Precision  Oncology  and  Big  Data  

Warren A. Kibbe, PhD wakibbe@northwestern.edu http://wiki.bioinformatics.northwestern.edu/index.php/Warren_Kibbe

Opportuni5es  

•  Big  Data  in  Cancer  – Mobility  and  pervasive  compu5ng  – Social  data  – NGS  –  Imaging  (fMRI,  CT  scans)  

•  EHR  integra5on  – Analy5cs  based  on  clinical  data  – Decision  support  

Challenges  

•  EHR  –  we  need  synop5c  and  seman5c  data  to  support  precision  medicine  

•  EHR  –  Truly  automated  and  useful  decision  support    

•  Handling  and  analyzing  big  data  •  Appropriate,  open  access  to  pa5ent-­‐derived  big  data  

•  Incorpora5ng  social  data  •  Mobile  compu5ng  

GeMng  Big  

•  Big  Data  is  about  emergent  proper5es    •  Big  Data  changes  the  sta5s5cal  paradigm  –  rather  than  modeling  whether  the  sample  is  representa5ve  of  the  popula5on,  you  have  all  the  data  from  the  popula5on  

•  How  do  we  combine  systems  biology  and  social  data  with  therapeu5cs  and  big  data  from  healthcare  providers  ?  

-­‐omics,  clinical,  nutri5on,  exposure  

•  Teasing  apart  the  factors  contribu5ng  to  risk  and  therapeu5c  efficacy  is  complicated!  

•  Sources  of  data  we  would  like  to  have  across  all  pa5ents:  

Genomic  data   Microbiome  data  Treatment   Metabolomics  Outcomes   Exposure  data  Nutri8on   Behavior  Labs   Medical  History  

-­‐omics,  clinical,  nutri5on,  exposure  

•  And  of  course  we  would  like  all  these  data  consistent  and  reliable!  

Genomic  data   Microbiome  data  Treatment   Metabolomics  Outcomes   Exposure  data  Nutri8on   Behavior  Labs   Medical  History  

-­‐omics,  clinical,  nutri5on,  exposure  

• We  aren’t  there  yet!  

• What  can  we  do  now?  

Examples  of  current  solu5ons  

•  Mobile  ePROs,  either  at  home  or  in  the  clinic  •  Care  diaries  on  tablets  –  response,  recovery  •  Integra5on  of  NLP  and  phenotype  algorithms  at  the  point  of  care  

•  Integra5on  of  clinically  ac5onable  genomic  variants  into  EHR  (think  Hercep5n  and  HER2)  

•  Decision  support  for  infec5ous  diseases  based  on  social  network  and  GPS  –  not  just  for  MRSA  

Mobile  compu5ng  

•  Measuring  depression,  pain  and  anxiety  directly  with  the  pa5ent    

•  Center  for  Behavioral  Interven5on  Technologies  at  Northwestern  – David  Mohr,  Director  and  PI  

Mobilyze  (P20  MH090318  PI:  Mohr)  

Burns,  M.  N.,  Mohr,  D.  C.  (2011).  J  Med  Internet  Res,  13(3),  e55.    

Mobilyze  is  a  mobile  applica5on  aimed  at  trea5ng  major  depression  and  includes  •  Didac5c  Content  (text,  video,  audio)  aimed  in  providing  educa5on  about  behavior  change  strategies  

•  Interac5ve  Tools  that  assist  in  implemen5ng  changes  

•  No5fica5ons  that  provide  reminders  

•  Feedback  that  provide  insight.  www.cbits.northwestern.edu  

Context  Awareness  •  Context  awareness  refers  to  the  idea  that  computers  can  

both  sense,  and  react  based  on  their  environment.  •  A  second  aim  of  the  Mobilyze  project  is  to  harness  sensor  

data  from  user  phones  to  develop  models  that  can  detect  treatment  relevant  states  in  real  5me,  which  can  then  be  used  to  posi5vely  reinforce  treatment  congruent  behavior  and  provide  assistance  when  need  is  detected.  

www.cbits.northwestern.edu  

Context  Inference  System  The  Machine  Learner  is  “trained”  using  EMA  (queries)  

www.cbits.northwestern.edu  

Purple  Robot  •  A  full  real-­‐5me  sensor  data  acquisi5on  placorm  for  

collec5ng  informa5on  about  the  user  and  their  immediate  surroundings.  Purple  Robot  provides  –  Full  access  to  the  Android  sensor  framework  (e.g.  the  

accelerometer,  gyroscope,  pressure  sensor,  light  sensor,  etc.)  

