Pediatric Cancer Genetics and Genomics.

Introduction: Recurrent somatic alterations associated with pediatric, childhood, and young adult cancers have not been as intensively studied as those associated with adult cancers. Consequently, whole-exome and transcriptome approaches are still being used to support discovery efforts. However, due to several initiatives aimed at profiling genomic alterations associated with childhood cancers, a set of recurrent somatic alterations has been defined. To accelerate research in this area, we have developed a novel targeted next-generation sequencing (NGS) assay to detect relevant somatic alterations previously reported in these cancer types. Methods: The assay was developed using Ion AmpliSeq targeted sequencing technology to cover the major gene variants associated with childhood cancers, including both solid tumor and hematologic cancer types. Over 200 gene targets were included on the basis of consultation with expert pediatric oncologists, literature review of the recent pediatric cancer genomic publications, as well as inclusion of relevant markers from adult cancers that are also observed in childhood cancers. Variant classes include mutations, copy number variations, gene fusions, and gene expression. Mutations in 130 genes, copy number variants in 28 genes, and over 1,400 distinct fusion isoforms in 88 fusion driver genes are analyzed. Variant calling algorithms for both DNA and RNA were optimized and combined into a single Ion Reporter workflow. Results: The assay generated an average read depth of >3,000 reads per DNA amplicon with high uniformity (>95%), when up to 7 sample DNA-RNA pairs were analyzed with the 540 chip of the Ion S5 sequencing instrument. Minimal allele frequency detected for key hotspots was 5%. Sensitive and reproducible detection of CNV and fusion variants associated with pediatric solid tumors (EWSR1-FL1 and KIAA1549-BRAF fusions, MYC and EGFR amplification) and hematologic cancers (ETV6-RUNX1 and PML-RARA fusions) was demonstrated in orthogonally profiled FFPE, blood, and bone marrow samples. Performance was robust across sample types. Similar results were observed with manual and automated library preparation. Conclusions: A novel NGS assay, designed specifically for pediatric, childhood, and young adult cancers, and capable of detecting relevant DNA and RNA alterations from the same sample, was developed and validated. The assay is useful for characterizing relevant alterations in a wide range of cancers, including childhood leukemias and lymphomas as well as solid tumors including neuroblastoma, rhabdomyosarcoma, retinoblastoma, osteosarcoma, Ewing sarcoma, Wilms tumor, and brain and spinal cord tumors. A review of the analytical studies will be presented. Citation Format: Nickolay A. Khazanov, Chaitali Parikh, Habib Hamidi, Scott P. Myrand, Efren Ballesteros-Villagrana, Jingwei Ni, Paul D. Williams, Karen L. Clyde, Dinesh Cyanam, Armand Bankhead, III, Manimozhi Manivannan, Mark Tomilo, Susan Ewald, Jon K. Sherlock, Janice K. Au-Young, Jaclyn Biegel, Jonathan Buckley, Matthew Hiemenz, Dejerianne Ostrow, Alex Judkins, Xiaowu Gai, Tracy Busse, Alan Wayne, Deepa Bhojwani, Raca Gordana, Matthew Oberley, David Parham, Seth Sadis, Timothy Triche. Development of a next-generation sequencing (NGS) assay for pediatric, childhood, and young adult cancer research with comprehensive DNA and RNA variant detection [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B45.

