Avoiding Fake Diseases and Biomarkup in Pediatrics · Avoiding Fake Diseases and Biomarkup In Pediatrics Isaac S. Kohane, MD, PhD -v ... Toward Precision Medicine: ... • ''Growth

Avoiding Fake Diseases and

Biomarkup In Pediatrics

Isaac S. Kohane, MD, PhD

- HARVARD OEPARTMENTOFv MEDICAL SCHOOL Biomedical Informatics

Google Maps: GIS mayers Organized by Geographic-al Positioning

Information Commons Organized Around Individual Patients

ExpoJ:oma

Signs and Symptoms

Genome

Toward Precision Medicine: Building a Knowledge Network

for Biomedical Research and a New Taxonomy of Disease

Report from National academy of science, USA, 2011

CErA.RTMEHTOr

lliomedical Inform atics

Some of the points I will make

Most population genetics and clinical medicine addicted to categorical diagnosis

Fake/Wrongly taxonomized diseases makes social & commercial construction of disease likely.

Genomics-first/Genomics only unnecessarily limiting

Clinical predictive accuracy does not imply shared physiology

Disease overlaps demonstrates a fundamental problem

Clinical data can be used to lesson the confusion.

But multi-modal approaches are the most robust.


Blood Gene Expression Detection

A

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Invited to HLS Meeting

"I think when Ari [Ne'eman] talks

about autism and I talk about

autism, we're talking about people

with different clusters of autism. I

know he doesn't like the word

'cure.' If my daughter could

function the way Ari could, I would

consider her cured," says Singer. "I

have to believe my daughter

doesn't want to be spending time

peeling skin off her arm."

DEPARTMENT OF Iii HARVARD Biomedical Informatics

MEDICAL SCHOOL

Extractand pool

pttu1tary g1ands Homogenize Inject

30-year CJD and APe incubabon deposits.

CEPARTMENT OfIii HARVARD Biomedical Informaticsv MEDICAL SCHOOL

GIANT study

lOO's of genes

Not like diseases at tails

So what do you call short stature and is that a diagnosis?

v DEPARTMENT orIll HARVARD Biomedical InformaticsMEDICAL SCHOOL

Criteria for Treatment

''Growth hormone deficiency (GHD)''

''Idiopathic short stature (ISS), defined by

height standard deviation score ~-2.25''

associated with growth rates unlikely to result in normal adult height, in whom other causes ofshort stature have been excluded

and a little story from 25 years ago


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NEWS Election U.S. World Entertainment Health Tech

But about once a mornth, an ambitious parent with cash to burn asks 44 SHARES Desrosiers. a pediatric endrocrinologist in Florida, if he would be willing

to g ive growth hormones to a short but otherwise healthy ch ild.

0 0 Desrosiers turns these patients away, but he says the requests still

come.

0 null Just last week. the father of a

young baseball player ~ a 14-yea old who was already 5 feet 6

inches tall - expected Desrosiers to prescribe recombinant growth hormone rGH to add hei ht to his biuddin athlete. rlHe wanted to make his kid big, and he th inks he1 s going to walk out

with the shots,1 r said Desrosiers, director of pediatrric endocrinollogy at

Arnold Palmer Children's Hospital in Orlando. "He was willing to pay

more than $45,000 a year, and didn1t even bat an eyelash."

Desrosiers said he 1even gets requests for growth hormone from 11Jolly

Green Gianr families, where children are likely to be tan.

In 2003, the U.S. Food and Drug Administration approved the use of

rGH for children with idiopathic - or unexplained -- short stature,

without a diagnosed metabolic hormone deficiency.

Pathological

Interaction Between

Clinical Annotation and

Genetics.

- HARVARD DEPARTMENTOF MEDICAL SCHOOL Biomedical Informatics

---- E1Cli8cellu4ar mallix

Dystrophln Associated Protein Complex

-lAMA4

Ul83 MYOZ2 NEBL NEXN TCAP VCl

CRYAB

Cytoplasm

- NUCiear membrane TMPO

Hypertrophic Cardiomyopathy

(HCM}

- Heart failure - Arrhythmias - Obstructed blood flow - Infective endocarditis - Sudden cardiac deat h

4 6503 individuals

NHLBIESP

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expected i100 individuals

6503 individuals

NHLBIESP

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observed

HCM Prevalence= 1 :500 HCM Inheritance = Autosomal Dominant

- HARVARD DEPARTMENTOF ,., MEDICAL SCHOOL Bio medical Informatics

Genetics-induced Health Disparities

30.00%

25.00%

Cl> u c 20.00% ~ IO >Cl> .... 0.

