Characterization of T cell repertoire of blood, tumor, and ascites in ovarian cancer patients using next generation sequencing
Bibliographic record
Abstract
Tumor-infiltrating lymphocytes (TILs) play an important role in regulating the host immune response and are one of key factors in defining tumor microenvironment. Some studies have indicated that T cell infiltration in malignant ascites is associated with clinical outcome, but few studies have performed detailed characterization of T cell diversity or clonality in malignant effusions. We have applied a next generation sequencing method to characterize T cell repertoire of a set of primary cancers, ascites, and blood from 12 ovarian cancer patients and also analyzed the T cell subtype populations in malignant fluids from 3 ovarian cancer patients. We observed enrichment of certain T cells in tumors and ascites, but most of the enriched T cell receptor (TCR) sequences in tumors and ascites were not common. Moreover, we analyzed TCR sequences of T cell subtypes (CD4+, CD8+, and regulatory T cells) isolated from malignant effusions and also found clonal expansion of certain T cell populations, but the TCR sequences were almost mutually exclusive among the three subgroups. Although functional studies of clonally expanded T cell populations are definitely required, our approach offers a detailed characterization of T cell immune microenvironment in tumors and ascites that might differently affect antitumor immune response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".