Tumor microenvironment before and after chemoradiation in locally advanced rectal cancer: Beyond PD-L1
Bibliographic record
Abstract
Abstract Background: In rectal cancer treatment, neoadjuvant chemoradiation therapy (CRT) is the standard of care and reduces local failure rate. The tumor microenvironment (TME) is a complex entity comprising of tumor cells, immune cells and surrounding stroma and is closely associated with tumor growth and survival, response to antitumor therapies and also resistance to antitumor therapies. The purpose of this study is to assess the change in biomarkers associated with TME following standard neoadjuvant CRT in rectal cancer. Methods: We accessed archival tissue from rectal cancer patients treated with neoadjuvant CRT at Allegheny Health Network (AHN) facilities over the past 14 years. Pre-treatment and post-treatment biopsies were assayed for PD-L1, CD8+ T-cells, CXCL9, TIM-3, IDO-1, IFN-G, IL17RE, LAG-3, and OX40 in 41 patients. Results: We found statistically significant upregulation in multiple biomarkers namely CD8, IL17RE, LAG3 and OX40 post neoadjuvant CRT and a trend towards upregulation, although not statistically significant, in biomarkers PD-L1, CXCL9, TIM-3, IDO-1 and IFN-G expression. Conclusions: This data has broad implications, not only in rectal cancer but also in other malignancies, and provides a glimpse into the TME before and after neoadjuvant CRT. We hypothesize that the biomarkers which were noted to be upregulated could be used for development of therapeutic targeted drug therapy and designing appropriate clinical trials in an effort to achieve better response to neoadjuvant therapy, increasing pathological complete response rates and improved overall outcomes.
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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 | 0.000 |
| 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 | 0.000 | 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".