Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives

Oncogenic gene fusions in cancer: from biology to therapy

Oncogenic gene fusions occur across a broad range of cancers and are a defining feature of some cancer types. Cancers driven by gene fusion products tend to respond well to targeted therapies, where available; thus, detection of potentially targetable oncogenic fusions is necessary to select optimal treatment. Detection methods include non-sequencing methods, such as fluorescence in situ hybridization and immunohistochemistry, and sequencing methods, such as DNA- and RNA-based next-generation sequencing (NGS). While NGS is an efficient way to analyze multiple genes of interest at once, economic and technical factors may preclude its use in routine care globally, despite several guideline recommendations. The aim of this review is to present a summary of oncogenic gene fusions, with a focus on fusions that affect tyrosine kinase signaling, and to highlight the importance of testing for oncogenic fusions. We present an overview of the identification of oncogenic gene fusions and therapies approved for the treatment of cancers harboring gene fusions, and summarize data regarding treating fusion-positive cancers with no current targeted therapies and clinical studies of fusion-positive cancers. Although treatment options may be limited for patients with rare alterations, healthcare professionals should identify patients most likely to benefit from oncogenic gene fusion testing and initiate the appropriate targeted therapy to achieve optimal treatment outcomes.

Accurate fusion transcript identification from long- and short-read isoform sequencing at bulk or single-cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics and prognostics and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short-read sequences. Recent advances in long-read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single-cell samples. Here, we developed a new computational tool, CTAT-LR-Fusion, to detect fusion transcripts from long-read RNA-seq with or without companion short reads, with applications to bulk or single-cell transcriptomes. We demonstrate that CTAT-LR-Fusion exceeds the fusion detection accuracy of alternative methods as benchmarked with simulated and genuine long-read RNA-seq. Using short- and long-read RNA-seq, we further apply CTAT-LR-Fusion to bulk transcriptomes of nine tumor cell lines and to tumor single cells derived from a melanoma sample and three metastatic high-grade serous ovarian carcinoma samples. In both bulk and single-cell RNA-seq, long isoform reads yield higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-Fusion, we are able to further maximize the detection of fusion splicing isoforms and fusion-expressing tumor cells.

Molecular features and clinical actionability of gene fusions in colorectal cancer

Colorectal cancer (CRC) is the third leading cause of cancer death and accounts for 10 % of cancer diagnoses worldwide. Despite the advancements achieved over the latest decades, CRC treatments are still based on conventional chemotherapy whose efficacy is limited by acquired resistance and unfavorable toxicity profile, making the search for novel actionable targets a priority. In this context, gene fusions are emerging as promising -albeit very rare - new markers because of their recurrence across different tumor types and their potential actionability. The aim of this review is to investigate the role of gene fusions in CRC by focusing on pathogenesis, screening strategies as well as their clinical implications.

The landscape of fusion transcripts in plants: a new insight into genome complexity

BACKGROUND

Fusion transcripts (FTs), generated by the fusion of genes at the DNA level or RNA-level splicing events significantly contribute to transcriptome diversity. FTs are usually considered unique features of neoplasia and serve as biomarkers and therapeutic targets for multiple cancers. The latest findings show the presence of FTs in normal human physiology. Several discrete reports mentioned the presence of fusion transcripts in planta, has important roles in stress responses, morphological alterations, or traits (e.g. seed size, etc.).

RESULTS

In this study, we identified 169,197 fusion transcripts in 2795 transcriptome datasets of Arabidopsis thaliana, Cicer arietinum, and Oryza sativa by using a combination of tools, and confirmed the translational activity of 150 fusion transcripts through proteomic datasets. Analysis of the FT junction sequences and their association with epigenetic factors, as revealed by ChIP-Seq datasets, demonstrated an organised process of fusion formation at the DNA level. We investigated the possible impact of three-dimensional chromatin conformation on intra-chromosomal fusion events by leveraging the Hi-C datasets with the incidence of fusion transcripts. We further utilised the long-read RNA-Seq datasets to validate the most reoccurring fusion transcripts in each plant species followed by further authentication through RT-PCR and Sanger sequencing.

CONCLUSIONS

Our findings suggest that a significant portion of fusion events may be attributed to alternative splicing during transcription, accounting for numerous fusion events without a proportional increase in the number of RNA pairs. Even non-nuclear DNA transcripts from mitochondria and chloroplasts can participate in intra- and inter-chromosomal fusion formation. Genes in close spatial proximity are more prone to undergoing fusion formation, especially in intra-chromosomal FTs. Most of the fusion transcripts may not undergo translation and serve as long non-coding RNAs. The low validation rate of FTs in plants indicated that the fusion transcripts are expressed at very low levels, like in the case of humans. FTs often originate from parental genes involved in essential biological processes, suggesting their relevance across diverse tissues and stress conditions. This study presents a comprehensive repository of fusion transcripts, offering valuable insights into their roles in vital physiological processes and stress responses.

