Accurate and efficient detection of gene fusions from RNA sequencing data.

The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.

Genomic architecture of FGFR2 fusions in cholangiocarcinoma and its implication for molecular testing.

BACKGROUND: Cholangiocarcinoma (CCA) is a primary malignancy of the biliary tract with a dismal prognosis. Recently, several actionable genetic aberrations were identified with significant enrichment in intrahepatic CCA, including FGFR2 gene fusions with a prevalence of 10-15%. Recent clinical data demonstrate that these fusions are druggable in a second-line setting in advanced/metastatic disease and the efficacy in earlier lines of therapy is being evaluated in ongoing clinical trials. This scenario warrants standardised molecular profiling of these tumours. METHODS: A detailed analysis of the original genetic data from the FIGHT-202 trial, on which the approval of Pemigatinib was based, was conducted. RESULTS: Comparing different detection approaches and displaying representative cases, we described the genetic landscape and architecture of FGFR2 fusions in iCCA and show biological and technical aspects to be considered for their detection. We elaborated parameters, including a suggestion for annotation, that should be stated in a molecular diagnostic FGFR2 report to allow a complete understanding of the analysis performed and the information provided. CONCLUSION: This study provides a detailed presentation and dissection of the technical and biological aspects regarding FGFR2 fusion detection, which aims to support molecular pathologists, pathologists and clinicians in diagnostics, reporting of the results and decision-making.

Comprehensive genomic analysis of refractory multiple myeloma reveals a complex mutational landscape associated with drug resistance and novel therapeutic vulnerabilities.

The outcomes of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs) remain poor. In this study, we performed whole genome and transcriptome sequencing of 39 heavily pretreated relapsed/refractory MM (RRMM) patients to identify mechanisms of resistance and potential therapeutic targets. We observed a high mutational load and indications of increased genomic instability. Recurrently mutated genes in RRMM, which had not been previously reported or only observed at a lower frequency in newly diagnosed MM, included NRAS, BRAF, TP53, SLC4A7, MLLT4, EWSR1, HCFC2, and COPS3. We found multiple genomic regions with bi-allelic events affecting tumor suppressor genes and demonstrated a significant adverse impact of bi-allelic TP53 alterations on survival. With regard to potentially resistance conferring mutations, recurrently mutated gene networks included genes with relevance for PI and IMiD activity; the latter particularly affecting members of the Cereblon and the COP9 signalosome complex. We observed a major impact of signatures associated with exposure to melphalan or impaired DNA double-strand break homologous recombination repair in RRMM. The latter coincided with mutations in genes associated with PARP inhibitor sensitivity in 49% of RRMM patients; a finding with potential therapeutic implications. In conclusion, this comprehensive genomic characterization revealed a complex mutational and structural landscape in RRMM and highlights potential implications for therapeutic strategies.

Bcl-xL as prognostic marker and potential therapeutic target in cholangiocarcinoma.

Intrahepatic, perihilar, and distal cholangiocarcinoma (iCCA, pCCA, dCCA) are highly malignant tumours with increasing mortality rates due to therapy resistances. Among the mechanisms mediating resistance, overexpression of anti-apoptotic Bcl-2 proteins (Bcl-2, Bcl-xL , Mcl-1) is particularly important. In this study, we investigated whether antiapoptotic protein patterns are prognostically relevant and potential therapeutic targets in CCA. Bcl-2 proteins were analysed in a pan-cancer cohort from the NCT/DKFZ/DKTK MASTER registry trial (n = 1140, CCA n = 72) via RNA-sequencing and transcriptome-based protein activity interference revealing high ranks of CCA for Bcl-xL and Mcl-1. Expression of Bcl-xL , Mcl-1, and Bcl-2 was assessed in human CCA tissue and cell lines compared with cholangiocytes by immunohistochemistry, immunoblotting, and quantitative-RT-PCR. Immunohistochemistry confirmed the upregulation of Bcl-xL and Mcl-1 in iCCA tissues. Cell death of CCA cell lines upon treatment with specific small molecule inhibitors of Bcl-xL (Wehi-539), of Mcl-1 (S63845), and Bcl-2 (ABT-199), either alone, in combination with each other or together with chemotherapeutics was assessed by flow cytometry. Targeting Bcl-xL induced cell death and augmented the effect of chemotherapy in CCA cells. Combined inhibition of Bcl-xL and Mcl-1 led to a synergistic increase in cell death in CCA cell lines. Correlation between Bcl-2 protein expression and survival was analysed within three independent patient cohorts from cancer centers in Germany comprising 656 CCA cases indicating a prognostic value of Bcl-xL in CCA depending on the CCA subtype. Collectively, these observations identify Bcl-xL as a key protein in cell death resistance of CCA and may pave the way for clinical application.

