Tumor genomic, transcriptomic, and immune profiling characterizes differential response to first‐line platinum chemotherapy in high grade serous ovarian cancer
Bibliographic record
Abstract
BACKGROUND: In high grade serous ovarian cancer (HGSOC), there is a spectrum of sensitivity to first line platinum-based chemotherapy. This study molecularly characterizes HGSOC patients from two distinct groups of chemotherapy responders (good vs. poor). METHODS: Following primary debulking surgery and intravenous carboplatin/paclitaxel, women with stage III-IV HGSOC were grouped by response. Patients in the good response (GR) and poor response (PR) groups respectively had a progression-free intervals (PFI) of ≥12 and ≤6 months. Analysis of surgical specimens interrogated genomic and immunologic features using whole exome sequencing. RNA-sequencing detected gene expression outliers and inference of immune infiltrate, with validation by targeted NanoString arrays. PD-L1 expression was scored by immunohistochemistry (IHC). RESULTS: A total of 39 patient samples were analyzed (GR = 20; PR = 19). Median PFI for GR and PR patient cohorts was 32 and 3 months, respectively. GR tumors were enriched for loss-of-function BRCA2 mutations and had a significantly higher nonsynonymous mutation rate compared to PR tumors (p = 0.001). Samples from the PR cohort were characterized by mutations in MGA and RAD51B and trended towards a greater rate of amplification of PIK3CA, MECOM, and ATR in comparison to GR tumors. Gene expression analysis by NanoString correlated increased PARP4 with PR and increased PD-L1 and EMSY with GR. There was greater tumor immune cell infiltration and higher immune cell PD-L1 protein expression in the GR group. CONCLUSIONS: Our research demonstrates that tumors from HGSOC patients responding poorly to first line chemotherapy have a distinct molecular profile characterized by actionable drug targets including PARP4.
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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) | 0.001 | 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) | 0.002 | 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".