Homologous Recombination Deficiency Testing to Inform Patient Decisions About Niraparib Maintenance Therapy for High-Grade Serous or Endometrioid Epithelial Ovarian Cancer: A Health Technology Assessment.
Bibliographic record
Abstract
Background: Ovarian cancer affects the cells of the ovaries, and epithelial cancer is the most common type of malignant ovarian cancer. The homologous recombination repair pathway enables error-free repair of DNA double-strand breaks. Damage of key genes associated with this pathway leads to homologous recombination deficiency (HRD), which results in unrepaired DNA and can lead to cancer. Tumours with HRD are believed to be sensitive to treatment with poly-adenosine diphosphate (ADP)-ribose polymerase (PARP) inhibitors, such as niraparib. We conducted a health technology assessment to evaluate the clinical utility and cost-effectiveness of HRD testing to inform patient decisions about the use of niraparib maintenance therapy for patients with high-grade serous or endometrioid epithelial ovarian cancer. We also evaluated the efficacy and safety of niraparib maintenance therapy in patients with HRD or homologous recombination proficiency (HRP), the cost-effectiveness of HRD testing, the budget impact of publicly funding HRD testing, and patient preferences and values. Methods: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Cochrane risk-of-bias tool for randomized trials version 2, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search and conducted a cost-utility analysis with a 5-year time horizon from a public payer perspective. We also analyzed the budget impact of publicly funding HRD testing in people with ovarian cancer in Ontario. We performed a literature search for quantitative evidence of patient and provider preferences with respect to HRD testing and maintenance therapy with PARP inhibitors. To contextualize the potential value of HRD testing, we spoke with people with ovarian cancer. Results: wild type) over the next 5 years.We identified no studies that evaluated quantitative preferences for HRD testing. Based on two studies that evaluated patients and oncologists' preferences for maintenance therapy with a PARP inhibitor in the recurrent setting, a decrease in moderate to severe adverse events was more important for patients than an improvement in progression-free survival; however, improvement in progression-free survival was more important for oncologists. Both patients and oncologists accepted some trade-offs between efficacy and safety. The people with ovarian cancer we spoke with demonstrated a shared value for access to information, prevention of cancer recurrence, and overall survival with minimal adverse effects. This was consistent with findings from another survey in patients with ovarian cancer and at least one episode of recurrence, which suggest that patients prioritize treatment benefit over some treatment adverse events in the context of niraparib maintenance therapy. Interviewees also emphasized the importance of the patient-doctor partnership, access to local health care services, and patient education. Conclusions: wild type could save $4,509 per person and lead to a loss of 0.116 QALY. The findings of our economic analyses are dependent on assumptions about the use of niraparib following HRD testing. We estimate that publicly funding HRD testing would lead to a total saving of $9 million to $12.67 million for newly diagnosed cancer, and a total saving of $16.31 million to $21.67 million for recurrent cancer over 5 years, assuming the use of niraparib maintenance therapy would be reduced following HRD testing.Patients prioritized decreasing the risk of moderate to severe adverse events of maintenance therapy with PARP inhibitors over improving progression-free survival, and oncologists prioritized improving progression-free survival over decreasing the risk of moderate to severe adverse events. However, both patients and oncologists were open to accepting certain trade-offs between treatment efficacy and toxicity. The people we interviewed, who had lived experience with ovarian cancer and genetic testing, valued the potential clinical benefits of HRD testing for themselves and their family members. They emphasized patient education as an important consideration for public funding in Ontario.
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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 | 0.074 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".