RNA Sequencing for Gene Fusion Detection in Cancer: A Comprehensive Guide for Researchers and Drug Developers

Abstract

This article provides a comprehensive overview of RNA sequencing (RNA-seq) for detecting clinically relevant gene fusions in cancer. It explores the foundational role of gene fusions as diagnostic biomarkers and therapeutic targets, detailing methodological advances from targeted panels to long-read sequencing. The content addresses key challenges in troubleshooting and optimization, including sample preparation and bioinformatic pipelines. Finally, it synthesizes evidence from comparative validation studies, demonstrating how integrating RNA-seq with DNA-based methods significantly improves detection rates for actionable fusions, thereby advancing precision oncology and drug development.

Gene Fusions as Oncogenic Drivers: From Basic Biology to Clinical Actionability

Gene fusions, also known as chimeric genes, are hybrid genes formed when two previously separate genes become juxtaposed due to chromosomal rearrangements. These genomic alterations represent a critical class of somatic alterations in cancer, functioning as strong oncogenic drivers in numerous malignancies [1] [2]. The resulting fusion proteins can exhibit novel functional properties or altered expression patterns that disrupt normal cellular processes, ultimately leading to tumorigenesis. The clinical importance of gene fusions has grown substantially with the development of targeted therapies, making their detection crucial for optimal treatment selection [1].

The processes of tumorigenesis and development are intricate, involving numerous genes and molecular pathways. Fusion genes, as direct products of abnormal chromosomal rearrangements, are now recognized as key factors in the formation of many types of tumors [2]. In recent years, advancements in sequencing technology and bioinformatics have accelerated the discovery of fusion genes associated with specific tumor types, expanding our understanding of their roles in cancer biology and their potential as therapeutic targets.

Mechanisms of Gene Fusion Formation

Genomic Rearrangements Leading to Fusion Genes

Gene fusions arise through several distinct mechanisms of DNA rearrangement, each involving different types of chromosomal alterations [1]:

• Reciprocal Translocations: Interchromosomal exchange of DNA between regions, which can be equal (balanced) or unequal (unbalanced). An example is the SLC34A2-ROS1 fusion [1].
• Insertions: Inter- or intrachromosomal movement of a DNA fragment from one region to another.
• Deletions: Genomic deletions can bring separate genes into proximity, such as the ATG7-RAF1 fusion [1].
• Tandem Duplications: Duplicated genomic regions fuse with genes in their original region, exemplified by FGFR3-TACC3 in glioblastoma [1].
• Inversions: Segments of a chromosome flip relative to the centromere (pericentric) or not relative to the centromere (paracentric), such as KIF5B-RET [1].
• Chromothripsis: Catastrophic events involving fragmentation and inaccurate reassembly of one chromosome or chromosomal region [1].

The majority of oncogenic fusions are in-frame mutations that affect exonic regions of two protein-coding genes [1]. Interestingly, chimeric proteins can also arise without genomic rearrangement through mechanisms such as aberrant read-through transcription, where the transcription process does not properly terminate at the end of a gene and continues into the next gene (e.g., SCNN1A-TNFRSF1A) [1]. Fusion transcripts may also arise by trans or cis splicing of mRNA [1].

Functional Consequences of Gene Fusions

Oncogenic fusion proteins drive cancer development through several distinct mechanisms:

• Promoter-Driven Overexpression: Fusions can join a strong promoter that drives overexpression to a proto-oncogene (e.g., TRABD-DDR2), leading to downstream deregulation [1].
• Transcription Factor Activation: Fusions affecting transcription factors are important oncogenic drivers, exemplified by PML-RARα in leukemia, ETS gene fusions in prostate cancer, and PAX3-FOXO1 in alveolar rhabdomyosarcoma [1].
• Receptor Tyrosine Kinase Activation: Rather than driving overexpression, some fusion proteins drive oncogenesis through constitutive activation of receptor tyrosine kinases (RTKs) [1]. Examples include NRG1 ligand gene fusions and EGFR fusions, which trigger aberrant signaling pathways.

Table 1: Common Gene Fusion Types and Their Functional Consequences

Fusion Type	Functional Consequence	Representative Examples	Primary Signaling Pathways Affected
Kinase Fusions	Constitutive kinase activation	BCR-ABL, EML4-ALK, TPM3-NTRK1	PI3K/AKT, MAPK, JAK/STAT
Transcription Factor Fusions	Altered gene expression programs	PML-RARα, TMPRSS2-ERG, PAX3-FOXO1	Cell differentiation, apoptosis
Ligand Fusions	Aberrant receptor activation	NRG1 fusions	ErbB signaling
Promoter Swap Fusions	Oncogene overexpression	IGH-FGFR3	Various oncogenic pathways

Oncogenic fusion proteins have been shown to drive or contribute to cancer development through both cell-autonomous and non-cell-autonomous mechanisms. For instance, in rhabdomyosarcoma, tumor cells with PAX3-FOXO1 fusion can modulate the tumor microenvironment to enhance cancer and recipient cell motility, favoring metastatic disease [1]. Similarly, cell-surface-bound NRG1 fusion proteins are thought to drive paracrine signaling via RTKs on neighboring cells [1].

Prevalence of Gene Fusions Across Malignancies

Hematologic Malignancies

Gene fusions were first discovered in hematologic malignancies, with the Philadelphia chromosome in chronic myeloid leukemia (CML) representing the inaugural example [1]. This chromosomal abnormality, identified in 1960 and later found to arise from a translocation between chromosomes 9 and 22, results in the BCR-ABL fusion gene [1]. This fusion generates a constitutively active tyrosine kinase that drives leukemogenesis and is found in almost all cases of CML [1].

Other significant fusions in hematologic cancers include PML-RARα in acute promyelocytic leukemia and various ALK fusions in anaplastic large cell lymphoma (ALCL). The TPM3-ALK fusion, for instance, has been reported in ALCL, where it drives aberrant ALK expression closely associated with malignant transformation of lymphoid cells [3].

Solid Tumors

In solid tumors, gene fusions occur across a broad spectrum of malignancies. The first fusion reported in solid tumors was CTNNB1-PLAG1 in salivary gland adenoma [1]. Large-scale genomic studies have since revealed the extensive landscape of fusion genes across solid tumors.

A comprehensive analysis of 9,624 tumors across 33 cancer types identified 25,664 fusions, with a 63.3% validation rate using whole-genome sequencing data [4]. This study suggested that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of cases [4].

Table 2: Prevalence of Select Gene Fusions Across Cancer Types

Cancer Type	Fusion	Prevalence	Clinical Actionability
Chronic Myeloid Leukemia	BCR-ABL	~100% [1]	FDA-approved TKIs
Prostate Adenocarcinoma	TMPRSS2-ERG	38.2% [4]	Under investigation
Lung Adenocarcinoma	EML4-ALK	1.0% [4]	FDA-approved ALK inhibitors
Thyroid Carcinoma	CCDC6-RET	4.2% [4]	FDA-approved RET inhibitors
Cholangiocarcinoma	FGFR2-BICC1	5.6% [4]	FDA-approved FGFR inhibitors
Head and Neck Cancer	FGFR3-TACC3	2.8% overall fusion prevalence [5]	Potential target
Various Solid Tumors	NTRK fusions	0.35% overall prevalence [6]	FDA-approved TRK inhibitors

Recent large-scale studies have provided further insights into fusion prevalence. A 2025 pan-cancer analysis of 67,278 patients receiving both RNA- and DNA-based next-generation sequencing (NGS) found that 2.2% had at least one of nine fusions with FDA-approved matched therapies [7]. Notably, 29% of these fusions were detected outside of FDA-approved indications, highlighting the potential for expanding targeted therapy applications [7].

The prevalence of specific fusion types varies considerably across cancer types. For NTRK fusions, a real-world study of 19,591 solid tumor samples found an overall prevalence of 0.35%, with the highest frequencies in glioblastoma (1.91%), small intestine tumors (1.32%), and head and neck tumors (0.95%) [6].

Detection Methodologies for Gene Fusions

Traditional Detection Methods

Historically, gene fusions were detected using traditional methods that remain relevant in clinical practice:

• Fluorescence In Situ Hybridization (FISH): Allows visualization of chromosomal rearrangements using fluorescently labeled DNA probes.
• Immunohistochemistry (IHC): Detects aberrant protein expression patterns resulting from gene fusions.

While these methods have been widely used, they have limitations, particularly poor compatibility with multiplexing, which prevents simultaneous interrogation of multiple fusion genes [8].

Next-Generation Sequencing Approaches

The emergence of NGS technology has revolutionized fusion detection by enabling simultaneous sequencing of numerous genes in parallel [8]. Both DNA-based and RNA-based NGS approaches are employed, each with distinct advantages and limitations:

DNA-based NGS identifies genomic rearrangements at the DNA level but requires extensive coverage due to unpredictable breakpoints and potential blind spots within targeted areas [8].

RNA-based NGS detects expressed fusion transcripts, providing direct evidence of functionally relevant fusions. However, RNA is more susceptible to degradation, especially in formalin-fixed paraffin-embedded (FFPE) samples [8].

Recent studies have demonstrated that combined DNA and RNA sequencing significantly improves fusion detection. A 2025 study showed that concurrent RNA- and DNA-based NGS increased the detection of driver gene fusions by 21% compared with DNA-NGS alone [7]. Another study developing an integrated DNA and RNA-based targeted sequencing assay reported 100% sensitivity and specificity in detecting fusions in clinical samples [8].

Protocol: Integrated DNA and RNA-Based NGS for Fusion Detection

The following protocol outlines an integrated approach for gene fusion detection using both DNA and RNA NGS:

Sample Preparation:

• Obtain FFPE tumor samples with minimum tumor purity of 30% [7].
• Extract total nucleic acids (both DNA and RNA) using commercial kits suitable for FFPE material.
• Assess DNA and RNA quality and quantity using appropriate methods.

Library Preparation:

• For DNA sequencing: Use hybrid capture-based panels covering intronic and exonic regions of target genes. The Tempus xT assay covers 648 genes with 500× coverage [7].
• For RNA sequencing: Use whole-transcriptome or targeted RNA-seq approaches. The Tempus xR assay provides whole-transcriptome RNA-seq [7].
• For targeted approaches: Custom-designed panels can focus on genes with known clinical relevance.

Sequencing and Analysis:

• Sequence libraries on appropriate NGS platforms.
• For DNA data: Use structural variant callers like LUMPY to identify genomic rearrangements [7].
• For RNA data: Apply fusion detection algorithms such as STAR-Fusion and Arriba [5].
• Integrate results from both DNA and RNA analyses to maximize sensitivity.

Validation:

• Confirm novel or uncertain fusions using orthogonal methods such as Sanger sequencing [8].
• Establish analytical validation through reference standards with known fusions [8].

This integrated approach has been shown to overcome the limitations of single-method approaches, with one study demonstrating that combined DNA and RNA sequencing identified a TPM3-NTRK1 fusion that was missed by DNA-only analysis [8].

Signaling Pathways Activated by Gene Fusions

Oncogenic fusion proteins typically activate key signaling pathways that drive tumorigenesis. The diagram below illustrates the major pathways activated by different types of gene fusions:

Diagram 1: Signaling Pathways Activated by Oncogenic Gene Fusions

The diagram above illustrates how different categories of gene fusions activate distinct signaling cascades that ultimately drive oncogenic processes. Kinase fusions typically activate multiple pathways simultaneously, including the RTK/RAS/MAPK, PI3K/AKT/mTOR, and JAK/STAT pathways, leading to enhanced cell proliferation and survival [1] [3]. Transcription factor fusions primarily alter gene expression programs, which can block differentiation and increase metastatic potential [1]. Ligand fusions, such as NRG1 fusions, activate receptor tyrosine kinase signaling through aberrant paracrine or autocrine mechanisms [1].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Gene Fusion Studies

Reagent Category	Specific Examples	Function/Application	Technical Notes
Nucleic Acid Extraction Kits	FFPE RNA/DNA extraction kits	Isolation of high-quality nucleic acids from archived specimens	Optimized for degraded FFPE material; include DNase/RNase treatment steps
Library Preparation Kits	Tempus xT (DNA), Tempus xR (RNA) [7]	Preparation of sequencing libraries	DNA panels should cover relevant intronic regions; RNA methods should capture fusion transcripts
Reference Standards	Commercial fusion standards (e.g., GeneWell) [8]	Assay validation and quality control	Contain known fusion variants at defined concentrations
Hybrid Capture Reagents	Custom bait panels	Enrichment of target genes	Design should include known and novel fusion partners
Reverse Transcription Kits	High-efficiency RT enzymes	cDNA synthesis for RNA-seq	Critical for obtaining full-length transcripts from degraded RNA
Sequencing Controls	Spike-in RNA controls, positive control samples	Monitoring technical performance	Should include samples with known fusion status
Analysis Software	STAR-Fusion, Arriba, EricScript [4] [5]	Bioinformatics detection of fusions	Use multiple algorithms to improve sensitivity/specificity
Orthogonal Validation Reagents	FISH probes, IHC antibodies, Sanger sequencing reagents	Confirmation of NGS findings	Essential for validating novel fusions

Experimental Workflow for Fusion Detection

The following diagram illustrates a comprehensive workflow for gene fusion detection integrating both DNA and RNA sequencing approaches:

Diagram 2: Integrated DNA and RNA Sequencing Workflow for Fusion Detection

This workflow highlights the importance of parallel DNA and RNA analysis to maximize detection sensitivity. Studies have demonstrated that this integrated approach increases the detection of actionable fusions by 21-127% compared to DNA sequencing alone [7]. The complementary nature of DNA and RNA sequencing helps overcome the limitations of each individual method - DNA sequencing can detect genomic rearrangements regardless of expression, while RNA sequencing confirms functionally expressed fusions and can identify events missed by DNA analysis due to breakpoint location or complexity [8].

Gene fusions represent critical oncogenic drivers across both hematologic malignancies and solid tumors. Understanding their mechanisms of formation, prevalence across cancer types, and the signaling pathways they activate is essential for advancing cancer research and therapy. The development of integrated detection methodologies combining DNA and RNA sequencing has significantly improved our ability to identify these important alterations, directly impacting therapeutic decisions and patient outcomes. As targeted therapies continue to advance, comprehensive fusion testing will play an increasingly vital role in precision oncology, enabling more patients to benefit from matched targeted treatments.

Oncogenic gene fusions are hybrid genes formed when two previously separate genes become juxtaposed through genomic rearrangements such as chromosomal translocations, inversions, deletions, or duplications [1]. These molecular events represent a critical class of oncogenic drivers across a broad spectrum of cancers, with profound implications for tumor initiation, progression, and therapeutic targeting [1]. Research indicates that gene fusions drive cancer development in approximately 16.5% of all cancer cases, playing a unique driving role in more than 1% of cases [9]. The clinical significance of these fusions stems from their dual role as defining diagnostic markers and actionable therapeutic targets, making their detection imperative for modern precision oncology.

The constitutive activation of tyrosine kinases through fusion events represents a common oncogenic mechanism. For instance, the BCR-ABL fusion in chronic myeloid leukemia and EML4-ALK fusion in non-small cell lung cancer result in aberrant, ligand-independent signaling that drives uncontrolled cellular proliferation and survival [1]. Beyond kinase activation, gene fusions can also drive oncogenesis through alternative mechanisms, including the juxtaposition of strong promoters that drive overexpression of proto-oncogenes or the creation of novel chimeric transcription factors that alter transcriptional programs [1]. The resulting fusion proteins can activate multiple critical signaling pathways, including PI3K-AKT, MAPK, and Rho GTPase pathways, establishing oncogenic addiction that can be therapeutically exploited [1].

Gene Fusions as Clinical Biomarkers

Diagnostic Biomarkers

Gene fusions serve as defining diagnostic markers for specific cancer types and subtypes, enabling precise pathological classification. The detection of particular gene fusions can distinguish histologically similar tumors and guide accurate diagnosis, which is fundamental for appropriate treatment selection [1]. Several fusion-driven cancers are now recognized as distinct entities in diagnostic classifications, including the World Health Organization (WHO) classification of tumors.

Table 1: Gene Fusions as Diagnostic Biomarkers in Specific Cancers

Cancer Type	Diagnostic Fusion	Clinical Significance
Chronic Myeloid Leukemia	BCR-ABL1	Defining diagnostic marker [9]
Secretory Breast Cancer	ETV6-NTRK3	Present in ~92% of cases; diagnostic biomarker [10]
Synovial Sarcoma	SS18-SSX	Characteristic marker [10]
Dermatofibrosarcoma Protuberans	COL1A1-PDGFB	Specific marker [10]
Ependymoma	RELA fusion	Distinct entity in WHO CNS tumor classification [10]
Inflammatory Myofibroblastic Tumor	ALK fusions	Clarifies diagnosis due to high specific expression [11]
Lipofibromatosis-like Neural Tumor	NTRK1 fusions	Distinguishes from histologically similar lipofibromatosis [11]

Predictive Biomarkers and Therapeutic Targeting

Gene fusions serve as powerful predictive biomarkers for response to targeted therapies. Cancers driven by fusion products, particularly those involving tyrosine kinases, often demonstrate remarkable responses to matched targeted agents, exemplifying the paradigm of precision oncology [1]. The predictive value of these fusions has led to the development of tumor-agnostic treatment approaches, where therapies are approved based on the molecular alteration rather than the tumor's tissue of origin.

The combined prevalence of actionable fusions with FDA-approved targeted therapies represents a significant proportion of cancer patients who can benefit from matched targeted treatments. A recent large-scale pan-cancer analysis of 67,278 patients found that 2.2% harbored at least one of nine fusions with an FDA-approved matched therapy, with RNA sequencing increasing the detection of these driver gene fusions by 21% compared to DNA sequencing alone [7]. Furthermore, 29% of these actionable fusions were detected outside of their FDA-approved indications, highlighting the potential for expanding targeted therapy benefits to additional patient populations [7].

Table 2: Clinically Actionable Gene Fusions and Approved Therapies

Gene Fusion	Primary Cancer Types	Approved Targeted Therapies	Level of Evidence
ALK fusions	NSCLC, Inflammatory Myofibroblastic Tumor	Crizotinib, Ceritinib, Alectinib [11] [1]	FDA-approved in specific indications
NTRK fusions	Multiple solid tumors (tumor-agnostic)	Larotrectinib, Entrectinib [1]	Tumor-agnostic FDA approval
RET fusions	NSCLC, Thyroid Cancer	Selpercatinib, Pralsetinib [11] [1]	FDA-approved in specific indications
ROS1 fusions	NSCLC	Crizotinib, Entrectinib [11] [1]	FDA-approved in specific indications
FGFR2/3 fusions	Cholangiocarcinoma, Bladder Cancer	Erdafitinib, Pemigatinib [1] [7]	FDA-approved in specific indications
NRG1 fusions	NSCLC, Pancreatic Cancer	Afatinib (under investigation) [1]	Clinical trials

Prognostic Biomarkers

The prognostic significance of gene fusions varies considerably across different cancer types and specific fusion events. Some fusions are associated with more aggressive disease courses and worse outcomes, while others may define cancer subtypes with more favorable prognoses [1]. For instance, in pediatric thyroid cancers, patients with RET or NTRK fusions were more likely to have metastatic disease and worse outcomes compared to those with BRAF-mutant disease [1]. In contrast, FGFR2 fusions in cholangiocarcinoma were grouped in a cluster of genetic alterations with the best prognosis [1]. This variability underscores the importance of context-specific interpretation of the prognostic implications of gene fusions.

Analytical Approaches for Fusion Detection

RNA Sequencing Methodologies

RNA sequencing has emerged as a powerful tool for gene fusion detection due to its ability to directly detect expressed fusion transcripts. Several targeted and whole transcriptome sequencing approaches have been developed and validated for clinical use:

Targeted RNA Sequencing The FoundationOneRNA assay is a hybrid-capture-based targeted RNA sequencing test designed to detect fusions in 318 genes and measure expression of 1521 genes. Analytical validation demonstrated a positive percent agreement (PPA) of 98.28% and negative percent agreement (NPA) of 99.89% compared to orthogonal methods [12]. The assay successfully identified a low-level BRAF fusion missed by orthogonal whole transcriptome RNA sequencing, confirming its high sensitivity [12].

Whole Transcriptome Sequencing (WTS) A novel WTS assay for detection of gene fusions, MET exon 14 skipping, and EGFR vIII alterations achieved 98.4% sensitivity, correctly identifying 62 out of 63 known gene fusions with 100% specificity [10]. The assay established optimal performance thresholds at DV200 ≥ 30% for RNA degradation, >100 ng RNA input, >40 copies/ng fusion expression, and >80 million mapped reads [10].

Integrated DNA and RNA Sequencing Combining RNA sequencing with whole exome sequencing (WES) from a single tumor sample substantially improves detection of clinically relevant alterations. Applied to 2230 clinical tumor samples, this integrated approach enabled direct correlation of somatic alterations with gene expression, recovery of variants missed by DNA-only testing, and improved detection of gene fusions [13]. The combined assay uncovered clinically actionable alterations in 98% of cases and revealed complex genomic rearrangements that would likely have remained undetected without RNA data [13].

