HUMAN DISEASE NETWORKTHE ROSETTA STONEOF GENOMIC MEDICINE

e-Clinical Avatars and PersonalizedMedicine Disease Networks

Artificial Life, Synthetic Genome, Semantic Maps of Medicine

e-CLINICAL AVATARSPERSONALIZED MEDICINEDISEASE NETWORKS

What if pharmaceutical company CEOs measured their success by their progress in curing diseases? What if patients were not recruited, but offered lifelong commitments for finding the disease cure? Would urgency of finding a cure have a different meaning if researchers were close to patients or were patients themselves? Could science, business, philanthropy and patients collaborate to change the culture and chaos of curing disease?

Artificial Life, Synthetic Genome, Semantic Maps of Medicine

The “God Moment” in drug development is here. “Life is basically the result of an information process — a software process,” says Craig Venter, “starting with the information in a computer, we put it into a recipient cell and convert it into a new species.” Craig Venter and his team have built the genome of a bacterium from scratch and incorporated it into a cell to make what they call the world’s first synthetic life form, a landmark experiment that paves the way for designer organisms that are built rather than evolve.1 Artificial genetics2 intends to apply personalized medicine to large patient populations by connecting large datasets of genomic information with clinical patient data. Leveraging its vast search capabilities, Google3 is making large, strategic, computing infrastructure investments in Adimab, a drug discovery startup, to develop “in-silico” drug models that pharmaceutical companies will use for accelerated research of targeted therapies. “e-Clinical avatars,”4 or simulated representations of patients, are being used to do “cool analysis” of clinical and genetic data.

A new patient-centric paradigm is emerging, where patients enroll in a lifelong protocol, where standard of care is driven by Personalized Medicine/Translational Medicine, and where pharmaceutical industry researchers, providers and academic medical centers collaborate to facilitate rapid channeling (near real time) of both safety and efficacy data back to researchers who can use this information to further improve the disease models for targeting of the drug in development — The Personalized Medicine Disease Network. These disease networks will allow researchers to adopt new ways for doing drug discovery; Fail-Often, Fail-Early and run clinical trials with fewer patients (and in less time — months compared to years). The networked nature of disease networks will allow pre-competitive collaborative exploration of many failures to uncover success (by pooling large datasets), much like the Coalition Against Major Diseases (CAMD)5 for finding cures for Alzheimer’s. Executing trials leveraging e-clinical avatars in tissue banks built on a foundation of co-morbidity driven, real-world data along with controlled clinical-trial data will ease patient recruitment, help in drug-repositioning, accelerate discoveries of best-in-class medicines and revolutionize R&D with information-based therapies developed under a new regulatory framework The time has come to usher in a new golden age of drug discovery/development — the networked age.

Given the thousands of clinical trials currently underway, and the incalculable billions of dollars of funding already spent, the pharmaceutical industry cannot possibly test all combinations on large patient populations for patients to see any benefits in their lifetime. The traditional clinical trial model fails as diseases like cancer and Alzheimer’s tend to be multi-factorial in nature, with molecular and phenotypic differentiation causing patient-level differences in effectiveness. A new approach in R&D innovation will come from genomically-enabled, semantically-interoperable, patient-centric collaborative disease networks of

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interconnected clinical research communities, research-based institutions, investigator networks, CROs, pharma, research informatics networks like CaBIG, providers, patients, labs and payers. Personalized healthcare will drive massive disease networks to facilitate molecular-level investigation of diseases, target optimal treatment for patients, and facilitate off-label use of personalized therapies supported by these participants and a whole ecosystem of trusted intermediaries and service providers.

The innovative paradigm is to create a distributed platform where dis-integrated information networks and ecosystems can semantically collaborate to accelerate drug discovery to find multiple genetic fingerprints of human diseases, pool data, combine results and share findings. The same paradigm is empowering patients and their advocacy groups to launch networks that would focus on discovery of drugs that might be beneficial for their co-morbidities. Leveraging these capabilities, patients can harness their own genomic data to self-generate their disease models, identify novel treatments and work with their providers in a highly involved manner — a true patient-centric revolution.

The days of the blockbuster “me too” drug are all but finished. With payers tightening up, it’s the cheapest drug that wins the market these days. That’s pushing pharmaceutical companies to develop new drugs that target smaller patient populations, while also examining their R&D portfolios to license new compounds and sponsor, build or become part of consortia-based disease networks. Payer awareness of what they buy and why they buy is not going to diminish. And if they have good alternatives which are less expensive, they will go to the less expensive, that’s the approach they will take. .

Pharma’s focus on unique pathways and models in an otherwise inherently complex and interdependent human system needs to be replaced with a semantically-interoperable disease network model. The key is a common standard, which allows us to prove therapies in small patient populations or in individual patients. Such disease biology models will fiercely evolve as semantic interoperability evolves, igniting several open-source public platforms, which will provide capabilities to make new genomic discoveries and develop new companion diagnostics by validating successes in similar molecular phenotypes, while pooled data on failed trials will contribute to better understanding of disease biology and more importantly, the failed drug’s mechanism of action.

Figure 1.

Phase IIIPhase II Ph I Discovery

Personalized Medicine-Based Disease Networks Can Save Patient Lives

Phase III Year 1 Years 12 – 15

Patient Diagnosedwith Cancer Patient Dies Domino Effect in Patient Mortality

Revolutionize Causality-Based, Experimental, Clinical Research withInformation-Based Therapies Developed under a New Regulatory Framework

(Accelerated Time Velocity in Finding Cures)

A Semantically interoperable disease network model can ignite true innovation in R&D.

Effectiveness of the treatments can be improved by tailoring treatments to specific groups of patients leveraging biomarkers (See Figure 2 below). Genomic sequencing and profiling can offer a fountain of information on an individual’s disease, the pathways involved and the interdependencies across diseases. In that sense, each patient represents an individual disease network on its own. Furthermore, aggregating the results at patient level can help drive novel research. Disease Networks are Personalized Medicine/Translational healthcare models integrating research and individual patient care. They are starting to appear in various business models (profit and non-profit), and there is an opportunity for pharmaceutical companies to create and sponsor these Disease Networks to drive new therapeutic insights and scientific discoveries while improving care delivery in specific diseases.

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Figure 2.

Drug discovery and delivery today is primarily population-based (one size fits all), and it takes too long (12-15 years) to bring a new effective therapy to the market. Clinical research is burdened with its own rules. What if research collaborations could create necessary incentives for all stakeholders, personalize healthcare and keep patients as part of a lifelong protocol? A new set of entrants are required who can create new methodologies built upon a foundation of personalized medicine/translational disease networks. Building a personalized medicine ecosystem will require large scale collaboration among stakeholders and extensive interconnected disease networks with robust biomarkers, biobanks, genomics labs, leading tools and applications for discovery, development, diagnostics and treatment. The time has come to bring drug discovery/development into the age of disease networks.

Many diseases like cancer or Alzheimer’s are multi-factorial diseases. Scientists and providers have known for a long time that only some patients respond to drugs, and understanding their genetic makeup will be critical to helping them with the disease. What if we could enter this information from patient samples obtained over their lifetime into a database combined with “Biomarker Factories”8 and then combine it with data from thousands of other patient samples? Several international and national efforts are afoot to create large biobank repositories where patient samples are maintained and the information obtained is managed in large databases. The potential to conduct database studies by mining the information in these biobanks for information-based therapies will be the new paradigm — a tremendous leap. Such approaches will identify compounds for drug-repurposing or create new insights where failed drugs may see a revival.

Personalized medicine labs are already using some Virtual Lab platforms to “fit the vision of ubiquitous access to the lab on the Web regardless of location.” Using the platform, researchers can do “cool analysis” of clinical and genetic data using “clinical avatars,” or simulated representations of patients. These “e-Clinical Avatars or computational biology algorithms” will help replace large trials with trials for sub-populations — replacing traditional drug discovery with information-based therapies for patients. In the past, Google has applied its algorithmic search to organizing patient health records and recently has applied large, venture-based investment to foray into drug discovery. Its capital and computing infrastructure investment in Adimab is a clear trend of new entrants leveraging their capabilities to develop “information-based therapies” in accelerated timeframes (weeks vs. months instead of years and decades). This ability to conduct virtual, in-silico simulations on large-scale protein structure data, build networked disease models, and then apply them to the targets’ protein structures to identify antibody binding sites could lead to new discoveries.

Effectiveness of treatment can be improved...

• 20–75% of patients do not receive effective treatment6

• > 100,000 deaths per year from adverse drug reactions in U.S.7

...by tailoring treatments to selected patient groups defined by biomarkers

Imagine if we had a thousand biomarkers!

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Artificial genetics intends to change personalized medicine by scaling it to large patient populations by connecting large datasets of genomic information with clinical patient data, stimulating creation of artificial life and chemical systems fully capable of evolution. Stemming creation of artificial life and applying semantics to understand how chemical structures are related to genetic behaviors will stimulate pursuit of chemical systems fully capable of evolution. Artificial genetics has become the new tool in genomic research to search biological samples in large datasets to extract known gene mutations.

Never having the full context of information is always a key issue in identifying disorders that are related to a particular target genes or working in new disease areas. We need to be able to assimilate thousands of datasets and understand the landscape quickly. How can we understand the different landscape for different questions posed? The data deluge always caused researchers to focus on few disorders rather than having the ability to study the inter-relationship of several diseases at once. The genomics revolution permits us to understand how all our molecular defects add up to all our diseases, not just a single disease. The ability to understand the interconnectedness and interdependency within all diseases — the network effect — is not possible in current pharmaceutical industry approaches driven by unique models.

Figure 3

Personalized Medicine/Translational Medicine Disease NetworksA new patient-centric paradigm is emerging where standard of care is driven by Personalized Medicine/Translational Medicine, which develops and integrates collaborative genomics-based branded disease networks, forges large-scale semantically interoperable information, and helps R&D develop new insights, and accelerates novel targeted personalized therapies and molecular biomarkers more effectively. Besides providing a new way to redefine human diseases and gain a broader understanding of disease mechanism, the genomic profile-based disease relationships can also help us to find potential new indications of existing drugs.

Semantic context is the quantum leap in innovation. Metadata enriches the semantic fabric over time and is key to finding insights within the data tombs.

Example of Human Disease Network Topology9

Connecting the Dots Is the Key to Meaningful Insights

The Implications of Human Metabolic Network Topology For Disease Comorbidity Ref: Lee et al. PNAS 105:9880-9885 (2008) Printed with Permission from PNAS “Copyright (2008) National Academy of Sciences, U.S.A.”

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Figure 4.

1) Silpa Suthram et al. PLoS computational biology (2010) vol. 6 (2) pp. e1000662 2) DNA Image — courtesy Berkeley 3) The human disease network. Ref: Goh K-I et al. PNAS vol. 104 no. 21 8685-8690 (2007) Printed with Permission from PNAS “Copyright (2007) National Academy of Sciences, U.S.A.”

Imagine if failures in drug discovery were rewarded and rewarded quickly. Pharma’s success rates in first-in-class medicines are dismal and with comparative effectiveness being pushed as part of healthcare reform, proving that a particular drug is best-in-class takes on a whole new meaning. To Fail-Often and Fail-Early is exactly what pharmaceutical companies are hoping for in the value of emerging personalized medicine disease networks. The decision to move ahead with larger trials needs to be based on key decisions and end points achieved. The ability to determine efficacy and safety at molecular levels is a new stage of translational approaches that will impact comparative effectiveness. These new personalized/translational medicine approaches shall lay out the decision frameworks necessary to promote the eventual first-in-class drugs, comparative effectiveness criteria to prove the best-in-class medicines and thus impact the scale of innovation across the entire pharmaceutical industry — a quantum leap in R&D innovation indeed.

The human disease network offers a fantastic model along with a graphical, semantic platform to explore all known genetic lineage and disease relationships. Physicians and other healthcare providers can apply this integrated information to personalize patient care through the interactive analysis of patient-specific medical/clinical variables. These cognitive networks shaped by inferencing engine capabilities can offer great insights for providers, clinical researchers and the biomedical community to identify molecular and disease-level linkages in a visually compelling view of data focusing and exploring only regions of interest.

Translational Approaches

Clnical Avatars Artificial Genetics Disease Network

Stratified Populations

Achieve Scale with ArtificialGenetics

Lead Compounds

Predictive Disease Networks Identify Lead Compounds

Leverage Semantically- Enabled Networked

Disease Models to Select Potential Candidates

Leverage Clinical Avatarsto Validate the Specific

Disease Model

Identify Patient Subgroups

Demonstrate Human POC

Leverage ArtificialGenetics to Scale

Personalized Medicine

Personalized Medicine

Applied to Stratified Patient Sub-Populations

Scientists Combine Results into the Disease Network

Artificial Intelligence Discovers New Information-Based Therapies

Patient updates his Clinical Avatar with his genomic profile; self-generates a

disease model; interrogates the network to identify

drug(s) for his co-morbidities; approaches the provider

with this knowledge

Translate Patient Findings into Transforming Drug Discovery in the Labs

Phases III & IVPhase II Phase I Preclinical

Combined Translational/Personalized Medicine Disease NetworksProspectively assessing

opportunities for patient selectionIdentifying patients who have an improved

clinical benefit to launched drugs

1 2 3

Efficacy and safety need to be investigated at a molecular levels and interrogated against Disease Networks.

