Abstract 5434: Screen for immune-related transcriptional targets of the Hippo pathway in human breast cancer cells
Bibliographic record
Abstract
Abstract The Hippo signaling pathway has recently emerged as a cellular network that is dysregulated in cancer. In breast cancer cells, aberrant activation of the Hippo transducers (and transcriptional co-activators) TAZ and YAP leads to altered expression of their downstream gene targets and endows cells with multiple “hallmarks of cancer”. While several transcriptional targets of TAZ and YAP underlying their pro-tumorigenic activity have been described (e.g. CTGF, CYR61), gene targets of TAZ and YAP that modulate immune cell behavior in the tumor microenvironment are poorly understood. We have performed a comprehensive screen for immune-related transcriptional targets of TAZ and YAP in human breast cells. We have identified many candidate genes that are potentially regulated by TAZ and YAP including the immune checkpoint molecules PD-L1 and PD-L2, the lymphocyte regulator S1PR1 and the inflammasome component NLRP3. We have further validated PD-L1 as a bona fide transcriptional target of TAZ and YAP. These findings reveal new functions for the Hippo pathway in modifying immune responses and implicate TAZ and YAP in cancer immune evasion. Citation Format: Helena J. Janse van Rensburg, Taha Azad, Min Ling, Yawei Hao, Lori M. Minassian, Brooke Snetsinger, Prem Khanal, Michael J. Rauh, Charles H. Graham, Xiaolong Yang. Screen for immune-related transcriptional targets of the Hippo pathway in human breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5434.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".