Abstract B60: Functional genomic landscape of T-cell mediated cytotoxicity
Bibliographic record
Abstract
Abstract The direct killing of cancer cells by cytotoxic T-lymphocytes (CTL) is critical for achieving effective antitumor immune responses. While several tumor-intrinsic mutations have been identified that promote evasion to CTL killing, there remains a paucity of data cataloging how individual genetic perturbations impact cancer cell fitness under immunotherapeutic selection pressure. Towards this, genetic screens using the CRISPR-Cas9 system have recently been employed, identifying novel intrinsic genetic regulators of cancer immunotherapy fitness. While these initial studies demonstrate the significant potential of unbiased genetic screens for discovery of novel immunotherapy targets, they have been limited in application to a small number of disease models. As such, the degree to which intrinsic genetic perturbations robustly modulate immunotherapy fitness across diverse cancer genotypes remains largely unexplored. To circumvent this knowledge gap, we performed genetic screens across a panel of commonly utilized syngeneic murine cancer cell lines to identify a core set of genes that render cancer cells more sensitive or resistant to CTL killing across diverse genetic backgrounds. An optimized genome-scale gRNA library targeting ~19,000 protein-coding genes in the murine genome (termed mTKO; mouse Toronto Knockout) was created in an analogous manner to our human library. Screens were performed in multiple murine cell lines engineered to express model tumor-associated antigens. Cells were propagated in the presence or absence of preactivated antigen-specific CTLs, and gRNA abundance was quantified by illumina sequencing to identify perturbed genes enriched or depleted in CTL treated versus untreated populations. These screens uncovered known genetic regulators of CTL killing including immune checkpoints (Cd274), antigen presentation machinery (B2m, Tap1/2) and interferon signaling components (Socs1, Jak1/2, Ifngr1/2), benchmarking the utility of this approach. Importantly, we leveraged this dataset to derive a core list of genes whose perturbation significantly modulates cancer-cell fitness to CTL killing across the majority of genotypes screened. Gene set enrichment analysis revealed critical roles for cytokine and TLR signaling, the NF-κB and MAPK pathways as well as the antiviral and antigen presentation machineries. As such, our dataset represents the first step towards a comprehensive understanding of the functional genomic landscape of CTL-mediated cytotoxicity. Citation Format: Keith Lawson, Xiaoyu Zhang, Cris Sousa, Rummy Akthar, Zi Fan, Eiru Kim, Medina Colic, Amy Tong, Katie Chan, Qian Huang, Xiaowei Wang, Kevin Brown, Michael Aregger, Antonio Finelli, Laurie Ailles, Traver Hart, Gillian Kingsbury, Charles Kung, Jason Moffat. Functional genomic landscape of T-cell mediated cytotoxicity [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B60.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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 teacher head, 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".