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Fanconi Anemia Research Symposium 2017 Hoatlin

Maureen Hoatlin
Maureen Hoatlin

Session overview and session wrap up "Rare Disease Research and Drug Development", presentation "Data Translator for Fanconi Anemia"

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Rare Disease Research & Drug Development Session Overview Maureen Hoatlin, PhD, MBA Fanconi Anemia Research Fund Annual Scientific Symposium Atlanta, GA September 2017 image credit: Max Ogden @denormalize
Ideas-to-Treatment Pipeline It takes too long The failure rate is unacceptable
Solutions explored in this session: embrace expertise of new stakeholders
Consider data integration
Data Integration method Easy to aggregate data from diverse sources that may differ widely in precision, accuracy and meaning. Flexible! New information can be added without affecting or being constrained by what is already there Can test inferences can reveal hidden knowledge https://goo.gl/q2K5Jx
Agenda Christine Colvis, PhD The Translator Project: Turning Biomedical Data Into Knowledge Melissa Haendel, PhD Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery Maureen Hoatlin A Data Translator for Fanconi Anemia Jeff Siegel, MD Challenges and Opportunities in Rare Disease Drug Development
A Data Translator for Fanconi Anemia image credit: Max Ogden @denormalize Maureen Hoatlin, PhD, MBA Fanconi Anemia Research Fund Annual Scientific Symposium Atlanta, GA September 2017
Intellectual input ** ** **First time at an FA meeting
Features of FA = Excellent Demonstrator Complex phenotype Many clinical and basic science questions Lots of genes and variants Multiple pathways, high complexity Environmental exposure component High unmet medical need Clinical relevance to broader population Incomplete data allows identification of gaps Model for other rare diseases
Example: How do defects in FA and Aldehyde pathways interact? Requires: Genes & variants for FA Genes & variants for ALDH Modifier variants Medical record integration Exposure history
Blackboard: flexible navigation of complex problems
Gene X For 12 of the 26 Fanconi gene set, we found other genes with similar regulatory regions (>300 total distinct). What genes contain similar transcription factor binding site ordering as those in the FA gene set? 1 2 3 Gene Y 24 YES NO FA gene set (1/26) TF Reg Elements 1 2 3Baseline >>Demonstrates data analysis from multiple sources ( JASPAR UCSC NCBI)
20 Fanconi Anemia Genes seed a network of: 3,058 interactions 972 genes Fanconi Gene (FG) FG -> FG Interaction What proteins are in the Fanconi anemia interaction network?
Result set seeded a network of: 10,259 Interactions 3,585 Genes Fanconi Gene (FG) FG -> FG Interaction Extended Fanconi Interaction Network
Gene Expression query Find genes that, when knocked down, induce gene-expression changes similar to knockdowns of FA core complex genes.
Experimental data underlying model A B C D E gene change in expression perturbation shRNA knockdown (3553 genes total, 6 FA genes) measurement gene expression (Luminex) (954 genes) FANCA Calculate intersections and union of signatures
500 databases
Future queries What candidate modifier variants should be examined in patients for phenotypic correlation and then functionally validated? What compounds compensate for the knock down of an FA gene?
Summary First steps to building a Knowledge Graph for Fanconi anemia --- inferences The complexity in FA makes it an ideal demonstrator project for Translator Teams leverage Monarch etc., work together, Open Science New data can be incorporated as it becomes available (e.g, drug screens, patient data).
www.probmods.org PGM structure: encodes assumptions (only 14 parameters are needed) conditional distributions: fit from experimental data Example probabilistic graphical model (PGM) encoding conditional dependence assumptions in model structure
Common mechanistic underpinnings of rare & common/complex disease Neutral ALDH KO: ? Rare, Detrimental Common, Subtle Rare and/or Beneficial DetrimentalBeneficial PopulationFrequency Acetaldehyde metabolism mutations: 500 Million People Affected Fanconi Anemia: 1 in 160,000 individuals worldwide ALDH2 OtherFANCgenes
SESSION WRAP UP
Session wrap up & suggestions (including Salvo La Rosas talk re NF Fndtn) Incorporate open & team science methods Shared data registries, curation and standardization needed Consider unified long-term strategy like NF example encompassing all phasesidea to treatment FARF: gap analysis, set strategy, consider opportunity costs, allocate funds where you want to go, locate expertise and partners Explore leveraging Translator and other efforts at NIH/NCATS including FA registry, natural history studies and curation Partner/consult with experienced and willing experts at FDA and drug development companies now, not later What questions do you have that Translator might answer? @hoatlinlab

