ArticleiScience2023
A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Biologically-informed integration of drug representations for breast cancer treatment using deep learning.Nature communications · 2025Trial
- A computer vision method to evaluate tumor-infiltrating lymphocytes and multiparametric modeling of neoadjuvant systemic therapy response in breast cancer.Therapeutic advances in medical oncology · 2026Article
- Single cell and bulk transcriptome analysis identified oxidative stress response-related features of Hepatocellular Carcinoma.Frontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as "immune-rich" or "immune-poor" phenotypes. The "immune-rich" phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).
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Registered trials
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