precision medicine / personalized medicine€¦ · precision / personalized medicine: dream/vision...

Precision Medicine / Personalized Medicine

Visioner, muligheder, barrierer

Professor, consultant, Research directorIvan Brandslund

14. Marts 2017

2

County Mayor E. Thiedemann, Denmark

”I finally understand the human genome:

To have it for the single person can be compared to having a list of telephone numbers for all the inhabitants on the globe…

…but if you don’t know their names, occupation, address or anything else about them it’s of no use.”

3

Kerneydelser

1. Diagnosticer sygdommen med bedste metoder

2. Behandl patienten efter højeste vidensniveau

3. Giv bedste pleje og trøst

Krav til pkt. 1

1. Sensitivitet

2. Specificitet

3. Positiv prediktiv værdi

4. Negativ prediktiv værdi

4

To use and practice precision medicine a very robust connection is needed between

Genomic sequenceBiomarker

Drug TreatmentPreventive intervention

Disease

Many diseases are unknown in ethiology. Many polymorphisms are unknown in importance. Many drugs fail or need dose adjustment based on mutational differences.

The 3 principles of PM

5

Such considerations have delayed a general acceptance of financing large scale sequencing projects.

A government report instead proposed smaller targeted research projects.

To enable these:

National Danish Bio – and Genebanks are organized for

CancerDiabetesRheumatologyEtc.

6

Precision Medicine is a

Hope DreamVision

for a compass direction of future research

to link

variation in genes, protein functions, metabolites

with

environmental conditions and lifestyles

to

enable individualized prevention and treatment

7

Personalized Medicine links

Precision Medicines 3 principles

with

patients own preferences

from

knowledge on effects and side-effects of treatment / drug in exactly this patient.

8

Precision Medicine and Targeted Treatment in Personalized Medicine

The Right Diagnosis Lab. Tests

The Right Person Genetics / biomarkers

The Right Drug PGx

The Right Time Natural history of disease

The Right Dose PGX and TDM

9

Old examples of PM / PM / TT

Diabetes 1

Diagnostics: Glucose, HbA1c, C-peptide, S-Insulin

Mechanisms known: Low / no Insulin production

Etiology: Unknown, GAD65 pos

Targeted treatment: Insulin

Companion DX: Insulin Ab?

Monitoring: Glucose, HbA1c

10

Old examples of PM / PM / TT

Blood Transfusion

Diagnostics: Low Hb

Genome: Blood Type

Companion DX : Donor blood typing, computer match

Targeted treatment: Transfusion

Effect / drug monitoring: B-Hb

Side-effects: Incompatibility, Type II – III allergic reaction, hemolysis, renal impairment

11

Precision / Personalized Medicine: dream/vision

1. A protein causing a disease is identified in a person (eg. BCR-ABL in CML).

2. The protein and the gene sequence is determined in the disease, CML.

3. A company succeeds in producing a drug, a small chemical molecule, that blocks the protein.

4. Gleevec!

5. Variations in protein amino acid gene sequence is observed to reduce effect of drug.

6. Produce another drug for this patient.

12

CML is Treated with a Tyrosinekinase Inhibitor

BCR-ABL fusionprotein blocked by Gleevec

13

BMS354825 response on BCR-ABL mutant isoforms

Shah et al. SCIENCE (2004) 305 p399

14

PM / PM

Personalized Medicine in its extreme meaning is creation of a tailored drug that targets exactly this – and other patients’ problem, provided the exact same gene/protein-sequence and function is present.

Expensive

The reality is that gene variations in the target protein can partition patients in smaller groups, that differs in their

• risk of getting a disease

• risk of treatment failure

• risk of adverse reactions

• probability for cure or relief of symptoms

Treat those who will benefit. Do not treat those that will not.