Experimental Protocol: RNA Sequencing for Fusion Detection

Sample Preparation and Quality Control

• Nucleic Acid Isolation: Extract RNA from FFPE tissues using commercially available kits (e.g., RNeasy FFPE Kit, Qiagen). For optimal results, use sections from tissue stored at 4°C for less than one year with tumor content exceeding 20% [10].
• RNA Quality Assessment: Quantify RNA using NanoDrop 8000 and Qubit 3.0. Assess integrity via Agilent 2100 Bioanalyzer system. Establish DV200 ≥ 30% as the threshold for acceptable RNA quality [10].
• rRNA Depletion: Remove ribosomal RNA using NEBNext rRNA Depletion Kit to enrich for mRNA and fusion transcripts of interest.

Library Preparation and Sequencing

• Library Construction: Use NEBNext Ultra II Directional RNA Library Prep Kit with custom adaptors and index primers. For highly degraded samples (DV200 ≤ 50%), omit the fragmentation step [10].
• Library Quality Control: Quantify libraries using Qubit 3.0 and assess quality with LabChip GX Touch system.
• Sequencing: Perform sequencing on platforms such as Illumina NovaSeq 6000 or comparable systems. Generate approximately 25 gigabases of 100 bp paired-end reads per sample [10].

Bioinformatic Analysis

• Alignment: Map sequencing reads to the reference genome (hg38) using STAR aligner or similar tools [13].
• Fusion Calling: Implement ensemble methods integrating multiple fusion detection algorithms (e.g., STAR-Fusion, Mojo) to improve accuracy [7].
• Filtering and Annotation: Apply filters based on supporting read count, expression levels, and known false-positive patterns. Annotate candidate fusions using databases such as FusionGDB and ChimerDB [10].

Research Reagent Solutions

Table 3: Essential Research Reagents for RNA Sequencing-Based Fusion Detection

Reagent/Category	Specific Examples	Function in Workflow
RNA Extraction Kits	RNeasy FFPE Kit (Qiagen), AllPrep DNA/RNA FFPE Kit	Nucleic acid isolation from challenging FFPE samples [13] [10]
RNA Quality Control	Agilent 2100 Bioanalyzer, TapeStation 4200, Qubit assays	Assessment of RNA integrity, quantity, and suitability for sequencing [13] [10]
Library Prep Kits	NEBNext Ultra II Directional RNA Library Prep Kit, TruSeq stranded mRNA kit	Construction of sequencing libraries from RNA templates [13] [10]
rRNA Depletion	NEBNext rRNA Depletion Kit	Removal of ribosomal RNA to enrich for coding transcripts [10]
Target Capture	SureSelect XTHS2 RNA kit (Agilent)	Hybrid-capture enrichment for targeted RNA sequencing approaches [13]
Sequencing Platforms	Illumina NovaSeq 6000, Gene+ seq 2000	High-throughput sequencing of RNA libraries [13] [10]

Signaling Pathways and Molecular Mechanisms

Oncogenic fusion proteins drive cancer development through multiple mechanisms, most commonly by constitutively activating tyrosine kinase signaling or altering transcriptional programs. The diagram below illustrates the key signaling pathways activated by oncogenic gene fusions and their downstream effects.

The molecular mechanisms of fusion-driven oncogenesis extend beyond cell-autonomous signaling. Fusion-positive cancer cells can modulate the tumor microenvironment through paracrine signaling. For example, in rhabdomyosarcoma, PAX3-FOXO1 fusion alters exosome content, driving pro-tumorigenic signaling in recipient cells [1]. Similarly, cell-surface-bound NRG1 fusion proteins can drive paracrine signaling via RTKs on neighboring cells [1]. These microenvironmental effects highlight the broad impact of oncogenic fusions on tumor biology.

Integrated Detection Workflow

The accurate detection of clinically relevant gene fusions requires an integrated approach that combines DNA and RNA sequencing methodologies. The workflow below illustrates the complementary nature of these technologies in identifying fusion events.

The complementary nature of DNA and RNA sequencing is evidenced by multiple studies demonstrating that combined approaches significantly improve fusion detection rates. In a large pan-cancer analysis, concurrent RNA and DNA sequencing increased the detection of driver gene fusions by 21% compared with DNA sequencing alone [7]. Similarly, a targeted sequencing study found that integrated DNA and RNA testing could identify fusions that would be missed by either method alone, with DNA and RNA assays independently showing false-negative rates that were compensated for by the complementary method [11].

The comprehensive characterization of gene fusions as diagnostic, prognostic, and predictive biomarkers represents a cornerstone of modern precision oncology. The integration of RNA sequencing with DNA-based genomic profiling has demonstrated significant improvements in detection sensitivity, with combined approaches identifying actionable alterations in up to 98% of cases [13]. As the landscape of fusion-targeted therapies continues to expand, with tumor-agnostic approvals for NTRK and RET functions and ongoing investigations for numerous other targets, the clinical imperative for robust fusion detection will only intensify.

Future developments in fusion detection technology, including long-read transcriptome sequencing and advanced computational methods like GFvoter, promise further enhancements in detection accuracy [9]. Meanwhile, the growing recognition of fusions occurring outside their classic indications highlights the importance of comprehensive molecular profiling across diverse cancer types. As biomarker-driven treatment strategies continue to evolve, the systematic implementation of integrated DNA and RNA sequencing approaches will be essential for maximizing therapeutic opportunities for cancer patients harboring these clinically significant genomic alterations.

Oncogenic gene fusions are hybrid genes arising from chromosomal rearrangements such as translocations, inversions, deletions, or tandem duplications, and represent a critical class of therapeutic targets in precision oncology [1]. These fusion events can result in constitutive activation of tyrosine kinases or aberrant expression of transcription factors, driving uncontrolled cell proliferation and survival through the disruption of key signaling pathways including RAS/MAPK, PI3K/AKT, and JAK/STAT [14] [1]. The detection of these fusions has become essential for optimal cancer diagnosis, prognosis, and treatment selection, particularly with the development of highly effective targeted therapies.

RNA sequencing has emerged as a powerful tool for fusion detection, offering several advantages over DNA-based approaches and traditional methods like FISH and IHC. While DNA-based NGS can identify genomic rearrangements, it often struggles with large intronic regions where breakpoints frequently occur. RNA-seq directly captures the expressed fusion transcript, providing functional evidence of the rearrangement and confirming the maintenance of the reading frame and integrity of kinase domains [14] [7]. The clinical utility of comprehensive fusion profiling is underscored by real-world data showing that concurrent RNA and DNA sequencing increases the detection of clinically actionable fusions by 21-127% compared to DNA sequencing alone [7].

Landscape of Key Targetable Fusions

Prevalence and Clinical Characteristics

The prevalence of targetable fusions varies significantly across cancer types, with some occurring at high frequencies in specific rare tumors while appearing at lower frequencies across more common malignancies. The table below summarizes the prevalence and clinical characteristics of key oncogenic fusions.

Table 1: Prevalence and Characteristics of Key Oncogenic Fusions

Fusion	Prevalence in NSCLC	Other Cancer Types with Significant Prevalence	Common Fusion Partners	Clinical Characteristics
ALK	3-8% [14]	Anaplastic Large Cell Lymphoma (50-80%) [14]; Inflammatory Myofibroblastic Tumors (50-60%) [14]	EML4, STRN, NPM1, TPM3 [14]	Oncogenic addiction to ALK signaling; responsive to TKIs [14]
RET	1-2% [15]	Papillary Thyroid Carcinoma [11]	KIF5B, CCDC6 [11]	Higher proportion of never smokers (36%) and adenocarcinoma histology (88%) [15] [16]
ROS1	1-2% [17]	-	CD74, SLC34A2 [11]	-
NTRK1/2/3	<1% [6]	Glioblastoma (1.91%) [6]; Small Intestine (1.32%) [6]; Secretory Breast Carcinoma (>90%) [6]	ETV6, TPM3, LMNA [6]	Tumor-agnostic FDA approvals; often mutually exclusive with other drivers [6]

Signaling Pathways of Oncogenic Fusions

Oncogenic fusions involving receptor tyrosine kinases typically result in constitutive activation of downstream signaling cascades that promote cell survival, proliferation, and differentiation. The diagram below illustrates the common signaling pathways activated by ALK, RET, ROS1, and NTRK fusions.

RNA Sequencing Methodologies for Fusion Detection

Comparison of RNA Sequencing Approaches

Multiple RNA-based NGS approaches have been developed for fusion detection, each with distinct advantages and limitations. The selection of an appropriate methodology depends on factors including the required sensitivity, need for novel fusion discovery, cost considerations, and sample quality.

Table 2: Comparison of RNA Sequencing Methodologies for Fusion Detection

Methodology	Principle	Advantages	Limitations	Representative Platforms
Whole Transcriptome Sequencing (WTS)	Sequencing of all polyadenylated RNA transcripts	Unbiased detection of known and novel fusions; comprehensive biomarker analysis [14]	Complex bioinformatic analysis; low sensitivity for fusions with low expression [14]	Standard RNA-seq protocols
Hybrid-Capture-Based RNA Sequencing	Solution-based hybridization to target transcripts using bait panels	High sensitivity for known fusions; robust performance with FFPE samples [6] [18]	Limited to pre-designed targets; may miss novel partners outside panel [6]	Tempus xR [7]; Illumina RNA Panels [6]
Amplicon-Based RNA Sequencing	Multiplex PCR amplification of target regions	High sensitivity for known targets; cost-effective [14]	Limited to predefined targets; false positives from primer artifacts [18]	TruSight RNA Fusion Panel [14]; OncoFu Elite [14]
Anchored Multiplex PCR	Gene-specific priming combined with universal adapters	Ability to detect fusions with unknown partners; requires less input RNA [14]	Limited by primer design; may miss some fusion variants	FusionPlex [14]

Integrated DNA-RNA Sequencing Workflow

The most comprehensive approach for fusion detection involves parallel sequencing of both DNA and RNA from tumor samples. This integrated workflow maximizes sensitivity and specificity while providing complementary information about genomic rearrangements and their functional transcriptional consequences.

Protocol: RNA Hybrid-Capture Sequencing for Fusion Detection

Principle: This protocol utilizes biotinylated oligonucleotide probes to enrich for target RNA sequences prior to sequencing, enabling highly sensitive detection of fusion transcripts even in degraded FFPE samples [6].

Sample Requirements:

• Input: 50-100ng total RNA from FFPE tissue
• Quality: DV200 ≥ 30% (percentage of RNA fragments >200 nucleotides)
• Tumor Purity: ≥ 20% tumor content recommended

Procedure:

• RNA Quality Control

  • Assess RNA quantity and quality using fluorometric methods
  • Determine RNA integrity (DV200) using Bioanalyzer or TapeStation

• Library Preparation

  • Perform ribosomal RNA depletion using commercially available kits
  • Convert RNA to cDNA using reverse transcriptase with random primers
  • Synthesize second strand to create double-stranded cDNA

• Hybrid Capture Enrichment

  • Fragment cDNA to 200-300bp using acoustic shearing
  • Add Illumina-compatible adapters with unique dual indexes
  • Hybridize with biotinylated probe library (e.g., comprehensive fusion panel)
  • Capture target regions using streptavidin-coated magnetic beads
  • Wash to remove non-specifically bound fragments

• Sequencing

  • Amplify captured libraries with 10-12 PCR cycles
  • Quantify libraries by qPCR
  • Pool libraries at equimolar ratios
  • Sequence on Illumina platform (minimum 20 million 2x75bp reads per sample)

• Bioinformatic Analysis

  • Align reads to reference genome (GRCh38) using STAR or HISAT2
  • Detect fusion transcripts using multiple algorithms (STAR-Fusion, Arriba, FusionCatcher)
  • Filter against database of normal samples and common artifacts
  • Annotate fusions with clinical significance (OncoKB, CIViC)

Quality Control Metrics:

• Minimum sequencing depth: 20 million reads
• Target coverage: >80% of targets at 100x
• Fusion supporting reads: ≥5 unique reads spanning breakpoint
• Expression level: TPM ≥1 for 3' gene

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Fusion Detection Studies

Reagent/Category	Specific Examples	Function	Application Notes
NGS Library Prep Kits	Illumina TruSight RNA Fusion Panel; Archer FusionPlex	Target enrichment and library preparation for RNA sequencing	Archer uses anchored multiplex PCR; Illumina uses hybrid capture [14]
Reference Standards	GeneWell Fusion Reference Standards (contain 10 fusions across ALK, ROS1, RET, NTRK) [11]	Assay validation and quality control	Essential for establishing limit of detection (5% for DNA, 250-400 copies for RNA) [11]
Hybrid Capture Panels	Tempus xR (whole transcriptome); Labcorp CGP Panel	Comprehensive fusion detection via RNA baits	Labcorp panel identified 73 NTRK fusions in 19,591 samples (0.35% prevalence) [6]
Bioinformatic Tools	STAR-Fusion; Arriba; AGFusion; FusionCatcher	Fusion detection from RNA-seq data	Ensemble approaches combining multiple algorithms improve accuracy [7]
Validation Reagents	FISH probes; IHC antibodies; Sanger sequencing	Orthogonal validation of NGS findings	Critical for confirming novel fusions and borderline positive cases [11]

Therapeutic Implications and Resistance Mechanisms

Approved Targeted Therapies

The identification of oncogenic fusions has led to the development of highly effective targeted therapies, with several receiving FDA approval in both tumor-agnostic and indication-specific contexts.

Table 4: Approved Targeted Therapies for Oncogenic Fusions

Fusion	Approved Therapies	Approval Context	Clinical Response
ALK	Crizotinib, Ceritinib, Alectinib [11]	NSCLC-specific	Standard care in ALK+ NSCLC [14]
RET	Selpercatinib, Pralsetinib [19] [15]	Tumor-agnostic for RET fusions	Pralsetinib: ORR 70.3%, mPFS 13.1 mos, mOS 44.3 mos [19]
NTRK	Larotrectinib, Entrectinib, Repotrectinib [6]	Tumor-agnostic for NTRK fusions	Larotrectinib: ORR 79% [6]; Entrectinib: ORR 61.2% [6]
ROS1	Crizotinib, Entrectinib [11]	NSCLC-specific	-

Resistance Mechanisms and Next-Generation Inhibitors

Despite initial efficacy, resistance to targeted therapies inevitably develops through multiple mechanisms. Understanding these pathways is essential for developing sequential treatment strategies.

Resistance Mechanisms:

• On-target mutations: Secondary mutations in the kinase domain that impair drug binding (e.g., ALK G1202R, RET G810X gatekeeper mutations) [19]
• Off-target bypass: Activation of alternative signaling pathways (e.g., HER3-mediated ERK reactivation following RET inhibition) [19]
• Histologic transformation: Epithelial-to-mesenchymal transition or conversion to small cell lung cancer morphology

Emeritting Therapeutic Strategies:

• Next-generation inhibitors: Vepafestinib (RET-selective) shows activity in treatment-naïve and pretreated patients with RET-altered cancers [19]
• Combination therapies: Pan-HER inhibitor afatinib combined with selpercatinib overcomes YAP-driven HER3-mediated resistance in RET-altered cancers [19]
• CNS-penetrant agents: FHND5071 (selective RET inhibitor) demonstrates high brain penetration and 100% CNS ORR in patients with brain metastases [19]

Emerging Directions and Future Applications

Novel Detection Modalities

Artificial Intelligence in Fusion Prediction: Deep learning models applied to H&E-stained whole slide images can predict ALK and ROS1 fusions with ROC AUCs of 0.84-0.85, serving as potential prescreening tools before confirmatory molecular testing [17]. These vision transformer models utilize a two-step training approach, first learning general cancer morphology patterns before specializing in specific fusion prediction.

Liquid Biopsy Applications: While not extensively covered in the current search results, circulating tumor DNA and RNA analyses are emerging as non-invasive methods for fusion detection and resistance monitoring, particularly valuable when tissue biopsies are impractical.

Expanding Therapeutic Landscapes

The future of fusion-targeted therapy includes several promising directions:

• Pan-fusion inhibitors: Drugs targeting common structural features of multiple fusion kinases
• Combination regimens: Rational pairing of fusion-directed therapy with complementary pathway inhibitors to delay resistance
• Degradation strategies: PROTACs and other targeted protein degradation approaches for complete oncoprotein elimination
• Immunotherapy combinations: Exploring synergy between targeted therapies and immune checkpoint inhibitors in selected contexts

The comprehensive detection of oncogenic fusions through RNA sequencing represents a critical component of precision oncology. The integration of multiple testing modalities, particularly combined DNA and RNA sequencing, significantly enhances detection rates of these therapeutic targets. As the field advances, the ongoing development of more sensitive detection methods, novel therapeutic agents, and sophisticated resistance-management strategies will continue to improve outcomes for patients with fusion-driven cancers. Research and clinical practice must prioritize comprehensive molecular profiling to fully realize the potential of targeted therapies across the spectrum of oncogenic fusions.

The identification of oncogenic gene fusions is critical for cancer diagnosis, prognosis, and targeted treatment selection. For years, fluorescence in situ hybridization (FISH), immunohistochemistry (IHC), and reverse transcription polymerase chain reaction (RT-PCR) have served as cornerstone techniques in clinical molecular pathology. However, as our understanding of cancer genomics expands, significant limitations of these traditional methods have emerged. Within the broader thesis on the superiority of RNA sequencing for gene fusion detection, this application note systematically details the technical and clinical constraints of FISH, IHC, and RT-PCR, supported by quantitative performance data and experimental protocols. The transition toward comprehensive genomic approaches like RNA-based next-generation sequencing (NGS) is becoming increasingly necessary to fully realize the potential of precision oncology.

Performance Comparison of Traditional Detection Methods

The table below summarizes the key characteristics and limitations of FISH, IHC, and RT-PCR based on current clinical studies.

Table 1: Comparative Analysis of Traditional Gene Fusion Detection Methods

Method	Typical Applications	Key Advantages	Major Limitations	Reported Sensitivity	Reported Specificity
FISH	Detection of ALK, ROS1, RET rearrangements in NSCLC [20]	Single-cell resolution; partner-agnostic [21]	Limited multiplexing capability; subjective interpretation; inability to identify fusion partners or breakpoints [21] [11]	~70-99% (varies by gene and platform) [22]	~80-99% (varies by gene and platform) [22]
IHC	Protein expression analysis; ALK, HER2 status detection [20] [23]	Low cost; rapid turnaround; preserves tissue architecture [22]	Variable sensitivity/specificity dependent on antibody and fusion partner; semi-quantitative [21]	~60% for RET [21]; 97% for HER2 (AI-assisted) [23]	40-85% for RET [21]; 82% for HER2 (AI-assisted) [23]
RT-PCR	Known fusion variant detection (e.g., EML4-ALK) [22]	Rapid; high sensitivity for known fusions; works with limited tissue [22]	Cannot detect novel fusion partners; susceptible to RNA degradation [11] [24]	100% (ALK vs FISH/Sequencing) [22]	94% (ALK vs FISH/Sequencing) [22]

Detailed Methodological Limitations and Protocols

Fluorescence In Situ Hybridization (FISH)

Experimental Protocol for FISH-Based Fusion Detection:

• Sample Preparation: Cut 4-5 μm sections from formalin-fixed paraffin-embedded (FFPE) tissue blocks and mount on charged slides.
• Deparaffinization and Pretreatment: Bake slides at 56°C for 4 hours, deparaffinize in xylene, and hydrate through graded ethanols.
• Pretreatment: Incubate in pretreatment solution (1M sodium thiocyanate) at 80°C for 30 minutes, then protease digest (pepsin in 0.2N HCl) at 37°C for 15-30 minutes.
• Denaturation and Hybridization: Denature target DNA and probe mixture at 85°C for 5 minutes, then hybridize at 37°C for 12-16 hours using break-apart FISH probes.
• Post-Hybridization Washes: Wash in 2× SSC/0.3% NP-40 at 75°C for 5 minutes, then counterstain with DAPI.
• Analysis: Score 50-100 tumor cells using fluorescence microscopy; positive result indicated by >15% cells with split signals [21] [22].

Key Limitations: FISH demonstrates particularly poor performance for pericentric fusions where partner genes are located close together (e.g., KIF5B and RET on chromosome 10) [21]. The method cannot identify the specific fusion partner, which has emerging clinical relevance for predicting treatment response [21]. Furthermore, FISH requires specialized expertise for interpretation, lacks standardized cutoff criteria across laboratories, and may yield positive results that are not confirmed at the transcript level [21].

Immunohistochemistry (IHC)

Experimental Protocol for IHC-Based Fusion Protein Detection:

• Sectioning and Deparaffinization: Cut 4 μm FFPE sections, bake at 60°C for 1 hour, and deparaffinize through xylene and graded alcohols.
• Antigen Retrieval: Heat slides in citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) using a pressure cooker or steamer for 20-30 minutes.
• Primary Antibody Incubation: Apply validated primary antibodies (e.g., D5F3 for ALK, 5A4 for ALK) for 60 minutes at room temperature [22].
• Detection and Visualization: Use enzyme-conjugated secondary antibodies and chromogenic substrates (DAB) for signal development.
• Counterstaining and Interpretation: Counterstain with hematoxylin, dehydrate, and mount. Score based on staining intensity and distribution [22] [23].

