Mutations affecting the p53 pathway are associated with aromatase inhibitor resistance.
Major finding: Mutations affecting the p53 pathway are associated with aromatase inhibitor resistance.
Clinical relevance: Pretreatment tumor DNA sequence and posttreatment tumor proliferation index were analyzed.
Impact: Genomic sequencing may identify patients more likely to respond to aromatase inhibitors.
Clinical trials have been initiated to study whether neoadjuvant treatment with aromatase inhibitors (AI), which block estrogen synthesis, improves surgical outcome of postmenopausal patients with estrogen receptor–positive luminal breast cancer. Ellis and colleagues performed whole genome or exome sequencing on 77 pretreatment biopsy samples collected during 2 such trials in order to identify molecular determinants of AI response. Because high cellular proliferation following AI treatment is associated with poor prognosis, posttreatment surgical specimens with widespread expression of the Ki67 proliferation biomarker were considered AI resistant, and those with minimal Ki67 staining were considered AI sensitive. Validation of the most significantly mutated genes in an additional 240 tumors identified PIK3CA, TP53, and MAP3K1 as the most commonly mutated, although many other recurring low-frequency mutations were identified. Both high- and low-frequency somatic mutations clustered into distinct signaling nodes, the majority of which were shared among AI-sensitive and AI-resistant tumors although several were distinct to each response group. Luminal B–subtype tumors bearing TP53 mutations with significantly higher Ki67 levels at both baseline and surgery were less likely to respond to AI therapy, whereas luminal A–subtype tumors with MAP3K1 mutations and lower baseline Ki67 levels were more likely to respond. Furthermore, pathway analyses revealed that mutations affecting DNA replication, mismatch repair, and p53 signaling were enriched in the AI-resistant group, which had a significantly higher background mutation rate than the AI-sensitive tumors and tended to have more somatic structural alterations. These findings suggest that, in concert with more traditional clinical characterization methods, genomic sequencing has the potential to identify patients who will respond to AI therapy, though prospective clinical trials based on breast tumor DNA sequencing will require large numbers of patients due to the prevalence of low-frequency mutations.