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Background Analysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample. Results Application of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas. Conclusions deconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine.

While genomic instability is a hallmark of cancer, its genetic vulnerabilities remain poorly understood. Identifying strategies that exploit genomic instability to selectively target cancer cells is a central challenge in cancer biology with major implications for anti-cancer drug development.

Poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) are rare and frequently lethal tumors that so far have not been subjected to comprehensive genetic characterization. We performed next-generation sequencing of 341 cancer genes from 117 patient-derived PDTCs and ATCs and analyzed the transcriptome of a representative subset of 37 tumors. Results were analyzed in the context of The Cancer Genome Atlas study (TCGA study) of papillary thyroid cancers (PTC). Compared to PDTCs, ATCs had a greater mutation burden, including a higher frequency of mutations in TP53, TERT promoter, PI3K/AKT/mTOR pathway effectors, SWI/SNF subunits, and histone methyltransferases. BRAF and RAS were the predominant drivers and dictated distinct tropism for nodal versus distant metastases in PDTC. RAS and BRAF sharply distinguished between PDTCs defined by the Turin (PDTC-Turin) versus MSKCC (PDTC-MSK) criteria, respectively. Mutations of EIF1AX, a component of the translational preinitiation complex, were markedly enriched in PDTCs and ATCs and had a striking pattern of co-occurrence with RAS mutations. While TERT promoter mutations were rare and subclonal in PTCs, they were clonal and highly prevalent in advanced cancers. Application of the TCGA-derived BRAF-RAS score (a measure of MAPK transcriptional output) revealed a preserved relationship with BRAF/RAS mutation in PDTCs, whereas ATCs were BRAF-like irrespective of driver mutation. These data support a model of tumorigenesis whereby PDTCs and ATCs arise from well-differentiated tumors through the accumulation of key additional genetic abnormalities, many of which have prognostic and possible therapeutic relevance. The widespread genomic disruptions in ATC compared with PDTC underscore their greater virulence and higher mortality. This work was supported in part by NIH grants CA50706, CA72597, P50-CA72012, P30-CA008748, and 5T32-CA160001; the Lefkovsky Family Foundation; the Society of Memorial Sloan Kettering; the Byrne fund; and Cycle for Survival.

Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing \"driver\" mutations from passively selected \"passenger\" mutations. In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.

Detection of recurrently mutated nucleotides identifies novel cancer hotspots in an analysis of >11,000 human tumor samples. Mutational hotspots indicate selective pressure across a population of tumor samples, but their prevalence within and across cancer types is incompletely characterized. An approach to detect significantly mutated residues, rather than methods that identify recurrently mutated genes, may uncover new biologically and therapeutically relevant driver mutations. Here, we developed a statistical algorithm to identify recurrently mutated residues in tumor samples. We applied the algorithm to 11,119 human tumors, spanning 41 cancer types, and identified 470 somatic substitution hotspots in 275 genes. We find that half of all human tumors possess one or more mutational hotspots with widespread lineage-, position- and mutant allele–specific differences, many of which are likely functional. In total, 243 hotspots were novel and appeared to affect a broad spectrum of molecular function, including hotspots at paralogous residues of Ras-related small GTPases RAC1 and RRAS2 . Redefining hotspots at mutant amino acid resolution will help elucidate the allele-specific differences in their function and could have important therapeutic implications.

Pituitary carcinoma is a rare and aggressive malignancy with a poor prognosis and few effective treatment options. A 35-year-old woman presented with an aggressive ACTH-secreting pituitary adenoma that initially responded to concurrent temozolomide and capecitabine prior to metastasizing to the liver. Following treatment with ipilimumab and nivolumab, the tumor volume of the dominant liver metastasis reduced by 92%, and the recurrent intracranial disease regressed by 59%. Simultaneously, her plasma ACTH level decreased from 45,550 pg/mL to 66 pg/mL. Both prospective clinical sequencing with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets and retrospective whole-exome sequencing were performed to characterize the molecular alterations in the chemotherapy-naive pituitary adenoma and the temozolomide-resistant liver metastasis. The liver metastasis harbored a somatic mutational burden consistent with alkylator-induced hypermutation that was absent from the treatment-naive tumor. Resistance to temozolomide treatment, acquisition of new oncogenic drivers, and subsequent sensitivity to immunotherapy may be attributed to hypermutation. Combination treatment with ipilimumab and nivolumab may be an effective treatment in pituitary carcinoma. Clinical sequencing of pituitary tumors that have relapsed following treatment with conventional chemotherapy may identify the development of therapy-induced somatic hypermutation, which may be associated with treatment response to immunotherapy.