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Fanconi Anemia Research Symposium 2017 Hoatlin

  • 1. Rare Disease Research & Drug Development Session Overview Maureen Hoatlin, PhD, MBA Fanconi Anemia Research Fund Annual Scientific Symposium Atlanta, GA September 2017 image credit: Max Ogden @denormalize
  • 2. Ideas-to-Treatment Pipeline It takes too long The failure rate is unacceptable
  • 3. Solutions explored in this session: embrace expertise of new stakeholders
  • 5. Data Integration method Easy to aggregate data from diverse sources that may differ widely in precision, accuracy and meaning. Flexible! New information can be added without affecting or being constrained by what is already there Can test inferences can reveal hidden knowledge https://goo.gl/q2K5Jx
  • 6. Agenda Christine Colvis, PhD The Translator Project: Turning Biomedical Data Into Knowledge Melissa Haendel, PhD Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery Maureen Hoatlin A Data Translator for Fanconi Anemia Jeff Siegel, MD Challenges and Opportunities in Rare Disease Drug Development
  • 7. A Data Translator for Fanconi Anemia image credit: Max Ogden @denormalize Maureen Hoatlin, PhD, MBA Fanconi Anemia Research Fund Annual Scientific Symposium Atlanta, GA September 2017
  • 9. Features of FA = Excellent Demonstrator Complex phenotype Many clinical and basic science questions Lots of genes and variants Multiple pathways, high complexity Environmental exposure component High unmet medical need Clinical relevance to broader population Incomplete data allows identification of gaps Model for other rare diseases
  • 10. Example: How do defects in FA and Aldehyde pathways interact? Requires: Genes & variants for FA Genes & variants for ALDH Modifier variants Medical record integration Exposure history
  • 11. Blackboard: flexible navigation of complex problems
  • 12. Gene X For 12 of the 26 Fanconi gene set, we found other genes with similar regulatory regions (>300 total distinct). What genes contain similar transcription factor binding site ordering as those in the FA gene set? 1 2 3 Gene Y 24 YES NO FA gene set (1/26) TF Reg Elements 1 2 3Baseline >>Demonstrates data analysis from multiple sources ( JASPAR UCSC NCBI)
  • 13. 20 Fanconi Anemia Genes seed a network of: 3,058 interactions 972 genes Fanconi Gene (FG) FG -> FG Interaction What proteins are in the Fanconi anemia interaction network?
  • 14. Result set seeded a network of: 10,259 Interactions 3,585 Genes Fanconi Gene (FG) FG -> FG Interaction Extended Fanconi Interaction Network
  • 15. Gene Expression query Find genes that, when knocked down, induce gene-expression changes similar to knockdowns of FA core complex genes.
  • 16. Experimental data underlying model A B C D E gene change in expression perturbation shRNA knockdown (3553 genes total, 6 FA genes) measurement gene expression (Luminex) (954 genes) FANCA Calculate intersections and union of signatures
  • 18. Future queries What candidate modifier variants should be examined in patients for phenotypic correlation and then functionally validated? What compounds compensate for the knock down of an FA gene?
  • 19. Summary First steps to building a Knowledge Graph for Fanconi anemia --- inferences The complexity in FA makes it an ideal demonstrator project for Translator Teams leverage Monarch etc., work together, Open Science New data can be incorporated as it becomes available (e.g, drug screens, patient data).
  • 20. www.probmods.org PGM structure: encodes assumptions (only 14 parameters are needed) conditional distributions: fit from experimental data Example probabilistic graphical model (PGM) encoding conditional dependence assumptions in model structure
  • 21. Common mechanistic underpinnings of rare & common/complex disease Neutral ALDH KO: ? Rare, Detrimental Common, Subtle Rare and/or Beneficial DetrimentalBeneficial PopulationFrequency Acetaldehyde metabolism mutations: 500 Million People Affected Fanconi Anemia: 1 in 160,000 individuals worldwide ALDH2 OtherFANCgenes
  • 23. Session wrap up & suggestions (including Salvo La Rosas talk re NF Fndtn) Incorporate open & team science methods Shared data registries, curation and standardization needed Consider unified long-term strategy like NF example encompassing all phasesidea to treatment FARF: gap analysis, set strategy, consider opportunity costs, allocate funds where you want to go, locate expertise and partners Explore leveraging Translator and other efforts at NIH/NCATS including FA registry, natural history studies and curation Partner/consult with experienced and willing experts at FDA and drug development companies now, not later What questions do you have that Translator might answer? @hoatlinlab