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Figure 5. Human Disease Map10

The Human Disease Network Ref: Goh K-I et al. PNAS vol. 104 no. 21 8685-8690 (2007) Printed with Permission from PNAS “Copyright (2007) National Academy of Sciences, U.S.A.”

Only around 1 in 9 compounds make it through development and into the clinic. The major causes of attrition are efficacy and safety. The ability to identify multiple genetic accomplices for the same disease will enable researchers to get further molecular-level insights into understanding disease and how to increase compound efficacy by targeting multiple targets with a single compound. Understanding genetics has traditionally focused on how single genes turn on and off. Biological pathways are complex and inhibiting one target causes feedback loops and other chemical processes to circumvent the target, decreasing efficacy. The mapping of the human genome and recent newer technologies have helped us leverage genome-wide association11 studies to learn about the complexity of relationships within and across disease area genes.

Disease Networks are Personalized Medicine/Translational healthcare models integrating research and individual patient care. They are starting to appear in various business models (non-profit and for-profit), and are exemplified by M2Gen,12 TGen,13 and CollabRx.14 Therse is an opportunity for pharmaceutical companies to create and support these Disease Networks to drive new therapeutic insights and scientific discoveries, while improving care delivery in specific disease modalities.

Figure 6.

3-methylglutaconicaciduria

Aarskog-Scottsyndrome

ABCDsyndrome

Abetalipoproteinemia

26

Achondrogenesis_Ib

Achondroplasia

Achromatopsia

Acquiredlong_QT_syndrome

Acromegaly

Adenocarcinoma

Adenoma,periampullary

Adenomas

Adenosine_deaminasedeficiency

Adrenocorticalcarcinoma

Adult_iphenotype

Afibrinogenemia

Alagillesyndrome

Albinism

Alcoholdependence

Alexanderdisease

Allergicrhinitis

96

Alzheimerdisease

Amyloidneuropathy

Amyloidosis

Amyotrophiclateral

sclerosis

Androgeninsensitivity

Anemia

Angelmansyndrome

Angiofibroma,sporadic

117

Aniridia,type_II

Anorexianervosa

126

129

Aorticaneurysm

Apertsyndrome

Apolipoproteindeficiency

137

Aquaporin-1deficiency

144

Arthropathy

Aspergersyndrome

Asthma

Ataxia

Ataxia-telangiectasia

Atelosteogenesis

Atherosclerosis

Atopy

Atrialfibrillation

Atrioventricularblock

Autism

Autoimmunedisease

Axenfeldanomaly

182

Bare_lymphocytesyndrome

Barthsyndrome

Bart-Pumphreysyndrome

Basal_cellcarcinoma

192

Beckermusculardystrophy

Benzenetoxicity

198

Birt-Hogg-Dubesyndrome

Bladdercancer

Bloodgroup

217

Bothniaretinal

dystrophy

Branchiooticsyndrome

Breastcancer

Brugadasyndrome

Butterflydystrophy,

retinal

Complement_componentdeficiency

Cafe-au-laitspots

Caffeydisease

Cancersusceptibility

Capillarymalformations

Carcinoidtumors,

intestinal

Cardiomyopathy

Carneycomplex

275

Cataract

287

Cerebellarataxia

Cerebralamyloid

angiopathy

Cervicalcarcinoma

Charcot-Marie-Toothdisease

Cleftpalate

Coatsdisease

Coffin-Lowrysyndrome

Coloboma,ocular

Coloncancer

347

Conedystrophy

Convulsions

Cornealdystrophy

Coronaryartery

disease

Costellosyndrome

Coumarinresistance

Cowdendisease

CPTdeficiency,

hepatic

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377

378

379

Craniosynostosis

Creatinephosphokinase

Creutzfeldt-Jakobdisease

Crouzonsyndrome

Cutislaxa

396

Deafness

Dejerine-Sottasdisease

Dementia

Dentindysplasia,

type_II418

Denys-Drashsyndrome

422

Desmoiddisease

Diabetesmellitus

Diastrophicdysplasia

434

439

441

Duchennemusculardystrophy

Dyserythropoieticanemia

Dysfibrinogenemia463

EBD

Ectodermaldysplasia

Ectopia

Ehlers-Danlossyndrome

Elliptocytosis

474

Emphysema

Endometrialcarcinoma

EnhancedS-cone

syndrome

Enlargedvestibularaqueduct

Epidermolysisbullosa

Epidermolytichyperkeratosis

Epilepsy

Epiphysealdysplasia

Episodicataxia

Epsteinsyndrome

Erythrokeratoderma

Esophagealcancer

Estrogenresistance

Exudativevitreoretinopathy

Eyeanomalies

Factor_xdeficiency

Fanconianemia

Fanconi-Bickelsyndrome

Favism

Fechtnersyndrome

Fovealhypoplasia

549

Frasiersyndrome

558

Fundusalbipunctatus

G6PDdeficiency

Gardnersyndrome

Gastriccancer

Gastrointestinalstromaltumor

Germ_celltumor

Gerstmann-Strausslerdisease

Giant-cellfibroblastoma

Glaucoma

Glioblastoma

594

604

Goiter

GRACILEsyndrome

Graft-versus-hostdisease

Gravesdisease

Growthhormone

HDL_cholesterollevel_QTL

Heartblock

Hemangioblastoma,cerebellar

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Hemiplegic_migraine,familial

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Hemolytic-uremicsyndrome

Hemorrhagicdiathesis

665

Hepaticadenoma

Hirschsprungdisease

Histiocytoma

HIV

Holoprosencephaly

Homocystinuria

Huntingtondisease

Hypercholanemia

Hypercholesterolemia

Hypereosinophilicsyndrome

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733

Hyperlipoproteinemia

Hyperostosis,endosteal

Hyperparathyroidism

Hyperproinsulinemia

Hyperprolinemia

Hyperproreninemia

Hypertension

Hyperthroidism

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Hypertriglyceridemia

Hypoalphalipoproteinemia

Hypobetalipoproteinemia

Hypocalcemia

Hypocalciurichypercalcemia

Hypoceruloplasminemia

Hypochondroplasia

Hypodontia

Hypofibrinogenemia

Hypoglycemia

Hypokalemicperiodicparalysis

Hypothyroidism

792

Ichthyosiformerythroderma Ichthyosis

IgE_levelsQTL

803

Incontinentiapigmenti

Infantile_spasmsyndrome

809

Insensitivityto_pain

Insomnia

Insulinresistance

Intervertebral_discdisease

Iridogoniodysgenesis

Iris_hypoplasiaand_glaucoma

Jackson-Weisssyndrome

Jensensyndrome

830

833

Kallmannsyndrome

Keratitis

843

Keratoconus

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Kniestdysplasia

Larsonsyndrome

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Leanness,inherited

Lebercongenital_amaurosis

Leighsyndrome

Leopardsyndrome

Leprechaunism

Leprosy

Leukemia

Lhermitte-Duclossyndrome

Liddlesyndrome

LiFraumenisyndrome

Li-Fraumenisyndrome

Lipodystrophy

Lipoma

Lissencephaly

Listeriamonocytogenes

Loeys-Dietzsyndrome

Long_QTsyndrome

913

Lungcancer

Lymphoma

930

Macrocyticanemia

Macrothrombocytopenia

Maculardegeneration

Maculopathy,bull’s-eye

Malaria

942

Maple_syrup_urinedisease

Marfansyndrome

Marshallsyndrome

MASSsyndrome

Mast_cellleukemia

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May-Hegglinanomaly

McCune-Albrightsyndrome

Medulloblastoma

Melanoma Memoryimpairment

Menieredisease

Meningioma

Menkesdisease

Mentalretardation

Merkel_cellcarcinoma

Mesangialsclerosis

Mesothelioma

Migraine

1016

Miyoshimyopathy

MODY

Mohr-Tranebjaergsyndrome

Morningglorydisc

anomaly

Muenkesyndrome

Muir-Torresyndrome

Multipleendocrineneoplasia

Musculardystrophy

Myasthenicsyndrome

Myelodysplasticsyndrome

Myelofibrosis,idiopathic

Myelogenousleukemia

Myeloperoxidasedeficiency

Myocardialinfarction

Myoclonicepilepsy

1056

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Myopathy

Myotilinopathy

Myotoniacongenita

Myxoma,intracardiac

Nasopharyngealcarcinoma

Nephropathy-hypertension

Nethertonsyndrome

Neuroblastoma

Neuroectodermaltumors

Neurofibromatosis

1096

Neurofibromatosis

Neurofibrosarcoma

Neuropathy

Neutropenia

Nevosyndrome

11041105

Nicotineaddiction

Nightblindness

Nijmegen_breakagesyndrome

1113

Non-Hodgkinlymphoma

Nonsmall_celllung_cancer

Noonansyndrome

Norriedisease

Obesity

Obsessive-compulsivedisorder

Occipital_hornsyndrome

Oculodentodigitaldysplasia

Oligodendroglioma

Oligodontia

1140

Omennsyndrome

Opticatrophy

Orolaryngealcancer

OSMEDsyndrome

Osseousheteroplasia

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Osteoarthritis

Osteogenesisimperfecta

Osteopetrosis

Osteoporosis 1164

Osteosarcoma

Ovariancancer

1174

Pancreaticcancer

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Paragangliomas

Paramyotoniacongenita

Parathyroidadenoma

Parietalforamina

Parkes_Webersyndrome

Parkinsondisease

Partingtonsyndrome

PCWH

Pelizaeus-Merzbacherdisease

Pendredsyndrome

Perinealhypospadias

Petersanomaly

Peutz-Jegherssyndrome

Pfeiffersyndrome

Pheochromocytoma

Pickdisease

Piebaldism

1229

Pilomatricoma

1232

Placentalabruption

Plateletdefect/deficiency

1239

Polycythemia

Polyposis

PPM-Xsyndrome

Preeclampsia

Primarylateral_sclerosis

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Prostatecancer

Proudsyndrome

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Pseudohypoaldosteronism

Pseudohypoparathyroidism

Pyropoikilocytosis

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Rabson-Mendenhallsyndrome

Renal_cellcarcinoma

Retinal_conedsytrophy

Retinitispigmentosa

Retinoblastoma

Rettsyndrome

Rhabdomyosarcoma

Rheumatoidarthritis

Rh-modsyndrome

Rh-negativeblood_type

Riegersyndrome

Ring_dermoidof_cornea

Rippling_muscledisease

Roussy-Levysyndrome

Rubenstein-Taybisyndrome

Saethre-Chotzensyndrome

Salivaryadenoma

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SARS,progression_of

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Sea-blue_histiocytedisease

Seasonalaffective_disorder

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Self-healingcollodion_baby

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Sezarysyndrome

Shah-Waardenburgsyndrome

Shprintzen-Goldbergsyndrome

Sick_sinussyndrome

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Simpson-Golabi-Behmelsyndrome

