Role of biomarkers in blood and CSF

Gavin Giovannoni

Barts and The London

Why MS biomarkers?

• Diagnostic testing

• Positive & negative predictive testing

• Pathogenesis

• Immunology

• Aetiology

• Disease progression & recovery

• Disease heterogeneity

• Pharmacovigilance

• Monitor disease processes

• Prognosis (high vs. low risk patients)

• Monitoring effect of therapeutic interventions

Diagnostic & pathogenic markers

The evolving clinical definition of MS

1. Schumacher, et al. Problems of Experimental Trials of Therapy in Multiple Sclerosis: Report by

the Panel on the Evaluation of Experimental Trials of Therapy in Multiple Sclerosis. Ann N Y

Acad Sci 1965;122:552-68.

2. Poser, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols.

Ann Neurol 1983;13:227-31.

3. McDonald, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the

International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121-7.

4. Polman, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald

Criteria". Ann Neurol 2005;58:840-6.

5. Polman, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald

criteria. Ann Neurol. 2011;69:292-302.

Will Rogers Phenomenon in Multiple Sclerosis

1879 - 1935

“When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states.”

Will Rogers Phenomenon in Multiple Sclerosis

Sormani et al. Ann Neurol 2008;64:428–433.

Poser

McDonald

Intrathecal synthesis of IgG

Images courtesy of Alastair Compston and Ed Thompson.

Kabat et al. J Clin Invest. 1942 Sep;21(5):571-7.

Carl Lange – Colloidal Gold Curve

Isoelectric focusing with immunfixation

Diagnostic criteria for Primary Progressive MS

Polman et al. Ann Neurol 2005;58:840-6.

Accumulation of disability in PPMS: stratified by intrathecal IgG abnormalities

Proportion Progressing as Percent

Epoch CSF- CSF+

6 mo 7.3 9.8

12 mo 15.0 20.4

18 mo 22.8 28.1

24 mo 25.4 34.3

Years to Progression

2.43 2.26

Based on data from a second meeting of the DSMB and assume no therapeutic effect

0 1 2 3 Years

0.0

0.2

0.4

0.6

0.8

1.0

Pro

po

rtio

n P

rogr

essi

ng

Positive Negative

CSF

Slide courtesy of Jerry Wolinsky

P =0.03

Dobson R, et al. J Neurol Neurosurg Psychiatry 2013;0:1–6.

What constitutes a useful diagnostic test or set of criteria?

TARGET DISORDER

PRESENT ABSENT

DIAGNOSTIC

TEST RESULT

+ a b a + b

- c d c + d

a + c b + d a + b + c + d

From these we determine the sensitivity and specificity as follows:

SENSITIVITY = a/(a+c) > 80%

SPECIFICITY = d/(b+d) > 80%

Neurobiol Aging 1998; 19:109-116.

A clinico-pathoanatomical study of multiple sclerosis diagnosis

SENSITIVITY = True+ve /(True+ve + False-ve)

Eye Department, Hvidovre Hospital, Denmark.

• Neuropathological examination of 518 consecutive patients with clinically definite MS revealed a correct diagnosis in 485 cases (94%).

• Clinical diagnosis had been established by a neurologist in all cases.

• Erroneous diagnosis included a variety of other neurological disorders.

• Also investigated was a randomly selected series of 33 patients with a clinical diagnosis of probable MS:

– post mortem confirmation of MS was obtained in circa 66%.

– The remainder the error pattern was similar to the above.

Engell T. Acta Neurol Scand. 1988 Jul;78(1):39-44.

Lennon et al. Lancet 2004;364:2106-12.

NMO

Kleinschmidt-DeMasters,et al. N Engl J Med. 2005 Jul 28;353(4):369-74.

