The science of tumor-infiltrating lymphocytes (TIL): perspectives from the SITC Surgery Committee
Bibliographic record
Abstract
Immunity to solid tumors is associated with the hallmarks of cancer-associated inflammation and the ability of immune mechanisms to limit tumor progression. Application of expanded tumor-infiltrating lymphocyte adoptive T cell therapy (TIL ACT) in clinical trials is now practiced at many sites around the world. Prior to immune checkpoint blockade (ICB), an approximate 50% objective response rate was consistently observed across multiple institutions for patients with melanoma. This now-approved strategy approaches 35% in recent studies from the USA and 49% with more highly selected patients in Europe. Here, we focus on early TIL studies in non-melanoma epithelial neoplasms. Increased understanding of cancer immunology has allowed changes in the TIL expansion process to include: (1) initial generation of TIL from fragments, (2) use of specialized large-scale culture vessels, (3) use of the rapid expansion protocol to enable 'young' TIL prosecution, and (4) treatment regimens employing non-myeloablative (NMA) chemotherapy followed by brief interleukin-2 administration. NMA leads to homeostatic proliferation of the transferred T cells, engraftment, profound neutropenia and lymphopenia, and improved clinical outcome. A key success of TIL ACT relies on the quality, specificity, and number of pre-existing TIL. This, in turn, is highly influenced by the suppressive tumor microenvironment. Thus, any means to alter 'cold tumor (non-T cell inflamed)' to 'hot tumor (T cell inflamed)' is theoretically desirable to improve both the quality and quantity of TIL obtained before harvest. Combinations of other immunotherapies such as application of ICB, co-stimulatory molecule agonist antibodies, autophagy inhibition, and dendritic cell support strategies could provide additional- improvements in TIL therapy and enable harnessing of the adaptive immune response to enhance the clinical outcome of TIL-ACT patients.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".