Abstract IA10: Developing rational combination therapy with checkpoint inhibitors
Bibliographic record
Abstract
Abstract Targeting antibodies to programmed cell death protein-1 (PD-1) is an effective treatment across multiple cancer types. While a subset of patients receiving these therapies experience favorable responses, many still show disease progression, highlighting the importance of other mechanisms influencing immune responsiveness in these tumors. Therefore, combining therapies that enhance antitumor immunity has been an area of great interest to the entire cancer community. We have recently tackled this challenge in the rapidly evolving field of cancer immunotherapy by using in vivo functional genomics to identify genes whose inhibition potentiates the response to anti-PD-1 immunotherapy. Using an in vivo screening approach with a customized shRNA pooled library, we identified a number of candidates including DDR2 as promising targets for the enhancement of response to anti-PD-1 immunotherapy. In the case of DDR2, using isogenic in vivo murine models across five different tumor histologies—bladder, breast, colon, sarcoma, and melanoma—we show that DDR2 depletion increases sensitivity to anti-PD-1 treatment compared to monotherapy. Combination treatment of tumor-bearing mice with anti-PD-1 and dasatinib, a tyrosine kinase inhibitor of DDR2, also led to tumor load reduction and in some cases, complete clearance. RNAseq and CyTOF analysis revealed higher CD8+ T-cell populations in tumors with DDR2 depletion and those treated with dasatinib when either was combined with anti-PD-1 treatment. Our work provides strong scientific rationale for targeting DDR2 in combination with PD-1 inhibitors. In addition, a number of other potential druggable targets have been identified in our screen that we are currently pursuing. Citation Format: Megan M. Tu, Francis Y. F. Lee, Robert T. Jones, Abigail K. Kimball, Elizabeth Saravia, Robert F. Graziano, Brianne Coleman, Krista Menard, Jun Yan, Erin Michaud, Han Chang, Hany A. Abdel-Hafiz, Andrii I. Rozhok, Jason E. Duex, Neeraj Agarwal, Ana Chauca-Diaz, Linda K. Johnson, Terry L. Ng, John C. Cambier, Eric T. Clambey, James C. Costello, Alan J. Korman, Dan Theodorescu. Developing rational combination therapy with checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr IA10.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".