Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data

Paleogene Radiation of a Plant Pathogenic Mushroom

Background: The global movement and speciation of fungal plant pathogens is important, especially because of the economic losses they cause and the ease with which they are able to spread across large areas. Understanding the biogeography and origin of these plant pathogens can provide insights regarding their dispersal and current day distribution. We tested the hypothesis of a Gondwanan origin of the plant pathogenic mushroom genus Armillaria and the currently accepted premise that vicariance accounts for the extant distribution of the species. Methods: The phylogeny of a selection of Armillaria species was reconstructed based on Maximum Parsimony (MP), Maximum Likelihood (ML) and Bayesian Inference (BI). A timeline was then placed on the divergence of lineages using a Bayesian relaxed molecular clock approach. Results: Phylogenetic analyses of sequenced data for three combined nuclear regions provided strong support for three major geographically defined clades: Holarctic, South American-Australasian and African. Molecular dating placed the initial radiation of the genus at 54 million years ago within the Early Paleogene, postdating the tectonic break-up of Gondwana. Conclusions: The distribution of extant Armillaria species is the result of ancient long-distance dispersal rather than vicariance due to continental drift. As these finding are contrary to most prior vicariance hypotheses for fungi, our result

Skeletal Muscle Phenotypically Converts and Selectively Inhibits Metastatic Cells in Mice

Skeletal muscle is rarely a site of malignant metastasis; the molecular and cellular basis for this rarity is not understood. We report that myogenic cells exert pronounced effects upon co-culture with metastatic melanoma (B16-F10) or carcinoma (LLC1) cells including conversion to the myogenic lineage in vitro and in vivo, as well as inhibition of melanin production in melanoma cells coupled with cytotoxic and cytostatic effects. No effect is seen with non-tumorigenic cells. Tumor suppression assays reveal that the muscle-mediated tumor suppressor effects do not generate resistant clones but function through the down-regulation of the transcription factor MiTF, a master regulator of melanocyte development and a melanoma oncogene. Our findings point to skeletal muscle as a source of therapeutic agents in the treatment of metastatic cancers

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Kagisano Number 9

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Integrated analysis of drug sensitivity and selectivity to predict synergistic drug combinations and target coaddictions in cancer

High-throughput drug sensitivity testing provides a powerful phenotypic profiling approach to identify effective drug candidates for individual cell lines or patient-derived samples. Here, we describe an experimental-computational pipeline, named target addiction scoring (TAS), which mathematically transforms the drug response profiles into target addiction signatures, and thereby provides a ranking of potential therapeutic targets according to their functional importance in a particular cancer sample. The TAS pipeline makes use of drug polypharmacology to integrate the drug sensitivity and selectivity profiles through systems-wide interconnection networks between drugs and their targets, including both primary protein targets as well as secondary off-targets. We show how the TAS pipeline enables one to identify not only single-target addictions but also combinatorial coaddictions among targets that often underlie synergistic drug combinations. © Springer Science+Business Media, LLC, part of Springer Nature 2019.Peer reviewe