Abstract A38: Oncogenes drive production of immunosuppressive cytokines to facilitate lung cancer progression
Bibliographic record
Abstract
Abstract Lung cancer development is driven by the expression of mutant oncogenes, with EGFR and KRAS being the most frequent mutations in lung adenocarcinoma. However, these mutations alone are not sufficient for tumorigenesis, and additional factors influence tumor development and progression, including the balance of antitumor immune effector cells and protumorigenic immune suppressor cells within the tumor environment. We hypothesized that oncogene signaling regulates the production of cytokines by tumor cells in order to modulate the immune microenviroment and promote lung tumor development. We used CIBERSORT to quantify 22 immune cell types in over 300 human lung adenocarcinomas (LUAD) and 100 matched normal lung tissues. Cells associated with inflammatory or antitumor response, macrophages (M1, M0), T follicular helper cells, and plasma cells, were enriched in LUAD compared to normal lung tissue. Additionally, immunosuppressive regulatory T cells (Tregs) were significantly enriched in LUAD tumors, even at the early stages. To identify cytokines that could be induced by oncogenic signaling early in lung tumorigenesis, we used normal cells expressing doxycycline-inducible mutant KRASG12V mutant EGFRL858R or wild-type EGFR and analyzed cytokine production with a multiplex assay (LUMINEX). Induction of oncogenic signaling in normal cells rapidly increased production of cytokines CCL5 and CCL2. These are capable of recruiting a variety of cells types, including Tregs. In KRAS mutant lung cancer cells, disruption of oncogene signaling with a MEK inhibitor (trametinib) decreased CCL5 production. We used transgenic mice that spontaneously develop lung tumors in response to tetracycline-inducible expression of mutant EGFRDelEx19 or mutant KRASG12V in type II alveolar cells to investigate immune cell populations that may change in response to oncogene-driven lung tumorigenesis. We found that Tregs were increased in the lungs of tumor-bearing mice. These Tregs express the CCL5 receptor, CCR5. Several cytokines were elevated in bronchioalveolar lavage fluid or in lung lysates of tumor-bearing mice, including IL-12(p40), CXCL1, CCL2, CCL3 and CCL5. Murine Lewis lung carcinoma (LLC) cells harbor a KRAS mutation and express high levels of CCL5. We are currently investigating the effects of disrupting oncogene signaling or CCL5 expression in LLC cells on cytokine production and immune cell recruitment in vivo using syngeneic implantation of the cells into mice. Our data suggest that oncogenic signaling regulates expression of cytokines in lung tumor cells. Targeted inhibition of cytokines directly or indirectly through oncogenic signaling may represent therapeutic strategies to block the recruitment of immune-suppressive cells to the tumor microenvironment, thereby enhancing the antitumor immune response. Citation Format: Elizabeth Franks, Etienne Melese, Bryant Harbourne, Liz Halvorsen, Rachel Cederberg, Jenna Collier, Natalie Firmino, Jennifer Luu, Arun Unni, Min Hee Oh, Vivian Lam, Gerry Krystal, Ninan Abraham, Kevin Bennewith, William Lockwood. Oncogenes drive production of immunosuppressive cytokines to facilitate lung cancer progression [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 A38.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".