Tumor microenvironment modulation enhances immunologic benefit of chemoradiotherapy
Bibliographic record
Abstract
<h3>Background</h3> Chemoradiotherapy (CRT) remains one of the most common cancer treatment modalities, and recent data suggest that CRT is maximally effective when there is generation of an anti-tumoral immune response. However, CRT has also been shown to promote immunosuppressive mechanisms which must be blocked or reversed to maximize its immune stimulating effects. <h3>Methods</h3> Therefore, using a preclinical model of human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC), we developed a clinically relevant therapy combining CRT and two existing immunomodulatory drugs: cyclophosphamide (CTX) and the small molecule inducible nitric oxide synthase (iNOS) inhibitor L-n6-(1-iminoethyl)-lysine (L-NIL). In this model, we treated the syngeneic HPV-HNSCC mEER tumor-bearing mice with fractionated (10 fractions of 3 Gy) tumor-directed radiation and weekly cisplatin administration. We compared the immune responses induced by CRT and those induced by combinatory treatment (CRT + CTX/L-NIL) with flow cytometry, quantitative multiplex immunofluorescence and by profiling immune-related gene expression changes. <h3>Results</h3> We show that combination treatment favorably remodels the tumor myeloid immune microenvironment including an increase in anti-tumor immune cell types (inflammatory monocytes and M1-like macrophages) and a decrease in immunosuppressive granulocytic myeloid-derived suppressor cells (MDSCs). Intratumoral T cell infiltration and tumor antigen specificity of T cells were also improved, including a 31.8-fold increase in the CD8<sup>+</sup> T cell/ regulatory T cell ratio and a significant increase in tumor antigen-specific CD8<sup>+</sup> T cells compared to CRT alone. CTX/LNIL immunomodulation was also shown to significantly improve CRT efficacy, leading to rejection of 21% established tumors in a CD8-dependent manner. <h3>Conclusions</h3> Overall, these data show that modulation of the tumor immune microenvironment with CTX/L-NIL enhances susceptibility of treatment-refractory tumors to CRT. The combination of tumor immune microenvironment modulation with CRT constitutes a translationally relevant approach to enhance CRT efficacy through enhanced immune activation.
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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".