Multiple Sclerosis dataBase Independent predictors of time to relapse after CIS in high-risk patients Tim Spelman 1*, Claire Meyniel 1,2*, Maria Trojano 3, Alessandra Lugaresi 4, Guillermo Izquierdo 5, Francois Grand’Maison 6, Cavit Boz 7, Raed Alroughani 8, Eva Havrdova 9, Gerardo Iuliano 10, Pierre Duquette 11, Murat Terzi 12, Pierre Grammond 13, Jose Antonio Cabrera-Gomez 14, Raymond Hupperts 15, Jeannette Lechner-Scott 16, Helmut Butzkueven 1, on behalf of the MSBASIS (an MSBase substudy) Investigators Dr Tim Spelman

Acknowledgements Tim Spelman received honoraria for consultancy and funding for travel from Biogen Idec Inc. Claire Meyniel did not declare any competing interests Maria Trojano received honoraria for consultancy and/or speaking from Biogen Idec, Genzyme-Sanofi, Merck Serono, Novartis, and Roche; research grants from Biogen Idec, Merck Serono, Novartis, and Teva. Alessandra Lugaresi is a Bayer Schering, Biogen Idec, Genzyme/Sanofi, Merck Serono Advisory Board Member. She received travel grants and honoraria from Bayer Schering, Biogen Idec, Genzyme, Merck Serono, Novartis, Sanofi and Teva and research grants from Bayer Schering, Biogen Idec, Merck Serono, Novartis, Sanofi and Teva. Prof Lugaresi has also received travel and research grants from the Associazione Italiana Sclerosi Multipla and was a Consultant of “Fondazione Cesare Serono”. Guillermo Izquierdo received consulting fees from Bayer Schering, Biogen Idec, Merck Serono, Novartis, Sanofi, and Teva. Francois Grand’Maison received an honorarium for organizing a CME event for Biogen Idec in 2013 and received consultation fees from Biogen Idec as well as from Novartis and Genzyme in 2013 and Cavit Boz did not declare any competing interests. Raed Alroughani received honororia from Biologix, Bayer, Merck Sorono, GSK and Novartis, and served on advisory board for Biologix, Novartis and Merck Sorono. Eva Havrdova received speaker honoraria and consultant fees from Biogen Idec, Merck Serono, Novartis, Genzyme and Teva, as well as support for research activities from Biogen Idec and Merck Serono. Gerardo Iuliano had travel/accommodations/meeting expenses funded by Bayer Schering, Biogen Idec, Merck Serono, Novartis, Sanofi Aventis, and Teva. Pierre Duquette has received honoraria for organising CME events and has obtained funding to attend meetings from Biogen Idec, EMD Serono, TEVA Neuroscience, Novartis, and Genzyme, has received funding for investigator-initiated trials with Biogen Idec, EMD Serono and Novartis, and has received peer-review funding from CIHR and from the MS Society of Canada. Murat Terzi did not declare any competing interests Pierre Grammond is a Novartis, Teva-neuroscience, Biogen Idec advisory board member, consultant for Merck Serono, received payments for lectures by Merck Serono, Teva-Neuroscience and Canadian Multiple sclerosis society, and received grants for travel from Teva-Neuroscience and Novartis. Jose Antonio Cabrera-Gomez did not declare any competing interests Raymond Hupperts received honoraria as consultant on scientific advisory boards from Merck-Serono, Biogen-Idec, Genzyme-Sanofi and Teva, research funding from Merck-Serono and Biogen-Idec, and speaker honoraria from Sanofi-Genzyme. Jeannette Lechner-Scott has accepted travel compensation from Novartis, Biogen and Merck Serono. Her institution receives the honoraria for talks and advisory board commitment and also clinic support from Bayer Health Care, Biogen Idec, CSL, Genzyme Sanofi, Merck Serono and Novartis. Helmut Butzkueven received compensation for serving on scientific advisory boards and as a consultant for Biogen Idec and Novartis; speaker honoraria from Biogen Idec Australia, Merck Serono Australia, and Novartis Australia; travel support from Biogen Idec Australia and Merck Serono Australia; research support from CASS Foundation (Australia), Merck Serono Australia, the Royal Melbourne Hospital Friends of the Neurosciences Foundation, and the University of Melbourne.