Yu Chen and colleagues describe a new constitutively activating mutation in the G-protein-coupled receptor CYSLTR2 in patients with uveal melanoma lacking mutations in the G-protein-encoding genes GNAQ and GNA11 . They find that expression of the mutant leads to increased expression of melanocyte-lineage signature genes and promotes tumorigenesis in vivo . Uveal melanomas are molecularly distinct from cutaneous melanomas and lack mutations in BRAF , NRAS , KIT , and NF1 . Instead, they are characterized by activating mutations in GNAQ and GNA11 , two highly homologous α subunits of G αq/11 heterotrimeric G proteins, and in PLCB4 (phospholipase C β4), the downstream effector of G αq signaling 1 , 2 , 3 . We analyzed genomics data from 136 uveal melanoma samples and found a recurrent mutation in CYSLTR2 (cysteinyl leukotriene receptor 2) encoding a p.Leu129Gln substitution in 4 of 9 samples that lacked mutations in GNAQ , GNA11 , and PLCB4 but in 0 of 127 samples that harbored mutations in these genes. The Leu129Gln CysLT 2 R mutant protein constitutively activates endogenous G αq and is unresponsive to stimulation by leukotriene. Expression of Leu129Gln CysLT 2 R in melanocytes enforces expression of a melanocyte-lineage signature, drives phorbol ester–independent growth in vitro , and promotes tumorigenesis in vivo . Our findings implicate CYSLTR2 as a uveal melanoma oncogene and highlight the critical role of G αq signaling in uveal melanoma pathogenesis.

Primary prostate cancer is the most common malignancy in men but has highly variable outcomes, highlighting the need for biomarkers to determine which patients can be managed conservatively. Few large prostate oncogenome resources currently exist that combine the molecular and clinical outcome data necessary to discover prognostic biomarkers. Previously, we found an association between relapse and the pattern of DNA copy number alteration (CNA) in 168 primary tumors, raising the possibility of CNA as a prognostic biomarker. Here we examine this question by profiling an additional 104 primary prostate cancers and updating the initial 168 patient cohort with long-term clinical outcome. We find that CNA burden across the genome, defined as the percentage of the tumor genome affected by CNA, was associated with biochemical recurrence and metastasis after surgery in these two cohorts, independent of the prostate-specific antigen biomarker or Gleason grade, a major existing histopathological prognostic variable in prostate cancer. Moreover, CNA burden was associated with biochemical recurrence in intermediate-risk Gleason 7 prostate cancers, independent of prostate-specific antigen or nomogram score. We further demonstrate that CNA burden can be measured in diagnostic needle biopsies using low-input whole-genome sequencing, setting the stage for studies of prognostic impact in conservatively treated cohorts.

Marc Ladanyi and colleagues show that the nuclear deubiquitinase BAP1 is commonly inactivated by somatic mutations and 3p21.1 losses in malignant pleural mesothelioma (MPM). They further show that knockdown of BAP1 in MPM cell lines affects E2F and Polycomb target genes, implicating transcriptional deregulation in disease pathogenesis. Malignant pleural mesotheliomas (MPMs) often show CDKN2A and NF2 inactivation, but other highly recurrent mutations have not been described. To identify additional driver genes, we used an integrated genomic analysis of 53 MPM tumor samples to guide a focused sequencing effort that uncovered somatic inactivating mutations in BAP1 in 23% of MPMs. The BAP1 nuclear deubiquitinase is known to target histones (together with ASXL1 as a Polycomb repressor subunit) and the HCF1 transcriptional co-factor, and we show that BAP1 knockdown in MPM cell lines affects E2F and Polycomb target genes. These findings implicate transcriptional deregulation in the pathogenesis of MPM.

Many mutations in cancer are of unknown functional significance. Standard methods use statistically significant recurrence of mutations in tumor samples as an indicator of functional impact. We extend such analyses into the long tail of rare mutations by considering recurrence of mutations in clusters of spatially close residues in protein structures. Analyzing 10,000 tumor exomes, we identify more than 3000 rarely mutated residues in proteins as potentially functional and experimentally validate several in RAC1 and MAP2K1. These potential driver mutations (web resources: 3dhotspots.org and cBioPortal.org) can extend the scope of genomically informed clinical trials and of personalized choice of therapy.