From  the  Virtual  Human  to  a    ‘Digital  Me’  

Nour  Shublaq,  Peter  Coveney  

Centre  for  Computa-onal  Science  University  College  London,  UK  

n.shublaq@ucl.ac.uk  

VPH 2012 – Infrastructures: Looking Ahead, Thursday Sept 20, 2012, London

Overview  

•  What  is  a  ‘Digital  Me’?    

•  Ingredients  

•  Some  challenges  ahead  and  key  to  implementa-on  

•  Conclusions  

A  ‘Digital  Me’:  Google  maps  of  the  human  body  

         ‘a  coherent  digital  representa-on  that  is  used  as  an  integra-ve  framework  for  the  consolida-on  of  fundamental  and  transla-onal  Integra-ve  Biomedical  Research,  and  the  provision  to  (European)  Ci-zens  of  an  affordable  Personalised,  Predic-ve,  and  Integra-ve  Medicine’  

•  Interface  to  informaAon  -­‐  having  an  efficient,  effec-ve  and  interac-ve  interface  to  the  combined,  heterogeneous  informa-on  based  on  innova-ve,  interac-ve  visualisa-on  technologies    

•  Blender  of  informaAon  -­‐  the  ability  to  combine,  integrate,  fuse  informa-on  in  a  synergis-c  way,  and  to  return  such  fusion  to  the  user  visually.  This  involves  knowledge  management,  data  fusion,  image  processing,  mul--­‐modal  visualisa-on,  and  visualisa-on  of  uncertainty    

•  PaAent  avatar  -­‐  Modelling  of  physiological  and  pathological  processes  and  their  representa-on  in  a  way  that  fosters  understanding,  explora-on  and  possibly  the  produc-on  of  new  knowledge  from  pa-ent-­‐specific  and  popula-on-­‐specific  informa-on  and  knowledge  

             Digital  Pa)ent  Roadmap    

•  What  is  a  ‘Digital  Me’  ?  

•  Ingredients  

•  Some  challenges  ahead  and  key  to  implementa-on  

•  Conclusions  

Human  Genome  Sequencing  Sixty years ago we barely understood the genetic basis of heredity. Today, next generation sequencing has led to fundamental shifts in our understanding of biology.

No more than 25,000 protein coding genes in the human genome and not more than 100,000 previously thought.

Thousands of DNA variants have now been associated with traits/diseases.

Physical characteristics and disease risk are partly determined by small genetic differences

Structure  Mol.  Profiles  Genomic  

2

10

3000 30,000

6

New  Sequencers   1 Human Genome in: 5 years (2001) 2 years (2004) 4 days (Jan 2008) 16 Hours (Oct 2008) 3 Hours (Nov 2009) 6 minutes (Now!)

Cost of whole genome sequencing expected to drop to $100 in a few years

Genotype-­phenotype  resources  

Molecular-­level  models  (GWAS,  PPI,  …)  

Translational  Systems  Biology  

Clinical  phenotypes  (EHR,  multi-­scale  

physiological  models…)  

Exposome  (drugs,  diet,  environmental  

chemicals,…)  

Developments  

1

2

3

System-­level  models  (organ  networks,…)  

Text  m

ining    

&  sem

antic  

web  

Complex  disease  networks  

Pharmacogenomics  

Disease  susceptibility  

Disease  gene  Oinding  

Phenotypic  variation  

Use  Case:  Cancer  Treatment    

8

Drug treatment recommendation

Genome and Transcriptome sequencing

Tumor sampling Tumor stem cell extraction/expansion

Modeling Drug Response

The Cancer Model

X

X

X

Patient Specific Model

Drug Database

Mutation Database

Consumer  led  Healthcare  

PaAentLikeMe    

US-­‐based  social  networking    

and  data  sharing  plaZorm  for    

people  with  a  range  of  mainly    chronic  and  serious  condi-ons  

-­‐  New  security  se\ngs    

23andme    

personal  genomics  company    

stores  and  analyses  the    

genotypes  of  thousands  of    individuals  at  over  500,000    

different  posi-ons      

MobileTech Special (Qualcomm)

Informationweek.com

RunK

eepe

r    

Best  App

 for  Exercise  

Fooducate    

Best  App  for  Healthy  Ea-ng  Sleep  Cycle    

Best  App  for  Snoozing  

Top  5  Health  Apps      The  Times    Aug  2012  

Lose  It!    

