ORIGINAL RESEARCH published: 17 December 2020 doi: 10.3389/fneur.2020.579438 New Algorithms Improving PML Risk Stratification in MS Patients Treated With Natalizumab Edited by: Roberta Magliozzi, Inmaculada Toboso 1 , Amalia Tejeda-Velarde 1 , Roberto Alvarez-Lafuente 2 , Rafael Arroyo 3 , University of Verona, Italy Harald Hegen 4 , Florian Deisenhammer 4 , Susana Sainz de la Maza 5 , Reviewed by: José C. Alvarez-Cermeño 5 , Guillermo Izquierdo 6 , Dolores Paramo 6 , Pedro Oliva 7 , Antonio Bertolotto, Bonaventura Casanova 8 , Eduardo Agüera-Morales 9 , Diego Franciotta 10 , San Luigi Gonzaga University Matteo Gastaldi 10 , Oscar Fernández 11 , Patricia Urbaneja 11 , José M. Garcia-Dominguez 12 , Hospital, Italy Fernando Romero 12 , Alicia Laroni 13 , Antonio Uccelli 13 , Angel Perez-Sempere 14 , Elisabeth Gulowsen Celius, Oslo University Hospital, Norway Albert Saiz 15 , Yolanda Blanco 15 , Daniela Galimberti 16 , Elio Scarpini 16 , Carmen Espejo 17 , Xavier Montalban 17 , Ludwig Rasche 18 , Friedemann Paul 18,19 , Inés González 20 , *Correspondence: Elena Álvarez 20 , Cristina Ramo 21 , Ana B. Caminero 22 , Yolanda Aladro 23 , Carmen Calles 24 , Luisa M. Villar Pablo Eguía 25 , Antonio Belenguer-Benavides 26 , Lluis Ramió-Torrentà 27 , Ester Quintana 27 , luisamaria.villar@salud.madrid.org José E. Martínez-Rodríguez 28 , Agustín Oterino 29 , Carlos López de Silanes 30 , Luis I. Casanova 30 , Lamberto Landete 31 , Jette Frederiksen 32 , Gabriel Bsteh 4 , Specialty section: Patricia Mulero 17 , Manuel Comabella 17 , Miguel A. Hernández 33 , Mercedes Espiño 1 , This article was submitted to Multiple Sclerosis and José M. Prieto 34 , Domingo Pérez 35 , María Otano 36 , Francisco Padilla 37 , Neuroimmunology, Juan A. García-Merino 38 , Laura Navarro 39 , Alfonso Muriel 40 , Lucienne Costa Frossard 5 a section of the journal and Luisa M. Villar 1* Frontiers in Neurology 1 Immunology Department, Hospital Universitario Ramon y Cajal, Madrid, Spain, 2 Instituto de Investigación Sanitaria San Received: 02 July 2020 Carlos (IDISSC), Hospital Clinico San Carlos, Madrid, Spain, 3 Department of Neurology, Hospital Universitario Quiron Salud, Accepted: 15 October 2020 Madrid, Spain, 4 Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria, 5 Neurology Department, Published: 17 December 2020 Hospital Universitario Ramon y Cajal, Madrid, Spain, 6 Neurology Department, Hospital Universitario Virgen Macarena, Sevilla, Citation: Spain, 7 Neurology Department, Hospital Universitario Central de Asturias, Oviedo, Spain, 8 Neurology Department, Hospital Toboso I, Tejeda-Velarde A, Universitario la Fe, Valencia, Spain, 9 Neurology Department, Hospital Universitario Reina Sofia, Cordoba, Spain, 10 Istituti di Alvarez-Lafuente R, Arroyo R, Recovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy, 11 Neurology Department, Hospital Hegen H, Deisenhammer F, Regional Universitario, Malaga, Spain, 12 Neurology Department, Hospital General Universitario Gregorio Marañón, Madrid, Sainz de la Maza S, Spain, 13 University of Genoa, Ospedale Policlinico San Martino, Genoa, Italy, 14 Neurology Department, Hospital General Alvarez-Cermeño JC, Izquierdo G, Universitario de Alicante, Alicante, Spain, 15 Neurology Service, Hospital Clinic and Institut d’Investigacions Biomèdiques Paramo D, Oliva P, Casanova B, August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain, 16 Centro Dino Ferrari, Fondazione Ca’ Granda, Agüera-Morales E, Franciotta D, Istituti di Recovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico, University of Milan, Milan, Italy, 17 Servei de Gastaldi M, Fernández O, Urbaneja P, Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya, Vall d’Hebron Institut de Recerca, Hospital Garcia-Dominguez JM, Romero F, Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain, 18 Department of Neurology, NeuroCure Laroni A, Uccelli A, Perez-Sempere A, Clinical Research Center, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Saiz A, Blanco Y, Galimberti D, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany, 19 Experimental and Clinical Research Center, Scarpini E, Espejo C, Montalban X, Charité—Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Berlin, Germany, 20 Neurology Department, Rasche L, Paul F, González I, Hospital Alvaro Cunqueiro, Vigo, Spain, 21 Neurology