Evidence map›Paper›PMID 34526986›Full record

ArticleFrontiers in immunology2021

An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer.

Changfang Fu, Yu Liu, Xinghua Han, Yueyin Pan, Hong-Qiang Wang, Hongzhi Wang, Haiming Dai, Wulin Yang

Abstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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

Changfang FuAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Yu LiuAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Xinghua HanThe First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yueyin PanThe First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Hong-Qiang WangBiological Molecular Information System Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Hongzhi WangAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Haiming DaiAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Wulin YangAnhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.

Indexed as

Breast NeoplasmsGene Expression Regulation, NeoplasticNeoadjuvant TherapyAdultAgedAnthracyclinesFemaleHumansMiddle AgedPaclitaxelAnthracyclinesPaclitaxelbreast cancerchemosensitivityLASSOneoadjuvant chemotherapypathologic complete response

Identifiers

PMID34526986
PMCPMC8435784

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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.