1401

SMEDStrudwick_type

1414

Somatotrophinoma

Spastic_ataxia/paraplegia

Spherocytosis

Spinal_muscularatrophy

Spinocereballarataxia

1432

Spondyloepiphysealdysplasia

Squamous_cellcarcinoma

Stargardtdisease

Sticklersyndrome

Stomachcancer

Stroke

1456

Supranuclearpalsy

Supravalvar_aorticstenosis

Syndactyly

Systemic_lupuserythematosus

Tangierdisease

1476

T-celllymphoblastic

leukemia

Tetralogyof_Fallot

1490

Thrombocythemia

Thrombocytopenia

Thrombophilia

Thyroidcarcinoma

Thyrotoxicperiodicparalysis

Tietzsyndrome

Toenaildystrophy,

isolated

1518

1528

Turcotsyndrome

1545

Urolithiasise

Ushersyndrome

Uterineleiomyoma

van_Buchemdisease

1555

Ventriculartachycardia

Verticaltalus

Viralinfection

Vitelliformmacular

dystrophy

Vohwinkelsyndrome

von_Hippel-Lindausyndrome

Waardenburg-Shahsyndrome

Waardenburgsyndrome

Wagnersyndrome

WAGRsyndrome

Walker-Warburgsyndrome

Watsonsyndrome

Wegenergranulomatosis

Weill-Marchesanisyndrome

1586

Williams-Beurensyndrome

Wilmstumor

Wiskott-Aldrichsyndrome

Witkopsyndrome

Wolff-Parkinson-Whitesyndrome

1614

Zlotogora-Ogursyndrome

Adrenaladenoma

Adrenal_corticalcarcinoma

Aneurysm,familial_arterial

Autoimmunethyroiddisease

Basal_cellnevus_syndrome

Carcinoid_tumorof_lung

Central_coredisease

Coronaryspasms

2385

2785

Maculardystrophy

Medullary_thyroidcarcinoma

Pancreaticagenesis

3212

3229

Thyroidhormoneresistance

3512

3558

Combinedimmunodeficiency

Multiplemalignancysyndrome

Optic_nervehypoplasia/aplasia

5233

Renaltubularacidosis

Multiplesclerosis

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dysgenesis

ABCA1

ABCA4

ADA

ADRB2

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JAG1

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APOE

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AR

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ATP7A

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BDNF

BMPR1A

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CASP10

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CBS

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COX15

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CYP2A6

DBH

ACE

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DCX

DES

TIMM8A

COCH

NQO1

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DSPP

SLC26A2

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EDN3

EDNRB

EGFR

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ELA2

ELN

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ESR1

EYA1

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F7

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FCGR3A

FCMD

FGA

FGB

FGD1

FGFR1

FGFR3

FGFR2

FGG

FOXC1

FLNB

G6PD

GABRG2

GARS

GATA1

GCK

GCNT2

GCSL

GDNF

GJA1

GJB2

GJB3

GPC3

GNAI2GNAS

GSS

MSH6

GYPC

GUCY2D

HEXB

CFH

HNF4A

HOXD10

HRAS

HSD11B2

HSPB1

HTR2A

IL2RG

IL10

IL13

INS

INSR

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IRF1

JAK2

KCNE1

KCNH2

KCNJ11

KCNQ1

KCNQ2

KIT

KRAS

KRT1

KRT10

LAMA3

LMNA

LOR

LPP

LRP5

SMAD4

MAPT

MATN3

MECP2

MEN1

MET

CIITA

MITF

MLH1 NR3C2

MPO

MPZ

MSH2

MSX1

MSX2

MUTYH

MXI1

MYF6MYH6

MYH7

MYH8

MYH9

MYO7A

NBN

NDP

NDUFV1

NDUFS4

NF1

NF2

NOS3

NRL

NTRK1

OPA1

SLC22A18

PAFAH1B1

PARK2

PAX3

PAX6

PAX9

PDGFB

PDGFRA

PDGFRL

PDE6B

PDHA1

ENPP1

SLC26A4

PGK1

SERPINA1

PIK3CA

PITX2 PITX3

PLEC1

PLOD1

PLP1

PMP22

PMS2

PPARG

PRKAR1A

PRNP

PRODH

PSEN1

PTCH

PTEN

PTPN11

PTPRC

PVRL1

RAG1

RAG2

RASA1

RB1

RDS

REN

RET

RHAG

RHCE

RHO

RLBP1

RP1

RPGR

RPE65

RPS6KA3

RYR1

RYR2

SCN4A

SCN5A

SCNN1B

SCNN1G

SDHA

SDHBSDHD

SGCD

SHH

SLC2A2

SLC4A1

SLC6A4

SLC6A8

SLC34A1

SNCA

SOX3

SOX10

SPTA1

SPTB

STAT5B

ELOVL4

STK11

ABCC8

TAP2

TAZ

TBP

TCF1

TCF2

TG

TGFBR2

TGM1

THBD

TNF

TP53

TPO

TSHR

TTN

TTR

TYR

USH2A

VHL

VMD2

WAS

WT1

XRCC3

PLA2G7

HMGA2

DYSF

AXIN2

MAD1L1

RAD54L

IKBKG

TCAP

PTCH2

WISP3

BCL10

PHOX2B

LGI1

VAPB

MYOT

KCNE2

NR2E3

USH1C

FBLN5

POMT1

GJB6

SPINK5

CHEK2

ACSL6

CRB1

AIPL1

RAD54B

PTPN22

BSCL2

VSX1FOXP3

PHF11

PRKAG2

NLGN3

CNGB3

RETN

RPGRIP1

NLGN4X

ALS2

CDH23

DCLRE1C

PCDH15

CDC73

OPA3

BRIP1

MASS1

ARX

FLCN

Abacavirhypersensitivity

18

Acrocallosalsyndrome

Acrocapitofemoraldysplasia

Acrokeratosisverruciformis

Acromesomelicdysplasia

53

Adrenoleukodystrophy

Adrenomyeloneuropathy

ADULTsyndrome

Agammaglobulinemia

AIDS

77

AldosteronismAlopecia

universalis

Alperssyndrome

87

Alpha-actinin-3deficiency

92

Alportsyndrome

Amelogenesisimperfecta

Analbuminemia

107

Andersondisease

Anhaptoglobinemia

Ankylosingspoldylitis

Antley-Bixlersyndrome

Aplasticanemia

Aromatasedeficiency

Arthrogryposis

162

Atransferrinemia

Atrichia w/papular lesions

171

Bardet-Biedlsyndrome

BCGinfection

Beckwith-Wiedemannsyndrome

Bernard-Souliersyndrome

Bethlemmyopathy

Blausyndrome

210

Blepharospasm

Blue-conemonochromacy

Bombayphenotype

Bosley-Salih-Alorainysyndrome

Brachydactyly

Buschke-Ollendorffsyndrome

Calcinosis,tumoral

Campomelicdysplasia

Cartilage-hairhypoplasia

279

292

294Ceroid

lipofuscinosis

Ceroid-lipofuscinosis

CETPdeficiency

Cholelithiasis

Cholestasis

313

Chondrocalcinosis

Chondrosarcoma

Chorea,hereditary

benign

320

Chudley-Lowrysyndrome

329

Chylomicronretentiondisease

Ciliarydyskinesia

CINCAsyndrome

Cleidocranialdysplasia

Cockaynesyndrome

Codeinesensitivity

344

Colorblindness

Congestiveheartfailure

Conjunctivitis,ligneous

357

Coproporphyria

Craniometaphysealdysplasia

CRASHsyndrome

Crigler-Najjarsyndrome

Crohndisease

Cystathioninuria

Cysticfibrosis

CystinuriaDarierdisease

Debrisoquinesensitivity

Dentalanomalies,

isolated

Dentdisease

De Sanctis-Cacchionesyndrome

DiGeorgesyndrome

438

Dosage-sensitivesex

reversal

Double-outletright ventricle

Downsyndrome

452

453

Dyskeratosis

Dysprothrombinemia

461

Dystonia

EEC syndrome

471

Ellis-van Creveldsyndrome

Enchondromatosis

Erythremias

Erythrocytosis

Ewingsarcoma

Exostoses

527

Fertileeunuch

syndrome

535

Fibromatosisl

539

Fish-eyedisease

Fitzgerald factordeficiency

544

545

Frontometaphysealdysplasia

Fumarasedeficiency

Gaucherdisease

584

Gilbertsyndrome

GM-gangliosidosis

Greenbergdysplasia

626

Guttmachersyndrome

Haim-Munksyndrome

Hand-foot-uterussyndrome

Harderoporphyrinuria

HARPsyndrome

Hay-Wellssyndrome

646

Heinzbody

anemia

HELLPsyndrome

Hemangioma

Hematuria,familial_benign

Hemochromatosis

Hemoglobi_Hdisease

Hemophilia

Heterotaxy

Heterotopia

Hex_Apseudodeficiency

679

Homocysteineplasma

level

699 701

Hoyeraal-Hreidarssonsyndrome

HPFH

HPRT-relatedgout

H._pyloriinfection

Hyalinosis,infantilesystemic

Hydrocephalus

Hyperalphalipoproteinemia

Hyperandrogenism

Hyperbilirubinemia

Hyperekplexia

727

Hyper-IgDsyndrome

734

Hypermethioninemia

Hyperphenylalaninemia

Hyperprothrombinemia

Hypertrypsinemia

Hyperuricemicnephropathy

Hypoaldosteronism

Hypochromicmicrocytic

anemia

Hypogonadotropichypogonadism

Hypohaptoglobinemia

Hypolactasia,adulttype

780

Hypophosphatasia

Hypophosphatemia

Hypophosphatemicrickets

785

Hypoprothrombinemia

Inclusionbody

myopathy

Ironoverload/deficiency

Joubertsyndrome

Juberg-Marsidisyndrome

Kanzakidisease

Kartagenersyndrome

Kenny-Caffeysyndrome-1

Kininogendeficiency

Langermesomelicdysplasia

Larondwarfism

LCHADdeficiency

Leadpoisoning

Leiomyomatosis

Leri-Weilldyschondrosteosis

Lesch-Nyhansyndrome,

891

Leydigcell

adenoma

LIG4syndrome

Limb-mammarysyndrome

Lipoproteinlipase

deficiency

Longevity

Lowesyndrome

Low reninhypertension

Lymphangioleiomyomatosis

Lymphedema

945

MASAsyndrome

McKusick-Kaufmansyndrome

969

Melnick-Needlessyndrome

982

Meningococcaldisease

Mephenytoinpoor metabolizer

Metachromaticleukodystrophy

Metaphysealchondrodysplasia

Methemoglobinemia

10011002

Mevalonicaciduria

Microcephaly

Micropenis

Microphthalmia

Muckle-Wellssyndrome

Mucopolysaccharidosis

Mycobacterialinfection

Myelokathexis,isolated

1050

Myeloproliferativedisorder

Nemalinemyopathy

1080

Nephrolithiasis

Nephronophthisis

1090

Neurodegeneration

Nonakamyopathy

Norumdisease

1119

Ocularalbinism

1133

Odontohypophosphatasia

Opremazolepoor metabolizer

Orofacial cleft

Osteolysis

Osteopoikilosis

Otopalatodigitalsyndrome

Ovarioleukodystrophy

Pachyonychiacongenita

Pagetdisease

Pallister-Hallsyndrome

Palmoplantarkeratoderma

Pancreatitis

Papillon-Lefevresyndrome

Pelger-Huetanomaly

Periodontitis

Phenylketonuria

1227 Plasminogendeficiency

1238

Polydactyly

Poplitealpterygiumsyndrome

Porphyria

Precociouspuberty,

male

Prematureovarianfailure

1265

Proguanilpoor metabolizer

Proteinuria

Pseudohermaphroditism,male

Psoraisis

Pulmonaryfibrosis

RAPADILINOsyndrome

Rapp-Hodgkinsyndrome

Red hair/fair skin

Refsumdisease

Restingheartrate

Restrictivedermopathy,

lethal

1325

1335

Rothmund-Thomsonsyndrome

Salladisease

Sarcoidosis

Schindlerdisease

1361

Senior-Lokensyndrome

1376

Septoopticdysplasia

Sexreversal

Shortstature

Sialidosis

Sialuria

Sicklecell

anemia

Situsambiguus

Smith-Fineman-Myerssyndrome

Smith-McCortdysplasia

Sotossyndrome

Spinabifida

Split-hand/footmalformation

Spondylometaphysealdysplasia

1438

Startledisease

STAT1deficiency

Steatocystomamultiplex

1446

Surfactantdeficiency

Sutherland-Haansyndrome-like

1466

Symphalangism,proximal

Synostosessyndrome

Synpolydactyly

Tallstature

1475

Tay-Sachsdisease

Thalassemias

Thymine-uraciluria

Tolbutamidepoor

metabolizer

1519

Trichodontoosseoussyndrome

Trichothiodystrophy

1526

Tropicalcalcific

pancreatitis

Tuberculosis

Tuberoussclerosis

Twinning,dizygotic

1542

UV-inducedskin damage

Velocardiofacialsyndrome

Virilization

1565

1580

Weaversyndrome

Weyersacrodentaldysostosis

WHIMsyndrome

Wolframsyndrome

Wolmandisease

Xerodermapigmentosum

1611

Yellownail

syndrome

Zellwegersyndrome

Adrenocorticalinsufficiency

Chondrodysplasia,Grebetype

2327

Combinedhyperlipemia

2354

Diabetesinsipidus

3037

3144

Ovariandysgenesis

3260van_der_Woude

syndromeBasal

gangliadisease

4291

5170

Pituitaryhormonedeficiency

Renalhypoplasia,

isolated

Ovariansex cordtumors

CombinedSAP deficiency

Multiplemyeloma

SERPINA3

ACTN3

ADRB1

NR0B1

ALAD

ALB

ABCD1

ALPL

ATP2A2

ATRX

AVPR2

FOXL2

BTK

RUNX2KRIT1

CETP

CTSC

CFTR

CLCN5COL4A4

COL6A1

COL6A2

COL6A3

COL10A1

CPOX

CTH

CYP2C19

CYP2C9

CYP2D6

CYP11B1

CYP11B2

CYP19A1

CYP21A2

DKC1

DLX3

DNAH5

DPYD

DRD5

ERCC2

ERCC3

ERCC5

ERCC6

EVC

EWSR1

EXT1

F2

F9

FH

FOXC2

FLNA

FLT4

FSHR

FTL

NR5A1

FUT2OPN1MW

GHR

GLB1

GLI3

GLRA1

GNRHR

GP1BB

HADHA

HBA1

HBA2

HBB

HEXA

HFE

HLA-B

HOXA1

HOXA13HOXD13

HP

HPRT1

HSPG2

IFNG

IFNGR1

IHHIRF6

KNG1

KRT16

KRT17

L1CAM

LBR

LCAT

LHCGR

LIG4

LIPA

LPL

MAT1A

MBL2

MC1R

MCM6

MTHFD1

MTRMTRR MVK

NAGA

NPHP1

ROR2

OCRL

PAH

PAX2

PDGFRB

PEX1

PEX7

PEX10

PEX13

ABCB4

PLG

POLG

POR

PPT1

PSAPPTHR1

PXMP3

PEX5

OPN1LW

RMRP

SFTPC

SHOX

SLC3A1

SOX9

SPINK1

STAT1

TBX1

TBCE

TERC

TF

TITF1

TPM2

TSC1TSC2

UMOD

WFS1

CXCR4

FGF23

MKKS

GDF5

TP73L

DNAH11

TNFRSF11A

HESX1

EIF2B4

EIF2B2EIF2B5

NOG

RECQL4

GNEENAM

ZMPSTE24

LEMD3

SLC17A5

DNAI1

SAR1B

SLC45A2

UGT1A1

DYM

BCOR

PEX26

HR

CFC1

ANKH

CARD15

NSD1

VKORC1

MCPH1

PANK2

CIAS1

ANTXR2

NPHP4

The human disease networkGoh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabasi A-L (2007) Proc Natl Acad Sci USA 104:8685-8690′

