Systems medicine and proactive P4
medicine have reached a tipping point:
transforming healthcare
predictive, preventive, personalized and participatory
Lee Hood
Institute for Systems Biology, Seattle
11-7-13
Paradigm change in medicine focusing on
the individual patient—systems medicine
• The genomic revolution – Democratization of genome
– Genetic variants cause many diseases
– High throughput genomic tools—sequencing, arrays, etc. leading to Big Data.
– Parts list of genes and proteins essential for systems biology
• The systems-biology revolution – Global and comprehensive analysis of biological
systems
– Deciphering biological complexity with Big Data
– Creating models of biology and disease that are predictive and actionable
I Participated in Five Paradigm Changes
in Biology over 40 years—managing
complexity
• Bringing engineering to biology—developed 5 instruments that led to high throughput biology and big data in biology
• The human genome project— invented enabling technology—automated DNA sequencing, advocate, participant, created one of first genomics companies, applying genomics to P4 medicine
• Cross-disciplinary biology—created 1st department—enabled technology development
• Systems biology—created 1st institute—deciphering biology complexity and applying it to disease
• Systems medicine and the emergence of proactive P4 medicine—early advocate and pioneer—is transforming healthcare
What I learned from the 5 paradigm changes
• Each fundamentally changed how we think about and practice biology and medicine.
• Each was met initially with enormous skepticism.
• Each new paradigm change required a new organizational structure to be realized.
What is system medicine?
• The digital information of the genome
Human Phenotypes are Specified by Two Types of Biological Information
• The environmental information that impinges upon and modifies the digital information
Phenotype
Big data is one essence of systems medicine: In 5-10
years each individual will be surrounded by a virtual
cloud of billions of multi-scale data points—big data
Transactional
11010100010101010110101010100100
0
Phenome
Na143 K 3.7 BP 110/70
HCT32 BUN 12.9 Pulse
110 PLT150 WBC 92
GCGTAG ATGCGTAGGCATGCATGCCATTATAGCTT
CCA
Genome
Proteome
arg-his-pro-gly-leu-ser-thr-ala-trp-
tyr-val-met-phe-
Transcriptome
UUAGUG AUGCGUCUAGGCAUGCAU
GCC
Epigenome
1101010001010101011010101010010001011010
10001
Single Cell
11010100010101010110101010100100
iPS Cells
11010100010101010110101010100100
Social Media
1101010001010101011010101010010001011010
10001
TeleHealth
1101010001010101011010101010010001011010
10001
Systems medicine: disease-perturbed
network of networks
Systems biology (medicine) infrastructure
and culture—drives discovery and
innovation
Holy Trinity: Biology Drives Technology Drives Computation
Learn languages Foster teams
Biologists Chemists Computer scientists Engineers Mathematicians Physicists Physicians
ISB
Broad
Whitehead
CSHL
HHMI
JCVI Sanger
SIB Novartis
George Institute
American Cancer Society
Joslin
Harvard-MIT
SCImago Institution Rankings of 3290 Research Institutions in 2012—ISB is 4th in the world
The Institute for Systems Biology (ISB)
has participated in creating 17 companies in
the 13 years of its existence—raised half a
billion dollars for investment and created
more than 500 jobs
Systems medicine has reached a tipping
point and is changing the practice of
healthcare
Systems Medicine Transforms Healthcare
• Provide fundamental insights into dynamical disease-perturbed networks – Enable mechanistic insights, diagnosis, therapy and prevention for the individual patient
• Family genome sequencing—identifying disease genes – Identify disease, wellness genes and drug-intolerant genes. For the identification for each
individual of 300 actionable genes
• Transform blood into a window to distinguish health from disease – Disease diagnostics, assess drug toxicity, assess wellness
– Human examples: lung cancer, PTSD, liver toxicity, liver hepatitis
• Stratify diseases into their distinct subtypes – For impedance match with appropriate drugs
– Human example: various cancers
• Stratify patients—drug adverse reactions, modifier genes to disease mechanisms, eg, early and late onset of Huntington’s disease, Variant genes increase mercury susceptibility in kids
• Permit a multi-organ approach to the study of disease – Unraveling the complexity of the individual patient’s disease
• Enable a new computational approaches to pioneering drug reuse and drug target discovery
– Re-engineer disease-perturbed networks to normalcy with drugs, Repurpose drugs.