~ >. 15.00% .... 0 c: Cl> Ol Cl>

"'ijj

IO 10.00%

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-cE

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European Americans

African Americans

European Americans

African Americans

!NE

Potential

JG UST 18, 2016

_J _I ... TNNT2 OBSCN MYBPC3 Remaining 89

TNNl3 (P82S) JPH2 (G505S) (K247R) (R4344Q) (G278E) mutat ions

2.88% 0.33% 0.03% 0.02% 0.80% 6.67%

27.14% 15.27% 4.07% 3.15% 2.92% 7.1 8%

DEPARTMENT OF 13Iii HARVARD Biomedical InfoTmatics

MEDICAL SCHOOL

46 Unavailable 2005 p B Cli nical Diagnosis of HCM

75 Unavailable 2005 p B Family Hi story and Clinical

Symptoms of HCM

32 Black or African American 2005 p B Clin ical Diagnosis of HCM

34 Black or African American 2005 u B Clinical Diagnosis and Family History of HCM

12 Black or African American 2006 u B Family History of HCM

40 Black or African American 2007 u B Clinical Diagnosis of HCM

45 Black or African American 2007 u B Clinical Features of HCM

16 Asian 2008 u B Clinical Diagnosis and Family History of HCM

59 Black or African American 2006 p B Clin ical Features of HCM

15 Black or African American 2007 p B Clinical Diagnosis of HCM

16 Black or African American 2007 p B Clinical Diagnosis of HCM

22 Black or African American 2007 p B Clinical Diagnosis and Family Hist ory of HCM

48 Black or African American 2008 u B Clinical Diagnosis of HCM

Pro82Ser

Gly278Glu

P = Pathogenic and Presumed Pathogenic U = Pathogenicity Debated and Unknown Significance

DEPARTMENT OF " HARVARD Biomedical InformaticsMEDICAL SCHOOL

Example: ' Heart attack" AND 'Los Angeles

Clinical Trials.gov Search for studies: J Seorch A service of the U.S. National Institutes of Health Advanced Search Help Studies by Topic Glossary

Find Studies About Clinical Studies Submit Studies Resources About This Site

Home > Find Studies > Study Record Detail Text Size "'

Valsartan for Attenuating Disease Evolution In Early Sarcomeric HCM (VANISH)

This study ls currently recruiting participants. (see Contacts and Locations) ClinicalTrials.gov Identifier:

NCT01912534Verified July 2013 byNew England Research Institutes First received: June 5, 2013

Sponsor: Last updated: October 5, 2015

New England Research Institutes Last verified: July 2013 History of Changes Collaborator:

National Heart, Lung, and Blood Institute (NHLBI)

Information provided by (Responsible Party): New England Research Institutes

Full Text View Tabular View No Study Results Posted Disclaimer El How to Read a Study Record

0(11,t,fl:TM(l';T (/" iill HARVARD Biomedic~J InformJticsv MED ICAL SCllOOL

http:ClinicalTrials.gov

Group 1 (Overt HCM Cohort)

1. LV wall thickness 2:12 mm and :S25 mm or z score 2:3 and :S18 as determined by rapid assessment by the echocardiographic core laboratory

2. NYHA functional class I or II; no perceived or only slight limitations in physical activities

3. No resting or provokable LV obstruction (peak gradient :s 30 mmHg) on clinically-obtained Exercise Tolerance Test (ETT)-echo within the past 24 months or transthoracic echo with Valsalva maneuver within the past 12 months

4. Age 8-45 years

5. Able to attend follow-up appointments, complete all study assessments, and provide written informed consent

Group 2 (Preclinical HCM Cohort (G+/LVH-))

1. LV Wall Thickness

Is Prediction the Acid

Test of Diagnosis?


