OSEBNA MEDICINA IN SISTEMSKA MEDICINA – ŽELJA ALI REALNOST?

http://cfgbc.mf.uni-lj.si/2013anniv8casym

Zakaj je vsak človeški genom drugačen? Posamezniki iste vrste imajo polimorfna DNA zaporedja

DNA je med posamezniki človeške vrste najmanj 99% identična.

Kje najdemo razlike?

Genom - dedna informacija celotnega organizma (jedrni, mitohondrijski, kloroplastni genom; pri bakterijah tudi

plazmidi in transpozoni; haploidni, poliploidni). Je vsota vseh genov organizma.

Enojni nukleotidni polimorfizmi (SNP) Mikrosateliti (ponovitve kratkih zaporedij),

Minisateliti (ponovitve dolgih zaporedij), Delecije,

Duplikacije, Druge prerazporeditve DNA.

Število ponovitev Bolezenski status <28 Zdrav

28–35 Zdrav 36–40 Prizadeti heterozigoti >40 Bolezensko stanje

PRIMER: CAG ponovitve pri Hungtingtonovi bolezni

POLIMORFIZNI (NPR. POMNOŽEVANJE MIKROSATELITOV) LAHKO VODIJO DO BOLEZNI

Bolezen se deduje avtosomno dominantno.

Genome sequenced (publication year) HGP (2003) Venter (2007) Watson (2008)

Time taken (start to finish) 13 years 4 years 4.5 months

Number of scientists listed as authors > 2,800 31 27

Cost of sequencing (start to finish) $2.7 billion $100 million < $1.5 million

Coverage 8–10 × 7.5 × 7.4 ×

Published online 16 April 2008 | 452, 788 (2008) | doi:10.1038/452788b James Watson's genome sequenced at high speed The first full genome to be sequenced using next-generation rapid-sequencing technology is published today, marking another milestone in the extraordinarily fastmoving field of human genome sequencing. It took just four months, a handful of scientists and less than US$1.5 million to sequence the 6 billion base pairs of DNA pioneer James Watson. The achievement is first proof of principle that these rapid-sequencing machines can decipher large, complex genomes. Made in this case by Connecticut-based 454 Life Sciences — a division of Roche Diagnostics — they allow many more sequencing reactions to proceed at the same time, on the same surface, than the previous generation of machines that produced the inaugural human genomes. That change has had big pay-offs in speed, efficiency and, ultimately, cost.

http://www.nature.com/nature

Zaporedje človeškega genoma, ki je bilo najavljeno junija 2000, je bil “reprezentativni” genom na osnovi zaporedja izbranega posameznika (moški belec). Želja določiti zaporedje mnogih posameznikov je sprva izgledalo izgleda kot Herculovo delo. Ker je genom vsakega posameznika “reprezentativen”, ni važno, čigav genom je bil določen prvi, saj je zaporedje identično cca 99%.

KAJ JE POMENILO SEKVENCIRANJE ČLOVEŠKEGA GENOMA?

Genom Jamesa Watsona je bil določen leta 2008 s tehnologijo 454.

GENOMI IN GENETSKA RAZNOLIKOST

- Zaporedje kateregakoli genoma NE odraža genetskih raznolikosti (polimorfizmov) posamezne vrste.

- Genetsko raznolikost lahko spoznamo, če primerjamo zaporedja genomov posameznikov znotraj iste vrste organizmov.

- GENOM torej ne ponazarja informacije o posameznem zaporedju DNA. Podaja informacijo o skupini zaporedij, ki imajo skupen biološki pomen.

