Abstract B042: Analysis of predictive indicators of invasive breast cancer: Modeling survival rates
Authors: Arigbede Olumide , Kiros Gebre-Egziabhe 
Published: February 2024

Objective: We aim to investigate indicators of risk for invasive breast cancer (IBC) and to predict and assess the impact of these risk indicators on survival rates in breast cancer (BC) patients who received various therapies. Background: It has been identified that several indicators including BC subtypes, age at diagnosis, history of breast disease, menopausal status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth receptor (HER2) status, tumor stage, tumor size, among others, can cause genomic alterations and driver mutations in BC, leading to invasive carcinogenesis. To assure improved BC outcomes, we investigate the influence of BC indicators on survival rates in patients receiving hormone therapy, chemotherapy and radiotherapy. Methods: This study uses the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which included 31 clinical indicators, m-RNA levels z-score for 331 genes, and mutation in 175 genes for 1904 BC patients. Both bivariate and multivariate associations were examined using logistic regression and Cox proportional hazard models. The multivariate logistic regression analysis is used to determine predictors of IBC. The Cox proportional hazard model was also used to evaluate the magnitude of the impacts of clinical and demographic variables on IBC, as well as the effects of therapy on survival. All analyses for this investigation were carried out using SAS. A p-value &lt; 0.05 was considered statistically significant. Results: We examined 1,043 patients (with the median age 61±13.1 years) from a total of 1904 for this study: 78.8% of patients had breast invasive ductal carcinoma; 87.4% were HER2 negative; 22.8%, 60.7%, and 67.6% received chemotherapy, hormone therapy, and radiotherapy treatments, respectively; 56.9% died but only 34.6% died from the disease; the average overall survival months was 127.3±77.6 months; and the most common age group was "65+ years" (39.4%). Chemotherapy (p=0.0474), neoplasm histologic grade (p=0.0015), Nottingham prognostic index (p&lt;.0001), radiation (p=0.0068), and BReast CAncer gene 1 (BRCA1) (p=0.0251) were all associated with IBC. Overall survival was also correlated to age at diagnosis (p=0.0002), neoplasm histologic grade (p&lt;.0001), lymph nodes (p&lt;.0001), mutation count (p&lt;.0001), Nottingham prognostic index (p&lt;.0001), radiotherapy (p=0005), tumor size (p&lt;.0001), tumor stage (p&lt;.0001), BRCA1 (p=0.250), PTEN (p&lt;.0001), and TP53 (p=0.0248). The overall accuracy of the model for each IBC and survival status was 82.1% and 74.2%, respectively. Conclusion: Our analysis demonstrated the importance of predictive analysis using the logistic regression model and survival analysis using the Cox proportional hazard model in cancer research. Predictive and survival analysis results aided in predicting the invasiveness and survival rate of BC among the study participants. In general, predictive and survival analyses are more accurate and may be used to make more realistic treatment decisions and precision oncology.

Citation Format: Olumide Arigbede, Gebre-Egziabhe Kiros. Analysis of predictive indicators of invasive breast cancer: Modeling survival rates [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B042.