Best  App  for  Weight  

Loss  

ZocDoc  

Best  App  for  Finding  a  Doctor  

E-­‐infrastructure  and  compuAng  in  the  ‘cloud’  

Amazon and Microsoft are providing cloud services for data storage and retrieval

•  What  is  a  ‘Digital  Me’  ?  

•  Ingredients  

•  Some  challenges  ahead  and  key  to  implementa-on  

•  Conclusions  

Some  challenges  ahead  

Biological  challenges  –  Do  we  understand  biology  and  

diseases  enough  to  develop  reliable  computa-onal  models?  

–  How  to  integrate  growing  knowledge  into  models?  

ICT  Challenges  –  Data  quality  –  Data  management  –  Data  security  –  User  interfaces  

Societal  challenges  –  Privacy  –  How  to  prevent  inequali-es  in  

access  to  health  care?  –  Health  care  economics  –  Implementa-on  in  health  care  –  How  to  prevent  adverse  

effects/misuse?  

•  Exploit  unprecedented  amounts  of  detailed  biological  data  being  accumulated  for  individual  people  (e.g.  at  GP  surgeries,  labs),  some  of  which  are  already  available  on  EHRs  

•  Harness  the  latest  developments  in  ICT  –  large  scale  data  integra-on  and  mining,  cloud  compu-ng,  high  performance  compu-ng,  advanced  modelling  and  simula-on,    

–  all  brought  together  in  a  highly  flexible  plaZorm.    

•  Turn  this  informa-on  into  knowledge  that  assists  in  taking  medical,  clinical  and  lifestyle  decisions  for  the  ci-zen  

•  Bridge  the  knowledge  gap  in  the  clinical/medical  community    

•  Pay  acen-on  to  the  ethical,  legal  and  societal  issues    

Key  to  ImplementaAon  

Clinicians  of  Tomorrow  

•  With  the  rush  of  genomic  data  into  hospitals,  and  an  increased  adop-on  of  electronic  health  records,  the  medical/clinical  community  is  faced  with  a  knowledge  gap.    

•  Match  the  knowledge  and  training  available  today  for  the  medical  and  clinical  communi-es  with  the  changing  landscape  of  medical  prac-ce  and  personalised  medicine  

•  Train  clinicians  today  to  be  comfortable  and  familiar  with  the  use  of  genomic  data  in  managing  their  pa-ents.  For  example,  although  it  might  be  more  useful  for  sequencing  and  genomic  research  to  freeze  tumor  samples,  surgeons  and  pathologists  most  oden  store  -ssue  in  formalin,  which  tends  to  make  meaningful  sequencing  more  difficult.  

           

E-­‐infrastructure  &  ICT  Layers  

Ethical,  legal  and  societal  issues  

Autonomy   Well-­‐being   JusAce  

Scien-sts   Freedom  to  research  

Facili-es  and  funding  

Appropriate  reward  e.g.  IP  

Pa-ents   Right  to  know  or  not  to  know  

Improved  treatment  op-ons  

Access  to  resources  

Vulnerable  groups   Right  to  be  heard   Allevia-on  of  disadvantage  

Equality  

Professional  groups  

Professional  judgment  

Increased  burden?  

Implica-ons  for  prac-ce  

Data  breach  is  the  unauthorised  acquisi-on,  access,  use,  or  disclosure  of  protected  health  informa-on  

       ownership  of  data,  consent,  compliance,  what  are  the  applicable  laws  and  regula-ons  

 governing  the  data?  Audi-ng  in  the  cloud?  

•  What  is  a  ‘Digital  Me’  ?  

•  Ingredients  

•  Some  challenges  ahead  and  key  to  implementa-on  

•  Conclusions  

•  Medicine  today  is  a  driver  of  ICT  innova-on  and  vice  versa    

•  Advanced  IT  allows  us  to  analyse  pa-ents  all  the  way  up  from  their  own  DNA  sequences  

•  A  personalised  ‘digital  Me‘  approach  is  expected  to  lead  to  improved    –  health  outcomes    –  drugs/treatments  –  disease  preven-on  –  evidence-­‐based  decision-­‐making  –  lifestyle  choices  for  global  ci-zens  

Conclusions  

Thank  you  for  your  a\enAon!  Nour  Shublaq  

University  College  London,  UK  n.shublaq@ucl.ac.uk