Serum HER-2 determinations for personalized care of breast cancer patients

Ivan Brandslund,

Professor MD, DMSc

University of Southern Denmark, Department of Clinical Biochemistry,

Vejle Hospital, Denmark

Rome, 2016

16

HER-2

CD340 Neu ErbB-2 HER 2

Cluster of Differentiation

Neuro/glioblastoma

Erythroblastic leukemia viraloncogene

Human Epidermal growth Receptor

chromosome 17

17

HER - family

1978 EGFR = HER 1 by Cohen

1982-84 HER2 by Weinberg at MIT

1984-86 Cloned by Ullrich et al

1986 McAb to HER2 produced

1987 Oncogene amplified in some cancers (Slamon)

1987 HER2 amplification: Shorter survival, aggressive disease

1988 Transfection with the gene cancer in mouse mammary tissue

1989 McAb against HER2 inhibits cell growth

1991 Herceptin produced, tested

1995 Phase 3 trial started

1996 The Dako Herceptest

1998 Herceptin approved: First antibody for targeted therapy in cancer

2003 Herceptest CE approved

2008 Lapatinib synergy with Herceptin

18

The Epidermal Growth Factor Signalling

19

Effect of HER-2 amplification on human breast cancer cells

Human breast cancer cells

Transfect with

HER2 gene

DNA synthesis 50–75%

Cell growth rate 30–50%

Growth in soft agar 225%

Tumourigenicity in nude mice

Metastatic potential 220%in nude mice

Transformedphenotype

HER2–ve

HER2+ve

MCF-7 MCF-7

20

The central dogme

DNA RNA Protein Function

Substrate Product

21

Cancer tissue Autologous

reference tissueNormal tissue

.1

1

10

100

1000

10000H

ER

2 n

g/m

g p

rote

in

(Centa

ur)

22

Indicators of increased HER-2 production

1 = gene copy number

2 = mRNA transcription

3 = cell surface receptor protein expression

4 = release of receptor extracellular domain

Normal Amplification/Overexpression

Cytoplasm

HER2 receptorprotein

Cytoplasmicmembrane

Nucleus

HER2 DNA

HER2mRNA

1

2

3

4

23

Positive or negative HER-2 status

IHC Images courtesy of MJ Kornstein, MD, Medical College of Virginia

Abnormal 2+ Abnormal 3+Normal 0

Normal

Normal 1+

Normal Abnormal low

amplification

Abnormal high

amplification

24

Ross JS, Fletcher JA. Stem Cells 1998; 16: 413–428

Survival of node-negative breast cancer patients related to HER-2 status

1.00

0.75

0.50

0.25

0Cu

mu

lati

ve p

rob

abili

ty

0 24 48 72 96 120 144

Not amplified

Amplified

Amplified: >10 copies/nucleusNot amplified: <3 copies/nucleus

Borderline: excluded

Time to death (months)

Log rank p<0.001

25

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80

Dis

ease f

ree s

urv

ival

Follow-up, months

BTC and HB-EGF in cancer tissue

BTC< 4.1 and HB-EGF<28.5

(11/74)

BTC>=4.1 and HB-EGF>=28.5

(31/76)

0.00

0.25

0.50

0.75

1.00

0 20 40 60 80

Dis

ease f

ree s

urv

ival

Follow-up, months

p=0.0006

Study on value of S-HER2 / HER2 of DNA in breast cancer

• S-HER2 was measured in 862 patients every 3–12 months, HER2 of DNA in selected.

• Tissue HER-2 status was determined by IHC and FISH

• Metastases were diagnosed according to the routine clinical methods using imaging/biopsy

26

27

0

200

400

600

800

1000

1200

1400

Se

rum

HE

R-2

(n

g/m

l)

Withouth relapse/progression With relapse/progression

Maximum serum HER-2 values in 35 patients who were serum HER-2 positive, tissue HER-2 positive

patients without (n=10) or with relapse/progression (n=25). Three tissue positive serum HER-2 values

between 3300 ng/mL and 14,000 ng/mL are not shown (p<0.00003).

S-HER2 before metastasis detection in tissue-positive patients using different cutoffs

Cutoff value 15µg/L Cutoff value 15µg/L + a

delta value of > 100 %

increase from individual

baseline after primary

therapy

Cutoff value 32 µg/L

Sensitivity 69 % (53-80 %) 50 % (35-64 %) 47 % (33-62 %)

Specificity 71 % (62-78 %) 96 % (91-98 %) 96 % (91-98 %)

Positive predictive value 47 % (35-59 %) 84 % (65-93 %) 83 % (64-93 %)

Negative predictive

value86 % (77-91 %) 83 % (76-89 %) 83 % (75-88 %)

29

(CI 95 %)

Probabilities for metastatic recurrence in relation to S-HER2 value

S-HER2 values taken at the time of confirmed metastatic recurrence by CT/US/MR in patients with recurrence. For patients without recurrence, the highest S-HER2 for each patient was used.