Key Limitations: IHC sensitivity is highly dependent on the specific fusion partner. For RET fusions, sensitivity ranges from 100% for KIF5B-RET to approximately 50% for NCOA4-RET [21]. The method suffers from significant inter-observer variability, with studies showing substantial discordance between laboratories due to lack of standardization in reagents and training [22]. While artificial intelligence approaches are emerging to address these limitations, they require further validation before widespread clinical implementation [23].

Reverse Transcription PCR (RT-PCR)

Experimental Protocol for RT-PCR Fusion Detection:

• RNA Extraction: Isolate total RNA from FFPE tissues using commercial kits with DNase treatment to remove genomic DNA contamination.
• RNA Quality Assessment: Determine RNA concentration and quality using spectrophotometry and fragment analysis (DV200 ≥ 30% recommended) [24].
• cDNA Synthesis: Convert 100-500 ng of total RNA to cDNA using reverse transcriptase with random hexamers or gene-specific primers.
• PCR Amplification: Perform quantitative PCR using primers targeting specific fusion junctions (e.g., EML4-ALK variants) with appropriate cycling conditions.
• Result Interpretation: Use ΔCt cut-off values (e.g., ΔCt ≤8 for ALK detection) for positive calls; confirm positive results with sequencing when possible [22].

Key Limitations: RT-PCR is fundamentally limited to detecting known fusion variants with predefined breakpoints [11]. The requirement for intact, high-quality RNA presents significant challenges with FFPE specimens, where RNA is often degraded [24]. The method's sensitivity decreases dramatically when fusion transcripts are expressed at low levels or when the RNA input is below optimal levels (typically <250-400 copies/100 ng) [11].

Signaling Pathways and Molecular Consequences

The diagram below illustrates the fundamental difference in what each traditional detection method actually measures in the central dogma of molecular biology.

Diagram Title: What Traditional Methods Detect in Molecular Pathology

The Scientist's Toolkit: Essential Research Reagents

Table 2: Key Research Reagents for Traditional Fusion Detection Methods

Reagent/Category	Specific Examples	Function and Application Notes
FISH Probes	Vysis ALK Break Apart FISH Probe Kit (Abbott Molecular) [22]	Designed to detect ALK rearrangements regardless of partner; requires fluorescence microscopy and specific expertise for interpretation
IHC Antibodies	Ventana ALK (D5F3) CDx Assay; Novocastra 5A4 (Leica) [22]	Clone D5F3 is FDA-approved companion diagnostic; 5A4 is widely validated; both require standardized antigen retrieval and detection systems
RNA Extraction Kits	RNeasy FFPE Kit (Qiagen) [24]	Critical for RT-PCR and RNA-based methods; includes DNase treatment step to remove genomic DNA contamination
RT-PCR Kits	ALK RGQ RT-PCR Kit (QIAGEN) [22]	Single-tube quantitative real-time PCR assay for automated ALK expression interpretation; requires high-quality RNA input
Control Materials	Commercial fusion reference standards (e.g., GeneWell) [11]	Contain spiked-in fusion transcripts (EML4::ALK, CD74::ROS1, CCDC6::RET) for assay validation and quality control

The limitations of traditional gene fusion detection methods - including limited multiplexing capability, inability to detect novel fusions, variable sensitivity and specificity, and technical challenges with sample quality - present significant constraints in the era of precision oncology. While FISH, IHC, and RT-PCR remain valuable for specific clinical scenarios, their individual shortcomings highlight the necessity for more comprehensive approaches. RNA-based next-generation sequencing emerges as a powerful solution that overcomes these limitations, enabling simultaneous detection of known and novel fusions across multiple genes with high sensitivity and specificity. The integration of advanced computational methods and multi-analyte approaches will further enhance the detection of clinically relevant gene fusions, ultimately improving patient outcomes through more accurate diagnosis and targeted treatment selection.

RNA-Seq Methodologies in Focus: From Targeted Panels to Long-Read Sequencing

Gene fusions represent a critical class of molecular alterations in cancer, serving as diagnostic, prognostic, and predictive biomarkers for targeted therapies. The detection of these transcripts in clinical specimens, particularly formalin-fixed paraffin-embedded (FFPE) tissue, presents significant technical challenges. Targeted RNA sequencing (RNA-Seq) has emerged as a powerful solution, offering enhanced sensitivity and specificity for fusion detection compared to whole transcriptome approaches. This application note details the fundamental design principles, experimental protocols, and validation frameworks for developing targeted RNA-Seq panels specifically optimized for capturing fusion transcripts in cancer research, providing researchers and drug development professionals with a comprehensive guide for implementing this technology in both basic and translational settings.

Gene fusions are hybrid genes formed through chromosomal rearrangements including translocations, deletions, inversions, or duplications. These molecular events can result in the expression of chimeric proteins with oncogenic properties or place proto-oncogenes under the control of strong promoter elements, driving tumorigenesis. Approximately one-third of soft tissue tumors and a wide array of other solid tumors harbor clinically relevant gene fusions [11]. Notably, fusions involving genes such as ALK, ROS1, RET, and NTRK family members have been well-characterized and serve as biomarkers for matched targeted therapies that have demonstrated remarkable clinical efficacy [11] [25].

The accurate detection of fusion transcripts is therefore paramount in modern cancer research and precision oncology. While traditional methods like fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) remain in use, they are limited by poor compatibility with multiplexing, preventing simultaneous interrogation of multiple fusion genes [11] [26]. Next-generation sequencing (NGS) technologies, particularly RNA-Seq, enable comprehensive profiling of fusion transcripts. However, the low quality and fragmented nature of RNA extracted from FFPE samples—the standard in pathology—poses a substantial challenge for sequencing assays [11] [26] [27]. Targeted RNA-Seq addresses these limitations by focusing sequencing power on specific genes of interest, thereby improving sensitivity, reducing costs, and enabling robust analysis of degraded samples typical in clinical research.

Design Principles for Targeted RNA-Seq Fusion Panels

Core Design Strategies: Amplicon vs. Hybridization Capture

Targeted RNA-Seq employs two primary strategies to enrich for specific RNA transcripts: amplicon sequencing and hybridization capture [28].

• Amplicon Sequencing (PCR-based): This approach uses multiple pairs of primers to selectively amplify target RNA regions through PCR. It is highly sensitive and requires less input RNA, making it suitable for fragmented FFPE-derived RNA. A key consideration is careful primer design to avoid gaps in coverage that could miss breakpoints in fusion events [28].
• Hybridization Capture: This method uses biotin-labeled oligonucleotide probes complementary to the target regions to pull down RNA fragments of interest from a complex library. While potentially offering more uniform coverage and flexibility in panel design, it generally requires higher input RNA and longer intact fragments, which can be a limitation for degraded FFPE samples [28].

The choice between these methods depends on the research application, sample quality, and the desired balance between sensitivity, specificity, and coverage.

Gene Content Selection

The selection of genes to include in a panel is driven by the research context. A well-designed panel should include:

• Clinically Actionable Oncogenes: Genes with established roles as therapeutic targets, such as ALK, ROS1, RET, NTRK1/2/3, FGFRs, and BRAF [11] [26] [28].
• Tumor-Type Specific Fusions: Genes recurrently fused in specific cancers, such as SS18-SSX in synovial sarcoma, EWSR1-FLI1 in Ewing's sarcoma, and TMPRSS2-ERG in prostate cancer [25] [26].
• 5' and 3' Partner Gene Coverage: Since the identity of the fusion partner can influence treatment response, panels should be designed to detect both known and novel partners for key driver genes.

Optimization for FFPE-Derived RNA

FFPE processing causes RNA fragmentation and chemical modification, making downstream analysis difficult [11] [26]. Key design adaptations include:

• Short Amplicon Design: Designing amplicons to be short (100-250 bp) to accommodate the fragmented nature of FFPE RNA and ensure efficient amplification and sequencing [28].
• High Target Capture Efficiency: Optimizing probe or primer sequences to ensure high efficiency in capturing degraded RNA, thereby maximizing the yield of informative sequences.

Experimental Protocol: A Step-by-Step Workflow

The following section outlines a standard workflow for fusion detection using a targeted RNA-Seq approach, from sample preparation to data analysis.

Sample Preparation and Quality Control

• RNA Extraction: Total RNA is extracted from biological samples (e.g., FFPE tissue sections, cells) using specialized kits for FFPE material (e.g., Maxwell RSC RNA FFPE Kit) [26]. The integrity of the extracted RNA should be checked using a bioanalyzer, though traditional RNA Integrity Number (RIN) is often low for FFPE samples; concentration is therefore a more critical parameter.
• RNA Fragmentation: While RNA from FFPE is already fragmented, further controlled fragmentation (physical or enzymatic) may be performed to achieve a uniform size distribution of around 100-500 bases [28].

Library Preparation for Targeted RNA-Seq

Table 1: Key Research Reagent Solutions for Library Preparation

Reagent / Kit	Function	Application Note
FusionPlex Solid Tumor Kit (ArcherDX)	Multiplex PCR-based library preparation for fusion detection.	Validated for FFPE samples; includes panels for sarcoma and carcinoma [26].
Biotin-labeled Probes	Hybridization and capture of target RNA sequences.	Used in capture-based targeted sequencing; require careful design for specificity [28].
Streptavidin Magnetic Beads	Enrichment of probe-bound target RNA fragments.	Essential for post-hybridization wash and capture in hybridization-based methods [28].
Reverse Transcriptase	Synthesis of complementary DNA (cDNA) from RNA templates.	First step in converting captured RNA into a sequencer-compatible library.
Platform-specific Adapters	Enable binding of library fragments to sequencing flow cells.	Contain indices for sample multiplexing.

The library preparation process varies by method but generally follows these steps:

• Reverse Transcription: The enriched or selected RNA fragments are reverse-transcribed into cDNA.
• Target Enrichment:
  • For Amplicon-based: A multiplex PCR is performed using primers designed for the targeted genes.
  • For Hybridization Capture: The cDNA is hybridized with the custom probe panel, and non-target sequences are washed away.
• Adapter Ligation & Amplification: Sequencing adapters are ligated to the cDNA fragments, followed by a limited number of PCR cycles to amplify the final library.
• Library QC: The quality and concentration of the final library are quantified using methods such as qPCR or bioanalyzer profiling.

Sequencing and Data Analysis

• Sequencing: The pooled libraries are sequenced on a high-throughput platform (e.g., Illumina), typically generating paired-end reads. A minimum of 10-20 million reads per sample is often sufficient for targeted panels.
• Bioinformatic Analysis: The resulting FASTQ files are processed through a specialized pipeline:
  • Quality Control & Trimming: Tools like Trimmomatic or Cutadapt remove low-quality bases and adapter sequences [29].
  • Alignment: Processed reads are aligned to a reference genome (e.g., GRCh38) using splice-aware aligners such as STAR.
  • Fusion Calling: Dedicated algorithms identify chimeric transcripts. Using at least two tools is recommended to improve accuracy [30] [26]. Commonly used tools include:
    • Arriba (ARR): A fast and sensitive fusion detection algorithm with high sensitivity for druggable fusions [30] [26].
    • STAR-Fusion (SFU): A widely used tool based on the STAR aligner, known for its high accuracy [30] [26].
    • CTAT-LR-Fusion: A tool designed for long-read RNA-Seq data, useful for resolving complex isoforms [25].
  • Annotation and Filtering: Called fusions are filtered against databases of known artifacts, normal genomic variations, and annotated for clinical relevance.

The following workflow diagram illustrates the complete process from sample to analysis:

Performance Validation and Benchmarking

Establishing Analytical Sensitivity and Specificity

Robust validation is critical for deploying a targeted RNA-Seq assay in a research setting. Performance is measured using well-characterized reference standards and clinical samples.

Table 2: Representative Performance Metrics from Validation Studies

Validation Parameter	Representative Data	Experimental Details
Limit of Detection (LOD) - DNA	Mutational abundance down to 5% [11].	Serial dilution experiments with fusion-spiked reference standards.
Limit of Detection (LOD) - RNA	250–400 copies/100 ng input RNA [11].	Serial dilution of RNA from positive cell lines (e.g., H2228 for EML4-ALK).
Sensitivity	100% (28/28 known positive clinical samples) [11].	Comparison of assay results against known fusion status from previous tests (NGS or FISH).
Specificity	96.9%-100% (after resolving false negatives) [11] [27].	Testing of fusion-negative samples and confirmation of discordant results by orthogonal methods (e.g., Sanger sequencing).
Reproducibility	100% concordance in intra-run and inter-run replicates [11].	Testing of multiple replicates (n=3) within a single run and across different sequencing runs.

Comparative Performance of Bioinformatics Tools

The choice of fusion-calling algorithm significantly impacts results. One study on 190 FFPE samples found that while the ArcherDX Analysis Suite (ADx) demonstrated high sensitivity, the open-source tools Arriba (ARR) and STAR-Fusion (SFU) showed lower sensitivity but could provide valuable orthogonal support, especially for low-quality data [26]. Combining multiple callers can therefore improve the robustness of fusion detection.

Integrated DNA and RNA Sequencing for Comprehensive Profiling

While RNA-Seq directly captures expressed fusion transcripts, DNA-based NGS can identify genomic rearrangements that may not be transcribed or may be difficult to capture due to RNA degradation. An integrated approach that simultaneously uses DNA and RNA-based NGS maximizes detection sensitivity [11]. Studies have shown that DNA and RNA results can complement each other, with some fusions being detected only at one level [11]. For instance, DNA-based assays may miss fusions due to large intronic regions or complex rearrangements, while RNA-based assays may miss fusions if the transcript is expressed at very low levels or is unstable.

The following diagram conceptualizes this complementary relationship:

Targeted RNA-Seq represents a highly sensitive, specific, and cost-effective methodology for the detection of clinically relevant gene fusions in cancer research. Its design, optimized for challenging, real-world samples like FFPE tissue, makes it particularly suited for both retrospective and prospective studies. Success hinges on several key factors: prudent panel design encompassing actionable genes, a robust laboratory workflow validated for degraded RNA, and a bioinformatics pipeline that leverages complementary algorithms to minimize false positives and negatives. As the landscape of therapeutic targets continues to expand, the implementation of rigorously designed and validated targeted RNA-Seq panels will be indispensable for unraveling the molecular drivers of cancer and advancing drug development.

In precision oncology, DNA sequencing reveals the genetic blueprint of a tumor, but it cannot determine which mutations are actively transcribed into messenger RNA and are therefore more likely to produce functional proteins that drive cancer progression. This fundamental limitation creates a "DNA-to-protein divide" in clinical diagnostics [31]. RNA sequencing (RNA-Seq) bridges this critical gap by providing a snapshot of the actively expressed mutational landscape, enabling more accurate cancer diagnosis, prognosis, and therapeutic targeting [32] [31]. This Application Note details experimental and bioinformatic protocols for using RNA-Seq to identify expressed mutations, with emphasis on clinically-actionable gene fusions in cancer research.

Performance Characteristics of RNA-Seq for Mutation Detection

RNA-Seq demonstrates high sensitivity and specificity for detecting expressed mutations, particularly gene fusions. The following table summarizes key performance metrics from recent studies:

Table 1: Performance Metrics of RNA-Seq Assays for Gene Fusion Detection

Metric	Performance Value	Experimental Context	Citation
Sensitivity	98.4% (62/63 known fusions)	Whole Transcriptome Sequencing (WTS) assay on clinical samples	[24]
Specificity	100% (0 false positives in 21 negative samples)	Same WTS assay on fusion-negative samples	[24]
Precision (Average)	58.6%	GFvoter performance across multiple datasets	[9]
Advantage over DNA-Seq	Identifies 18% additional somatic SNVs in lung cancer	Comparative analysis of paired RNA-seq and DNA-seq data	[31]

These performance characteristics make RNA-Seq particularly valuable for clinical applications where detecting expressed mutations directly influences treatment decisions. For instance, one study found that nearly one-fifth of somatic single nucleotide variants (SNVs) detected by DNA sequencing were not transcribed, suggesting they may have limited clinical relevance for targeted therapies [31].

Experimental Protocol: RNA-Seq for Fusion Detection in Cancer Research

Sample Preparation and Quality Control

Proper sample preparation is critical for successful RNA-Seq analysis. The following protocol outlines key steps for processing cancer specimens:

• RNA Extraction: Use RNeasy FFPE Kit (Qiagen) or similar for formalin-fixed paraffin-embedded (FFPE) tumor samples. For optimal results, use tissue stored at 4°C for less than one year with tumor content exceeding 20% [24].
• RNA Quality Assessment: Evaluate RNA integrity using multiple methods:
  • Quantification with NanoDrop 8000 and Qubit 3.0
  • Quality assessment with Agilent 2100 Bioanalyzer system
  • Determine DV200 value (percentage of RNA fragments >200 nucleotides). A DV200 ≥30% indicates sufficient RNA quality for sequencing [24].
• Library Preparation:
  • Remove ribosomal RNA using NEBNext rRNA Depletion Kit
  • For samples with DV200 ≤50%, skip fragmentation step
  • Perform cDNA synthesis and library preparation using NEBNext Ultra II Directional RNA Library Prep Kit
  • Use unique dual indices to enable sample multiplexing [24]

Sequencing Parameters

• Platform: Illumina NextSeq 500 or similar high-throughput platform
• Read Configuration: 75-cycle single-end or 100bp paired-end
• Recommended Depth: Minimum 80 million mapped reads per sample
• Output: Approximately 25 gigabases of data per sample [33] [24]

Bioinformatic Analysis Workflow

The computational analysis of RNA-Seq data involves multiple steps to identify expressed mutations accurately:

• Alignment and Preprocessing:
  • Align reads to reference genome using spliced aligners (STAR, GSNAP, or TopHat2)
  • Generate raw count tables using tools like HTSeq [33]
• Fusion Detection:
  • Apply multiple fusion callers (e.g., GFvoter, LongGF, JAFFAL) to improve accuracy
  • GFvoter employs a multivoting strategy using Minimap2, Winnowmap2, and dedicated fusion detection tools [9]
• Differential Expression Analysis:
  • Filter low-expressed genes (keep genes expressed in ≥80% of samples)
  • Normalize counts using TMM method in edgeR [34]
  • Identify differentially expressed genes using limma-voom pipeline [34]

Diagram: RNA-Seq Fusion Detection Workflow

Applications in Cancer Research and Precision Oncology

RNA-Seq provides critical functional validation of DNA-identified mutations, with several key applications in oncology:

• Therapeutic Target Prioritization: RNA-Seq confirms whether mutations identified by DNA sequencing are actually expressed, helping prioritize targets with clinical relevance. Studies show RNA-Seq uniquely identifies variants with significant pathological relevance that were missed by DNA-Seq alone [31].

• Gene Fusion Detection: Gene fusions are important drivers of cancer and serve as diagnostic biomarkers and therapeutic targets. RNA-Seq enables unbiased detection of both known and novel fusion events within any expressed gene [9] [24].

• Comprehensive Mutation Profiling: Beyond fusions, RNA-Seq detects alternative splicing events, exon skipping variants (e.g., MET exon 14 skipping in NSCLC), and expressed single nucleotide variants that may impact protein function [24].

• Biomarker Discovery: Expression patterns from RNA-Seq can classify cancer subtypes, predict treatment response, and identify resistance mechanisms, contributing to more personalized treatment approaches [32].

Table 2: Clinically-Actionable Mutations Detectable by RNA-Seq

Mutation Type	Cancer Examples	Clinical Significance
Gene Fusions (ALK, ROS1, RET, NTRK)	Non-small cell lung cancer (NSCLC)	FDA-approved targeted therapies available	[24]
Exon Skipping (MET exon 14)	Lung adenocarcinoma, lung sarcomatoid carcinoma	Emerging therapeutic target; responds to MET inhibitors	[24]
Expressed SNVs	Various solid tumors	Indicates clinically relevant mutations; 18% of DNA-level SNVs not transcribed	[31]

The Scientist's Toolkit: Essential Research Reagents and Tools

Table 3: Essential Reagents and Tools for RNA-Seq Mutation Detection

Category	Specific Product/Tool	Application/Function
RNA Extraction	RNeasy FFPE Kit (Qiagen)	RNA isolation from FFPE tissue samples	[24]
Library Prep	NEBNext Ultra II Directional RNA Library Prep Kit	cDNA synthesis and library construction	[24]
rRNA Depletion	NEBNext rRNA Depletion Kit	Remove ribosomal RNA to enrich coding transcripts	[24]
Sequencing Platforms	Illumina NovaSeq, PacBio SMRT, Oxford Nanopore	High-throughput sequencing; long-read technologies ideal for isoform detection	[35] [36]
Alignment Tools	STAR, GSNAP, TopHat2, Minimap2	"Splicing-aware" alignment of RNA-Seq reads	[33] [9]
Fusion Detection	GFvoter, LongGF, JAFFAL, FusionSeeker	Specialized algorithms for identifying fusion transcripts	[9]
Differential Expression	edgeR, limma, DESeq2	Statistical analysis of gene expression changes	[34]

Pathway Analysis: From DNA Mutation to Expressed Protein

Diagram: Bridging the DNA-to-Protein Divide with RNA-Seq

RNA sequencing provides a critical functional bridge between DNA-level mutations and their protein products, enabling more accurate cancer diagnostics and therapeutic decision-making. By confirming which mutations are actively expressed, RNA-Seq addresses fundamental limitations of DNA-only approaches in precision oncology. The experimental and computational protocols detailed in this Application Note provide researchers with robust methods for detecting expressed mutations, particularly gene fusions, across various cancer types. As sequencing technologies and bioinformatic tools continue to advance, RNA-Seq is poised to play an increasingly central role in cancer research and clinical oncology, ultimately improving patient outcomes through more precise molecular profiling.

