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Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact

2014; Nature Portfolio; Volume: 5; Issue: 1 Linguagem: Inglês

10.1038/ncomms6469

2041-1723

Lei Li, Yu‐Hong Wei, Christine To, Chang‐Qi Zhu, Jiefei Tong, Nhu‐An Pham, Paul Taylor, Vladimir Ignatchenko, Alexandr Ignatchenko, Wei Zhang, Dennis Wang, Naoki Yanagawa, Ming Li, Melania Pintilie, Geoffrey Liu, Lakshmi Muthuswamy, Frances A. Shepherd, Ming‐Sound Tsao, Thomas Kislinger, Michael F. Moran,

Gene expression and cancer classification

Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA → RNA → protein → disease. However, the extent to which cancer is a manifestation of the proteome is unknown. Here we present an integrated omic map representing non-small cell lung carcinoma. Dysregulated proteins not previously implicated as cancer drivers are encoded throughout the genome including, but not limited to, regions of recurrent DNA amplification/deletion. Clustering reveals signatures composed of metabolism proteins particularly highly recapitulated between patient-matched primary and xenograft tumours. Interrogation of The Cancer Genome Atlas reveals cohorts of patients with lung and other cancers that have DNA alterations in genes encoding the signatures, and this was accompanied by differences in survival. The recognition of genome and proteome alterations as related products of selective pressure driving the disease phenotype may be a general approach to uncover and group together cryptic, polygenic disease drivers.