Neoadjuvant Chemotherapy of Ovarian Cancer Results in Three Patterns of Tumor-Infiltrating Lymphocyte Response with Distinct Implications for Immunotherapy
Bibliographic record
Abstract
Abstract Purpose: Some forms of chemotherapy can enhance antitumor immunity through immunogenic cell death, resulting in increased T-cell activation and tumor infiltration. Such effects could potentially sensitize tumors to immunotherapies, including checkpoint blockade. We investigated whether platinum- and taxane-based chemotherapy for ovarian cancer induces immunologic changes consistent with this possibility. Experimental Design: Matched pre- and post-neoadjuvant chemotherapy tumor samples from 26 high-grade serous carcinoma (HGSC) patients were analyzed by immunohistochemistry (IHC) for a large panel of immune cells and associated factors. The prognostic significance of post-chemotherapy TIL patterns was assessed in an expanded cohort (n = 90). Results: Neoadjuvant chemotherapy was associated with increased densities of CD3+, CD8+, CD8+ TIA-1+, PD-1+ and CD20+ TIL. Other immune subsets and factors were unchanged, including CD79a+ CD138+ plasma cells, CD68+ macrophages, and MHC class I on tumor cells. Immunosuppressive cell types were also unchanged, including FoxP3+ PD-1+ cells (putative regulatory T cells), IDO-1+ cells, and PD-L1+ cells (both macrophages and tumor cells). Hierarchical clustering revealed three response patterns: (i) TILhigh tumors showed increases in multiple immune markers after chemotherapy; (ii) TILlow tumors underwent similar increases, achieving patterns indistinguishable from the first group; and (iii) TILnegative cases generally remained negative. Despite the dramatic increases seen in the first two patterns, post-chemotherapy TIL showed limited prognostic significance. Conclusions: Chemotherapy augments pre-existing TIL responses but fails to relieve major immune-suppressive mechanisms or confer significant prognostic benefit. Our findings provide rationale for multipronged approaches to immunotherapy tailored to the baseline features of the tumor microenvironment. Clin Cancer Res; 23(4); 925–34. ©2016 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".