Department, Hospital Germans Trias i Pujol, Badalona, Spain, Álvarez E, Ramo C, Caminero AB, 22 Neurology Department, Hospital Nuestra Señora de Sonsoles, Avila, Spain, 23 Neurology Department, Hospital Universitario Aladro Y, Calles C, Eguía P, Getafe, Getafe, Spain, 24 Neurology Department, Hospital Universitario Son Espases, Palma de Mallorca, Spain, 25 Neurology Belenguer-Benavides A, Department, Hospital Doctor Jose Molina Orosa, Arrecife, Spain, 26 Neurology Department, Hospital General Universitario de Ramió-Torrentà L, Quintana E, Castellón, Castellón, Spain, 27 Neurology Department, Hospital Universitario Doctor Josep Trueta, Girona, Spain, 28 Neurology Martínez-Rodríguez JE, Oterino A, Department, Hospital del Mar, Barcelona, Spain, 29 Neurology Department, Hospital Universitario Marqués de Valdecilla, López de Silanes C, Casanova LI, Santander, Spain, 30 Neurology Department, Hospital Universitario de Torrejón, Torrejón de Ardoz, Spain, 31 Neurology Landete L, Frederiksen J, Bsteh G, Department, Hospital Universitario Dr. Peset, Valencia, Spain, 32 Glostrup Hospital, University of Copenhagen, Copenhagen, Mulero P, Comabella M, Denmark, 33 Neurology Department, Hospital Universitario Nuestra Señora de Candelaria, Tenerife, Spain, 34 Neurology Hernández MA, Espiño M, Prieto JM, Department, Hospital Clínico de Santiago, Santiago de Compostela, Spain, 35 Neurology Department, Hospital del Bierzo, Pérez D, Otano M, Padilla F, Ponferrada, Spain, 36 Neurology Department, Complejo Hospitalario de Navarra, Pamplona, Spain, 37 Neurology Department, García-Merino JA, Navarro L, Hospital Clinico de Malaga, Malaga, Spain, 38 Neurology Department, Hospital Puerta de Hierro, Majadahonda, Madrid, Muriel A, Frossard LC and Villar LM Spain, 39 Neurology Department, Hospital General de Elche, Elche, Spain, 40 Biostatistics Unit, Hospital Univesitario Ramon y (2020) New Algorithms Improving Cajal, Instituto Ramon y Cajal para la Investigación Sanitaria (IRYCIS), Madrid, Spain PML Risk Stratification in MS Patients Treated With Natalizumab. Front. Neurol. 11:579438. Overview: We assessed the role of age and disease activity as new factors contributing doi: 10.3389/fneur.2020.579438 to establish the risk of progressive multifocal leucoencephalopathy in multiple sclerosis Frontiers in Neurology | www.frontiersin.org 1 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS patients treated with natalizumab in 36 University Hospitals in Europe. We performed the study in 1,307 multiple sclerosis patients (70.8% anti-John Cunninghan virus positive antibodies) treated with natalizumab for a median time of 3.28 years. Epidemiological, clinical, and laboratory variables were collected. Lipid-specific IgM oligoclonal band status was available in 277 patients. Factors associated with progressive multifocal leucoencephalopathy onset were explored by uni- and multivariate logistic regression. Results: Thirty-five patients developed progressive multifocal leucoencephalopathy. The multivariate analysis identified anti-John Cunninghan virus antibody indices and relapse rate as the best predictors for the onset of this serious opportunistic infection in the whole cohort. They allowed to stratify progressive multifocal leucoencephalopathy risk before natalizumab initiation in individual patients [area under the curve (AUC) = 0.85]. The risk ranged from <1/3,300 in patients with anti-John Cunninghan virus antibody indices <0.9 and relapse rate >0.5, to 1/50 in the opposite case. In patients with lipid-specific IgM oligoclonal bands assessment, age at natalizumab onset, anti-John Cunninghan virus antibody indices, and lipid-specific IgM oligoclonal band status predicted progressive multifocal leucoencephalopathy risk (AUC = 0.92). The absence of lipid-specific IgM oligoclonal bands was the best individual predictor (OR = 40.94). The individual risk ranged from <1/10,000 in patients younger than 45 years at natalizumab initiation, who showed anti John Cunningham virus antibody indices <0.9 and lipid-specific IgM oligoclonal bands to 1/33 in the opposite case. Conclusions: In a perspective of personalized medicine, disease activity, anti-lipid specific IgM oligoclonal bands, anti Jonh Cunninghan virus antibody levels, and age can help tailor natalizumab therapy in multiple sclerosis