Search

Supplementary Table S2. Drugs and their concentrations used in the isogenic drug screen.

Supplementary Table S3. Chemical-genetic interaction scores derived in this study

Supplementary Figure 1. Distribution of gene alterations in Breast TCGA and verification of expression of MCF10A cells. Supplementary Figure 2. Analysis of the MCF10A drug screen. Supplementary Figure 3. Significance of overlap between interactions found in this study and in the CGP. Supplementary Figure 4. Response of isogenic engineered cells to dasatinib. Supplementary Figure 5. Dasatinib sensitivity of CML versus AML cancer cell lines. Supplementary Figure 6. Verification of LYN knockdown via siRNA and response of LYN T319I to dasatinib. Supplementary Figure 7. Co-­�expression of MYC and LYN in breast cancer cell lines.

Supplementary Table S1. List of constructs used to generate stable cell lines.

There is an urgent need in oncology to link molecular aberrations in tumors with altered cellular behaviors, such as metabolic derangements, and to identify novel therapeutics for cancer treatment. We have sought to identify synthetic-lethal genetic interactions that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated an unbiased and quantitative chemical-genetic interaction map that measures the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model more complex cellular contexts. Applied to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene including resistance to PI3K/AKT pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner. These studies provide new drug and biomarker pairs for clinical investigation. We have also performed global metabolomics analysis in a subset of the isogenic cell lines demonstrating alterations in metabolic pathways that are shared across multiple oncogenes, as well as those that are distinct to specific oncogenic drivers. This scalable approach enables the prediction of drug responses from patient data and can be used to accelerate the development of new genotype-directed therapies. This abstract is also presented as a poster at the Translation of the Cancer Genome conference. Citation Format: Maria M. Martins, Alicia Y. Zhou, Alexandra Corella, Dai Horiuchi, Christina Yau, Taha Rakshandehroo, John D. Gordan, Rebecca S. Levin, Jeff Johnson, John Jascur, Mike Shales, Antonio Sorrentino, Jaime Cheah, Paul A. Clemons, Alykhan Shamji, Stuart Schreiber, Stuart Schreiber, Nevan J. Krogan, Kevan M. Shokat, Kevan M. Shokat, Frank McCormick, Daniel Nomura, Sourav Bandyopadhyay, Andrei Goga. Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR07.

There is an urgent need in oncology to link molecular aberrations in tumors with altered cellular behaviors, such as metabolic derangements, and to identify novel therapeutics for cancer treatment. We have sought to identify synthetic-lethal genetic interactions that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated an unbiased and quantitative chemical-genetic interaction map that measures the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model more complex cellular contexts. Applied to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene including resistance to PI3K/AKT pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner. These studies provide new drug and biomarker pairs for clinical investigation. We have also performed global metabolomics analysis in a subset of the isogenic cell lines demonstrating alterations in metabolic pathways that are shared across multiple oncogenes, as well as those that are distinct to specific oncogenic drivers. This scalable approach enables the prediction of drug responses from patient data and can be used to accelerate the development of new genotype-directed therapies. Citation Format: Maria M. Martins, Alicia Y. Zhou, Alexandra Corella, Dai Horiuchi, Christina Yau, Taha Rakshandehroo, John D. Gordan, Rebecca S. Levin, Jeff Johnson, John Jascur, Mike Shales, Antonio Sorrentino, Jaime Cheah, Paul A. Clemons, Alykhan Shamji, Stuart Schreiber, Stuart Schreiber, Nevan J. Krogan, Kevan M. Shokat, Kevan M. Shokat, Frank McCormick, Daniel Nomura, Sourav Bandyopadhyay, Andrei Goga. Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR15.