–  Access  to  other  device  informa5on  (e.g.  badery  level,  running  soeware  &  apps,  and  hardware  informa5on).  

–  Op5ons  to  scan  for  external  devices  such  as  wireless  access  points  and  visible  Bluetooth  devices.  

–  Loca5on  sensors  that  use  the  built-­‐in  GPS  and  cellular  triangula5on  op5ons  to  map  the  user’s  loca5on.  

–  Local  environmental  data  sources  such  as  solar  event  5ming  (sunrise  &  sunset)  and  weather  condi5ons.  

–  Communica5on  paderns,  including  phone  logs  and  text-­‐message  transcripts.  

–  Cryptographic  anonymiza5on  of  personally-­‐iden5fiable  informa5on  before  it  leaves  the  device.  

•  Purple  Robot  has  been  open  sourced.    •  hdp://tech.cbits.northwestern.edu/purple-­‐robot/  

www.cbits.northwestern.edu  

EHR  Integra5on  

•  EHR  systems  are  rela5vely  closed  •  Implemen5ng  two  way  integra5on  is  difficult  •  Innova5on  using  EHRs  is  difficult  

•  Example:  Lightweight  coupling  of  electronic  pa5ent  reported  outcomes  (ePROs)    

Mobile  Devices  in  the  Clinic  

Andrew  Gawron,  MD,  PhD  Center  for  Healthcare  Studies    John  Pandolfino,  MD  Division  of  Gastroenterology  and  Hepatology  Northwestern  University    

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Outpa5ent    care  

Inpa5ent  care  

Procedures  

Our  approach  

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https://enotis.northwestern.edu/login

Ø  eNOTIS: Open source web-based subject registration system

Ø Meets federal guidelines for electronic reporting and addresses a mandate that accrual information be tracked, validated, and reported.

https://github.com/NUBIC/eNOTIS

Informatics

Integration with eNOTIS

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Ø  eCapture is a web-based system delivers forms for administrator or patient facing data collection and it is linked to eNOTIS.

Ø  Information, collected

through these systems, can be linked with other clinical information available in the EHR

https://github.com/NUBIC/surveyor

Delivery on an iPad

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Dashboard view by study

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Results

0

50

100

150

200

250

300

350

400

450

Aug Sep Oct Nov Dec Jan Feb Mar April May Jun Jul Aug

Num

ber o

f Pat

ient

s

>4000 ePROs collected in 1 year in 2 clinics

Ø  482 patients recruited v  434 patients completed at least one measure

Ø Mean age 48yrs v  52.5% female, 87.7% white

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Results: Time burden Patient Reported Outcome Measure

# of items

Patients (N)

Median time, min (IQR)

Disease Specific GerdQ 6 413 1.0 (1.6) Heartburn Symptom/Experience 13 432 1.3 (1.7) Heartburn Vigilance/Awareness 16 424 1.8 (1.8) Impaction Dysphagia Questionnaire 6 426 1.3 (1.8) Visceral Sensitivity Index 15 432 1.9 (2.0) Not Disease Specific Discomfort Tolerance Scale 7 391 1.4 (1.4) Anxiety Sensitivity Index 16 432 1.8 (1.7) BSI-18 18 430 1.4 (1.4) PANAS 20 432 1.8 (1.5) Perceived Stress Scale 4 434 0.8 (0.8)

Ø  Most patients required ≤ 2 minutes for each ePRO measure

Ø  Average time to complete all measures: ∼ 20 minutes

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Results: Usability

N=93 patients

Ø ~90% of patients reported the system easy to use

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Results: Satisfaction and Patient Recall

N=93 patients

Ø  95.7% would recommend the system to other patients

Ø  46.2% reported that the system helped them remember symptom occurrence

Ø  35.5% said that it encouraged them to discuss medical issues with their doctor

Decision  Support  and  EHRs  

•  eMERGE  project  –  NHGRI  funded  study  to  examine  the  validity  of  the  EHR  for  iden5fying  disease  cohorts  for  gene5c  studies  – Automated  disease  /  phenotype  algorithms    – Genera5on  of  SNP  variants  from  a  pa5ent  cohort  

•  Integra5on  with  EpicCare  the  phenotype  algorithms  

•  Integra5on  of  genomic  variants  with  care  

eMERGE  I  Ques5ons  

•  Technical  –  Is  the  informa5on  in  the  EHR?  – How  to  get  it  out?  – Does  it  work  across  ins5tu5ons?  

