Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations

By David Tamborero, C. Rubio-Perez, Jordi Deu-Pons, Michael P. Schroeder, A. Vivancos, A. Rovira, I. Tusquets, J. Albanell, J. Rodón, J. Tabernero and 4 more. Genome Medicine, 2017

501 citations recorded by the source corpus.

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7ced88d13806943ae2340c42a6fbc49700265ef6

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Citations in the corpus

Open Targets Platform: new developments and updates two years on (2018, methodology): “We incorporate a collection of genomic biomarkers of drug responses (sensitivity, resistance and toxicity) and their level of clinical significance from the Cancer Biomarkers database (27).”
A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) (2018, background): “Only very few of the somatic mutations found in cancer genes (2% across 7000 tumours of a 28cancer type cohort) [41] are known oncogenic events.”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “…approach, López-Cortés and colleagues integrated numerous cancer databases such as PharmGKB, Cancer Genome Interpreter, and TCGA, among others, to predict proteins associated with breast cancer pathogenesis, and reported several viable candidates to pursue as biomarkers or drug targets (4, 67–70).”
The next-generation Open Targets Platform: reimagined, redesigned, rebuilt (2022, methodology): “A new data widget was created to bring in cancer biomarker annotations sourced from the Cancer Genome Interpreter, which provide evidence for target– disease associations (and importantly the level of clinical evidence from pre-clinical to approved drug standard-of-care guidelines) (28).”
A compendium of mutational cancer driver genes (2020, background): “According to this generalization, as a result of driver alterations, malignant cells become capable of (1) resisting apoptosis, (2) maintaining proliferative signalling (even in the absence of extracellular signals), (3) evading suppressors of cell growth, (4) initiating invasion and metastasis, (5)”
Analyses of non-coding somatic drivers in 2,658 cancer whole genomes (2020, methodology): “We (1) aligned the human RMRP sequence with the Saccharomyces cerevisiae RMRP sequence using the sequence family covariance model from Rfam and (2) mapped the locations of RNA-protein interactions within 4Å64 from the crystal structure and the experimentally determined RNA-protein crosslinking sites”