An Integrated DNA and RNA NGS Assay for Comprehensive Fusion Detection

Oncogenic gene fusions are hybrid genes resulting from chromosomal rearrangements such as translocations, inversions, deletions, or tandem duplications [1]. These fusions act as powerful drivers in numerous cancers, with products often constituting constitutively active tyrosine kinases or overexpressed transcription factors that lead to uncontrolled cell growth and proliferation [1]. The reliable detection of these fusions is critical for personalized cancer therapy, especially with the advent of targeted treatments like TRK inhibitors for NTRK fusions and selective RET inhibitors for RET fusions, which can produce profound responses in patients whose cancers harbor these alterations [37] [1].

While traditional methods like fluorescence in situ hybridization (FISH) and reverse transcription-polymerase chain reaction (RT-PCR) have proven utility, they possess inherent limitations in identifying novel and noncanonical fusion genes [38]. Next-generation sequencing (NGS) technologies have therefore become the cornerstone of modern molecular diagnostics. DNA-based NGS (DNA-seq) is adept at identifying genomic breakpoints but can miss fusions involving large introns or complex rearrangements [38]. RNA-based NGS (RNA-seq) directly captures expressed fusion transcripts, offering enhanced sensitivity and the ability to detect unknown partners, but its effectiveness depends on RNA quality and expression levels [37] [38]. An integrated DNA and RNA sequencing approach overcomes the limitations of either method alone, ensuring comprehensive detection of both known and novel oncogenic fusions for optimal therapeutic decision-making.

Methodologies and Experimental Protocols

Integrated Assay Workflow

The following diagram outlines the comprehensive workflow for integrated DNA and RNA sequencing, from sample preparation to final analysis.

Pre-Analytical Considerations and Sample Preparation

The pre-analytical phase is critical for assay success, particularly for labile RNA.

• Tissue Selection and Enrichment: A board-certified pathologist must review hematoxylin and eosin (H&E)-stained sections to select tumor-rich areas. Macrodissection is preferred over coring to maintain FFPE block integrity, especially for small tumors [37]. A post-dissection H&E slide should be assessed to confirm the target tissue was obtained.
• Nucleic Acid Co-Extraction: DNA and RNA are co-extracted from the same tumor sample. For challenging specimens with low input, testing can proceed at the validated lower limit of the assay, with complementary testing (e.g., IHC) or report caveats considered for negative results [37].
• Quality Control and Input: DNA and RNA quantity and quality are assessed using systems like Nanodrop 2000 and Qubit fluorometer [38]. For RNA-based NGS, input typically ranges from 10–30 ng, reflecting common use of amplicon methods [37]. International standards should guide RNA extraction from FFPE tissue to manage pre-analytic variability [37].

DNA Sequencing (DNA-seq) for Fusion Detection

DNA-seq identifies genomic rearrangements at the DNA level.

• Library Preparation and Target Enrichment: Genomic DNA from FFPE samples is used to prepare NGS libraries with a comprehensive gene panel, such as a 425-gene panel, using kits like the KAPA Hyper Prep kit [38].
• Sequencing and Data Processing: Libraries are sequenced on platforms like the Illumina HiSeq 4000. Sequence reads in FASTQ format undergo quality control with tools like Trimmomatic. High-quality reads are aligned to the human genome (e.g., hg19) using the Burrows-Wheeler Aligner (BWA-MEM) algorithm [38].
• Variant and Fusion Calling: The Genome Analysis Toolkit (GATK) is used for local realignment and base quality score recalibration. Somatic gene fusion variants are called by tools like Delly with default parameters [38].

RNA Sequencing (RNA-seq) for Fusion Detection

RNA-seq directly identifies expressed fusion transcripts, overcoming limitations of DNA-seq.

• Library Preparation from RNA: Starting with total RNA or RNA enriched for mRNA, cDNA libraries are prepared using kits such as the NEBNext Ultra DNA Library Prep Kit for Illumina [33]. The quality of input RNA is paramount; an RNA integrity number (RIN) >7.0 is ideal [33].
• Sequencing and Analysis Workflow: Libraries are sequenced, and the resulting reads are demultiplexed. FASTQ files are aligned to a reference genome (e.g., mm10 for mouse, GRCh37/hg19 for human) using aligners like TopHat2 [33]. Reads are then mapped to genes using tools like HTSeq to generate a raw counts table [33].
• Fusion Transcript Identification: Both whole-transcriptome sequencing (WTS) and more focused targeted RNA-seq panels can be employed. Targeted panels often demonstrate enhanced sensitivity for detecting low-abundance or actionable fusions that may be missed by WTS [38].

Bioinformatic Analysis and Data Integration

• Data Normalization and Processing: RNA-seq count data requires normalization to remove technical biases and enable comparisons across samples. Multiple normalization approaches exist and should be selected based on the experimental design [29].
• Differential Expression Analysis: Tools for differential expression analysis, such as edgeR, employ a negative binomial generalized log-linear model to identify statistically significant changes in gene expression between conditions [33].
• Integrated Caller and Validation: Fusion calls from DNA-seq and RNA-seq are integrated, with discrepancies resolved by orthogonal methods like FISH. This combined approach significantly enhances detection sensitivity and specificity.

Performance and Validation Data

Concordance Between Methodologies

A systematic comparison of DNA-seq, RNA-seq, and FISH for detecting RET fusions in early-stage NSCLC demonstrates the strengths of an integrated approach [38].

Table 1: Concordance Rates Between Detection Methods for RET Fusions

Comparison	Concordance Rate	Key Findings
DNA-seq vs. RNA-seq	92.3% (36/39 cases)	High concordance, but some fusions missed by each method individually.
RNA-seq vs. FISH	84.6% (33/39 cases)	Targeted RNA-seq identified 5 additional RET+ cases missed by WTS.
DNA-seq vs. FISH	82.5% (33/40 cases)	FISH provides visual confirmation but may miss non-canonical fusions.

Detection Rates of RNA-seq Modalities

The same study highlighted critical performance differences between RNA-seq approaches [38].

Table 2: Detection Performance of RNA-seq Methods

Method	Detection Rate	Sensitivity	Advantages
Whole-Transcriptome Sequencing (WTS)	79.5% (31/39 cases)	Moderate	Unbiased detection of known and novel fusions.
Targeted RNA-seq	Higher than WTS	Enhanced	Identified 5 additional RET+ cases missed by WTS; optimal for low-quality RNA.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Kits for Integrated Fusion Detection

Item	Function	Example Product
FFPE DNA/RNA Extraction Kit	Simultaneous co-extraction of nucleic acids from archived tumor tissue.	QIAamp DNA FFPE Tissue Kit [38]
DNA Library Prep Kit	Preparation of sequencing-ready libraries from gDNA.	KAPA Hyper Prep Kit [38]
RNA Library Prep Kit	Construction of strand-specific RNA-seq libraries.	NEBNext Ultra DNA Library Prep Kit for Illumina [33]
Target Enrichment Panel	Hybrid-capture or amplicon-based panels for focused sequencing.	GeneseeqPrime 425-gene panel [38]
NGS Platform	High-throughput sequencing of prepared libraries.	Illumina HiSeq 4000 [38]
Alignment & Analysis Software	Processing of raw sequence data, alignment, and variant calling.	BWA-MEM, GATK, Delly [38]

Signaling Pathways in Fusion-Driven Cancers

Oncogenic gene fusions activate key signaling pathways that drive tumor growth and survival. The diagram below illustrates the primary signaling cascades dysregulated by kinase fusion proteins.

These constitutive signals promote tumorigenesis through multiple mechanisms [1]:

• MAPK/ERK Pathway Activation: Drives continuous cellular proliferation.
• PI3K/AKT/mTOR Pathway Activation: Enhances cell survival and inhibits apoptosis.
• JAK/STAT Pathway Activation: Modulates the tumor microenvironment and immune responses.

Targeted therapies, such as small-molecule inhibitors, directly bind to and inhibit the constitutively active kinase domain of the fusion protein, thereby blocking these oncogenic signals [1].

The integrated DNA and RNA NGS assay provides a comprehensive diagnostic solution for detecting oncogenic gene fusions. This approach leverages the unique strengths of each method: DNA-seq reliably identifies genomic breakpoints and structural rearrangements, while RNA-seq confirms the expression of functional fusion transcripts and detects events missed by DNA-based methods due to large introns or complex rearrangements [38].

The clinical implications of this integrated approach are profound. It ensures that patients with rare or non-canonical fusions are identified, making them eligible for highly effective targeted therapies. This is the cornerstone of tumor-agnostic treatment, where the molecular alteration, rather than the tumor's tissue of origin, dictates the therapeutic strategy [1]. Furthermore, characterizing the specific fusion partner and structure can provide insights into clinical behavior and potential resistance mechanisms.

For implementing this assay, several best practices are recommended. Laboratories should standardize pre-analytical procedures, including tumor enrichment via macrodissection and optimal handling of FFPE tissue to preserve RNA integrity [37]. A validated bioinformatics pipeline is essential for accurate fusion calling from both DNA and RNA data. Finally, maintaining flexibility in RNA input and having protocols for complementary testing (e.g., FISH or IHC) for challenging specimens is crucial for maximizing clinical utility [37] [38].

In conclusion, an integrated DNA and RNA NGS assay represents a superior methodology for comprehensive fusion detection in oncology. It aligns with the principles of precision medicine by ensuring that all patients with actionable oncogenic fusions are identified, thereby optimizing their treatment outcomes and paving the way for continued advances in cancer therapy.

Long-read transcriptome sequencing has emerged as a transformative technology for detecting complex structural variants and gene fusions in cancer research. Unlike short-read sequencing, long-read technologies from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) generate reads spanning thousands to millions of bases, enabling direct observation of full-length transcripts and complex rearrangement events. This capability is particularly valuable for identifying driver mutations and therapeutic targets in oncology, where gene fusions play a significant role in approximately 16.5% of cancer cases. This application note details experimental protocols, benchmarking data, and analytical workflows for implementing long-read sequencing in cancer genomics research, providing researchers with practical frameworks for detecting clinically relevant structural variants.

Long-read sequencing technologies have revolutionized structural variant detection by overcoming inherent limitations of short-read approaches. The two leading platforms, PacBio and Oxford Nanopore, employ fundamentally different methodologies each with distinct advantages for transcriptome analysis:

• Pacific Biosciences (PacBio) HiFi Sequencing: Utilizes circular consensus sequencing (CCS) to produce highly accurate reads (>99.9% accuracy) typically ranging from 10-25 kilobases. This technology is particularly valuable for clinical applications where variant calling precision is critical [39].
• Oxford Nanopore Technologies (ONT): Sequences single DNA or RNA molecules as they pass through protein nanopores, generating ultra-long reads that can exceed 1 megabase. Recent improvements in chemistry and basecalling algorithms have increased accuracy beyond 99%, making ONT particularly suitable for detecting large structural variants and complex rearrangements [39].

The key advantage of both platforms is their ability to span repetitive regions and complex genomic architectures that typically confound short-read technologies, enabling more comprehensive characterization of the cancer transcriptome [40].

Table 1: Comparison of Long-Read Sequencing Platforms

Feature	PacBio HiFi	Oxford Nanopore (ONT)
Read Length	10-25 kb (HiFi reads)	Up to >1 Mb (typical reads 20-100 kb)
Accuracy	>99.9% (HiFi consensus)	~98-99.5% (Q20+ with recent improvements)
Throughput	Moderate–High (up to ~160 Gb/run Sequel IIe)	High (varies by device; PromethION > Tb)
Instrument Cost	High (Sequel IIe system)	Lower (MinION, GridION, scalable options)
Best Applications	Clinical-grade variant detection, haplotype phasing	Large SV detection, point-of-care, real-time analysis

Bioinformatics Workflow for Structural Variant Detection

The analysis of long-read transcriptome sequencing data requires specialized bioinformatics workflows tailored to the unique characteristics of long reads. A robust analytical pipeline includes the following critical steps:

Read Alignment and Quality Control

Long reads must be aligned to a reference genome using specialized aligners that accommodate their length and error profiles. Minimap2 is widely used for this purpose, though alternatives like Winnowmap2 and ngmlr offer complementary strengths [9]. Quality control should assess read length distribution, base-calling quality scores, and adapter contamination.

Fusion Detection and Validation

Specialized algorithms identify potential gene fusions by detecting reads that align to multiple genomic locations. The GFvoter pipeline employs a multivoting strategy that integrates multiple alignment and fusion detection tools to improve accuracy [9]. This approach demonstrates how combining evidence from multiple methods reduces false positives while maintaining sensitivity.

Functional Annotation

Detected variants require annotation to determine their potential functional impact. This includes assessing whether fusions preserve open reading frames, affect functional domains, or disrupt regulatory elements. Integration with cancer gene databases helps prioritize clinically relevant events.

Performance Benchmarking of Fusion Detection Tools

Recent evaluations of fusion detection tools demonstrate significant variability in performance metrics. GFvoter, which employs a multivoting strategy combining multiple aligners and fusion callers, has shown superior performance compared to existing methods [9].

Table 2: Performance Comparison of Fusion Detection Tools on Real and Simulated Datasets

Tool	Average Precision	Average Recall	Average F1 Score	Key Strengths
GFvoter	58.6%	Variable by dataset	0.569	Best precision-recall balance, multivoting strategy
LongGF	39.5%	Variable by dataset	0.407	Good recall for certain fusion types
JAFFAL	30.8%	Variable by dataset	0.386	Comprehensive fusion annotation
FusionSeeker	35.6%	Variable by dataset	0.291	High precision on specific datasets

In a comparative analysis using both simulated datasets and real cancer cell line data (including MCF-7, HCT-116, and A549 lines), GFvoter consistently achieved the highest F1 scores across nine experimental datasets, with values ranging from 0.080 to 0.972 [9]. Notably, GFvoter uniquely detected the RPS6KB1-VMP1 gene fusion in the MCF-7 breast cancer cell line, which was missed by all other tools evaluated [9].

The performance advantage of GFvoter stems from its multivoting methodology, which integrates:

• Two specialized RNA-seq aligners (Minimap2 and Winnowmap2)
• Two fusion detection tools (LongGF and JAFFAL)
• A novel scoring mechanism that prioritizes high-confidence fusions

This approach demonstrates how combining evidence from multiple analytical methods can overcome the limitations of individual tools, particularly for detecting challenging fusion events with complex breakpoints or occurring in repetitive regions [9].

Detailed Experimental Protocol: Gene Fusion Detection Using GFvoter

Sample Preparation and Quality Control

Begin with high-quality RNA extracted from cancer cells or tissues:

• RNA Integrity: Assess RNA quality using Agilent Bioanalyzer or TapeStation; RIN > 7.0 is recommended for optimal results [33].
• Quantity Requirements: A minimum of 300 ng/μL total RNA is typically required for library preparation [41].
• mRNA Enrichment: Use poly(A) selection to enrich for mRNA using kits such as NEBNext Poly(A) mRNA Magnetic Isolation Module [33].

Library Preparation for PacBio HiFi Sequencing

The following protocol is adapted from methods described in recent publications [9] [42]:

• cDNA Synthesis: Convert mRNA to double-stranded cDNA using reverse transcriptase and DNA polymerase.
• Size Selection: Perform size selection to remove fragments <1 kb using AMPure PB beads.
• Adapter Ligation: Ligate SMRTbell adapters to the cDNA fragments using T4 DNA ligase.
• Purification: Remove unligated adapters and contaminants with exonuclease treatment.
• Quality Control: Validate library quality and quantity using Fragment Analyzer or Bioanalyzer.

Sequencing Run

• Load the prepared library onto a PacBio Sequel IIe system according to manufacturer specifications.
• Aim for a minimum of 50 Gb of data per sample to ensure sufficient coverage for fusion detection.
• Target a read length N50 of at least 10 kb to span multiple exons and detect fusion junctions.

Computational Analysis with GFvoter

• Install GFvoter: Download from https://github.com/xiaolan-z/GFvoter and install dependencies.
• Input Preparation: Provide raw sequencing data in FASTQ format.
• Execute Analysis:

• Interpret Results: The output includes a ranked list of candidate fusions with confidence scores.

Validation

• Confirm high-confidence fusions using orthogonal methods such as RT-PCR and Sanger sequencing.
• For clinical applications, validate with fluorescence in situ hybridization (FISH) or nanopore amplicon sequencing.

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of long-read transcriptome sequencing requires specific reagents and computational resources. The following table details essential components for a complete workflow:

Table 3: Essential Research Reagents and Computational Tools for Long-Read Transcriptome Sequencing

Category	Specific Product/Tool	Function/Purpose
RNA Extraction	Trizol reagent, PicoPure RNA Isolation Kit	High-quality RNA extraction from cells/tissues
Library Prep	NEBNext Ultra DNA Library Prep Kit, SMRTbell Express Template Prep Kit	cDNA library construction for sequencing
Sequencing	PacBio Sequel IIe SMRT Cells, ONT PromethION Flow Cells	Platform-specific sequencing substrates
Alignment	Minimap2, Winnowmap2, ngmlr	Mapping long reads to reference genomes
Fusion Detection	GFvoter, LongGF, JAFFAL	Identifying gene fusions from aligned reads
Quality Control	FastQC, Trimmomatic	Assessing and improving read quality
Visualization	Integrative Genomics Viewer (IGV)	Visualizing aligned reads and fusion events

Case Study: Resolving Complex Structural Variants in Cancer

The application of long-read sequencing to cancer genomics has yielded significant insights into complex structural variants driving oncogenesis. In a recent study of acute myeloid leukemia (AML), long-read transcriptome sequencing identified previously undetected gene fusions that had escaped detection by short-read technologies [9]. These findings demonstrate how long-read approaches can resolve the full complexity of cancer transcriptomes.

Another illustrative case comes from analysis of the MCF-7 breast cancer cell line, where GFvoter detected the RPS6KB1-VMP1 fusion while other tools failed [9]. This fusion represents a clinically relevant event that may serve as both a diagnostic biomarker and therapeutic target. The ability to detect such events with high confidence enables researchers to build more complete inventories of driver mutations across cancer types.

Long-read sequencing also excels at characterizing variants in repetitive regions and segmental duplications, which are frequently overlooked in short-read analyses [39]. This capability is particularly valuable for understanding genome instability in cancer and identifying non-canonical gene fusions that may contribute to therapy resistance.

Future Perspectives and Clinical Translation

As long-read sequencing technologies continue to evolve, several trends are shaping their future application in cancer research:

• Multi-omics Integration: Combining long-read transcriptome data with epigenetic information from the same platforms provides insights into regulation of fusion genes [40].
• Single-Cell Applications: Adapting long-read protocols for single-cell RNA sequencing will enable characterization of fusion heterogeneity within tumors [42].
• Clinical Implementation: Decreasing costs and improving throughput are making long-read sequencing increasingly viable for clinical diagnostics, particularly for cases where standard approaches have failed to identify driver mutations [39].
• Pan-Cancer Atlases: Large-scale efforts to comprehensively characterize structural variants across cancer types using long-read sequencing will provide valuable resources for biomarker discovery and therapeutic development [43].

The integration of long-read sequencing into mainstream cancer research requires continued development of user-friendly analytical tools and standardized protocols. As these technologies become more accessible, they are poised to significantly advance our understanding of cancer genomics and expand the repertoire of actionable targets for precision oncology.

• GFvoter: Gene fusion detection in long-read transcriptome sequencing data [9]
• Long-read sequencing and structural variant detection in rare genetic diseases [39]
• RNA-seq analysis and bioinformatics protocols [41]
• A beginner's guide to analysis of RNA sequencing data [33]
• Application of long-read sequencing to detect structural variants in human cancer genomes [40]
• RNA sequencing and its applications in cancer and rare diseases [42]
• Long-read sequencing of 945 Han individuals identifies structural variants [43]

Overcoming Technical Hurdles: Optimizing Sensitivity, Specificity, and Workflow

Formalin-fixed, paraffin-embedded (FFPE) tissues represent an invaluable resource for cancer genomics research, particularly in the context of RNA sequencing for gene fusion detection. However, the degraded nature of FFPE-derived RNA, chemical modifications from fixation, and low yields present significant analytical challenges. This application note synthesizes current methodologies and data to provide optimized protocols for FFPE RNA quality assessment, library preparation, and sequencing analysis. By implementing rigorous quality control metrics and selecting appropriate sequencing strategies, researchers can reliably overcome these challenges to detect clinically relevant gene fusions, including ALK, ROS1, RET, and NTRK fusions, with significant implications for cancer diagnosis and therapeutic development.