Supporting Information Figure 13 | Bipartite-graph representation of the diseasome. A disorder (circle) and a gene (rectangle) are connected if the gene is implicated in the disorder. The size of the circle represents the number of distinct genes associated with the disorder. Isolated disorders (disorders having no links to other disorders) are not shown. Also, only genes connecting disorders are shown.

Disorder Class

Disorder Name

BoneCancerCardiovascularConnective tissue disorderDermatologicalDevelopmentalEar, Nose, ThroatEndocrineGastrointestinalHematologicalImmunologicalMetabolicMuscularNeurologicalNutritionalOphthamologicalPsychiatricRenalRespiratorySkeletalmultipleUnclassified

5233 Placental steroid sulfatase deficiency5170 Ovarian hyperstimulation syndrome4291 Cerebral cavernous malformations3558 Ventricular fibrillation, idiopathic3512 Total iodide organification defect3260 Premature chromosome condensation w/ microcephaly, mental retardation3229 Pigmented adrenocortical disease, primary isolated3212 Persistent hyperinsulinemic hypoglycemia of infancy3144 Optic nerve coloboma with renal disease3037 Multiple cutaneous and uterine leiomyomata2785 Hypoplastic left heart syndrome2385 Creatine deficiency syndrome, X-linked2354 Congenital bilateral absence of vas deferens2327 Chronic infections, due to opsonin defect1614 Yemenite deaf-blind hypopigmentation syndrome1611 XLA and isolated growth hormone deficiency1586 Weissenbacher-Zweymuller syndrome1580 Warfarin resistance/sensitivity1565 Vitamin K-dependent coagulation defect1555 VATER association with hydrocephalus1545 Unna-Thost disease, nonepidermolytic1542 Ullrich congenital muscular dystrophy1528 Trismus-pseudocomptodactyly syndrome1526 Trifunctional protein deficiency1519 Transposition of great arteries, dextro-looped1518 Transient bullous of the newborn1490 Thanatophoric dysplasia, types I and II1476 Tauopathy and respiratory failure1475 Tarsal-carpal coalition syndrome1466 Sweat chloride elevation without CF1456 Subcortical laminar heterotopia1446 Stevens-Johnson syndrome, carbamazepine-induced1438 Stapes ankylosis syndrome without symphalangism1432 Spondylocarpotarsal synostosis syndrome1414 Solitary median maxillary central incisor1401 Skin fragility-woolly hair syndrome1396 Silver spastic paraplegia syndrome1383 Severe combined immunodeficiency1376 Sensory ataxic neuropathy, dysarthria, and ophthalmoparesis1361 Schwartz-Jampel syndrome, type 11347 Sandhoff disease, infantile, juvenile, and adult forms1335 Robinow syndrome, autosomal recessive1325 Rhizomelic chondrodysplasia punctata1297 Pyruvate dehydrogenase deficiency1267 Prolactinoma, hyperparathyroidism, carcinoid syndrome1265 Progressive external ophthalmoplegia with mitochondrial DNA deletions1263 Prion disease with protracted course1239 Pneumothorax, primary spontaneous1238 Pneumonitis, desquamative interstitial1232 Pituitary ACTH-secreting adenoma1229 Pigmented paravenous chorioretinal atrophy1227 Pigmentation of hair, skin, and eyes, variation in1183 Papillary serous carcinoma of the peritoneum1174 Pallidopontonigral degeneration1164 Osteoporosis-pseudoglioma syndrome1153 Ossification of the posterior longitudinal spinal ligaments1140 Oligodontia-colorectal cancer syndrome1133 Oculofaciocardiodental syndrome1119 Norwalk virus infection, resistance to1113 Noncompaction of left ventricular myocardium1105 Newfoundland rod-cone dystrophy1104 Nevus, epidermal, epidermolytic hyperkeratotic type1096 Neurofibromatosis-Noonan syndrome1090 Neural tube defects, maternal risk of1080 Nephrogenic syndrome of inappropriate antidiuresis1057 Myokymia with neonatal epilepsy1056 Myoglobinuria/hemolysis due to PGK deficiency1050 Myelomonocytic leukemia, chronic1016 Mitochondrial complex deficiency1002 Methylcobalamin deficiency, cblG type1001 Methionine adenosyltransferase deficiency, autosomal recessive982 Melorheostosis with osteopoikilosis969 Medullary cystic kidney disease959 Mastocytosis with associated hematologic disorder945 Mandibuloacral dysplasia with type B lipodystrophy942 Malignant hyperthermia susceptibility930 Lynch cancer family syndrome II913 Lower motor neuron disease, progressive, without sensory symptoms891 Leukoencephalopathy with vanishing white matter868 Laryngoonychocutaneous syndrome847 Keratosis palmoplantaria striata845 Keratoderma, palmoplantar, with deafness843 Keratitis-ichthyosis-deafness syndrome833 Juvenile polyposis/hereditary hemorrhagic telangiectasia syndrome830 Jervell and Lange-Nielsen syndrome809 Infundibular hypoplasia and hypopituitarism803 Immunodysregulation, polyendocrinopathy, and enteropathy, X-linked792 Hystrix-like ichthyosis with deafness785 Hypoplastic enamel pitting, localized780 Hypoparathyroidism-retardation-dysmorphism syndrome734 Hyperkeratotic cutaneous capillary-venous malformations733 Hyperkalemic periodic paralysis727 Hyperferritinemia-cataract syndrome701 Homozygous 2p16 deletion syndrome699 Homocystinuria-megaloblastic anemia, cbl E type679 High-molecular-weight kininogen deficiency665 Hemosiderosis, systemic, due to aceruloplasminemia646 Hearing loss, low-frequency sensorineural626 Greig cephalopolysyndactyly syndrome604 Glutathione synthetase deficiency594 Glomerulocystic kidney disease, hypoplastic584 Giant platelet disorder, isolated558 Fuchs endothelial corneal dystrophy549 Foveomacular dystrophy, adult-onset, with choroidal neovascularization545 Focal cortical dysplasia, Taylor balloon cell type544 Fluorouracil toxicity, sensitivity to539 Fibular hypoplasia and complex brachydactyly535 Fibrocalculous pancreatic diabetes527 Fatty liver, acute, of pregnancy474 Emery-Dreifuss muscular dystrophy471 Elite sprint athletic performance463 Dystransthyretinemic hyperthyroxinemia461 Dyssegmental dysplasia, Silverman-Handmaker type453 Dysalbuminemic hyperthyroxinemia452 Dyggve-Melchior-Clausen disease441 Dopamine beta-hydroxylase deficiency439 Dissection of cervical arteries438 Disordered steroidogenesis, isolated434 Dilated cardiomyopathy with woolly hair and keratoderma422 Dermatofibrosarcoma protuberans418 Dentinogenesis imperfecta, Shields type396 Cyclic ichthyosis with epidermolytic hyperkeratosis379 Craniofacial-skeletal-dermatologic dysplasia378 Craniofacial-deafness-hand syndrome377 Craniofacial anomalies, empty sella turcica, corneal endothelial changes357 Conotruncal anomaly face syndrome347 Colonic aganglionosis, total, with small bowel involvement344 Cold-induced autoinflammatory syndrome329 Chylomicronemia syndrome, familial320 Choreoathetosis, hypothyroidism, and respiratory distress313 Cholesteryl ester storage disease294 Cerebrovascular disease, occlusive292 Cerebrooculofacioskeletal syndrome287 Central hypoventilation syndrome279 Cavernous malformations of CNS and retina275 Carpal tunnel syndrome, familial217 Bone mineral density variability210 Blepharophimosis, epicanthus inversus, and ptosis198 Beta-2-adrenoreceptor agonist, reduced response to192 Beare-Stevenson cutis gyrata syndrome182 Bannayan-Riley-Ruvalcaba syndrome171 Attention-deficit hyperactivity disorder162 Athabaskan brainstem dysgenesis syndrome144 Arrhythmogenic right ventricular dysplasia137 Apparent mineralocorticoid excess, hypertension due to129 Anxiety-related personality traits126 Anterior segment anomalies and cataract117 Angiotensin I-converting enzyme107 Analgesia from kappa-opioid receptor agonist, female-specific96 Alternating hemiplegia of childhood92 Alpha-thalassemia/mental retardation syndrome87 Alpha-1-antichymotrypsin deficiency77 Aldosterone to renin ratio raised53 Adrenal hyperplasia, congenital26 Achondrogenesis-hypochondrogenesis, type II18 Acampomelic campolelic dysplasia

Quantifying Diseasethrough Networks

Proteomic Networks

Disease Heatmapof AutoimmuneDiseases

Functional GeneModule Networks

Exploring the Human Disease Networks for Novelty, Biomarkersand Genetic Switches15, 16, 17, 18, 19

Silpa Suthram et al. PLoS computational biology (2010) vol. 6(2) pp.e100662

Joel Dudley and AutlButte. PacificSymposium on Biocomputing(2009) pp.27-38

Good Signal Detectionin Public Data Networks

Marina Sirota etal. PLoS genetics(2009) vol.5(12)pp.e1000792

What is the genetic uniqueness across metabolic diseases?

Can similar diseases be treated by same drugs, leading to new indications?

How do we identify biomarkers across diseases?

What is the role of inter-disease genomic relationships?

What are the genes with opposing risk profiles?

How do we identify similarities and differences across disease pairs?

Disease Networks are the key to understanding the disease at a molecular level, along with the complexity of relationships, multiple genetic accomplices and the target feedback loops.

| e-Clinical Avatars And Personalized Medicine Disease Networks | 8

Disease Networks can be leveraged for research across many therapeutic classes which have economic and scientific attractiveness for companion diagnostics but also provide these additional benefits:

• Newdriversforpre-clinicaldiscovery(intellectualproperty)• Newmarketchannelsfordiagnostic/prognosticproducts• Providecaptivepatientpopulationstoperformclinicaltrials• Enablecomparativeeffectivenessanalysisforproducts/diagnostics• Providechannelsforvalue-addedservices

Building Disease Networks incorporating genotype and phenotype will enable biomarker-driven adaptive trials resulting in integration of data to build patient-centric, predictive disease models that impact individual health.

Leveraging such network models to interrogate comprehensive repositories of biological screening data and internal experiment data will permit identification of compounds that are potent against multiple targets in single or multiple pathways and predictive insights into mechanisms of action. By integrating compound data into network analysis, new models will be developed that are able to predict complex human networks and facilitate direct identification of genes which are causal for disease. Disease-drug relationships are of great interest because such knowledge can not only significantly enhance our understanding of disease mechanisms, but also accelerate many aspects of drug discovery. A large-scale disease-drug network provides a valuable resource to revisit disease classification, reposition therapeutic agents, and identify potential drug side effects and targets.

