Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth.

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1 -mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1 + interstitial macrophages and SiglecF Hi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1 -mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1 -mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1 + interstitial macrophages and SiglecF Hi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1 -mutant tumors.

Molecular characterization of colorectal cancer related peritoneal metastatic disease.

A significant proportion of colorectal cancer (CRC) patients develop peritoneal metastases (PM) in the course of their disease. PMs are associated with a poor quality of life, significant morbidity and dismal disease outcome. To improve care for this patient group, a better understanding of the molecular characteristics of CRC-PM is required. Here we present a comprehensive molecular characterization of a cohort of 52 patients. This reveals that CRC-PM represent a distinct CRC molecular subtype, CMS4, but can be further divided in three separate categories, each presenting with unique features. We uncover that the CMS4-associated structural protein Moesin plays a key role in peritoneal dissemination. Finally, we define specific evolutionary features of CRC-PM which indicate that polyclonal metastatic seeding underlies these lesions. Together our results suggest that CRC-PM should be perceived as a distinct disease entity.

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.

First-in-Human Phase I Study of Fisogatinib (BLU-554) Validates Aberrant FGF19 Signaling as a Driver Event in Hepatocellular Carcinoma.

Outcomes for patients with advanced hepatocellular carcinoma (HCC) remain poor despite recent progress in drug development. Emerging data implicate FGF19 as a potential HCC driver, suggesting its receptor, FGFR4, as a novel therapeutic target. We evaluated fisogatinib (BLU-554), a highly potent and selective oral FGFR4 inhibitor, in a phase I dose-escalation/dose-expansion study in advanced HCC using FGF19 expression measured by IHC as a biomarker for pathway activation. For dose escalation, 25 patients received 140 to 900 mg fisogatinib once daily; the maximum tolerated dose (600 mg once daily) was expanded in 81 patients. Fisogatinib was well tolerated; most adverse events were manageable, grade 1/2 gastrointestinal events, primarily diarrhea, nausea, and vomiting. Across doses, the overall response rate was 17% in FGF19-positive patients [median duration of response: 5.3 months (95% CI, 3.7-not reached)] and 0% in FGF19-negative patients. These results validate FGFR4 as a targetable driver in FGF19-positive advanced HCC. SIGNIFICANCE: Fisogatinib elicited clinical responses in patients with tumor FGF19 overexpression in advanced HCC. These results validate the oncogenic driver role of the FGFR4 pathway in HCC and the use of FGF19 as a biomarker for patient selection.See related commentary by Subbiah and Pal, p. 1646.This article is highlighted in the In This Issue feature, p. 1631.

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

TYRO3 as a molecular target for growth inhibition and apoptosis induction in bladder cancer.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is an aggressive neoplasm with poor prognosis, lacking effective therapeutic targets. Oncogenic dependency on members of the TAM tyrosine kinase receptor family (TYRO3, AXL, MERTK) has been reported in several cancer types, but their role in bladder cancer has never been explored. METHODS: TAM receptor expression was evaluated in two series of human bladder tumours by gene expression (TCGA and CIT series), immunohistochemistry and western blotting analyses (CIT series). The role of the different TAM receptors was assessed by loss-of-function experiments and pharmaceutical inhibition in vitro and in vivo. RESULTS: We reported a significantly higher expression of TYRO3, but not AXL or MERTK, in both non-MIBCs and MIBCs, compared to normal urothelium. Loss-of-function experiments identified a TYRO3-dependency of bladder carcinoma-derived cells both in vitro and in a mouse xenograft model, whereas AXL and MERTK depletion had only a minor impact on cell viability. Accordingly, TYRO3-dependent bladder tumour cells were sensitive to pharmacological treatment with two pan-TAM inhibitors. Finally, growth inhibition upon TYRO3 depletion relies on cell cycle inhibition and apoptosis associated with induction of tumour-suppressive signals. CONCLUSIONS: Our results provide a preclinical proof of concept for TYRO3 as a potential therapeutic target in bladder cancer.

Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles.

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance.

Genomic Correlate of Exceptional Erlotinib Response in Head and Neck Squamous Cell Carcinoma.

IMPORTANCE: Randomized clinical trials demonstrate no benefit for epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in unselected patients with head and neck squamous cell carcinoma (HNSCC). However, a patient with stage IVA HNSCC received 13 days of neoadjuvant erlotinib and experienced a near-complete histologic response. OBJECTIVE: To determine a mechanism of exceptional response to erlotinib therapy in HNSCC. DESIGN, SETTING, AND PARTICIPANTS: Single patient with locally advanced HNSCC who received erlotinib monotherapy in a window-of-opportunity clinical trial (patients scheduled to undergo primary cancer surgery are treated briefly with an investigational agent). Whole-exome sequencing of pretreatment tumor and germline patient samples was performed at a quaternary care academic medical center, and a candidate somatic variant was experimentally investigated for mediating erlotinib response. INTERVENTION: A brief course of erlotinib monotherapy followed by surgical resection. MAIN OUTCOMES AND MEASURES: Identification of pretreatment tumor somatic alterations that may contribute to the exceptional response to erlotinib. Hypotheses were formulated regarding enhanced erlotinib response in preclinical models harboring the patient tumor somatic variant MAPK1 E322K following the identification of tumor somatic variants. RESULTS: No EGFR alterations were observed in the pretreatment tumor DNA. Paradoxically, the tumor harbored an activating MAPK1 E322K mutation (allelic fraction 0.13), which predicts ERK activation and erlotinib resistance in EGFR-mutant lung cancer. The HNSCC cells with MAPK1 E322K exhibited enhanced EGFR phosphorylation and erlotinib sensitivity compared with wild-type MAPK1 cells. CONCLUSIONS AND RELEVANCE: Selective erlotinib use in HNSCC may be informed by precision oncology approaches.

