Abstract P2-03-20: Clinical profiling and comprehensive analysis of candidate genes related to breast cancer estrogen receptor intratumour heterogeneity

Abstract

Abstract 1. Background and Aim Breast cancer has the highest incidence among the world’s population. One third of patients diagnosed with early breast cancer progressed into metastatic outcomes due to the inherent heterogeneity and evolutionary features of tumors. The main clinical manifestation is the discordance of receptor expression in metastases. ESR1 the estrogen receptor (ER) encoding gene, has been proved affected ER expression. Nevertheless, the correlation between ER transformation and the somatic mutation profiles in breast cancer metastatic lesions remains unclear. Availability of advanced high-throughput technologies, and the development of bioinformatics tools has greatly accelerated our cognition of the molecular basis of cancer. In this study, we aimed to evaluate the frequency, clinical characteristics and prognostic value of ER receptor conversion between primary and first metastatic lesions in 115 patients whose primary ER status are positive. We also compared the results of NGS of 42 patients and explored latent genes related to ER discordance in expression. 2. Method Qualified metastatic tumor tissue sequencing information was available for 42 patients. Samples were accessed using the FoundationOne CDx assay (Foundation Medicine, Cambridge, MA, USA) panel including 733 tumor related genes. High frequency mutant genes were defined as the genes count more than 4 times in each group. All data were analyzed using SPSS 25.0 software (Chicago, IL, USA). Pearson’s chi-squared (χ2) test, Fisher’s exact test, were used to test the associations between different variables. The Cox proportional hazard models was used to evaluate the relationship between KMT2C mutation and Disease free survival (DFS) in the patients cohorts. The results with p < 0.05 were considered statistically significant. 3. Results and Conclusions All 115 patients (primary ER status is positive) were divided into two groups, 70(60.87%) patients ER status remained positive while 45(39.14%) patients ER status transformed into negative. The histological type (p=0.008) and DFS (p=0.004) were significantly different in two subgroups. There were no significant correlations between the age at the time of diagnosis, BMI classification, menopausal status, surgery, histological grade, tumor size, lymph node status, metastasis, Ki67, adjuvant radiotherapy, neoadjuvant, metastasis site before second biopsy, first treatment after recurrent and first PFS after rebiopsy. Univariable analysis of DFS proved metastasis to be distinct risk factor for poor survival (hazard ratio [HR] > 1, p = 0.001). By contrast, neoadjuvant proved to be protective factor for better survival (hazard ratio [HR] < 1, p < 0.001). The surgery, histological type, histological grade, tumorsize, metastasis, neoadjuvant, first treatment after recurrent, first PFS after rebiopsy were subsequently analyzed with the multivariable Cox analysis. Metastasis (HR>1, p=0.007) and neoadjuvant (HR<1, p<0.001) remained independent prognostic factors of DFS. We compared the differentially mutated genes in the ER differential expression BC patient groups. Mutations in TP53 (n=24) and PIK3CA (n=19) occurred most frequently in both groups and account for 57.14% and 49.23% separately. Notably, mutations in KMT2C (n=6) was detected in ER+ to – subgroup and accounted for 37.5%. According to the results of NGS, the distribution of KMT2C mutations was different among the two cohorts. We identified the mutation in codon 321 was the hot spot of KMT2C in our patient cohort. Next, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to analyze the enriched pathways of KMT2C. KEGG pathway analysis revealed that patients with KMT2C mutations harbored significantly more mutations in genes involved in the Ubiquitin mediated proteolysis and Lysine degradation signaling pathway. Table 1. Patients’ basic clinicopathological characteristics among two groups Table 2. Univariate and Multivariate Analysis Between Clinicopathological Characteristics and DFS of 115 BC patients Table 3. Mutated Genes Rank and patients number Citation Format: Xi Wang, Yongmei Yin, Ziyi Fu. Clinical profiling and comprehensive analysis of candidate genes related to breast cancer estrogen receptor intratumour heterogeneity [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-20.