Lecture 2: Informing Cancer Biology using Computational Proteogenomics
Cancer has been well established as a disease of the genome, with a subset of somatic mutations frequently acting as drivers of tumor progression, and thereby influencing diagnosis, prognosis and treatment. The integration of cancer genomics with mass spectrometry-based proteomics and phosphoproteomics can be used to supplement genomic information, determining the effect of genomic aberrations at the protein level, guiding biomarker development and predicting effective drug combinations for treatment. Our lab focuses on the development and application of informatics methods focused on cancer proteogenomics, and predictive modeling. During the seminar, I will discuss a subset of these methods, which we have used to identify novel peptides and outlier kinase expression in cancer proteomics data. Additionally, I will discuss our current efforts in applying related techniques to identify aberrant kinase gene expression and phosphorylation in diverse tumor and sample types.