faster and cheaper, drugs that prevent networks from becoming disease-perturbed
• Increasing focus on wellness
• Large-scale,multiparameter, Framingham-like clinical trials to permit all of the above
Family genome sequencing—
integrating genetics and genomics
to identify disease genes
Whole Genome Sequencing of Family of Four
The genome sequences of a family
permit one to use the principles
of Mendelian genetics to:
• Identify 70% sequencing errors—
current error rate—less than 1/106
• Identify rare variants
• Determine chromosomal haplotypes—
reduce disease search space
• Determine intergenerational mutation
rate—35 mutations per child
• Identify candidate genes for simple
Mendelian diseases
Unaffected parents
Children with
craniofacial
Malformation
(Miller Syndrome)
and lung disease
(ciliary dyskinesia)
Alternating Hemiplegia of Childhood
* *
N
* * * * * *
* * * * * * * * *
?
ATP1A3
spl /wt
spl /wt
spl /spl
spl /wt
CYYR1
del /wt
G>W /wt
G>W /del
G>W /del
del /wt
del /wt
del /del
del /del
wt /wt
C22orf25
del /wt
wt /wt
del /wt
del /wt
del /wt
INADL
Systems diagnostics--making blood a
window into distinguishing health from
disease
A systems approach to blood diagnostic for
identifying benign lung nodules in human
lung cancer
Integrated Diagnostics—Paul Kearney, Xiao-jun Li,
etc.
Indeterminate Pulmonary Nodules
Integrated Diagnostics
Is this cancer?
~3 million cases annually in the USA
Patrick Nana-Sinkham, MD Ohio State University
Lung Nodules Found by CT Scan in USA
PET Scan Needle Aspiration Bronchoscopic Biopsy
Repeat CT studies
Surgery for nodule removal
(Ost DE and Gould MK. Decision Making in the Patient with Pulmonary Nodules. Am. J. Respir. Crit. Care Med. October 6, 2011 as doi:10.1164/rccm.201104-0679C)
Cancer Risk
lower intermediate higher
Watchful waiting for 2
years
Look for cancer
surgery threshold “watchful waiting” threshold
~0.8 – 2.0 cm
3 million cases/yr 600,000 in “dilemma zone”
Lung cancer blood biomarker panel
• From 400 candidates to a panel of 13 proteins—using systems approaches to deal with signal to noise issues
• 92% accurate detection of malignant lung nodules
• Rule out about 70% of the benign nodules
• Save the healthcare system in US about $3.5 billion
• Bring “peace of mind” to many patients
• Panel is independent of 3 classical criteria for lung cancer—age, smoking history and size of lung nodule
Blood 13-protein Panel: Lung Rule Out
Function – Prevalence Adjusted
Rule Out
May 2013
Three Lung Cancer Networks Monitored:
12/13 biomarkers map to these networks
Blood Biomarker Panels for Detecting
Disease—Five Features
• Distinguish normal individuals from
diseased individuals
• Early diagnosis
• Follow progression
• Follow response to therapy
• Stratification of disease into different
subgroups for impedance match against
effective drugs—and proper prognosis
ISB Approaches to Disease Stratification—
One of the Grand Challenges for Medicine
• Patient iPS cells differentiated to relevant
cells in a test tube—and perturbation with
environmental signals
• Delineate dynamic networks in diseased
tissues
• Employ organ-specific blood proteins and
blood miRNA fingerprints
Personalized use of drugs for cancer
therapy
• Sequencing tumor genomes to identify
mutated protein targets for which we have
drugs—examples, melanoma, breast
cancer, colon cancer, etc.