Microbiome's Universe: Impact on health, disease and cancer treatment

The human microbiome is a diverse ecosystem of microorganisms that reside in the body and influence various aspects of health and well-being. Recent advances in sequencing technology have brought to light microbial communities in organs and tissues that were previously considered sterile. The gut microbiota plays an important role in host physiology, including metabolic functions and immune modulation. Disruptions in the balance of the microbiome, known as dysbiosis, have been linked to diseases such as cancer, inflammatory bowel disease and metabolic disorders. In addition, the administration of antibiotics can lead to dysbiosis by disrupting the structure and function of the gut microbial community. Targeting strategies are the key to rebalancing the microbiome and fighting disease, including cancer, through interventions such as probiotics, fecal microbiota transplantation (FMT), and bacteria-based therapies. Future research must focus on understanding the complex interactions between diet, the microbiome and cancer in order to optimize personalized interventions. Multidisciplinary collaborations are essential if we are going to translate microbiome research into clinical practice. This will revolutionize approaches to cancer prevention and treatment.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

FindDNAFusion: An Analytical Pipeline with Multiple Software Tools Improves Detection of Cancer-Associated Gene Fusions from Genomic DNA

Detection of cancer-associated gene fusions is crucial for diagnosis, prognosis, and treatment selection. Many bioinformatics tools are available for the detection of fusion transcripts by RNA sequencing, but there are fewer well-validated software tools for DNA next-generation sequencing (NGS). A 542-gene solid tumor NGS panel was designed, with exonic probes supplemented with intronic bait probes against genes commonly involved in oncogenic fusions, with a focus on lung cancer. Three software tools for the detecting gene fusions in this DNA-NGS panel were selected and evaluated. The performance of these tools was compared after a pilot study, and each was configured for optimal batch analysis and detection rate. A blacklist for filtering common tool-specific artifacts, and criteria for selecting clinically reportable fusions, were established. Visualization tools for annotating and confirming somatic fusions were applied. Subsequently, a full clinical validation was used for comparing the results to those from in situ hybridization and/or RNA sequencing. With JuLI, Factera, and GeneFuse, 94.1%, 88.2%, and 66.7% of expected fusions were detected, respectively. With a combinatorial pipeline (termed FindDNAFusion), developed by integrating fusion-calling tools with methods for fusion filtering, annotating, and flagging reportable calls, the accuracy of detection of intron-tiled genes was improved to 98.0%. FindDNAFusion is an accurate and efficient tool in detecting somatic fusions in DNA-NGS panels with intron-tiled bait probes when RNA is unavailable.

A Protocol for the Detection of Fusion Transcripts Using RNA-Sequencing Data

Fusion transcripts are formed when two genes or their mRNAs fuse to produce a novel gene or chimeric transcript. Fusion genes are well-known cancer biomarkers used for cancer diagnosis and as therapeutic targets. Gene fusions are also found in normal physiology and lead to the evolution of novel genes that contribute to better survival and adaptation for an organism. Various in vitro approaches, such as FISH, PCR, RT-PCR, and chromosome banding techniques, have been used to detect gene fusion. However, all these approaches have low resolution and throughput. Due to the development of high-throughput next-generation sequencing technologies, the detection of fusion transcript becomes feasible using whole genome sequencing, RNA-Seq data, and bioinformatics tools. This chapter will overview the general computational protocol for fusion transcript detection from RNA-sequencing datasets.

Comprehensive genomic profiling reveals prognostic signatures and insights into the molecular landscape of colorectal cancer

Background

Colorectal cancer (CRC) is a prevalent malignancy with diverse molecular characteristics. The NGS-based approach enhances our comprehension of genomic landscape of CRC and may guide future advancements in precision oncology for CRC patients.

Method

In this research, we conducted an analysis using Next-Generation Sequencing (NGS) on samples collected from 111 individuals who had been diagnosed with CRC. We identified somatic and germline mutations and structural variants across the tumor genomes through comprehensive genomic profiling. Furthermore, we investigated the landscape of driver mutations and their potential clinical implications.

Results

Our findings underscore the intricate heterogeneity of genetic alterations within CRC. Notably, BRAF, ARID2, KMT2C, and GNAQ were associated with CRC prognosis. Patients harboring BRAF, ARID2, or KMT2C mutations exhibited shorter progression-free survival (PFS), whereas those with BRAF, ARID2, or GNAQ mutations experienced worse overall survival (OS). We unveiled 80 co-occurring and three mutually exclusive significant gene pairs, enriched primarily in pathways such as TP53, HIPPO, RTK/RAS, NOTCH, WNT, TGF-Beta, MYC, and PI3K. Notably, co-mutations of BRAF/ALK, BRAF/NOTCH2, BRAF/CREBBP, and BRAF/FAT1 correlated with worse PFS. Furthermore, germline AR mutations were identified in 37 (33.33%) CRC patients, and carriers of these variants displayed diminished PFS and OS. Decreased AR protein expression was observed in cases with AR germline mutations. A four-gene mutation signature was established, incorporating the aforementioned prognostic genes, which emerged as an independent prognostic determinant in CRC via univariate and multivariate Cox regression analyses. Noteworthy BRAF and ARID2 protein expression decreases detected in patients with their respective mutations.