PML complicating treatment with natalizumab and IFNb-1a for MS

Pathogenic markers

“Inflammation”

“Oligodendrocyte Toxicity & Demyelination”

Axonal Toxicity (conduction block)

Axonal & Neuronal Loss

Gliosis

Remyelination & Axonal Recovery

“Inflammation”

Central Adaptation & Plasticity

Key pathological processes in MS

Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology

Magliozzi et al. Brain 2007; 130:1089-1104.

Increased urinary free immunoglobulin light chain excretion in MS

Brain atrophy

Slider courtesy of Dr Nic Losseff, Institute of Numerology, Queen Square.

Control Multiple sclerosis

Brain atrophy

Petzold et al. J Neurol Neurosurg Psychiatry. 2005 Feb;76(2):206-11.

Spinal fluid neurofilament levels

Gunnarsson et al. Ann Neurol 2010; Epub.

CSF NFL

Natalizumab treatment of progressive multiple sclerosis reduces

inflammation and tissue damage

- results of a phase 2A proof-of-concept study

ClinicalTrials.gov Identifier: NCT01077466

J. Romme Christensen1, R. Ratzer1, L. Börnsen1, E. Garde2, M. Lyksborg2, H.R. Siebner2, T.B. Dyrby2, P. Soelberg Sørensen1 and F. Sellebjerg1

Natalizumab treatment of progressive MS reduces inflammation and tissue damage: CSF markers of axonal damage

Romme Christensen et al. ECTRIMS 2012.

-1.0%

-0.8%

-0.6%

-0.4%

-0.2%

0.0% Years 0-2

-0.82%

-0.80%

P=0.822

Placebo (N=315) Natalizumab (N=627)

Year 0-1* Year 1-2

-0.40%

-0.56%

-0.43%

-0.24%

P=0.004

P=0.002

Miller DH et al. Neurology 2007;68:1390-1401.

Natalizumab and brain atrophy Mean (SE) percentage change in BPF

Bas

elin

e

Follow-u

p

0

500

1000

1500

2000

2500

NfL

(p

g/m

l)

Bas

elin

e

Follow-u

p

0

1000

2000

10000

NfL

(p

g/m

l)

Cerebrospinal fluid NfL Fingolimod 0.5mg/1.25 mg versus placebo treated patients

p<0.001

Fingolimod, n=23 Placebo, n=12

p=0.470

Fingolimod 0.5 mg Fingolimod 1.25 mg

Baseline Follow-up Baseline Follow-up

Median (pg/ml)

644 321 (-50%) 886 738 (-17%)

*Non-parametric Wilcoxon matched pairs test; p value is calculated with inclusion of outliers Dr Jens Khule, ECTRIMS 2013

Fingolimod has an early and sustained effect on the rate of brain atrophy compared with placebo and IFNb-1a IM

FREEDOMS, 2 years

Fingolimod 0.5 mg (n = 356)

Placebo (n = 329)

***

* **

6 0 12 24

Time (months)

0

-0.4

-0.8

-1.2

-1.6

-2.0

−38%

vs placebo p<0.001

Ch

ange

in m

ean

BV

fro

m

bas

elin

e (%

)

TRANSFORMS, 1 year

0 12

Time (months)

0.0

-0.4

-0.6

-1.0

IFNb-1a IM (n = 359)

Fingolimod 0.5 mg (n = 368)

−40%

vs IFNb-1a IM p<0.001

*** -0.2

-0.8

Ch

ange

in m

ean

BV

fro

m

bas

elin

e (%

)

ITT population with evaluable MRI images. Note: n numbers for FREEDOMS data reflect the number of patients with available data at 24 months. *p<0.05; **p<0.01; ***p<0.001 vs comparator; p-values are for comparisons over Months 0-6, Months 0-12, Months 0-24 BV, brain volume; ITT, intent-to-treat. Gilenya™ Prescribing Information 19 April 2012. Reproduced with permission. Kappos L et al. N Engl J Med 2010; 362: 387-401, and Cohen JA et al. N Engl J Med 2010; 362: 402-415. Copyright © 2011 Massachusetts Medical Society. All rights reserved

