ArticleScientific reports2019
A Predictor of Pathological Complete Response to Neoadjuvant Chemotherapy Stratifies Triple Negative Breast Cancer Patients with High Risk of Recurrence.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 32 citations in OpenAlex.
- Tumor-infiltrating lymphocytes display prognostic signatures associated with chemotherapy response in TNBC patients.iScience · 2026Article
- Article
- EGFR and AR expression and co-expression in Indian triple-negative breast cancer with patient outcome association.Scientific reports · 2026Article
- Ensemble model for neoadjuvant chemotherapy response prediction and treatment sensitivity in TNBC based on DNA replication stress signatures.Scientific reports · 2025Article
- Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025Review
- Development of Patient-Derived Conditionally Reprogrammed 3D Breast Cancer Culture Models for Drug Sensitivity Evaluation.Oncology research · 2025Article
- GrB-Fc-KS49, an anti-EMP2 granzyme B fusion protein therapeutic alters immune cell infiltration and suppresses breast cancer growth.Journal for immunotherapy of cancer · 2024Article
- Predicting pathological complete response after neoadjuvant chemotherapy in breast cancer by clinicopathological indicators and ultrasound parameters using a nomogram.Scientific reports · 2024Article
- Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer.EMBO molecular medicine · 2024Article
- Decoding gene regulatory circuitry underlying TNBC chemoresistance reveals biomarkers for therapy response and therapeutic targets.NPJ precision oncology · 2024Article
- A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.Clinical breast cancer · 2023Review
- Transcriptome Meta-Analysis of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy.Cancers · 2023Article
- Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool.JCO clinical cancer informatics · 2023Article
- Article
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Aberrant calcium signalling downstream of mutations in TP53 and the PI3K/AKT pathway genes promotes disease progression and therapy resistance in triple negative breast cancer.Cancer drug resistance (Alhambra, Calif.) · 2022Review
- Circulating T-Cell Repertoires Correlate With the Tumor Response in Patients With Breast Cancer Receiving Neoadjuvant Chemotherapy.JCO precision oncology · 2022Article
- Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer.Frontiers in immunology · 2021Article
- Housekeeping genes involved in non-malignant breast phenotypes are widely expressed in multiple cancers and provide novel biomarkers of tumor classification.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a test to predict which patients will achieve pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and which will have residual disease (RD). Gene expression data from pretreatment biopsies of patients with all breast cancer subtypes were combined into a 519-patient cohort containing 177 TNBC patients. Two RNA classifiers of 16 genes each were sequentially applied to the total cohort, classifying patients into 3 distinct classes. The test performance was further validated in an independent 304-patient cohort. The test accurately identified 70.5% (79/112) of pCR and 83.5% (340/407) of RD patients in the total population, and 75.0% (45/60) of pCR and 75.2% (88/117) of RD patients in the TNBC subset. For the independent cohort, the test identified 91.5% RD patients in the total population and 86.2% RD patients in the TNBC subset. However, the identification of pCR in both total and TNBC population are as low as 21.1% and 30%, respectively. The TNBC RD patients were subdivided by our classifiers, with one class showing significantly higher levels of Ki67 expression and having significantly poorer survival rates than the other classes. This stratification of patients may allow predicted residual disease classes to be assigned an alternative therapy.
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