Dr. Sidong Huang
Rosalind and Morris Goodman Cancer Research Centre
Departments of Biochemistry, Faculty of Medicine
Functional Genomics to Guide Cancer Therapy
Our laboratory uses functional genomic tools to study cancer-relevant pathways and to guide targeted cancer therapy. We aim to identify novel genes and networks that modulate response to cancer drugs, and to uncover genetic dependencies of cancer-relevant pathways that can be exploited therapeutically.
Overcoming drug resistance to targeted cancer therapeutics
Cancer therapy is often hampered by the rapid emergence of drug resistance (acquired resistance). This is true not only for the conventional chemotherapies, but also for genotype-directed drugs targeting those components that are mutated or deregulated in tumor cells. In addition, presence of an oncogenic driver mutation does not always confer sensitivity to the drug that inhibits that driver (intrinsic resistance). This is often due to the feedback loops and crosstalk between the major signaling pathways in cancer. Therefore, a better understanding of resistance mechanisms is essential to enable the rational development of treatment strategies to overcome this clinical challenge. Using unbiased functional genetic tools, we aim to uncover novel genes and network interactions that causally modulate response to targeted cancer therapeutics. Whenever possible, the expressions of these candidate biomarkers are examined in tumour samples of cancer patients to correlate with clinical response to the cancer drugs. Complementary genetic and biochemical approaches are used to investigate the mechanism of action and to provide treatment strategies to overcome resistance.
Targeting genetic dependencies of cancer driver mutations
Majority of genotype-directed anticancer drugs target the gain-of-function oncogenic mutations. However, some cancer driver mutations such as loss-of-function alterations in tumour suppressors are not directly actionable. When oncogenic mutations are not directly targetable, “synthetic lethality” is often explored to derive highly specific cancer therapies that have minimal side effects in normal tissue. To uncover genetic dependencies of novel cancer driver mutations that can be exploited therapeutically, we use customized druggable gene-family shRNA libraries and compound libraries to identify the targets whose inhibitions are synthetic lethal with the driver mutations in the cancer types of interests. Our studies aim to deliver novel insights into the genetic dependencies of cancer-relevant pathways and provide effective treatment strategies, resulting in clinical benefit for cancer patients.