Cancer Pharmacogenomics

Caroline F. Thorn, Ph.D. [email protected]

Stanford – South Africa Biomedical Informatics Program Overview

• Host genome and cancer genome • Etiology of cancer/genetics of cancer predisposition • Whole genome approaches – Patient samples / ex vivo – Cell lines / in vitro

Stanford – South Africa Biomedical Informatics Program Host Genome and Cancer Genome

• What is the cancer genome? • How is cancer cell different from host cell? – genetic change(s) in cancer cell may alter its response to drug

Stanford – South Africa Biomedical Informatics Program Host Genome and Cancer Genome

What makes a cancer cell different? • Loss of cell cycle control so cancer cell is not contact inhibited and can replicate • Evasion of normal methods of cell death, loss of tumour suppressors, decreased apoptosis • Amplification of detoxifying pathways, increased activity of drug transporters • Increase in growth factor receptors – E.g. Her-2/neu

Stanford – South Africa Biomedical Informatics Program Host Genome and Cancer Genome

• So what if we target drugs to those differences ?

Stanford – South Africa Biomedical Informatics Program Her/neu and trastuzumab

• Her-2 oncogene, over expressed in ~30% breast cancers • Associated with aggressive tumours and poor prognosis • Is expressed on cancer cell surface • Drug is antibody to target it - trastuzumab aka Herceptin, • Limits toxicity as targets only the cancer cells = successful example

Stanford – South Africa Biomedical Informatics Program Gefitinib (Iressa)

• Inhibits the growth factor receptor EGFR • Promising early trials - but only improved survival in subset of patients, 10-15% – Female, – Japanese – Non-smokers

Example that shows we have to remember about the host genome as well

Stanford – South Africa Biomedical Informatics Program Gefitinib (Iressa)

• Iressa works better in those with variant EGFR that makes it more susceptible to the drug

Stanford – South Africa Biomedical Informatics Program Etiology of cancer

• 2-hit hypothesis, Knudsen : mutation (may be germ line) in one tumour suppressor gene or oncogene can be tolerated, but second somatic change then tips balance from controlled growth to uncontrolled growth • More likely to be several hits

Stanford – South Africa Biomedical Informatics Program Cancer predisposition

• Even though all cancer has genetic changes and there are several well known examples of inherited genetic risk factors e.g. BRCA1 and breast cancer, only 5-10% of cancer is inherited • Most cancers come from random mutations over lifetime – mistake when cells are going through cell division – response to injuries from environmental agents such as radiation or chemicals. – viruses

Stanford – South Africa Biomedical Informatics Program Other omics

• Worth mentioning efforts to examine gene-environment interactions of importance in cancer research – Nutrigenomics, interaction between diet and genes – Toxigenomics, interactions between toxins (e.g. Carcinogens in environment) and genes

Stanford – South Africa Biomedical Informatics Program Whole genome approaches

• Can’t test cancer drugs on healthy volunteers in same way as other drug classes so need to exploit other methods for PGx discovery – Expression profiling in patient samples – In vitro drug response in cell lines From McLeod review, JCO 2005 – mouse models (will not cover in this course)

Stanford – South Africa Biomedical Informatics Program Expression profiling

• Using microarray or RT-PCR in high- throughput system to find a set of genes that respond to drug (up or down regulated) or are found in certain disease type and give characteristic “signature”

Stanford – South Africa Biomedical Informatics Program Expression profiling on patient samples