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Kryukov GV, Berger MF, Stransky N, Barretina J, Onofrio R, Caponigro G, Sougnez C, Monahan J, Shefler E, Venkhatesan K, Cibulskis K, Morais P, Sivachenko A, Meltzer J, Lawrence M, Ramos A, Getz G, Platform BGS, Thibault J, Mahan S, Jones M, Morrissey M, Sonkin D, Ardlie KG, Golub T, Weber B, Warmuth M, Sellers W, Harris J, Schlegel R, Garraway LA. Abstract 923: Separating the wheat from the chaff: A first look at the Cancer Cell Lines Encyclopedia sequencing data. Cancer Res 2011. [DOI: 10.1158/1538-7445.am2011-923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Comprehensive characterization of cancer genomic alterations and understanding their functional roles are important steps in the development of personalized cancer treatments. The Cancer Cell Lines Encyclopedia project aims to relate genomic alterations to drug sensitivity of cancer cells. Cancer cell lines are indispensable resource for researchers because of their convenience for high-throughput profiling and availability for follow-up experiments.
We sequenced the coding regions of 1645 genes in over 800 cancer cell lines representing 32 different tumor types. Genes were selected on their likelihood to be cancer-related and sequenced using next-generation Illumina technology after hybrid selection of exonic regions. Although, the absence of matched normal cell lines precludes direct distinction of somatic from germline mutations, we were able to select a subset of mutations highly enriched in somatic events combining the following three approaches: subtraction of known polymorphisms, prediction of strongly detrimental mutations (including computational predictions of amino acid substitutions’ effects on protein function) and detection of abnormal linkage disequilibrium patterns for recurring mutations.
We then searched for the three independent indicators of the potential involvement of mutated genes in cancer: the presence of an unusually high fraction of strongly damaging mutations within a gene, statistically significant deviation of distribution of mutations between cancer types from random expectation and clustering of mutations within a gene. Combined analysis of these lines of evidence revealed both known and novel potential tumor suppressors and oncogenes.
These data should become an import resource for cancer researchers in their search for personalized cancer therapies.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 923. doi:10.1158/1538-7445.AM2011-923
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Affiliation(s)
| | | | | | | | | | | | | | - John Monahan
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | - Erica Shefler
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | - Paula Morais
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Jodi Meltzer
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | | | - Alex Ramos
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | - Gad Getz
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Joseph Thibault
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - Scott Mahan
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | - Michael Jones
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | | | - Dmitry Sonkin
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | | | - Todd Golub
- 1The Broad Institute of MIT and Harvard, Cambridge, MA
| | - Barbara Weber
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | - Markus Warmuth
- 2Novartis Institutes for BioMedical Research, Cambridge, MA
| | | | - Jennifer Harris
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | | | - Levi A. Garraway
- 4Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
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Barretina J, Caponigro G, Kim S, Stransky N, Venkhatesan K, Reddy V, Berger M, Morrissey M, Morais P, Meltzer J, Thibault J, Mahan S, Sonkin D, Che J, Raman P, Slind J, Johannessen C, Gupta S, Niu L, Kehoe S, Hatton C, Jones M, Monahan J, Meyer V, Wilson C, Shipway A, Li N, Engels I, Su A, Callahan A, Ding Y, Liefeld T, Ziaugra L, Sougnez C, Onofrio R, Winckler W, MacConaill L, Reich M, Gabriel S, Ardlie K, Getz G, Warmuth M, Meyerson M, Finan P, Golub T, Weber B, Harris J, Sellers W, Schlegel R, Garraway L. Abstract 2620: The Cancer Cell Line Encyclopedia project: From integrative cancer genomics to personalized cancer therapy. Cancer Res 2010. [DOI: 10.1158/1538-7445.am10-2620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Cancer genome characterization efforts such as The Cancer Genome Atlas project are rapidly improving our knowledge of tumor genetic alterations. With the expanded use of massively parallel sequencing, the catalogue of known genetic alterations in cancer is expected to expand at an accelerating rate. In this context, the emphasis is shifting towards systematic identification of the genes and pathways targeted by recurrent genetic alterations, their functional impact in tumor biology, and the resulting cellular dependencies that might be exploited therapeutically. Anticipating the need for a companion resource to systematically probe tumor biology armed with cancer genomics knowledge, we have assembled a compendium of experimentally tractable cancer model systems consisting of ∼1000 human cancer cell lines and performed extensive genomic analysis (at the level of gene expression, DNA copy number and mutations) coupled with pharmacological profiling. This resource, which we call the Cancer Cell Line Encyclopedia (CCLE), is being used not only to identify the putative targets of prevalent genetic alterations, but also to systematically link the presence or absence of certain genetic alterations to drug sensitivity or resistance.
To date, we have identified several previously unappreciated genomic predictors of response or intrinsic resistance to targeted anticancer agents. For instance, through integrative analysis, we have discovered additional mechanisms that may underlie sensitivity to MET inhibitors, beyond amplification of the MET receptor, highlighting the fact that response prediction in the clinic may require assessment of multiple variables. We have also broadened the potential relevance of known predictive biomarkers that might provide a rationale for future genotype-driven clinical trials. As an example, we have expanded on existing knowledge of resistance to receptor tyrosine kinase (RTK) inhibitors, showing that the presence of RAS mutations may predict lack of response to a broad spectrum of RTK inhibitors in addition to EGFR inhibitors. This work demonstrates that pharmacological profiling of large, genomically-annotated cancer model systems may uncover new tumor dependencies as well as positive and negative predictors of drug response. The results of this study are being made publicly available at a CCLE online portal, with the hope they will become a valuable resource for the cancer community to propel translation of the knowledge generated through in vitro integrative genomics into personalized cancer medicine.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2620.
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Affiliation(s)
| | | | - Sungjoon Kim
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | | | | | | | | | | | | | - Jodi Meltzer
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Joseph Thibault
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | | | - Dmitriy Sonkin
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - John Che
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - Pichai Raman
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | | | | | | | - Lili Niu
- 4Dana-Farber Cancer Institute, Boston, MA
| | | | | | - Mike Jones
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - John Monahan
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Vic Meyer
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Chris Wilson
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Aaron Shipway
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - Nanxin Li
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - Ingo Engels
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - Andrew Su
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Markus Warmuth
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | | | - Peter Finan
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | | | - Barbara Weber
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Jennifer Harris
- 3Genomics Institute of the Novartis Research Foundation, San Diego, CA
| | - William Sellers
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
| | - Robert Schlegel
- 2Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA
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