Targeting Intracellular Innate RNA-Sensing Systems Overcomes Resistance to CAR T-cell Therapy in Solid Tumors
Bibliographic record
Abstract
Despite the remarkable success of chimeric antigen receptor (CAR) T cells in certain hematologic malignancies, only modest responses have been achieved in solid tumors. Defective cell death pathways have recently been suggested as a tumor-intrinsic form of resistance to CAR T-cell treatment. In this study, we showed that insufficient activity of the innate RNA-sensing receptor system retinoic acid-inducible gene I (RIG-I)/mitochondrial antiviral signaling protein (MAVS) leads to tumor cell-inherent resistance to CAR T-cell attack. Active RIG-I/MAVS signaling in tumor cells primed intrinsic mitochondrial apoptosis pathways and expression of cell death receptors, which funneled into CAR T-cell-triggered cell death. CAR T-cell reliance on tumor-intrinsic RIG-I signaling was observed in various murine and human cancer types, independent of the CAR construct used, and the dependence was most pronounced under conditions with low target antigen expression or low effector/target ratios. RIG-I-induced proapoptotic priming of CAR T-cell susceptibility involved auto-/paracrine type-I IFN signaling loops and could spread to bystander tumor cells. Strong tumor-intrinsic RIG-I/MAVS signaling imprinted an activated cytolytic phenotype on tumor-interacting CAR T cells. Agonist-mediated targeting of the RIG-I pathway in the tumor microenvironment rendered murine melanoma susceptible to CAR T-cell therapy in vivo with enhanced infiltration of active CAR T cells. Together, these data identify insufficient RIG-I/MAVS activity and associated impaired cell death signaling in malignant cells as a resistance mechanism to CAR T cells. Targeting tumor-intrinsic RIG-I is a potential strategy to sensitize solid tumors to CAR T-cell treatment. SIGNIFICANCE: Insufficient activity of the RIG-I/MAVS pathway is a tumor intrinsic resistance mechanism to CAR T cells, providing the rationale for targeting RIG-I to optimize CAR T efficacy in patients with solid cancers.
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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) | 0.001 | 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) | 0.001 | 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".