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. 2024 Jul 6;16(13):2473.
doi: 10.3390/cancers16132473.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

Alex Ap Rosini Silva  1 , Marcella R Cardoso  2   3 , Danilo Cardoso de Oliveira  1 , Pedro Godoy  1 , Maria Cecília R Talarico  2 , Junier Marrero Gutiérrez  1 , Raquel M Rodrigues Peres  1 , Lucas M de Carvalho  4 , Natália Angelo da Silva Miyaguti  1 , Luis O Sarian  2 , Alessandra Tata  5 , Sophie F M Derchain  2 , Andreia M Porcari  1
Affiliations

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

Alex Ap Rosini Silva et al. Cancers (Basel). .

Abstract

Background: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

Methods: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

Results: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

Conclusion: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Keywords: breast cancer; cancer biology; drug resistance; metabolomics; neoadjuvant chemotherapy response.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Principal component analysis (PCA) results. PCA using 19 identified features. △ (pink) represents resistant plasma samples, and ◯ (blue) represents sensitive plasma samples. The ellipses indicate the confidence interval (CI = 95%).
Figure 2
Figure 2
Results obtained with the SVM model, considering resistant and sensitive samples. (A) Training set sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). (B) Validation set sensitivity, specificity, accuracy, NPV, and PPV. (C) Representation of the model’s ability to make predictions of NACT response.
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