Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
Metadatos
Mostrar el registro completo del ítemAutor
Díaz, Caridad; Camacho Páez, José; Mena García, Patricia; Martín Blázquez, Ariadna; Marchal Corrales, Juan Antonio; Vicente, Francisca; Pérez del Palacio, JoséEditorial
Wiley
Materia
ASCA Breast cancer LC-HRMS Neoadjuvant chemotherapy Personalized medicine Treatment response
Fecha
2022-03-26Referencia bibliográfica
Díaz, C... [et al.] (2022), Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach. Mol Oncol. [https://doi.org/10.1002/1878-0261.13216]
Patrocinador
Fundacion MEDINA; Fundacion Bancaria Unicaja; Agencia Andaluza del Conocimiento, Regional Government of Andalucia; European Commission B-TIC-136-UGR20Resumen
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.