Lynn Petukhova, Ph.D.lynn.petukhova@columbia.edu

Assistant ProfessorDepartments of Dermatology and Epidemiology

Columbia University

Alopecia Areata Research SummitNovember 15, 2016

Precision Medicine

“And that’s the promise of precision medicine -- delivering the right treatments, at the right time, every time to the right person.”

- President Barack Obama, January 30, 2015

Precise definitions of diseaseimprove patient outcomes and decrease healthcare costs.

How do we define disease mechanisms?

NIH Roadmap for Precision Medicine

Adapted from Francis Collins, ASHG, 2015

Large cohorts of engaged patients

Genomic datainforms on

biology

mHealth datainforms on

environment & behavior

EHR datainforms on

natural history and clinical trajectories

New therapeutic interventions and companion diagnostics

Large cohorts of engaged patientsLarge cohorts of engaged patients

Precise definitions of diseaseimprove patient outcomes and decrease healthcare costs.

Precision Medicine

“And that’s the promise of precision medicine -- delivering the right treatments, at the right time, every time to the right person.”

- President Barack Obama, January 30, 2015

Precise definitions of diseaseidentify patient subtypes and mechanistic links among diseases.

Disease subtypes and mechanistic links

disease subtypes

mechanistic link

Precisely defined disease mechanismsare at the crux of precision medicine.

alopecia areata rheumatoid arthritis

JAK-STATsignaling

HFvulnerability

Costimulatory

pathway

clinical implications

Disease subtypes and mechanistic links

Framework for Disease Comorbidities in Precision Medicine

Increased riskfor

Diagnosis B

A cohort of comorbid patients will be enriched for the shared disease mechanism.

Population riskfor

Diagnosis B

How do we identify comorbidities?

Epidemiological Studies of Comorbidities

Rzhetsky et al., 2007

Eaton et al., 2007

1. Ask the patient (lack power)2. Look in medical records (confounding)

Type 1 diabetes

Rheumatoid arthritis

Psoriasis

Systemic lupus erythromytosis

Multiple sclerosis

Goiter (hypothyroidism)

Myositis

Type 1 diabetes

Rheumatoid arthritis

Psoriasis

Vitiligo

Thyrotoxicosis/thyroiditis

Systemic lupus erythromytosis

Inflammatory bowel disease

ICD co-occurrence

autoimmune rheumatoid arthritis

multiple sclerosis

systemic lupus

erythematosus

type 1 diabetes

psoriasis

inflammatory hypersensitivity angiitis

allergic rhinitis

goiter

infection susceptibility Hepatitis C

meningococcus

streptococcus

tuberculosis

virus

CNS viral disease

helicobacter pilori

mumps

Hepatitis B

pertussis

neoplasm benign neoplasms

carcinoma in situ

neurofibromatosis

metabolic

disorders of lipid

metabolism

type 2 diabetes

cholelithiasis

AA metabolism (aromatic)

neuropsychiatric depression

migraine

epilepsy

bipolar

attention deficit

EHR studies of ICD co-occurrences at CUMC

Rhetsky et al, PNAS, 2007

GWAS

Biological Validation with PheWAS

PheWAS (requires a cohort with genetic data linked to EHR)

Leverage Public Databases linking EHR to Genome Data

Outcome• Groups are defined by disease status (case or control)

Exposure• Obtain genotypes

Statistical test

• Test for allele frequency differences between disease groups

• Identify Risk alleles

Outcome• Groups are defined by allele status at risk SNPs (risk or protective allele)

Exposure• Obtain all phenotypes in EHR

Statistical test

• Test for ICD frequency differences between allele groups

• Identify comorbid condition with a biological basis

Genetic Studies reveal comorbidities

Genetic Studies reveal comorbidities

11,410,409 SNPs imputed in our meta-analysis cohort,

revealing 16,848 associated SNPs across 14 GWAS loci

Biological Validation with PheWAS

https://phewascatalog.org/phewasDenny JC et al. Nat Biotechnol. 2013 Dec;31(12):1102-10

AA SNPS implicated 275 conditions, including autoimmune, inflammatory, cancers, cardiometabolic, and anxiety disorders.