Conclusion

The integration of our analyses furnishes crucial insights into CRC's molecular characteristics, drug responsiveness, and the construction of a four-gene mutation signature for predicting CRC prognosis.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

mRNA Capture Sequencing and RT-qPCR for the Detection of Pathognomonic, Novel, and Secondary Fusion Transcripts in FFPE Tissue: A Sarcoma Showcase

We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints. Next, whole-transcriptome TruSeq RNA Exome sequencing was applied to 17 fusion gene-negative alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS) tumors, for whom fluorescence in situ hybridization (FISH) did not identify the classical pathognomonic rearrangements. For six patients, a pathognomonic fusion transcript was readily detected, i.e., PAX3-FOXO1 in two ARMS patients, and EWSR1-FLI1, EWSR1-ERG, or EWSR1-NFATC2 in four URCS patients. For the 11 remaining patients, 11 newly identified fusion transcripts were confirmed by RT-qPCR, including COPS3-TOM1L2, NCOA1-DTNB, WWTR1-LINC01986, PLAA-MOB3B, AP1B1-CHEK2, and BRD4-LEUTX fusion transcripts in ARMS patients. Additionally, recurrently detected secondary fusion transcripts in patients diagnosed with EWSR1-NFATC2-positive sarcoma were confirmed (COPS4-TBC1D9, PICALM-SYTL2, SMG6-VPS53, and UBE2F-ALS2). In conclusion, this study shows that mRNA capture sequencing enhances the detection rate of pathognomonic fusions and enables the identification of novel and secondary fusion transcripts in sarcomas.

SUMOylation of SYNJ2BP-COX16 promotes breast cancer progression through DRP1-mediated mitochondrial fission

Treatments targeting oncogenic fusion proteins are notable examples of successful drug development. Abnormal splicing of genes resulting in fusion proteins is a critical driver of various tumors, but the underlying mechanism remains poorly understood. Here, we show that SUMOylation of the fusion protein Synaptojanin 2 binding protein-Cytochrome-c oxidase 16 (SYNJ2BP-COX16) at K107 induces mitochondrial fission in breast cancer and that the K107 site regulates SYNJ2BP-COX16 mitochondrial subcellular localization. Compared with a non-SUMOylated K107R mutant, wild-type SYNJ2BP-COX16 contributed to breast cancer cell proliferation and metastasis in vivo and in vitro by increasing adenosine triphosphate (ATP) production and cytochrome-c oxidase (COX) activity. SUMOylated SYNJ2BP-COX16 recruits dynamin-related protein 1 (DRP1) to the mitochondria to promote ubiquitin-conjugating enzyme 9 (UBC9) binding to DRP1, enhance SUMOylation of DRP1 and phosphorylation of DRP1 at S616, and then induce mitochondrial fission. Moreover, Mdivi-1, an inhibitor of DRP1 phosphorylation, decreased the localization of DRP1 in mitochondria, and prevents SYNJ2BP-COX16 induced mitochondrial fission, cell proliferation and metastasis. Based on these data, SYNJ2BP-COX16 promotes breast cancer progression through the phosphorylation of DRP1 and subsequent induction of mitochondrial fission, indicating that SUMOylation at the K107 residue of SYNJ2BP-COX16 is a novel potential treatment target for breast cancer.

Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care.

Discovery of clinically relevant fusions in pediatric cancer

Background

Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.

Results

Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.

Conclusions

The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08094-z.

Transcriptional configurations of myeloproliferative neoplasms

Myeloproliferative neoplasms (MPNs) is an umbrella term for several heterogenous diseases, which are characterized by their stem cell origin, clonal hematopoiesis and increase of blood cells of the myeloid lineage. The focus will be on BCR-ABL1 negative MPNs, polycythemia vera (PV), primary myelofibrosis (PMF), essential thrombocythemia (ET). Seminal findings in the field of MPN were driven by genomic analysis, focusing on dissecting genomic changes MPN patients. This led to identification of major MPN driver genes, JAK2, MPL and CALR. Transcriptomic analysis promises to bridge the gap between genetic and phenotypic characterization of each patient's tumor and with the advent of single cell sequencing even for each MPN cancer cell. This review will focus on efforts to mine the bulk transcriptome of MPN patients, including analysis of fusion genes and splicing alterations which can be addressed with RNA-seq technologies. Furthermore, this paper aims to review recent endeavors to elucidate tumor heterogeneity in MPN hematopoietic stem and progenitor cells using single cell technologies. Finally, it will highlight current shortcoming and future applications to advance the field in MPN biology and improve patient diagnostics using RNA-based assays.

Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer

Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts.

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.

Diagnostics, therapeutics and RET inhibitor resistance for RET fusion-positive non-small cell lung cancers and future perspectives

Selective RET inhibitors is the current hot topic, making multikinase inhibitors a thing of the past. However, the limitation of various test approaches, coupled with lack of knowledge of acquired resistance mechanisms, and specific patient groups that bear special consideration, remains a challenge. Herein, we outline utility of various diagnostic techniques, provide evidence to guide management of RET-fusion-positive Non-Small Cell Lung Cancer (NSCLC) patients, including specific patient groups, such as EGFR-mutant NSCLC patients who acquired RET fusions after resisting EGFR TKIs, and offer a compendium of mechanisms of acquired resistance to RET targeted therapies. This review further provides a list of ongoing clinical trials and summarizes perspectives to guide future development of drugs and trials for this population.