Computational approaches to identifying
drug targets—re-engineering disease-
perturbed networks to be more normal and
repurposing drugs
Wellness assays of the future using
a fraction of a droplet of blood to
follow wellness/disease in 50
human organs—organ-specific
blood proteins
APLP1,SNAP25,LGI1,NACM1, CLSTN2
KINESIN,MAP1B,SYT3,CT
NND1
CAMKII,PCLO,GRIA4,GLUR3,NSF,ANK2,ENO2,DOCK3
,SCG3,
L1CAM,CTF1, ARF3,ANK3,
MAP3K12,CTNNA2,KIF3A,GFAP,CNTN1,ENC1
, CRMP2, SYNAPSIN1
NEUROMODULIN,HUC,CAMKIIA,RIN,SYNAPSIN1,RGS4,PEA15,RASGRF1,NR1
GNAO1, GNA13,
GABBR1, GLUR1,GRI
A1
MAP1A,SPTBN,
SPTBN4,FOXG1,EPHA5,N
CAM2, ELAVL3
TAU,MAP2, CAMKII, EPHA5,
UCHL1,NCAM1
RGS4,PEA15,CAMKII,RASGRF1,
NR1
Synaptic vesicle transport
Calcium mediated signaling
Synaptic Transmission
Neurogenesis
Cell surface receptor signaling
GPCR signaling
Cellular differentiation
Anatomical structure
development
Nerve growth factor signaling
179 Brain-Specific Blood Proteins
Reflect Key Networks (SRM assays)
Brian
Lymph node
lung
Spleen
Bone marrow
Stomach
Pancreas
Bladder Small intestine
Bone Marrow
Kidney
Liver
Heart
Muscle
Larynx Eye
Colon
PBMC
Skin
Tongue
Uterus
Cervix
Placenta Breast
Ovary
Testis Prostate
Thymus
Peripheral nerve
*: highlighted in GREEN circles
We have generated list of organ-specific blood proteins covering 19 major organs* in human body
Making Blood a Window into Health and Disease for 100s millions of patients:
50 organ-specific blood proteins from each of 50
organs—measure 2500 blood proteins
Integrated nanotech/microfluidics platform
Jim Heath, et al
cells out
300 nanoliters of plasma
Assay region
5 minute measurement 1. Uses fraction of droplet of blood
2. Assay takes 5 minutes to measure 50 proteins
3. Mid amole level of sensitivity
4. Already being used in hospitals
P4 pilot project: studying wellness in
100,000 patients longitudinally—20-30 years
We are rarely able to study what we
really want to related to health and
disease
Health: What do we really want to
understand from 100,000 well patients?
Wellness
Time
Wellness
Disease transition
Me
asu
rem
en
ts
Classic Blood Monitoring Blood collection and monitoring of traditional blood biomarkers • lipid panel, minerals, CRP, liver enzymes etc. • Blood tests 3 times a year – complete regeneration of blood in 120 days • Personalized and molecular feedback from changes in behavior
Self-Tracking (Quantified Self) Health monitoring though self-tracking - including digital devices • Activity, sleep, weight, blood pressure, stress, mood… • Instant & personalized feedback • Leveraging social networking for health
Genomics Genome sequencing & personalized interpretation • 300 actionable variants, and growing • Identify disease predisposition - personalized interventions to reduce disease • Pharmacogenomic analysis to optimize medication choices and dosages • Nutrigenomic analysis to optimize nutrition
Emerging/Novel Biomarkers Emerging: New applications for existing biomarkers • Microbiome
Novel: ISB tests–organ specific blood markers • Identify disease subtypes for proper drug match • Identify disease state and track disease progression
Big Data / Analytics
1. Collection, integration
& analysis of all accumulating health data
2. Correlations between different types and sources of health info
3. Discovery of novel indicators and patterns of health
4. Fuel scientific discovery
5. Personalize health information
6. Optimize clinical care
Follow transitions in 100,000 individuals
from
1) wellness to greater wellness,
2) from wellness to disease for all
major diseases and
3) from disease to wellness
Wellness/Health Demonstration Project:
Objectives/Benefits
• Short term—optimize wellness and reduce disease for each individual patient and reduce the costs
• Intermediate term—create a data base of wellness measurements to mine for the “wellness metrics”
• Long term—generate a data base from individuals that will allow us to follow transitions from wellness to disease for major diseases
• Long term—create a “Silicon Valley of Wellness and Disease Transitions” through the 100,000 person longitudinal database
P4 medicine arises from a convergence of
three healthcare thrusts
The emergence of P4 medicine:
predictive, preventive, personalized
and participatory
Digital Revolution Big Data
Systems Biology and Systems
Medicine
Consumer-Driven Healthcare and Social Networks
P4 Medicine
Three converging megatrends Driving the transformation of healthcare for patients
The “new medicine” or P4 medicine
• Is far more that just personalized
medicine—hence P4 medicine—nuanced
description of major features—predictive,
preventive, personalized and participatory
• Precision medicine is a terrible term
– It not precise—big data is all about dealing
with enormous signal to noise problems
– Precise does not describe any of the features
of the new medicine
P4 Medicine • Predictive
• Probabilistic health history from patient data clouds
• DNA sequence & longitudinal multi-parameter (blood) measurements
• Preventive
• Design of therapeutic and preventive drugs/vaccines via systems approaches
• Wellness
• Personalized
• Unique individual human genetic variation and environments mandates individual treatment
• Deal with patient as an individual not as population
• Patient will be their own control for longitudinal (lifelong) data analyses
• Participatory:
• Patient-driven social networks for disease and wellness will be a driving force in P4 medicine
• Society must access patient data and make it available to biologists for pioneering predictive medicine of the future
• How does one educate patients, physicians and the healthcare community about P4?