Purpose: Risk stratification and molecular targeting have been key to increasing cure rates for pediatric cancers in high-income countries. Precise diagnosis and successful treatment of pediatric cancer in low-resourced settings is often hindered by insufficient pathology infrastructure, including lack of laboratory platforms for molecular analysis. Given the high frequency of gene fusions in pediatric cancers, identifying such fusions would greatly aid cost-effective pediatric cancer diagnosis, risk stratification, and precision medicine in low-resourced settings. Methods: To allow for implementation of gene fusion detection at Global HOPE sites in Sub-Saharan Africa (SSA), methodologies were reviewed to consider minimal technical expertise required, the ability to utilize samples with sub-optimal RNA quality, and rapid turn-around-time. Literature review, clinical laboratory results, public databases, and large-scale genomic studies were used to obtain exact breakpoint sequence information for gene fusions associated with pediatric and adolescent cancers. Results: Two custom pediatric gene fusions panels were designed using the NanoString Elements technology. The hematologic malignancy panel was designed to detect 439 breakpoints for 223 non-IGH/TCR fusions reported in ALL, AML, lymphomas, and histiocytosis. The solid tumor panel was designed to detect 204 breakpoints for 436 fusions associated with pediatric sarcomas, brain tumors, and renal malignancies. Each panel was tested using 96 samples with known fusion status at Texas Children's Hospital to determine specificity, sensitivity, precision, and ease of workflow. Conclusions: The design, testing, and implementation of a rapid assay to detect gene fusions with diagnostic, prognostic, and therapeutic impact would be transformational in the care of pediatric cancer patients in low-resourced settings. The custom designed panels will allow for large-scale fusion detection in 2-3 days with only 15 minutes of technician time after RNA isolation. Additional steps are needed to identify and address any challenges upon initiation in SSA to fully realize the potential of such technology. Citation Format: Julie Gastier-Foster, Fredrick Lutwama, Joseph Lubega, Kevin Fisher, Dolores Lopez-Terrada, Angshumoy Roy, Nmazuo Ozuah, Jeremy Slone, Peter Wasswa, Carl Allen, David Poplack. Custom Gene Fusion Assays for the Rapid Diagnosis of Pediatric Cancers in Low-Resourced Settings [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 98.

Recurrent gene fusions in cancers have been successfully applied in clinical diagnoses and treatments. Specific gene rearrangements or other specific cytogenetic translocations may be helpful in separating cancers from benign lesions. Also, the detection of gene fusions has brought great benefits to distinguish molecular subclassifications of cancers. Numerous approaches have been used to identify cancer-specific abnormalities, including FISH, RT-PCR, next-generation sequencing, etc. In addition to diagnostic and genetic values, molecular testing has been becoming a valuable tool in the therapeutic research. Recent clinical trials involving gene fusions in cancers have also been developing under a rapid speed. Generally, we review gene fusions in cancers, emphasizing on relevant diagnostic biomarkers, method selections, and treatments.

BackgroundGene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions.MethodsWe developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing.ResultsIn a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects.ConclusionsOur results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.

