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 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 | 0.000 |
| 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 | 0.000 |
| 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".