•  Ethics,  Legal,  Social  (ELSI)  – Recrui5ng  (Purposeful  /  Opportunis5c)  – Consen5ng  (Opt  in  /  Opt  out)  – Privacy  – Data  Use   EHRs and Genomic Discovery

Vanderbilt Internal + Epic

Group Health Cooperative Epic

Mayo Clinic GE Centricity+

Cerner

Marshfield Clinic Internal

Northwestern Epic Cerner eClinicalWorks

Coordinating center

Geisinger Epic

Mt. Sinai/Columbia Epic/Allscripts

•  Rex  Chisholm  •  Maureen  Smith  

•  Jennifer  Pacheco  •  Will  Thompson  • Arun  Muthalagu  • Anna  Roberts  • Tony  Miqueli  

• Geoff  Hayes  • Laura  Rasmussen-­‐Torvik  • Loren  Armstrong  • Doug  Scheener  

•  Jus5n  Starren  •  Abel  Kho  •  Steve  Persell  •  Phil  Greenland  •  Bill  Lowe  •  Mark  Graves  •  Sharon  Aufox  

Validated Phenotype

Statistical Modeling

Validate

Algorithms

Identify

Subjects

Translate Definition to Data

Defin

ePh

enot

ype Data

Warehouse

Multi-diciplinary Team

Validate

Algorithm

80+ Years ofClinical Notes

AnalyzeData

Complexity / Maturity

Universal Omics

Disease  Risk:    Order  Test  

Disease  Risk:    Change  Behavior  

Disease  Risk:    Watchful  Wai8ng  

Low  Disease  Associa8on  

Gene8c  Varia8on  of  Unknown  Clinical  Significance  (GVUCS)  

Act

iona

bilit

y Primary  Actor  

Clinician  

Pa8ent  

Consumer  

Researcher  Informa8cian  

Not  Computable  

Ancillary  ‘Omics  

Actionable Results

EHR  

“Lab”  Results  

Observa5ons  

Patient Information

EHR  Integra8on  –  Overview  

Results  from  CLIA  cer5fied  lab  

Simple Data

Complex Data

‘Omic  Repository  

External  DSS   EHR  DSS  

Option 1 Ac5onable    Adributes  

Analy5cs  

Option 2

High-Throughput Sequence Data, Methylation, Tissue Array, Tertiary Structure, etc.

SNP calls, Regulatory Network Analysis, etc.

SNPs, Network Activation, Indels, CNVs, Rearrangements, etc.

Filter for Actionable Clinical Significance

Na8onal  DB  of  Clinically  Significant  Variants    

Clinically Relevant “Omic” Findings

EHR Integration

Pa8ent  Specific  Clinical  and  

Environmental  Data  

Personalized Health Care

Raw  “Omics”  Data  

Informa8on  

Knowledge  

Ac8on  

Bedside                                                                                                                    Be

nch  

Scien8fic  Literature  

Na8onal  DB  of  ‘Omic  CDS  Rules  

Open  Ques5ons  

•  What  data  goes  “in  the  EHR”  and  what  data  is  stored  in  ancillary  systems.  

•  What  clinical  decision  support  is  internal  to  the  EHR  and  what  is  external?  

•  How  do  we  incorporate  mobile  data?    

We  need  to  speed  up  our  ability  to  transform  findings  into  ac5onable  calls  

Issues  

•  Appropriate  access  to  precision  oncology  data  –  big  data  in  cancer  needs  innova5on  

•  How  do  we  promote  pipeline  innova5on  –  data  handling,  mining,  analy5cs?  

•  How  do  we  build  true  healthcare  learning  systems,  where  every  pa5ent  contributes  to  our  knowledge  of  cancer?  

CI4CC  

 

Cancer  Informa5cs  For  Cancer  Centers    

www.ci4cc.org      

Thank  you  

Warren  A.  Kibbe  wakibbe@northwestern.edu