Transcriptomic Features Associated to Neoadjuvant Chemotherapy Response in Four Molecular Breast Cancer Subtypes
Bibliographic record
Abstract
PURPOSE Breast cancer (BC) represents a major public health issue. The effectiveness of neoadjuvant chemotherapy (NAC) varies among breast cancer patients, with some experiencing incomplete pathological responses. This variability in treatment response may be attributed to differences in tumor heterogeneity and its microenvironment (TME). This study investigates gene expression patterns associated to non-response to NAC in invasive BC patients, emphasizing the role of genes, pathways and the variability among BC subtypes. METHODS A transcriptomic study analyzed 58 baseline samples from women with advanced breast cancer at the Colombian National Cancer Institute, categorizing them into 29 NAC responders and 29 non-responders. The study comprised gene expression comparison, enrichment analysis, tumor microenvironment estimation via xCell, and therapeutic efficacy of targeted drugs using PrRophetic package. RESULTS Different gene expression profiles distinguished responders from non-responders among various breast cancer subtypes, highlighting immune-related pathways like IL-17 and TNF signaling, B and T cell receptor signaling, complement and coagulation cascades, natural killer cell-mediated cytotoxicity, and NF-kB signaling. Non-responders in Luminal B HER2- subtype exhibited increased endothelial cells and immune and microenvironment scores, while Luminal B HER2+ non-responders showed higher levels of CD4 Tcm, CD4 Tem, and megakaryocytes. Sensitivity to multiple potential therapeutic drugs (Lestaurtinib, Avagacestat, GSK3 inhibitor, Veliparib, Tretinoin, Afatinib, Vinorelbine, RSK1 inhibitor, Motesanib) varied distinctly among non-responders. CONCLUSION Immune-related genes and diverse immune cell subtypes within the TME correlate with response to NAC, with significant changes observed between response groups and subtypes. Comprehensive detection and evaluation of TME components are crucial for predicting NAC efficacy and preventing disease relapse. These findings underscore the importance of studying mixed populations to uncover novel insights and address disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".