- Človeški genom med posamezniki je 99% identičen, kar pomeni, da se genom dveh posameznikov med sebom razlikuje v cca 3 milijone bp.

http://www.personalgenomes.org/

Steven Pinker

NASLEDNJA (NEXT) IN TRETJA GENERACIJA VISOKOZMOGLJIVEGA PARALELNEGA SEKVENCIRANJA

Naslednja generacija (HTS-NG) – PCR pomnoževanje

- Sekveniranje z amplifikacijo na kroglicah (Roche/454FLX; GS Junior)

- Sekvenciranje s sintezo (Illumina/Solexa Genome analyzer, MiSeq)

- Sekvenciranje z ligacijo (Applied Biosystems SOLID System)

Tretja generacija – sekvenciranje brez pomnoževanja ali ojačitve signala

- Heliscope (tSMS – true single molecule sequencing, 2007)

- SMRT (sekvenciranje v realnem času)

- RNAP (sekvenciranje v realnem času)

- Nanopore sekvenator

- Ion Torrent, Ion Proton (ABI)

- Etc……..

NOVE GENERACIJE SEKVENCIRANJA OMOGOČAJO VISOKOZMOGLJIVO IN HITRO PREISKOVANJE VELIKEGA ŠTEVILA GENOMOV.

ŠTEVILNE MEDNARODNE INICIATIVE!

PROJEKT HAPMAP

Cilj tega mednarodnega projekta je razkriti pogoste vzorce variacij človeškega genoma – zemljevid haplotipov (haplotip je zaporedje alelov na posameznem kromosomu). Gre za različice, ki so povezane z boleznimi in z odgovorom posameznika na zdravila in druge okoljske dejavnike. Sodelujejo raziskovalci različnih držav (Kanada, Kitajska, Japonska, ZDA, UK, Nigerija…). Projekt se je pričel leta 2002. Rezultati 1. faze so bili objavljeni oktobra 2005, 2. faze oktobra 2007, in 3. faze spomladi 2009. Podatki so javno dostopni.

http://hapmap.ncbi.nlm.nih.gov/

ENCODE – Enciklopedija človeških DNA elementov

http://www.genome.gov/10005107

EPIGENOM sestoji iz vrste kemijskih sprememb DNA in histonov posameznega organizma (metilacije, acetilacije, ubikvitinacje, itd. ). Te spremembe so v različnih tkivih lahko različne. Nekatere se prenašajo na potomce. Spremembe epigenoma povzročijo spremembo kromatina, kar lahko vodi do spremenjene biološke aktivnosti.

PROJEKT 1000 GENOMOV

Pričel se je januarja 2008, s ciljem narediti najbolj natančen katalog genetskih različic pri človeku. Znanstveniki bodo določili zaporedja genomov ljudi različnih etničnih skupin in obeh spolov. Sodelujejo ZDA, UK in Kitajska. Podatkovna zbirka je prosto dostopna. Podatki bodo imeli velik vpliv na medicino.

http://www.1000genomes.org/

Oktober 2010

http://www.1000genomes.org/

10 LET ČLOVEŠKEGA GENOMA

Ob tej obletnici je velo rahlo razočaranje, saj sekvenciranje genomov ni še ni prineslo revolucije v medicini, razumevanju bolezni in zdravljenju. Desetletje je postreglo s skokovitim napredkom tehnologij. Študijami GWAS in 1000 genomov, ter razcvetom osebne genomike.

Craig Venter

April 2010

DOKONČANIH PREKO 3800 GENOMOV

KEGG Organisms: Complete Genomes

Eukaryotes: 180 Bacteria: 2127 Archaea: 147

Vedno več podjetij ponuja sekveniranje predelov posameznikovega genoma za ceno nekaj 100 €. Kako posameznik lahko uporabi informacijo o svojem genomu? Kaj si s to informacijo na sedanji stopnji znanja lahko pomaga zdravnik? “Manjkajoča dednost” predstavlja največjo oviro pri odkrivanju z boleznimi povezanih genov. Vzrok je prispevek velikega števila lokusov, ki imajo posamezno majhen učinek in lahko učinkujejo v različnih kombinacijah.

