What Does Take to Identify the Signal From the Noise in Molecular Profiling of Tumors

Cancer is a complex, heterogeneous disease that is driven by continually evolving genomic changes. Our current efforts to identify and the cure or demise of patients has utilized snap shots of DNA, RNA, proteins, and/or protein/nucleic acid interactions among numerous assays. For example, sequencing genomes or exomes distinguishes germline variants from somatic mutations as one step toward identifying nucleotide changes that are truly driving mutations. However, these assays identify very large numbers of variants and substantially reducing the noise requires considering the potential impact of variants (missense, non-sense, synonomous), quality of the call, prevalence of mutations in tumor versus normal cells, and whether a gene carrying a mutation is even expressed. Consequently, molecular profiling of tumors, benefits from data obtained from several different kinds of DNA sequencing-based assays. Using data from paired tumor and normal samples we will show an example workflow that combines exome and transcriptome sequencing to identify putative driver mutations that display high signal for being impactful in cancer.