Cytogenetic and molecular genetic methods for chromosomal translocations detection with reference to the KMT2A/MLL gene

Acute leukemias (ALs) are often associated with chromosomal translocations, in particular, KMT2A/MLL gene rearrangements. Identification or confirmation of these translocations is carried out by a number of genetic and molecular methods, some of which are routinely used in clinical practice, while others are primarily used for research purposes. In the clinic, these methods serve to clarify diagnoses and monitor the course of disease and therapy. On the other hand, the identification of new translocations and the confirmation of known translocations are of key importance in the study of disease mechanisms and further molecular classification. There are multiple methods for the detection of rearrangements that differ in their principle of operation, the type of problem being solved, and the cost-result ratio. This review is intended to help researchers and clinicians studying AL and related chromosomal translocations to navigate this variety of methods. All methods considered in the review are grouped by their principle of action and include karyotyping, fluorescence in situ hybridization (FISH) with probes for whole chromosomes or individual loci, PCR and reverse transcription-based methods, and high-throughput sequencing. Another characteristic of the described methods is the type of problem being solved. This can be the discovery of new rearrangements, the determination of unknown partner genes participating in the rearrangement, or the confirmation of the proposed rearrangement between the two genes. We consider the specifics of the application, the basic principle of each method, and its pros and cons. To illustrate the application, examples of studying the rearrangements of the KMT2A/MLL gene, one of the genes that are often rearranged in AL, are mentioned.

MET-dependent solid tumours - molecular diagnosis and targeted therapy

Attempts to develop MET-targeted therapies have historically focused on MET-expressing cancers, with limited success. Thus, MET expression in the absence of a genomic marker of MET dependence is a poor predictor of benefit from MET-targeted therapy. However, owing to the development of more sensitive methods of detecting genomic alterations, high-level MET amplification and activating MET mutations or fusions are all now known to be drivers of oncogenesis. MET mutations include those affecting the kinase or extracellular domains and those that result in exon 14 skipping. The activity of MET tyrosine kinase inhibitors varies by MET alteration category. The likelihood of benefit from MET-targeted therapies increases with increasing levels of MET amplification, although no consensus exists on the optimal diagnostic cut-off point for MET copy number gains identified using fluorescence in situ hybridization and, in particular, next-generation sequencing. Several agents targeting exon 14 skipping alterations are currently in clinical development, with promising data available from early-phase trials. By contrast, the therapeutic implications of MET fusions remain underexplored. Here we summarize and evaluate the utility of various diagnostic techniques and the roles of different classes of MET-targeted therapies in cancers with MET amplification, mutation and fusion, and MET overexpression.

Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts

Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

BACKGROUND

Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.

RESULTS

We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.

CONCLUSION

The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Visual Detection of Fusion Genes by Ligation-Triggered Isothermal Exponential Amplification: A Point-of-Care Testing Method for Highly Specific and Sensitive Quantitation of Fusion Genes with a Smartphone

Fusion genes, playing a causal role in human tumorigenesis and developments, are deemed as gold standard molecular biomarkers in cancer diagnosis, therapy, and prognosis. A rapid, robust, and sensitive method of detection of fusion genes for point-of-care (POC) diagnosis is urgently needed. Here, taking the advantages of the superior specificity of the ligation reaction and the highly amplified efficiency of isothermal exponential amplification with a pH indicator, we developed a colorimetric method for visual detection of fusion genes with high sensitivity and specificity by the naked eye. More importantly, we first found that fusion genes can be accurately quantified in a wide dynamic range (2 zmol to 2 fmol) by an open-source app with a smartphone-assisted RGB (red, green, and blue value) reading mode. The proposed method for Visual detection of Fusion genes by Ligation-triggered Isothermal Exponential Amplification is termed Vis-Fusion LIEXA. We have demonstrated that the Vis-Fusion LIEXA is a practical and reliable method for accurate quantitative detection of the fusion gene in a complex biological sample at zmol level in 40 min only with a smartphone, thereby providing a user-friendly and point-of-care testing (POCT) tool for molecular diagnostics.

Alternative tumour-specific antigens

The study of tumour-specific antigens (TSAs) as targets for antitumour therapies has accelerated within the past decade. The most commonly studied class of TSAs are those derived from non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens. However, to increase the repertoire of available therapeutic TSA targets, 'alternative TSAs', defined here as high-specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated. Among these alternative TSAs are antigens derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other processes. Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the advantage of being widely shared by multiple tumours, allowing for universal, off-the-shelf therapies. In this Opinion article, we will outline the biology, available computational tools, preclinical and/or clinical studies and relevant cancers for each alternative TSA class, as well as discuss both current challenges preventing the therapeutic application of alternative TSAs and potential solutions to aid in their clinical translation.

FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells

Fusion proteoforms are translation products derived from gene fusion. Although very rare, the fusion proteoforms play important roles in biomedical science. For example, fusion proteoforms influence the development of tumors by serving as cancer markers or cell cycle regulators. Although numerous studies have reported bioinformatics tools that can predict fusion transcripts, few proteogenomic tools are available that can predict and identify proteoforms. In this study, we develop a versatile proteogenomic tool "FusionPro," which facilitates the identification of fusion transcripts and their potential translatable peptides. FusionPro provides an independent gene fusion prediction module and can build sequence databases for annotated fusion proteoforms. FusionPro shows greater sensitivity than the available fusion finders when analyzing simulated or real RNA sequencing data sets. We use FusionPro to identify 18 fusion junction peptides and three potential fusion-derived peptides by MS/MS-based analysis of leukemia cell lines (Jurkat and K562) and ovarian cancer tissues from the Clinical Proteomic Tumor Analysis Consortium. Among the identified fusion proteins, we molecularly validate two fusion junction isoforms and a translation product of FAM133B:CDK6. Moreover, sequence analysis suggests that the fusion protein participates in the cell cycle progression. In addition, our prediction results indicate that fusion transcripts often have multiple fusion junctions and that these fusion junctions tend to be distributed in a nonrandom pattern at both the chromosome and gene levels. Thus, FusionPro allows users to detect various types of fusion translation products using a transcriptome-informed approach and to gain a comprehensive understanding of the formation and biological roles of fusion proteoforms.

FusionScan: accurate prediction of fusion genes from RNA-Seq data

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study

Assessing the impact of kinase in gene fusion is essential for both identifying driver fusion genes (FGs) and developing molecular targeted therapies. Kinase domain retention is a crucial factor in kinase fusion genes (KFGs), but such a systematic investigation has not been done yet. To this end, we analyzed kinase domain retention (KDR) status in chimeric protein sequences of 914 KFGs covering 312 kinases across 13 major cancer types. Based on 171 kinase domain-retained KFGs including 101 kinases, we studied their recurrence, kinase groups, fusion partners, exon-based expression depth, short DNA motifs around the break points and networks. Our results, such as more KDR than 5'-kinase fusion genes, combinatorial effects between 3'-KDR kinases and their 5'-partners and a signal transduction-specific DNA sequence motif in the break point intronic sequences, supported positive selection on 3'-kinase fusion genes in cancer. We introduced a degree-of-frequency (DoF) score to measure the possible number of KFGs of a kinase. Interestingly, kinases with high DoF scores tended to undergo strong gene expression alteration at the break points. Furthermore, our KDR gene fusion network analysis revealed six of the seven kinases with the highest DoF scores (ALK, BRAF, MET, NTRK1, NTRK3 and RET) were all observed in thyroid carcinoma. Finally, we summarized common features of 'effective' (highly recurrent) kinases in gene fusions such as expression alteration at break point, redundant usage in multiple cancer types and 3'-location tendency. Collectively, our findings are useful for prioritizing driver kinases and FGs and provided insights into KFGs' clinical implications.

RNA Analysis as a Tool to Determine Clinically Relevant Gene Fusions and Splice Variants

CONTEXT

- Technologic advances have contributed to the increasing relevance of RNA analysis in clinical oncology practice. The different genetic aberrations that can be screened with RNA include gene fusions and splice variants. Validated methods of identifying these alterations include fluorescence in situ hybridization, immunohistochemistry, reverse transcription-polymerase chain reaction, and next-generation sequencing, which can provide physicians valuable information on disease and treatment of cancer patients.

OBJECTIVE

- To discuss the standard techniques available and new approaches for the identification of gene fusions and splice variants in cancer, focusing on RNA analysis and how analytic methods have evolved in both tissue and liquid biopsies.

DATA SOURCES

- This is a narrative review based on PubMed searches and the authors' own experiences.

CONCLUSIONS

- Reliable RNA-based testing in tissue and liquid biopsies can inform the diagnostic process and guide physicians toward the best treatment options. Next-generation sequencing methodologies permit simultaneous assessment of molecular alterations and increase the number of treatment options available for cancer patients.

NCLcomparator: systematically post-screening non-co-linear transcripts (circular, trans-spliced, or fusion RNAs) identified from various detectors

BACKGROUND

Non-co-linear (NCL) transcripts consist of exonic sequences that are topologically inconsistent with the reference genome in an intragenic fashion (circular or intragenic trans-spliced RNAs) or in an intergenic fashion (fusion or intergenic trans-spliced RNAs). On the basis of RNA-seq data, numerous NCL event detectors have been developed and detected thousands of NCL events in diverse species. However, there are great discrepancies in the identification results among detectors, indicating a considerable proportion of false positives in the detected NCL events. Although several helpful guidelines for evaluating the performance of NCL event detectors have been provided, a systematic guideline for measurement of NCL events identified by existing tools has not been available.

RESULTS

We develop a software, NCLcomparator, for systematically post-screening the intragenic or intergenic NCL events identified by various NCL detectors. NCLcomparator first examine whether the input NCL events are potentially false positives derived from ambiguous alignments (i.e., the NCL events have an alternative co-linear explanation or multiple matches against the reference genome). To evaluate the reliability of the identified NCL events, we define the NCL score (NCLscore) based on the variation in the number of supporting NCL junction reads identified by the tools examined. Of the input NCL events, we show that the ambiguous alignment-derived events have relatively lower NCLscore values than the other events, indicating that an NCL event with a higher NCLscore has a higher level of reliability. To help selecting highly expressed NCL events, NCLcomparator also provides a series of useful measurements such as the expression levels of the detected NCL events and their corresponding host genes and the junction usage of the co-linear splice junctions at both NCL donor and acceptor sites.