ZMOŽNOST HITREGA DOLOČANJA NUKLEOTIDNEGA ZAPOREDJA POSAMEZNIKOVEGA GENOMA JE PRIVEDLO DO OBDOBJA INDIVIDUALNE

(OSEBNE, POSAMEZNIKU PRILAGOJENE) GENOMIKE

Velja predpostavka, da je višina dedna v 80 – 90%. Če bi 29 cm ločilo najvišjih 5% ljudi od najnižjih, bi bila za 27 cm odgovorna dednost. V letu 2008 so 3 skupine raziskovalcev naredile študijo na 30.000 ljudeh. Našli so 40 (pogostih) lokusov, ki so povezani z razlikami v višini. Vendar so ti lokusi predstavljali le 5% (!!) dedne komponente višine (cca 6 cm). Nauk: za dednost so pomembne tudi redke različice, ki se med posamezniki razlikujejo!

KAJ JE MANJKAJOČA DEDNOST? Primer: določanja višine človeka

REALNOST INDIVIDUALIZIRANE (OSEBNE) MEDICINE

Informacija o genomu naj bi v perspektivi imela poseben status v klinični medicini. Zato so se že zelo zgodaj razmahnile komercialne ponube genomskih testov, ki bazirajo na omejenih študijah klinične veljave, pogosto brez analize dobrobiti za zdravje. V principu naj bi osebna genomika zmanjšala stroške zdravljenja zaradi boljše diagnostike, bolj efektivnega zdravljenja, vendar je zaenkrat zelo malo z dokazi podprtih tovrstnih dejstev. Dobre znanstvene ideje same po sebi ne morejo spremeniti medicinske prakse. Zato mora osebna genomika prestati prav takšne teste in standarde, kot katerokoli drugo polje medicine.

Poleg realnih obetov za izboljšanje diagnoze in zdravljenja, osebna genomika posamezniku lahko prinaša dileme v etičnem in psihološkem smislu, v primeru lažnih pozitivnih/negativnih podatkov ali nezmožnosti razumevanja podatkov. Genomika je velik izziv za medicino. Nikdar do sedaj še ni bilo tako velikega razkoraka med količino podatkov in našo zmožnostjo, da jih razumemo in koristno uporabimo. Zato je nujno, da nadalje razvijamo orodja (tudi bioinformatična!) in čim hitreje izobrazimo nove generacije zdravnikov in raziskovalcev. Tako bomo ohranili dobrobit genomike in minimizirali potencialne negativne učinke.

ZADRŽKI PRED OSEBNO GENOMIKO?

Personalised Medicine in the FP7 Health Programme

Dr Patrik Kolar Head of Unit

Directorate for Health DG Research & Innovation

European Commission

FP7 Health/Personalised Medicine Information Day, Olomouc, 16 June 2011

Health Care Challenges

● 30-60 % of patients responds to common drugs ● 5-7 % of all hospital admissions resulting from adverse drug reactions ● Costly and time consuming to develop new treatments, high failure rate ● The ageing population ● Increasing cost of health care ● Cost containment for health care spending ● Well informed patients putting higher demands on health care providers

Time for change !!!

Health care opportunities

● Avalanche of new –omics and molecular information following the sequencing of the human genome

● Translation of – omics from basic to clinical research can bring us better understanding of health and disease

Clinical definition of disease

Molecular definition of disease

Molecular definition of health

● Which can bring us innovative approaches for the prediction, prevention,

treatment and cure of disease that can make health care more effective

Personalised Medicine

● Personalised medicine aims at better predicting, preventing and treating or curing diseases based on a patient's individual characteristics

● This emerging field may bring

radical changes in healthcare but we are still at the early stages its development

● But a long term structured approach to foster innovation in this area and to facilitate the rapid uptake of personalised medicine into clinical practice is still lacking

Omics

Technolo-gies

Data

Samples

Statistics

Personalised Medicine: complex but many posibilities for research & innovation action

R&D the

basics

R&D stratifying

tools

R&D test in human

In patients

Uptake in health-

care

Towards the

market

Biomarkers Identification Qualification Validation

Data modelling tools Technical aspects and challenges

Clinical trials Methodologies Ethics

Patient -recruitment

Diagnostics and Therapies Approval process – regulatory aspects

Pricing and Reimbursement Health economy

HTA

Prediction - Prevention – Treatment - Cure

Availability and usability in the clinic

Patient perspective Equal treatment Ethics

Training of professionals

Bruselj, 29. – 30. aprila 2010 NOVE TEHNOLOGIJE V OSEBNI MEDICINI

Bruselj, 10. – 11. junija 2010 DIFERENCIRANI BIOMARKERJI IN OSEBNA MEDICINA Bruselj, 14. – 15. junija 2010 OD SISTEMSKE BIOLOGIJE DO SISTEMSKE MEDICINE

Številna posvetovanja na temo osebne in sistemske medicine

Organizator

Evropska komisija Direktorat za raziskave

(DG Research)

Personalised medicine – highlights of workshops’

outcomes (2010)

…link clinical bioinformatics with

–omics expertise and clinical research……

CASyM – a brief overview

Walter Kolch on behalf of the CASyM Consortium

HLA-MED

22 PARTNERS & 11 COUNTRIES:

GERMANY (6) UNITED KINGDOM (3) FRANCE (3) SWEDEN (2) LUXEMBOURG (2) NETHERLANDS (1) SLOVENIA (1) IRELAND (1) ICELAND (1) ISRAEL (1) ITALY (1)

Who is CASyM?

27

The vision of CASyMs

Harnessing the advances in biology, computational biology and systems biology for the benefit of the patient

We can produce more data on patients than ever before

Metabolomics Proteomics Imaging Clinical laboratory

Genomics & Transcriptomics

Cell Biology & Molecular Biology

Technology Platforms

Pathology & Biomarkers

MS-data Images Biochemical data

Metabolomics data

Gene array data Sequencing data 101

102

103

100

FACS data Tissue

microarrays

What can Systems Biology do for Medicine?

But how efficiently can we use these data?

Metabolomics Proteomics Imaging Clinical laboratory

Genomics & Transcriptomics

Cell Biology & Molecular Biology

Technology Platforms

Pathology & Biomarkers

MS-data Images Biochemical data

Metabolomics data

Gene array dataSequencing data101

102

103

100

FACS dataTissue

microarrays

What can Systems Biology do for Medicine?

What can Systems Biology do for Medicine?

Systems Biology Approaches can provide the Heads-up-Display that allows the clinician to navigate patients’ data for making optimal decisions about diagnosis and therapy

A vision for Systems Medicine

Clinical samples

Mutation data

Pathway mapping

RASSF1A

APOPTOSIS

Mitogens Growth factors

Receptor receptor

Ras

RAFPP

P

P

MEKP

ERKPP

MST2

LATS1

PROLIFERATION

p53p73

Patients’ samples

Pathway literature

Clinical literature

Evidence & data

Clinical data

Over

all s

urvi

val

Survival in months

Dukes’ B (N=34)

Dukes’ A (N=24)

RKIP weak/negative

Dukes’ C (N=55)

0.5

P values:A vs. B: 0.70B vs. C: 0.03A vs. C: 0.08

Dukes’ Stage Survival Time Standard Error 95% Confidence IntervalA Mean: 59 months 7 46 - 72

Median: 72 months 14 44 - 100 B Mean: 70 months 7 56 - 84

Median: not applicableC Mean: 49 months 7 36 - 62

Median: 36 months 10 17 - 55

Signalling networks

Plasma membrane EGFR Frizzled

WntEGF

β-cat

β−cat/APC*/Axin*/GSK3β

β-cat*/APC*/Axin*/GSK3β APC*/Axin*

/GSK3β

Dshi Dsha

2

β-cat/TCF

TCF

Axin a

Slug

E-cadLPDM

SOS/Grb2

RasRas-GTP

Raf-1Raf-1*

MEKMEKpp

ERKERKppRKIPp RKIP

Snail

E-cad

c

c

GSK3β

2

SnailSlug

PKCδ

GSK3β

2

4

4

Slug

E-cad

RKIP

Axin

ERK pathway

Gene regulation

∅

∅

TranscriptionStoichiometric conversionTranscriptional repressionInhibitionFacilitationEnzymatic catalysis

