Abstract A043: Identifying critical vulnerabilities that sensitise <i>Lkb1</i>-mutant lung adenocarcinoma to T cell mediated killing
Bibliographic record
Abstract
Abstract KRAS is the most frequently altered oncogene in lung adenocarcinoma (LUAD). While the clinical development of targeted therapies has improved the treatment of a subset of patients, the heterogeneity of KRAS mutant LUAD remains a clinical challenge. Identifying tumor intrinsic dependencies through common co-occurring mutations are one approach to more effectively target KRAS-mutant LUAD. Loss-of-function mutations in STK11/Lkb1, are frequently found co-mutated with KRAS, and comprise an aggressive form of the disease. Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis, which aims to unleash CD8 effector T cells against tumors, are currently used as a first-line treatment strategy for KRAS mutant patients. However, KRAS mutant tumors that harbour co-mutations in STK11 fail to respond to ICIs. A greater understanding of the tumor-intrinsic processes that allow STK11/Lkb1 mutant tumors to evade immune detection is required to enhance the response of ICIs and improve patient outcomes. Here, we have used an unbiased genome-wide CRISPR knockout screening approach in lung cancer cell lines generated from the Kras G12D/+/Lkb1 fl/fl (KL) mouse model to unveil tumor-intrinsic mechanisms involved in anti-tumor immunity. To identify gene targets that sensitise tumor cells to T cell mediated killing, a co-culture method using the OVA/OT-I system was employed. Importantly, sgRNAs targeting genes encoding components of interferon signalling (Jak1, Stat1, Ifngr1) and MHC-I presentation (B2m, H2-k1) were enriched following serial application of OT-I T cells, consistent with the role these pathways play in resistance to ICIs. Conversely, sgRNAs targeting several genes were found to sensitise KL tumor cells to T cell mediated killing. This included genes (Serpinb9, Ptpn2) previously implicated in increasing T cell-mediated tumor cell killing, supporting the robustness of our screening platform. Excitingly, sgRNAs targeting novel genes, not previously implicated in mediating anti-tumor immunity were also identified. Current investigations validating the ability of candidate genes to sensitise KL tumor cells to T cell killing will be presented. Together, these results demonstrate the power of whole genome CRISPR screens in identifying candidate genes that may serve as therapeutic targets to improve treatment responses in KRAS/STK11/Lkb1 mutant LUAD patients. Citation Format: Jackson A McDonald, Danielle Boyd, Sarah Diepstraten, Leanne Scott, Lin Tai, Andrew Kueh, Marian Burr, Sarah A Best, Marco J Herold, Kate D Sutherland. Identifying critical vulnerabilities that sensitise Lkb1-mutant lung adenocarcinoma to T cell mediated killing [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 A043.
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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 | 0.001 |
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".