Integrating proteomics into precision oncology.

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.

Deconvolution of sarcoma methylomes reveals varying degrees of immune cell infiltrates with association to genomic aberrations.

BACKGROUND: Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments. RESULTS: To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas. CONCLUSIONS: In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.

Germline SDHB-inactivating mutation in gastric spindle cell sarcoma.

Gastrointestinal stromal tumors (GISTs) are the most frequent mesenchymal tumors of the gastrointestinal tract. Inactivating mutations or epigenetic deregulation of succinate dehydrogenase complex (SDH) genes are considered defining features of a subset of GIST occurring in the stomach. Based on comprehensive molecular profiling and biochemical analysis within a precision oncology program, we identified hallmarks of SDH deficiency (germline SDHB-inactivating mutation accompanied by somatic loss of heterozygosity, lack of SDHB expression, global DNA hypermethylation, and elevated succinate/fumarate ratio) in a 40-year-old woman with undifferentiated gastric spindle cell sarcoma that did not meet the diagnostic criteria for other mesenchymal tumors of the stomach, including GIST. These data reveal that the loss of SDH function can be involved in the pathogenesis of non-GIST sarcoma of the gastrointestinal tract.

PingPongPro: a tool for the detection of piRNA-mediated transposon-silencing in small RNA-Seq data.

Summary: Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs which guide endonucleases to mRNAs of actively transcribed transposons in order to prevent their translation. The resulting mRNA fragments induce a positive feedback loop (the 'ping-pong cycle'), which reinforces piRNA production and hence the transposon-silencing effect. PingPongPro is a command-line tool to scan small RNA-Seq data for signs of ping-pong cycle activity. It implements a novel algorithm that combines empirical probabilities in a multi-factor model to accurately identify transposons which are suppressed through the ping-pong cycle. Availability and implementation: Source code, a user manual, and binaries for Microsoft Windows and Linux are available at https://github.com/suhrig/pingpongpro under the GPLv3 license. Supplementary information: Supplementary data are available at Bioinformatics online.

RSPO2 gene rearrangement: a powerful driver of ß-catenin activation in liver tumours.

OBJECTIVE: We aimed at the identification of genetic alterations that may functionally substitute for CTNNB1 mutation in ß-catenin-activated hepatocellular adenomas (HCAs) and hepatocellular carcinoma (HCC). DESIGN: Large cohorts of HCA (n=185) and HCC (n=468) were classified using immunohistochemistry. The mutational status of the CTNNB1 gene was determined in ß-catenin-activated HCA (b-HCA) and HCC with at least moderate nuclear CTNNB1 accumulation. Ultra-deep sequencing was used to characterise CTNNB1wild-type and ß-catenin-activated HCA and HCC. Expression profiling of HCA subtypes was performed. RESULTS: A roof plate-specific spondin 2 (RSPO2) gene rearrangement resulting from a 46.4 kb microdeletion on chromosome 8q23.1 was detected as a new morphomolecular driver of ß-catenin-activated HCA. RSPO2 fusion positive HCA displayed upregulation of RSPO2 protein, nuclear accumulation of ß-catenin and transcriptional activation of ß-catenin-target genes indicating activation of Wingless-Type MMTV Integration Site Family (WNT) signalling. Architectural and cytological atypia as well as interstitial invasion indicated malignant transformation in one of the RSPO2 rearranged b-HCAs. The RSPO2 gene rearrangement was also observed in three ß-catenin-activated HCCs developing in context of chronic liver disease. Mutations of the human telomerase reverse transcriptase promoter-known to drive malignant transformation of CTNNB1-mutated HCA-seem to be dispensable for RSPO2 rearranged HCA and HCC. CONCLUSION: The RSPO2 gene rearrangement leads to oncogenic activation of the WNT signalling pathway in HCA and HCC, represents an alternative mechanism for the development of b-HCA and may drive malignant transformation without additional TERT promoter mutation.

Variant classification in precision oncology.

