- Define and explain carcinogenesis and cancer genetics.
- Identify the most common germline variants and describe how these can modulate treatment outcome.
- Describe how somatic variants can predict drug response.
- Discuss pharmacogenomics in oncology clinical trials.
Most types of cancer show considerable variability in their response to chemotherapy. In addition, anti-cancer therapies for treatment of a particular type of cancer, although significantly different in their mechanism of action, show only a marginal difference in outcome when compared with one another. This variability is thought to be due to inter-tumor and intra-tumor heterogeneity and host-specific factors and may reflect the lack of knowledge on how the molecular abnormalities in cancer cells affect responsiveness to anti-cancer therapies. In this aspect, pharmacogenomics can play an important role in predicting efficacy and toxicities of anti-cancer drugs.
Currently, there are over 100 drugs approved for treating cancer,1 and over 800 more in clinical development.2 Compared with other non-neoplastic diseases, selection of an anti-cancer treatment regimen is more critical since the mortality in patients increases significantly with the progression of disease.3 Due to a large number of therapeutic options, low response rates, high incidence of de novo and acquired resistance to therapies, and severe toxicities associated with anti-cancer agents, pharmacogenomic applications to develop predictive markers for drug response or toxicity are essential in oncology therapeutics.
Traditionally, most cytotoxic chemotherapy dosing is calculated with the use of weight, body surface area, or area under the curve. However, patients with inherited deficiencies in enzymes responsible for drug metabolism and disposition can have severe toxicities at these traditional doses. Patients who have a deficiency in thiopurine S-methyltransferase (TPMT) can have greatly elevated concentrations of active drug metabolites and are at risk for life-threatening, drug-induced myelosuppression. On the other hand, patients with increased enzymatic activity may be at risk for treatment failure resulting in cancer progression. Other examples of enzyme deficiencies and the drugs they metabolize include uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) and irinotecan, cytochrome P450 2D6 (CYP2D6) and tamoxifen, and dihydropyrimidine dehydrogenase (DPD) and 5-fluorouracil (5-FU).
In addition to pharmacogenomics of drug-metabolizing enzymes, there are mutations in drug targets that influence treatment outcome by conferring either sensitivity or resistance to therapy. One classical example is trastuzumab, a monoclonal antibody targeting human epidermal growth factor receptor 2 (ERBB2/HER2), which is overexpressed in some of the breast tumors. Trastuzumab resistance is seen in tumors that express p95, a truncated form of HER2, with no extracellular domain containing the trastuzumab binding site. In these cases, it is suggested that lapatinib may be a more effective treatment choice. Another example is breakpoint cluster region–v-abl Abelson murine leukemia viral oncogene homolog 1 (BCR-ABL)–targeted therapy in the treatment of chronic myeloid leukemia (CML). Treatment resistance to all the currently available BCR-ABL inhibitors (imatinib, dasatinib, and nilotinib) is seen when there is a T315I mutation.
Pharmacogenomic markers that predict response to treatment will ...