30

31

Summary

Tissue neg. pts.: PPV at 15 ng/ml 42%

PPV at 25 µg/L 100%

Tissue pos. pts.: PPV at 15 ng/ml 71%

PPV at 80 µg/L 100%

Lead-time between S-HER2 increase and the clinical

diagnosis of symptomatic metastasis (at cutoff 15 µg/L)

• 0-38 months

• Median 4 months32

• A prospective follow-up study on Herceptin treatment of BC

• Followed for up to 6 years or until death (2004-2011)

• S-HER2 and S-Trastuzumab was measured at clinically determined intervals of between 3 weeks and 12 months

• Patients were followed routinely for relapse by physical examination. If symptoms of relapse CT/MR/US was performed

33

Dot plot showing delta S-HER2% values in patient events with no progression and with progression, respectively.29 patients (27 patients in the progression group and two patients in the no progression group) with a change > 100% are not depicted in the figure.

34

27 patients

2 patients

S-HER2 ’s ability by increase to predict progression

An increase in S-HER2 of ≥20% was correlated to progression in the disease in 40 out of 44 clinical courses (p < 0.0001).

35

Events with progression Events with no

progression

≥20 % increase in S-HER2 40 4

Not ≥20 % increase in S-HER2 9 32

Sensitivity 82% (68% – 91%)

Specificity 89% (74% – 97%)

PPV 91% (78% – 97%)

NPV 78% (62% – 89%)

(95% CI)

Sensitivity 56% (38% – 72%)

Specificity 98% (89% – 100%)

PPV 95% (76% – 100%)

NPV 75% (63% – 85%)

(95% CI)

A decrease in S-HER2 of ≥20% was correlated to no progression in the disease in 20 out of 21 clinical courses (p < 0.0001).

36

Events with progression Events with no progression

≥20 % decrease in S-HER2 1 20

Not ≥20 % decrease in S-HER2 48 16

S-HER2 ’s ability by decrease to predict response to treatment

37

No. 39

0

100

200

300

400

500

600

700

27-01-09 16-02-09 08-03-09 28-03-09 17-04-09 07-05-09 27-05-09

Date of sample

S-H

ER

2 (

ng

/ml)

0

100

200

300

400

500

600

S-H

erc

ep

tin

(n

g/m

l)

No 39 January 2009 mastitis carcinomatosis, ER and PR negative. Neoadjuvant therapy Herceptin/Taxol 1 serie 05.02.09, because of reduced Muqa continues only Taxol with good effect but operation not possible, continues Taxol. December 2009 progression, treatment changes to Faslodex.

38

No. 67

0

10

20

30

40

50

05-09-

05

24-03-

06

10-10-

06

28-04-

07

14-11-

07

01-06-

08

18-12-

08

06-07-

09

Date of sample

S-H

ER

2 (

ng

/ml)

0

100

200

300

400

500

600

S-H

erc

ep

tin

(n

g/m

l)

No 67

Surgery 25.04.06 after neoadjuvant EC and Iressa (02.02.06 – 06.04.06). 19.05.06 local relapse. Herceptin/Taxotere(24.05.06 – 06.09.06 ). July 2006 complete remission of local relapse. Local radiotherapy. Herceptin monotherapy (27.09.06 – 25.04.07). Currently without relapse.

A typical course for a patient with an initially good response but subsequent lack of response to trastuzumab treatment is that S-HER2 remains at the normal level during the first trastuzumabtreatment period and then increases prior to the second trastuzumab treatment period. S-HER2 decreases markedly and rapidly during the second trastuzumab treatment period.Eventually S-HER2 continues to increase despite therapy and the patients die.

