A distinct pre-existing inflammatory tumour microenvironment is associated with chemotherapy resistance in high-grade serous epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Chemotherapy resistance is a major determinant of poor overall survival rates in high-grade serous ovarian cancer (HGSC). We have previously shown that gene expression alterations affecting the NF-κB pathway characterise chemotherapy resistance in HGSC, suggesting that the regulation of an immune response may be associated with this phenotype. METHODS: Given that intrinsic drug resistance pre-exists and is governed by both tumour and host factors, the current study was performed to examine the cross-talk between tumour inflammatory microenvironment and cancer cells, and their roles in mediating differential chemotherapy response in HGSC patients. Expression profiling of a panel of 184 inflammation-related genes was performed in 15 chemoresistant and 19 chemosensitive HGSC tumours using the NanoString nCounter platform. RESULTS: A total of 11 significantly differentially expressed genes were found to distinguish the two groups. As STAT1 was the most significantly differentially expressed gene (P=0.003), we validated the expression of STAT1 protein by immunohistochemistry using an independent cohort of 183 (52 resistant and 131 sensitive) HGSC cases on a primary tumour tissue microarray. Relative expression levels were subjected to Kaplan-Meier survival analysis and Cox proportional hazard regression models. CONCLUSIONS: This study confirms that higher STAT1 expression is significantly associated with increased progression-free survival and that this protein together with other mediators of tumour-host microenvironment can be applied as a novel response predictive biomarker in HGSC. Furthermore, an overall underactive immune microenvironment suggests that the pre-existing state of the tumour immune microenvironment could determine response to chemotherapy in HGSC.
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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 | 0.000 | 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 | 0.001 |
| 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".