Resident Memory-Like Tumor-Infiltrating Lymphocytes (TILRM): Latest Players in the Immuno-Oncology Repertoire
Bibliographic record
Abstract
Resident memory T cells (TRM) are a recently identified subset of long lived memory T cells that are characterized in terms of their unique surface phenotype combined with a non-recirculating pattern of localization to non-lymphoid, peripheral tissues. TRM have quickly become a key area of focus in understanding immune responses to microbial infection in so called ‘barrier’ tissues, and appear to be particularly critical for protection against repeat exposure at the same site. More recently, tumor infiltrating T cells with canonical TRM features are being identified in human cancers, in particular cancers of epithelial origin, and their presence is broadly found to be associated with favorable long term prognosis. In addition, recent studies have found that these tumor infiltrating TRM (TILRM) are uniquely activated in melanoma patients undergoing PD-1 directed checkpoint blockade therapy. Accordingly, there is much interest at present regarding the biology of these cells and their role in anti-cancer immunity. Herein, we review the current state of the literature regarding TILRM with a specific emphasis on their specificity, origins and relationship to conventional pathogen-specific TILRM and speculate upon the way(s) in which they might contribute to improved prognosis for cancer patients. We discuss the growing body of evidence that suggests TILRM may represent a population of bona-fide tumor reactive T cells and the attractive possibility of leveraging this cell population for future immunotherapy.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".