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Biography

Kian Kani has completed his Postdoctoral Research at Cedars Sinai Medical Center, where he utilized proteomics in order to study therapeutic response to HER axis targeted drugs in cancer and during his graduate studies at UCLA utilized molecular and bioinformatic tools in order to study control mechanisms regulating aberrant activity of the Human Epidermal Growth Factor Receptor (HER) family in cancer. His focus at USC is to identify and characterize novel cancer biomarkers and his goal is to enhance patient outcome by empowering physicians with the necessary tools for personalized medicine.

Abstract

A number of seminal reports have revealed the genomic landscape of human cancer. Collectively, these reports identified similar patterns of genomic alterations that can initiate or promote tumorigenesis. Approximately 150 genes that represent 12 signaling pathways comprise the ‘hot’ list in cancer research. Translation of genomic alterations into distinct phenotypic traits is challenging, especially for a complex systemic disease like cancer. In cases where mutations are identified, it is often difficult to connect the genomic heterogeneity to tumor phenotype. Since complex cellular systems are formed by interaction between gene products, or protein interactome networks, a complete understanding of the genotype-phenotype relationship in cancer will require tools that can better characterize these networks. Our goal is to develop a systems biology approach to study how cellular networks control biological processes and how perturbations in such networks can explain phenotypes associated with cancer initiation and progression. We rely on affinity purification (AP) and mass spectrometry (MS) based proteomics approaches to quantitatively produce protein-protein interaction networks. A number of new AP-MS strategies have recently been developed. Our group utilizes a combination of antibody enrichment (epitope-tag), chemical crosslinking, and proximity sensors (BioID) to elucidate the organization and stoichiometry of protein complexes. We also describe a number of strategies to reduce inclusion of false-positive or ‘contaminants’ proteins from our results.