ICD co-occurrence Phewas

autoimmune rheumatoid arthritis ✔

multiple sclerosis

systemic lupus erythematosus ✔

type 1 diabetes ✔

psoriasis ✔

inflammatory hypersensitivity angiitis

allergic rhinitis ✔

goiter

infection susceptibility Hepatitis C

meningococcus

streptococcus

tuberculosis

virus

CNS viral disease ✔

helicobacter pilori

mumps

Hepatitis B

pertussis ✔

neoplasm benign neoplasms ✔

carcinoma in situ

neurofibromatosis

metabolic disorders of lipid metabolism ✔

type 2 diabetes ✔

cholelithiasis ✔

AA metabolism (aromatic)

neuropsychiatric depression

migraine

epilepsy

bipolar

attention deficit

https://www.jax.org/strain/000659

Mouse Phenotyping

Alopecia areata mouse model (C3H/HeJ)

C3H/HeJ Phenotyping Results

autoimmune prone to colitis

prone to IgA nephropathy (exaggerated IgA responses)

infection susceptibility lethal infection by Gram-negative bacteria (defective lipopolysaccharide response; TLR4-LPS-d)

increased susceptibility to viral infection

abnormal T-helper 2 physiology

abnormal macrophage function

neoplasm high incidence of hepatomas

low incidence of mammary tumors

metabolic resistent to diet-induced atherosclerosis

high lipid levels; high cholesterol

elevated heme oxygenase

decreased circulating alanine transaminase level

neuropsychiatric absence seizures (Gria4spkw)

attenuated responses to tactile and thermal stimulation

retinal degeneration (100% prevalent; Pdebrd1)

abnormal glial cell apoptosis

prone to anxiety and impulsivity

disruptions in social behavior

[a characteristic of depression, autism, bipolar and schizophrenia]

https://www.jax.org/strain/000659

Mouse Phenotyping

Alopecia areata mouse model (C3H/HeJ)

Dermatological Diagnoses at Columbia University

Data on 22,291 patients

17,575 only a single diagnosis

Alopecia,

unspecified AA

benign

neoplasm keratosis acne

Other

disorders of

skin and

subcutaneous

tissue

Alopecia, unspecified 2369 1701 136 227 124 331 71

AA 752 136 503 53 29 104 16

benign neoplasm 7871 227 53 4838 1172 1816 263

keratosis 5787 124 29 1172 3668 1133 57

acne 9424 331 104 1816 1133 6139 445

Other disorders of skin and subcutaneous tissue 1412 71 16 263 57 445 726

Lipid Panel Patient Counts Cholesterol Total HDL Cholesterol LDL Cholesterol Triglyceride

AA 275 187.28 56.16 108.65 116.82

acne 5864 176.55 53.03 100.25 118.64

Benign neoplasm of skin 1318 185.28 54.48 106.12 124.48

keratosis 7844 171.86 52.90 94.06 127.74

Other disorders of skin and subcutaneous tissue 1049 186.45 52.54 107.64 129.73

Unspecified alopecia 1704 183.75 54.94 105.00 120.09

Grand Total 178.10 53.49 100.28 123.88

Preliminary Results

EHR data of Human Alopecia Areata Patients

Alopecia areata may include disease manifestations in cells and tissues other than hair follicle and immune system.

Disease mechanisms may contribute to dysregulation in lipid metabolism.

Biological basis to psychosocial conditions frequently reported by patients

Wrap up

Conclusions

Future directions NAAF funded study of comorbidities in National Alopecia

Areata Registry. Updated the questionaire to validate existing data.

Characterize the distribution of risk alleles for possibly comorbid conditions in the GWAS cohort.

Leverage EHR cohorts linked to genetic data to further pursue investigation of biological validation

Rheumatology

Joan Bathon

Gastroenerology

Ben Lebohwl

Govind Bhagat

Ali Jabbari

Jane Cerise

Annemieke de Jong

Zhengpeng Dai

Stephanie Erjavec

Alexa Abdelaziz

Claire Higgins

Muhammad Wajid

Sivan Harel

Yutaka Shimomura

Tarek Yamany

Esther Drill

Mazen Kurban

Hynumi Kim

Katie Fantauzzo

Courtney Luke

Rita Cabral

Gina DeStefano

Ming Zhang

Hazi Lam

Department of Dermatology

Angela M. Christiano

Julian MacKay-Wiggan

Neuropsychiatric Epidemiology

Ruth Ottman

Sharon S. Schwartz

Biomedical Informatics

Chunhua Weng

George Hripcsak

Neurology

Claire S. Riley

Cardiology

Alan Tall