Dataset Information

Spatial and temporal diversity in genomic instability processes define early stage lung cancer evolution.

ABSTRACT: Deciphering the complexity of non-small cell lung cancer (NSCLC) evolutionary histories may elucidate the basis for its dismal outcome. We performed multi-region sequencing on 25 regions from seven early-stage NSCLCs. Spatial dissection revealed branched tumor evolution, with driver mutations occurring before and after subclonal diversification. Intra-tumor heterogeneity in DNA copy number alterations, translocations and endogenous APOBEC mutational processes, demonstrates that NSCLC may follow multiple distinct evolutionary trajectories simultaneously. In smokers, despite maintained carcinogen exposure, temporal dissection reveals a proportional decrease in smoking-related mutations during tumor progression, accompanied by an increase in APOBEC-related mutations. We observed genome-doubling events early in tumor development of ex-smokers, suggesting prolonged latency periods before clinical detection. Multi-region sequencing demonstrates the relentless and heterogeneous nature of genomic instability processes in early-stage NSCLCs.

PROVIDER: EGAS00001000840 | EGA |

REPOSITORIES: EGA

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Spatial and temporal diversity in genomic instability processes defines lung cancer evolution.

de Bruin Elza C EC McGranahan Nicholas N Mitter Richard R Salm Max M Wedge David C DC Yates Lucy L Jamal-Hanjani Mariam M Shafi Seema S Murugaesu Nirupa N Rowan Andrew J AJ Grönroos Eva E Muhammad Madiha A MA Horswell Stuart S Gerlinger Marco M Varela Ignacio I Jones David D Marshall John J Voet Thierry T Van Loo Peter P Rassl Doris M DM Rintoul Robert C RC Janes Sam M SM Lee Siow-Ming SM Forster Martin M Ahmad Tanya T Lawrence David D Falzon Mary M Capitanio Arrigo A Harkins Timothy T TT Lee Clarence C CC Tom Warren W Teefe Enock E Chen Shann-Ching SC Begum Sharmin S Rabinowitz Adam A Phillimore Benjamin B Spencer-Dene Bradley B Stamp Gordon G Szallasi Zoltan Z Matthews Nik N Stewart Aengus A Campbell Peter P Swanton Charles C

Science (New York, N.Y.) 20141001 6206

Spatial and temporal dissection of the genomic changes occurring during the evolution of human non-small cell lung cancer (NSCLC) may help elucidate the basis for its dismal prognosis. We sequenced 25 spatially distinct regions from seven operable NSCLCs and found evidence of branched evolution, with driver mutations arising before and after subclonal diversification. There was pronounced intratumor heterogeneity in copy number alterations, translocations, and mutations associated with APOBEC cy ...[more]

PMID: 25301630

Publication: 1/2

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Project description:PURPOSE: Non-small cell lung cancers (NSCLC) comprise multiple distinct biological groups with different prognoses. For example, patients with epithelial-like (EL) tumors have a better prognosis and exhibit greater sensitivity to inhibitors of the epidermal growth factor receptor (EGFR) pathway than patients with mesenchymal-like (ML) tumors. Here we test the hypothesis that EL NSCLCs can be distinguished from ML NSCLCs on the basis of global DNA methylation patterns. EXPERIMENTAL DESIGN: To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. RESULTS: We show that patterns of methylation divide NSCLCs into EL and ML subsets as defined by gene expression and that these signatures are similarly correlated in NSCLC cell lines and tumors. We identify multiple DMRs, including ERBB2 and ZEB2, whose methylation status is strongly associated with an epithelial phenotype in NSCLC cell lines, surgically resected tumors, and formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. CONCLUSIONS: Our data demonstrate that patterns of DNA methylation can divide NSCLCs into two phenotypically distinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used for predictive biomarker discovery and development. To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy.

Dataset Information

Spatial and temporal diversity in genomic instability processes define early stage lung cancer evolution.

Publications

Spatial and temporal diversity in genomic instability processes defines lung cancer evolution.

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