39

1

10

100

1000

10000

Group 1

Free of recurrence

Group 2

Recurrence alive

Group 3

Recurrence dead

S-H

ER

2

1

10

100

1000

10000

Group 1

Free of recurrence

Group 2

Recurrence alive

Group 3

Recurrence dead

S-H

ER

2

N=18 N=13 N=17

41

Cancertype Gene/protein Analysis Drug No tests per year

Lungcancer EFGRC-METALK

DNA mutationMutationExpression

Gefitinib, Erlotinib 500

Breastcancer HER-2

HER-2BRCA1BRCA2

IHC, FISH, amplification

S-HER2

Mutation

Trastuzumab, Lapatinib

Trastuzumab, Lapatinib

PARB-inh

400

3.000

50

Colorectal cancer EGFRKRAS

Mutation Cetuximab 150

Ovary cancer HER-2EFGRVEGFVEGGF-R

Mutation div. 400

Prostatecancer BRCA1 og 2 Mutation PARP-inh 100

Melanoma BRAF Mutation Vemura Fenib

Leukemia ABL/BCR Translocation PCR Imatinib 50

Myelomatosis FGFR3 Translocation mutation Div.Daratumumab

100

Molecular test repertoire in cancer at Lillebaelt Hospital, Vejle, Denmark

42

2011

CA125 Ovarycancer 2.000

PSA Prostatecancer 20.000

YKL-40 Lungcancer 1.500

HCG Mola/testescancer 2.000

AFP Liver/testes cancer 2.000

CEA Coloncancer 1.000

DNA-methylation 4.000

Mikro RNA-analyser 4.000

Pattern – profiles/arrays 1.000

GWA 100

M-komponent screen/diagnostics 10.000

S-light chains / myleolomatosis 6.000

Min. Resid disease monitor, specific heavy chain 300

CYP-45 <10

YKL-40 + K-RAS effect of Cetuximab 1.000

Other tests and cancersERBB2, FGFR1, FGFR2, PDGFRA, PDGFRB, ALK, IGF1R, c-KIT, FLT3, RET, JAK2, SRC, Aurora A and B kinases, Polo-like kinases, MTOR, PI3K

44

Future of Personalized medicine

CPR (civil registration number)

Every person In Denmark has one

and all information is tied to this

number.

Danish health IT-map




number.


FMK (common prescription system)

Cosmic (regional medical records)

GP-medical records




number.

Danish health IT-map – Prescription National Database





number.




GP-medical records

BCC (Clinical Chemistry database)


FMK (common prescription system) BCC (Clinical Chemistry database)

CSO/AC(Anticoagulation)




number.

Sundhed.dk (health.dk)

Nemid (Easy ID)

a common secure logon system for self service


GP-medical records

Laboratory




GP-medical records

BCC (Clinical Chemistry database)





number.

Laboratory

Reimbursement

by the stok

Pharmacy

Drug


FMK (common prescription system) BCC (Clinical Chemistry database)

CSO/AC(Anticoagulation)

PGx-

hospitalCosmic (regional medical records)

GP-medical records




number.




number.


Nemid (Easy ID)

a common secure logon system for self service

Flow of data till generation of dosage letter

Presentation of patients Patient record

Dosage prescriptionLetter to patient

E-mail correspondence with the patient via the CSO-system

Letter to the patient

Login - Patient Self Testing

58

The history about the Tempus 600 system

Background

Conclusion from engineer students

• Reduction of delivery times (from blood test to answer)

• Optimizing of workflow in the blood test part

- Focus on the values (VA) and the non-value activities.

Venepuncture

VA 3,48 Min.

Non VA 39,06 Min.

Total 42,54 Min.

Transport 16 %

Wait for other tests 74 %

Wait /search (patients) 2 %

Value 8 %

59

Dispatch

60

GLP Conveyer belt

2016 Vejle Hospital

First inn / First out robot

(FIFO)

VS 2016

Morning round, time of sampling and reporting 2012 versus 2015

61

07:45

08:00

08:15

08:30

08:45

09:00

09:15

09:30

09:45

10:00

10:15

10:30

10:45

26 28 30 32 34 36 38 40 42 44 46 48 50

Tim

e

Week no.

2015

2015

2012

2012

62

2012 2015 Gain

Time of sampling mean2 SD min.

8:1218

8:026

1012

Received in Lab min.2 SD min.

3126

1614

1512

Time to report mean2 SD min.

9640

5614

4026

Effect of Tempus System on ToTAT

Morning round

64

From patient to waste container- automatized transport- and analyze production platform for (blood)samples –

From patient to waste container- automatized transport- and analyze production platform for (blood)samples –

Effect of Reduced TAT of 1 hour

• 1000 patients per day: 1000 hours less waiting

120 Parking lots

120 m2 waiting rooms

120 chairs

1000 hours less idle time for patients, doctors, nurses

Value? > 100.000 DKKr per day

> 25 Mill DKKr per year

66

GLP/Sysmex preanalytical centrifugation delay 2015

General Coagulation

Centrifugation time, min 10 14

Average time for passage, min.24 30

Minimum, min.Maximum, min. 2 SD, min.

146018

196018

67

3 centrifuges batch–production.

Morning 8-12 h, 300-600 samples per h.

68

CACS

HC Smede, Børkop

2015

Patent

69

Projekt DESERT

AIM Safer, faster and cheaper care of acute admission patients by Hospitals of Region of Southern Denmark

70

Success criteria

Shorter hospital stay

Reduced mortality

Reduces readmission number

Reduces critical outcomes – sepsis

Reduced use of intensive care units

71

Necessities

Necessary laboratory service

Short turnaround time: < 60 min

Large repertoire: 60-70 components

Necessary computer facilities

Transfer of lab. Data for BCC to algorithm analysis system.

72

User presentation

1. Comment on critical lab results

2. Risk assessment of patient

3. Top 3 possible diagnosis and probability

Costs in Region Southern Denmark

50 analyses ekstra per patient:

150 Kr. x 150.000 pts. 23 M.kr./year

SAS platform and program: 5 mill. Kr. 10 years + 1,5 mill. Kr./year 2 M.kr./year

25 M. Kr./year

5 h shorter hosp. stay: 1000 kr. x 150.000 pts. 150 M.kr./year

Cost reduction 125 M.kr./year

73

74

• Risk assessment

• Screen for disease

• Diagnose disease

• Predict effect of treatment

• Select best treatment according to serum analyzes: Theragnostics

• Monitor effect of surgery

• Detect effect of treatment

• Detect side effects of drug treatment

• Adjust treatment dose according to individual test results

• Discontinue treatment / change treatment in case of resistance to drug

75

Economy

Patients sampled per year Costs per year

v. 50 kr./pt.

Vejle Hospital 300.000 2 Million Euro

Vejle County 800.000 5 Million Euro

Denmark 12 Million 70 Million Euro

Europe 650 Million 4 Billion Euro

4 AU G U S T 2 0 1 6 | VO L 5 3 6 | N AT U R E | 4 1

Diabetes articles 1997-2016

Nature Genetics 46: 357-361, 2014

I omfattende genome-wide-associationsstudier Identificeres mutationer i et gen, som danner en zinktransportør i de insulin-producerende betaceller i pancreas

Mutationerne beskytter mod udvikling af type 2 diabetes og zinktransportøren undersøges nu af flere farma-virksomheder, som et muligt mål for ny udvikling af diabetes medicin

Vejle Diabetes Biobank

Nature Genetics 46:295-299, 2014

Igen et eksempel på hvorledes internationale genome-wide-

associationsstudier er I stand til at kortlægge nye områder i

det humane genom, som enten øger eller mindsker risiko for

type 2 diabetes


Nature Genetics 48: 1151-1161, 2016

Kortlægning af 30 nye områder i det humane

genom som bidrager til udvikling af

hypertension



18 SEPTEMBER 2015 • VOL 349 ISSUE 6254

J Med Genet. 2016 Sep;53(9):616-23. doi: 10.1136/jmedgenet-2015-103728. Epub 2016 Apr 11.


BMJ Open Diabetes Research and Care 2015;3:e000095. doi:10.1136/bmjdrc-2015-000095


PLOS ONE | DOI:10.1371/journal.pone.0120890 March 23, 2015


J Clin Endocrinol Metab, April 2015, 100(4):E664–E671


October 2014 | Volume 9 | Issue 10 | e109646


BMC Endocr Disord. 2014 Aug 28;14:74


Accepted 27 May 2015


Rates of Infection in Type 2 Diabetes • CID 2016:63 (15 August) 2016


Diabetic Retinopathy

• Most frequent diabetic complication

• Risk increases with the duration of diabetes

• Hypertension, poor glycemic control and elevated serum

lipids are risk factors

Results

P=0.48 P=0.50 P=0.81

”Whole Genome” sekventering Targeteret sekventering

Maturity-Onset Diabetes of the Young (MODY)

X X X X X

MODY1 MODY2

• Mutation in one of 13 know genes.

