Abstract B021: In vivo gain-of-function screen identifies CREB5, a novel ECM modulator that promotes immunotherapy resistance via the Collagen-Lair1 axis
Bibliographic record
Abstract
Abstract Treatment with immune checkpoint inhibitors induce remarkable clinical responses in several cancer types. However, most cancer patients do not respond to immunotherapy, and patients who initially respond often exhibit acquired resistance. Understanding the universe of immune evasion strategies will enable the design of more effective immunotherapies. Here, we identified genes that drive immune evasion by performing a genome scale in vivo CRISPR gain-of-function screen in tumors treated with anti-PD-1 antibodies; and discovered the transcription factor CREB5 as a top resistance mediator. Using transcriptional profiling, we showed CREB5 drives upregulation of extracellular matrix genes including collagen and collagen-stabilizing factors. We found CREB5 or collagen (Col1a1, Col4a1, or Col16a1) overexpressing tumors exhibit poor responses to anti-PD-1. Collagen is the major ligand for the inhibitory receptor LAIR1, which is broadly expressed on T cells, B cells, NK cells, and myeloid cells. Deletion of LAIR1 in mice or overexpression of the decoy receptor LAIR2 in tumors abrogates the resistance-causing effect of CREB5 or collagen overexpression, suggesting that CREB5 overexpression drives resistance partly via collagen-LAIR1 inhibitory signaling. In summary, we have identified a gene that can induce fibroblast-like features in tumor cells to promote immunotherapy resistance. Further investigation of pathways that induces stromal mimicry in cancer cells could identify novel immunotherapy targets. Citation Format: Payal Tiwari, Kayla Colvin, Sarah Kim, Ashwin Kammula, Seth Anderson, Or-Yam Revach, Maulik Vyas, Jessica A Talamas, Shadmehr Demehri, Kathleen Yates, John Doench, William C Hahn, Robert Manguso. In vivo gain-of-function screen identifies CREB5, a novel ECM modulator that promotes immunotherapy resistance via the Collagen-Lair1 axis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B021.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".