Genomic Instability and Protumoral Inflammation Are Associated with Primary Resistance to Anti–PD-1 + Antiangiogenesis in Malignant Pleural Mesothelioma
Bibliographic record
Abstract
Cancer immunotherapy combinations have recently been shown to improve the overall survival of advanced mesotheliomas, especially for patients responding to those treatments. We aimed to characterize the biological correlates of malignant pleural mesotheliomas' primary resistance to immunotherapy and antiangiogenics by testing the combination of pembrolizumab, an anti-PD-1 antibody, and nintedanib, a pan-antiangiogenic tyrosine kinase inhibitor, in the multicenter PEMBIB trial (NCT02856425). Thirty patients with advanced malignant pleural mesothelioma were treated and explored. Unexpectedly, we found that refractory patients were actively recruiting CD3+CD8+ cytotoxic T cells in their tumors through CXCL9 tumor release upon treatment. However, these patients displayed high levels of somatic copy-number alterations in their tumors that correlated with high blood and tumor levels of IL6 and CXCL8. Those proinflammatory cytokines resulted in higher tumor secretion of VEGF and tumor enrichment in regulatory T cells. Advanced mesothelioma should further benefit from stratified combination therapies adapted to their tumor biology. SIGNIFICANCE: Sequential explorations of fresh tumor biopsies demonstrated that mesothelioma resistance to anti-PD-1 + antiangiogenics is not due to a lack of tumor T-cell infiltration but rather due to adaptive immunosuppressive pathways by tumors, involving molecules (e.g., IL6, CXCL8, VEGF, and CTLA4) that are amenable to targeted therapies. This article is highlighted in the In This Issue feature, p. 799.
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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".