• IT for healthcare
How P4 medicine differs from
Evidence-based Medicine
• Proactive
• Focus on Individual
• Focus on Wellness
• Generate, mine and integrate enormous amounts of data on individual patients to produce predictive and actionable models of wellness/disease
• Large patient populations analyzed at single individual level (not population averages!) to generate quantized stratification of patient populations and create the predictive medicine of the future. N=1 experiments.
• Patient-driven social networks are a key to driving the acceptance of P4 medicine. The emergence of the quantified self networks in many cities demonstrates crowd sourcing and the ability to drive physician to start learning about wellness.
Conceptual Themes of P4 Medicine
Disease Demystified Wellness Quantified
P4 Medicine Predictive
Preventive
Personalized
Participatory
Two Challenges for Bringing Paradigm-
Changing P4 Medicine to Patients • Technical
– Biology informational science
– Strategies
– Technologies
– Computational/mathematical tools
• Societal – Education of patients, physicians and healthcare community
– Social networks will be key to educational process
– Bring P4 to the healthcare delivery system—a radical change
– Access to patient records and materials for mining the predictive medicine of the future
– Others: ethics, legal, social, security, privacy, policy, regulation, economics
How to bring P4 medicine to patients and the healthcare system? Through pilot projects for proof of principle—100K wellness project
Through creation of Institutes for Systems Medicine at medical schools to disseminate the vision Patient-activated social networks
Systems Medicine has Five Societal
Implications
• Turn sharply around escalating costs of healthcare—democratization of healthcare
• Digitalize medicine for the individual patient—a larger revolution than the digitization of information technologies and communication—patient-driven medicine and wellness
• Force a revision of business plans of every sector of healthcare industry—enormous opportunities for innovation and economic gain
• Systems Medicine will create significant wealth
• Transform the practice of medicine – Improved healthcare
– Decreased costs
– Enhanced innovation
Why the P4 Medicine Will Turn Around the
Sharply Escalating Costs of Healthcare
• Blood is a window into health and disease—early diagnosis will lead to saving
• Diagnosis will stratify disease and patients and create an impedance match effective drugs—companion diagnostics
• Re-engineering disease-perturbed networks to normalicy with drugs—new and less expensive strategy for drug target discovery
• Benefits of wellness-- survey biannually 2500 blood organ-specific protein measurements—50 from each of 50 organs—global early detection of the transition from health to disease
• Digital technologies exponentially increasing in measurement potential and decreasing in cost--sculpt for individuals the dimensions of health/disease while dramatically decreasing measurement costs, e.g. sequencing a human genome in 2000 about $300 million dollars; in 2012 about $3000—a 100,000-fold decrease in cost—digitalization of medicine
• Other medical advances arising from mechanistic insights—stem cells, neurodegenerative, aging, vaccines, cancer etc.
Health Policy Recommendations • Support the science of Systems Medicine and
application of P4 Medicine to healthcare
• Support the education of patients, physicians and the healthcare systems about P4 medicine
• Create effective patient (consumer)-driven social networks around P4 pilot projects
• Pioneer a truly effective IT for healthcare system that can manage the aggregation, mining, integration and modeling of patient data clouds of billions of features
• Create a “gold standard” web site of modern medical information—accessible for patients (and physicians)
• Encourage the industrial innovation that is emerging in the areas of wellness, P4 medicine and the emerging digitization of medicine
• Make certain that the patient’s data clouds are available for mining for the predictive medicine of the future—for our children and grandchildren.