Survival 3 Years After a WBC Test (White, Male, 50-69 Years; Using Last VVBC Between 7128105 and 7127106)

Repeat

Interval

Result

Time

WBCValue Patients

Low Normal High Any

< 1 Day 12a-8a 43.33% 84.68% 63.24% 76.39% 1830

8a-4p 54.55% 86.61% 79.40% 83.15% 1442

4p-12a 77.30% 67.53% 72.49% 229

< 1 Year 12a-8a 47.83% 79.58% 66.67% 74.39% 1644

8a-4p 76.96% 90.73% 80.80% 88.53% 8812

4p-12a 81 .65% 92.99% 86 .01% 91.69% 2769

> 1 Year 12a-8a 95.65% 96.97% 96.00% 175

8a-4p 97.30% 98.13% 91.98% 97.83% 4280

4p-12a 92 .68% 97.35% 96.67% 97.20% 1932

Any 73.17% 91.79% 78.11% 88.95% 23113

Patients 1286 18775 3052 23113

v ota-.~itTMe:r.ro'1111 HARVARD Biomedical Informatics MEDICAL SCHOOL

Healthcare System Dynamics

Clinical data reflect both patients' health AND their interactions with the healthcare system.

Patient P athophysiology Healthcare System Dynamics Data Quality

Patient Demographics

Diagnoses

Laboratory Test Results

Vital Signs

Genetic Markers

Number of Observations

Time of Day of Observations

Time Between Observations

Cost of a Test or Treatment

Clinical Setting I Clinician Type

Data Entry Errors

Dictation Mistakes

Data Compression Loss

Unstructured Data

Missing Data

Clinical Encounter

Patient + Clinician

Electronic Health Record Data

Healthcare System Dynamics

Normal

Abnormal

Patient Pathophysiology

Normal

Best

Outcomes

Moderate Outcomes

Abnormal

Moderate

Outcomes

Worst Outcomes

DEPARTMENT or

Biomedical Informatics

Predicting Survival from Ordering a Lab Test

--

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CtS CtS .::!:: a:: ~~ ::J = 10.00% en CtS -~ 0 0 Q) :!E (.) "C c: Q)CtS ...... 'i:: II) CtS :::::J>:o

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Predicting Survival Using Lab Value & HSD

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MHCT

URIC -

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F-NON JG __. FE POL~ ___!. I e-- MONO ALC-TSTES -. - e ESR_ e e e e e uAS-RBC UA-PH LYM ~ ~12- FOLATa A osN+ TRIG UIUNA UA-SPGR

U/UOSM LDH 25VITD PHOS EOSP JI ~ /CPK e VPC02 CHOL e e ~ ,[ T H 9 .,, e UA-KET 1 e NEUT e ...- e e UA-BLD

OSM HDL \ CORT e , e e DIG MONOS e LDL e e CREACJ ~ VLDL e -[!_-UNID CBASOUUN T

e e e B~HGHBA~C e ; CWBC UK ~ OI L LACT F-WBC e GLU e Vi2 _,,,. U~~~~I~~ ~ ~ I .t\fC 2 PTT ._A YPS

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APH TRVA C CP:..MB ~ TROP-T MYELO UA-UROBI

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0.1 0.2 0.3 0.5 2 3 1005 20 30 50 DEPARTMENT OF Improvement from Only HSD I lmproveme .' ~ .t"J~VM.D Biomedical Informatics

MEDICAL SCHOOL

Clinical Data To

Clarify Diagnostic

Boundaries?


Mothers told me about bowel

problems but pediatricians told me...

code counts code counts code counts 0-6 months 6-12 months 12-18 months ___A A A I T' V' --,

-"' -

-,, ' I I I I

patient clustering

patients :

DEPARTMENT OFlliv H.PtRVARD Biomedical InformaticsMEDICAL SCHOOL

Autism or

Autisms?

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What about

increasing overlaps

across diagnoses?


What genes are shared across co-

Nazeen et al. Genome Biology 17 (1): 228, 2016.

