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Exploring the evolution process of cancers and its related complex molecular mechanisms at the genomic level through pathological staging angle is particularly important for providing novel therapeutic strategies most relevant to every cancer patient diagnosed at each stage. This is because the genomic level involving copy number variation (CNV) has been recognized as a critical genetic variation, which has a large influence on the progression of a variety of complex diseases. Great efforts have been devoted to the identification of recurrent aberrations, single genes and individual static pathways related to cancer progression. However, we still have little knowledge about the most important aberrant genes related to the pathology stages and their interconnected pathways from genomic profiles. In this study, we propose an identification framework that allows determining cancer-stages specific patterns dynamically. Firstly, a two-stage GAIA method is employed to identify stage-specific aberrant copy number variants segments. Secondly, stage-specific cancer genes fully located within the aberrant segments are then identified according to the reference annotation dataset. Thirdly, a pathway evolution network is constructed based on the impacted pathways functions and their overlapped genes. The involved significant functions and evolution paths uncovered by this network enabled investigation of the real progression of cancers, and thus facilitated the determination of appropriate clinical settings that will help to assess risk in cancer patients. Those findings at individual levels can be integrated to identify robust biomarkers in cancer progressions.

Predicting Stage-Specific Recurrent Aberrations From Somatic Copy Number Dataset