CONCLUSION

NCLcomparator provides useful guidelines, with the input of identified NCL events from various detectors and the corresponding paired-end RNA-seq data only, to help users selecting potentially high-confidence NCL events for further functional investigation. The software thus helps to facilitate future studies into NCL events, shedding light on the fundamental biology of this important but understudied class of transcripts. NCLcomparator is freely accessible at https://github.com/TreesLab/NCLcomparator .

Structural Variation Detection by Proximity Ligation from Formalin-Fixed, Paraffin-Embedded Tumor Tissue

The clinical management and therapy of many solid tumor malignancies depends on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed, paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call Fix-C, which is similar in concept to Hi-C. Fix-C enables structural variation detection from archival FFPE samples. This method was applied to 15 clinical adenocarcinoma- and sarcoma-positive control specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with fluorescence in situ hybridization assays, the current clinical gold standard. In addition, novel structural variation undetected by other methods could be identified, and long-range chromatin configuration information recovered from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material and will inform the development of targeted molecular diagnostic assays for patient care.

Discovery of New Fusion Transcripts in a Cohort of Pediatric Solid Cancers at Relapse and Relevance for Personalized Medicine

We hypothetized that pediatric cancers would more likely harbor fusion transcripts. To dissect the complexity of the fusions landscape in recurrent solid pediatric cancers, we conducted a study on 48 patients with different relapsing or resistant malignancies. By analyzing RNA sequencing data with a new in-house pipeline for fusions detection named ChimComp, followed by verification by real-time PCR, we identified and classified the most confident fusion transcripts (FTs) according to their potential biological function and druggability. The majority of FTs were predicted to affect key cancer pathways and described to be involved in oncogenesis. Contrary to previous descriptions, we found no significant correlation between the number of fusions and mutations, emphasizing the particularity to study pre-treated pediatric patients. A considerable proportion of FTs containing tumor suppressor genes was detected, reflecting their importance in pediatric cancers. FTs containing non-receptor tyrosine kinases occurred at low incidence and predominantly in brain tumors. Remarkably, more than 30% of patients presented a potentially druggable high-confidence fusion. In conclusion, we detected new oncogenic FTs in relapsing pediatric cancer patients by establishing a robust pipeline that can be applied to other malignancies, to detect and prioritize experimental validation studies leading to the development of new therapeutic options.

Maximizing the Utility of Cancer Transcriptomic Data

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

Relatively favorable prognosis for MLL-rearranged childhood acute leukemia with reciprocal translocations

BACKGROUND

Mixed-lineage leukemia (MLL) with multifarious partner genes leads to aggressive leukemia with dismal outcomes.

METHODS

Using panel-based targeted sequencing, we examined 90 cases with MLL-rearranged (MLL-r) childhood acute leukemia, including 55 with acute lymphoblastic leukemia (ALL) and 35 with acute myeloid leukemia (AML).

RESULTS

MLL breakpoints and complete rearrangements were identified. A total of 37.8% (34/90) of patients displayed a single direct MLL fusion gene, 15.6% (14/90) carried a single reciprocal fusion, and 27.8% (25/90) had both reciprocal MLL fusion alleles. The remaining 17 MLL-r cases exhibited complex translocations with homozygous disruptions on chromosome 11 or two breakpoints on the same MLL allele with a deletion of functional regions. A total of 77 patients (45 ALL and 32 AML) received chemotherapy with a median follow-up of 2.5 years. Unexpectedly, we identified children with reciprocal MLL fusions who exhibited relatively favorable outcomes compared with those in children with complex translocations or a single direct MLL fusion allele (66.1% vs. 24.6% and 27.6%, P = 0.001). Reciprocal MLL fusion may be functionally rescued by a partially truncated MLL protein.

CONCLUSION

Comprehensive MLL-r analysis by targeted next-generation sequencing can provide detailed molecular information and is helpful for precise stratified treatment and clinical prognosis determination.

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .

A computational tool to detect DNA alterations tailored to formalin-fixed paraffin-embedded samples in cancer clinical sequencing

Advanced cancer genomics technologies are now being employed in clinical sequencing, where next-generation sequencers are used to simultaneously identify multiple types of DNA alterations for prescription of molecularly targeted drugs. However, no computational tool is available to accurately detect DNA alterations in formalin-fixed paraffin-embedded (FFPE) samples commonly used in hospitals. Here, we developed a computational tool tailored to the detection of single nucleotide variations, indels, fusions, and copy number alterations in FFPE samples. Elaborated multilayer noise filters reduced the inherent noise while maintaining high sensitivity, as evaluated in tumor-unmatched normal samples using orthogonal technologies. This tool, cisCall, should facilitate clinical sequencing in everyday diagnostics. It is available at https://www.ciscall.org.