Next-generation sequencing has become a cornerstone of therapy guidance in cancer precision medicine and an indispensable research tool in translational oncology. Its rapidly increasing use during the last decade has expanded the options for targeted tumor therapies, and molecular tumor boards have grown accordingly. However, with increasing detection of genetic alterations, their interpretation has become more complex and error-prone, potentially introducing biases and reducing benefits in clinical practice. To facilitate interdisciplinary discussions of genetic alterations for treatment stratification between pathologists, oncologists, bioinformaticians, genetic counselors and medical scientists in specialized molecular tumor boards, several systems for the classification of variants detected by large-scale sequencing have been proposed. We review three recent and commonly applied classifications and discuss their individual strengths and weaknesses. Comparison of the classifications underlines the need for a clinically useful and universally applicable variant reporting system, which will be instrumental for efficient decision making based on sequencing analysis in oncology. Integrating these data, we propose a generalizable classification concept featuring a conservative and a more progressive scheme, which can be readily applied in a clinical setting.

Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia.

Drug sensitivity and resistance testing on diagnostic leukemia samples should provide important functional information to guide actionable target and biomarker discovery. We provide proof of concept data by profiling 60 drugs on 68 acute lymphoblastic leukemia (ALL) samples mostly from resistant disease in cocultures of bone marrow stromal cells. Patient-derived xenografts retained the original pattern of mutations found in the matched patient material. Stromal coculture did not prevent leukemia cell cycle activity, but a specific sensitivity profile to cell cycle-related drugs identified samples with higher cell proliferation both in vitro and in vivo as leukemia xenografts. In patients with refractory relapses, individual patterns of marked drug resistance and exceptional responses to new agents of immediate clinical relevance were detected. The BCL2-inhibitor venetoclax was highly active below 10 nM in B-cell precursor ALL (BCP-ALL) subsets, including MLL-AF4 and TCF3-HLF ALL, and in some T-cell ALLs (T-ALLs), predicting in vivo activity as a single agent and in combination with dexamethasone and vincristine. Unexpected sensitivity to dasatinib with half maximal inhibitory concentration values below 20 nM was detected in 2 independent T-ALL cohorts, which correlated with similar cytotoxic activity of the SRC inhibitor KX2-391 and inhibition of SRC phosphorylation. A patient with refractory T-ALL was treated with dasatinib on the basis of drug profiling information and achieved a 5-month remission. Thus, drug profiling captures disease-relevant features and unexpected sensitivity to relevant drugs, which warrants further exploration of this functional assay in the context of clinical trials to develop drug repurposing strategies for patients with urgent medical needs.

Mutational patterns and regulatory networks in epigenetic subgroups of meningioma.

DNA methylation patterns delineate clinically relevant subgroups of meningioma. We previously established the six meningioma methylation classes (MC) benign 1-3, intermediate A and B, and malignant. Here, we set out to identify subgroup-specific mutational patterns and gene regulation. Whole genome sequencing was performed on 62 samples across all MCs and WHO grades from 62 patients with matched blood control, including 40 sporadic meningiomas and 22 meningiomas arising after radiation (Mrad). RNA sequencing was added for 18 of these cases and chromatin-immunoprecipitation for histone H3 lysine 27 acetylation (H3K27ac) followed by sequencing (ChIP-seq) for 16 samples. Besides the known mutations in meningioma, structural variants were found as the mechanism of NF2 inactivation in a small subset (5%) of sporadic meningiomas, similar to previous reports for Mrad. Aberrations of DMD were found to be enriched in MCs with NF2 mutations, and DMD was among the most differentially upregulated genes in NF2 mutant compared to NF2 wild-type cases. The mutational signature AC3, which has been associated with defects in homologous recombination repair (HRR), was detected in both sporadic meningioma and Mrad, but widely distributed across the genome in sporadic cases and enriched near genomic breakpoints in Mrad. Compared to the other MCs, the number of single nucleotide variants matching the AC3 pattern was significantly higher in the malignant MC, which also exhibited higher genomic instability, determined by the numbers of both large segments affected by copy number alterations and breakpoints between large segments. ChIP-seq analysis for H3K27ac revealed a specific activation of genes regulated by the transcription factor FOXM1 in the malignant MC. This analysis also revealed a super enhancer near the HOXD gene cluster in this MC, which, together with general upregulation of HOX genes in the malignant MC, indicates a role of HOX genes in meningioma aggressiveness. This data elucidates the biological mechanisms rendering different epigenetic subgroups of meningiomas, and suggests leveraging HRR as a novel therapeutic target.

