Using chemotherapy response by KELIM score to predict response to first line maintenance PARP inhibitor therapy in non-BRCA mutant/homologous recombination deficiency (HRD) unknown high grade serous ovarian cancer (HGSOC).
Bibliographic record
Abstract
e17547 Background: Maintenance PARP inhibitor therapy after response to first line chemotherapy is now standard of care in advanced HGSOC. Niraparib is available to all patients based on the PRIMA trial data; however, the progression free survival (PFS) benefit for patients without BRCA mutations or homologous recombination deficiency (HRD) is limited. Funded HRD testing is not accessible in many countries. Patient selection for PARPi therapy in non-BRCA mutant HGSOC is challenging. The calculated CA-125 ELIMination of Rate Constant K (KELIM) score is a mathematical model developed to evaluate CA-125 kinetics during chemotherapy. KELIM has been shown to correlate with chemosensitivity, with scores ≥1 associated with better clinical outcomes. This project aims to use real-world patient data to assess if surrogate markers, such as KELIM score and/or pathological chemotherapy response score, can predict response to 1st line maintenance PARPi in the absence of funded HRD testing. Methods: A retrospective review of non-BRCA mutant HGSOC cases on first line maintenance PARPi therapy at BC Cancer, Canada between April 2020 and June 2022. Only cases confirmed to be non-BRCA mutant (by both germline and tumour testing) were included in the study. Data collection was through electronic medical records and included patient demographics, chemotherapy intent (neoadjuvant vs. adjuvant), pathological (p) chemotherapy response score (CRS), progression-free survival (PFS) defined as start of niraparib to radiological evidence of disease progression. PFS analysis was performed in all patients provided 1 full cycle had been completed. The rate of not progressing at 12 months (PFS12) was calculated. KELIM score was calculated from at least 3 CA125 values taken within 100 days from the chemotherapy start date and using a validated calculation software. Results: 70 patients met the full eligibility criteria for analysis. All patients received niraparib as PARPi therapy. Mean age was 67 years and 40 patients (57%) were ≥ 65 years. Most patients were FIGO stage 3C at presentation (56%). Median number of niraparib cycles was 10 (range 1-28). 35/70 patients (50%) had disease progression at time of data analysis with a median follow-up of 13.2 months. 59 patients had evaluable KELIM scores. pCRS was not associated with any statistically significant differences in PFS. Patients with KELIM scores ≥1 had a trend to greater PFS from niraparib vs. those with KELIM <1 with median PFS of 15 months vs. 8.3 months respectively ( p=0.06). PFS12 rate was higher at 64% with KELIM scores ≥1 vs. 43% with KELIM <1; ( p=0.18). Conclusions: We show that KELIM score could be a useful tool to predict PARP inhibitor response and aid clinical decision-making by oncologists and patients in the real world setting where HRD testing is unfunded.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".