Evidence map›Paper›PMID 39762945›Full record

ArticleBreast cancer research : BCR2025

Serum metabolomic profiling for predicting therapeutic response and toxicity in breast cancer neoadjuvant chemotherapy: a retrospective longitudinal study.

Zhihao Fang, Guohong Ren, Shouyu Ke, Qimin Xu, Yuhua Chen, Xiaoyuan Shi, Cheng Guo, Jian Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025
    Review
  9. Review
  10. 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

8 authors.

Zhihao Fang *Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China.
Guohong Ren *Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China.
Shouyu Ke *Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China.
Qimin XuDepartment of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China.
Yuhua ChenDepartment of Laboratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaoyuan ShiAnalytical Instrument Trading Co., Ltd, SCIEX, Shanghai, China.
Cheng GuoCancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. cheng_guo@zju.edu.cn.
Jian HuangDepartment of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road, Hangzhou, Zhejiang, China. drhuangjian@zju.edu.cn.

Funding

Joint Funds of the National Natural Science Foundation of China U22A20321Key Program of National Natural Science Foundation of China 81930079Key R&D Program of Zhejiang Province 2022C03019The National Key R&D Program of China 2022YFA1105200
6 · The paper itself

Abstract

backgroundNeoadjuvant chemotherapy (NACT) is the standard-of-care treatment for patients with locally advanced breast cancer (LABC), providing crucial benefits in tumor downstaging. Clinical parameters, such as molecular subtypes, influence the therapeutic impact of NACT. Moreover, severe adverse events delay the treatment process and reduce the effectiveness of therapy. Although metabolic changes during cancer treatment are crucial determinant factors in therapeutic responses and toxicities, related clinical research remains limited.

methodsOne hundred paired blood samples were collected from 50 patients with LABC before and after a complete NACT treatment cycle. Untargeted metabolomics was used by liquid chromatography-mass spectrometry (LC-MS) to investigate the relationship between dynamically changing metabolites in serum and the responses and toxicities of NACT.

resultsFirstly, we observed significant alterations in serum metabolite levels pre- and post-NACT, with a predominant enrichment in the sphingolipid and amino acid metabolism pathways. Second, pre-treatment serum metabolites successfully predicted the therapeutic response and hematotoxicities during NACT. In particular, molecular subtype variations in favorable treatment responses are linked to acyl carnitine levels. Finally, we discovered that the therapeutic effects of NACT could be attributed to essential amino acid metabolism.

conclusionThis study elucidated the dynamic changes in metabolism during NACT treatment, providing a possibility for developing responsive metabolic signatures for personalized NACT treatment.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsBreast NeoplasmsMetabolomeMetabolomicsNeoadjuvant TherapyAdultAgedBiomarkers, TumorCarnitineChemotherapy, AdjuvantChromatography, LiquidFemaleHumansLongitudinal StudiesMiddle AgedPrognosisacylcarnitineBiomarkers, TumorCarnitineAcylcarnitineBreast cancerEssential amino acidMetabolomicsNeoadjuvant chemotherapySerum

Identifiers

PMID39762945
PMCPMC11706045

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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 OpenQuestion graph.