Precision oncology for rare gynecologic malignancies: Integrating molecular tumor boards and real-time treatment matching.
Bibliographic record
Abstract
5523 Background: Patients with rare gynecologic cancers often face limited systemic therapy options and participation in clinical trials is challenging due to a paucity of available options. As many as 30% of women with gynecologic cancer have rare subtypes with tumors often harbouring unique molecular aberrations that may serve as potential therapeutic targets. With accessibility of Next-Generation Sequencing (NGS), targeted drug matching in real time is an ever-increasing interest for developing personalized treatment strategies. Methods: Within the gynecologic site group at Princess Margaret (PM), we have developed a comprehensive program whereby coordination of molecular profiling, pathology review and consensus therapeutic recommendations are made for patients with rare or complex diagnoses. Patients undergo NGS predominantly through a translational study (VENUS; NCT03420118 or BioDiva; NCT03419689) and results are reviewed at ‘ComplexDiva’ rounds. These rounds gather a multidisciplinary team for review of patient demographics, treatment history, and imaging followed by literature review based on histopathology and molecular profile. This process facilitates collective analysis of genomic data and discussion of targeted treatment options to ensure recommendations are individually tailored. Results: The ComplexDiva pilot assessing feasibility of this approach has facilitated the review and discussion of 67 patients between November 2021 and December 2023. NGS was performed on at least one tissue sample for 65 patients. Whole Genome and Transcriptome Sequencing (WGTS) was performed on 14 archival tissue samples from 12 patients. Ovarian tumors comprised 63%, cervical 26%, endometrial 9%, and vulvar 2% of cases. More than 60% of patients (42/67) had rare tumor types, such as female adnexal tumor of wolffian origin (FATWO), small cell carcinoma of the ovary, hypercalcemic type (SCCOHT), steroid cell tumor, gynandroblastoma, and mesonephric adenocarcinoma. Recommendations were made for clinical trial screening in eight patients (12%) and for Nof1 treatment (any off-label indication) in 30 patients (45%). Nof1 treatment was suggested as next line of therapy in 77% of these patients and to be considered as a future treatment line in 23%. Conclusions: Integration of molecular tumor boards, identification of targetable mutations, and real-time treatment matching at PM has allowed a functional molecular and consensus tumor board to facilitate personalized treatment strategies for patients with rare gynecologic cancers. This innovative approach holds promise for advancing precision oncology and improving therapeutic outcomes in challenging clinical contexts. This scalable initiative allows collaboration with other cancer centres. Patient outcomes are followed through a dedicated registry to systematically evaluate the efficacy of our approach.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".