The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers

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Abstract

<p class="first" id="P1">The Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) is an expert-curated description of the genes driving human cancer, used as a standard in cancer genetics across basic research, medical reporting and pharmaceutical development. After a major expansion and complete re-evaluation, the 2018 CGC describes in detail the effect of 719 cancer-driving genes. Recent expansion includes functional and mechanistic descriptions of how each gene contributes to disease generation, described in terms of the key cancer hallmarks and the impact of mutations on gene and protein function. These functional characteristics depict the extraordinary complexity of cancer biology, and suggest multiple cancer-related functions for many genes, which are often highly tissue- or tumour stage-dependent. The 2018 CGC encompasses a second-tier, describing an expanding list of genes (currently 145) from more recent cancer studies which show supportive but less detailed indications of a role in cancer. </p>

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Alex H Ramos, Lee Lichtenstein, Manaswi Gupta … (2015)

Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.

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ESR1 mutations—a mechanism for acquired endocrine resistance in breast cancer.

Rinath M Jeselsohn, Gilles Buchwalter, Carmine De Angelis … (2015)

Approximately 70% of breast cancers are oestrogen receptor α (ER) positive, and are, therefore, treated with endocrine therapies. However, about 25% of patients with primary disease and almost all patients with metastases will present with or eventually develop endocrine resistance. Despite the magnitude of this clinical challenge, the mechanisms underlying the development of resistance remain largely unknown. In the past 2 years, several studies unveiled gain-of-function mutations in ESR1, the gene encoding the ER, in approximately 20% of patients with metastatic ER-positive disease who received endocrine therapies, such as tamoxifen and aromatase inhibitors. These mutations are clustered in a 'hotspot' within the ligand-binding domain (LBD) of the ER and lead to ligand-independent ER activity that promotes tumour growth, partial resistance to endocrine therapy, and potentially enhanced metastatic capacity; thus, ER LBD mutations might account for a mechanism of acquired endocrine resistance in a substantial fraction of patients with metastatic disease. In general, the absence of detectable ESR1 mutations in patients with treatment-naive disease, and the correlation between the frequency of patients with tumours harbouring these mutations and the number of endocrine treatments received suggest that, under selective treatment pressure, clonal expansion of rare mutant clones occurs, leading to resistance. Preclinical and clinical development of rationale-based novel therapeutic strategies that inhibit these ER mutants has the potential to substantially improve treatment outcomes. We discuss the contribution of ESR1 mutations to the development of acquired resistance to endocrine therapy, and evaluate how mutated ER can be detected and targeted to overcome resistance and improve patient outcomes.

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Open Targets: a platform for therapeutic target identification and validation

Gautier Koscielny, Peter An, Denise Carvalho-Silva … (2016)

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.