FFPE tissues are the most widely available clinical specimens, offering unparalleled access to retrospective cohorts with extensive clinical annotation. Their importance in oncology is underscored by the critical role that gene fusions play as diagnostic biomarkers and therapeutic targets in solid tumors. Gene fusions derived from genomic rearrangements occur in approximately one-third of soft tissue tumors and drive oncogenesis in 16.5% of all cancer cases [11] [9]. However, the formalin fixation process fragments RNA, induces chemical modifications, and cross-links nucleic acids to proteins, resulting in suboptimal RNA integrity that complicates downstream sequencing applications [44]. The poly-A tails essential for many library preparation methods are often degraded in FFPE-RNA, limiting the effectiveness of oligo-dT based reverse transcription [44]. This technical guide addresses these challenges through systematic quality control, optimized protocols, and analytical frameworks specifically designed for fusion detection in FFPE specimens.

Quality Control and Sample Assessment

Critical QC Metrics for FFPE-RNA

Precise quality assessment is fundamental for successful FFPE-RNA sequencing. Traditional RNA Quality Indicators (RIN) are often unreliable for FFPE-RNA due to extensive fragmentation. Instead, fragment size distribution metrics provide more accurate quality predictions:

• DV200: Percentage of RNA fragments >200 nucleotides. Samples with DV200 > 40% are generally suitable for sequencing [44].
• DV100: Percentage of RNA fragments >100 nucleotides. For highly degraded sample sets (DV200 < 40%), DV100 provides better stratification, with DV100 > 50% recommended for library preparation [44].

Research demonstrates that samples with median RNA concentration <18.9 ng/µL and pre-capture library Qubit values <2.08 ng/µL frequently fail bioinformatics QC, whereas successful samples typically exhibit concentrations >40.8 ng/µL and library yields >5.82 ng/µL [45]. A decision tree model incorporating these metrics achieved an F-score of 0.848 in predicting sequencing success [45].

Practical QC Workflow

Table 1: Quality Control Decision Thresholds for FFPE-RNA

Metric	Threshold for Success	Threshold for Failure	Assessment Method
RNA Concentration	>25 ng/µL	<18.9 ng/µL	Qubit Fluorometer
DV200 Value	>40%	<30%	Agilent Bioanalyzer
DV100 Value	>50%	<40%	Agilent Bioanalyzer
Library Concentration	>1.7 ng/µL	<1.7 ng/µL	Qubit dsDNA HS Assay
28S:18S Ratio	~2:1 (for intact RNA)	<1.5:1	Denaturing Agarose Gel

The recommended workflow includes:

• Extraction: Use FFPE-specific nucleic acid extraction kits with RNase-free conditions [44]
• Assessment: Determine concentration (Qubit) and size distribution (Bioanalyzer)
• Replication: When material allows, extract from multiple FFPE regions for biological replicates [44]
• Aliquoting: Preserve a QC aliquot to avoid freeze-thaw degradation [44]

Input Requirements and Library Preparation Strategies

RNA Input Guidelines by Methodology

Table 2: RNA Input Requirements for Different Sequencing Approaches

Sequencing Approach	Example Kits	Recommended Input	Optimal FFPE Application
Short-read RNA-seq	Illumina TruSeq Stranded mRNA	100 ng	Higher quality samples (DV200 > 50%)
Short-read RNA-seq (Low-input)	NEBNext Ultra II Directional RNA	10 ng	Limited samples with moderate quality
Long-read RNA-seq	PacBio SMRTbell Prep 3.0	300-500 ng	Intact transcript sequencing (rare for FFPE)
Ultra-low-input RNA-seq	SMART-Seq mRNA LP	10 pg	Extremely limited or poor quality samples
3' mRNA-seq	QuantSeq FFPE	10 ng-1 μg	Degraded samples, gene expression focus
Whole Transcriptome	CORALL FFPE	Varies	Fusion detection, isoform analysis

The selection of appropriate input requirements depends on both sample availability and research objectives. For gene fusion detection, studies demonstrate that an integrated DNA and RNA-based approach achieves optimal results, with DNA-based NGS reliably detecting fusions at 5% mutational abundance and RNA-based NGS requiring 250-400 copies/100 ng input [11]. This combined approach increased detection of clinically actionable fusions by 21% compared to DNA-NGS alone in a pan-cancer study of 67,278 patients [7].

Library Preparation Protocol Comparison

Two predominant strategies have emerged for FFPE-RNA library preparation:

A. Ribosomal Depletion Workflow:

• Utilizes probes to remove abundant rRNA (e.g., NEBNext rRNA Depletion Kit)
• Preserves both coding and non-coding RNA species
• Better suited for degraded samples as it doesn't rely on intact poly-A tails
• Recommended for samples with DV200 < 30% [44]

B. 3' mRNA-Seq Workflow:

• Targets the 3' end of transcripts through oligo(dT) priming
• Focuses sequencing on the 3' UTR, tolerant of 5' degradation
• Provides cost-effective gene expression quantification
• Demonstrated 89% correlation with whole transcriptome methods in FFPE samples [46]

Table 3: Performance Comparison of FFPE-Compatible Library Prep Kits

Kit Name	Workflow	Input Requirement	Key Advantages	Fusion Detection Capability
TaKaRa SMARTer Stranded Total RNA-Seq v2	Ribosomal depletion	1 ng-200 ng	20-fold lower input requirement	Suitable with sufficient coverage
Illumina Stranded Total RNA Prep with Ribo-Zero Plus	Ribosomal depletion	Standard input	Superior alignment rates (61.65% unique mapping)	Excellent with uniform coverage
NEBNext Ultra II Directional RNA	Ribosomal depletion	10 ng	Cost-effective, well-validated	Reliable with confirmatory DNA-seq
QuantSeq FFPE	3' mRNA-seq	10 ng-1 μg	Focused on 3' end, cost-efficient	Limited to 3' fusion partners

Comparative studies indicate that while ribosomal depletion methods (Kit B) achieve better alignment performance and lower duplication rates (10.73% vs 28.48%), low-input methods (Kit A) can achieve comparable gene expression quantification with 20-fold less RNA input [47]. Both approaches show 83.6-91.7% concordance in differential expression analysis, confirming their reliability for FFPE transcriptomics [47].

Experimental Protocol: Integrated DNA-RNA Fusion Detection

Sample Preparation and Quality Control

Materials:

• FFPE tissue sections (5-10 μm thickness)
• FFPE nucleic acid extraction kit (e.g., AllPrep DNA/RNA FFPE Kit)
• RNase-free reagents and consumables
• Agilent 2100 Bioanalyzer with RNA Nano Kit
• Qubit Fluorometer with RNA HS Assay

Procedure:

• Macrodissection: Perform pathologist-guided dissection to enrich tumor content (>30% purity) [47] [7]
• Nucleic Acid Extraction: Co-extract DNA and RNA using dedicated FFPE kits
• Quality Assessment:
  • Quantify RNA concentration using Qubit
  • Analyze RNA integrity using Bioanalyzer to calculate DV200/DV100
  • Confirm DNA quality and concentration
• Sample Selection: Proceed with samples meeting minimum thresholds (RNA concentration >25 ng/μL, DV200 > 30% or DV100 > 50%)

Library Preparation and Sequencing

Dual-Modality Approach: For comprehensive fusion detection, implement both DNA and RNA sequencing:

DNA Library Preparation:

• Use targeted NGS panels covering intronic regions of fusion driver genes (e.g., 648-gene panel)
• Achieve minimum 500× coverage with enhanced detection for 22 fusion-related genes
• Include both exonic and select intronic probes for breakpoint detection [7]

RNA Library Preparation:

• Select ribosomal depletion method for degraded samples (DV200 < 40%)
• Incorporate unique molecular identifiers (UMIs) for accurate quantification
• Use random priming rather than poly-A selection to avoid 3' bias [44]

Sequencing Parameters:

• DNA-seq: Minimum 500× coverage
• RNA-seq: 75-150 bp paired-end reads, minimum 25 million reads mapped to gene regions [45]

Bioinformatics Analysis

Fusion Detection Pipeline:

• DNA-based Fusion Calling:
  • Align to reference genome (GRCh37/38)
  • Use structural variant callers (e.g., LUMPY) with filters based on supporting read count
  • Annotate with tools like AGFusion [7]

• RNA-based Fusion Calling:

  • Employ ensemble methods integrating multiple algorithms (STAR-Fusion, Mojo)
  • Filter against technical and biological noise databases
  • Require minimum 4 total supporting reads for fusion calling [7]

• Validation:

  • Integrate DNA and RNA evidence for confident fusion calls
  • Use orthogonal validation (Sanger sequencing, FISH) for novel fusions
  • Cross-reference with known fusion databases (Mitelman database) [11] [9]

Table 4: Key Research Reagent Solutions for FFPE RNA Fusion Detection

Product Category	Specific Examples	Function and Application
Nucleic Acid Extraction	AllPrep DNA/RNA FFPE Kit (Qiagen)	Co-extraction of DNA and RNA from FFPE samples
RNA Quality Assessment	Agilent RNA 6000 Nano Kit	Fragment analysis and DV200 calculation
RNA Quantitation	Qubit RNA HS Assay	Accurate RNA concentration measurement
rRNA Depletion Kits	NEBNext rRNA Depletion Kit	Removal of ribosomal RNA for total RNA-seq
Low-Input RNA Library Prep	TaKaRa SMARTer Stranded Total RNA-Seq Kit v2	Library preparation from limited RNA input (1ng)
3' mRNA-Seq Kits	QuantSeq FPPE (Lexogen)	3' focused gene expression profiling
Whole Transcriptome Kits	CORALL FFPE (Lexogen)	Full transcriptome coverage for fusion detection
DNA NGS Panels	Tempus xT (648 genes)	Targeted DNA sequencing for fusion detection
RNA NGS Panels	Tempus xR (Whole transcriptome)	RNA sequencing for fusion validation
Fusion Validation	FusionPlex BBI Solid Tumor Panel	Orthogonal validation of fusion events

FFPE tissues remain indispensable for cancer research, particularly in the era of precision oncology where gene fusion detection directly informs therapeutic decisions. By implementing rigorous quality control metrics (DV200/DV100, concentration thresholds), selecting appropriate library preparation strategies based on RNA quality, and utilizing integrated DNA-RNA sequencing approaches, researchers can successfully overcome the inherent challenges of FFPE-derived nucleic acids. The combined DNA-RNA NGS approach increases detection of clinically actionable fusions by 21% compared to DNA sequencing alone, potentially expanding patient eligibility for matched targeted therapies [7]. As sequencing technologies evolve, particularly long-read transcriptome sequencing and advanced computational tools like GFvoter, the detection of complex fusion events and their isoforms will continue to improve, further unlocking the potential of archived FFPE collections for transformative cancer research.

Oncogenic gene fusions are potent drivers of cancer and represent critical biomarkers for targeted therapies [1]. The reliable detection of these fusions via RNA sequencing is paramount for personalized cancer treatment, making the rigorous validation of analytical methods a cornerstone of clinical genomics. Establishing robust detection limits—specifically the Limit of Detection (LOD) and reproducibility—ensures that assays can reliably identify therapeutically relevant fusions, even at low expression levels or in challenging sample types like Formalin-Fixed, Paraffin-Embedded (FFPE) tissues [48] [27]. This application note details the experimental protocols and metrics essential for validating an RNA sequencing assay for gene fusion detection in cancer research, providing a framework for researchers, scientists, and drug development professionals.

Key Experimental Approaches and Performance Metrics

Various targeted RNA sequencing approaches have been developed and validated to detect gene fusions with high sensitivity and specificity. The table below summarizes the performance metrics reported for different assay methodologies.

Table 1: Analytical Validation Metrics of RNA Sequencing Assays for Fusion Detection

Assay / Study	Methodology	Positive Percent Agreement (PPA)	Negative Percent Agreement (NPA)	Limit of Detection (LoD)	Reproducibility
FoundationOneRNA [49] [50]	Hybrid-capture-based targeted RNA-seq (318 fusion genes)	98.28%	99.89%	21-85 supporting reads; 1.5-30 ng RNA input	100% (10/10 pre-defined fusions)
RNA Fusion Panel (ArcherDX) [48]	Anchored Multiplex PCR (AMP) targeting 17 genes	>99%	>99%	50 copies for most fusion transcripts	>99%
Tempus xR [7]	Whole-transcriptome RNA-seq	98.2%	99.993%	LOB set at 3 total supporting reads; ≥4 reads for positive call	Established via GLMM*
Targeted RNA-seq (Research) [51]	Two panels for hematological (188 genes) and solid (241 genes) tumors	Increased diagnostic rate from 63% to 76% vs. FISH/RT-PCR	N/A	50% detection at 2 pM input (using spike-in standards)	High concordance with previous diagnoses

*GLMM: Generalized Linear Mixed Model, a statistical approach for characterizing reproducibility [52].

Determining the Limit of Detection (LOD)

The LOD is the lowest concentration of an analyte that can be reliably detected by an assay. For gene fusion detection, this is often defined in terms of input RNA and supporting sequencing reads.

Protocol: LOD Determination via Cell Line Dilution

This protocol outlines the standard procedure for establishing the analytical sensitivity of an RNA sequencing assay for fusion detection.

I. Materials and Reagents

• Fusion-Positive Cell Lines: Select cell lines harboring known, clinically relevant fusions (e.g., H2228 for EML4-ALK) [27].
• Fusion-Negative Background RNA: RNA from a cell line known not to contain the target fusions (e.g., GM12878) [51].
• RNA Extraction Kit: A robust kit suitable for the sample type (e.g., Qiagen AllPrep DNA/RNA FFPE kit for clinical samples) [48].
• Library Preparation Kit: Kit compatible with the chosen NGS methodology (e.g., Archer FusionPlex for AMP, or hybrid-capture reagents) [49] [48].
• NGS Platform: Illumina MiSeq, NextSeq, or similar.

II. Experimental Procedure

• Prepare Serial Dilutions: Create a dilution series of RNA from the fusion-positive cell line into the fusion-negative background RNA. A typical series may include dilutions of 1:10, 1:100, 1:1000, and 1:10,000 [51].
• Extract and Quantify RNA: Extract total RNA from all samples and dilutions. Quantify RNA concentration using a fluorometric method (e.g., Qubit) [48].
• Library Preparation and Sequencing: Process all samples through the established RNA-seq workflow, including cDNA synthesis, library preparation, and sequencing on the NGS platform. Maintain consistent sequencing depth across runs.
• Bioinformatic Analysis: Process the raw sequencing data through the fusion detection pipeline (e.g., using tools like STAR-Fusion, FusionCatcher, or custom algorithms) [51] [9].

III. Data Analysis and LOD Calculation

• The LOD is determined as the lowest dilution level at which the fusion is consistently detected with 95% confidence [7].
• This is expressed as the minimum number of supporting reads (e.g., 21-85 reads) and the minimum input RNA (e.g., 1.5 ng) at that dilution [49] [50].
• An alternative method uses spike-in controls (e.g., fusion sequins) at known concentrations to establish an absolute detection limit (e.g., 50% detection at 2 pM input) [51].

Assessing Assay Reproducibility

Reproducibility measures the precision of an assay under varying conditions, such as different operators, instruments, or days. It ensures consistent results across routine clinical use.

Protocol: Reproducibility and Precision Testing

I. Materials and Reagents

• Reference Standards: Well-characterized samples, such as fusion-positive cell lines or synthetic RNA controls. A calibrated reference standard is recommended for identifying inconsistencies [48].
• Multiple Reagent Lots: Use at least two different lots of key reagents (e.g., library prep kits, capture probes) [7].

II. Experimental Procedure

• Intra-Run Precision: Process the same reference standard sample in multiple replicates (n≥3) within a single sequencing run.
• Inter-Run Precision: Process the same reference standard across multiple different sequencing runs conducted on different days.
• Inter-Operator Precision: Have multiple trained operators independently prepare libraries from the same sample set.
• Inter-Lot Precision: Perform the assay using different lots of critical reagents.

III. Data Analysis and Interpretation

• Calculate the positive percent agreement for the fusion calls across all conditions. A result of 100% reproducibility (e.g., 10/10 fusions consistently detected) is achievable in validated assays [49] [50].
• For a more statistical characterization, a Generalized Linear Mixed Model (GLMM) can be employed. This model estimates the variability of the LOD across different laboratories or experimental conditions, providing a quantitative measure of reproducibility [52].

Integrated Workflow for Fusion Detection and Validation

The following diagram illustrates the complete workflow, from sample preparation to analytical validation, for establishing robust detection limits in gene fusion detection.

The Scientist's Toolkit: Essential Research Reagents

The table below lists key reagents and materials critical for successfully establishing detection limits for gene fusion detection assays.

Table 2: Essential Research Reagents for RNA Fusion Assay Validation

Reagent / Material	Function	Example Products / Notes
FFPE RNA Extraction Kit	Isolves high-quality RNA from archived clinical samples.	Qiagen AllPrep DNA/RNA FFPE kit [48].
RNA Quantitation Kit	Accurately measures RNA concentration; more reliable for FFPE RNA than absorbance.	Qubit fluorometric quantification [48].
cDNA Synthesis Kit	Generates complementary DNA from RNA templates for library prep.	Archer FusionPlex reagents [48].
Targeted RNA Library Prep Kit	Prepares sequencing libraries enriched for fusion-related genes.	FoundationOneRNA; Archer FusionPlex [49] [48].
Fusion-Positive Control RNA	Serves as a positive control for assay development and LOD studies.	RNA from cell lines like K562 (BCR-ABL1), H2228 (EML4-ALK) [51] [27].
Fusion Spike-in Controls	Synthetic RNA molecules used for absolute quantification of LOD.	Fusion sequins [51].
NGS Platform	Executes high-throughput sequencing of prepared libraries.	Illumina MiSeq, NextSeq [48].
Bioinformatic Tools	Software for identifying fusion transcripts from raw sequencing data.	STAR-Fusion, FusionCatcher, JAFFAL, GFvoter [51] [9].

In the context of RNA sequencing for gene fusion detection in cancer research, bioinformatic pipelines face a fundamental challenge: minimizing false negatives to ensure that clinically actionable fusions are not missed, while controlling false positives to avoid misleading clinical conclusions and unnecessary follow-up testing. Gene fusions are a hallmark of cancer, driving approximately 20% of human cancer morbidity, and their accurate identification is paramount for diagnosis, prognosis, and guiding targeted therapies [24]. However, fusion detection remains challenging due to artifacts introduced during library preparation, sequence alignment difficulties, and the inherent complexity of genomic rearrangements [30]. This application note details established protocols and data analysis strategies to optimize this critical balance, enabling reliable identification of therapeutically relevant gene fusions in clinical and research settings.

Performance Benchmarking of Fusion Detection Tools

The choice of computational tools significantly impacts the balance between sensitivity and specificity. Independent benchmarking studies provide crucial quantitative data for informed tool selection.

Table 1: Performance Comparison of Short-Read RNA-Seq Fusion Detection Tools

Tool	Sensitivity (Recall)	Precision	Key Strengths	Runtime Efficiency
Arriba	88/150 (58.7%) fusions at 5x expression level [30]	High (Superior enrichment of validated predictions) [30]	Fast; sensitive detection of low-expression fusions; identifies fusions with intergenic breakpoints [30]	<1 hour per sample [30]
STAR-Fusion	Varies by dataset and expression level [30]	Varies by dataset and expression level [30]	Commonly used; part of a widely adopted RNA-seq aligner suite [30]	Computationally demanding [30]
FusionCatcher	Lower than Arriba on benchmark data [30]	Lower than Arriba on benchmark data [30]	Can use a list of known fusions for sensitive parameters [30]	Computationally demanding [30]
JAFFA	Lower than Arriba on benchmark data [30]	Lower than Arriba on benchmark data [30]	Hybrid (assembly and alignment) approach [30]	Computationally intensive, scalability challenges [53]

For long-read transcriptome sequencing (PacBio, Nanopore), new tools are emerging. GFvoter, a tool that employs a multivoting strategy, has demonstrated superior performance on real and simulated datasets, achieving an average F1 score (harmonic mean of precision and recall) of 0.569, outperforming LongGF, JAFFAL, and FusionSeeker [9]. GFvoter successfully identified the RPS6KB1::VMP1 fusion in the MCF-7 cell line, which was missed by other tools tested [9].

Table 2: Performance of Long-Read RNA-Seq Fusion Detection Tools (Real Datasets)

Tool	Average Precision	Average Recall	Average F1 Score
GFvoter	58.6%	Varies by dataset	0.569
LongGF	39.5%	Varies by dataset	0.407
JAFFAL	30.8%	Varies by dataset	0.386
FusionSeeker	35.6%	Varies by dataset	0.291

Experimental Protocols for Robust Fusion Detection

Protocol 1: Whole Transcriptome Sequencing (WTS) from FFPE Samples

This protocol is validated for formalin-fixed, paraffin-embedded (FFPE) tumor samples, a common but challenging material source in clinical practice [24].

