Dense, Longitudinal Phenotyping of Individuals Generates the Data to Transform Healthcare

TRISH Workshop 2018

Lee Hood

Senior VP and Chief Strategy OfficerInstitute for Systems Biology, Seattle

Senior Vice President and Chief Science OfficerProvidence St. Joseph Health, Seattle

July 31, 2018

The grand challenge for biology and medicine: Deciphering biological complexity—Caltech 1970

6

Scientific (quantitative)

Wellness: dense, longitudinal, personal phenotyping—2014Arivale 2015-today

7

Bringing P4 healthcare to

Providence St.Joseph Health

(and the US and other

Healthcare Systems)—2016-today

2016

I Participated in Seven Paradigm Changes in Biology Dealing with Complexity and Led to

Precision Medicine1

Brought engineering to biology—19701970

2The Human Genome Project—1990

3Cross-disciplinary biology—1992

4Systems biology—2000

Conceptualization of systems medicine and P4 healthcare 2000-2004

5

2014--The 108 Person Scientific (Quantitative) Wellness Project (Pioneers)Principal Investigators: Lee Hood and Nathan PriceUsing personal, dense, dynamic data clouds or dense phenotyping for wellness IRB approved study

Determinants of Health in U.S.

60%

30%

10%

Genetics

Behavior & environment

Health Care

Dense, dynamic, personal data clouds assess the integration of individual genetics and environment

Assays / Measurements for 108 Pioneers

Database of actionable

possibilities that will grow over

time

GENOME

Whole Genome Sequencing SNPs Millions

LABS

Detailed lab tests 3x(blood, urine, saliva)Clinical chem. 150

Metabolites 700Proteins 400Almost 1200

analytes

Continual self-tracking& lifestyle monitoring

MICROBIOME

Gut MicrobiomeRation of

microbial species 3x

Creating dense, dynamic, personal data clouds—dense phenotyping

SELF-TRACKING

Wellness Coaching for 108 ParticipantsA Critical Component of Scientific Wellness

Sandi Kaplan, MS, RD Craig Keebler, MD

Wellness Coach Study Physician

Education of pioneers about wellnessCompliance—70%Long term longitudinal recruitment of participants

Clinical Labs Discovery: Improvements in blood health with behavioral coaching

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Cardiovascular Diabetes Inflammation Nutrition

% ch

ange

in o

ut-o

f-ran

ge m

easu

rem

ents

Baseline 3 months 6 months

Improved by 33%Improved by 6% Improved by 12% Improved by 21%

Examples of Improved Wellness

• Arthritis and hemachromatosis• High cortisol and colon cancer• B12 deficiency and Bell’s palsy• Vitamin D and blocking genetic variants for

uptake• Increased WBC count and leukemia• LDL particle size abnormal with normal LDL

cholesterol—significant carotid arterial plaque• Ferritin trending down—anemia• Liver markers trending up—fatty acid liver

Creation of a Consumer-Based Scientific Wellness Company

2015 LAUNCH

Arivale Clients

• Currently about 4000 pioneers

• In all US states except New York. nationally distributed clinical trials

• To date, more than 60

• Wellness to disease transitions—for most major chronic diseases

• ISB agreement to analyze all data in collaboration with Arivale

The Hubble Telescope allows us to probe the dark matter of the universe just as dense and dynamic personal data clouds allow us to probe the dark matter of human biology and disease.

Personal Dense, Dynamic Data Clouds: Probing the DarkMatter of WellnessAnd Disease

Arivale/ISB Computation Platform to deal with personal data clouds

Statistical Correlations

Deriving Insights from Data: New Frontiers—3500 Statistical Correlations

A

B C

Price, Magis, Earls , Hood, et al, Nature Biotechnology (2017)

Identification of 70 multi-omic functional communities (modules) in the correlation

network.

Serotonin

Essential Fatty Acids

Bladder Cancer Risks

Microbiome diversity

Nutritional status

CardioMetabolic

• Cholesterol is positively associated with alpha-tocopherol (Vitamin E)

• Cholesterol is negatively associated with endogenous thyroxine

• A beneficial side effect of the drug thryroxine(Synthroid) is lowering LDL cholesterol

Total cholesterol community as one of70 communities

Price, Magis, Earls, Hood et al, Nature Biotechnology, 2017

Price, Magis, Earls…Hood, Nature Biotechnology, 2017

Dynamical Statistical Correlations between State 1 and State 2 Are Informative

Systems Medicine and Big Data

We can determine your polygenic risk for more than 100 diseases.

GWAS Variants Have Been Identified for Over 127 Diseases and Disease Traits

ADHD COPD MyopiaAlzheimer's disease Crohn's disease Obesity

Anorexia Esophageal cancer OsteoarthritisAsthma Gout Osteoporosis

Atrial fibrillation Grave's disease Ovarian cancerBreast cancer Hematocrit Pancreatic cancer

Bipolar disorder Hypertension Parkinson's diseaseBlood pressure Hypothyroidism Primary biliary cirrhosis

Bone mineral density Inflammatory bowel disease Prostate cancerInflammation Iron levels Psoriasis

Calcium Lung Cancer Rheumatoid arthritisCardiovascular disease Lupus Schizophrenia

Celiac disease Macular degeneration StrokeCholesterol levels Magnesium levels Type 1 Diabetes

Chronic kidney disease Metabolic syndrome Type 2 DiabetesColorectal cancer Migraine Ulcerative colitis

Coronary heart disease Multiple sclerosis Urate levels

LDL cholesterol in Participants Shows Monotonic Relationship with ‘Genetic Risk’

75

85

95

105

115

125

135

0

5

10

15

20

25

30

35

40

Very Low Low Medium High Very High

LDL C

hole

stre

ol (m

g/dL

)

Num

ber o

f par

ticip

ants

in th

is ris

k ra

nge

Genetic risk vs. Disease state

LDL Cholesterol Levels vs Genetic Risk (59 variants)Genetic Risk Baseline (No Meds)

Example: Cystine Is Negatively Correlated with Increasing Genetic Risk for Inflammatory Bowel Disease

cystine (plasma) glutathione (intracellular)cysteine (intracellular)

Cystine and cysteine are limiting precursors of glutathione synthesis.

reactive oxygen species (ROS)glutathione (intracellular)

O2-

H2O2

O2-OH

OHOxidative stress

Glutathione consumes ROS associated with chronic inflammation and colonic damage.

Only one individual in our study had been diagnosed with IBD.Independent case-control study identified abnormally low cystine in IBD patients1.

1. Sido, B. et al. (1998). Impairment of intestinal glutathione synthesis in patients with inflammatory bowel disease. Gut 42, 485–492.

Increasing genetic risk

State Transition: Metabolites, Clinical Chemistries, and Protein Expression Levels Diverge with Age and Provide Metrics for Wellness

Incr

easin

g Va

rianc

e of

Exp

ress

ion

Observe Increasing Outliers with Age:Metabolomics

Increasing decades of life

IS BIOLOGICAL AGE RELATED TO HEALTH?

John Earls

Type 2 diabetes INCREASED biological age across all analyte classes tested

Coronary Artery Disease INCREASED biological age across (almost) all analyte classes tested

Activity DECREASED biological age across all analyte classes tested

Health Aging

• Assess how Arivale program decreases ones biological age

• Do N=1 experiments to decrease one’s biological age

Assess Resilience

• Determine how resistant biological age changes are to relevant stressors—either short term or long term.

State Transitions: Wellness to Disease

Aug Dec Apr SepBlood &

MicrobiomeBlood Blood &

MicrobiomeBlood

Example – identifying outlier forpioneer with pancreatic cancer

Levels of protein of interest

N=1990All draws/All pioneers

Dec2015

Apr2016

Sep2016

Aug2015

Over the course of the four blood draws, this pioneer has consistently been the largest outlier for the protein of interest.

DiagnosedJanuary 2017

2015 2016 2017

1. Yachida, S et al. (2010). Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature, 467(7319), 1114–1117.

A 2010 study by Bert Vogelstein estimates at least 5 years between parental, non-metastatic founder cell and metastatic ability in pancreatic cancer1.

Identifying disease-perturbed networks pre-diagnosis: Early example for pancreatic cancer

0 1 2 3 4 5 6 7 8

VISIT 1

VISIT 2VISIT 3

VISIT 4

Distribution of # of significantly low p-values

Eve

948 clients/how many of 9 potentialcancer-related networks perturbed

Number of disease-perturbednetworks with >3 outlier proteins

Building computational systems for truly personalized disease diagnostics

• Define wellness state(s) from cohort

• Identify divergent values for each individual—analytes, networks and correlations—at each blood draw– Informed by dynamics– How unlikely?

• Functional analysis of individual divergences at each blood draw (signal to noise issue)—many divergences prior to diagnosis– Deep context learning to interpret

meaning– AI/Machine Learning/Systems

Biology• Every individual will know all

their disease-related outliers which eventually will trigger early reversal of most chronic diseases

• Scientific wellness will be the front-end of healthcare system

Early Reversal of Chronic Diseases: Preventive Medicine of the 21st Century

• In following about 4,000 or more patients over an extended time period, we have started to see wellness to 60 earliest disease transitions for all common diseases (as measured by blood analytes).

• Use data clouds to develop blood biomarkers for the earliest transitions for each disease and disease-perturbed network biology analyses to identify drug candidates/life style changes for therapies to reverse each disease at its earliest transition.

• Thus individuals will have diseases reversed before the diseases manifest themselves as a chronic disease phenotype—an approach to eventually eliminating chronic diseases--preventive medicine of the 21st century

Cognitive Learning –clinically proven—cell

phone deliveryMike Merzenich UCSFSynaptic Health with

Multimodal, IndividuallyAdaptive therapy (36

elements)

Dense Phenotyping Hood and Price ISB

PET Metabolic Scanning Mike Phelps UCLA

PSJH Strategic Partnership for Alzheimer’sHoag, Swedish, St. John’s, ISB and others

Multimodal Therapies

DiagnosticsBlood biomarkers for

1) earliest wellnessto disease

transition and 2) distinguish 3 AD subtypes

Rod Shankle HoagCocoa AD Trial

4-10 years before clinical diagnosis

Alzheimer’s ClinicsN=1 trials with ISB

Swedish, and possibly St. John’s

N=1 Experiments on Cancer

M.M., 69 yo female - Diagnosis: Invasive Bladder Carcinoma (pT3a; High grade)

Transurethral Bladder Biopsy

(TURBT)

PIONEER 100 PROGRAMPersonal GenomeRegular Blood Draws

Blood works MetabolomicsProteomicsMicrobiome

ACTION: “Anti-inflammatory Diet”à Very low w6/w3

Fall 2017~ 2015

Blood in urine

à Her2Copy number gainà CDKN2A p16 focal deletionà Cyclin D copy number gain

TUMOR DNA 262-sitesTargeted Exome sequencing

BLUE: MEASUREMENTGREEN = INTERVENTIONRED = specific patient findings

TRADITIONAL “PRECISION MEDICINE”

SURGERY: CYSTECTOMY

CHEMO-THERAPY

NEW: N=1 in P4 MEDICINEPERSONAL DENSE DYNAMIC DATA CLOUDS

TARGETED THERAPYü Trastuzumabü palbociclib

TRANSCRIPTOMEà Her2 increasedà Cyclin D higher (?)

PROTEOME (Somalogic 5000 proteins)

3D Tumor Organoidà drug susceptibiliy

Multi-plex IHC

Spring 2018

TUMOR WGSTumor MutationalBurden (TBD)

• High Tumor Lymphocyte Score• High IFNg signature score• Low TGF• Low Inflammation scoreconf

irmsconfirm

s

confirms

Indication for futureCHECKPOINT INHIBITORAs next line therapy

confirms//

Applications of Dense Phenotyping

Personal, Dense, Longitudinal Data Clouds on Human Individuals Will Allow Us To:

• Optimize human wellness (with coaching)• Follow disease progression, response to therapy

and return to health—understand disease mechanisms

• Identify earliest wellness to disease transitions and reverse early—preventive medicine of the 21st

century (follow those at high risk for a disease to find earliest transition)

• N=1 experiments, then stratification, are the key to deconvoluting some biological complexities, e.g., nutrition, resilience and aging

• 2-step clinical trails—First, assess with 50 patients. Second, then 50 patients all responders.

• Genentech took Herceptin to FDA for successful approval with only 46 patients.

Alaska, California, Oregon, Montana, Washington, New Mexico, Texas

ISB & Providence St. Joseph HealthAffiliation

States served 7

Hospitals 50

Physicians 7500

RNs 36,000

Unique patients served each year

5 million

Total Assets $23 billion

Third largest not-for-profit healthcare system in the USIntegrated Medical Electronic Health Records for 30 million patients

TRANSLATIONAL PILLAR:

ScientificWellness

Scientific Wellness: Opportunities for Astronauts• Optimize wellness: before, during and after space• Assess genetic risks for relevant diseases• Follow high risk individuals for earliest relevant

disease transitions. Look for analytes to reverse.• N=1 experiments to determine what the stress of

space does to 10s of individuals• Determine dynamical biological ages to assess

wellness and follow it as an astronaut goes though 3 state changes: before, during and after space.

• Employ more accurate assessments of bone density, immune competence, and other relevant physiological features

• Digital devices could be powerful—e.g. heart rate variability

• Assess resilience during training• Statistical correlations between earth, space, and

earth data will be informative.

Reduce Cost of Scientific Wellness

• Reduce the costs of the assays more than 10-fold in 5-10 years. Driven by scale of wellness studies. Genome. Metabolites. Immunity.

• Reduce dimensionality and optimize analyteanalyses. Tricorder with 5000 measurements in 10 years at home

• Use avatars to amplify enormous what coaches can do and so scale coaching.

• Insurance will pay for scientific wellness in 5 years. Wellness clinical trial

• Improve wellness for the individual• Reverse diseases at their earliest

transitions--eliminate many chronic diseases

• Reverse ever escalating healthcare costs

• Scientific wellness will be the front-end of healthcare systems—scientific wellness will be the key to dealing with individual health and chronic diseases

What Will 21st

Scientific Wellness Achieve?