Abstract IA1: Cancer dependencies defined by genomic alterations and targeted by small molecules
Authors: Schreiber Stuart L. 
Published: August 2014

Abstract
The ability to sequence cancer genomes coupled with advances in small-molecule science provides a new foundation for creating safe and effective cancer therapeutics – ones delivered to patients based on the specific genomic alterations present in their cancer. Several examples now exist of small-molecule cancer drugs targeting protein kinases encoded by both wild-type and oncogenic alleles, and yielding high clinical response rates in patients with specific genetic features. However, these drugs target only a narrow range of proteins, benefit &lt;1% of patients suffering today from cancer, and provide therapeutic benefits that are not always durable.
All current genetically matched drugs target the driver oncogene or its wild-type allele directly (“oncogene dependencies”). It is unknown whether similar clinical responses will result from drugs targeting non-oncogenes working in cooperation with oncogenes in patients with cancers having the relevant genetic features (“non-oncogene co-dependencies”). To accelerate the discovery of genetically matched therapies, systematic approaches are needed to identify: 1) the direct and indirect dependencies cancers acquire as a consequence of their specific mutations, translocations, and copy number alterations, and 2) small-molecule drugs that target the dependencies.
Genomic cancer cell line profiling has been used to reveal patterns of small-molecule sensitivities across diverse cancer cell lines (CCLs). These efforts initially focused on relating sensitivity to the lineage of CCLs (ref NCI-60 and others), but now increasingly focus on relating sensitivity to genetic and epigenetic features of the cell lines. But these are still early days of genomic CCL profiling – there are many issues to iron out. Nevertheless, there are also signs that this approach will eventually be fruitful and impacting. For example, there are no examples of targeted cancer therapeutics today that were not predicted by genomic CCL profiling.
In my lecture, I will describe an Interactive Resource to analyze and visualize a rich body of quantitative CCL sensitivity measurements with the aim of identifying novel candidate cancer dependencies targeted by small molecules. To enable the Resource, we measured the sensitivity of 243 genetically characterized CCLs to 355 small-molecule probes and drugs (the ‘Informer Set’) at eight concentrations in duplicate. The CCLs are part of a 949-member Cancer Cell Line Encyclopedia (CCLE) whose extensive genomic characterization includes the mutational status of &gt;1,600 genes, global assessment of DNA copy number, and global gene expression analysis. These characterizations are also available freely via an interactive web portal (broadinstitute.org/ccle). The compounds included in the Informer Set are known to have selective interactions with their target, and collectively target many distinct nodes in cancer cell circuitry, including but not limited to pathways modulating apoptosis, oxidative stress, chromatin signaling, mitotic stress, hypoxic stress, proteotoxic stress, and metabolism.
Using robust analytical methods including cell-line and compound filtering, we correlated the sensitivity measurements with genomic alterations in cancer cell lines to identify dependencies conferred by specific genotypes. I will describe the current content of the Resource and provide multiple illustrations of its use, including the use of additional analytical tools not included in the Resource but readily and freely accessible elsewhere.
Citation Format: Stuart L. Schreiber. Cancer dependencies defined by genomic alterations and targeted by small molecules [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA1.