patients, as predictors of progressive multifocal leucoencephalopathy. Keywords: multiple sclerosis, demyelinating diseases, biomarkers, natalizumab, progressive multifocal leucoencephalopathy, disease modifying treatments INTRODUCTION inflammatory course prior natalizumab onset can also predict PML risk. The use of natalizumab, a highly effective therapy approved It was also demonstrated that mean age is higher in multiple for the treatment of active relapsing-remitting multiple sclerosis patients suffering PML during natalizumab treatment sclerosis (1), is limited by the risk of progressive multifocal (10, 12, 13). However, the role of age as PML risk factor has not leucoencephalopathy (PML), a serious opportunistic been fully explored. infection of the central nervous system caused by John We studied in a multicenter cohort of multiple sclerosis Cunninghan virus (JCV), appearing in about 1/250 treated patients treated with natalizumab whether patients’ clinical and patients (2, 3). demographic characteristics can be useful in predicting PML The factors most frequently used to stratify PML risk in onset. Moreover, we further investigated the utility of LS-OCMB multiple sclerosis patients treated with natalizumab are the for the stratification of PML risk in combination with other presence of anti-JCV antibodies or high anti-JCV indexes in clinical and laboratory variables. serum; prior immunosuppressive therapies; and duration of natalizumab treatment (3–7). These factors have proven to be effective in reducing the risk of PML in the clinical setting (8, 9). MATERIALS AND METHODS However, these strategies present some limitations. They depend on treatment duration and anti-JCV antibody levels, or negative This was a multicenter cross-sectional study including 1,307 anti-JCV status may change a long time, and this modifies patient patients treated with natalizumab (natalizumab treatment prognosis (10, 11). Therefore, the search for new factors to stratify duration: 3.73 ± 2.13 years, mean ± SD) in 36 European PML risk is of great clinical relevance. A highly inflammatory hospitals. The study was approved by the ethical committee disease, revealed by the presence of lipid-specific oligoclonal of Ramon y Cajal University Hospital. All patients signed and IgM bands (LS-OCMB) in cerebrospinal fluid (CSF), associates informed consent before entering. with a lower PML risk in natalizumab treated patients (10). Patients were followed every 3–6 months in the neurology However, it remains unknown if clinical data indicating high clinics at every participating center, with additional visits in Frontiers in Neurology | www.frontiersin.org 2 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS case of relapses. Demographic, clinical, and laboratory data Normality of the different variables in PML and not PML prospectively collected at every center were anonymized and groups was assessed with Kolmogorov–Smirnov test. No variable sent to the coordinator center. All patients signed an informed passed normality test in PML group. Thus, Mann–Whitney consent obtained according to the Declaration of Helsinki U-test (two tailed) was applied for non-parametric tests and before entry. Fisher exact test (two sided) was used for comparisons of categorical variables between groups. Univariate tests based on Inclusion Criteria logistic regression were used to explore variables associated to We established the following inclusion criteria: PML risk and to calculate odds ratios (OR) and confidence Patients had to be treated with natalizumab for at least intervals (CI). Significant results obtained in the univariate a year to avoid the effect of a short time of treatment as analyses were explored by multivariate tests, and minimal confounder factor. models were established by eliminating variables loosing Clinical data had to be obtained prospectively since statistical significance. disease onset to avoid the lack of accuracy of retrospective To assess PML risk in individual patients, a nomogram was data acquisition. generated from the minimal model logistic regression results. In this analysis, the program assigns a score to every factor increasing PML risk. It also creates two parallel scales with Data Collection total scores and the correspondent probability of PML. To We established a