Advanced development of the Cancer Dependency Map portal (DepMap.org)

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PROJECT SUMMARY/ABSTRACT: The ability to predict vulnerabilities given the molecular features of a patient’s tumor is central to operationalizing cancer precision medicine. While sequencing of patient tumors is increasingly common, researchers, clinicians, and drug developers currently lack the ability to identify which somatically altered genes and variants are required for tumor survival and/or confer a requirement for other genes (synthetic lethality). The “Cancer Dependency Map (DepMap)” project directly addresses this challenge. This effort, which continues to generate and release pre-publication data on a quarterly basis without restriction, currently encompasses over 1,000 genomically annotated cancer cell lines and organoid models, over 750 genome-wide CRISPR/Cas9 viability screens, and large scale drug repurposing screens totaling over 1,000,000 data points. In addition, we have created a wide range of computational algorithms to discover dependencies and to infer them from molecular features. To ensure that the scientific community can easily use these data and tools to make scientific discoveries, we launched the pilot of depmap.org on April 2018. This pilot aimed to learn how best to support the analysis and visualization of such data (whether created at the Broad Institute or elsewhere) by researchers everywhere. The pilot proved to be a success: currently, depmap.org has 62,000 users and is visited by >800 unique researchers from >200 laboratories daily. Here, we propose the advanced development of depmap.org to address the emerging needs of distinct user communities: ● cancer biologists: use depmap.org to discover the function of genes and variants and how these induce network changes that result in vulnerabilities (users have limited programming experience, we will emphasize user experience, enabling the upload of researchers’ own data and the interoperability with other experimental research tools); ● translational cancer researchers: use depmap.org to prioritize new targets from CRISPR data and mechanism of action of existing drugs within specific tumor type contexts to advance drug discovery (users have limited programming experience, we will emphasize user experience and tumor-type functionality, connectivity with cBioPortal and patient data); ● computationalists: aim to develop new predictive modeling applications and data analysis tools that can be readily shared back with the depmap.org community (users have extensive programing experience, we will emphasize creating application programming interface (API) protocols and support sharing of new computational tools back with depmap.org) Our revised proposal focuses on three complementary Specific Aims: In Aim 1, we will develop new functionalities to support pre-defined scientific inquiries of cancer biologists and translational researchers. Here, we will (a) enable users to prioritize cancer targets via the integrated analysis of drug and CRISPR viability data, (b) create tools to connect patient tumors with cell models, (c) develop mechanism of action functionality and (d) support tumor- and genotype-specific inquiries. In Aim 2, we will develop new visualization and interactive analysis tools for cancer biologists and translational researchers as well as APIs for advanced computationalists. This will include data generated by multiple institutions as well as new functionality for interoperability with user uploaded data and APIs to export harmonized data for outside analysis. In Aim 3, we will develop a set of resources to train and engage a diverse user community. This work will include a major training and outreach program and real-time communication channels for user feedback and support. This ITCR proposal will put us on a path towards the routine use of depmap.org by a majority of cancer researchers worldwide. If funded, this proposal would represent the only dedicated source of funds to support the maintenance and expansion of this popular portal which simply cannot be sustained at the needed level without dedicated funding. As such, it will have a significant impact on both basic and translational cancer research and enable computationalists and biologists to continue to make key cancer discoveries.