Detection of gene fusions is important for discovery of cancer drivers and clinical oncology testing, but existing software tools for fusion detection usually take hours to run and may fail to find lowly expressed fusions. To overcome these limitations, we developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions in unmapped paired-read RNA-Seq data. Given a set of patterns representing fusion transcript breakpoints, Fuzzion2 finds every read pair matching any of the patterns. Both exact and inexact (fuzzy) matches are detected; the fuzzy matching tolerates variations caused by sequencing errors, SNVs, and indels. By employing a novel index of frequency minimizers, Fuzzion2 needs only minutes to process a sample. We have also developed pipelines to produce patterns for Fuzzion2, from fusion contig sequences, from genomic breakpoints in DNA and RNA, and from fusion protein sequences. To evaluate its applicability in clinical testing, we ran Fuzzion2 on ~2,000 RNA-seq samples profiled by the St. Jude clinical genomics program and confirmed its sensitivity in identifying lowly expressed fusions, such as KIAA1549-BRAF in low-grade glioma, which are frequently missed by commonly used fusion detection programs. Notably, Fuzzion2 detected a subclonal BCR-ABL1 fusion expressed at 1% and 6% of the wild-type BCR and ABL1 transcription level, respectively, in a B-lineage ALL sample that also has an IGH-CRLF fusion. Processing RNA-seq data from BCR-ABL1 cell lines, K562 with p210 fusion and OP1 with p190 fusion, diluted at 1:10, 1:100, and 1:1000 showed that Fuzzion2 can detect the fusion at 1:10-1:100 dilution, achieving a sensitivity 10 times greater than that of other fusion detection programs. We also evaluated the performance of Fuzzion2 for large-scale data mining in a study to compare the prevalence of gene fusions in pediatric versus adult cancers. We assembled a set of 15,474 patterns representing 5,480 fusions identified in the Pediatric Cancer Genome Project, NCI TARGET, clinical sequencing, and the COSMIC database. Fuzzion2 was deployed to the NCI Cancer Genomics Cloud and analyzed 9,464 TCGA RNA-seq samples from adult solid and brain tumors. Processing took an average of 6 minutes at a cost of only US$0.16 per sample. Among the 105 recurrent fusions identified in pediatric cancers, only 11 were also found in adult cancers. These shared fusions can be classified into two categories: 1) gene fusions present in cancers that occur in both children and young adults, e.g., synovial sarcoma, papillary thyroid cancer, and fibrolamellar hepatocellular carcinoma; and 2) kinase fusions involving ABL1, NTRK, and FGFR. Our experience with Fuzzion2 demonstrates that it is a powerful tool for time-critical clinical application and large-scale data mining. It is publicly available at https://github.com/stjude/fuzzion2. Citation Format: Stephen V. Rice, Michael N. Edmonson, Liqing Tian, Michael Rusch, David A. Wheeler, Jennifer L. Neary, Scott Newman, Lu Wang, Patrick R. Blackburn, Michael Macias, Andrew Thrasher, Jian Wang, Mark R. Wilkinson, Xin Zhou, Jinghui Zhang. Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4092.

Background, Rationale and Experimental Approach Oncogenic fusions are generated via chromosomal rearrangements resulting in an exchange of coding or regulatory DNA sequences. These mutations play an important role in disease onset and subsequent cancer progression, however the exact timing and mechanisms by which they arise are unknown. Through the SickKids clinical sequencing program, KiCS, we explored how and when canonical fusions arise by studying the whole-genomes of childhood cancers with diagnostic or driver fusions. Our investigation began with the pediatric bone cancer, Ewing sarcoma, and later expanded to include other solid, blood, and brain cancers such as papillary thyroid carcinoma, myeloid leukemia, and ependymoma among others. Results The starting point of our investigation was ES, where we sequenced the whole-genomes of 124 cases. Ewing sarcoma (ES) represents the prototypical fusion-driven sarcoma as it is characterized and driven by the EWSR1-ETS fusion. In ~42% of cases, we found that the ES fusion gene arises by chromoplexy, a sudden burst of complex, loop-like rearrangements, rather than by simple reciprocal translocations as previously thought. We show that these rearrangements rapidly and dramatically altered the chromosomal landscape of ES tumors, producing the driver EWSR1-ETS fusion and disrupting numerous other genes in a short time. Remarkably, these complex rearrangements are enriched for genes, including those with a clear role in oncogenesis, and are associated with the earliest replicating portions of the genome. We then sequenced the genomes of 30 other childhood cancers with oncogenic fusions to study their timing and formation mechanisms. In doing so, we have identified several novel fusions in many cancer types, which have been validated by RNA sequencing and cytogenetics. In some cases, the presence of these chromoplectic fusions indicates these patients may benefit from targeted therapy due to the generation of druggable fusions. . Conclusions Our findings provide fundamental insights into the pathogenesis of gene fusions in human cancer. They reveal complex DNA rearrangements to be a mutational process underpinning gene fusions in cancer that influences tumorigenesis. Citation Format: Nathaniel D. Anderson, Richard de Borja, Matthew D. Young, Fabio Fuligni, Andrej Rosic, Nicola D. Roberts, Nischalan Pillay, Jeffrey A. Toretsky, Yoshida Akihiko, Tatsuhiro Shibata, Markus Metzler, Gino Somers, Stephen W. Scherer, Adrienne M. Flanagan, Peter J. Campbell, Joshua D. Schiffman, Mary Shago, Ludmil B. Alexandrov, Jay S. Wunder, Irene L. Andrulis, David Malkin, Sam Behjati, Adam Shlien. Exploring the complex etiology of oncogenic fusions in childhood cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2506.

