Abstract B41: Neutrophils modulate T-cell recruitment and promote hepatic metastases in lung cancer
Bibliographic record
Abstract
Abstract Immunotherapy using checkpoint inhibitors has shown success in the treatment of late-stage non-small cell lung carcinomas (NSCLC). Indeed, nivolumab (anti-PD1 antibody) increased overall response by 9 % and overall survival by 3 months compared to chemotherapy (docetaxel). Combining Ipilimumab (anti-CTLA-4 antibody), another immune checkpoint inhibitor, with nivolumab was shown to improve response rates and survivals in NSCLC patients compared to nivolumab alone (phase 1/2 study). Despite these advances, there are still many patients that are non-responsive to available immunotherapies. We hypothesize that the immune system can overtake the inhibition of immune suppression via other mechanisms leading to a more immune suppressive tumor microenvironment. Neutrophils, being amongst the first cells of our innate immune system to be recruited to the tumor microenvironment, are key players in modulating other immune cells. Indeed, we and others have demonstrated that neutrophils play a critical role during cancer progression and from a clinical standpoint elevated neutrophil counts both in circulation and within growing lesions have been associated with poor oncologic outcomes. Therefore, neutrophil could well be the immunosuppressive switch in the tumor microenvironment. To test our hypothesis, we injected liver metastatic lung carcinoma cells into mice treated with daily injection of neutrophil elastase inhibitor (NEi, Sivelestat) and into untreated (control) mice. We then collected the livers of those mice at multiple time points post-tumor inoculation and analyzed the immune profile of these mice. As expected, NEi-treated mice had significantly less liver metastases than control mice (p < 0.05) indicating that neutrophils play a pro-tumor role in cancer progression. Furthermore, NEi-treated mice had a significant decrease in the number of tumor infiltrating granulocytes (GR1+ cells, p <0.05) and tumor associated neutrophils (TANs, Ly6G-1A8 + cells) (p<0.0005). This significant decrease in TANs correlated with a significant decrease in CD3+ T-cells (p<0.05) in NEi-treated mice compared to control mice 1-week post-tumor injection. The number of tumor associated macrophages (F4/80+ cells) was the same in both groups (p=0.65) and thus not affected by the significant decrease in TANs. These findings show that neutrophils play a role in T-cell recruitment in liver metastases of lung cancer cells and thus neutrophil can be an important target for new immunotherapies. Citation Format: Roni F. Rayes, Alexandra Tinfow, Dorothy Antonatos, France Bourdeau, Betty Giannias, Jonathan D. Spicer. Neutrophils modulate T-cell recruitment and promote hepatic metastases in lung cancer [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr B41.
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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 | 0.001 |
| 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".