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Sammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy

ABSTRACT: In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection. Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.

SUBMITTER: Divyang Deep Tiwari  

PROVIDER: BIOMD0000001075 | BioModels | 2023-08-28

REPOSITORIES: BioModels

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Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment<sup>1</sup>. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy<sup>2</sup>. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encod  ...[more]

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