Materials and Reagents:

• RNA Extraction: RNeasy FFPE Kit (Qiagen) [24].
• RNA Quality Control: NanoDrop 8000, Qubit 3.0, Agilent 2100 Bioanalyzer [24].
• rRNA Depletion: NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (NEB) [24].
• Library Preparation: NEBNext Ultra II Directional RNA Library Prep Kit (NEB) with custom adapters and indexes [24].
• Sequencing Platform: Gene+seq 2000 or equivalent for 100 bp paired-end reads [24].

Procedure:

• Sample Selection: Use FFPE samples stored at 4°C for less than one year. Sections should be 10 slices of a 5x5 mm² tissue piece with tumor content >20% [24].
• RNA Extraction: Extract total RNA using the RNeasy FFPE Kit according to the manufacturer's instructions [24].
• Quality Control (QC): Assess RNA concentration and integrity. A DV200 value ≥ 30% is critical for proceeding. The optimal input is >100 ng of RNA with a concentration of >40 copies/ng [24].
• rRNA Depletion: Deplete ribosomal RNA using the NEBNext rRNA Depletion Kit. For samples with DV200 ≤ 50%, skip the fragmentation step [24].
• Library Preparation: Construct sequencing libraries using the NEBNext Ultra II Directional RNA Library Prep Kit. Use custom primers for multiplexing [24].
• Sequencing: Sequence libraries to a minimum depth of 80 million mapped reads per sample to ensure sensitivity [24].

Protocol 2: Integrated DNA and RNA-Based Targeted Sequencing

This protocol uses a custom panel to simultaneously interrogate fusions at the DNA and RNA level, providing complementary information that enhances detection accuracy [11].

Materials and Reagents:

• Custom Targeted Panel: A panel targeting 16 or more therapy-related genes (e.g., ALK, ROS1, RET, NTRK) for both DNA and RNA analysis [11].
• Orthogonal Validation: Equipment for Sanger sequencing [11].

Procedure:

• Nucleic Acid Extraction: Co-extract DNA and RNA from the same FFPE sample block.
• Library Preparation: Prepare separate DNA and RNA sequencing libraries using the custom targeted panel.
• Sequencing: Sequence both libraries on an NGS platform.
• Data Analysis: Analyze DNA and RNA data independently and then integrate results.
• Analytical Validation: Establish that fusions can be detected down to 5% DNA mutational abundance and 250-400 RNA copies/100 ng input. Assess precision with intra- and inter-assay reproducibility tests [11].

Key Finding: The DNA and RNA assays are complementary. In one study, the DNA assay missed fusions (e.g., ETV6::NTRK3, CCDC6::RET) that the RNA assay detected, and conversely, the RNA assay missed some fusions (e.g., TRIM46::NTRK1, CD74::ROS1) that the DNA assay detected. The integrated approach achieved 100% sensitivity and specificity after resolving discrepancies [11].

Wet-Lab Workflow Optimization

The following diagram illustrates the critical steps and quality control checkpoints in the sample preparation workflow that directly influence the false positive/negative rate.

Bioinformatic Pipeline and Filtering Strategy

A multi-layered bioinformatic approach is essential to filter out artifacts while retaining true positive fusions. The following diagram outlines a consensus strategy.

Key Filtering Steps:

• Primary Filtering: Remove calls with low supporting read counts or poor mapping quality. The required number of supporting reads may vary by tool and sequencing depth [24] [30].
• Annotation Filtering: Filter against a curated list of reportable genes (e.g., 553 genes with diagnostic, prognostic, or therapeutic value) to reduce false positives from non-relevant fusions. This also excludes common artifacts such as read-through transcripts and pseudogenes [24].
• Advanced Filters:
  • RNA-seq Coverage Imbalance: For kinase fusions like RET, analyze the 5'/3' coverage imbalance. True oncogenic fusions often show a distinct pattern of high 3' coverage (kinase domain) and low 5' coverage, which can discriminate true positives from false positives with 100% sensitivity and specificity when optimized [21].
  • Reading Frame Analysis: For protein-coding fusions, check if the fusion junction maintains an open reading frame, which is more likely for a functional fusion protein.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents and Kits for RNA-Seq Based Fusion Detection

Reagent/Kits	Primary Function	Application Note
RNeasy FFPE Kit (Qiagen)	RNA extraction from FFPE tissue	Maintains RNA integrity for degraded samples; critical for archival clinical material [24].
NEBNext rRNA Depletion Kit (NEB)	Removal of ribosomal RNA	Enriches for mRNA transcripts; preferred over poly-A selection for degraded RNA [24].
NEBNext Ultra II Directional RNA Library Prep Kit (NEB)	Preparation of sequencing-ready libraries	Compatible with low-input and degraded RNA from FFPE samples [24].
QIAseq RNAscan Custom Panel (Qiagen)	Targeted PCR-based RNA-seq	Uses UMI for error correction; highly sensitive for low-expression fusions in a defined gene set [53].
ERCC RNA Spike-In Mix (Thermo Fisher)	External RNA controls	Monitors technical performance and enables cross-laboratory benchmarking of expression data [54].
Commercial Fusion Reference Standards (e.g., GeneWell)	Positive controls for validation	Contains validated fusion transcripts for determining assay LOD, precision, and accuracy [11].

Optimizing a bioinformatic pipeline for gene fusion detection requires a holistic approach that integrates stringent wet-lab protocols, judicious selection of computational tools, and multi-step bioinformatic filtering. Adherence to strict RNA quality controls (DV200 ≥ 30%), sufficient sequencing depth (>80M reads), and the use of integrated DNA/RNA or targeted RNA-seq approaches can significantly enhance detection accuracy. Employing modern tools like Arriba for short-read data or GFvoter for long-read data, combined with advanced filtering strategies such as coverage imbalance analysis, provides a robust framework for balancing sensitivity and specificity. This optimized workflow ensures reliable identification of clinically actionable gene fusions, ultimately supporting precise diagnosis and personalized treatment in oncology.

Determining Adequate Sample Size and Sequencing Depth for Reliable Results

Within cancer research, the reliable detection of gene fusions via RNA sequencing (RNA-Seq) is a critical component of molecular diagnostics and therapeutic targeting. Achieving reliable results, however, is profoundly dependent on two fundamental experimental parameters: sample size (the number of biological replicates) and sequencing depth (the number of reads per sample). Inadequate attention to either can lead to both false-positive and false-negative findings, compromising the validity of the study and its potential for clinical translation. This Application Note provides a structured framework, grounded in empirical evidence and statistical principles, to guide researchers in determining these parameters for robust gene fusion detection in the context of cancer.

Determining Sample Size for Robust Statistical Power

Statistical Foundations and Key Parameters

Sample size estimation for RNA-Seq experiments is based on a negative binomial distribution, which accurately models the over-dispersed nature of count data generated by sequencing [55]. The core formula for calculating the required number of samples per group (n) is [55]:

Where the key parameters are:

• μ (Average Read Count): The expected mean number of reads for a gene of interest.
• σ (Coefficient of Variation): The biological variation observed for that gene across replicates.
• Δ (Fold Change): The minimum effect size (e.g., 1.5 for a 50% change) deemed biologically significant.
• α (Significance Level): The probability of a Type I error (false positive), typically set at 0.05.
• β (Power): The probability of correctly rejecting a false null hypothesis (1-β), often set at 0.8 or 0.9.

The values zα/2 and zβ are the critical values from the standard normal distribution corresponding to α and β. For example, for α=0.05 and β=0.10 (90% power), zα/2 = 1.96 and zβ = 1.28 [55].

Empirical Evidence and Practical Guidelines

While power calculations are essential, empirical down-sampling studies from large datasets provide practical guidance. A recent large-scale analysis in murine models offers critical insights applicable to cancer studies [56].

Table 1: Impact of Sample Size on False Discovery Rate (FDR) and Sensitivity [56]

Sample Size (N per group)	False Discovery Rate (FDR)	Sensitivity	Recommendation
N ≤ 4	Unacceptably High (e.g., >50%)	Very Low	Highly misleading; results are unreliable
N = 5	High	Low	Fails to recapitulate the full expression signature
N = 6-7	Decreases to ~50%	Increases to ~50%	Minimum threshold for consistent results
N = 8-12	Further reduced, tapering observed	Significantly improved (e.g., >50%)	Optimal range for reliable discovery

This analysis demonstrated that raising the fold-change cutoff is not an effective substitute for increasing sample size, as this strategy inflates effect sizes and substantially reduces detection sensitivity [56]. For gene fusion studies, where the goal is to detect a qualitative "present/absent" event rather than a quantitative fold-change, sufficient biological replicates remain crucial for accurately estimating the prevalence of a fusion within a population and for accounting for biological variability in expression.

Implementation with Reference Data and RnaSeqSampleSize

Using a single, conservatively chosen value for read count (μ) and dispersion (σ) often leads to over-estimated sample sizes [57]. The recommended approach is to utilize distributions of these parameters from real RNA-seq data of a similar type (e.g., from public repositories like The Cancer Genome Atlas - TCGA).

The RnaSeqSampleSize R package implements this empirical data-based method [57]. The workflow involves:

• Input a reference dataset (e.g., a TCGA cancer dataset) or a list of genes of interest (e.g., a pathway known to harbor fusions).
• Set the experimental parameters: desired power (e.g., 0.8), false discovery rate (FDR, e.g., 0.05), and fold change (e.g., 2).
• The software calculates the required sample size by randomly selecting genes from the reference distribution and estimating the power for each, providing a more accurate and often lower sample size estimate than the single-value method [57].

Table 2: Comparison of Sample Size Estimation Methods for a Hypothetical Study

Estimation Method	Key Features	Input Parameters	Estimated Sample Size (Example)
Single-Value (Conservative)	Uses one minimal read count and maximal dispersion; often overestimates.	Min read count=10, Max dispersion=2.0, FC=2, Power=0.8, FDR=0.05	168 [57]
Empirical Data-Based (RnaSeqSampleSize)	Uses real distributions of read counts and dispersions from a reference dataset.	TCGA READ dataset, FC=2, Power=0.8, FDR=0.05	42 [57]

The following diagram illustrates the statistical workflow for determining sample size.

Optimizing Sequencing Depth for Gene Fusion Detection

Sequencing Depth Guidelines by Application

Sequencing depth must be aligned with the primary analytical goal of the study. While standard gene expression analysis can be performed with moderate depth, the confident detection of gene fusions and other complex features demands greater sequencing effort [58].

Table 3: Recommended Sequencing Depth and Read Length by RNA-Seq Application

Application	Recommended Depth (Mapped Reads)	Recommended Read Length	Rationale
Standard Gene Expression	20 - 40 million [59] [58]	50 - 75 bp, paired-end [60]	Cost-effective for robust quantification of highly expressed genes.
Isoform Detection & Splicing	≥ 100 million [58]	2x75 bp or 2x100 bp, paired-end [58] [60]	Increased depth and length are required to cover low-abundance junctions and resolve complex splice variants.
Fusion Gene Detection	60 - 100 million [58]	2x75 bp as baseline, 2x100 bp for better resolution [58]	Ensures sufficient "split-read" support to anchor breakpoints and identify novel fusion partners.
Allele-Specific Expression	~100 million [58]	2x75 bp or longer, paired-end	Higher depth is essential to accurately estimate variant allele frequencies and minimize sampling error.

For fusion detection, the use of paired-end reads is critical. This approach sequences both ends of a DNA fragment, providing two independent data points that are invaluable for mapping reads across breakpoint junctions and resolving complex rearrangements [61] [62]. A read length of 2x100 bp provides cleaner junction resolution compared to 2x75 bp [58].

The Interplay of Sample Size and Sequencing Depth

A key principle in experimental design is that for a fixed budget, increasing the number of biological replicates (sample size) often provides a greater return on investment than increasing the sequencing depth per sample [56]. While deep sequencing is necessary for fusion detection, an underpowered study with few replicates sequenced very deeply will still yield biologically irreproducible results. The priority should be to first determine the adequate sample size to account for biological variation, then allocate remaining resources to achieve the recommended sequencing depth for the application.

Experimental Protocol for Gene Fusion Detection

A Targeted RNA-Seq Workflow for Clinical Diagnostics

Targeted RNA sequencing panels offer a highly sensitive and cost-effective approach for detecting recurrent gene fusions in a clinical diagnostic setting, with a demonstrated turnaround time of fewer than five days [63]. The following protocol, adapted from a clinical leukemia study, details this workflow.

Workflow Overview: The process involves using an anchored multiplex PCR panel for target enrichment. This method uses gene-specific primers combined with universal adapters to amplify sequences of interest without prior knowledge of the exact partner gene or breakpoint, making it ideal for discovering novel fusions [63].

Step-by-Step Protocol:

• RNA Extraction and QC: Extract total RNA from patient samples (e.g., bone marrow or blood) using a standard Trizol-based or column-based protocol. Assess RNA integrity (RIN or RQS) and quantity. For formalin-fixed, paraffin-embedded (FFPE) samples, use DV200 as a metric; a DV200 > 50% is generally acceptable, but rRNA depletion is preferred over poly(A) selection for degraded samples [58].

• cDNA Synthesis: Convert 1.0 - 1.5 μg of total RNA into complementary DNA (cDNA) using reverse transcriptase (e.g., M-MLV) and oligo(dT) or random hexamer primers [63].

• Targeted Library Preparation (Anchored Multiplex PCR):

  • First PCR (Target Enrichment): Amplify the cDNA using a pool of gene-specific primers for known fusion partners (e.g., a panel covering 20 genes recurrent in leukemia [63]). These primers are paired with universal adapters.
  • Second PCR (Nested PCR): Perform a second, nested PCR using primers that bind to the universal adapter sites. This step adds full sequencing adapters and sample-specific index barcodes, enabling sample multiplexing and increasing amplicon specificity.

• Library QC and Pooling: Quantify the final libraries using a fluorometric method (e.g., Qubit). Assess library size distribution using a bioanalyzer or tape station. Normalize and pool the barcoded libraries in equimolar ratios.

• Sequencing: Sequence the pooled library on a benchtop sequencer (e.g., Illumina MiSeq). Aim for a sequencing depth of 3-5 million reads per sample for a targeted panel, which is sufficient for sensitive fusion detection [60]. Use a 2x75 bp or 2x100 bp paired-end run configuration.

• Bioinformatic Analysis: Demultiplex the sequenced data and align reads to the reference genome using a splice-aware aligner like STAR [59]. Use a fusion detection algorithm (e.g., FusionScan [61]) to identify split reads and discordant read pairs that map to two distinct genes. FusionScan achieves high precision and recall by requiring multiple split reads that join intact exons and implementing extensive filtering to remove false positives.

• Experimental Validation: Confirm putative fusion candidates identified by RNA-Seq using an independent method, such as RT-PCR followed by Sanger sequencing. This is a critical step for verifying results, especially for novel fusions [63].

The Scientist's Toolkit: Essential Reagents and Materials

Table 4: Key Research Reagent Solutions for Targeted RNA-Seq Fusion Detection

Item	Function / Description	Example Product / Method
Targeted RNA-Seq Panel	A pre-designed set of probes/primers to enrich for genes known to be involved in fusions in a specific cancer type.	Archer FusionPlex Panels, Illumina TruSight RNA Panels [63] [60]
Reverse Transcriptase	Enzyme that synthesizes cDNA from an RNA template.	M-MLV Reverse Transcriptase [63]
High-Fidelity DNA Polymerase	Enzyme for PCR amplification with low error rate, critical for accurate sequencing.	Q5 Hot-Start Polymerase
Library Quantification Kit	Fluorometric assay for accurate quantification of DNA library concentration prior to sequencing.	Qubit dsDNA HS Assay Kit
Bioanalyzer/TapeStation	Microfluidic capillary electrophoresis system for assessing library fragment size distribution and quality.	Agilent Bioanalyzer 2100
Stranded RNA Library Prep Kit	For whole-transcriptome approaches, preserves strand information of originating transcript.	Illumina Stranded Total RNA Prep [62]
rRNA Depletion Kit	Removes abundant ribosomal RNA, increasing the sequencing capacity for mRNA and other RNAs.	Illumina Ribozero rRNA Removal Kit
Fusion Detection Software	Bioinformatics tool to identify split reads and discordant read pairs indicative of gene fusions.	FusionScan [61], STAR-Fusion

The rigorous detection of gene fusions in cancer research using RNA-Seq is a process heavily dependent on sound experimental design. Researchers must justify their choices of sample size and sequencing depth based on statistical principles, empirical evidence, and the specific requirements of their biological question. As demonstrated, a sample size of fewer than six biological replicates per group carries a high risk of generating unreliable and irreproducible data, while a depth of 60-100 million paired-end reads provides a solid foundation for confident fusion detection. By adhering to the protocols and guidelines outlined in this document, researchers can optimize resource allocation, enhance the reliability of their findings, and ensure that their conclusions stand up to scientific and clinical scrutiny.

Benchmarking Performance: RNA-Seq vs. Orthogonal Technologies and Real-World Impact

Gene fusions are a critical class of oncogenic drivers in cancer pathogenesis, with significant implications for disease classification, prognosis, and therapeutic targeting [64] [7]. The detection of these structural variants has evolved substantially with the advent of advanced genomic technologies. While DNA sequencing (DNA-Seq) has been a cornerstone in genomic profiling, it faces limitations in detecting certain fusion events due to large intronic regions and complex structural rearrangements [7]. RNA sequencing (RNA-Seq) emerged as a powerful complementary approach that directly captures expressed fusion transcripts, overcoming some of DNA-Seq's limitations [7]. More recently, optical genome mapping (OGM) has introduced a novel approach for comprehensive structural variant detection without sequencing [64] [65]. This application note provides a comparative analysis of these technologies, focusing on their respective strengths, limitations, and implementation protocols for gene fusion detection in cancer research and drug development.

Detection Capabilities Across Platforms

Table 1: Comparative Performance of Genomic Technologies for Fusion Detection

Technology	Detection Principle	Key Advantages	Major Limitations	Best Applications
DNA-Seq	DNA-level variant detection across targeted genes	Detects coding variants, CNVs, and some fusions; established workflows	Limited fusion detection due to large introns; misses enhancer hijacking	First-line genomic profiling; SNV/indel detection
RNA-Seq	Sequence expressed transcripts	Direct evidence of functional fusion transcripts; unaffected by breakpoint location	Limited to expressed fusions; misses events without fusion transcript generation	Therapeutic target validation; expressed fusion confirmation
OGM	Imaging of ultra-high molecular weight DNA	Genome-wide SV detection; identifies cryptic rearrangements and enhancer hijacking	May miss small intrachromosomal deletions interpreted as simple deletions	Comprehensive SV profiling; complex rearrangement analysis

Recent large-scale studies have demonstrated the complementary nature of these technologies. A pan-cancer analysis of 67,278 patients revealed that combining RNA-Seq with DNA-Seq increased the detection of clinically actionable gene fusions by 21% compared to DNA-Seq alone [7]. The study identified 1,497 patients (2.2%) with at least one of nine therapeutically targetable fusions, with 29% of these fusions detected outside their FDA-approved cancer indications, highlighting the value of comprehensive genomic profiling [7].

A focused analysis in acute leukemia compared a 108-gene targeted RNA-Seq panel with OGM in 467 cases [64] [66]. The overall concordance rate between methods was 74.7% for 234 detected gene rearrangements and fusions. However, significant differences emerged in specific detection capabilities: OGM uniquely identified 37 of 234 (15.8%) clinically relevant rearrangements, while RNA-Seq exclusively identified 22 of 234 (9.4%) [64]. The technologies showed markedly different performance profiles depending on the biological mechanism of the structural variant.

Quantitative Performance Metrics

Table 2: Detection Performance Across Acute Leukemia Types

Leukemia Type	Cases with ≥1 Rearrangement	Tier 1 Aberration Detection Rate	RNA-Seq/OGM Concordance
AML (n=360)	36.1%	23.9%	High (specific rate not provided)
B-ALL (n=89)	~75% (estimated)	60.7%	80.2%
T-ALL (n=12)	75%	Not specified	41.7%
Overall (n=467)	43.6%	31.5%	74.7%

Enhancer-hijacking lesions demonstrated particularly poor concordance between platforms (20.6% versus 93.1% for all other aberrations, p < 0.001) [64]. These events, including MECOM, BCL11B, and IGH rearrangements, were predominantly detected by OGM, as they often do not generate fusion transcripts targeted by RNA-Seq panels [64] [66]. Conversely, RNA-Seq slightly outperformed OGM for fusions arising from intrachromosomal deletions that were sometimes labeled by OGM as simple deletions [64].

Experimental Protocols

RNA-Seq Fusion Detection Protocol

Sample Preparation and Library Construction

• RNA Extraction: Isolate total RNA from fresh or frozen tissue (tumor biopsy), peripheral blood, or bone marrow aspirate using standardized extraction kits (e.g., PAXgene Blood RNA kit, QIAsymphony SP/AS platform) [67] [68]. Assess RNA quality and integrity using appropriate methods (e.g., Qubit fluorometer, Agilent Bioanalyzer).
• Library Preparation: Utilize either:
  • Whole Transcriptome Approach: Deplete rRNA using NEBNext Globin and rRNA Depletion Kit, followed by library preparation with NEBNext Ultra Directional RNA Library Prep Kit [68].
  • Targeted Panels: Employ anchored multiplex PCR (AMP) for target enrichment, using gene-specific primers to capture known and novel fusion partners [64]. The 108-gene panel described in acute leukemia studies uses unidirectional GSP2 primers targeting exons of translocated genes [64].
• Sequencing: Perform bidirectional sequencing on Illumina platforms (NovaSeq PE150, SeqStudio) with approximately 100 million reads per sample for whole transcriptome, or sufficient coverage for targeted panels [64] [68].

