The recent event held at CNIO has made possible to meet and greet computational cancer researchers (CCR) from all over the world, including Dana-Farber Cancer Institute, Ontario Institute for Cancer Research (OICR, Toronto, Canada), EMBL (Heidelberg, Germany), and The Francis Crick Institute (CRICK,London, UK), to name but a few. The attendees were interested in cancer as a heterogeneous disease at multiple levels (patient,cellular and molecular levels), due its being an important clinical determinant of patient outcomes. The conference focused on recent progress in cancer genome analysis for understanding the causes, consequences of cancer heterogeneity and its implications for cancer treatment. The meeting was indeed a great networking as well as an absolutely brilliant educational opportunity.
Some fantastic computational resources and tools were shared for facilitating cancer research. We list here only a few, as a toolbox for CCR begginers:
ICGC (www.icgc.org). ICGC Goal: To obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in 50 different tumor types and/or subtypes which are of clinical and societal importance across the globe. Ongoing consortium effort. It includes TCGA data.
EVolutionary Couplings (http://evfold.org/evfold-web/evfold.do). The EVolutionary Couplings server provides functional and structural information about proteins derived from the evolutionary sequence record using methods from statistical physics, folds the protein when unknown structure, and analyzes two known interacting domains.
3DHOTSPOTS (http://3dhotspots.org/3d/#/home). A resource for statistically significant mutations clustering in 3D protein structures in cancer
INTOGEN (http://www.intogen.org/search). Catalog of somatic mutations. It specifies if they are drivers or not. IntOGen mutations uses several data sources (including ICGC and TCGA) and tools:
– OncodriveFM: Identifies genes with a bias towards high functional mutations
– OncodriveCLUST: Identifies genes with a significant regional clustering of mutations
– MutSigCV: Identifies genes mutated more frequently than background mutation rate
– OncodriveROLE: Classifies driver genes according to its mode of action in Activation or Loss of Function.
CANCER GENOME INTERPRETER (https://www.cancergenomeinterpreter.org/home; https://www.cancergenomeinterpreter.org/biomarkers). Cancer Genome Interpreter (CGI) is designed to support the identification of therapeutically actionable genomic alterations in tumors. CGI receives as input the list of genomic alterations detected in a tumor sample. It first identifies validated driver events and predicts the potential effect of mutations of uncertain significance using OncodriveMUT. Then, CGI identifies the alterations that are known to date to affect the response to anti-cancer therapies (sensitivity, resistance and toxicity) of the tumour according to several levels of clinical evidence based on the information of our manually curated database of genomic biomarkers of drug response.
CANCER GENE CENSUS (http://cancer.sanger.ac.uk/census/). The cancer Gene Census is an ongoing effort to catalogue those genes for which mutations have been causally implicated in cancer. Recommended: http://www.nature.com/nrc/journal/v4/n3/full/nrc1299.html
CANCER LANDSCAPES (http://cancerlandscapes.org/). Cancer Landscapes (CL) is a new pipeline attempting to bridge the gap between patient genomic data and biological interpretation. They use data from the The Cancer Genome Atlas (TCGA) and local projects, such as the Human Glioma Cell Culture (HGCC) project, as sources for statistical network models describing data interactions. Recommended: http://science.sciencemag.org/content/339/6127/1546.full
CRAVAT (http://cravat.us/CRAVAT/). CRAVAT is a web server where cancer-related analyses of variants are performed.
ONCOPAD (http://bg.upf.edu/oncopad/index_p). Web-tool for the design of cancer NGS panels based on mutational data.
MANTRA (http://mantra.tigem.it/). Mode of Action by NeTwoRk Analysis (MANTRA) is a computational tool for the analysis of the Mode of Action (MoA) of novel drugs and the identification of known and approved candidates for “drug repositioning”.
EpiDISH (https://github.com/sjczheng/EpiDISH). This package contains a reference-based function to infer the proportions of a priori known cell subtypes present in a sample representing a mixture of such cell-types. Inference proceeds via one of 3 methods (Robust Partial Correlations-RPC, Cibersort (CBS), Constrained Projection (CP)), as determined by user.
SCITE. Algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. Recommended: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0936-x
MCP-counter. The Microenvironment Cell Populations-counter (MCP-counter) method allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. Available as an R package. Recommended: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1070-5
The Resistant Cancer Cell Line Collection (https://www.kent.ac.uk/stms/cmp/RCCL/RCCLabout.html). Cancer cell lines with acquired drug resistance.
canSAR (https://cansar.icr.ac.uk/).CanSAR is an integrated knowledge-base that brings together multidisciplinary data across biology, chemistry, pharmacology, structural biology, cellular networks and clinical annotations, and applies machine learning approaches to provide drug-discovery useful predictions.
WikiPathways (http://wikipathways.org/index.php/WikiPathways). WikiPathways is an open, public platform dedicated to the curation of biological pathways by and for the scientific community. It is linked to many other databases.