Targeting cancer with kinase inhibitors.

Kinase inhibitors have played an increasingly prominent role in the treatment of cancer and other diseases. Currently, more than 25 oncology drugs that target kinases have been approved, and numerous additional therapeutics are in various stages of clinical evaluation. In this Review, we provide an in-depth analysis of activation mechanisms for kinases in cancer, highlight recent successes in drug discovery, and demonstrate the clinical impact of selective kinase inhibitors. We also describe the substantial progress that has been made in designing next-generation inhibitors to circumvent on-target resistance mechanisms, as well as ongoing strategies for combining kinase inhibitors in the clinic. Last, there are numerous prospects for the discovery of novel kinase targets, and we explore cancer immunotherapy as a new and promising research area for studying kinase biology.

First Selective Small Molecule Inhibitor of FGFR4 for the Treatment of Hepatocellular Carcinomas with an Activated FGFR4 Signaling Pathway.

UNLABELLED: Aberrant signaling through the fibroblast growth factor 19 (FGF19)/fibroblast growth factor receptor 4 (FGFR 4) signaling complex has been shown to cause hepatocellular carcinoma (HCC) in mice and has been implicated to play a similar role in humans. We have developed BLU9931, a potent and irreversible small-molecule inhibitor of FGFR4, as a targeted therapy to treat patients with HCC whose tumors have an activated FGFR4 signaling pathway. BLU9931 is exquisitely selective for FGFR4 versus other FGFR family members and all other kinases. BLU9931 shows remarkable antitumor activity in mice bearing an HCC tumor xenograft that overexpresses FGF19 due to amplification as well as a liver tumor xenograft that overexpresses FGF19 mRNA but lacks FGF19 amplification. Approximately one third of patients with HCC whose tumors express FGF19 together with FGFR4 and its coreceptor klotho ß (KLB) could potentially respond to treatment with an FGFR4 inhibitor. These findings are the first demonstration of a therapeutic strategy that targets a subset of patients with HCC. SIGNIFICANCE: This article documents the discovery of BLU9931, a novel irreversible kinase inhibitor that specifically targets FGFR4 while sparing all other FGFR paralogs and demonstrates exquisite kinome selectivity. BLU9931 is efficacious in tumors with an intact FGFR4 signaling pathway that includes FGF19, FGFR4, and KLB. BLU9931 is the first FGFR4-selective molecule for the treatment of patients with HCC with aberrant FGFR4 signaling.

Genomic analyses of gynaecologic carcinosarcomas reveal frequent mutations in chromatin remodelling genes.

Malignant mixed Müllerian tumours, also known as carcinosarcomas, are rare tumours of gynaecological origin. Here we perform whole-exome analyses of 22 tumours using massively parallel sequencing to determine the mutational landscape of this tumour type. On average, we identify 43 mutations per tumour, excluding four cases with a mutator phenotype that harboured inactivating mutations in mismatch repair genes. In addition to mutations in TP53 and KRAS, we identify genetic alterations in chromatin remodelling genes, ARID1A and ARID1B, in histone methyltransferase MLL3, in histone deacetylase modifier SPOP and in chromatin assembly factor BAZ1A, in nearly two thirds of cases. Alterations in genes with potential clinical utility are observed in more than three quarters of the cases and included members of the PI3-kinase and homologous DNA repair pathways. These findings highlight the importance of the dysregulation of chromatin remodelling in carcinosarcoma tumorigenesis and suggest new avenues for personalized therapy.

The landscape of kinase fusions in cancer.

Human cancer genomes harbour a variety of alterations leading to the deregulation of key pathways in tumour cells. The genomic characterization of tumours has uncovered numerous genes recurrently mutated, deleted or amplified, but gene fusions have not been characterized as extensively. Here we develop heuristics for reliably detecting gene fusion events in RNA-seq data and apply them to nearly 7,000 samples from The Cancer Genome Atlas. We thereby are able to discover several novel and recurrent fusions involving kinases. These findings have immediate clinical implications and expand the therapeutic options for cancer patients, as approved or exploratory drugs exist for many of these kinases.

A small molecule that binds and inhibits the ETV1 transcription factor oncoprotein.

Members of the ETS transcription factor family have been implicated in several cancers, where they are often dysregulated by genomic derangement. ETS variant 1 (ETV1) is an ETS factor gene that undergoes chromosomal translocation in prostate cancers and Ewing sarcomas, amplification in melanomas, and lineage dysregulation in gastrointestinal stromal tumors. Pharmacologic perturbation of ETV1 would be appealing in these cancers; however, oncogenic transcription factors are often deemed "undruggable" by conventional methods. Here, we used small-molecule microarray screens to identify and characterize drug-like compounds that modulate the biologic function of ETV1. We identified the 1,3,5-triazine small molecule BRD32048 as a top candidate ETV1 perturbagen. BRD32048 binds ETV1 directly, modulating both ETV1-mediated transcriptional activity and invasion of ETV1-driven cancer cells. Moreover, BRD32048 inhibits p300-dependent acetylation of ETV1, thereby promoting its degradation. These results point to a new avenue for pharmacologic ETV1 inhibition and may inform a general means to discover small molecule perturbagens of transcription factor oncoproteins.

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene ß-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

Mutational heterogeneity in cancer and the search for new cancer-associated genes.

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.

ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens.

Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of off-target transcripts. Here we present a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for data sets that contain as few as 10 samples and can be used to analyze screens of any number of targeted genes. We used this analytic approach to interrogate RNAi data derived from screening more than 100 human cancer cell lines and identified HNF1B as a transforming oncogene required for the survival of cancer cells that harbor HNF1B amplifications. ATARiS is publicly available at http://broadinstitute.org/ataris.