New Pharmacogenomic Paradigm in Breast Cancer Treatment
Authors: Kenneth Offit, Mark E. Robson
Journal: Journal of Clinical Oncology (2010)

The impact of genetics and genomics on clinical oncology has been profound. Panels that detect the expression of oncogenes (eg, breast cancer expression arrays) and assays for acquired mutations in tumors (eg, EGFR mutations in lung cancer) now guide treatment and prognostic assessment. Testing for germline (inherited) genetic markers (eg, BRCA1/2 testing) informs the risk assessment and management of hereditary cancers. An emerging application of cancer genetics, pharmacogenetics, involves the use of inherited or acquired genetic abnormalities to predict treatment toxicity or outcomes. Recently, methods that involve the scanning of entire genomes in individuals for common variants have begun to influence studies of cancer causation. As illustrated by the study by Ingle et al, genomic scanning methodologies are now being applied to transform pharmacogenetics into pharmacogenomics. Pharmacogenetic studies in cancer have previously linked genetic variants in known pathways with treatment response. For example, TPMT genetic variants have been correlated with 6-mercaptopurine response, CYP2D6 variants have been correlated with tamoxifen response, and UGT1A1 testing has been correlated with irinotecan toxicity. The study by Ingle et al is one of the first to use an agnostic, whole-genome scanning approach to identify variants in a genetic pathway that are linked to a common adverse effect of an anticancer drug. In this study, 293 patients with early-stage, estrogen receptor– positive breast cancer who were treated with aromatase inhibitors (AIs) and who developed musculoskeletal toxicity were matched with 585 patients with the same disease and treatment who did not develop musculoskeletal toxicity. A genome-wide scan of 551,395 single nucleotide polymorphisms (SNPs) was carried out in the two groups. Strikingly, a set of SNPs near the gene TCL1A were associated with a risk for musculoskeletal toxicity. To bolster the biologic plausibility of this association, the authors report an altered estrogen response in cell lines with these same TCL1A variants, one of which, rs11849538, creates an estrogen-response element. Although the results of the study by Ingle et al are scientifically important and serve to establish a new paradigm for the potential power of pharmacogenomics in oncology, one may inquire as to the clinical significance of these findings. One may also ask about the potential clinical efficacy of using TCL1A locus genotyping to personalize treatment plans for patients receiving antiestrogen therapy. Is it time to translate into clinical practice the newly reported relationships between AIs, adverse effects, and SNPs? With regard to clinical significance, the context must first be considered. The AIs—anastrozole, letrozole, and exemestane—have shown advantages in both disease-free survival and toxicity profiles compared with tamoxifen in adjuvant treatment of breast cancer as well as in treatment of metastatic breast cancer. Although treatment using AIs allows the tamoxifen-associated risks of thromboembolic phenomena and endometrial cancer to be avoided, AIs present another potential risk. AI treatment is accompanied by a reduction in bone mineral density, and in 35% to 47% of patients, AIs cause joint symptoms or arthralgia. This rate may be as high as 61% in some community-based series of patients who are treated with AIs. Such musculoskeletal adverse events (MS-AEs) can be so severe that they affect compliance and the overall efficacy of treatment, with up to 17% of patients discontinuing AI use in some of these series. It is also notable that musculoskeletal complaints less frequently accompany treatment with tamoxifen. With regard to the clinical efficacy of this study, several caveats must be noted. These relate to the magnitude of the risk associated with the genetic variant identified, the effect size, and the predictive value of the proposed pharmacogenetic test. The level of statistical significance, on the order of P 10 6 in the study by Ingle et al, seems impressive. However, in a strict statistical sense, the primary association reported, which involves MS-AEs and the SNPs near the TCL1A locus, may simply be the result of chance. This is because the P value of the association is just below the threshold required for statistical significance in a study with more than 500,000 comparisons. The odds ratio (approximation of the relative risk) of the variants is approximately 2.1, with a range from 1.5 to 2.9. This means that an individual carrying one copy of the genetic variant that places her at risk (eg, rs7158782) would experience an approximately two-fold increased risk for MS-AEs compared with a woman without this genetic variant. (The rare homozygote for these alleles would present nearly a fourfold increase in risk.) It is possible to calculate the populationattributable risk of MS-AEs that results from this genetic variant by using a formula that incorporates the frequency of the variant in the general population (approximately 10%) and the risk of MS-AEs if carrying the risk allele (an approximately two-fold increase). This leads to the suggestion that only approximately 11% of clinically significant MS-AEs overall are attributable to this particular genetic variant. JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L S VOLUME 28 NUMBER 31 NOVEMBER 1 2010