• Type-2 diabetes diagnosis wrong.

• 5-20 % in patients MODY are found.

Case

• Male 62 years

• BMI: 23

• Type-2 Diabetes diagnosis as 30-years

• PDX1 mutation

99

3 New Steno Diabetes Centers in Denmark

• 1 Billion Euro funding from Novo Nordisk Foundation

• To increase quality of clinical treatment

• Do research in Type 1 and 2 Diabetes

• Introduce personalized medicine in the treatment of the single patient

101

Danish Ministry of Health

Mapping of international activities concerning

Personalized Medicine

June, 2016

102

Personalized medicine – improving outcomes

103

The precision-medicine ecosystem

104

Kilde:

https://www.genomicsengland.co.uk/taking-

part/genomic-medicine-centres/

Oversigt over placeringen af de 13 Genomic Medicin Centres

105

Overordnet it-arkitektur for Genomics England og The 100,000 Genomes Project

106

Eksempel på online-kursus ved Genomics Education Programme

107

Forståelsesramme for kortlægning af internationale erfaringer med personlig medicin

108

Fordeling af udvalgte lande

109

Report on Personalized Medicine

October 2016

Suggestions for projects, organization and management

110

Skitse til overordnet arkitektur for genomdata

111

Eksempel på sammenhængen mellem forskning og klinisk praksis ved personlig medicin

112

Udgifter til personlig medicin og stor skala-sekventering –Bemærk illustration af estimater

113

Grove overslag for økonomiske hovedelementer for etablering og drift af national satsning på personlig medicin – Bemærk mulig skalering

114

Opdeling af afledte økonomiske effekter af personlig medicin på kort sigt (fem år)

115

Recommendation in Denmark

1. National strategy for direction and priorities.

2. Justicial/law-based activities. Patient rights, safety, security

3. Education and information to Citizens: Insight and transparency

4. Effects in society: outcomes and economic gain? Health care efficiency? Investments and growth in biotech commercial area?

5. Formal networks and coordination of cooperation.

6. PM and genome data needs more people with many and more competences and educational level

7. Technological and IT infra structure IT standards, registers, databases, architecture and compatibility.

116

Best links:

https://www.nih.gov/precision-medicine-initiative-cohort-program

http://precisionmedicine.ucsf.edu/elements-precision-medicine

https://ghr.nlm.nih.gov/primer/precisionmedicine

https://www.nih.gov/precision-medicine-initiative-cohort-program

http://precisionmedicine.ucsf.edu/elements-precision-medicine

https://ghr.nlm.nih.gov/primer/precisionmedicine

Trombose / Trombofili

APTT

Protein C-resistens

F5- Gen Leiden

Protrombin – Gen/DNA

Cøliaki

Autoantistoffer Transglutaminase

Gliadin

+ vævstype

HLA-DQ8 + HLA-DR2

Immunologi? Genetik?

We don’t care

just an analytical test

STAT !

Urin – Flow Cytometry

Metode: Detekterer og kvantitererLeucocytter, Blod, CastsBakterier(samt U-stix analyser)

Outcome: Nedbringer antal dyrkninger i MikrobiologiGør U-stix obsolete

U-dyrk (MADS-flow) U-stix (Biokemi)

Tempus Tempus

Flow (på fælles GLP conveyer belt)

Negative svares i BCC < 10 minPositive dyrkes

Svares i MADS / ROS

123

Dobbeltfunktion / konfliktområder

PatologiMikroskopisk diagnostik på væv diagnostik af cancer

Biokemi analyser på vævMol.biol. analyser på væv

Mol.biol. analyser på blod/plasma

Mol.biol. Og Biokemiske analyser på væv, celler, blod, plasma, urin, fæces m.m.

Biokemi

124

Vejle Hospital

precision medicine / personalized medicine€¦ · precision / personalized medicine: dream/vision...

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