Abstract
Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor‐related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI‐treated patients with stage I‐III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine‐learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias.
Highlights
Aromatase inhibitors (AIs) are critical to the management of women with hormone receptor-positive breast cancer (BC)
After we determined and rank ordered the most discriminatory single nucleotide polymorphisms (SNP) (n = 400), we identified the smallest number of SNPs that were most predictive of risk using the LOOCV algorithm described above
We found that a signature consisting of 70 specific SNPs had the highest predictive accuracy of 75.93% (Table 2)
Summary
Aromatase inhibitors (AIs) are critical to the management of women with hormone receptor-positive breast cancer (BC). Their use has evolved to include premenopausal women with high-risk BC in combination with ovarian suppression [1, 2]. Sequential therapy, or extended therapy, AIs favorably impact disease free survival [3]. AIs are associated with a number of toxicities, of which arthralgia (AIA) is among the most common and significant. In two studies designed to identify AIA, the incidence of AIA was consistently reported near 50%, Therapy-R elated Arthralgia Prediction and did not vary across different third-generation AIs [5, 6]. AIA frequently results in noncompliance with AI regimens or treatment discontinuation entirely, both of which adversely impact clinical outcomes [5, 7]
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