Dataset Information

Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions.

ABSTRACT: Somatic genetic alterations in cancers have been linked with response to targeted therapeutics by creation of specific dependency on activated oncogenic signaling pathways. However, no tools currently exist to systematically connect such genetic lesions to therapeutic vulnerability. We have therefore developed a genomics approach to identify lesions associated with therapeutically relevant oncogene dependency. Using integrated genomic profiling, we have demonstrated that the genomes of a large panel of human non-small cell lung cancer (NSCLC) cell lines are highly representative of those of primary NSCLC tumors. Using cell-based compound screening coupled with diverse computational approaches to integrate orthogonal genomic and biochemical data sets, we identified molecular and genomic predictors of therapeutic response to clinically relevant compounds. Using this approach, we showed that v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations confer enhanced Hsp90 dependency and validated this finding in mice with KRAS-driven lung adenocarcinoma, as these mice exhibited dramatic tumor regression when treated with an Hsp90 inhibitor. In addition, we found that cells with copy number enhancement of v-abl Abelson murine leukemia viral oncogene homolog 2 (ABL2) and ephrin receptor kinase and v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) (SRC) kinase family genes were exquisitely sensitive to treatment with the SRC/ABL inhibitor dasatinib, both in vitro and when it xenografted into mice. Thus, genomically annotated cell-line collections may help translate cancer genomics information into clinical practice by defining critical pathway dependencies amenable to therapeutic inhibition.

SUBMITTER: Sos ML

PROVIDER: S-EPMC2689116 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions.

Sos Martin L ML Michel Kathrin K Zander Thomas T Weiss Jonathan J Frommolt Peter P Peifer Martin M Li Danan D Ullrich Roland R Koker Mirjam M Fischer Florian F Shimamura Takeshi T Rauh Daniel D Mermel Craig C Fischer Stefanie S Stückrath Isabel I Heynck Stefanie S Beroukhim Rameen R Lin William W Winckler Wendy W Shah Kinjal K LaFramboise Thomas T Moriarty Whei F WF Hanna Megan M Tolosi Laura L Rahnenführer Jörg J Verhaak Roel R Chiang Derek D Getz Gad G Hellmich Martin M Wolf Jürgen J Girard Luc L Peyton Michael M Weir Barbara A BA Chen Tzu-Hsiu TH Greulich Heidi H Barretina Jordi J Shapiro Geoffrey I GI Garraway Levi A LA Gazdar Adi F AF Minna John D JD Meyerson Matthew M Wong Kwok-Kin KK Thomas Roman K RK

The Journal of clinical investigation 20090518 6

Somatic genetic alterations in cancers have been linked with response to targeted therapeutics by creation of specific dependency on activated oncogenic signaling pathways. However, no tools currently exist to systematically connect such genetic lesions to therapeutic vulnerability. We have therefore developed a genomics approach to identify lesions associated with therapeutically relevant oncogene dependency. Using integrated genomic profiling, we have demonstrated that the genomes of a large p ...[more]

PMID: 19451690

Similar Datasets

Project description:BackgroundNon-small cell lung cancer (NSCLC), a leading cause of cancer deaths, represents a heterogeneous group of neoplasms, mostly comprising squamous cell carcinoma (SCC), adenocarcinoma (AC) and large-cell carcinoma (LCC). The objectives of this study were to utilize integrated genomic data including copy-number alteration, mRNA, microRNA expression and candidate-gene full sequencing data to characterize the molecular distinctions between AC and SCC.MethodsComparative genomic hybridization followed by mutational analysis, gene expression and miRNA microarray profiling were performed on 123 paired tumor and non-tumor tissue samples from patients with NSCLC.ResultsAt DNA, mRNA and miRNA levels we could identify molecular markers that discriminated significantly between the various histopathological entities of NSCLC. We identified 34 genomic clusters using aCGH data; several genes exhibited a different profile of aberrations between AC and SCC, including PIK3CA, SOX2, THPO, TP63, PDGFB genes. Gene expression profiling analysis identified SPP1, CTHRC1 and GREM1 as potential biomarkers for early diagnosis of the cancer, and SPINK1 and BMP7 to distinguish between AC and SCC in small biopsies or in blood samples. Using integrated genomics approach we found in recurrently altered regions a list of three potential driver genes, MRPS22, NDRG1 and RNF7, which were consistently over-expressed in amplified regions, had wide-spread correlation with an average of ~800 genes throughout the genome and highly associated with histological types. Using a network enrichment analysis, the targets of these potential drivers were seen to be involved in DNA replication, cell cycle, mismatch repair, p53 signalling pathway and other lung cancer related signalling pathways, and many immunological pathways. Furthermore, we also identified one potential driver miRNA hsa-miR-944.ConclusionsIntegrated molecular characterization of AC and SCC helped identify clinically relevant markers and potential drivers, which are recurrent and stable changes at DNA level that have functional implications at RNA level and have strong association with histological subtypes.

Dataset Information

Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions.

Publications

Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets