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Decoding the Genetic Language of the Blood

Explore the potential of real-time measurements and liquid biopsy using blood tests to decode the genetic language of the blood for disease monitoring. Discover the importance of DNA and RNA biomarkers, deep learning in bioinformatics, and the clinical utility of measuring RNA in blood.

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Decoding the Genetic Language of the Blood

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  1. Decoding the Genetic Language of the Blood Howard Urnovitz Doctorate Univ. Michigan, 1979 Microbiology and Immunology

  2. Problem Only a minority of patients are benefiting from the breakthroughs in medicine today Current markers of disease stability or progression take months after treatment to get results Solution Real time measurements of disease using blood tests

  3. Biological Biomarkers – Which to choose DNA – Discovered as basis for heredity in 1945 RNA -Structural -Regulatory Proteins – end product of heredity coding

  4. Repetitive DNA codes for regulatory RNA DNA Biomarkers Options Hereditary Blueprints Modern Genetic Analysis

  5. Biological Biomarkers – Where to Measure Blood Tests or Liquid Biopsy Liquid Biopsy -Eliminates need for invasive tissue sample

  6. Hypothesis in late 1990’s: Foreign DNA good target for monitoring cancer US Cancer Market Segments 2017 DNA Technology Copy Number Instability Single Point Mutations or SNPs Gene Panels and Methylation/Epigenetics Ultra Deep Sequencing-Deep Learning Other Technology CTC-Circulating Tumor Cells Autoantibody Protein biomarker Result: $30 billion liquid biopsy Cancer Dx market

  7. Targeting Selected Mutations vs Whole Genome Sequencing US Cancer Market Segments 2017 DNA Technology Copy Number Instability Single Point Mutations or SNPs Gene Panels and Methylation/Epigenetics Ultra Deep Sequencing-Deep Learning Other Technology CTC-Circulating Tumor Cells Autoantibody Protein biomarker Most technologies target known mutations and compete in smallest slice of liquid biopsy market

  8. Liquid Biopsy-Advantage of Counting Whole Genome Plasma DNA

  9. Chronix Reports 10 ml sample of blood taken and processed to separate plasma DNA fragments (cell free DNA, cfDNA) are sequenced by Chronix lab or third party licensee Discrepancies from normal genome highlighted and displayed in easy readable visual format Results analysed and matched against Chronix database The CNI Score derived from Gains/Losses

  10. CNI Treatment Evaluation Test • Copy Number Instability score measures cancer mutations of DNA gains and losses • What about monitoring diseases that are non-malignant and have limited mutations • Would AI/Deep Learning help?

  11. The role of deep Learning in genomic bioinformatics • Identify interactions or clusters of messages • Predict disease regression or progression  • Suggest treatment options based on evidenced based medicine Min, Seonwoo, Byunghan Lee, and Sungroh Yoon. "Deep learning in bioinformatics." Briefings in bioinformatics 18.5 (2017): 851-869.

  12. Many entities announcing their entrance to AI and Personalized Medicine this week • Most protocols focused on bundling some-to-many biomarkers • Protocols mostly focused on treatment decision making • Good news: Studies to-date show statistical separation between disease and control • Next steps • Larger blinded studies for clinical utility • Improve accuracy • Test hypothesis that AI/DL can create liquid biopsies for monitoring non-malignant diseases

  13. AI/DL is looking for separations of disease vs healthy signals by bundling low accuracy tests for metabolites, proteins or DNA Question: what about measuring RNA?

  14. Hypothesis: Decoding the Command Language of the Blood will have Clinical Utility • RNA • Has powerful regulatory properties • RNA is found in plasma • High probability that RNA is the command language of a possible genome operating system

  15. RNA List of targets

  16. Repetitive Nucleic Acids

  17. Non-coding RNA Programming Language? Gomes, Clarissa PC, et al. Non-coding RNA research (2018)

  18. 1996 Observation: Mysterious blood RNA in Blood of Persian Gulf War Vets -enterovirus primers used on serum RNA

  19. Mysterious blood RNA • • Fragments of 22q11.2 • • Antibody Light Variable Region • 3/3 vets with same gene sequence

  20. Gene Rearrangements powers the immune system • Can AI/DL assist in understanding the rules behind gene rearrangements • Will this knowledge base assist in predicting early stage inflammatory reactions related to infections (microbiomes), chemicals, radiation or other stressors? Boyle, Eileen Mary et al. “B-cell malignancies : capture-sequencing strategies for identification of gene rearrangements and translocations into immunoglobulin gene loci.” (2014).

  21. Measure Everything – Because We Can • Measuring the entire genome in blood has clinical utility in monitoring patients undergoing cancer therapy • Hypothesis: measuring all RNA in blood will monitor the dynamics of inflammatory processes

  22. The Columbus Conundrum • Do we sail more ships to known destinations to increase knowledge base • Set sail for new worlds? 
 Hypothesis: Measuring total blood RNA could create new liquid biopsy markets: neurologic DX, arthritis DX

  23. From my perspective • Decoding the programming language of blood-RNA is the brave new world • The genome contains programming language mediated through unique RNA sequences (the vocabulary) that have “grammatical rules” for instructing a biologic processes to perform specific tasks • AI/DL would be a powerful approach for decoding and learning the language of health and disease • One important specialty for discovery of the new world of biomarkers will be expertise in the the world of RNA biology

  24. How remarkable is it: • that here in Martinsreid was one of the sites known for amazing advancements in RNA biochemisty at the Max Planck Institute for Biochemistry

  25. From an Investment Viewpoint • Bundling existing biomarkers • One needs to be cautious of GIGO (garbage in/garbage out) when creating new tests using existing sub-optimal biomarkers • Good chance for healthcare reimbursement since existing biomarkers have clinical history • Discovering new biomarkers • risk on achieving clinical utility • large return on investment

  26. CONCLUSION It’s my expert opinion that the breakthroughs in medicine and biology will occur most efficiently if RNA biologists/virologists are given the opportunity to use the powerful tools of AI/Deep Learning

  27. If interested on how we got here I'm starting to see this amazing pattern that there is a common thread throughout these diseases like cancer, AIDS, Gulf War Illnesses: you can always find a transposable or repeat element. So it makes sense that we figure out how to tie these diseases all together.

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