The Hippo Pathway Component TAZ Promotes Immune Evasion in Human Cancer through PD-L1
Bibliographic record
Abstract
Abstract The Hippo pathway component WW domain-containing transcription regulator 1 (TAZ) is a transcriptional coactivator and an oncogene in breast and lung cancer. Transcriptional targets of TAZ that modulate immune cell function in the tumor microenvironment are poorly understood. Here, we perform a comprehensive screen for immune-related genes regulated by TAZ and its paralog YAP using NanoString gene expression profiling. We identify the immune checkpoint molecule PD-L1 as a target of Hippo signaling. The upstream kinases of the Hippo pathway, mammalian STE20-like kinase 1 and 2 (MST1/2), and large tumor suppressor 1 and 2 (LATS1/2), suppress PD-L1 expression while TAZ and YAP enhance PD-L1 levels in breast and lung cancer cell lines. PD-L1 expression in cancer cell lines is determined by TAZ activity and TAZ/YAP/TEAD increase PD-L1 promoter activity. Critically, TAZ-induced PD-L1 upregulation in human cancer cells is sufficient to inhibit T-cell function. The relationship between TAZ and PD-L1 is not conserved in multiple mouse cell lines, likely due to differences between the human and mouse PD-L1 promoters. To explore the extent of divergence in TAZ immune-related targets between human and mouse cells, we performed a second NanoString screen using mouse cell lines. We show that many targets of TAZ may be differentially regulated between these species. These findings highlight the role of Hippo signaling in modifying human/murine physiologic/pathologic immune responses and provide evidence implicating TAZ in human cancer immune evasion. Significance: Human-specific activation of PD-L1 by a novel Hippo signaling pathway in cancer immune evasion may have a significant impact on research in immunotherapy. Cancer Res; 78(6); 1457–70. ©2018 AACR.
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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) | 0.001 | 0.000 |
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".