Outcome-Related Differences in Gene Expression Profiles of High-Grade Serous Ovarian Cancers Following Neoadjuvant Chemotherapy
Bibliographic record
Abstract
Abstract Large-scale genomic studies have detailed the molecular landscape of tumors from patients with high-grade serous ovarian cancers (HGSC) who underwent primary debulking surgery and correlated the identified subgroups to survival. In recent years, there is increased use of neoadjuvant chemotherapy (NACT) for patients with HGSC and while abundant data exist for patients who underwent primary debulking, little data are available on the cancer cells remaining after NACT that could lead to recurrences. We aimed to analyze gene expression profiles of NACT-treated HGSC tumor samples, and correlate them to treatment response and outcome. Tumor samples were collected from patients with stage III or IV HGSC (NACT cohort, N = 57) at the time of surgery and diagnosis (biopsy samples N = 8). Tumor content was validated by histologic examination and bioinformatics. Gene expression analysis was performed using a tailored NanoString-based assay, while sequencing was performed using MiSeq. A cross-validated survival classifier revealed patient clusters with either a “Better” or “Worse” prognostic outcome. The association with overall survival remained significant after controlling for clinical variables, and differential gene expression, gene set enrichment analyses, and the appropriate survival models were used to assess the associations between alterations in gene expression in cancer cells remaining after NACT and outcome. Pathway-based analysis of the differentially expressed genes revealed comparatively high levels of cell cycle and DNA repair gene expression in the poor outcome group. Implications: Our work suggests mRNA expression patterns in key genes following NACT may reflect response to treatment and outcome in patient with 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".