Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Abstract

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer

“…For example, Rodriguez et al identified that patients with pCR had lower level of valine at baseline and those with relapse had lower level of succinate by GC-MS based targeted metabolomics 26 . Diaz et al performed metabolomics analysis in breast cancer patients with nCRT and found that glycohyocholic acid and glycodeoxycholic acid can classify triple-negative patients regarding treatment response 59 . Wang et al identified numerous differentially expressed genes and miRNAs from microarray datasets of nCRT responder group in patients with esophageal squamous cell carcinoma 60 .…”

Permutation tests for ASCA in multivariate longitudinal intervention studies

“…In a related publication to assess differential responses to cancer treatment with metabolomics, 9 we observed that different permutation tests provided inconsistent outcomes in the significance of the interaction between time and treatment. This is a fundamental flaw in the testing ability of permutation tests in ASCA and should be treated carefully since inconsistencies imply totally contrary conclusions in the study.…”

“…This technique allows to derive power curves tailored to the size and (un)balanced nature of the data set in the study, and it is useful to identify misleading permutation tests, which have a lack of power or overly optimistic outcomes. The application of the proposed technique to the data sets in Díaz et al 9 demonstrates that the choice of the best permutation approach is far from intuitive and that there is a significant risk of deriving incorrect conclusions in real‐life analyses.…”

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

“…Metabolic studies have identified biomarkers that predict response to treatment. For example, glycohyocholic and glucodeoxycholic acids can stratify TNBC patients according to response to neoadjuvant chemotherapy and OS ( 146 ). On the other hand, when comparing patients with large primary breast cancer who had received neoadjuvant chemotherapy plus bevacizumab with those who had received chemotherapy alone, higher levels of leucine, acetoacetate and trihydroxybutyrate and lower levels of formate were observed 12 weeks after treatment ( 147 ).…”