Changes in the Tumor Immune Microenvironment during Disease Progression in Patients with Ovarian Cancer
Bibliographic record
Abstract
Anti-PD1/PDL1 therapy has proven efficacious against many cancers but only reached modest objective response rates against recurrent ovarian cancer. A deeper understanding of the tumor microenvironment (TME) may reveal other immunosuppressive mechanisms that warrant investigation as immunotherapeutic targets for this challenging disease. Matched primary and recurrent tumors from patients with high-grade serous ovarian carcinoma (HGSC) were analyzed by multicolor immunohistochemistry/immunofluorescence for the presence of T cells, B cells, macrophages, and for the expression of immunosuppressive and HLA molecules. Cancer- and immune-related gene expression was assessed by NanoString analysis. Recurrent tumors showed increased infiltration by immune cells, displayed higher expression of PDL1, IDO, and HLA molecules, and contained more stromal tissue. NanoString analysis demonstrated increased expression of gene signatures related to chemokines and T cell functions in recurrent tumors. The ovarian tumors showed high gene expression of LAG3 and HAVCR2 (TIM3) and enhanced levels of TIGIT and CTLA4 in recurrent tumors compared to primary tumors. The majority of HGSC developed into a more inflamed phenotype during progression from primary to recurrent disease, including indications of adaptive immune resistance. This suggests that recurrent tumors may be particularly sensitive to inhibition of adaptive immune resistance mechanisms.
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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 | 0.000 | 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) | 0.001 | 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".