Abstract 728: Identification of high-risk luminal early breast cancer patients based on a p53 signature

Abstract Estrogen receptor (ER) positive luminal early breast cancer (BC) benefits from endocrine therapy based on the selective ER modulator tamoxifen (TAM) and aromatase inhibitors (AI). Yet, tumor recurrence occurs in up to one third of patients due to primary and secondary endocrine treatment resistance (ETR). The causes of primary ETR are not well defined by predictive tumor markers, however, TP53 mutations have been linked with primary ETR in a preoperative short-term endocrine treatment study. We used a previously learned TP53 mutation-predictive p53 score as part of the NanoString®BC360 mRNA expression panel to identify patients with p53 mutant-like status, and tested its applicability for the prediction of primary ETR in the adjuvant setting. In the TCGA BRCA cohort (N=1078), the p53 score distribution was analyzed for the presence of different subgroups using Gaussian finite mixture modeling. The same procedure was applied in breast cancer patients from the BreastMark database (N=1936) as an independent test of methodological and clinical validity. An ad-hoc binary classifier was trained to apply the cutoff identified in TCGA patients to patients of a prospectively collected, postmenopausal early breast cancer test cohort (IKP211) of hormone receptor-positive patients treated with TAM and/or AI (median follow up 6 years, N=632; DRKS00000605). Cluster modeling revealed that the p53 score distribution in the TCGA cohort is composed of two different patient groups: 363 patients (33.7%) with higher p53 scores were separated from the remaining patients with lower p53 scores. Likewise, the BreastMark cohort also showed two clusters with 419 patients (21.6%) forming the patient subgroup with high p53 scores. Cancer-specific survival (TCGA) and recurrence-free survival (BreastMark) were significantly shorter in p53-high patients of Luminal A subtype (P=0.0045 and P=0.04, respectively), with no significant differences observed in the remaining subtypes. When applying the binary classifier (mean AUC 0.95) on patients of the IKP211 test cohort, 14 patients (2.2%) were predicted as p53 mutant-like. This p53-positive patient group was strongly associated with higher recurrence or death rates in multivariate Cox regression (HR=3.3, 95% confidence interval 1.5-7.4; P=0.0037). As an independent verification, TP53 amplicon sequencing of these patients confirmed a positive mutation status in all samples that passed quality control (N=11, 100%). We conclude that this p53-based division of Luminal A patients is useful to predict high-risk hormone receptor-positive early breast cancer patients with p53 mutations upfront, whose tumors likely show primary ETR and who are not sufficiently treated with endocrine agents. Citation Format: Florian A. Büttner, Stefan Winter, Johanna Dahlen, Thomas E. Mürdter, Reiner Hoppe, Peter Fritz, Patrick Danaher, Stephen F. Madden, Matthias Schwab, Werner Schroth. Identification of high-risk luminal early breast cancer patients based on a p53 signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 728.