Gene Fusion in Malignant Glioma: An Emerging Target for Next-Generation Personalized Treatment

Malignant gliomas are heterogeneous diseases in genetic basis. The development of sequencing techniques has identified many gene rearrangements encoding novel oncogenic fusions in malignant glioma to date. Understanding the gene fusions and how they regulate cellular processes in different subtypes of glioma will shed light on genomic diagnostic approaches for personalized treatment. By now, studies of gene fusions in glioma remain limited, and no medication has been approved for treating the malignancy harboring gene fusions. This review will discuss the current characterization of gene fusions occurring in both adult and pediatric malignant gliomas, their roles in oncogenesis, and the potential clinical implication as therapeutic targets.

Gene Fusions in Thyroid Cancer

BACKGROUND

Gene fusions are known in many cancers as driver or passenger mutations. They play an important role in both the etiology and pathogenesis of cancer and are considered as potential diagnostic and prognostic markers and possible therapeutic targets. The spectrum and prevalence of gene fusions in thyroid cancer ranges from single cases up to 80%, depending on the specific type of cancer. During last three years, massive parallel sequencing technologies have revealed new fusions and allowed detailed characteristics of fusions in different types of thyroid cancer.

SUMMARY

This article reviews all known fusions and their prevalence in papillary, poorly differentiated and anaplastic, follicular, and medullary carcinomas. The mechanisms of fusion formation are described. In addition, the mechanisms of oncogenic transformation, such as altered gene expression, forced oligomerization, and subcellular localization, are given.

CONCLUSION

The prognostic value and perspectives of the utilization of gene fusions as therapeutic targets are discussed.

Fusion genes: A promising tool combating against cancer

The driving roles of fusion genes during tumorigenesis have been recognized for decades, with efficacies demonstrated in clinical diagnosis and targeted therapy. With advances in sequencing technologies and computational biology, a surge in the identification of fusion genes has been witnessed during the past decade. The discovery and presence of splicing based fusions in normal tissues have challenged our canonical conceptions on fusion genes and offered us novel medical opportunities. The specificity of fusion genes to neoplastic tissues and their diverse functionalities during carcinogenesis foster them as promising tools in the battle against cancer. It is time to re-visit and comb through our cutting-edge knowledge on fusion genes to accelerate clinical translation of these internal markers. Urged as such, we are encouraged to categorize fusion events according to mechanisms leading to their generation, oncological consequences and clinical implications, offer insights on fusion occurrence across tumors from the system level, highlight feasible practices in fusion-related pharmaceutical development, and identify understudied yet important niches that may lead future research trend in this field.

Read-through transcripts in normal human lung parenchyma are down-regulated in lung adenocarcinoma

Read-through transcripts result from the continuous transcription of adjacent, similarly oriented genes, with the splicing out of the intergenic region. They have been found in several neoplastic and normal tissues, but their pathophysiological significance is unclear. We used high-throughput sequencing of cDNA fragments (RNA-Seq) to identify read-through transcripts in the non-involved lung tissue of 64 surgically treated lung adenocarcinoma patients. A total of 52 distinct read-through species was identified, with 24 patients having at least one read-through event, up to a maximum of 17 such transcripts in one patient. Sanger sequencing validated 28 of these transcripts and identified an additional 15, for a total of 43 distinct read-through events involving 35 gene pairs. Expression levels of 10 validated read-through transcripts were measured by quantitative PCR in pairs of matched non-involved lung tissue and lung adenocarcinoma tissue from 45 patients. Higher expression levels were observed in normal lung tissue than in the tumor counterpart, with median relative quantification ratios between normal and tumor varying from 1.90 to 7.78; the difference was statistically significant (P < 0.001, Wilcoxon's signed-rank test for paired samples) for eight transcripts: ELAVL1–TIMM44, FAM162B–ZUFSP, IFNAR2–IL10RB, INMT–FAM188B, KIAA1841–C2orf74, NFATC3–PLA2G15, SIRPB1–SIRPD, and SHANK3–ACR. This report documents the presence of read-through transcripts in apparently normal lung tissue, with inter-individual differences in patterns and abundance. It also shows their down-regulation in tumors, suggesting that these chimeric transcripts may function as tumor suppressors in lung tissue.

ALK fusion variants detection by targeted RNA-next generation sequencing and clinical responses to crizotinib in ALK-positive non-small cell lung cancer

OBJECTIVES

The aim of the present study was firstly to assess in a clinical setting the yields of an amplicon-based parallel RNA sequencing (RNA-seq) assay for ALK fusion transcript variants detection in comparison with immunohistochemistry (IHC) and fluorescent in-situ hybridization (FISH) in a selected population of ALK-positive and ALK-negative non-small cell lung cancer (NSCLC) cases, and secondly to evaluate the impact of the ALK variant on crizotinib efficacy.

MATERIALS AND METHODS

The cohort used for the assessment of the RNA-seq assay comprised 53 samples initially diagnosed as being ALK-positive based on the results obtained by IHC and/or FISH, and 23 ALK-negative samples. A distinction was made between 'truly' IHC/FISH positive or 'truly' IHC/FISH negative samples, and those for which the IHC and/or FISH were equivocal (IHC) or borderline-positive (FISH).