Figure 7. Open-access platforms, such as SAGE,20 to share and extract intellectual property to develop knowledge for screening compounds against Disease Networks, represent the future

Case Study:TRAIN Patients funding their own disease networksThe Redstone Acceleration and Innovation Network (TRAIN),21 a transformational disease network formed from a group of non-profit foundations, works to create translational medicine approaches across multiple disease areas, where traditional clinical research has not created meaningful drugs. This represents an example where patients are increasingly taking charge of their health, funding and creating patient-lead, patient-centric, collaborative health ecosystems, which share information to accelerate findings from bench to bedside. Such large-scale innovation across multiple disease areas is key to tapping into the interdependencies of multi-factorial disease networks.

Generate Predictive Inter-Disease Molecular Networks

Semantic Data Aggregation

“Drug A”Signature

Profile

“Drug B” Signature

Profile

Predictive Networks (Disease State Signatures) Combinedwith Drug Signatures Can Identify Biomarkers

ContributoryNetwork

Enrichment

Disease Subtype 2

Disease Subtype 1 Disease Subtype 3

Disease Subtype 4

Disease Subtype 6

Disease Subtype 5

Disease Subtype 7

Disease Subtype 8

Disease Subtype 9

2

Achieve Scale with ArtificialGenetics

| e-Clinical Avatars And Personalized Medicine Disease Networks | 9

Figure 8.

Case Study: Cystic Fibrosis Foundation Connecting the Scientist to the PatientThe Cystic Fibrosis Foundation23 is disease network focused on a single disease area, and has thousands of patients who receive care while their data is pooled into a longitudinal patient database. The foundation has created an incentive-based mechanism to work with scientists and researchers in order to work with this central database, but also to return the results so that they could be pooled across the patients. The access to patient communities and the ability to manage data centrally while delivering pipeline benefits to patients, pharma, providers, researchers and scientists makes this an enviable network.

Case Study: The Alpha-1 Foundation Aggregating Individual Disease Models for Therapeutic ResearchThe Alpha-1 Foundation24 leverages several elements of personalized medicine/ translational medicine disease networks beginning with the patient at its core. It is supported by a biorepository, access to the research community, genetics research along with a family linkage program, several clinical resource centers, and a medical and scientific advisory committee. With a keen focus on this particular disease area supported by a DNA and a tissue biobank, the foundation knows its patients populations which investigators can tap into for reasons of clinical study enrollment, and pharmaceutical companies can leverage to develop new treatments.

Case Study: CollabRx A Personalized Healthcare Collaborative Ecosystem Commercial entities and healthcare providers are beginning to organize research and care delivery around disease states — focusing efforts to develop deep knowledge of disease to translate into the best care and outcomes. CollabRx25

has created a personalized research service to guide oncologists and their patients in making treatment decisions based on deep molecular understanding of tumors. CollabRx is a model for the future of Pharma and Personalized Healthcare. It has been focused on finding therapies leveraging virtual therapy development and focused care.

CollabRx is a virtual biotech, leveraging a distributed network of scientists, research labs, clinicians and contract research organizations to accelerate the research cycle. It is enabled by a web-based collaborative research platform and has partnered with non-profit associations, government agencies, and other

The Redstone Acceleration and Innovation Network22

Collaborative, mission-driven, results-oriented, and strategic in their use of capital, these groups are motivated solely by moving promising therapies

from the laboratory bench to the patient’s bedside as rapidly as possible — even those that do not

directly fund therapy development.

| e-Clinical Avatars And Personalized Medicine Disease Networks | 10

organizations to draw together leading-edge research to translate into treatment insights. It offers a personalized research service called CollabRx ONE, which provides patients and their providers with deep insight into specific treatment options. The particular research service leverages genomic technologies to analyze tumor biopsies at the molecular level and identify therapies that will have the most impact given the molecular profile of the cancer.

Case Study: Merck-Moffitt Cancer Center Alliance (M2Gen)A Personalized Medicine/Personalized Healthcare Biobank — Leadership in InnovationM2Gen26 is a patient-centric, provider-supported personalized medicine disease network (consortium network) with a vision to accelerate new drug development, while improving patient care. It is an alliance between Merck, a pharmaceutical company and Moffitt cancer center, a non-profit institution. This network brings unique value to the market through several key innovations.

Process innovations that inform discovery of new medicines and diagnostics include a large prospective translational research project (Total Cancer Care) with lifelong patient consent, support from a consortium of cancer care treatment centers and community-based physicians; collection, relation and interpretation of tissues, clinical data, genomic data and images from a large population; a mechanism to identify molecular signatures for diagnosis, prognosis and prediction of response to therapy — a means to personalize cancer therapy and to improve the quality of medicine. Product and service innovations to speed clinical research, improve patient care and inform evidence-based medicine include trial-matching-powered clinical trial services, personalized therapy and follow-up by matching “pipeline” drugs to patients harboring molecular targets, enablement of an evidence-based approach to cancer care, measurement of short- and long-term outcomes, and a robust dataset for observational studies (safety, outcomes and health economics). The business model provides high-value services to pharmaceutical companies, health plans and government that in turn fund expansion of a cancer care network and informatics infrastructure with a potential to make cancer care more efficient for physicians and more convenient for patients through electronic health records that integrate and make available patient information from multiple sources.

Moffitt’s Total Cancer Care™ (TCC®)27 initiative is focused on creating evidence-based protocols for oncology treatment, derived from molecular and genomic insights into tumors, with a plan to combine IT, science and clinical treatment and provide evidence-based guidelines to improve care and outcomes for cancer patients.

Figure 9. Moffitt Center Figure28

Making the New Human

| e-Clinical Avatars And Personalized Medicine Disease Networks | 11

Rare Diseases Clinical Research and Pre-Competitive Drug Discovery NetworksRecently, the U.S. Food and Drug Administration formed a rare disease review group to start building competencies, ideas and necessary incentives to engage the pharmaceutical industry in this neglected area. Although some companies like Genzyme have had profitable operations focusing on a rare disease portfolio, it has not been a focus of large pharmaceutical companies due to the economics of smaller patient populations. But a strategic shift has started to occur. As rare diseases tend to represent smaller patient populations, the industry stalwarts have recognized application of personalized medicine to this area and are stepping up to the plate. Pfizer and GSK have ramped up new rare disease research units, and Novartis recently received approval for its personalized medicine drug, Ilaris for rare inflammatory disease with genetic underpinnings. Although these signs of activity are a boon for patients with rare diseases, the industry needs incentives such as market exclusivity for longer periods. At a national level in the United States, The Rare Diseases Clinical Research Network (RDCRN)29 is funded by the NIH, and in Europe, an alliance-based network, EURODIS30 is being created to increase collaboration across rare diseases.

To stimulate drug discovery via collaboration (and not competition), several pharmaceutical companies contributed patient data from failed clinical trials for Alzheimer’s to the Coalition Against Major Diseases (CAMD)31 database, a research network, working under the auspices of the Critical Path Institute.

Eli Lilly has launched a new collaboration initiative, called the Lilly Phenotypic Drug Discovery Initiative,32 or PD2 (pronounced PD-squared), which leverages the strategies of a FIPNet33 — a fully integrated pharmaceutical network — to create a new interface with researchers across the world, especially to leverage research talent out of India and China to focus on several disease areas. Merck is also engaged in a FIPNet for its Asian strategies.

Semantic Maps of MedicineCreativity requires insights into mountains of information, which in turn needs tools that give visual and intuitive access to semantic data networks. Making sense of all this connectivity is challenging. Scientific and commercial value is waiting to be unlocked via mining vast stores of connected data. The ability to mine and find correlations across various types of data to make better decisions will help researchers make discoveries across and among information sources which previously had no connectivity. These types of systems provide scientists powerful research-friendly environments where they can quickly and effectively explore the wealth of connected information. These new “semantic engines” organize and present information in a visually appealing manner, highlight connections and help scientists rapidly find correlations between previously unavailable data.

As part of building a common infrastructure and community, semantically aware networks can compile a metadata repository of semantically interoperable data for use in integrative genomics and build predictive computational disease models. For example, a super disease network like SAGE is working on collating, curating and hosting these datasets for the community. Such super disease networks could be used to enhance the human disease network model of disorders and disease genes linked by previously known disorder-gene associations (see Figure 10 below). Semantic interoperability would need to include standardized nomenclature for synonyms for genotypic, phenotypic gene expression34 and “omics” information across large patient populations in order to conduct genome-wide association studies. The human disease network, when combined with semantic interoperability, could offer such a model along with a graphical, semantic platform to explore all known and previously unknown relationships across disease areas.

Semantic interoperability is the foundation for building coherent Disease Networks.

| e-Clinical Avatars And Personalized Medicine Disease Networks | 12

Figure 10. Human Disease Network35

The Human Disease Network Ref: Goh K-I et al. PNAS vol. 104 no. 21 8685-8690 (2007) Printed with Permission from PNAS “Copyright (2007) National Academy of Sciences, U.S.A.”

Disease Network models with knowledge of protocolized medicine will reduce the importance of physicians in terms of deciding on patient treatment. Physicians are currently central to the decision-making process regarding patient treatment. The rise of health technology assessment bodies has led to a number of “best practice” protocols being developed at the national level, facilitated by developments in IT systems. As physicians become increasingly performance-managed on compliance to protocols, they are becoming less important as decision makers. The relative role of physicians in decision making is likely to diminish with Intensive Care and Oncology already heavily protocolized, and referral management systems, such as that which is based on Map of Medicine36 (www.mapofmedicine.com), are increasingly used.

Artificial Life, Synthetic Genome and e-Clinical AvatarsAs per the ScienceDaily (Mar. 23, 2009) article, “Artificial genetics: New Type of DNA Has 12 Chemical Letters Instead of Usual 4”, the work on Artificial Genetics being done by Steven Benner, Ph.D., “is the basis for the viral load detector, which helps personalize the health care of those 400,000 patients annually infected with hepatitis B, hepatitis C, and HIV, the cause of AIDS. Benner says that the artificial DNA system is poised to become an essential tool in genomics research. The 12 letter alphabet already underlies new work at the National Human Genome Research Institute to connect large quantities of genomic data with human medicine.”37

Artificial Genetics represents a tidal wave in changing the scale of personalized medicine to leverage large patient populations by connecting large datasets of genomic information with clinical patient data, stimulating creation of artificial life and chemical systems fully capable of evolution. Stemming creation of artificial life and applying semantics to understand how chemical structures are related to genetic behaviors will stimulate pursuit of chemical systems fully capable of evolution. Artificial genetics has become the new tool in genomic research to search biological samples in these large datasets and extract known gene mutations.