Data Analysis Pipeline

• Alignment: Map reads to reference genome (GRCh37/hg19 or GRCh38) using STAR aligner in two-pass mode [68].
• Fusion Calling: Utilize specialized algorithms (STAR-Fusion, Archer Analysis Software v6.2.7, or ensemble methods combining multiple callers) [64] [7].
• Filtering and Annotation: Remove technical artifacts, filter by supporting read count (typically ≥4 reads), and annotate fusions using reference databases [7].

Optical Genome Mapping Protocol

Sample Preparation and Imaging

• DNA Extraction: Isolate ultra-high molecular weight (UHMW) DNA from fresh or frozen cells (peripheral blood, bone marrow) using specialized kits (Bionano Prep SP Blood and Cell DNA Isolation Kit) [64] [65]. Critical: maintain DNA integrity with minimal fragmentation.
• DNA Labeling: Fluorescently label DNA at specific sequence motifs (CTTAAG for DLE-1 enzyme) using direct label and stain (DLS) chemistry [65]. Label density typically achieves 14-17 signals per 100 kb.
• Array Processing: Load labeled DNA onto Saphyr chips for linearization through nanochannels and image using Saphyr system [64] [67]. Target effective genome coverage >300× with molecule N50 values >250 kb.

Data Analysis

• Genome Mapping: Perform de novo assembly using Bionano Solve software (v3.7) and align to reference genome (GRCh38/hg38) [64] [69].
• Variant Calling: Identify SVs using Rare Variant Analysis Pipeline with standard filter settings in Bionano Access software (v1.6-1.8.2) [64] [67].
• Variant Annotation: Filter SVs against control databases (frequency <1%), annotate with relevant disease genes, and prioritize based on location and potential functional impact [69].

DNA-Seq Fusion Detection Protocol

Sample Preparation and Sequencing

• DNA Extraction: Isolate genomic DNA from formalin-fixed paraffin-embedded (FFPE) tumor tissue or fresh samples using standard kits (QIAamp DNA Mini Kit) [67] [7].
• Library Preparation: Utilize targeted NGS panels (e.g., Tempus xT, 648 genes) with enhanced intronic coverage for select genes to improve fusion detection [7].
• Sequencing: Sequence on appropriate NGS platforms (Illumina S5, NovaSeq) with sufficient coverage (typically 500× for DNA-Seq) [7].

Structural Variant Analysis

• Variant Calling: Use structural variant callers (LUMPY) to identify rearrangements from discordant read pairs and split reads [7].
• Annotation and Filtering: Annotate SVs with gene information and filter against population databases and internal controls.

The Scientist's Toolkit

Table 3: Essential Research Reagents and Platforms

Category	Product/Platform	Application	Key Features
RNA Extraction	PAXgene Blood RNA kit (Qiagen)	RNA stabilization from blood samples	Maintains RNA integrity for transcriptome studies
RNA-Seq Library	NEBNext Ultra Directional RNA Library Prep Kit	Whole transcriptome library preparation	Preserves strand information; compatible with rRNA depletion
Targeted RNA-Seq	Archer FusionPlex Panels	Fusion detection in targeted genes	AMP chemistry for novel partner identification
UHMW DNA Isolation	Bionano Prep SP Blood and Cell DNA Isolation Kit	DNA extraction for OGM	Maintains long DNA fragments essential for mapping
OGM Labeling	Bionano DLS Labeling Kit	Fluorescent DNA labeling	Sequence-specific labeling for pattern recognition
OGM Platform	Bionano Saphyr System	Genome imaging and analysis	High-throughput imaging with nanochannel arrays
Analysis Software	Bionano Access & Solve	OGM data analysis	SV calling, visualization, and annotation
Fusion Callers	STAR-Fusion, Archer Analysis	RNA-Seq fusion detection	Sensitive algorithms for fusion transcript identification

Integration Strategies and Clinical Applications

Complementary Roles in Diagnostic Workflows

The integration of RNA-Seq and OGM provides a comprehensive approach for structural variant detection, with each technology compensating for the limitations of the other. In pediatric acute lymphoblastic leukemia (ALL), the combination of digital MLPA and RNA-Seq achieved precise classification in 95% of cases, significantly outperforming standard-of-care techniques (46.7%) [67]. OGM as a standalone test demonstrated superior resolution in detecting chromosomal gains and losses (51.7% vs. 35%) and gene fusions (56.7% vs. 30%) compared to conventional methods [67].

For complex structural variants, OGM provides unparalleled capability in resolving intricate rearrangement patterns. In one study of neurodevelopmental disorders, OGM detected a complex rearrangement involving chromosomes 2 and 6 that was much more complex than indicated by conventional cytogenetic analysis [69]. The technology revealed that 17 segments of 6q15 spanning 9.3 Mb were disarranged and joined to 2q11.2, demonstrating its power in resolving complicated genomic architectures.

Functional Validation through Transcriptomic Analysis

RNA-Seq plays a critical role in validating the functional consequences of structural variants identified at the DNA level. In the analysis of constitutional abnormalities, RNA-Seq confirmed the pathogenicity of three SVs detected by OGM by demonstrating aberrant expression of the affected genes [69]. This integrated approach solved three previously undiagnosed cases of neurodevelopmental disorders, including a deletion encompassing the promoter and 5'UTR of MBD5 and an intragenic duplication of PAFAH1B1 [69].

The synergy between these technologies is particularly evident in cancer research, where both DNA-level rearrangements and their functional RNA consequences are critical for understanding disease mechanisms and identifying therapeutic targets. The combination provides a complete picture from structural variant to functional outcome, enabling more accurate diagnosis and targeted treatment strategies.

RNA-Seq, DNA-Seq, and OGM offer complementary approaches for gene fusion detection in cancer research. RNA-Seq excels at identifying expressed fusion transcripts with direct therapeutic relevance, while OGM provides comprehensive genome-wide structural variant detection, particularly for cryptic rearrangements and enhancer hijacking events. DNA-Seq remains valuable for detecting coding variants and copy number changes. The integration of these technologies, through workflows and protocols outlined in this application note, provides researchers with a powerful toolkit for comprehensive genomic characterization. This multi-platform approach maximizes detection of clinically actionable variants, ultimately advancing precision oncology and drug development efforts.

The accurate detection of gene fusions is a critical component of precision oncology, directly influencing diagnostic stratification and therapeutic decisions. While fluorescence in situ hybridization (FISH) and reverse transcription-polymerase chain reaction (RT-PCR) have long been the standard methods in clinical practice, RNA sequencing (RNA-seq) presents a powerful alternative with the potential for higher multiplexing capability and the discovery of novel fusion partners. This document details the experimental protocols and summarizes key concordance data from recent clinical studies that validate targeted RNA-seq against FISH and RT-PCR across various patient cohorts and cancer types. The evidence supports the integration of RNA-seq into comprehensive genomic profiling workflows to enhance the detection of clinically actionable gene fusions.

Recent validation studies across different solid tumors and hematological malignancies have consistently demonstrated high concordance between RNA-based next-generation sequencing (NGS) and traditional methods.

Table 1: Concordance of RNA-seq with Orthogonal Methods in Clinical Cohorts

Cancer Type / Cohort	RNA-seq Method	Orthogonal Method	Key Concordance Findings	Citation
Diverse Solid Tumors (n=160)	FoundationOneRNA (Targeted)	Orthogonal NGS (DNA- or RNA-based)	PPA: 98.28%; NPA: 99.89%	[70] [50]
Early-stage NSCLC (RET+) (n=39)	Whole-Transcriptome Sequencing (WTS)	FISH	Concordance: 84.6%	[38]
Early-stage NSCLC (RET+) (n=40)	DNA-seq	FISH	Concordance: 82.5%	[38]
Broad Clinical Cancer Cohort	Targeted RNA-seq (FusionPlex)	FISH & RT-PCR	Increased diagnostic rate to 76%, from 63% with conventional methods alone	[51]
Hematological Malignancies (n=21)	Targeted RNA-seq (FusionPlex)	Whole-Transcriptome Sequencing	Concordance for known fusions: 100%	[71]

PPA: Positive Percent Agreement; NPA: Negative Percent Agreement

These studies underscore the robustness of RNA-seq. The FoundationOneRNA assay demonstrated exceptional accuracy in a diverse set of clinical solid tumor specimens [70] [50]. In a focused study on early-stage non-small cell lung cancer (NSCLC) with RET fusions, RNA-seq showed high concordance with FISH, comparable to that of DNA-seq [38]. Furthermore, targeted RNA-seq has been shown to increase the overall diagnostic yield of fusion genes in a clinical cohort, identifying actionable fusions that were missed by conventional testing algorithms [51].

Detailed Experimental Protocols

Protocol 1: Retrospective Concordance Study Using Archived FFPE Samples

This protocol is adapted from validation studies that compared RNA-seq with FISH and RT-PCR using existing patient samples [38] [70].

Sample Selection and Preparation

• Sample Type: Formalin-Fixed, Paraffin-Embedded (FFPE) tumor tissue samples.
• Cohort Selection: Select samples with prior FISH and/or RT-PCR results, enriching for both positive and negative cases to ensure a robust validation. Include samples with borderline or equivocal results if available.
• Macrodissection: Review H&E-stained slides and macrodissect or microdissect (e.g., Laser Capture Microdissection) areas with high tumor cellularity (≥70%) to minimize non-neoplastic background [72].
• Nucleic Acid Co-Extraction: Co-extract DNA and RNA from adjacent FFPE sections (e.g., 5-10 μm thick) using commercial kits such as the AllPrep DNA/RNA FFPE Kit (Qiagen). Assess DNA and RNA quantity and quality using a fluorometer (e.g., Qubit) and systems like the TapeStation to determine RNA Integrity Number (RIN) or similar metrics [13] [70].

Targeted RNA Sequencing Library Preparation

• Input: Use 10-200 ng of total RNA.
• Library Construction: Perform library preparation using kits such as the TruSeq stranded mRNA kit (Illumina) for fresh-frozen tissue or SureSelect XTHS2 RNA kit (Agilent) for FFPE-derived RNA [13].
• Target Enrichment: Hybridize libraries with biotinylated oligonucleotide probes designed to target exons of hundreds of genes known to be involved in fusions (e.g., 318 genes in the FoundationOneRNA panel) [70]. A double-capture step can be employed to increase the on-target rate, achieving >90% alignment to targeted regions [51].
• Sequencing: Pool libraries and sequence on an Illumina platform (e.g., NovaSeq 6000 or HiSeq4000) to a depth of 30-50 million total read pairs per sample to ensure adequate coverage for fusion detection [13] [70].

Bioinformatic Analysis for Fusion Detection

• Alignment: Map sequencing reads to the human reference genome (e.g., hg19 or hg38) using aligners such as STAR or BWA.
• Fusion Calling: Utilize a consensus approach from multiple fusion detection algorithms (e.g., STAR-Fusion and FusionCatcher) to increase specificity [51]. Filtering criteria typically include:
  • A minimum number of supporting chimeric reads (e.g., ≥10 for known fusions, ≥50 for novel putative drivers) [70].
  • Removal of calls in repetitive genomic regions.
  • Annotation of fusion partners and breakpoints.
• Orthogonal Confirmation: Plan for orthogonal validation (e.g., by FISH or digital PCR) for any novel or unexpected high-impact fusions identified by RNA-seq [71].

Protocol 2: Analytical Validation for Sensitivity and Reproducibility

This protocol outlines the steps for determining the analytical sensitivity (Limit of Detection) and precision of an RNA-seq fusion assay, which is critical for clinical application [70].

Limit of Detection (LoD) Determination

• Sample Preparation: Use RNA from fusion-positive cell lines (e.g., K562 for BCR-ABL1). Create a dilution series of the fusion-positive RNA into fusion-negative RNA (e.g., from GM12878 cell line) at ratios from 1:10 down to 1:10,000, simulating varying tumor purity and fusion transcript abundance [51].
• Testing and Analysis: Process each dilution in multiple replicates (n≥5). The LoD is defined as the lowest input quantity (e.g., 1.5 ng RNA) or dilution factor at which the fusion is detected in ≥95% of replicates. For absolute quantification, synthetic RNA spike-in controls (e.g., "fusion sequins") with known concentrations can be used [51] [70].

Precision (Reproducibility and Repeatability) Assessment

• Experimental Design: Select 10-20 FFPE samples harboring different fusions. Process each sample across multiple replicates (n=3), multiple sequencing runs (n=3), and different days/operators [70].
• Analysis: Calculate the positive percent agreement for fusion detection across all replicates. The assay is considered precise if it demonstrates 100% concordance for all pre-defined target fusions across all experimental conditions [70] [50].

Visual Workflow and Pathway Diagrams

Integrated RNA-seq Clinical Validation Workflow

The diagram below illustrates the end-to-end process for validating RNA-seq for fusion detection against standard methods.

Method Comparison and Complementarity Logic

This diagram outlines the decision logic for when different molecular methods are most applicable and how their results complement each other.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents and Kits for RNA-seq Fusion Detection Studies

Item	Function/Application	Example Products
Nucleic Acid Co-Extraction Kit	Simultaneous purification of DNA and RNA from a single FFPE sample, preserving the limited specimen.	AllPrep DNA/RNA FFPE Kit (Qiagen) [13]
Targeted RNA-seq Panel	Biotinylated probe sets for enriching fusion-related transcripts prior to sequencing, increasing sensitivity.	FoundationOneRNA Panel [70], Custom Panels (e.g., 241-gene solid tumor panel) [51]
RNA Spike-in Controls	Synthetic RNA molecules added to samples to quantitatively assess sensitivity, enrichment efficiency, and detectability limits.	ERCC Spike-in Mix, Fusion Sequins [51]
Library Prep Kit	Prepares RNA sequencing libraries from input RNA, often optimized for degraded FFPE-derived RNA.	TruSeq Stranded mRNA Kit (Illumina) [13], SureSelect XTHS2 RNA Kit (Agilent) [13]
Bioinformatic Tools	Software packages for aligning sequencing reads and calling fusion events with high specificity.	STAR-Fusion, FusionCatcher [51]
Orthogonal Validation Assay	Independent method to confirm novel or uncertain fusion calls identified by RNA-seq.	Digital PCR Assays [71], FISH Probes [70]

The protocols and data summarized herein provide a robust framework for the clinical validation of RNA sequencing in the detection of gene fusions. The high concordance rates with established methods like FISH and RT-PCR, combined with the inherent advantages of multiplexing and novel fusion discovery, position targeted RNA-seq as an indispensable tool in modern oncology. Its integration into comprehensive genomic profiling, often alongside DNA-based analysis, ensures the most complete molecular characterization of tumors, ultimately facilitating personalized treatment strategies and improving patient outcomes.

The emergence of tumour-agnostic therapeutic paradigms, where treatment is guided by molecular alterations rather than tissue of origin, has fundamentally shifted diagnostic requirements in clinical oncology [73]. This precision medicine approach creates a pressing need for comprehensive genomic profiling that can reliably detect a broad spectrum of biomarker classes across diverse cancer types. While DNA-based next-generation sequencing (NGS) has become a cornerstone of molecular diagnostics, it possesses inherent limitations for detecting certain structural variants and expressed biomarkers. The integration of RNA sequencing with DNA analysis presents a powerful solution to these limitations, significantly expanding the diagnostic capability available to researchers, scientists, and drug development professionals. This application note synthesizes recent pan-cancer evidence demonstrating the substantial added diagnostic yield of combined RNA/DNA sequencing, provides validated experimental protocols for implementation, and illustrates key bioinformatic workflows essential for clinical-grade fusion detection.

The Diagnostic Limitation of DNA-Only Sequencing

DNA-based sequencing approaches, whether employing targeted panels or whole exome sequencing, effectively detect single nucleotide variants (SNVs), small insertions/deletions (indels), and copy number variations (CNVs). However, they face inherent challenges in identifying gene fusions, exon-skipping events, and other expressed alterations that are critical therapeutic targets.

The fundamental limitation stems from the nature of genomic rearrangements. DNA sequencing relies on sufficient coverage across breakpoint regions, which can be expansive and unpredictable. For instance, DNA-based assays may miss functionally critical fusions due to several factors [27]:

• Breakpoints in non-coding or intronic regions not covered by targeted panels
• Large genomic rearrangements with breakpoints outside sequenced regions
• Complex structural variations difficult to resolve from short-read DNA data
• Expression-independent events that may not be biologically relevant

These limitations are not merely theoretical. In one validation study, an RNA sequencing assay identified a previously missed MET fusion in a clinical sample that had been characterized as fusion-negative by a DNA panel. This fusion was subsequently confirmed by RT-PCR and Sanger sequencing, revealing a false-negative result in the DNA-only approach [27].

Table 1: Limitations of DNA-Only Sequencing for Fusion Detection

Limitation Factor	Impact on Detection	Clinical Consequence
Breakpoints in non-coding regions	Inaccessible to targeted panels	Missed actionable fusions (e.g., NTRK, RET)
Large intronic regions	Incomplete coverage of potential breakpoints	Reduced sensitivity for known fusion drivers
Complex rearrangements	Difficult to reconstruct from short reads	Failure to detect novel fusion combinations
Expression ambiguity	Inability to confirm transcription of rearranged genes	Potential reporting of biologically irrelevant fusions

Pan-Cancer Evidence for Combined Sequencing

Enhanced Fusion Detection Sensitivity

The most consistently documented benefit of integrated RNA/DNA sequencing is the significant improvement in gene fusion detection. Evidence from large-scale clinical cohorts demonstrates that RNA sequencing complements DNA analysis by both verifying rearrangements identified at the DNA level and discovering additional fusions missed by DNA-only approaches.

In a comprehensive analysis of 2230 clinical tumor samples, the integration of whole exome sequencing (WES) with RNA sequencing improved the detection of gene fusions and enabled direct correlation of somatic alterations with gene expression profiles [13]. The combined approach recovered clinically actionable fusions that would have been missed by DNA-only testing, with complex genomic rearrangements particularly likely to remain undetected without RNA data.

Similarly, a pan-cancer study of 1166 tissue samples encompassing 29 cancer types utilized a comprehensive DNA/RNA profiling panel and found that 62.3% of samples harbored at least one actionable biomarker [73]. While the vast majority of somatic variants (4.6%) were identified through DNA analysis, a critical 0.1% of significant findings—particularly fusions—were exclusively detected via RNA sequencing. This study further identified at least one tumour-agnostic biomarker (including MSI-high, TMB-high, NTRK/RET fusions, and BRAF V600E) in 8.4% of samples across 26 different cancer types, highlighting the importance of comprehensive profiling for tumour-agnostic treatment strategies.

Table 2: Added Diagnostic Yield of Combined RNA/DNA Sequencing in Pan-Cancer Studies

Study Cohort	Detection Method	Key Findings on Added Yield	Clinical Impact
2230 clinical tumor samples [13]	WES + RNA-seq	Improved fusion detection; Recovery of variants missed by DNA-only; Complex rearrangement identification	Actionable alterations found in 98% of cases
1166 Asian cohort samples (29 cancer types) [73]	DNA/RNA CGP panel	0.1% of significant findings exclusively from RNA; Tumor-agnostic biomarkers in 8.4% across 26 cancers	62.3% samples had actionable biomarkers
60 FFPE solid tumors [11]	Integrated DNA/RNA NGS	Additional TPM3::NTRK1 fusion identified; 100% sensitivity after calibration	Complementarity of DNA and RNA levels
101 NSCLC samples [24]	Whole transcriptome sequencing	68.9% of identified fusions were potentially actionable	Higher actionability rate in NSCLC

Technical Validation and Performance Metrics

The analytical performance of combined sequencing approaches has been rigorously validated against reference standards and orthogonal methods. In one development study, a custom-designed integrated DNA/RNA NGS assay accurately identified all 10 different fusion types in commercial reference standards and 29 fusions (including 16 different forms) in 60 clinical solid tumor samples [11]. The assay demonstrated 100% sensitivity and 96.9% specificity after identifying an additional TPM3::NTRK1 fusion that was initially missed by previous testing methods.

For fusion detection specifically, RNA sequencing alone has demonstrated exceptional performance characteristics. One whole transcriptome sequencing (WTS) assay successfully identified 62 out of 63 known gene fusions, achieving a sensitivity of 98.4% with 100% specificity across validation cohorts [24]. The same study established optimal RNA quality thresholds (DV200 ≥ 30%), input requirements (>100 ng), and sequencing depth (>80 million mapped reads) for reliable fusion detection in clinical samples.

The clinical actionability of findings from combined sequencing is particularly noteworthy. In non-small cell lung cancer (NSCLC), where fusion drivers are well-established, 68.9% of fusions identified by WTS were potentially actionable [24]. This high actionability rate underscores the therapeutic importance of comprehensive fusion detection, particularly in molecularly-defined cancer subtypes.

