Abstract 6362: Overcoming resistance to immunotherapy due to loss of antigen presentation
Bibliographic record
Abstract
Abstract Resistance to immune checkpoint inhibitors represents a major therapeutic challenge, as only 40% of patients with melanoma (and less with other tumor types) have a long-term response to anti-PD-1 therapy. Resistance can arise because of somatic mutations in cancer cells that allow evasion of T cell-mediated killing. One commonly acquired resistance mutation in melanoma, loss of beta-2 microglobulin (B2m), prevents T cell killing by hiding cancer cells from CD8+ T cell recognition. To understand the failed immune response against resistant tumors, we used single-cell RNA-seq to characterize tumor-infiltrating immune cells in antigen presentation-deficient human melanoma biopsies and CRISPR-modified mouse melanoma tumors. Our data demonstrate an increase in immunosuppressive M2-like macrophages and absence of CD8+ T cells in B2m-null tumors. To overcome this resistance, we treated tumor-bearing mice with CD40 agonist antibody, which promotes differentiation of macrophages towards a pro-inflammatory phenotype and increases dendritic cell priming of CD8+ T cells. Treatment with CD40 agonist reduced tumor growth and improved tumor clearance in B2m-null melanoma and colorectal cancer models. To determine how CD40 agonist treatment works, we depleted different immune populations from the tumor microenvironment. We hypothesized that by depleting M2 macrophages, CD40 agonist treatment would remove an immunosuppressive brake to allow natural killer (NK) cells to kill tumor cells lacking MHC expression. To our surprise, NK cells were not required for the efficacy of CD40 agonist. Instead CD8+ T cells were required, even though the CD8+ T cells cannot directly recognize the tumor cells. scRNA-seq identified a transcriptionally unique state of CD8+ T cells that is recruited to the tumor microenvironment after CD40 agonist treatment. These CD8+ T cells produce IFNγ, which is required for the efficacy of CD40 agonist treatment. These data demonstrate that CD8+ T cells, a key mediator of anti-tumor immunity, can still be recruited to control tumors deficient in antigen presentation. More broadly, they suggest that strategies to activate CD8+ T cells may be effective even in the context of acquired resistance to checkpoint inhibitor therapy. Citation Format: Brian C. Miller, Yacine Choutri, Rose Al Abosy, Amy Huang, Emily K. Cox, Matthew P. Zimmerman, Wan Lin Chong, Katherine J. Vietor, Jenna Collier, Sarah A. Weiss, Debattama Sen, W. Nicholas Haining, Arlene H. Sharpe. Overcoming resistance to immunotherapy due to loss of antigen presentation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6362.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".