DegradationConstitutive protein synthesis

Line connection

APC/Axin/GSK3β

GSK3β

Axin

∅

APC/Axin

APC

β−cat/APC a

β−cat*

∅

Wnt pathway

EMT (metastasis)

x1 x2

x3

x4

x5

x7

x8

x9

x10x11

x12

x13

x14

x15

x16

x18

x19x20

x21x22

x23x24

x25x26 x27

x28

x29

x30x31

x27

x5

x17

x13

x5

∅

∅ ∅

RKIP

GSK3β*x6

v1

v2

v3

v4v5 v6

v7v8

v9

v10

v11

v12

v13

v14

v15

v16

v17

v28

v29v30

v31

v32

v33v34

v35

v37

v36

∅

∅

v19v20

v21 v22

v23

v24v25

v26

v27

v38

v39

v40v41

v42

v43v44

v45v46

v47v18 ∅

Notation

∅

Omic profiles

Multidimensional inputs

Computational models Patient

stratification

Therapy response

P=0.0002

Survival in months

Over

all su

rviva

l

0.5

5 year survival: 82%

5 year survival: 48%

Prognosis

Validation

Virtual patients

Personalised diagnostics

Improved diagnostics & therapies

Personalised therapies

&

Systems medicine lifelong training Current state and future goals

Why Systems Medicine Training? The post-genome wave combined with in-depth mathematical approaches changed the perspective of understanding human health and disease but this has not been sufficiently explored in medicine. Complexity of the human chronic multifactorial diseases, that combine with aging, urges to broaden the pool of researchers in the medical sciences that apply quantitative techniques and systems approaches. Clinical phenotypes require re-definition after inclusion of data and knowledge from omics and other quantitative biological/medical data that are relevant for any complex disease. Only the new generations of medical doctors and researchers and can fully accomplish such tasks, by being exposed to systems approaches as early as possible in their education or research paths.

Systems Medicine interdisciplinary training : towards a new generations of MDs and scientists that are trained within the

three pillars of systems medicine (laboratory, computing, clinical work) and can apply this in daily practice to improve prognosis, diagnosis and treatment regimens

of multifactorial chronic diseases.

CLINICS Patient data

LABORATORY “omics”, other multi-level data, bioinformatics

Modeling &

integration

HEALTH

DISEASE

Genetics Environment (drugs, diet)

Biological clocks

ICT

Overall Action Traning Plan: 1. To design tailored interdisciplinary programmes and propose training

implementation actions at the master’s, doctoral and/or postdoctoral level, as well as for clinicians at different stages of their careers.

2. To identify the need and specifications for course modules in Systems Medicine that can be incorporated in curricula for medical and biology students at undergraduate/graduate levels and will become the basis of the “Systems Medicine” curriculum.

3. To organize meetings, expert guided workshops and targeted lecture series and design the first European Summer School in Systems Medicine.

4. To develop accredited CPD (Continuous Professional Development) courses for clinicians and researchers available as blended on line and face-to-face opportunities across the community.

Offering course modules in Systems Medicine. Task leaders: D. Rozman, F Levi Task participants: D. Harisson, M. Benson, I. Thiele, S Parodi 1. To offer course modules of Systems medicine that can be incorporated into

the existing undergraduate and/or graduate studies. The aspects of Systems Medicine within each module, through interdisciplinary understanding, diagnosing and curing diseases.

CASyM Modules: (a) Aetiology, pathophysiology, mechanisms, phenotyping; (b) Molecular diagnostics, prevention, prognosis, therapy control; (c) Therapy, clinical studies, optimization drug development. Modules will include omics, technologies , data analysis, patient stratification, multivariate analyses, descriptive statistics, access to patient cohorts, registries, basic mathemathical approaches (statistics, computer programming, modeling techniques) , etc. Each module will have a dedicated amount of credits that will be in line with the Bologna agreement.

12. – 15. junij 2013