1. Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer.
- Author
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Cardoso, Marcella R., Silva, Alex Ap. Rosini, Talarico, Maria Cecília R., Sanches, Pedro H. Godoy, Sforça, Maurício L., Rocco, Silvana A., Rezende, Luciana M., Quintero, Melissa, Costa, Tassia B. B. C., Viana, Laís R., Canevarolo, Rafael R., Ferracini, Amanda C., Ramalho, Susana, Gutierrez, Junier Marrero, Guimarães, Fernando, Tasic, Ljubica, Tata, Alessandra, Sarian, Luís O., Cheng, Leo L., and Porcari, Andreia M.
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LEUCINE metabolism , *PROLINE metabolism , *THERAPEUTIC use of antineoplastic agents , *VALINE metabolism , *ADJUVANT chemotherapy , *DRUG efficacy , *METABOLOMICS , *IMMUNOHISTOCHEMISTRY , *EPIDERMAL growth factor receptors , *NUCLEAR proteins , *NUCLEAR magnetic resonance spectroscopy , *MACHINE learning , *CELL receptors , *CANCER patients , *CARBOXYLIC acids , *COMBINED modality therapy , *SENSITIVITY & specificity (Statistics) , *TUMOR markers , *PREDICTION models , *ACYCLIC acids , *BREAST tumors , *LONGITUDINAL method , *DRUG resistance in cancer cells , *EVALUATION - Abstract
Simple Summary: Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT. Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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