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 demonstrate Arriba's utility in both basic cancer research and clinical translation.

The clinical relevance of comprehensive molecular analysis in rare cancers is not established. We analyzed the molecular profiles and clinical outcomes of 1,310 patients (rare cancers, 75.5%) enrolled in a prospective observational study by the German Cancer Consortium that applies whole-genome/exome and RNA sequencing to inform the care of adults with incurable cancers. On the basis of 472 single and six composite biomarkers, a cross-institutional molecular tumor board provided evidence-based management recommendations, including diagnostic reevaluation, genetic counseling, and experimental treatment, in 88% of cases. Recommended therapies were administered in 362 of 1,138 patients (31.8%) and resulted in significantly improved overall response and disease control rates (23.9% and 55.3%) compared with previous therapies, translating into a progression-free survival ratio >1.3 in 35.7% of patients. These data demonstrate the benefit of molecular stratification in rare cancers and represent a resource that may promote clinical trial access and drug approvals in this underserved patient population. Significance: Rare cancers are difficult to treat; in particular, molecular pathogenesis–oriented medical therapies are often lacking. This study shows that whole-genome/exome and RNA sequencing enables molecularly informed treatments that lead to clinical benefit in a substantial proportion of patients with advanced rare cancers and paves the way for future clinical trials. See related commentary by Eggermont et al., p. 2677. This article is highlighted in the In This Issue feature, p. 2659

Tumor mutational burden (TMB) is a new biomarker for prediction of response to PD-(L)1 treatment. Comprehensive sequencing approaches (i.e., whole exome and whole genome sequencing) are ideally suited to measure TMB directly. However, as their applicability in routine diagnostics is currently limited by high costs, long turnaround times and poor availability of fresh tissue, targeted next-generation sequencing (NGS) of formalin-fixed and paraffin-embedded (FFPE) samples appears to be a more feasible and straightforward approach for TMB approximation, which can be seamlessly integrated in already existing diagnostic workflows and pipelines. In this work, we provide an overview of the clinical implications of TMB testing and highlight key parameters including pre-analysis, analysis and post-analytical steps that influence and shape TMB approximation by panel sequencing. Collectively, the data will not only serve as a field guide and state of the art knowledge source for molecular pathologists who consider implementation of TMB measurement in their lab, but also enable clinicians in understanding the specific parameters influencing TMB test results and reporting.

Tumor mutational burden (TMB) represents a new determinant of clinical benefit from immune checkpoint blockade that identifies responders independent of PD-L1 expression levels and is currently being explored in clinical trials. Although TMB can be measured directly by comprehensive genomic approaches such as whole-genome and exome sequencing, broad availability, short turnaround times, costs and amenability to formalin-fixed and paraffin-embedded tissue support the use of gene panel sequencing for approximating TMB in routine diagnostics. However, data on the parameters influencing panelbased TMB estimation are limited. Here, we report an extensive in silico analysis of the TCGA data set that simulates various panel sizes and compositions. We demonstrate that panel size is a critical parameter that influences confidence intervals (CIs) and cutoff values as well as important test parameters including sensitivity, specificity, and positive predictive value. Moreover, we evaluate the Illumina TSO500 panel, which will be made available for TMB estimation, and propose dynamic, entity-specific cutoff values based on current clinical trial data. Optimizing the cost-benefit ratio, our data suggest that panels between 1.5 and 3 Mbp are ideally suited to estimate TMB with small CIs, whereas smaller panels tend to deliver imprecise TMB estimates for low to moderate TMB (0-30 muts/Mbp), connected with insufficient separation of hypermutated tumors from non-hypermutated tumors.

To identify new tumor-suppressor gene candidates relevant for human hepatocarcinogenesis, we performed genome-wide methylation profiling and vertical integration with arraybased comparative genomic hybridization (aCGH), as well as expression data from a cohort of well-characterized human hepatocellular carcinomas (HCCs). Bisulfite-converted DNAs from 63 HCCs and 10 healthy control livers were analyzed for the methylation status of more than 14,000 genes. After defining the differentially methylated genes in HCCs, we integrated their DNA copy-number alterations as determined by aCGH data and correlated them with gene expression to identify genes potentially silenced by promoter hypermethylation. Aberrant methylation of candidates was further confirmed by pyrosequencing, and methylation dependency of silencing was determined by 5-aza-2 0 -deoxycytidine (5-aza-dC) treatment. Methylation profiling revealed 2,226 CpG sites that showed methylation differences between healthy control livers and HCCs. Of these, 537 CpG sites were hypermethylated in the tumor DNA, whereas 1,689 sites showed promoter hypomethylation. The hypermethylated set was enriched for genes known to be inactivated by the polycomb repressive complex 2, whereas the group of hypomethylated genes was enriched for imprinted genes. We identified three genes matching all of our selection criteria for a tumor-suppressor gene (period homolog 3 [PER3], insulin-like growth-factor-binding protein, acid labile subunit [IGFALS], and protein Z). PER3 was down-regulated in human HCCs, compared to peritumorous and healthy liver tissues. 5-aza-dC treatment restored PER3 expression in HCC cell lines, indicating that promoter hypermethylation was indeed responsible for gene silencing. Additionally, functional analysis supported a tumor-suppressive function for PER3 and IGFALS in vitro. Conclusion: The present study illustrates that vertical integration of methylation data with high-resolution genomic and transcriptomic data facilitates the identification of new tumor-suppressor gene candidates in human HCC. (HEPATOLOGY 2012;56:1817-1827 H epatocellular carcinoma (HCC) is the fifthmost frequent cancer worldwide and has a poor prognosis. 1 Various etiologies have been linked to HCC development, most of which cause chronic liver damage and finally lead to liver cirrhosis.The most prevalent etiological factors are chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, chronic alcohol consumption, and, in certain geographical areas, aflatoxin B1 food contamination. 2 Approximately 10% of HCC patients lack viral

Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel-based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using i) in silico analysis of TCGA (The Cancer Genome Atlas) data and ii) wet-lab sequencing of a total of 92 formalin-fixed and paraffin-embedded (FFPE) cancer samples grouped in three independent cohorts (non-small cell lung cancer, NSCLC; colorectal cancer, CRC; and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel >1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in NSCLC determined by wet-lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, our study is a valuable data source for researchers working in the field of immuno-oncology as well as for diagnostic laboratories planning TMB testing. What's new?Tumor mutational burden (TMB) is an emerging biomarker to predict response to immune checkpoint inhibitors. While TMB can be measured by whole exome sequencing (WES), costs, turn-around time, and tissue availability currently favor a panel sequencing approach using FFPE tissue for routine diagnostics. However, performance remains to be clarified. Our study is the first worldwide that analyses three major commercial gene panels by comparing TMB approximation across panels as well as against the technical reference standard WES. The data suggest that TMB approximation using gene panel sequencing of FFPE tumor tissue is feasible and can be implemented in routine diagnostics.

Tyrosine kinase inhibitors currently confer the greatest survival gain for nonsmall cell lung cancer (NSCLC) patients with actionable genetic alterations. Simultaneously, the increasing number of targets and compounds poses the challenge of reliable, broad and timely molecular assays for the identification of patients likely to benefit from novel treatments. Here, we demonstrate the feasibility and clinical utility of comprehensive, NGS‐based genetic profiling for routine workup of advanced NSCLC based on the first 3,000 patients analyzed in our department. Following automated extraction of DNA and RNA from formalin‐fixed, paraffin‐embedded tissue samples, parallel sequencing of DNA and RNA for detection of mutations and gene fusions, respectively, was performed using PCR‐based enrichment with an ion semiconductor sequencing platform. Overall, 807 patients (27%) were eligible for currently approved, EGFR‐/BRAF‐/ALK‐ and ROS1‐directed therapies, while 218 additional cases (7%) with MET, ERBB2 (HER2) and RET alterations could potentially benefit from experimental targeted compounds. In addition, routine capturing of comutations, e.g. TP53 (55%), KEAP1 (11%) and STK11 (11%), as well as the precise typing of fusion partners and involved exons in case of actionable translocations including ALK and ROS1, are prognostic and predictive tools currently gaining importance for further refinement of therapeutic and surveillance strategies. The reliability, low dropout rates (<5%), minimal tissue requirements, fast turnaround times (6 days on average) and lower costs of the diagnostic approach presented here compared to sequential single‐gene testing, highlight its practicability in order to support individualized decisions in routine patient care, enrollment in molecularly stratified clinical trials, as well as translational research.

Mouse Double Minute homolog 4 (MDM4) gene upregulation often occurs in human hepatocellular carcinoma (HCC), but the molecular mechanisms responsible for its induction remain poorly understood. Here, we investigated the role of the phosphoinositide-3-kinase/v-akt murine thymoma viral oncogene homolog/mammalian target of Rapamycin (PI3K/AKT/mTOR) axis in the regulation of MDM4 levels in HCC. The activity of MDM4 and the PI3K/AKT/mTOR pathway was modulated in human HCC cell lines via silencing and overexpression experiments. Expression of main pathway components was analyzed in an AKT mouse model and human HCCs. MDM4 inhibition resulted in growth restraint of HCC cell lines both in vitro and in vivo. Inhibition of the PI3K-AKT and/or mTOR pathways lowered MDM4 protein levels in HCC cells and reactivated p53-dependent transcription. De-ubiquitination by ubiquitin-specific protease 2a and AKT-mediated phosphorylation protected MDM4 from proteasomal degradation and increased AKT protein stability. The eukaryotic elongation factor 1A2 (EEF1A2) was identified as an upstream inducer of PI3K supporting MDM4 stabilization. Also, we detected MDM4 protein upregulation in an AKT mouse model and a strong correlation between the expression of EEF1A2, activated/phosphorylated AKT, and MDM4 in human HCC (each rho>.8, P<.001). Noticeably, a strong activation of this cascade was associated with shorter patients' survival. Conclusions The EEF1A2/PI3K/AKT/mTOR axis promotes the protumorigenic stabilization of the MDM4 protooncogene in human HCC via a post-transcriptional mechanism. The activation level of the EEF1A2/PI3K/AKT/mTOR/MDM4 axis significantly influences the survival probability of HCC patients in vivo and may thus represent a promising molecular target.