Expression of carcinoma ecotypes in the tumor microenvironment predicts response to neoadjuvant therapy in early-stage breast cancer
Bibliographic record
Abstract
Immunotherapy is associated with modest pathologic complete response (pCR) rates in early-stage breast cancer, and a subset of patients still achieves a pCR after neoadjuvant chemotherapy (NAC) only. Identifying biomarkers in the complex tumor ecosystem which define the subsets of patients who achieve pCR benefit on immunotherapy versus not is of a critical need. Transcriptomic data for patients enrolled in the two neoadjuvant immunotherapy arms of 'pembrolizumab' (n = 69) and 'durvalumab' (n = 71), and the chemotherapy arm 'control' (n = 210) of the I-SPY2 breast cancer clinical trial were included. Using a machine learning algorithm for tumor ecosystem-based classification, we deconvoluted transcriptomic data into the established 10 multicellular organization systems known as 'Ecotypes'. We found that the most pro-inflammatory carcinoma ecotype (CE)9 predicts pCR in the 'pembrolizumab' arm (OR = 2.07; 95% CI 1.35-3.8; adjusted P value = 0.01), in the 'control' arm (OR = 1.89; 95% CI 1.36-2.63; adjusted P value = 0.002), and in the 'durvalumab' arm (OR = 1.66; 95%CI 1.18-2.34; adjusted P value = 0.03). In contrast, the basal-enriched ecotype CE2 was the most significant predictor of pCR in the 'durvalumab' arm, which included the addition of olaparib (OR = 3.22; 95%CI 2.25-4.60; adjusted P value < 0.0001), but not in NAC (OR = 1.18; 95%CI 0.81-1.72; adjusted P value = 0.94). Our findings suggest that CE9 could identify early-stage breast cancer patients who achieve a pCR after neoadjuvant therapy and may have a good prognosis. Whether CE9 patients could still be considered for immunotherapy or be candidates for de-escalation strategies in the neoadjuvant setting requires further investigation in future studies with link to survival outcomes. In contrast, CE2 tumors would benefit from the combination of immunotherapy with olaparib. Integrating tumor ecosystem-based patient classification could guide effective clinical management in early-stage breast cancer.
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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 | 0.000 | 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) | 0.000 | 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 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".