Abstract 1296: MEK inhibition overcomes chemoimmunotherapy resistance by inducing CXCL10 in cancer cells
Bibliographic record
Abstract
Abstract Chemotherapy with anti PD-1/PD-L1 mAb has become the standard of care for patients with metastatic non-small cell lung cancer (NSCLC). Using lung tumor models, where pemetrexed-platinum chemotherapy (PEM/CDDP) remains unable to synergize with immune checkpoint inhibitors (ICI), we linked failure of this treatment with its inability to induce CXCL10 expression and CD8+ T cell recruitment. Using drug screening, we showed that combining a MEK inhibitor (MEKi) with PEM/CDDP triggers CXCL10 secretion by cancer cells and CD8+ T cell recruitment, and restores ICI efficacy. PEM/CDDP plus MEKi promotes optineurin (OPTN)-dependent mitophagy, resulting in CXCL10 production in a mitochondrial DNA and TLR9-dependent manner. TLR9 or autophagy/mitophagy processes genetic inactivation of abort the antitumor efficacy of PEM/CDDP plus MEKi/anti PD-L1 therapy. In human NSCLC, high OPTN, TLR9 and CXCL10 expression is associated with better response to ICI. Our results underline the role of TLR9 and OPTN-dependent mitophagy in enhancing chemoimmunotherapy efficacy. Citation Format: Emeric Limagne, Lisa Nuttin, Marion Thibaudin, Elise Jacquin, Romain Aucagne, Marjorie Bon, Elise Ballot, Solène Revy, Robby Barnestein, Caroline Truntzer, Valentin Derangère, Jean-David Fumet, Rébé Cédric, Pierre-Simon Bellaye, Coureche-Guillaume Kaderbhai, Aodrenn Spill, Bertrand Collin, Mary Callanan, Aurélie Lagrange, Laure Favier, Bruno Coudert, Laurent Arnould, Sylvain Ladoire, Bertrand Routy, Philippe Joubert, François Ghiringhelli. MEK inhibition overcomes chemoimmunotherapy resistance by inducing CXCL10 in cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1296.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".