Evidence map›Paper›PMID 38740665›Full record

ArticleBreast cancer research and treatment2024

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

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

Abstract read
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In one paragraph

Article in Breast cancer research and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025
    Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Miki YamadaDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.ORCID http://orcid.org/0000-0001-7448-7956
Hiromitsu JinnoDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan. jinno@med.teikyo-u.ac.jp.
Saki NaruseDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
Yuka IsonoDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
Yuka MaedaDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
Ayana SatoDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
Akiko MatsumotoDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.ORCID http://orcid.org/0000-0003-0990-0075
Tatsuhiko IkedaDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi, Tokyo, 173-8606, Japan.
Masahiro SugimotoInstitute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.ORCID http://orcid.org/0000-0003-3316-2543

Funding

KAKENHI JP21K08676 and JP18K08602
6 · The paper itself

Abstract

purposePreoperative 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.

methodsPlasma 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.

resultsSignificant 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.

conclusionThe 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.

Indexed as

Breast NeoplasmsMetabolomicsNeoadjuvant TherapyAdultAgedAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorChemotherapy, AdjuvantChromatography, LiquidElectrophoresis, CapillaryFemaleHumansMass SpectrometryMetabolomeMiddle AgedPrognosisBiomarkers, TumorBreast cancerCapillary electrophoresis-mass spectrometry (CE-MS)Liquid chromatography-mass spectrometry (LC–MS)MetabolomicsNeoadjuvant chemotherapy

Identifiers

PMID38740665

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.