Experimental Protocols for Combined Sequencing

Nucleic Acid Extraction and Quality Control

Sample Requirements:

• Input Material: Formalin-fixed paraffin-embedded (FFPE) tissue sections, fresh frozen tissue, or cell lines
• Tumor Content: >20% tumor nuclei for FFPE samples [24]
• Section Requirements: 10 sections of 5 × 5 mm² tissue piece for FFPE [24]
• Storage Conditions: FFPE blocks stored at 4°C for optimal RNA preservation [24]

Extraction Protocol:

• Co-extraction: Use AllPrep DNA/RNA FFPE Kit (Qiagen) or similar for simultaneous DNA/RNA extraction [13] [74]
• DNA Quantification: Measure using Qubit dsDNA HS Assay Kit (Thermo Fisher)
• RNA Quantification: Measure using Qubit RNA HS Assay Kit (Thermo Fisher)
• Quality Assessment:
  • DNA/RNA integrity: Agilent TapeStation 4200 or Bioanalyzer [13]
  • RNA degradation: DV200 value ≥30% as acceptable threshold [24]
  • Purity: NanoDrop spectrophotometry (A260/280 ratio 1.8-2.1)

Quality Control Thresholds:

• RNA Input: >100 ng for library preparation [24]
• RNA Concentration: >40 copies/ng for optimal sensitivity [24]
• DNA Input: 10-200 ng for exome sequencing [13]

Library Preparation and Sequencing

Dual-Stranded RNA Library Preparation:

• rRNA Depletion: Use NEBNext rRNA Depletion Kit (Human/Mouse/Rat) [24]
• Fragmentation Optimization: Omit fragmentation step for samples with DV200 ≤50% [24]
• cDNA Synthesis: NEBNext Ultra II Directional RNA Library Prep Kit with custom adapters [24]
• Library QC: LabChip GX Touch for size distribution; Qubit for quantification [24]

Whole Exome Library Preparation:

• Library Construction: SureSelect XTHS2 DNA Kit (Agilent Technologies) [13]
• Exome Capture: SureSelect Human All Exon V7 (Agilent) or similar [13]
• Target Enrichment: Hybridization-based capture with biotinylated probes
• Library QC: Average fragment size 200-300bp; Qubit quantification

Sequencing Parameters:

• Platform: Illumina NovaSeq 6000 or equivalent [13]
• RNA Sequencing: 100bp paired-end reads; ~25Gb data per sample [24]
• WES Sequencing: 100-150bp paired-end reads; target coverage >100x for tumor, >60x for normal [13]
• QC Metrics: Q30 >90%; PF >80% [13]

Figure 1: Integrated RNA and DNA Sequencing Workflow. The protocol encompasses co-extraction, quality control, library preparation, and integrated bioinformatic analysis for comprehensive genomic profiling.

Bioinformatic Analysis of Multi-Omic Data

Alignment and Quality Control

Reference Genome: GRCh38/hg38 with alt-aware alignment [13]

DNA Sequencing Analysis:

• Alignment: BWA-MEM (v0.7.17) for WES data [13]
• Duplicate Marking: GATK MarkDuplicates (v4.1.2) [13]
• Coverage Metrics: mosdepth (v0.2.1) for coverage statistics [13]
• Contamination Check: BAF-based or allele frequency-based methods

RNA Sequencing Analysis:

• Alignment: STAR (v2.4.2) for splice-aware alignment [13]
• Transcript Quantification: Kallisto (v0.43.0) for gene expression [13]
• Strandness Check: RSeQC (v3.0.1) for strand-specificity [13]
• Sample Identity: HLA typing (OptiType v1.3.5) and germline SNP concordance [13]

Variant Calling and Fusion Detection

Somatic Variant Calling (DNA):

• SNVs/Indels: Strelka2 (v2.9.10) with tumor-normal pairing [13]
• Filtering: Basic filter (tumor depth ≥10x, normal depth ≥20x, normal VAF ≤0.05) followed by complex scoring [13]
• CNV Calling: Read-depth based approaches with GC correction
• MSI/TMB: Analysis of microsatellite loci and total mutation burden [75]

RNA-Based Fusion Detection:

• Fusion Callers: Multiple algorithm approach (e.g., Arriba, STAR-Fusion, FusionCatcher)
• Validation: GFvoter for long-read data integrates multiple callers for consensus [9]
• Filtering: Expression-based filtering (≥5 supporting reads, FFPM thresholds) [11]
• Annotation: Reportable range of 553 genes with clinical relevance [24]

Integrated Analysis:

• DNA-RNA Concordance: Verify DNA-level rearrangements with RNA expression
• Allele-Specific Expression: Identify expression imbalances from RNA data
• Pathway Analysis: Correlate genomic alterations with transcriptional programs

Figure 2: Bioinformatic Pipeline for Integrated DNA and RNA Analysis. Parallel analysis of DNA and RNA sequencing data followed by integrated interpretation enhances variant detection and clinical actionability.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Research Reagents for Combined RNA/DNA Sequencing

Reagent/Category	Specific Examples	Function & Application
Nucleic Acid Extraction	AllPrep DNA/RNA FFPE Kit (Qiagen); RNeasy FFPE Kit (Qiagen)	Co-extraction of DNA and RNA while preserving quality; Specialized for challenging FFPE samples [13] [24]
Library Preparation	TruSeq stranded mRNA kit (Illumina); NEBNext Ultra II Directional RNA Library Prep Kit; SureSelect XTHS2	Construction of sequencing libraries; Maintains strand specificity; Compatible with degraded RNA [13] [24]
Target Enrichment	SureSelect Human All Exon V7 + UTR (Agilent); Custom targeted panels	Capture of exonic regions and untranslated regions; Focus on clinically relevant genes [13] [73]
RNA Depletion	NEBNext rRNA Depletion Kit (Human/Mouse/Rat)	Removal of ribosomal RNA to enrich for mRNA; Improves sequencing efficiency [24]
Reference Materials	GeneWell fusion reference standards; Commercial FFPE reference materials	Analytical validation; Quality control; Establishing limits of detection [11]
Bioinformatic Tools	GFvoter; LongGF; JAFFAL; FusionSeeker; Strelka2; STAR	Specialized fusion detection; Somatic variant calling; Splice-aware alignment [13] [9]

Signaling Pathways in Fusion-Driven Carcinogenesis

Gene fusions identified through combined RNA/DNA sequencing frequently drive oncogenesis through constitutive activation of key signaling pathways. Understanding these pathways is essential for interpreting the functional significance of fusion events and identifying potential therapeutic vulnerabilities.

The most clinically significant fusions typically function as oncogenic drivers through several mechanisms:

• Receptor Tyrosine Kinase Activation: Fusions involving ALK, ROS1, RET, and NTRK genes create constitutively active tyrosine kinases that dimerize and signal independent of normal ligand binding [11]
• Transcriptional Deregulation: Fusions such as EWSR1::FLI1 in Ewing sarcoma alter transcriptional programs, promoting proliferation and blocking differentiation
• Signal Transduction Activation: Fusion proteins can hyperactivate downstream pathways including MAPK/ERK, PI3K/AKT, and JAK/STAT signaling cascades

For example, MET exon 14 skipping alterations—detectable through RNA sequencing—lead to increased MET protein stability and subsequent activation of downstream RAS-RAF-MEK-ERK and PI3K/AKT pathways, promoting tumor growth and proliferation [24].

Figure 3: Signaling Pathways Activated by Oncogenic Fusions. Gene fusions detected through combined RNA/DNA sequencing drive oncogenesis through constitutive activation of key proliferative and survival pathways.

The accumulated pan-cancer evidence unequivocally demonstrates that combined RNA/DNA sequencing provides substantial added diagnostic yield compared to DNA-only approaches. This integrated methodology enhances detection of clinically actionable gene fusions, resolves ambiguous findings from DNA sequencing alone, and provides a more comprehensive molecular portrait of individual tumors. The protocols and analytical frameworks presented herein provide researchers and drug development professionals with validated methodologies for implementing this powerful approach in both basic research and clinical translation contexts. As precision medicine continues to evolve toward increasingly biomarker-driven treatment paradigms, the integration of transcriptional data with genomic profiling will become increasingly essential for optimizing therapeutic strategies and advancing drug development.

Evaluating Bioinformatics Tools for Fusion Calling in Short- and Long-Read Data

Gene fusions are pivotal drivers in oncogenesis, serving as critical biomarkers for cancer diagnosis, prognosis, and targeted therapy development [25] [76]. The detection of these chimeric transcripts has been revolutionized by RNA sequencing (RNA-seq), with both short-read and long-read technologies offering distinct advantages and challenges for accurate fusion calling [77] [78]. This application note provides a comprehensive evaluation of bioinformatics tools for fusion detection across sequencing platforms, presenting structured performance comparisons, detailed experimental protocols, and standardized workflows to guide researchers in selecting and implementing optimal fusion calling strategies in cancer research.

The evolution of sequencing technologies has fundamentally transformed fusion detection paradigms. Short-read RNA-seq from Illumina platforms has enabled cost-effective, high-throughput profiling but struggles to resolve complex fusion isoforms and breakpoints due to read length limitations [25]. Conversely, long-read technologies from PacBio and Oxford Nanopore Technologies (ONT) capture full-length transcript sequences, providing unambiguous fusion isoform characterization but historically facing higher error rates and throughput challenges [78] [79]. Recent advancements in both platforms have significantly improved accuracy and throughput, making long-read fusion detection increasingly viable for clinical and research applications [25] [79].

Performance Benchmarking of Fusion Calling Tools

Short-Read RNA-seq Fusion Callers

Multiple comprehensive studies have evaluated the performance of fusion detection tools for short-read RNA-seq data. Table 1 summarizes the key performance metrics of leading tools based on benchmarking studies involving simulated data and real cancer cell line RNA-seq [77].

Table 1: Performance Comparison of Short-Read Fusion Detection Tools

Tool	Sensitivity	Precision	Execution Speed	Key Strengths
Arriba	High	High	Fast (minutes)	Excellent sensitivity for driver fusions; fast runtime [77] [80]
STAR-Fusion	High	High	Fast	Robust alignment-based approach; user-friendly output [77]
STAR-SEQR	High	High	Fast	Integrates with STAR aligner; good performance on real data [77]
FusionCatcher	Moderate	Moderate	Moderate	Comprehensive filtering; detects wide range of fusion types [81]
JAFFA	Moderate	High	Slow	Assembly-based approach; good precision [77]
FusionMap	Moderate	Moderate	Moderate	Windows-compatible; reference comparison tool [81]

Benchmarking analyses reveal that Arriba, STAR-Fusion, and STAR-SEQR consistently demonstrate superior accuracy and efficiency for fusion detection in cancer transcriptomes [77]. These tools effectively identified clinically relevant driver fusions in pancreatic cancer samples, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1 fusions, which were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway [80]. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba specifically identified various driver fusions affecting druggable proteins [80].

Long-Read RNA-seq Fusion Callers

The development of specialized tools for long-read data has accelerated with improvements in PacBio and ONT technologies. Table 2 presents performance metrics for long-read fusion callers based on benchmarking with simulated and genuine long-read RNA-seq [25] [78].

Table 2: Performance Comparison of Long-Read Fusion Detection Tools

Tool	Platform	Sensitivity	Precision	Unique Capabilities
CTAT-LR-Fusion	PacBio, ONT	High	High	Bulk and single-cell support; short-read integration [25]
JAFFAL	PacBio, ONT	High	Moderate	Comprehensive fusion annotation; filters false positives [78]
LongGF	PacBio, ONT	Moderate	High	Good for full-length fusion transcripts [25] [78]
FusionSeeker	PacBio, ONT	Moderate	Moderate	Specialized for isoform sequencing [25] [78]
Anchored-fusion	Short-read	High	High	Targeted detection; deep learning filtering [76]

Recent benchmarking demonstrates that CTAT-LR-Fusion exceeds the fusion detection accuracy of alternative methods on both simulated and genuine long-read RNA-seq data [25]. The tool's modularized software includes chimeric read extraction, fusion transcript identification, expression quantification, gene fusion annotation, and interactive visualization capabilities [25]. When combining short and long reads in CTAT-LR-Fusion, researchers can maximize the detection of fusion splicing isoforms and fusion-expressing tumor cells in both bulk and single-cell RNA-seq applications [25].

For challenging detection scenarios involving sequence homology, Anchored-fusion employs a novel approach that anchors a user-specified gene of interest and incorporates a hierarchical view learning and distillation (HVLD) deep learning framework to filter false positives while maintaining sensitivity [76]. This method is particularly valuable for detecting fusion genes with low sequencing depth, such as in single-cell and clinical contexts [76].

Multi-Platform Fusion Calling Strategies

Integrating multiple tools and sequencing technologies can significantly enhance fusion detection accuracy. A benchmarking study of eight long-read structural variant callers demonstrated that combining multiple tools and testing different combinations substantially improves validation of somatic alterations in cancer genomes [82]. The study employed Sniffles, cuteSV, Delly, DeBreak, Dysgu, NanoVar, SVIM, and Severus on paired tumor and matched normal samples from lung cancer and melanoma cell lines, revealing that different tools have complementary strengths for various variant types [82].

For comprehensive fusion characterization, a hybrid approach leveraging both short-read and long-read technologies provides optimal results. Short-read data offers higher sequencing depth for initial detection, while long-read data enables complete isoform resolution [25] [79]. This strategy is particularly valuable in single-cell contexts, where long-read sequencing retains transcripts shorter than 500 bp and enables removal of degraded cDNA contaminated by template switching oligos, artifacts identifiable only from full-length transcripts [79].

Experimental Protocols

Standardized Fusion Detection Workflow

The following protocol outlines a comprehensive workflow for fusion detection from RNA-seq data, adaptable to both short-read and long-read platforms.

Sample and Library Preparation

RNA Extraction and Quality Control

• Extract total RNA using silica-membrane column methods with DNase I treatment
• Assess RNA integrity using Agilent Bioanalyzer or TapeStation (RIN > 8.0 recommended)
• Quantify RNA using fluorometric methods (Qubit RNA HS Assay)
• For nanopore sequencing, incorporate inverted terminal repeats and unique molecular identifiers (UMIs) to prevent over-representation of short fragments and enable duplicate removal [78]
• Implement Exonuclease I treatment between dT primer annealing and reverse transcription to prevent internal priming [78]

Library Preparation Optimization

• For short-read: Use Illumina Stranded mRNA Prep with TruSeq adapters
• For long-read PacBio: Apply MAS-ISO-seq (Kinnex) chemistry for increased throughput
• For long-read ONT: Optimize cDNA protocol with SQK-LSK114 kit and structure-specific RT primers [78]
• Use high-processivity reverse transcriptase enzymes to stabilize RT complex for full-length cDNA synthesis [78]
• Optimize amplification parameters to inhibit artificial chimeric products [78]

Sequencing and Data Generation

Platform-Specific Considerations

• For Illumina short-read: Sequence with 2×100 bp or longer reads; aim for 50-100 million read pairs per sample
• For PacBio long-read: Use Sequel IIe system with 3.2 binding chemistry; target >2 million reads per SMRT cell [79]
• For ONT long-read: Use PromethION or GridION with R9.4 or later flow cells; aim for high N50 read length (>2 kb) [82] [78]

Computational Analysis Pipeline

The computational workflow for fusion detection involves sequential steps from raw data processing to final fusion calling, with platform-specific considerations at each stage.

Quality Control and Preprocessing

Short-Read Data

• Perform quality assessment using FASTQC (v0.12.1+) [82]
• Trim adapters and low-quality bases using Trimmomatic or cutadapt
• Remove PCR duplicates using UMI-based tools if UMIs were incorporated

Long-Read Data

• Assess read quality and length distribution using NanoPlot or PacBio SMRT Link
• Filter reads by quality (Q-score > 7 for ONT; read quality > 0.99 for PacBio HiFi)
• Remove concatemers and artifacts using tool-specific filters (e.g., IsoSeq3 for PacBio)

Read Alignment and Fusion Calling

Short-Read Alignment

• Align reads to GRCh38 reference genome using STAR (v2.7.+) with chimera-aware parameters [77] [81]
• Use GCA000001405.15GRCh38noaltanalysisset.fa reference genome [82]
• Generate alignment BAM files with chimeric output included

Long-Read Alignment

• Align using minimap2 (v2.22+) with appropriate preset parameters (-ax splice for ONT, -ax splice:hq for PacBio HiFi) [82] [25]
• For specialized fusion detection, use customized minimap2 in CTAT-LR-Fusion to preliminarily identify reads mapping to multiple genomic loci [25]

Fusion Calling Execution

• For short-read: Run Arriba (v1.1.0+) and STAR-Fusion (v1.8.1+) in parallel [77] [80] [81]
• For long-read: Execute CTAT-LR-Fusion or JAFFAL with minimum supporting read threshold of 3 [25] [78]
• For targeted detection: Use Anchored-fusion with specified genes of interest and HVLD filtering [76]

Post-processing and Validation

Result Filtering and Annotation

• Filter out low-confidence calls with fewer than 5 supporting reads (short-read) or 3 supporting reads (long-read)
• Remove common false positives in normal tissues using GTEx and other normal reference databases
• Annotate fusions with known cancer associations using COSMIC, Mitelman, and ChimerKB databases
• Prioritize fusions with protein-domain integrity and in-frame coding potential

Visual Validation

• Validate high-priority fusions using Integrative Genomics Viewer (IGV) [82] [25]
• Inspect read alignments spanning fusion junctions
• For long-read data, use CTAT-LR-Fusion's interactive IGV-report for visualization [25]

The Scientist's Toolkit

Essential Research Reagent Solutions

Table 3: Key Research Reagents and Materials for Fusion Detection Studies

Reagent/Material	Function	Example Products
RNA Extraction Kits	High-quality RNA isolation with DNA removal	QIAGen RNeasy Plus, Zymo Quick-RNA
RNA Integrity Tools	Assess RNA quality for library preparation	Agilent Bioanalyzer RNA Nano, TapeStation
Library Prep Kits	Platform-specific cDNA library construction	Illumina Stranded mRNA Prep, ONT PCR-cDNA Kit, PacBio MAS-ISO-seq Kit
UMI Adapters	Unique molecular identifiers for duplicate removal	IDT UMI adapters, ONT UMI kits
Reverse Transcriptase	High-processivity cDNA synthesis	SuperScript IV, Induro RT (for direct RNA)
Exonuclease I	Prevent internal priming in cDNA synthesis	NEB Exonuclease I [78]
Reference Standards	Positive controls for fusion detection	Universal Human Reference RNA, Seraseq Fusion RNA standards

Table 4: Essential Computational Resources for Fusion Analysis

Resource Type	Specific Tools/Resources	Application Context
Quality Control	FastQC, MultiQC, NanoPlot	Pre-alignment quality assessment
Alignment Tools	STAR, minimap2, HISAT2	Read mapping to reference genome
Fusion Callers	Arriba, STAR-Fusion, CTAT-LR-Fusion, JAFFAL	Platform-specific fusion detection
Annotation Databases	COSMIC, Mitelman, ChimerDB	Biological and clinical interpretation
Visualization Tools	IGV, IGV.js, FusionInspector	Result validation and exploration
Benchmarking Sets	COLO829 truth set, UHRR with spike-ins	Performance validation [82] [78]

The evolving landscape of fusion detection tools presents researchers with multiple robust options for identifying cancer-relevant gene fusions across sequencing platforms. Short-read methods like Arriba and STAR-Fusion offer speed and sensitivity for large-scale studies, while long-read tools like CTAT-LR-Fusion provide unparalleled resolution of fusion isoforms and breakpoints. The optimal fusion detection strategy often involves combining multiple tools and sequencing technologies to leverage their complementary strengths, with careful attention to platform-specific library preparation and computational analysis parameters.

As sequencing technologies continue to advance, with improvements in read length, accuracy, and throughput, fusion detection methodologies will likely converge towards long-read-dominated workflows that provide complete transcriptome characterization. The integration of machine learning approaches, as demonstrated by Anchored-fusion's HVLD framework, represents a promising direction for enhancing detection sensitivity while controlling false positives. By adopting the standardized protocols and performance benchmarks outlined in this application note, researchers can implement reliable, reproducible fusion detection pipelines to advance cancer genomics research and precision oncology applications.

Conclusion

RNA sequencing has firmly established itself as an indispensable tool for the precise detection of oncogenic gene fusions, directly impacting cancer diagnosis and the expansion of targeted therapeutic options. The integration of RNA-seq with DNA-based NGS is critical, with real-world pan-cancer studies demonstrating a significant (over 21%) increase in the detection of clinically actionable fusions compared to DNA-seq alone. While targeted panels offer sensitive and cost-effective clinical profiling, long-read sequencing presents a promising frontier for resolving complex rearrangements. Future directions must focus on standardizing bioinformatic pipelines, validating assays across diverse cancer types, and further integrating RNA-seq data into clinical trial designs to unlock the full potential of precision medicine. This multi-faceted approach will ultimately improve patient outcomes by ensuring that fusion-driven cancers are accurately identified and matched with effective therapies.

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