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Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

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Abstract

Purpose

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

Methods

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC–MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

Results

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

Conclusion

The application of plasma metabolomics, utilizing CE-MS and LC–MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

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Data availability

The datasets generated and analyzed during the current study are not publicly accessible due to privacy considerations. However, they can be made available by the corresponding author upon reasonable request.

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Funding

This study received financial support from KAKENHI (JP21K08676 and JP18K08602).

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Authors and Affiliations

  1. Department of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan

    Miki Yamada, Hiromitsu Jinno, Saki Naruse, Yuka Isono, Yuka Maeda, Ayana Sato, Akiko Matsumoto & Tatsuhiko Ikeda

  2. Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan

    Masahiro Sugimoto

  3. Institute of Medical Science, Tokyo Medical University, Shinjuku, Shinjuku-ku, Tokyo, 160-8402, Japan

    Masahiro Sugimoto

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  1. Miki Yamada
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Contributions

Conceptualization: Miki Yamada, Hiromitsu Jinno. methodology: Masahiro Sugimoto. formal analysis and investigation: Miki Yamada, Masahiro Sugimoto, Saki Naruse, Yuka Isono, Yuka Maeda, Ayana Sato, Akiko Matsumoto, Tatsuhiko Ikeda writing—original draft preparation: Miki Yamada, Masahiro Sugimoto writing—review and editing: Miki Yamada, Hiromitsu Jinno, Saki Naruse, Yuka Isono, Yuka Maeda, Ayana Sato, Akiko Matsumoto, Tatsuhiko Ikeda, Masahiro Sugimoto funding acquisition: Hiromitsu Jinno supervision: Hiromitsu Jinno.

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Correspondence to Hiromitsu Jinno.

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This study was conducted in compliance with the Declaration of Helsinki principles. The Ethics Committee of Teikyo University provided approval (no. 19–288).

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10549_2024_7370_MOESM1_ESM.pdf

Supplementary file 1 (PDF 100 KB)—Figure S1: Metabolomic data before preoperative treatment (Pre). A) Heatmap illustrating the top 25 metabolites with the highest variability as determined by the ANOVA test among the four groups, categorized by subtype. B) Box plots depicting substances with significant differences (P<0.05) as determined by the Kruskal-Wallis test among the three groups. C) Box plots illustrating substances with significant differences (P<0.05) as determined by the Kruskal-Wallis test among the three groups.

10549_2024_7370_MOESM2_ESM.pdf

Supplementary file 2 (PDF 173 KB)—Figure S2: Plasma metabolomic profile of the second course of preoperative treatment. A) Heatmap categorizing data by subtype, displaying the top 25 metabolites with the highest variability as determined by the ANOVA test among the four groups. B) Box plots highlighting substances with significant differences (P<0.05) as determined by the Kruskal-Wallis test among the three groups.

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Yamada, M., Jinno, H., Naruse, S. et al. Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics. Breast Cancer Res Treat (2024). https://doi.org/10.1007/s10549-024-07370-2

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