In Vitro Suppression of T Cell Proliferation Is a Conserved Function of Primary and Immortalized Human Cancer-Associated Fibroblasts
Bibliographic record
Abstract
T cell immunotherapy is now a mainstay therapy for several blood-borne cancers as well as metastatic melanoma. Unfortunately, many epithelial tumors respond poorly to immunotherapy, and the reasons for this are not well understood. Cancer-associated fibroblasts (CAFs) are the most frequent non-neoplastic cell type in most solid tumors, and they are emerging as a key player in immunotherapy resistance. A range of immortalized CAF lines will be essential tools that will allow us to understand immune responses against cancer and develop novel strategies for cancer immunotherapy. To study the effect of CAFs on T cell proliferation, we created and characterized a number of novel immortalized human CAFs lines (Im-CAFs) from human breast, colon, and pancreatic carcinomas. Im-CAFs shared similar phenotypes, matrix remodeling and contraction capabilities, and growth and migration rates compared to the primary CAFs. Using primary isolates from breast carcinoma, colorectal carcinoma, and pancreatic ductal adenocarcinoma, we report that CAFs across major tumor types are able to potently suppress T cell proliferation in vitro. Im-CAFs retained this property. Im-CAFs are a key tool that will provide important insights into the mechanisms of CAF-mediated T cell suppression through techniques such as CRISPR-Cas9 modification, molecular screens, and pipeline drug testing.
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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) | 0.001 | 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".