Abstract P1-08-07: Prediction model of the response of neoadjuvant chemotherapy and long term survival according to multi-omic profiling in cooperation with clinicopathologic features in patients with breast cancer
Authors: Ji-Yeon Kim, Kyunghee Park, Woong-Yang Park, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Se Kyung Lee, Zhengyan Kan, Yeon Hee Park
Journal: Cancer Research (2022)

Abstract Background: The advance of next generation sequencing (NGS) leads to give the abundant genetic information of breast cancer (BC). We developed the prediction model of neoadjuvant chemotherapy (NAC) response and long term survival using genetic characteristics in cooperation with clinicpathologic features. Methods: Early and locally advanced BCs which would be planned to receive standard NAC (four cycles of anthracycline plus cyclophosphamide and four cycles of docetaxel or docetaxel plus trastuzumab for human epidermal growth factor receptor 2[HER2+] disease) followed by curative surgery. We prospectively collected tumor tissue and matched blood three times for each patients: at BC diagnosis (T1), three weeks after the first cycle of NAC (T2), and curative surgery (T3). Whole exome sequencing (WES) and RNASeq were performed to detect somatic mutation, mutational signature, tumor mutational burden (TMB) and PAM50 prediction was to classify intrinsic subtype. In terms of clinical variables, clinical stage and IHC subtype at diagnosis, pathologic complete response (pCR), residual cancer burden (RCB) class and distant recurrence free survival (DRFS) were used for prediction model development. Logistic regression was used for predicting RCB class and pCR with clinical and genomic variables at T1. Univariate and multivariate Cox regression were performed to identify prognostic factors for DRFS. Results: In total, 210 patients who were treated with scheduled NAC were enrolled. Finally, WES in 252 BC tissues (T1:123, T2:106 and T3:23) RNASeq in 198 BC tissues (T1:100, T2:77 and T3:21) were conducted from 123 patients. In NAC response, 52 patients achieved pCR(42.3%) and 14 patients were in RCB class 3, 39 in class 2, 14 in class 1 and 51 in class 0. Median follow up duration was 44months and distant recurrence was observed in 13 patients. TP53 mutation (60%) was the most commonly detected genetic alteration followed by TTN(31%), ERBB2(25%) and PIK3CA(18%). Eight BCL11A (3%) and 3 MPL mutations (1%) were also observed respectively. Serial tumor sequencing suggested that mutation profile have not changed during NAC. In terms of mutational signature, signature 3 was most frequently observed and no difference was observed in serial sequencing data. In addition, APOBEC (sig.2) and HRD (sig.3) were observed differently according to intrinsic subtype. In terms of intrinsic subtype, basal subtype decreased during NAC and luminal A type increased. Median TMB was 87 (range, 14-570). In prediction model, clinical N3(cN3) stage (Odds ratio [OR] of cN3 vs. not : 4.536, 95% confidence interval [CI]: 0.925, 44.016, p=0.042) and intrinsic subtype at BC diagnosis (OR of non-luminal vs. luminal types: 0.208, 95% CI: 0.062, 0.604, p=0.001) were associated with pCR. Clinical stage (OR of clinical stage III vs. II: 4.115, 95% CI: 1.094, 19.217, p=0.021), TMB (OR of TMB high vs. low: 0.255, 95% CI: 0.043,1.042, p.value=0.045), MPL genetic alteration (OR of mutation vs. wild type: 16.347, 95% CI: 0.797,1013.88, p.value=0.037), BCL11A (OR of mutation vs. wild type: 16.347, 95% CI: 0.797,1013.88, p.value=0.037) were associated with RCB class (3 vs. others). In terms of DRFS, prediction model consisted of clinical stage (Hazard ratio [HR] of stage III vs. II: 3.496, 95% confidence interval [CI]:1.337, 5.654), PIK3CA (HR of mutation vs. wild type: 1.572, 95% CI:-0.008, 3.152) TMB (HR of high vs. low: -1.999. 95% CI:-3.669, -0.329) had c-index of 0.861(95% CI:0.805, 0.916). Conclusions: During NAC, somatic mutation and mutation signature were consistently maintained while intrinsic subtype dynamically changed. In prediction model, TMB, PIK3CA mutation and clinical stage showed predictive roles on DRFS of BC in NAC setting. Citation Format: Ji-Yeon Kim, Kyunghee Park, Woong-Yang Park, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Se Kyung Lee, Zhengyan Kan, Yeon Hee Park. Prediction model of the response of neoadjuvant chemotherapy and long term survival according to multi-omic profiling in cooperation with clinicopathologic features in patients with breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-07.