Background Predictors of conversion for disease management Early initiation of treatment? - High risk patients 1,2 - RCTs: IFN/GLA reduce conversion 3,4,5,6 1. Van den Noort, S., Eidelman, B., & Rammohan, K. (1998). National Multiple Sclerosis Society (NMSS): disease management consensus statement. New York: National MS Society. 2. Freedman, M. S., Blumhardt, L. D., Brochet, B., Comi, G., Noseworthy, J. H., Sandberg-Wollheim, M., & Sørensen, P. S. (2002). International consensus statement on the use of disease- modifying agents in multiple sclerosis. Multiple sclerosis, 8(1), Comi, G., Martinelli, V., Rodegher, M., Moiola, L., Bajenaru, O., Carra, A.,... & Filippi, M. (2009). Effect of glatiramer acetate on conversion to clinically definite multiple sclerosis in patients with clinically isolated syndrome (PreCISe study): a randomised, double-blind, placebo-controlled trial. The Lancet, 374(9700), Kappos, L., Freedman, M. S., Polman, C. H., Edan, G., Hartung, H. P., Miller, D. H.,... & Sandbrink, R. (2007). Effect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT study. The Lancet,370(9585), Beck, R. W., Chandler, D. L., Cole, S. R., Simon, J. H., Jacobs, L. D., Kinkel, R. P.,... & Sandrock, A. W. (2002). Interferon β‐1a for early multiple sclerosis: CHAMPS trial subgroup analyses. Annals of neurology, 51(4), Comi, G., Filippi, M., Barkhof, F., Durelli, L., Edan, G., Fernández, O.,... & Hommes, O. R. (2001). Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study. The Lancet, 357(9268),

Study question To examine demographic, disease activity and examination characteristics as predictors of time to second attack following CIS.

Methods Data MSBase Incident Study (MSBASIS) Inclusions: minimum dataset, minimum annual follow up, baseline cerebral MRI, exc PPMS n=3296, 50 clinics, 22 countries, 5379 person-years, >30k observation points Outcome First relapse post-CIS (second attack) Predictors Age, sex, EDSS, DMD exposure, cerebral & spinal MRI lesion frequency, CSF OCB, country Model Cox Proportional Hazards Regression, Conditional risk-set model (sensitivity) Scaled Schoenfeld residuals, subgroup analyses (evaluation)

Patient characteristics SD = standard deviation, IQR = inter-quartile range, EDSS = Expanded Disability Status Scale, DMT=Disease Modifying Drug, MRI=Magnetic Resonance Imaging, Gd+ = Gadolinium enhancing, HTN = hyperintensive, CSF = cerebrospinal fluid, OCB = oligoclonal bands CharacteristicLevelAll (n=3296) Second events - n (%)-1953 (59.3) Cumulative follow-up a - person-years Time to second event (years) Mean (SD)1.09 (1.42) Median (IQR)0.50 (0.23, 1.27) Gender - n (%) Female2324 (70.5) Male972 (29.5) Age at MS onset - median (IQR) (25.26, 39.33) EDSS at CIS - median (IQR) 2 (1, 2.5) Medication Possession Ratio (MPR) a - mean (SD) 0.18 (0.33) DMT exposure DMT 910 (27.6) No first DMT 2386 (72.4) Baseline Cerebral MRI 1+ T1 Gd+ lesion571 (17.3) 9+ T2 HTN lesions909 (27.6) 1+ infratentorial lesion1246 (37.8) 1+ juxtatentorial lesion1479 (44.9) 2+ periventricular lesions1547 (46.9) Baseline Spinal MRI b 1+ T1 Gd+ lesion49 (1.5) 1+ T2 HTN lesion535 (16.2) Baseline CSFOCB detected1059 (32.1)

aHR = unadjusted Hazard Ratio, EDSS = Expanded Disability Status Scale, DMT=Disease Modifying Drug, MRI=Magnetic Resonance Imaging, Gd+ = Gadolinium enhancing, HTN = hyperintensive * hazard proportionality test p=0.6119, adjusted for country PredictorLevelaHR (95% CI) p-value* Gender Female1.04 (0.95, 1.15) Male1.00 Age at disease onset (5 years)-0.90 (0.88, 0.92) <0.001 EDSS (1.13, 1.20) <0.001 Exposed to DMD during follow up-0.57 (0.46, 0.72) <0.001 Medication possession ratio (MPR)-0.35 (0.25, 0.49) <0.001 Baseline cerebral MRI lesion frequency T1 Gd+ 1+ vs (1.10, 1.43) T2 HTN 3-8 vs (0.82, 1.22) T2 HTN 9+ vs (0.78, 1.21) Infratentorial 1+ vs (1.10, 1.38) <0.001 Juxtacortical 1+ vs (1.06, 1.37) Periventricular 1-2 vs (0.94, 1.39) Periventricular 3+ vs (1.38, 2.02) <0.001 Baseline spinal MRI lesion frequency T1 Gd+ 1+ vs 0 Co-linear with OCB T2 HTN 1+ vs 0 Oligoclonal banding in CSF Absent1.00 Present1.52 (1.23, 1.89) <0.001 Predictors of first relapse post-CIS

