Umbrella Trial in Myeloid Malignancies: The Myelomatch National Clinical Trials Network Precision Medicine Initiative
Bibliographic record
Abstract
Introduction: To accelerate myeloid cancer therapeutics, the National Clinical Trials Network (NCTN) is launching the National Cancer Institute (NCI) Myeloid Malignancies Molecular Analysis for Therapy Choice (myeloMATCH) precision medicine clinical trial. Sponsored by NCI and conducted across the entire NCTN system with initial trials to be led by SWOG, Alliance, ECOG-ACRIN, and CCTG, the initiative leverages public-private partnerships with pharmaceutical industry and biotech companies to create an efficient regulatory model incorporating cross-company novel-novel combinations in trials for acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). The goal is to create a portfolio of rationally designed treatment substudies onto which patients sequentially enroll over their treatment journey. As patients transition to higher tiers with increasingly lower remaining tumor burden, the focus will be to target residual disease more precisely. Methods: Newly diagnosed patients are enrolled onto the Master Screening and Reassessment Protocol (MSRP) for baseline clinical and laboratory evaluation. Specimens are sent to the Molecular Diagnostics Network (MDNet) with a 72-hour turn-around for patient assignment to an initial treatment substudy via an integrated informatics system developed by the NCI Precision Medicine Analysis and Coordination Center (PMACC). Assignments will be based on algorithms adjusted for prevalence of co-mutations to enhance accrual of rare molecular subsets to specific targeted treatment trials. As shown in figure 1, there are 4 tiers and 5 clinical baskets. Tier-1 is for initial therapy grouped by MDS, younger AML, and older AML. These are typically randomized phase 2 studies testing sensitivity to novel drug combinations with measurable residual disease (MRD) assessment conducted centrally by MDNet. Subsequent therapy occurs in higher tiers. These assignments are made by MDNet/PMACC based on prior treatment substudy outcome. Flow cytometry and duplex sequencing will be employed in Tier-4 clinical trials that will target residual disease. Statistical designs will evaluate the clinical utility of the assays and biomarkers to determine if targeting residual disease confers clinical benefit. Planned activation is quarter 4 of 2022 with the MSPRP, 2-young adult tier-1 studies and 1 tier-2 study. These are testing combinations of azacitidine, venetoclax, CPX351, 7+3 for ELN defined high risk AML, standard risk AML, and in tier-2 the ability to "erase” residual disease after tier-1 treatment. Additional studies in development include agents for mutant TP53, KIT, FLT-3, NPM1, IDH 1/2, higher and lower-risk MDS and a study for reduced intensity transplant and maintenance to include efforts for diverse populations. Launch for these studies is planned for mid 2023. Tier-4 studies to target KIT, IDH, FLT3 and others are in discussion. Discussion: MyeloMATCH is the largest focused investment of infrastructure and researchers ever coordinated by the US Network Groups and NCI that follows patients from diagnosis thorough all treatment in a single disease area. The charge is to rapidly advance therapeutics in myeloid malignancies. MyeloMATCH is designed to efficiently screen and assign patients to precision treatment trials of promising therapeutic combinations. By using early endpoints to identify large activity signals, myeloMATCH will generate data with promising findings for definitive study. The clinical and laboratory data can be interrogated across the initiative to generate hypotheses for additional focused testing. Participants receiving their treatment journey through myeloMATCH will contribute to a unique clinically annotated database with specimens for "omics” serially collected from pre-treatment and through follow up. This will provide a national resource for understanding drug sensitivity and resistance, as well as clonal evolution. In this manner, myeloMATCH aims to reduce the time and investment in failed phase 3 studies, and instead aims to provide high-quality randomized trial data that will enhance selection of phase 3 priorities. We believe this new paradigm for the collaborative conduct of clinical trials may provide meaningful advances for patients with AML and MDS, mentor investigators, and accelerate drug development. Updates and specific treatment-trial designs will be discussed at the meeting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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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 | 0.040 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".