ITOLL-LIKE RECEPTOR SIGNALING PATHWAy I

- ASD [=:J .-U1hm [=:J a lil!!!!!!I IBD [=:J lafctiou i=J URI

Cheniolao:t>: effects (NutropNI. lnunahlre DCLPS(O-) ----- - .\SD,II(NK cell) - ASD, 1ar..doa

~ Alduna,D(Li~=~) Htitl--liiiiilf'.--~~~-r - A>thma,IBDC:::J :Ulbu,Wttdoa - CXD, Dr:::J DC.a

( Flogn.t ass.nilly ) Cosbmulalory !OOletW.. (=:J D,IBD

FlogeDin ----i I CD-II I [=:J D, W..Uoa i=J IBD, ~IDI CDSO 1- [=:J mo, 1.rttdoa.

1 CD86 I (=:J ASD, II, loft

Classification accuracy reveals shared

biology

1 -.------------------0. '9 -+------------------

0.8 -------

~ 0.7 ------- 'a 0.16 u

What is the disease?

Shared Genes?

Shared Symptoms?


Autism(s)

Implications for study and treatment

Currently not obvious from a geneticsfirst/only approach

Why clinicians might miss it

Unsupervised vs self-referential & circular supervised

Multi-modal-first exploration.


What about when

there is NO

diagnosis?


n UDN D1ata1 P'rocess Overview Undiagnosed Diseases Network

Data Mgt. Step #1_;_ PublicII UDN Patient AppIi catio nr summaries Data& inina l response

Warehouse

M'etncs outcomes

II UDN Record Archive and Electronic Tri al Mgt.

.__rn.__, f .lclh~~ -'~~~~ ~~~WR O.ata IMgt. St!ep #2 :

Data Mgt. Step #4-: pos.-t v isit reporting and reviewref erra I and data tran sfer to a UDN ..

------ ~ --------~ - -clin ical cente r Digita( files, workflowJ admJn reports, phenotype, genotvpe

~T~"'~-~~-~ ~=- I[!][j]~__ . - ~~,;, I~~- D DEPARTMENT OF IData1 Mgt. Step #3: irlllr~RM'AlRD Biomedical Informatics

MEDICAL SCHOOL

-

Seven clinical sites 1 BaylorCollegeofMedicine and

TexasChildren's Hospital

2 Duke Medicinewilh COiumbia univt'l'sity Medical center

3 Harvard Teaching Hospitals (BCH, BWH, MGH)

4 NaUooallnstitutes ofHeallh

5 Stmford Medicine

6 UCLA SChool ofMedicine

7 l/anI CAL SCHOOL.

When there is no diagnosis

If there is a high probability causal variant:

- Diagnostic label links clinical findings in that

patient to that variant

Do all individuals with that variant have disease?

- How does genetic background/environment contribute in the general population

- p(D IV) --t- p(V ID) {cf. HFE & Hemochromatosis}


Why is medicine so

dependent on

categorical diagnoses?


RESEARCH LETTER

Medicine's Uncomfortable Relationship With Math: Calculating Positive Predictive Value

Survey Responses n-61

Most common answer - 950/o n=27

. () 0.4 0.6 0.8 1.0

HARVARD DEPARTMENT OFResponse MEDICAL SCHOOL Biomedical Informatics9

Summary

Addiction to categorical diagnoses is cognitively useful

- But our patients have beaten us to the Google-reflex

Categorical diagnosis can result in spurious biological & clinical inference - Often manipulated for$$$, 2ndry agenda - Single measurement modality (incl. genomics) easier to manipulate

Prediction of diagnostic class not necessarily evidence of biological etiology

Diagnoses are more robust and useful when Data-driven Formally model health systems dynamics Statistically-informed Multi-modal Unsupervised or lightly supervised.

v DEPARTMENT orIllHARVARD Biomedical InformaticsMEDICAL SCHOOL

Acknowledgments

Nathan Palmer

Susanne Churchill

Arjun "Raj" Manrai

Andrew Beam

Denis Agniel

MattMight

Griffin Weber

Rachel Ramoni

Sumaiya Nazeen

Bonnie Berger

Alexa McCray

R HARVARD Biomedtc.al lnf

Avoiding Fake Diseases and Biomarkup in Pediatrics · Avoiding Fake Diseases and Biomarkup In Pediatrics Isaac S. Kohane, MD, PhD -v ... Toward Precision Medicine: ... • ''Growth

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