Clinical landscape of precision oncology for rare cancers among diverse Asian populations: Insights from the MASTER KEY registry.
Bibliographic record
Abstract
3024 Background: Despite considerable strides in novel cancer treatments, disparities in access and care persist globally. This is particularly pronounced for rare cancers and among Asian patient cohorts, presenting a dual challenge. Methods: We conducted an analysis on clinical and biomarker data from the MASTER KEY Study, a multi-regional, prospective, observational precision oncology initiative encompassing 3,764 rare cancer patients across Asia. Clinical treatment and biomarker data, inclusive of DNA/RNA sequencing, were scrutinized to compare precision oncology adoption across various countries. Results: Within the Japanese cohort (3,268 patients), predominant rare cancer types included soft tissue sarcomas (22.0%), CNS/brain tumors (12.8%), and head and neck tumors (9.2%). The broader Asian cohort (496 patients) included soft tissue sarcomas (14.6%), liver/biliary tract tumors (14.4%), and head and neck tumors (13.8%), drawing patients from Malaysia, Korea, Taiwan, Philippines, Thailand, and Vietnam. Clinical trial participation for these rare cancers was notably higher in Taiwan (8.2%), followed by Japan (6.5%) and Malaysia (2.0%). The utilization of molecular target agents and/or immune checkpoint inhibitors was highest in Japan (20.3%), trailed by Taiwan (18.9%) and Korea (12.8%). DNA/RNA targeted sequencing data was available for 1,852 patients in Japan (56.7%) and 321 patients (64.7%) across the rest of Asia. 19.5% and 7.0% received on-target therapy in Japan and the rest of Asia, respectively. Notably, in Japan, patients harboring detectable targetable genes like BRAF V600E, BRCA1, and BRCA2 received BRAF/MEK inhibitors and PARP inhibitors at rates of 78.3%, 37.8%, and 25.0%, respectively, with varying response rates of 33.3%, 18.2%, and 20.2% each. Intriguingly, TP53 mutation acted as a negative predictor for response to BRAF/MEK inhibitors. Notably, no patients in the Asian cohort received BRAF/MEK inhibitors or PARP inhibitors despite the detection of these genes. Conclusions: The MASTER KEY Study demonstrates the feasibility of a prospective precision oncology platform, spotlighting rare cancers and enabling on-label targeted therapy as well as off-label targeted therapy for a subset of patients through trials. However, the broader Asian population outside Japan encounters heightened limitations in accessing precision oncology. Urgent expansion of clinical trials throughout Asia is crucial to effectively address the disparity that lies within Asian rare cancer patients.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".