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.