Very low risk

age place of residence

outdoor activity / sun exposure / sun screen diet / vitamin D supplements

age of exposure to EBV smoking

At risk High Risk

Low risk

RIS CIS MS

family history genetics

sex month of birth place of birth

Unfavourable disease-modifying factors dynamic risk factors static risk factors

dynamic protective factors static protective factors

MRI / evoked potentials changes

Peripheral immunological changes T-regs (), NK cells, CD8 ()

Clinical disease

In utero childhood Adolescence / early adulthood adulthood

1. Declining Physiology – “peripheral immunological endophenotype” 2. Biological disease threshold – “CNS endophenotype” 3. Asymptomatic disease – RIS (abnormal MRI and/or evoked potentials) 4. Clinical disease

a. Clinically isolated syndrome (CIS) b. Relapsing MS c. Relapsing secondary progressive MS d. Non-relapsing secondary progressive MS

Favourable disease-modifying factors

protective HLA haplotypes

CNS changes (OCBs and microscopic pathology)

2

3

2 4b 2 4c 2 4d

2 4a

1

The MS ‘Endophenotype’

d25-OH D3

Cox univariate HR 95% CI P value

Q1 1.00

Q2 0.74 0.60-0.92 0.008

Q3 0.69 0.55-0.90 0.001

Q4 0.74 0.60-0.92 0.007

Median Survival (days)

Log-rank p

> Median 1267 0.021

< Median 973

Dr Jens Khule, ECTRIMS 2013

EBNA-1 IgG*

Cox univariate HR 95% CI P value

EBNA-1 nOD 1.01 0.996-1.029 0.137

* similar results in OCB pos and MRI T2 pos patients only

Median Survival (days)

Log-rank p

< Median 1247 0.216

> Median 1032

Dr Jens Khule, ECTRIMS 2013

Pharmacovigilance markers

What is the diagnosis?

Take special care with Interferon-beta-1b: If you might have a disorder of the immune system in which abnormal proteins are found in the blood (monoclonal gammopathy), you must check this with your doctor before you use interferon beta-1b. Patients who have the rare condition known as monoclonal gammopathy may develop problems with their small blood vessels (capillaries) leading to shock (collapse) which can be fatal, when they use medicines like interferon-beta-1b. See also 4. Possible side effects.

?

Natalizumab

Progressive multifocal leukoencephalopathy (PML)

Kleinschmidt-DeMasters,et al. N Engl J Med. 2005 Jul 28;353(4):369-74.

207 cases -1st February 2012 44 (21%) died 163 (79%) alive

Mild disability – 10% Moderate disability – 50% Severe disability – 40%

5% NAbs – infusion reactions

Natalizumab PML risk stratification tool

Anti-JC virus antibody status

Negative Positive

Prior immunosuppressant use

Natalizumab treatment

>2 Years

Natalizumab treatment

>2 Years

No Yes

No Yes No Yes

Lowest Highest Relative PML Risk

< 1 in 10,000 1 in 94 1 in 256 1 in 668 1 in 1887

Mitoxantrone

Azathioprine

Methotrexate

Cyclophosphamide

Mycophenolate

Cladribine

Rituximab

Etc.

PML risk estimates for anti-JCV antibody index thresholds were calculated based on the current PML risk stratification algorithm (September 2012)

and predicted probabilities (using data from patients with no prior IS use: 2242 non-PML patients and 51 patients who developed PML using all

available anti-JCV antibody index data at least 6 months prior to PML diagnosis) for the population at or below that particular index (0.9−1.5) and

for the population above an index of 1.5. For index thresholds below 0.9, patient numbers were insufficient to allow for calculation of risk estimates.