Pediatric cancers have a very different genomic profile from adult cancers. For example, single-nucleotide variants (SNVs) are common drivers in many adult cancers but are not as prevalent in many pediatric cancers. In particular, a large subset of solid tumors is driven by copy number alterations and structural variations (SV), including translocation-induced gene fusions. These SVs can be difficult to profile using commercial sequencing panels or DNA-only sequencing. However, by integrating the results from RNAseq and whole-genome sequencing (WGS), we can start to better understand the mechanisms behind these rare malignancies. The primary results of WGS analysis are SNVs, SVs, or CNAs. Each of these somatic classes of variation can be further refined using genome annotation tools and databases to prioritize variants and identify likely drivers. However, if one looks at DNA data alone, it is impossible to validate these predictions. We may identify a known oncogenic SNV, but due to a complex rearrangement, that particular SNV may not be expressed. By including RNA in the analysis, we now have the ability to assess how functional these variants truly are. With SNVs, we examine the expression of a variant in RNA, including a comparison of the allele frequencies. For both somatic and germline variants, we use RNAseq to identify allele-specific expression patterns. We also use RNAseq to confirm the expression of predicted gene fusions, and the functional significance of copy number gains or losses, even at modest levels. In tumor profiling, RNAseq is primarily used for the identification of gene fusions and gene expression outliers. At the present, both of these techniques produce a high degree of false positives. However, due to the potential for complex rearrangements, RNAseq can be used to identify gene fusions that may be missed by DNA specific methods. For example, RNAseq can effectively “rescue” the results of WGS that may have identified individual (non-viable) SVs but missed the overall combination of rearrangements that would result in a viable fusion. In a single-patient analysis, outlier expression is quite difficult. Each gene can have a wide range of “normal” expression, which is tissue specific. However, gene expression outliers can be validated with WGS analysis (CNA, SV, promoter hijacking, or SNVs in transcription factor binding sites) to prioritize outlier genes based upon those that can be mechanistically explained with a somatic (DNA) variant. Here we will describe the techniques and analysis pipelines used for the integrated analysis of RNA and DNA in a cohort of rare and high-risk pediatric cancer patients. RNAseq can provide a functional output whereas WGS can be used to provide a potential mechanism. Importantly, using both techniques lets us capture signal that may be otherwise missed with only one method. Together, we believe that the integration of RNA and DNA produces a more comprehensive analysis to better understand the mechanisms of each individual cancer. Citation Format: Marcus R. Breese, Alex G. Lee, Avanthi T. Shah, Henry J. Martell, Alejandro Sweet-Cordero. Methods for integrated analysis of RNA and DNA sequencing in pediatric cancers [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B03.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the ‘Rare Variant Genome Wide Association Study’ (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

