A novel immune cell signature predicts pathological complete response to neoadjuvant chemotherapy in triple negative breast cancer patients in the Q-CROC3 trial.
Bibliographic record
Abstract
e12614 Background: Tumor infiltrating lymphocytes (TILs) have been associated with good prognosis and response to neoadjuvant chemotherapy. Several reports have shown that the heterogeneity of tumor infiltrating immune cells affects the response to chemotherapy, with for example low levels of FOXP3 expressing T cells associated with good prognosis and pathological complete response (pCR) to chemotherapy. Methods: We examined different immune cell markers on 52 pre-chemotherapy biopsy specimens obtained from triple negative breast cancer patients undergoing neo-adjuvant chemotherapy from the Q-CROC-03 trial. Slides were stained for CD8, CD3,PD-1, PDL-1, FOXP-3 and Granzyme B using multi-colour immunohistochemistry and automated cell counting of stroma and epithelial counts was conducted using the Vectra/inForm image analysis platform. We had total of 39 variables for analysis and we performed Penalized logistic regression for variable selection. Results: Nine variables were found statistically significant to predict response to chemotherapy, PD1+ stroma counts being the one with the highest probability of association with response. A tree algorithm was then used on all 9 variables to identify the best variable and threshold combination to identify patients who respond to chemotherapy. We separated our cohort in test (25% of samples n = 13) and training (75% of samples n = 39) sets for this analysis. Restricting the tree depth to 2 variables for clinical interpretability identified the combination of average counts of stromal PD1+ and average density of stromal FOXP3+ as predictors of chemo response (accuracy 0.82). Both stromal average PD1+ counts and average stromal FOXP3+ density positively correlated with the levels of TILS. Conclusions: Combining FOXP3 and PD1 protein expression in the stroma of pre-treatment biopsies of triple negative breast cancers receiving neoadjuvant chemotherapy is highly predictive of pCR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".