Anti-JCV antibody index values may differentiate PML risk for those with no prior immunosuppression

Index 1−24 months

≤0.9 0.1 (0, 0.41)

≤1.1 0.1 (0, 0.34)

≤1.3 0.1 (0.01, 0.39)

≤1.5 0.1 (0.03, 0.42)

>1.5 1.0 (0.64, 1.41)

46

PML risk estimates (95% CI) per 1000 patients with no prior IS use

Plavina T et al. Poster DX51, CMSC May 29–June 1 2013, Orlando, USA; Ticho B. et al, Presented at ENS, June 8–11 2013, Barcelona, Spain O228.

46

25−48 months 49−72 months

0.3 (0.04, 1.13)

0.4 (0.01, 2.15)

0.7 (0.21, 1.53)

0.7 (0.08, 2.34)

1.0 (0.48, 1.98)

1.2 (0.31, 2.94)

1.2 (0.64, 2.15)

1.3 (0.41, 2.96)

8.1 (6.64, 9.80)

8.5 (6.22, 11.38)

Predicting autoimmunity following treatment of MS with alemtuzumab

• 30% of alemtuzumab-treated pts develop autoimmune side-effects (primarily thyroid disease and idiopathic thrombocytopenia)

• Aim: To define predictive factors for autoimmune side-effects

• Sera of 141 pts screened at baseline for 8 different cytokines/chemokines

A combined IL-21/IL-7 test on pre-treatment serum may be useful to identify patients at low risk of developing autoimmunity following treatment with alemtuzumab

Jones JL, et al. ECTRIMS 2011, Amsterdam. P1009

Sensitivity NPV Specificity PPV

IL-21 alone 81 84 70 66

IL-7 alone 76 76 54 54

CCL21 alone 63 65 49 47

IL-21 or IL-7 98 97 41 55

IL-21 OR IL-7 OR CCL21

98 91 12 45

Given that pts may elect to receive treatment based

on results of this test – most weight given to

minimizing false negative results. Combining IL-21

and IL-7 into a single test offers improved test

accuracy over IL-21 alone. CCL21 did not improve

test accuracy

0

10

20

30

40

IL-7

Autoimmunity No autoimmunity

0

500

1000

1500

IL-2

1

1.0

Se

nsit

ivit

y

0.8

0.6

0.4

0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0

1.0

Se

nsit

ivit

y

0.8

0.6

0.4

0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0

1-Specificity

IL-21 and IL-7 levels in sera

of pts who did or did not

develop autoimmunity

Receiver operating

characteristic (ROC) curves

Neurology 2012;78(Suppl.): [S41.006]

Anti-natalizumab Antibodies

Number of Patients at Risk

Placebo

Antibody Negative

Transiently Positive

Persistently Positive

315

568

20

37

296

550

19

32

283

538

18

26

264

526

16

25

248

506

16

24

240

487

16

22

229

480

15

22

216

470

14

19

208

460

14

16

200

449

14

15

Weeks

0.0

0.1

0.2

0.3

0.4

0.5

0 12 24 36 48 60 72 84 96 108 120

29%

Placebo

17%

Antibody Negative

17%

Transiently Antibody Positive

34%Persistently Antibody Positive

Cu

mu

lati

ve

Pro

po

rtio

n o

f P

ati

en

ts

wit

h S

us

tain

ed

Dis

ab

ilit

y

Pro

gre

ss

ion

(E

DS

S) *,†

*p ≤0.05 vs. antibody-negative patients†p=0.66 vs. placebo

Number of Patients at Risk

Placebo

Antibody Negative

Transiently Positive

Persistently Positive

315

568

20

37

296

550

19

32

283

538

18

26

264

526

16

25

248

506

16

24

240

487

16

22

229

480

15

22

216

470

14

19

208

460

14

16

200

449

14

15

Weeks

0.0

0.1

0.2

0.3

0.4

0.5

0 12 24 36 48 60 72 84 96 108 120

29%

Placebo

17%

Antibody Negative

17%

Transiently Antibody Positive

34%Persistently Antibody Positive

Cu

mu

lati

ve

Pro

po

rtio

n o

f P

ati

en

ts

wit

h S

us

tain

ed

Dis

ab

ilit

y

Pro

gre

ss

ion

(E

DS

S) *,†

*p ≤0.05 vs. antibody-negative patients†p=0.66 vs. placebo

0.73

0.220.16

0.48*

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Ad

jus

ted

An

nu

ali

ze

d R

ela

pse

Ra

te (

95

% C

I)