e15568 Background: Next-generation sequencing (NGS) based molecular profiling technologies have revealed several oncogenic fusion genes that are actionable with small molecule inhibitors leading to practice change, particularly in lung cancer. The molecular and clinical characteristics of these gene fusions are not well defined in colorectal cancer patients (CRC). In this study, we aimed to define clinical and molecular characteristics of fusion genes in patients with CRC who underwent molecular profiling. Methods: Molecular characteristics of tissue confirmed 917 CRC patients were retrieved from the Moffit Cancer Center Clinical Genomics Action Committee database. Patients’ demographic and clinicopathological features and treatment history were collected from the database. All fusion genes were shown by hybridization-based NGS computational algorithms that determined cancer‐related genes, including single‐nucleotide variations, indels, microsatellite instability (MSI) status. Results: Among a total of 917 patients, 24 patients with CRC (2.6%) were found to have at least one fusion gene with a total number of 26 pathogenic fusions. The gene fusions are shown in Table. The most common, potentially targetable, fusion genes in our cohort were (1) RET fusions 0.5% (5/917), (2) ALK fusions 0.4% (4/917), (3) ROS1 fusions 0.2% (2/917), (4) NTRK1 fusion 0.1% (1/917), (5) NRG1 fusion 0.1% (1/917). Fusion genes were more common in MSI-H CRC (N = 27), and 3 (11.1%) patients with MSI-H CRC were found to have fusion genes [(RET (2) and NTRK(1)]. Fusion genes were present in both RAS wild-type (54%; 13/24) and RAS mutant (46%; 11/24) tumors. Most patients were older than 50 years (75%, 18/24) and had left-sided tumor (61.1%) tumor. Conclusions: Fusion genes are rare events in CRC. While fusion genes seem to be more prevalent in MSI-H CRC, RAS status does not correlate with the frequency of fusion genes. Actionable RET and ALK/ROS gene fusion are more common than NTRK fusion genes in this cohort of CRC patients.[Table: see text]

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

High-throughput detection of fusion genes in cancer using the Sequenom MassARRAY platform

Gene fusions or rearrangements are widely recognized as a significant player in driving tumorigenesis. Several key findings implicating fusion events have been reported in hematological malignancies (e.g. BCR-ABL in CML) and more recently in solid tumors (e.g. EML4-ALK in NSCLC). Next-generation sequencing, particularly RNA-seq, has rapidly led to discoveries of novel recurrent gene fusions in a wide variety of tumors (e.g. lung, breast, bladder, etc.). While the overall frequency of specific fusion events may be considered low, they have demonstrated significant value in guiding or developing treatment options and improving clinical outcomes. The advent of large-scale public sequencing efforts such as The Cancer Genome Atlas (TCGA) provide a tremendous opportunity in implementing creative analysis approaches and expanding the therapeutic opportunities of targeting gene fusions in cancer. In this study, we describe a comprehensive survey of fusion events of a select number of genes identified across various tumor types in TCGA. The computational demands of gene fusion discovery using RNA-seq data generated across thousands of tumors pose a serious challenge in pan-cancer analyses of clinically relevant gene fusions. To address this problem, we have developed an analytical pipeline to query genes of interest for 5′ or 3′ rearrangements that drastically lowers computational cost and increases throughput, compared to existing fusion analysis pipelines. This efficiency is primarily achieved by restricting the “alignment search space” of sequenced transcript reads to a fraction of the full read count, thereby enabling tangible gains in speed without sacrificing specificity or sensitivity. We applied this approach to search for fusion events involving well characterized genes in approximately 5700 paired-end RNA-seq tumor samples from 20 different cancer types sequenced by the TCGA project, completing the analysis in around 2 months on a modest hardware setup. Our results were validated against reported findings of prevalence in the respective cancer types (e.g. ETS-family gene fusions in prostate cancer and EML4-ALK fusions in lung cancer). As a striking result, we were able to identify several novel fusion partners for known fused oncogenes. Furthermore, the newly identified and also previously known fusion genes were discovered in novel tumor types, thus expanding the fusion landscape of well-known genes. For example, some of the 27 NTRK fusion genes found were observed in other indications than previously reported. In summary, we have successfully executed a valid functional and efficient analysis pipeline to reveal oncogenic rearrangements that play a key role in the initiation and progression of cancer. Furthermore, these events molecularly define clinical subsets of disease and as such, can guide personalized targeted therapies. Citation Format: Henrik Edgren, Kalle Ojala, Anja Ruusulehto, Gopi Ganji. Rapid pan-cancer identification of previously unidentified fusion genes to enable novel targeted therapeutics. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4793. doi:10.1158/1538-7445.AM2015-4793

When should we order a next generation sequencing test in a patient with cancer?

Fusion genes as biomarkers in pediatric cancers: A review of the current state and applicability in diagnostics and personalized therapy.