3-methylglutaconicaciduria

Aarskog-Scottsyndrome

ABCDsyndrome

Abetalipoproteinemia

26

Achondrogenesis_Ib

Achondroplasia

Achromatopsia

Acquiredlong_QT_syndrome

Acromegaly

Adenocarcinoma

Adenoma,periampullary

Adenomas

Adenosine_deaminasedeficiency

Adrenocorticalcarcinoma

Adult_iphenotype

Afibrinogenemia

Alagillesyndrome

Albinism

Alcoholdependence

Alexanderdisease

Allergicrhinitis

96

Alzheimerdisease

Amyloidneuropathy

Amyloidosis

Amyotrophiclateral

sclerosis

Androgeninsensitivity

Anemia

Angelmansyndrome

Angiofibroma,sporadic

117

Aniridia,type_II

Anorexianervosa

126

129

Aorticaneurysm

Apertsyndrome

Apolipoproteindeficiency

137

Aquaporin-1deficiency

144

Arthropathy

Aspergersyndrome

Asthma

Ataxia

Ataxia-telangiectasia

Atelosteogenesis

Atherosclerosis

Atopy

Atrialfibrillation

Atrioventricularblock

Autism

Autoimmunedisease

Axenfeldanomaly

182

Bare_lymphocytesyndrome

Barthsyndrome

Bart-Pumphreysyndrome

Basal_cellcarcinoma

192

Beckermusculardystrophy

Benzenetoxicity

198

Birt-Hogg-Dubesyndrome

Bladdercancer

Bloodgroup

217

Bothniaretinal

dystrophy

Branchiooticsyndrome

Breastcancer

Brugadasyndrome

Butterflydystrophy,

retinal

Complement_componentdeficiency

Cafe-au-laitspots

Caffeydisease

Cancersusceptibility

Capillarymalformations

Carcinoidtumors,

intestinal

Cardiomyopathy

Carneycomplex

275

Cataract

287

Cerebellarataxia

Cerebralamyloid

angiopathy

Cervicalcarcinoma

Charcot-Marie-Toothdisease

Cleftpalate

Coatsdisease

Coffin-Lowrysyndrome

Coloboma,ocular

Coloncancer

347

Conedystrophy

Convulsions

Cornealdystrophy

Coronaryartery

disease

Costellosyndrome

Coumarinresistance

Cowdendisease

CPTdeficiency,

hepatic

Cramps,potassium-aggravated

377

378

379

Craniosynostosis

Creatinephosphokinase

Creutzfeldt-Jakobdisease

Crouzonsyndrome

Cutislaxa

396

Deafness

Dejerine-Sottasdisease

Dementia

Dentindysplasia,

type_II418

Denys-Drashsyndrome

422

Desmoiddisease

Diabetesmellitus

Diastrophicdysplasia

434

439

441

Duchennemusculardystrophy

Dyserythropoieticanemia

Dysfibrinogenemia463

EBD

Ectodermaldysplasia

Ectopia

Ehlers-Danlossyndrome

Elliptocytosis

474

Emphysema

Endometrialcarcinoma

EnhancedS-cone

syndrome

Enlargedvestibularaqueduct

Epidermolysisbullosa

Epidermolytichyperkeratosis

Epilepsy

Epiphysealdysplasia

Episodicataxia

Epsteinsyndrome

Erythrokeratoderma

Esophagealcancer

Estrogenresistance

Exudativevitreoretinopathy

Eyeanomalies

Factor_xdeficiency

Fanconianemia

Fanconi-Bickelsyndrome

Favism

Fechtnersyndrome

Fovealhypoplasia

549

Frasiersyndrome

558

Fundusalbipunctatus

G6PDdeficiency

Gardnersyndrome

Gastriccancer

Gastrointestinalstromaltumor

Germ_celltumor

Gerstmann-Strausslerdisease

Giant-cellfibroblastoma

Glaucoma

Glioblastoma

594

604

Goiter

GRACILEsyndrome

Graft-versus-hostdisease

Gravesdisease

Growthhormone

HDL_cholesterollevel_QTL

Heartblock

Hemangioblastoma,cerebellar

Hematopoiesis,cyclic

Hemiplegic_migraine,familial

Hemolyticanemia

Hemolytic-uremicsyndrome

Hemorrhagicdiathesis

665

Hepaticadenoma

Hirschsprungdisease

Histiocytoma

HIV

Holoprosencephaly

Homocystinuria

Huntingtondisease

Hypercholanemia

Hypercholesterolemia

Hypereosinophilicsyndrome

Hyperinsulinism

733

Hyperlipoproteinemia

Hyperostosis,endosteal

Hyperparathyroidism

Hyperproinsulinemia

Hyperprolinemia

Hyperproreninemia

Hypertension

Hyperthroidism

Hyperthyroidism

Hypertriglyceridemia

Hypoalphalipoproteinemia

Hypobetalipoproteinemia

Hypocalcemia

Hypocalciurichypercalcemia

Hypoceruloplasminemia

Hypochondroplasia

Hypodontia

Hypofibrinogenemia

Hypoglycemia

Hypokalemicperiodicparalysis

Hypothyroidism

792

Ichthyosiformerythroderma Ichthyosis

IgE_levelsQTL

803

Incontinentiapigmenti

Infantile_spasmsyndrome

809

Insensitivityto_pain

Insomnia

Insulinresistance

Intervertebral_discdisease

Iridogoniodysgenesis

Iris_hypoplasiaand_glaucoma

Jackson-Weisssyndrome

Jensensyndrome

830

833

Kallmannsyndrome

Keratitis

843

Keratoconus

845

847

Kniestdysplasia

Larsonsyndrome

868

Leanness,inherited

Lebercongenital_amaurosis

Leighsyndrome

Leopardsyndrome

Leprechaunism

Leprosy

Leukemia

Lhermitte-Duclossyndrome

Liddlesyndrome

LiFraumenisyndrome

Li-Fraumenisyndrome

Lipodystrophy

Lipoma

Lissencephaly

Listeriamonocytogenes

Loeys-Dietzsyndrome

Long_QTsyndrome

913

Lungcancer

Lymphoma

930

Macrocyticanemia

Macrothrombocytopenia

Maculardegeneration

Maculopathy,bull’s-eye

Malaria

942

Maple_syrup_urinedisease

Marfansyndrome

Marshallsyndrome

MASSsyndrome

Mast_cellleukemia

959

May-Hegglinanomaly

McCune-Albrightsyndrome

Medulloblastoma

Melanoma Memoryimpairment

Menieredisease

Meningioma

Menkesdisease

Mentalretardation

Merkel_cellcarcinoma

Mesangialsclerosis

Mesothelioma

Migraine

1016

Miyoshimyopathy

MODY

Mohr-Tranebjaergsyndrome

Morningglorydisc

anomaly

Muenkesyndrome

Muir-Torresyndrome

Multipleendocrineneoplasia

Musculardystrophy

Myasthenicsyndrome

Myelodysplasticsyndrome

Myelofibrosis,idiopathic

Myelogenousleukemia

Myeloperoxidasedeficiency

Myocardialinfarction

Myoclonicepilepsy

1056

1057

Myopathy

Myotilinopathy

Myotoniacongenita

Myxoma,intracardiac

Nasopharyngealcarcinoma

Nephropathy-hypertension

Nethertonsyndrome

Neuroblastoma

Neuroectodermaltumors

Neurofibromatosis

1096

Neurofibromatosis

Neurofibrosarcoma

Neuropathy

Neutropenia

Nevosyndrome

11041105

Nicotineaddiction

Nightblindness

Nijmegen_breakagesyndrome

1113

Non-Hodgkinlymphoma

Nonsmall_celllung_cancer

Noonansyndrome

Norriedisease

Obesity

Obsessive-compulsivedisorder

Occipital_hornsyndrome

Oculodentodigitaldysplasia

Oligodendroglioma

Oligodontia

1140

Omennsyndrome

Opticatrophy

Orolaryngealcancer

OSMEDsyndrome

Osseousheteroplasia

1153

Osteoarthritis

Osteogenesisimperfecta

Osteopetrosis

Osteoporosis 1164

Osteosarcoma

Ovariancancer

1174

Pancreaticcancer

1183

Paragangliomas

Paramyotoniacongenita

Parathyroidadenoma

Parietalforamina

Parkes_Webersyndrome

Parkinsondisease

Partingtonsyndrome

PCWH

Pelizaeus-Merzbacherdisease

Pendredsyndrome

Perinealhypospadias

Petersanomaly

Peutz-Jegherssyndrome

Pfeiffersyndrome

Pheochromocytoma

Pickdisease

Piebaldism

1229

Pilomatricoma

1232

Placentalabruption

Plateletdefect/deficiency

1239

Polycythemia

Polyposis

PPM-Xsyndrome

Preeclampsia

Primarylateral_sclerosis

1263

1267

Prostatecancer

Proudsyndrome

Pseudoachondroplasia

Pseudohypoaldosteronism

Pseudohypoparathyroidism

Pyropoikilocytosis

1297

Rabson-Mendenhallsyndrome

Renal_cellcarcinoma

Retinal_conedsytrophy

Retinitispigmentosa

Retinoblastoma

Rettsyndrome

Rhabdomyosarcoma

Rheumatoidarthritis

Rh-modsyndrome

Rh-negativeblood_type

Riegersyndrome

Ring_dermoidof_cornea

Rippling_muscledisease

Roussy-Levysyndrome

Rubenstein-Taybisyndrome

Saethre-Chotzensyndrome

Salivaryadenoma

1347

SARS,progression_of

Schizophrenia

Schwannomatosis

Sea-blue_histiocytedisease

Seasonalaffective_disorder

Sebastiansyndrome

Self-healingcollodion_baby

Sepsis

1383

Sezarysyndrome

Shah-Waardenburgsyndrome

Shprintzen-Goldbergsyndrome

Sick_sinussyndrome

1396

Simpson-Golabi-Behmelsyndrome

1401

SMEDStrudwick_type

1414

Somatotrophinoma

Spastic_ataxia/paraplegia

Spherocytosis

Spinal_muscularatrophy

Spinocereballarataxia

1432

Spondyloepiphysealdysplasia

Squamous_cellcarcinoma

Stargardtdisease

Sticklersyndrome

Stomachcancer

Stroke

1456

Supranuclearpalsy

Supravalvar_aorticstenosis

Syndactyly

Systemic_lupuserythematosus

Tangierdisease

1476

T-celllymphoblastic

leukemia

Tetralogyof_Fallot

1490

Thrombocythemia

Thrombocytopenia

Thrombophilia

Thyroidcarcinoma

Thyrotoxicperiodicparalysis

Tietzsyndrome

Toenaildystrophy,

isolated

1518

1528

Turcotsyndrome

1545

Urolithiasise

Ushersyndrome

Uterineleiomyoma

van_Buchemdisease

1555

Ventriculartachycardia

Verticaltalus

Viralinfection

Vitelliformmacular

dystrophy

Vohwinkelsyndrome

von_Hippel-Lindausyndrome

Waardenburg-Shahsyndrome

Waardenburgsyndrome

Wagnersyndrome

WAGRsyndrome

Walker-Warburgsyndrome

Watsonsyndrome

Wegenergranulomatosis

Weill-Marchesanisyndrome

1586

Williams-Beurensyndrome

Wilmstumor

Wiskott-Aldrichsyndrome

Witkopsyndrome

Wolff-Parkinson-Whitesyndrome

1614

Zlotogora-Ogursyndrome

Adrenaladenoma

Adrenal_corticalcarcinoma

Aneurysm,familial_arterial

Autoimmunethyroiddisease

Basal_cellnevus_syndrome

Carcinoid_tumorof_lung

Central_coredisease

Coronaryspasms

2385

2785

Maculardystrophy

Medullary_thyroidcarcinoma

Pancreaticagenesis

3212

3229

Thyroidhormoneresistance

3512

3558

Combinedimmunodeficiency

Multiplemalignancysyndrome

Optic_nervehypoplasia/aplasia

5233

Renaltubularacidosis

Multiplesclerosis

Renaltubular

dysgenesis

ABCA1

ABCA4

ADA

ADRB2

AGRP

JAG1

AGTAGTR1

ALOX5

ALOX5AP

APC

APOA1

APOA2

APOB

APOE

APPFAS

AQP1

AR

STS

ATM

ATP1A2

ATP7A

BAX

CCND1

BCS1L

BDNF

BMPR1A

BRCA1

BRAF

BRCA2

C6

CACNA1A

CACNA1S

CACNB4

CASP8

CASP10

CASR

CAV3

RUNX1

CBS

CD36

CDH1

CDKN2A

CHRNA4

COL1A1

COL1A2

COL2A1

COL3A1

COL7A1

COL8A2

COL9A2

COL9A3

COL11A1

COL11A2

COMP

KLF6

COX15

CP

CPT2

CRX

CRYAB

NKX2-5

CTLA4

CTNNB1

CYP1B1

CYP2A6

DBH

ACE

DCTN1

DCX

DES

TIMM8A

COCH

NQO1

DMDDSP

