Development of a Data Portal for Aggregation and Analysis of Genomics Data in Familial Platelet Disorder with Predisposition to Myeloid Malignancy - the RUNX1.DB
Bibliographic record
Abstract
Abstract Background: It has been known for approximately 19 years that germline mutations in RUNX1, lead to familial platelet disorder with predisposition to myeloid malignancy (FPD-MM, OMIM 601399). Since that time researchers have identified a broad range of different RUNX1 mutations, in over 100 families. In large families, the diagnosis of malignancy shows variable penetrance among family members with the same mutation; some carriers of RUNX1 mutations do not develop malignancy. The causes of this heterogeneity are currently not known, complicating counselling and risk analysis for individual carriers. Recent advances in genetic sequencing technology applied by many FPD-MM research groups around the world have highlighted their value in understanding the somatic genetic changes that are associated with development of malignancies in germline RUNX1 mutation carriers. Collectively this information could lead to powerful insights essential for more precise risk assessment, monitoring, and therapeutic intervention. Specifically, a growing catalogue of somatic mutations associated with germline RUNX1 malignancy offers the opportunity for informed monitoring of asymptomatic RUNX1 carriers for additional high-risk somatic mutations, in turn providing the possibility for early therapeutic intervention to arrest the leukemic process. The challenge in advancing these goals for FPD-MM is the relative rarity of the disorder in individual populations. Global data sharing in a highly interactive FPD-MM research community offers a solution to this problem that benefits all patients world-wide. Aims: To create a global RUNX1 network through identifying and contacting researchers and clinicians with known and novel germline RUNX1 families, seeking their collaboration to share genomics data. To create a RUNX1.db portal to collectively house and analyse genomics data from germline RUNX1 carriers, with associated phenotype and clinical information, such that researchers have an ongoing means by which to combine their data with those generated by other groups around the world. Methods: RUNX1 network : Through existing collaborative networks, a systematic review of the literature, and referrals from initial contacts, we have identified a global network of researchers managing FPD-MM cases and families. RUNX1.db: We have adapted a custom-built variant analysis platform, VariantGrid, that is a visual web application and database designed to help scientists manage and analyse DNA variants that can be used to aggregate and analyse multiple datasets. Results: Preliminary analysis of aggregated data from the literature suggests there are features of germline RUNX1 syndrome that can be ascertained. These include frequent somatic mutation of RUNX1 in malignancy development, as well as mutations in genes associated with clonal hematopoiesis that may precede development of overt leukemia. This analysis also suggests that different types of germline RUNX1 mutations may be associated with different combinations of somatic mutations in the tumour. To further this analysis, we have identified over 70 groups internationally that either manage RUNX1 families or have identified potential germline carriers through genomics initiatives. This represents a large and growing resource for both scientific studies and clinical programs to benefit individuals with FPD-MM. Many of these groups have generated NGS data sets from patient samples which will be available for analysis through this portal. Further details and activities of the network, and results from the genomics aggregation database will be presented. Conclusion: We have created a global RUNX1 network, aggregated their data, and generated a RUNX1.db genomics portal for the continuous curation of genomics data from germline RUNX1 carriers. A preliminary analysis has already identified specific features of germline RUNX1 mutated malignancies that have clinical importance. Ongoing scientific and clinical studies through the RUNX1 network will enhance the power of aggregated data analysis, with RUNX1.db providing a central link, driving new insights that benefit patients. Disclosures Natsoulis: Imago BioSciences, Inc.: Consultancy, Equity Ownership. Guzman:Cellectis: Research Funding. DiNardo:Bayer: Honoraria; Karyopharm: Honoraria; Celgene: Honoraria; Agios: Consultancy; Medimmune: Honoraria; Abbvie: Honoraria. Borate:Novartis: Consultancy; Agios: Consultancy. Wei:Amgen: Honoraria, Other: Advisory committee, Research Funding; Pfizer: Honoraria, Other: Advisory committee; Celgene: Honoraria, Other: Advisory committee, Research Funding; Abbvie: Honoraria, Other: Advisory board, Research Funding, Speakers Bureau; Servier: Consultancy, Honoraria, Other: Advisory committee, Research Funding; Novartis: Honoraria, Other: Advisory committee, Research Funding, Speakers Bureau. Dokal:MRC, Bloodwise, Telomerase Activator Sciences: Research Funding; The Gary Woodward Dyskeratosis Congenita Trust: Membership on an entity's Board of Directors or advisory committees; Action Medical Research, European School of Haematology: Membership on an entity's Board of Directors or advisory committees; Barts and The London School of Medicine and Dentistry, Queen Mary University of London,: Employment, Research Funding; Telomerase Activator Sciences: Research Funding; Barts and The London, Queen Mary University of London: Employment; Gary Woodward Dyskeratois Congenita Trust: Membership on an entity's Board of Directors or advisory committees. Hiwase:Novartis: Research Funding; Celgene: Research Funding. Branford:Cepheid: Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Qiagen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Kramer:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Daiichi Sankyo: Consultancy. Owen:Merck: Honoraria; Teva: Honoraria; AbbVie: Research Funding; Pharmacyclics: Research Funding; Janssen: Honoraria, Research Funding; Celgene: Research Funding; AstraZeneca: Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Honoraria, Research Funding. Fitzgibbon:Epizyme: Consultancy, Research Funding; Gilead: Consultancy. Rienhoff:Imago BioSciences, Inc.: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Kurokawa:Astellas Pharma: Research Funding; Sumitomo Dainippon Pharma: Research Funding; Nippon Sinyaku: Honoraria, Research Funding; Kyowa Hakko Kirin: Honoraria, Research Funding; Eizai: Research Funding; MSD: Honoraria, Research Funding; Ono Pharmaceutical: Honoraria, Research Funding; Pfizer: Research Funding; Takeda Pharmaceutical: Research Funding; Otsuka Pharmaceutical: Research Funding; Teijin Pharma: Research Funding; Chugai Pharmaceutical: Research Funding.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".