Precision oncology based on omics data: The NCT Heidelberg experience.

Precision oncology implies the ability to predict which patients will likely respond to specific cancer therapies based on increasingly accurate, high-resolution molecular diagnostics as well as the functional and mechanistic understanding of individual tumors. While molecular stratification of patients can be achieved through different means, a promising approach is next-generation sequencing of tumor DNA and RNA, which can reveal genomic alterations that have immediate clinical implications. Furthermore, certain genetic alterations are shared across multiple histologic entities, raising the fundamental question of whether tumors should be treated by molecular profile and not tissue of origin. We here describe MASTER (Molecularly Aided Stratification for Tumor Eradication Research), a clinically applicable platform for prospective, biology-driven stratification of younger adults with advanced-stage cancer across all histologies and patients with rare tumors. We illustrate how a standardized workflow for selection and consenting of patients, sample processing, whole-exome/genome and RNA sequencing, bioinformatic analysis, rigorous validation of potentially actionable findings, and data evaluation by a dedicated molecular tumor board enables categorization of patients into different intervention baskets and formulation of evidence-based recommendations for clinical management. Critical next steps will be to increase the number of patients that can be offered comprehensive molecular analysis through collaborations and partnering, to explore ways in which additional technologies can aid in patient stratification and individualization of treatment, to stimulate clinically guided exploratory research projects, and to gradually move away from assessing the therapeutic activity of targeted interventions on a case-by-case basis toward controlled clinical trials of genomics-guided treatments.

Multi-omic and functional analysis for classification and treatment of sarcomas with FUS-TFCP2 or EWSR1-TFCP2 fusions.

Linking clinical multi-omics with mechanistic studies may improve the understanding of rare cancers. We leverage two precision oncology programs to investigate rhabdomyosarcoma with FUS/EWSR1-TFCP2 fusions, an orphan malignancy without effective therapies. All tumors exhibit outlier ALK expression, partly accompanied by intragenic deletions and aberrant splicing resulting in ALK variants that are oncogenic and sensitive to ALK inhibitors. Additionally, recurrent CKDN2A/MTAP co-deletions provide a rationale for PRMT5-targeted therapies. Functional studies show that FUS-TFCP2 blocks myogenic differentiation, induces transcription of ALK and truncated TERT, and inhibits DNA repair. Unlike other fusion-driven sarcomas, TFCP2-rearranged tumors exhibit genomic instability and signs of defective homologous recombination. DNA methylation profiling demonstrates a close relationship with undifferentiated sarcomas. In two patients, sarcoma was preceded by benign lesions carrying FUS-TFCP2, indicating stepwise sarcomagenesis. This study illustrates the potential of linking precision oncology with preclinical research to gain insight into the classification, pathogenesis, and therapeutic vulnerabilities of rare cancers.

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment.

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. To collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uni-heidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity.

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived in vitro and in vivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with high sensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

NCT/DKFZ MASTER handbook of interpreting whole-genome, transcriptome, and methylome data for precision oncology.

Analysis of selected cancer genes has become an important tool in precision oncology but cannot fully capture the molecular features and, most importantly, vulnerabilities of individual tumors. Observational and interventional studies have shown that decision-making based on comprehensive molecular characterization adds significant clinical value. However, the complexity and heterogeneity of the resulting data are major challenges for disciplines involved in interpretation and recommendations for individualized care, and limited information exists on how to approach multilayered tumor profiles in clinical routine. We report our experience with the practical use of data from whole-genome or exome and RNA sequencing and DNA methylation profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program of the National Center for Tumor Diseases (NCT) Heidelberg and Dresden and the German Cancer Research Center (DKFZ). We cover all relevant steps of an end-to-end precision oncology workflow, from sample collection, molecular analysis, and variant prioritization to assigning treatment recommendations and discussion in the molecular tumor board. To provide insight into our approach to multidimensional tumor profiles and guidance on interpreting their biological impact and diagnostic and therapeutic implications, we present case studies from the NCT/DKFZ molecular tumor board that illustrate our daily practice. This manual is intended to be useful for physicians, biologists, and bioinformaticians involved in the clinical interpretation of genome-wide molecular information.