DSPP

SLC26A2

ECE1

EDN3

EDNRB

EGFR

EGR2

ELA2

ELN

EP300

EPHX1ERBB2

EYA4

ESR1

EYA1

F5

F7

FBN1

FCGR3A

FCMD

FGA

FGB

FGD1

FGFR1

FGFR3

FGFR2

FGG

FOXC1

FLNB

G6PD

GABRG2

GARS

GATA1

GCK

GCNT2

GCSL

GDNF

GJA1

GJB2

GJB3

GPC3

GNAI2GNAS

GSS

MSH6

GYPC

GUCY2D

HEXB

CFH

HNF4A

HOXD10

HRAS

HSD11B2

HSPB1

HTR2A

IL2RG

IL10

IL13

INS

INSR

IPF1

IRF1

JAK2

KCNE1

KCNH2

KCNJ11

KCNQ1

KCNQ2

KIT

KRAS

KRT1

KRT10

LAMA3

LMNA

LOR

LPP

LRP5

SMAD4

MAPT

MATN3

MECP2

MEN1

MET

CIITA

MITF

MLH1 NR3C2

MPO

MPZ

MSH2

MSX1

MSX2

MUTYH

MXI1

MYF6MYH6

MYH7

MYH8

MYH9

MYO7A

NBN

NDP

NDUFV1

NDUFS4

NF1

NF2

NOS3

NRL

NTRK1

OPA1

SLC22A18

PAFAH1B1

PARK2

PAX3

PAX6

PAX9

PDGFB

PDGFRA

PDGFRL

PDE6B

PDHA1

ENPP1

SLC26A4

PGK1

SERPINA1

PIK3CA

PITX2 PITX3

PLEC1

PLOD1

PLP1

PMP22

PMS2

PPARG

PRKAR1A

PRNP

PRODH

PSEN1

PTCH

PTEN

PTPN11

PTPRC

PVRL1

RAG1

RAG2

RASA1

RB1

RDS

REN

RET

RHAG

RHCE

RHO

RLBP1

RP1

RPGR

RPE65

RPS6KA3

RYR1

RYR2

SCN4A

SCN5A

SCNN1B

SCNN1G

SDHA

SDHBSDHD

SGCD

SHH

SLC2A2

SLC4A1

SLC6A4

SLC6A8

SLC34A1

SNCA

SOX3

SOX10

SPTA1

SPTB

STAT5B

ELOVL4

STK11

ABCC8

TAP2

TAZ

TBP

TCF1

TCF2

TG

TGFBR2

TGM1

THBD

TNF

TP53

TPO

TSHR

TTN

TTR

TYR

USH2A

VHL

VMD2

WAS

WT1

XRCC3

PLA2G7

HMGA2

DYSF

AXIN2

MAD1L1

RAD54L

IKBKG

TCAP

PTCH2

WISP3

BCL10

PHOX2B

LGI1

VAPB

MYOT

KCNE2

NR2E3

USH1C

FBLN5

POMT1

GJB6

SPINK5

CHEK2

ACSL6

CRB1

AIPL1

RAD54B

PTPN22

BSCL2

VSX1FOXP3

PHF11

PRKAG2

NLGN3

CNGB3

RETN

RPGRIP1

NLGN4X

ALS2

CDH23

DCLRE1C

PCDH15

CDC73

OPA3

BRIP1

MASS1

ARX

FLCN

Abacavirhypersensitivity

18

Acrocallosalsyndrome

Acrocapitofemoraldysplasia

Acrokeratosisverruciformis

Acromesomelicdysplasia

53

Adrenoleukodystrophy

Adrenomyeloneuropathy

ADULTsyndrome

Agammaglobulinemia

AIDS

77

AldosteronismAlopecia

universalis

Alperssyndrome

87

Alpha-actinin-3deficiency

92

Alportsyndrome

Amelogenesisimperfecta

Analbuminemia

107

Andersondisease

Anhaptoglobinemia

Ankylosingspoldylitis

Antley-Bixlersyndrome

Aplasticanemia

Aromatasedeficiency

Arthrogryposis

162

Atransferrinemia

Atrichia w/papular lesions

171

Bardet-Biedlsyndrome

BCGinfection

Beckwith-Wiedemannsyndrome

Bernard-Souliersyndrome

Bethlemmyopathy

Blausyndrome

210

Blepharospasm

Blue-conemonochromacy

Bombayphenotype

Bosley-Salih-Alorainysyndrome

Brachydactyly

Buschke-Ollendorffsyndrome

Calcinosis,tumoral

Campomelicdysplasia

Cartilage-hairhypoplasia

279

292

294Ceroid

lipofuscinosis

Ceroid-lipofuscinosis

CETPdeficiency

Cholelithiasis

Cholestasis

313

Chondrocalcinosis

Chondrosarcoma

Chorea,hereditary

benign

320

Chudley-Lowrysyndrome

329

Chylomicronretentiondisease

Ciliarydyskinesia

CINCAsyndrome

Cleidocranialdysplasia

Cockaynesyndrome

Codeinesensitivity

344

Colorblindness

Congestiveheartfailure

Conjunctivitis,ligneous

357

Coproporphyria

Craniometaphysealdysplasia

CRASHsyndrome

Crigler-Najjarsyndrome

Crohndisease

Cystathioninuria

Cysticfibrosis

CystinuriaDarierdisease

Debrisoquinesensitivity

Dentalanomalies,

isolated

Dentdisease

De Sanctis-Cacchionesyndrome

DiGeorgesyndrome

438

Dosage-sensitivesex

reversal

Double-outletright ventricle

Downsyndrome

452

453

Dyskeratosis

Dysprothrombinemia

461

Dystonia

EEC syndrome

471

Ellis-van Creveldsyndrome

Enchondromatosis

Erythremias

Erythrocytosis

Ewingsarcoma

Exostoses

527

Fertileeunuch

syndrome

535

Fibromatosisl

539

Fish-eyedisease

Fitzgerald factordeficiency

544

545

Frontometaphysealdysplasia

Fumarasedeficiency

Gaucherdisease

584

Gilbertsyndrome

GM-gangliosidosis

Greenbergdysplasia

626

Guttmachersyndrome

Haim-Munksyndrome

Hand-foot-uterussyndrome

Harderoporphyrinuria

HARPsyndrome

Hay-Wellssyndrome

646

Heinzbody

anemia

HELLPsyndrome

Hemangioma

Hematuria,familial_benign

Hemochromatosis

Hemoglobi_Hdisease

Hemophilia

Heterotaxy

Heterotopia

Hex_Apseudodeficiency

679

Homocysteineplasma

level

699 701

Hoyeraal-Hreidarssonsyndrome

HPFH

HPRT-relatedgout

H._pyloriinfection

Hyalinosis,infantilesystemic

Hydrocephalus

Hyperalphalipoproteinemia

Hyperandrogenism

Hyperbilirubinemia

Hyperekplexia

727

Hyper-IgDsyndrome

734

Hypermethioninemia

Hyperphenylalaninemia

Hyperprothrombinemia

Hypertrypsinemia

Hyperuricemicnephropathy

Hypoaldosteronism

Hypochromicmicrocytic

anemia

Hypogonadotropichypogonadism

Hypohaptoglobinemia

Hypolactasia,adulttype

780

Hypophosphatasia

Hypophosphatemia

Hypophosphatemicrickets

785

Hypoprothrombinemia

Inclusionbody

myopathy

Ironoverload/deficiency

Joubertsyndrome

Juberg-Marsidisyndrome

Kanzakidisease

Kartagenersyndrome

Kenny-Caffeysyndrome-1

Kininogendeficiency

Langermesomelicdysplasia

Larondwarfism

LCHADdeficiency

Leadpoisoning

Leiomyomatosis

Leri-Weilldyschondrosteosis

Lesch-Nyhansyndrome,

891

Leydigcell

adenoma

LIG4syndrome

Limb-mammarysyndrome

Lipoproteinlipase

deficiency

Longevity

Lowesyndrome

Low reninhypertension

Lymphangioleiomyomatosis

Lymphedema

945

MASAsyndrome

McKusick-Kaufmansyndrome

969

Melnick-Needlessyndrome

982

Meningococcaldisease

Mephenytoinpoor metabolizer

Metachromaticleukodystrophy

Metaphysealchondrodysplasia

Methemoglobinemia

10011002

Mevalonicaciduria

Microcephaly

Micropenis

Microphthalmia

Muckle-Wellssyndrome

Mucopolysaccharidosis

Mycobacterialinfection

Myelokathexis,isolated

1050

Myeloproliferativedisorder

Nemalinemyopathy

1080

Nephrolithiasis

Nephronophthisis

1090

Neurodegeneration

Nonakamyopathy

Norumdisease

1119

Ocularalbinism

1133

Odontohypophosphatasia

Opremazolepoor metabolizer

Orofacial cleft

Osteolysis

Osteopoikilosis

Otopalatodigitalsyndrome

Ovarioleukodystrophy

Pachyonychiacongenita

Pagetdisease

Pallister-Hallsyndrome

Palmoplantarkeratoderma

Pancreatitis

Papillon-Lefevresyndrome

Pelger-Huetanomaly

Periodontitis

Phenylketonuria

1227 Plasminogendeficiency

1238

Polydactyly

Poplitealpterygiumsyndrome

Porphyria

Precociouspuberty,

male

Prematureovarianfailure

1265

Proguanilpoor metabolizer

Proteinuria

Pseudohermaphroditism,male

Psoraisis

Pulmonaryfibrosis

RAPADILINOsyndrome

Rapp-Hodgkinsyndrome

Red hair/fair skin

Refsumdisease

Restingheartrate

Restrictivedermopathy,

lethal

1325

1335

Rothmund-Thomsonsyndrome

Salladisease

Sarcoidosis

Schindlerdisease

1361

Senior-Lokensyndrome

1376

Septoopticdysplasia

Sexreversal

Shortstature

Sialidosis

Sialuria

Sicklecell

anemia

Situsambiguus

Smith-Fineman-Myerssyndrome

Smith-McCortdysplasia

Sotossyndrome

Spinabifida

Split-hand/footmalformation

Spondylometaphysealdysplasia

1438

Startledisease

STAT1deficiency

Steatocystomamultiplex

1446

Surfactantdeficiency

Sutherland-Haansyndrome-like

1466

Symphalangism,proximal

Synostosessyndrome

Synpolydactyly

Tallstature

1475

Tay-Sachsdisease

Thalassemias

Thymine-uraciluria

Tolbutamidepoor

metabolizer

1519

Trichodontoosseoussyndrome

Trichothiodystrophy

1526

Tropicalcalcific

pancreatitis

Tuberculosis

Tuberoussclerosis

Twinning,dizygotic

1542

UV-inducedskin damage

Velocardiofacialsyndrome

Virilization

1565

1580

Weaversyndrome

Weyersacrodentaldysostosis

WHIMsyndrome

Wolframsyndrome

Wolmandisease

Xerodermapigmentosum

1611

Yellownail

syndrome

Zellwegersyndrome

Adrenocorticalinsufficiency

Chondrodysplasia,Grebetype

2327

Combinedhyperlipemia

2354

Diabetesinsipidus

3037

3144

Ovariandysgenesis

3260van_der_Woude

syndromeBasal

gangliadisease

4291

5170

Pituitaryhormonedeficiency

Renalhypoplasia,

isolated

Ovariansex cordtumors

CombinedSAP deficiency

Multiplemyeloma

SERPINA3

ACTN3

ADRB1

NR0B1

ALAD

ALB

ABCD1

ALPL

ATP2A2

ATRX

AVPR2

FOXL2

BTK

RUNX2KRIT1

CETP

CTSC

CFTR

CLCN5COL4A4

COL6A1

COL6A2

COL6A3

COL10A1

CPOX

CTH

CYP2C19

CYP2C9

CYP2D6

CYP11B1

CYP11B2

CYP19A1

CYP21A2

DKC1

DLX3

DNAH5

DPYD

DRD5

ERCC2

ERCC3

ERCC5

ERCC6

EVC

EWSR1

EXT1

F2

F9

FH

FOXC2

FLNA

FLT4

FSHR

FTL

NR5A1

FUT2OPN1MW

GHR

GLB1

GLI3

GLRA1

GNRHR

GP1BB

HADHA

HBA1

HBA2

HBB

HEXA

HFE

HLA-B

HOXA1

HOXA13HOXD13

HP

HPRT1

HSPG2

IFNG

IFNGR1

IHHIRF6

KNG1

KRT16

KRT17

L1CAM

LBR

LCAT

LHCGR

LIG4

LIPA

LPL

MAT1A

MBL2

MC1R

MCM6

MTHFD1

MTRMTRR MVK

NAGA

NPHP1

ROR2

OCRL

PAH

PAX2

PDGFRB

PEX1

PEX7

PEX10

PEX13

ABCB4

PLG

POLG

POR

PPT1

PSAPPTHR1

PXMP3

PEX5

OPN1LW

RMRP

SFTPC

SHOX

SLC3A1

SOX9

SPINK1

STAT1

TBX1

TBCE

TERC

TF

TITF1

TPM2

TSC1TSC2

UMOD

WFS1

CXCR4

FGF23

MKKS

GDF5

TP73L

DNAH11

TNFRSF11A

HESX1

EIF2B4

EIF2B2EIF2B5

NOG

RECQL4

GNEENAM

ZMPSTE24

LEMD3

SLC17A5

DNAI1

SAR1B

SLC45A2

UGT1A1

DYM

BCOR

PEX26

HR

CFC1

ANKH

CARD15

NSD1

VKORC1

MCPH1

PANK2

CIAS1

ANTXR2

NPHP4

The human disease networkGoh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabasi A-L (2007) Proc Natl Acad Sci USA 104:8685-8690′

Supporting Information Figure 13 | Bipartite-graph representation of the diseasome. A disorder (circle) and a gene (rectangle) are connected if the gene is implicated in the disorder. The size of the circle represents the number of distinct genes associated with the disorder. Isolated disorders (disorders having no links to other disorders) are not shown. Also, only genes connecting disorders are shown.

