Antigen-presenting tumor B cells impact the phenotype of CD4 tumor infiltrating T cells in lung cancer patients
Bibliographic record
Abstract
Abstract The focus of immunotherapy has been on CD8 and CD4 tumor infiltrating lymphocytes (TILs), however, tumor infiltrating B cells (TIL-Bs) are understudied with no focus on their role as antigen presenting cells. We hypothesize that TIL-Bs help generate potent, long-term immune responses against cancer by presenting tumor antigens to CD4 TILs. Using un-manipulated, primary human B cells from fresh tumor, we generated a specific in vitro antigen presentation assay and observed three types of CD4 TIL responses when TIL-Bs presented autologous tumor antigens. There were activated responder CD4 TILs that proliferated when combined with TIL-Bs alone, which indicates stimulation with endogenous tumor antigens. There were antigen-associated responders that required autologous tumor lysate to elicit a CD4 TIL response, and there were patient CD4 TILs that did not respond to antigen presentation. Exhausted B cells have been described in chronic infection i.e. HIV, and thus, to parallel our studies to a model of chronic infection, we analyzed activated and exhausted TIL-Bs with our antigen presentation assay. If TIL-Bs were activated (HLADR+CD69+CD27+CD21+), the resultant CD4 TILs were T helper (anti-tumor) CD4 T cells and if the TIL-Bs were exhausted (HLADR+CD69+CD27−CD21−), the resultant CD4 TILs were T regulatory cells (pro-tumor). These data suggest that TIL-Bs influence CD4 TILs in NSCLC patient tumors. In conclusion, determining if TIL-Bs are activated or exhausted in NSCLC patients will determine the extent of their anti-tumor function in human cancer. Ultimately, results from this study will dictate how to target TIL-Bs in future immunotherapies.
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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) | 0.004 | 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".