RESULTS

On the overall population, RNA-seq sensitivity (Se) and specificity (Spe) were of 80% and 100%, respectively when IHC and FISH were combined. For the 31 'truly positive' samples, Se and Spe of 100% were reached. An ALK status could be assigned by RNA-seq in 10/10 of the equivocal and/or borderline-positive IHC/FISH cases, 2/7 IHC/FISH discordant cases. When crizotinib efficacy was evaluated according to the type of ALK variant, better clinical outcomes were observed in crizotinib-treated patients with EML4-ALK v1/v2/others variants compared to v3a/b variants.

CONCLUSION

RNA-seq detects ALK rearrangements with a high sensitivity and specificity using only 10 ng of RNA. It appears to be a promising rescue technique for non-clear-cut IHC/FISH cases and also offers a unique opportunity to identify ALK fusion variants and evaluate their predictive value for ALK inhibitors efficacy.

Domain retention in transcription factor fusion genes and its biological and clinical implications: a pan-cancer study

Genomic rearrangements involving transcription factors (TFs) can form fusion proteins resulting in either enhanced, weakened, or even loss of TF activity. Functional domain (FD) retention is a critical factor in the activity of transcription factor fusion genes (TFFGs). A systematic investigation of FD retention in TFFGs and their outcome (e.g. expression changes) in a pan-cancer study has not yet been completed. Here, we examined the FD retention status in 386 TFFGs across 13 major cancer types and identified 83 TFFGs involving 67 TFs that retained FDs. To measure the potential biological relevance of TFs in TFFGs, we introduced a Major Active Isofusion Index (MAII) and built a prioritized TFFG network using MAII scores and the observed frequency of fusion positive samples. Interestingly, the four TFFGs (PML-RARA, RUNX1-RUNX1T1, TMPRSS2-ERG, and SFPQ-TFE3) with the highest MAII scores showed 50 differentially expressed target genes (DETGs) in fusion-positive versus fusion-negative cancer samples. DETG analysis revealed that they were involved in tumorigenesis-related processes in each cancer type. PLAU, which encodes plasminogen activator urokinase and serves as a biomarker for tumor invasion, was found to be consistently activated in the samples with the highest MAII scores. Among the 50 DETGs, 21 were drug targetable genes. Fourteen of these 21 DETGs were expressed in acute myeloid leukemia (AML) samples. Accordingly, we constructed an AML-specific TFFG network, which included 38 DETGs in RUNX1-RUNX1T1 or PML-RARA positive samples. In summary, this study revealed several TFFGs and their potential target genes, and provided insights into the clinical implications of TFFGs.

Integrating RNA sequencing into neuro-oncology practice

Malignant tumors of the central nervous system (CNS) cause substantial morbidity and mortality, yet efforts to optimize chemo- and radiotherapy have largely failed to improve dismal prognoses. Over the past decade, RNA sequencing (RNA-seq) has emerged as a powerful tool to comprehensively characterize the transcriptome of CNS tumor cells in one high-throughput step, leading to improved understanding of CNS tumor biology and suggesting new routes for targeted therapies. RNA-seq has been instrumental in improving the diagnostic classification of brain tumors, characterizing oncogenic fusion genes, and shedding light on intratumor heterogeneity. Currently, RNA-seq is beginning to be incorporated into regular neuro-oncology practice in the form of precision neuro-oncology programs, which use information from tumor sequencing to guide implementation of personalized targeted therapies. These programs show great promise in improving patient outcomes for tumors where single agent trials have been ineffective. As RNA-seq is a relatively new technique, many further applications yielding new advances in CNS tumor research and management are expected in the coming years.

Statistical algorithms improve accuracy of gene fusion detection

Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.

De novo assembly and characterization of breast cancer transcriptomes identifies large numbers of novel fusion-gene transcripts of potential functional significance

Background

Gene-fusion or chimeric transcripts have been implicated in the onset and progression of a variety of cancers. Massively parallel RNA sequencing (RNA-Seq) of the cellular transcriptome is a promising approach for the identification of chimeric transcripts of potential functional significance. We report here the development and use of an integrated computational pipeline for the de novo assembly and characterization of chimeric transcripts in 55 primary breast cancer and normal tissue samples.

Methods

An integrated computational pipeline was employed to screen the transcriptome of breast cancer and control tissues for high-quality RNA-sequencing reads. Reads were de novo assembled into contigs followed by reference genome mapping. Chimeric transcripts were detected, filtered and characterized using our R-SAP algorithm. The relative abundance of reads was used to estimate levels of gene expression.

Results

De novo assembly allowed for the accurate detection of 1959 chimeric transcripts to nucleotide level resolution and facilitated detailed molecular characterization and quantitative analysis. A number of the chimeric transcripts are of potential functional significance including 79 novel fusion-protein transcripts and many chimeric transcripts with alterations in their un-translated leader regions. A number of chimeric transcripts in the cancer samples mapped to genomic regions devoid of any known genes. Several ‘pro-neoplastic’ fusions comprised of genes previously implicated in cancer are expressed at low levels in normal tissues but at high levels in cancer tissues.

Conclusions

Collectively, our results underscore the utility of deep sequencing technologies and improved bioinformatics workflows to uncover novel and potentially significant chimeric transcripts in cancer and normal somatic tissues.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0289-7) contains supplementary material, which is available to authorized users.