Abstract PS5-41: Machine learning model of gut microbiota predicts neratinib induced diarrhea in patients with breast cancer

Abstract

Abstract Background: Neratinib is a potent small molecule tyrosine kinase inhibitor (TKI) of human epidermal growth factor receptors (HER1,2,4). One of the major side effects of neratinib is diarrhea. The human gut contains a dense microbiome ecosystem that is essential in maintaining a healthy host physiology, and its disruption may lead to increased risk of toxicities from cancer therapy. In this study, we aimed to develop a machine learning model based on analysis of gut microbiota data to predict neratinib-induced diarrhea. Methods: Patients were enrolled in a phase II trial evaluating safety and tolerability of neratinib in older adults with HER2+ breast cancer (NCT02673398). Neratinib was administered as single agent, 240 mg oral daily in a 28-day cycle. Stool samples were collected at baseline and during treatment for 16S rRNA gene sequencing. Using microbial relative abundance data, we developed gradient-boosted tree models with two nested loops of cross validations to classify whether diarrhea would occur or not after treatment onset. For the inner validation loop, we used ten-fold cross validation to determine the optimal model from hyper-parameters including regularization. For the outer validation loop, we utilized a leave-one-patient-out cross validation to test this model on the hold-out patient’s baseline data and the predictions were used for model assessment. Results: A total of 11 patients and 50 longitudinal stool samples were collected. The median age was 66 years. 73% developed grade ≥ 1 diarrhea attributed to neratinib. Shannon diversity index of gut microbiome was not associated with diarrhea. For predictive modeling, the outer validation loop Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC) were 0.92 and 0.97, respectively. The two most important taxa predictive of protection from diarrhea were Ruminiclostridium 9, and Bacteroides sp. HPS0048. We found that patients with a larger relative abundance of Ruminiclostridium 9 and Bacteroides sp. HPS0048 have reduced risk of neratinib-related diarrhea. Conclusions: The machine learning model can identify breast cancer patients at risk of diarrhea prior to neratinib use. Future studies are required to validate this finding. Citation Format: Chi Wah Wong, Susan E. Yost, Jin Sun Lee, John D. Gillece, Megan Folkerts, Lauren Reining, Sarah K. Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan. Machine learning model of gut microbiota predicts neratinib induced diarrhea in patients with breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-41.