Disorder Class

Disorder Name

BoneCancerCardiovascularConnective tissue disorderDermatologicalDevelopmentalEar, Nose, ThroatEndocrineGastrointestinalHematologicalImmunologicalMetabolicMuscularNeurologicalNutritionalOphthamologicalPsychiatricRenalRespiratorySkeletalmultipleUnclassified

5233 Placental steroid sulfatase deficiency5170 Ovarian hyperstimulation syndrome4291 Cerebral cavernous malformations3558 Ventricular fibrillation, idiopathic3512 Total iodide organification defect3260 Premature chromosome condensation w/ microcephaly, mental retardation3229 Pigmented adrenocortical disease, primary isolated3212 Persistent hyperinsulinemic hypoglycemia of infancy3144 Optic nerve coloboma with renal disease3037 Multiple cutaneous and uterine leiomyomata2785 Hypoplastic left heart syndrome2385 Creatine deficiency syndrome, X-linked2354 Congenital bilateral absence of vas deferens2327 Chronic infections, due to opsonin defect1614 Yemenite deaf-blind hypopigmentation syndrome1611 XLA and isolated growth hormone deficiency1586 Weissenbacher-Zweymuller syndrome1580 Warfarin resistance/sensitivity1565 Vitamin K-dependent coagulation defect1555 VATER association with hydrocephalus1545 Unna-Thost disease, nonepidermolytic1542 Ullrich congenital muscular dystrophy1528 Trismus-pseudocomptodactyly syndrome1526 Trifunctional protein deficiency1519 Transposition of great arteries, dextro-looped1518 Transient bullous of the newborn1490 Thanatophoric dysplasia, types I and II1476 Tauopathy and respiratory failure1475 Tarsal-carpal coalition syndrome1466 Sweat chloride elevation without CF1456 Subcortical laminar heterotopia1446 Stevens-Johnson syndrome, carbamazepine-induced1438 Stapes ankylosis syndrome without symphalangism1432 Spondylocarpotarsal synostosis syndrome1414 Solitary median maxillary central incisor1401 Skin fragility-woolly hair syndrome1396 Silver spastic paraplegia syndrome1383 Severe combined immunodeficiency1376 Sensory ataxic neuropathy, dysarthria, and ophthalmoparesis1361 Schwartz-Jampel syndrome, type 11347 Sandhoff disease, infantile, juvenile, and adult forms1335 Robinow syndrome, autosomal recessive1325 Rhizomelic chondrodysplasia punctata1297 Pyruvate dehydrogenase deficiency1267 Prolactinoma, hyperparathyroidism, carcinoid syndrome1265 Progressive external ophthalmoplegia with mitochondrial DNA deletions1263 Prion disease with protracted course1239 Pneumothorax, primary spontaneous1238 Pneumonitis, desquamative interstitial1232 Pituitary ACTH-secreting adenoma1229 Pigmented paravenous chorioretinal atrophy1227 Pigmentation of hair, skin, and eyes, variation in1183 Papillary serous carcinoma of the peritoneum1174 Pallidopontonigral degeneration1164 Osteoporosis-pseudoglioma syndrome1153 Ossification of the posterior longitudinal spinal ligaments1140 Oligodontia-colorectal cancer syndrome1133 Oculofaciocardiodental syndrome1119 Norwalk virus infection, resistance to1113 Noncompaction of left ventricular myocardium1105 Newfoundland rod-cone dystrophy1104 Nevus, epidermal, epidermolytic hyperkeratotic type1096 Neurofibromatosis-Noonan syndrome1090 Neural tube defects, maternal risk of1080 Nephrogenic syndrome of inappropriate antidiuresis1057 Myokymia with neonatal epilepsy1056 Myoglobinuria/hemolysis due to PGK deficiency1050 Myelomonocytic leukemia, chronic1016 Mitochondrial complex deficiency1002 Methylcobalamin deficiency, cblG type1001 Methionine adenosyltransferase deficiency, autosomal recessive982 Melorheostosis with osteopoikilosis969 Medullary cystic kidney disease959 Mastocytosis with associated hematologic disorder945 Mandibuloacral dysplasia with type B lipodystrophy942 Malignant hyperthermia susceptibility930 Lynch cancer family syndrome II913 Lower motor neuron disease, progressive, without sensory symptoms891 Leukoencephalopathy with vanishing white matter868 Laryngoonychocutaneous syndrome847 Keratosis palmoplantaria striata845 Keratoderma, palmoplantar, with deafness843 Keratitis-ichthyosis-deafness syndrome833 Juvenile polyposis/hereditary hemorrhagic telangiectasia syndrome830 Jervell and Lange-Nielsen syndrome809 Infundibular hypoplasia and hypopituitarism803 Immunodysregulation, polyendocrinopathy, and enteropathy, X-linked792 Hystrix-like ichthyosis with deafness785 Hypoplastic enamel pitting, localized780 Hypoparathyroidism-retardation-dysmorphism syndrome734 Hyperkeratotic cutaneous capillary-venous malformations733 Hyperkalemic periodic paralysis727 Hyperferritinemia-cataract syndrome701 Homozygous 2p16 deletion syndrome699 Homocystinuria-megaloblastic anemia, cbl E type679 High-molecular-weight kininogen deficiency665 Hemosiderosis, systemic, due to aceruloplasminemia646 Hearing loss, low-frequency sensorineural626 Greig cephalopolysyndactyly syndrome604 Glutathione synthetase deficiency594 Glomerulocystic kidney disease, hypoplastic584 Giant platelet disorder, isolated558 Fuchs endothelial corneal dystrophy549 Foveomacular dystrophy, adult-onset, with choroidal neovascularization545 Focal cortical dysplasia, Taylor balloon cell type544 Fluorouracil toxicity, sensitivity to539 Fibular hypoplasia and complex brachydactyly535 Fibrocalculous pancreatic diabetes527 Fatty liver, acute, of pregnancy474 Emery-Dreifuss muscular dystrophy471 Elite sprint athletic performance463 Dystransthyretinemic hyperthyroxinemia461 Dyssegmental dysplasia, Silverman-Handmaker type453 Dysalbuminemic hyperthyroxinemia452 Dyggve-Melchior-Clausen disease441 Dopamine beta-hydroxylase deficiency439 Dissection of cervical arteries438 Disordered steroidogenesis, isolated434 Dilated cardiomyopathy with woolly hair and keratoderma422 Dermatofibrosarcoma protuberans418 Dentinogenesis imperfecta, Shields type396 Cyclic ichthyosis with epidermolytic hyperkeratosis379 Craniofacial-skeletal-dermatologic dysplasia378 Craniofacial-deafness-hand syndrome377 Craniofacial anomalies, empty sella turcica, corneal endothelial changes357 Conotruncal anomaly face syndrome347 Colonic aganglionosis, total, with small bowel involvement344 Cold-induced autoinflammatory syndrome329 Chylomicronemia syndrome, familial320 Choreoathetosis, hypothyroidism, and respiratory distress313 Cholesteryl ester storage disease294 Cerebrovascular disease, occlusive292 Cerebrooculofacioskeletal syndrome287 Central hypoventilation syndrome279 Cavernous malformations of CNS and retina275 Carpal tunnel syndrome, familial217 Bone mineral density variability210 Blepharophimosis, epicanthus inversus, and ptosis198 Beta-2-adrenoreceptor agonist, reduced response to192 Beare-Stevenson cutis gyrata syndrome182 Bannayan-Riley-Ruvalcaba syndrome171 Attention-deficit hyperactivity disorder162 Athabaskan brainstem dysgenesis syndrome144 Arrhythmogenic right ventricular dysplasia137 Apparent mineralocorticoid excess, hypertension due to129 Anxiety-related personality traits126 Anterior segment anomalies and cataract117 Angiotensin I-converting enzyme107 Analgesia from kappa-opioid receptor agonist, female-specific96 Alternating hemiplegia of childhood92 Alpha-thalassemia/mental retardation syndrome87 Alpha-1-antichymotrypsin deficiency77 Aldosterone to renin ratio raised53 Adrenal hyperplasia, congenital26 Achondrogenesis-hypochondrogenesis, type II18 Acampomelic campolelic dysplasia

Imagine a Semantically Connected Human Disease Network (Genetic, Proteomic, Metabolic Info)

Artificial genetics will push the frontiers of information-based therapies by actually stimulating evolutionary creation of chemical avatars, capable of genetic behaviors we still don’t completely fathom.

| e-Clinical Avatars And Personalized Medicine Disease Networks | 13

With a foundation in artificial molecular genetics, biology and chemical structures, synthetic genomes are now reality. The methods used in artificial genetics and synthetic genome-driven commercial solutions will closely align to bringing together the vision of Personalized Medicine, where semantic ontologies for connecting genomic data with clinical data will create effective therapies for specific patient groups.

Building personalized medicine/translational disease networks can be accelerated by using millions of clinical avatar populations and dosing simulations to develop platforms for supporting virtual clinical trials, and exploration of clinical efficacy parameters, outcomes including PK/PD models, and comparative effectiveness simulations.

Simulated patients need to be created to reflect actual population-wide and individual, demographic, clinical and laboratory characterizations including rapid simulation analysis of a wide selection of patient population scenarios to produce predictive evidence.

Figure 11.

ConclusionCare is being organized around specific disease states — driving deep knowledge and insight to deliver evidence-based care. Disease Networks bring together the various stakeholders with a focus on the patient. Payers are already aggregating detailed data and can mine this data to stratify patients and use this information to promote development of therapies to target problematic disease states. Pharmaceutical companies are collaborating with others, including research institutions and medical centers, to create a unified location for data and results. Networks of providers and patients are utilizing personalized healthcare models and leveraging those insights into patients to deliver better care. New approaches to integrating and analyzing information will shift profit pools, with non-pharmaceutical industry players potentially better positioned.

Developing and sharing open-source, networked disease models in a non-competitive framework or through large consortium collaborations, will generate a quantum leap in meaningful insights. Pharmaceutical companies have the opportunity to create and support Disease Networks to drive new therapeutic insights and scientific discoveries while improving care delivery in specific disease modalities. Pharmaceutical company leaders can enter this market and shape next generation disease models by applying advanced algorithms to provide richer diagnostic, prognostic and protocol/therapy management support. Pharmaceutical companies also have the opportunity to identify information partnerships that will enable them to derive new insights, coupling existing knowledge with rich, new sources of intelligence. These partnerships can drive better health outcomes and discovery by bringing together clinical, diagnostic/prognostic, research and scientific information, which can also be leveraged to enhance Disease Networks and Decision Support. Opportunities exist for pharmaceutical companies to explore establishing partnerships with commercial and non-commercial information networks (e.g., Science Commons38, Kaiser Permanente39,PatientsLikeMe40).

Systems Biology andGenomic Stratification

e-Clinical Avatars

Replace Large Trials with Smaller, Biomarker-Driven Adaptive Trialsfor Stratified Populations Enable Information-Based Therapies for Stratified Populations

Disease Networks represent quantum leaps in meaningful insights.

| e-Clinical Avatars And Personalized Medicine Disease Networks | 14

Capturing relevant clinical and research data and doing comparative studies for diseases41 as part of the disease networks is the key to innovation in patient-centric clinical research.

These Disease Networks will create tremendous value for their stakeholders. The value to the providers is in the new revenue opportunities (participation in research), enhanced care delivery, demonstrating quality of care to payers and contribution to scientific progress. Value to the patients is in the access to the newest personalized treatment options, assurance that they are receiving the best care protocols and contribution to the greater good by participating in research. Value to the bio-pharmaceutical industry is in the fact that the disease networks will be a source for drug discovery, representing an efficient channel to deliver personalized medicines while providing access to patient populations to perform clinical trials and enabling outcomes and safety monitoring.42

Disease Network-based molecular models will require significant resources and collaboration, but will create tremendous predictive value for the patients in terms of drug response. The end-state Vision is to leverage semantic interoperability of biomedical data into predictive networks to evolve complex, open-source disease models and create research collaborations for sharing data and resultant knowledge. Organization of data into intelligent, malleable, artificial intelligence-based semantically interoperable structures is absolutely fundamental to realizing the Disease Network paradigm.

CSC can assist in building such disease networks in trusted clouds for collaboration and knowledge-sharing in data-intensive research and analysis. Trusted clouds can become “a point of integration” between pharmaceutical companies and outside researchers. They will enable data sharing among drug makers and contract research organizations and other partners, and facilitate sharing of algorithms for analytics and securely exchanging data with collaborators.

Combining the Patient Disease Models to Create a Human Disease Network

| e-Clinical Avatars And Personalized Medicine Disease Networks | 15

About the Author Sanjeev Wadhwa is a Partner, Senior Strategy Expert and Global Leader of R&D solutions within CSC’s Life Sciences Practice. He directs CSC’s Life Sciences R&D Practice, where he concentrates on making organizations competitively agile, determining strategic investments, and fueling organic and

acquisitive growth through identification of attractive industries or market segments for investment focus, with excellent exposure to end markets and corporate spending arenas within life sciences and healthcare industries.

Mr. Wadhwa provides board-level advisory services to executive management councils at several companies to streamline development of business and technology strategies in personalized medicine, comparative effectiveness research, consumer-directed healthcare, biomarkers and adaptive trials. He has led the corporate strategy and large-scale business improvement operating model efforts for several Fortune 500 companies conducting global rationalization studies to enable identification and commercialization of promising candidate innovations.

Mr. Wadhwa can be reached at 908.451.3833 or at swadhwa@csc.com

| e-Clinical Avatars And Personalized Medicine Disease Networks | 16

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| e-Clinical Avatars And Personalized Medicine Disease Networks | 17

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