K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer

• Published in: Cancers Vol. 14; no. 21; p. 5322
• Main Authors: Johnson, Heather, Ali, Amjad, Zhang, Xuhui, Wang, Tianyan, Simoulis, Athanasios, Wingren, Anette Gjörloff, Persson, Jenny L
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.10.2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biomarkers; Breast cancer; breast cancer biomarkers; Cancer therapies; Cell cycle; Chemotherapy; Decision making; Diagnosis; E-cadherin; ErbB-2 protein; ESR1 protein; Estrogens; GATA-3 protein; Gene expression; Gene mutations; Genomes; Growth factors; Health aspects; K-Ras protein; Kinases; KRAS; Learning algorithms; luminal a breast cancer; Machine learning; machine learning algorithm; Mammography; Medical prognosis; Metastasis; Mutation; p53 Protein; Patients; Point mutation; progression-free survival; PTEN protein; Runx1 protein; Survival; Tomography; treatment response; triple-negative breast cancer; Tumors; Taiwan; triple-negative breast cancer; progression-free survival; treatment response; breast cancer biomarkers; KRAS; gene mutations; luminal a breast cancer; machine learning algorithm
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14215322

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.