Systems Biology and Integrated Computational Methods for Cancer-Associated Mutation Analysis

Systems biology offers great promise for the interpretation of genetic variation from functional standpoint. Sequence variation analysis holds a potential to inform systems biology by highlighting genes and their pathways. Genomic sequencing enables extensive amount of somatic mutations in tumors. Cancer genomic research aims to identify all genes associated to cancer and involvements of these genes in initiation of cancer and progression; however, this endeavor is characterized by numerous challenges like intricacy of biological networks affected by local mutation spectrum and huge amount of other passenger mutations. To date, development of multiple refined computational methods for screening cancer-driven mutations and pathways on the basis of their network information and biological pathways. These approaches can be classified into (1) network-based, (2) mutation frequency-based, (3) functional impact-based, (4) data integration-based, and (5) structural genomic-based methods. In silico modeling may pinpoint the effects caused by the missense mutations in order to deal with disease morphology. Hence, analyzing the mutations affecting a protein with the help of computational approaches and methods may intimate recognition of its functional importance and understanding the molecular mechanism of cancer progression. Here, we summarize some of the major computational methods and approaches that are useful in mutational studies, along with their recent advances and existing limitations. Nevertheless, better estimation of genetic variant association and genomics data interpretation with cancer or other disorders still requires significant efforts and improvement.