Placebo

(n=315)

Antibody Negative

(n=568)

Transiently

Antibody Positive

( n=20)

Persistently

Antibody Positive

(n=37)

*p=0.009 vs. antibody-negative patients

0.73

0.220.16

0.48*

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Ad

jus

ted

An

nu

ali

ze

d R

ela

pse

Ra

te (

95

% C

I)

Placebo

(n=315)

Antibody Negative

(n=568)

Transiently

Antibody Positive

( n=20)

Persistently

Antibody Positive

(n=37)

*p=0.009 vs. antibody-negative patients

Calabresi et al, Neurol 2007

Impact of anti-natalizumab antibodies on . . . . .

Annualized relapse rate Progressive disability

Natalizumab infusion reactions

• Acute hypersensitivity reactions are well-recognized

• Generalized urticaria, dizziness, fever, rash, rigors, pruritus, nausea, flushing, dyspnea, chest pain

• Onset generally during or within 1 hour of second infusion

• Incidence ~4%

• severe anaphylactic/anaphylactoid reactions <1%

• Most reactions are associated with anti-natalizumab antibodies

• Treatment:

• immediate and permanent cessation of natalizumab

• antihistamines

Rudick et al, NEJM 2006

Monitoring effect of therapeutic interventions

Reduced efficacy due to NAbs – systematic review

Farrell & Giovannoni, Multiple Sclerosis 2007; 13: 567-577.

Clinical importance of neutralising antibodies against interferon beta in patients with relapsing-remitting multiple sclerosis

Sorensen et al. Lancet 2003; 362: 1184–91.

Jacob Elkins, James Sheridan, Lakshmi Amaravadi, Katherine Riester, Gilmore O’Neill

Neurology 2012;78(Suppl.): S31.004

Prognostic markers

Intrathecal synthesis of IgG

Images courtesy of Alastair Compston and Ed Thompson.

Kabat et al. J Clin Invest. 1942 Sep;21(5):571-7.

Carl Lange – Colloidal Gold Curve

Isoelectric focusing with immunfixation

Petzold, J Neurol Sci. 2005 Jun 15;233(1-2):183-98.

Conclusion • Diagnostic/prognostic biomarkers

• Intrathecal OCBs

• IgG Index

• Pharmacovigilance

• Baseline screening

• Monoclonal gammaopathy (IFNbeta)

• Serology – VZV, JCV (immunosuppression)

• Monitoring

• FBC, LFTs, U&E, TFTs

• Monthly platelets and possibly urine (alemtuzmab)

• Serology – JCV (natalizumab)

• CD56-bright cells (daclizumab)

• NABs (IFNbeta and natalizumab)

• Potential surrogate treatment markers

• CSF neurofilament levels

• Potential future baseline response markers

• Type 1 interferon signature

• PBMC transcriptomic profiles

Acknowledgements

• Giovannoni

• Sharmilee Gnanapavan

• David Baker

• Gareth Pryce

• Sarah Al-Izki

• Sam Jackson

• Katie Lidster

• Yuti Chernajovsky

• Alex Annenkov

• Anne Rigby

• Michelle Sclanders

• Larry Steinman

• Peggy Ho

• Charles ffrench-Constant • Robin Franklin

• Siddharthan Chandran • David Hampton

• Ian Duncan • Sam Jackson

• Peter Calabresi • Avi Nath

• Raj Kapoor • John Zajicek • Doug Brown • UK MS Clinical Trial Network • BioMS

• Co-investigators

• NABINMS • Affirm study • Care MS 1 & 2 studies • Select trial