27P Impact of chemotherapy (CT) use and stromal tumor-infiltrating lymphocytes (sTILs) in stage I triple-negative breast cancer (TNBC)
Bibliographic record
Abstract
The optimal systemic treatment for stage I TNBC remains unclear, given the lack of well-designed randomized trials. sTILs have emerged as a prognostic biomarker in early TNBC. We aimed to study the impact of (neo)adjuvant CT use and sTILs in the prognosis of pts with stage I TNBC. Pts with stage I TNBC (ER<10% and HER2-0/low) treated at Vall d’Hebron Hospital between 2006 and 2021 were reviewed. sTILs were evaluated at the surgery specimen and/or at the diagnostic biopsy, as per the international immuno-oncology working group guidelines. The effect of (neo)adjuvant CT and sTILs on invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS) was evaluated. Statistical significance (p<0.05) was determined using the log-rank test. 108 patients were identified (median age 56, 35% pre-menopausal). The majority of patients had tumors ≥10mm (75%), grade 3 (61%), and received CT (79%). Patients not receiving CT had a median age of 75, 17% were pT1a tumors and 30% had favorable histotypes. sTILs could be assessed in 79/108 (73%); 34% had sTILs >50%, which associated with higher grade and Ki67. With a median follow-up of 7.2 years (IC: 3.0 – 21.8), 19/108 (18%) had a progression event, 9 (8%) distant metastases. 5y iDFS, DDFS, and OS rates are presented in the table. (Neo)adjuvant CT was not associated with better iDFS (p=.53), DDFS (p=.66) or OS (p=.89). In multivariate analyses for the survival endpoints, no interaction was observed between sTILs (both as categorical and continuous variables) and CT use.Table: 27P5-year ratesNiDFS (%)DDFS (%)OS (%)Overall population10883.691.298.9sTILs >50%2788.7100100sTILs <50%5285.6∗p=.63.93.8†p=.34.97.6‡p=.68.CT, overall8583.191.3100CT and sTILs>50%2387100100CT and sTILs<50%368997100No CT, overall2386.190.995.2No CT and sTILs>50%4100100100No CT and sTILs<50%16808793∗ p=.63.† p=.34.‡ p=.68. Open table in a new tab In this retrospective cohort, a high proportion of patients with stage I TNBC received CT, which was not associated with better outcomes, irrespectively of the abundance of sTILs. The role of (neo)adjuvant CT in stage I TNBC should be studied in prospective trials to identify patients that can be spared systemic treatment.
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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) | 0.002 | 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".