DMD exposure pre-conversion

DMD exposure aHR = unadjusted Hazard Ratio, EDSS = Expanded Disability Status Scale, DMT=Disease Modifying Drug, MRI=Magnetic Resonance Imaging, Gd+ = Gadolinium enhancing, HTN = hyperintensive, NS = not significant b hazard proportionality test p=0.3688, adjusted for country c hazard proportionality test p = , adjusted for country Exposed to DMD during follow up (n=910) a Not exposed to DMD during follow up (n=2386) a Second event - n=438Second event - n=1515 Predictor LevelaHR (95% CI) p-value b aHR (95% CI) p-value c Gender FemaleNS Male1.00 Age at MS onset (years) (0.87, 0.97) (0.89, 0.93) <0.001 EDSS (continuous) 1.12 (1.03, 1.21) (1.12, 1.21) <0.001 Baseline Cerebral MRI charactertistics T1 Gd+ 1+ vs (1.08, 1.91) (1.11, 1.50) T2 HTN 3-8 vs 0-2 NS T2 HTN 9+ vs 0-2 NS Infratentorial 1+ vs 0 NS1.29 (1.14, 1.47) <0.001 Juxtacortical 1+ vs 0 NS1.17 (1.01, 1.34) Periventricular 1-2 vs <3 NS Periventricular 3+ vs 0 NS1.70 (1.39, 2.08) <0.001 Baseline Spinal MRI charactertistics T1 Gd+ 1+ vs 0 Co-linear with OCB T2 HTN 1+ vs 0 Oligoclonal banding in CSF Absent 1.00 Present NS1.40 (1.11, 1.77) Time to first DMT (months) (0.96, 0.98) <0.001N/A

First DMD product identity Including binary DMD exposureIncluding first DMT identity PredictorLevelaHR (95% CI) p-value Gender Female1.04 (0.95, 1.15) (0.94, 1.15) Male1.00 Age at MS onset (5 years)-0.90 (0.88, 0.92) <0.001 EDSS (continuous) (1.13, 1.20) < (1.13, 1.21) <0.001 Exposed to DMT during follow up (0.46, 0.72) <0.001 First DMT product initiated IM-IFNβ-1a 0.60 (0.46, 0.78) <0.001 IFNβ-1b 0.63 (0.47, 0.85) Glatiramer Acetate 0.56 (0.41, 0.78) <0.001 SC-IFNβ-1a 0.56 (0.41, 0.78) <0.001 Fingolimod 0.32 (0.10, 1.01) No first DMT 1.00 Medication possession ratio (MPR) (0.25, 0.49) <0.001 Baseline cerebral MRI lesion frequency T1 Gd+ 1+ vs (1.10, 1.43) (1.11, 1.44) <0.001 T2 HTN 3-8 vs (0.82, 1.22) (0.83, 1.24) T2 HTN 9+ vs (0.78, 1.21) (0.78, 1.22) Infratentorial 1+ vs (1.10, 1.38) <0.001 Juxtacortical 1+ vs (1.06, 1.37) (1.06, 1.37) Periventricular 1-2 vs <31.15 (0.94, 1.39) (0.95, 1.40) Periventricular 3+ vs (1.38, 2.02) < (1.38, 2.03) <0.001 Baseline spinal MRI lesion frequency T1 Gd+ 1+ vs 0 Co-linear with OCB T2 HTN 1+ vs 0 Oligoclonal banding in CSF Absent1.00 Present1.52 (1.23, 1.89) <0.001

OCB & Spinal T2 lesions Co-linearity Observation: Greater MRI lesion load in CIS correlate with OCB in CSF 1,2 ?More progressed at CIS 3 ?risk stratification 1. Andersson, M., Alvarez-Cermeno, J., Bernardi, G., Cogato, I., Fredman, P., Frederiksen, J.,... & Wurster, U. (1994). Cerebrospinal fluid in the diagnosis of multiple sclerosis: a consensus report. Journal of Neurology, Neurosurgery & Psychiatry, 57(8), Tintore, M., Rovira, A., Rio, J., Tur, C., Pelayo, R., Nos, C.,... & Montalban, X. (2008). Do oligoclonal bands add information to MRI in first attacks of multiple sclerosis?. Neurology, 70(13 Part 2), Tur, C., & Montalban, X. (2013). CSF oligoclonal bands are important in the diagnosis of multiple sclerosis, unreasonably downplayed by the McDonald Criteria 2010: No. Multiple Sclerosis Journal, 19(6),

Conclusions Younger age at onset, no DMD exposure, presence of T1 Gd+, infratentorial, juxtacortical, peroventricular lesions, OCB on baseline CSF (T2 lesions on spinal MRI) predict increased rate of conversion Early identification– ?monitoring, ?early treatment intervention Extensions: Risk calculator