"Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human Logevity, Inc.)
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Transcript of "Hacking the Software for Life" - Brad Perkins (Chief Medical Officer, Human Logevity, Inc.)
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Hacking the Software for Life
Brad Perkins, MDChief Medical OfficerHuman Longevity, Inc.@BradleyAPerkins
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Reduced Sequencing Costs
Cost of human whole genome sequence drops from $100M in2001 to ~$1000 in 2014
Computational PowerMoore’s Law dynamics continue to decrease computing costs by
half every 24 months
Value-based CareHealth systems moving from
volume- to value-based models, providing opportunity for potential
disruption.
Machine LearningMachine learning goes mainstream due to quality breakthroughs and exponential increases in data availability
Four Trends Converge
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To predict everything that can be predicted from the human whole
genome sequence.
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Merging Genotype with Phenotype Data to Interpret Our Genetic Code
BIOLOGICAL DATA
INSIGHTS AND OUTCOMES
Genomics Phenotype
Computation
Diagnostics& Therapies
Medical Care Models
Machine Learning
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• Uncovered 150 million new single nucleotide genetic variants (SNVs), 82 million of which were novel
• Each sequenced genome contributed more than 8,500 novel variants
• Using billions of data points, the team mapped the invariant sites in the genome
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Single Gene (Mendelian) and Genome-Wide Association Studies (GWAS)
5% to 10% of all cancers result from inheritedsingle gene defects
• Cancers occurring at younger ages than usual
• Uncommon types of cancer• More than one type of cancer in a single
person• More than one childhood cancer in a set of
siblings
• Multiple common genetic variants contribute to risk
• Size effects are comparable to environmental factors (e.g. 2-5 fold)
• Strong support from GWAS on tens of thousands of individuals
Heritable Cancer (Mendelian): Mechanistic
Common Cancers (GWAS): Epidemiologic
Cancer Risk
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Human Microbiome: Early But Provocative Data
•Diabetes, Type 2•Cardiovascular disease•Cancer• Inflammatory Bowel Disease•Non-Alcoholic Fatty Liver Disease (NAFLD)
•Obesity•Autoimmune disorders•Autism
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1.77m 1.76m
MM
35y 38y
76kg82kg
PredictionSubject
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Age-Related Diseases are Leading Causes of Pre-Mature Mortality in US Adults, 50-74 years
* Ages 50 – 74, 2013 in USA; Adapted from Institute of Health Metrics and Evaluation, University of Washington. http://www.healthdata.org/results/data-visualizations
MALE30%
RISK OF DEATH*
CARDIOVASCULAR32%
CANCER36%
FEMALE20%
RISK OF DEATH*
CANCER40%
CARDIOVASCULAR25%
NEUROLOGIC3%
CIRRHOSIS3%
DIABETES6%
RESPIRATORY7%
OTHER13%
RESPIRATORY10%
DIABETES10%
CIRRHOSIS5%
NEUROLOGIC5%OTHER
5%
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Approaches to Curative and Preventive MedicineCurative PreventiveIdentify pathology Identify riskRestore health Decrease riskPromote individual
service ethicResponsibility is to the population
Clinical intervention predominates
Behavioral and social interventions are prominent, accompanied by clinical interventions
Fineberg H. JAMA 2013; 310(1):85-90
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Health Nucleus: PROACTIVE Curative AND Preventive Medicine
PROACTIVE Curative AND PreventiveIdentify pathology and riskRestore health and decrease riskPromote individual (and family/community) service ethicClinical, behavioral, and social interventions
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Multi-Modality Data GatheringGenomics Sciences, Historical Context, Quantitative Testing
Whole-genome (30x) Microbiome Metabolomics
Family & Med Hx Social Environmental Nutrition/Exercise
Advanced Imaging (full body MRI, Coronary CT)
Multi-Functional Testing Exhaustive Labs
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HEALTH NUCLE I CASE STUDY
150GB OVER AN 8 HOUR DAY
CollectedHeath Data(6-8 hr visit)
1. WHOLE BODY• MRI (Musculoskeletal/
Lipid Analysis)• MRI (Organ Specific
RSI)• DEXA Scan• In Body Biometrics• Over 70 Laboratory
Diagnostic Tests• 3D Forensic Imaging
2. SEQUENCING• Whole Genome• Metabolome• Microbiome
3. BRAIN• NeuroQuant® Brain MRI• 3D T2 Brain MRI• Diffusion MRI• Neurocognitive Testing• Quant. Gait Analysis
4. PULMONOLOGY• Pulmonary Function • Testing
5. CARDIO-Vascular• Echo (2D, 3D, 4D)• ECG• CT Scan (Coronary
Calcium Scoring)• 2 Week Ambulatory
Rhythm Testing
2
1
3
4
5
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HEALTH NUCLE I CASE STUDY
Fully-Automated, PACS-Integrated, Volumetric MRI
Alzheimer’sNormal
HEALTH NUCLE I CASE STUDY
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hindered
Magnetic Resonance-Based Restricted Spectrum Imaging for Early Cancer Detection
Cellularity Index
Nathan S. White et al. Cancer Res 2014;74:4638-4652
restricted
Time1/2
Displacement
RSI Model
Time
10 ms 20 ms 60 ms
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HEALTH NUCLE I CASE STUDY
A DYNAMIC EXPERIENCE THAT ALLOWS YOU TO EXPLORE YOUR HEALTH
• Review your report with you and your physician
• Develop a personalized healthcare strategy
• Personal avatar and healthcare record
The Living Avatar Report
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Genomic & Phenotype Findings to Identify Disease & Predict Risk (n=209)
Cardiovascular
Genomic RiskMendelian GWAS*20 (10%) 79 (38%)
Cancer
Phenotype Findings151 (72%)
78 (37%)3 (1%) 5 (2%)
Metabolic
Neurodegenerative
27 (13%) 24 (11%) 91 (44%)
11 (5%) 26 (12%) 4 (2%)
*Genome-Wide Association Study
Other 64 (31%) 95 (46%) 6 (3%)
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Medicine has been a clinical science, supported by data.
Medicine is about to become a data science, supported by clinicians.
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PRESENTED TO
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