A novel cell-based screen identifies chemical entities that reverse the immune-escape phenotype of metastatic tumours
Bibliographic record
Abstract
Genetic and epigenetic events have been implicated in the downregulation of the cellular antigen processing and presentation machinery (APM), which in turn, has been associated with cancer evasion of the immune system. When these essential components are lacking, cancers develop the ability to subvert host immune surveillance allowing cancer cells to become invisible to the immune system and, in turn, promote cancer metastasis. Here we describe and validate the first high-throughput cell-based screening assay to identify chemical extracts and unique chemical entities that reverse the downregulation of APM components in cell lines derived from metastatic tumours. Through the screening of a library of 480 marine invertebrate extracts followed by bioassay-guided fractionation, curcuphenol, a common sesquiterpene phenol derived from turmeric, was identified as the active compound of one of the extracts. We demonstrate that curcuphenol induces the expression of the APM components, TAP-1 and MHC-I molecules, in cell lines derived from both metastatic prostate and lung carcinomas. Turmeric and curcumins that contain curcuphenol have long been utilized not only as a spice in the preparation of food, but also in traditional medicines for treating cancers. The remarkable discovery that a common component of spices can increase the expression of APM components in metastatic tumour cells and, therefore reverse immune-escape mechanisms, provides a rationale for the development of foods and advanced nutraceuticals as therapeutic candidates for harnessing the power of the immune system to recognize and destroy metastatic cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".