Pfizer / Peter Mac Cancer Genomics Program

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Pfizer / Peter Mac Cancer Genomics Program - Research at Peter Mac

The goal of the Pfizer/Petermac Cancer Genomics Program is to identify tumour signatures that will guide the use of chemotherapy to cancer patients that are most likely to respond.

Research Focus

Melanoma and Ovarian Cancer Screening tumours for drug sensitivity Using genomic, proteomic and metabolomic profiling to obtain a drug predictive signature Functional genomic RNAi screens to identify genes associated with resistance

Research Overview

Cancer develops as a result of multiple gene mutations and individuals with the same type of cancer often have dissimilar genetic defects; these differences underlie the clinical spectrum of disease outcomes, progression and drug effectiveness. One of the major challenges in treating cancer is the selection of the most effective chemotherapy agents for individual patients. To address this challenge the aim of the Pfizer/Petermac Cancer Genomics Program is to generate Proteomic, Metabolomic and Genomic profiles of human tumours and use this information to predict drug effectiveness in patients. As a first step toward achieving this goal we are screening a panel of human ovarian tumour and melanoma cell lines for their sensitivity to Pfizer drugs. Concurrently these cells are being profiled using genomic, proteomic and metabolomic approaches. Bioinformatics analysis is then being employed to look for signatures that predict drug efficacy. Genomic profiling is being achieved by using microarray technology to determine gene expression and gene copy number, and gene mutations are being identified using a broad candidate approach utilizing exon capture and next generation sequencing. A candidate Proteomic approach using both Reverse Phase Protein arrays and Western Analysis is been undertaken to identify key signaling pathway components that are altered in these cells. Cellular and excreted metabolites are being analysed by gas chromatography and Mass Spectrometry in collaboration with Metabolomics Australia at Bio21. To date we have successfully generated a “ gene signature” using a continuous predictor model and principal component analysis based feature reduction that is 95% accurate in classifying cells as either resistant or sensitive to a drug that targets the PI3 kinase pathway.