Tumor-Infiltrating Lymphocytes and Response to Neoadjuvant Chemotherapy With or Without Carboplatin in Human Epidermal Growth Factor Receptor 2–Positive and Triple-Negative Primary Breast Cancers
Bibliographic record
Abstract
PURPOSE: Modulation of immunologic interactions in cancer tissue is a promising therapeutic strategy. To investigate the immunogenicity of human epidermal growth factor receptor 2 (HER2) -positive and triple-negative (TN) breast cancers (BCs), we evaluated tumor-infiltrating lymphocytes (TILs) and immunologically relevant genes in the neoadjuvant GeparSixto trial. PATIENTS AND METHODS: GeparSixto investigated the effect of adding carboplatin (Cb) to an anthracycline-plus-taxane combination (PM) on pathologic complete response (pCR). A total of 580 tumors were evaluated before random assignment for stromal TILs and lymphocyte-predominant BC (LPBC). mRNA expression of immune-activating (CXCL9, CCL5, CD8A, CD80, CXCL13, IGKC, CD21) as well as immunosuppressive factors (IDO1, PD-1, PD-L1, CTLA4, FOXP3) was measured in 481 tumors. RESULTS: Increased levels of stromal TILs predicted pCR in univariable (P < .001) and multivariable analyses (P < .001). pCR rate was 59.9% in LPBC and 33.8% for non-LPBC (P < .001). pCR rates ≥ 75% were observed in patients with LPBC tumors treated with PMCb, with a significant test for interaction with therapy in the complete (P = .002) and HER2-positive (P = .006), but not the TNBC, cohorts. Hierarchic clustering of mRNA markers revealed three immune subtypes with different pCR rates (P < .001). All 12 immune mRNA markers were predictive for increased pCR. The highest odds ratios (ORs) were observed for PD-L1 (OR, 1.57; 95% CI, 1.34 to 1.86; P < .001) and CCL5 (OR, 1.41; 95% CI, 1.23 to 1.62; P < .001). CONCLUSION: Immunologic factors were highly significant predictors of therapy response in the GeparSixto trial, particularly in patients treated with Cb. After further standardization, they could be included in histopathologic assessment of BC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".