minimum sample size of 1,000 patients explore individual risk in a patient, the total score is calculated to analyze all the variables projected. A form was sent to and the corresponding risk read in the probability scale. To the participating centers comprising the following variables: avoid overestimating PML risk, probabilities were corrected by a sex, age at first relapse, age at natalizumab initiation, time factor obtained by dividing previously described PML frequency between multiple sclerosis onset and natalizumab initiation, in natalizumab treated patients (3) and the one obtained in duration of natalizumab treatment, Expanded Disability Status our cohorts. Scale (EDSS) at natalizumab initiation, Multiple Sclerosis Severity Scale (MSSS) (14) at natalizumab initiation, relapse rate measured from multiple sclerosis onset to natalizumab Data Availability initiation, previous treatments, serum anti-JCV antibody status The study protocol, statistical analysis plan, and data not (positive or negative), anti-JCV antibody index (which is provided in the article because of space limitations will proportional to serum anti-JCV antibody levels) (5), IgG be shared upon request by any qualified investigator for oligoclonal bands (OCGB), and PML onset. LS-OCMB were purposes of replicating procedures and results during 3 years available in a sub-cohort of 277 patients recruited at 29 different after publication. hospitals. LS-OCMB were determined by isoelectric focusing and immunoblotting, as previously described (15). After receiving the first set of results, the database was RESULTS debugged three times to complete data collection and correct We included in the study 1,240 multiple sclerosis patients treated inconsistent results. Finally, 69 patients were excluded, because with natalizumab at 36 different hospitals. Thirty-five developed of incomplete data or treatment duration shorter than 1 PML during natalizumab treatment, and 1,205 did not suffer this year. All the analyses were performed in the remaining 1,240 opportunistic infection. Clinical and demographic data of the multiple sclerosis patients. Missing data were found in the patients classified according to PML onset are shown in Table 1A. following variables: Anti-John Cunninghan (JC) antibodies were The highest differences were found in age at natalizumab only available in 1,174 patients (97.5%). Thirty-four of them initiation (p = 0.004), relapse rate before natalizumab (p < developed PML, and 1,140 did not. Of note, in two PML cases 0.0001), anti-JCV antibody positivity (p = 0.004), and anti-JCV anti-JC antibodies were negative 4 and 6 months before PML index levels (p < 0.0001). PML patients were older at treatment onset, when the last control test was performed. In both cases, the initiation, showed a lower relapse rate, a higher proportion anti-JC test became positive at PML diagnosis. Anti-JC antibody tested positive for anti-JCV antibodies before PML, and had levels were only available in 1,016 patients (82%). Twenty-seven increased anti-JCV antibody indices. We also found that PML developed PML, and 989 did not; relapse rate before natalizumab group showed an increased proportion of males (p = 0.04) initiation was only obtained in 1,224 cases (98.7%). Thirty-five and had longer disease duration at natalizumab initiation (p = developed PML, and 1,189 did not. Finally, data on OCGB were 0.02). No variation in other clinical or demographic variables only available in 756 patients (61%). Thirty-two developed PML, was associated with PML, including prior immunosuppression and 726 did not. Data collection comprised from 31 March 2017 or duration of natalizumab treatment. We further explored if to 15 June 2018. the values of this variable could change depending on anti- JC antibody values. The median time of treatment was 3.28 Statistical Analysis years in the whole cohort, the range going from 1.00 to 13.40 Results were analyzed with STATA v.14 (StataCorp.2014. years, and the interquartile range (IQR) from 2.06 to 4.82 years. Statistical Software: Release 14. College Station, TX, USA). p < These values did not change substantially in patients with anti-JC 0.05 were considered as significant. antibody levels higher (median = 3.32, range: 1.00–11.46, IQR: Frontiers in Neurology | www.frontiersin.org 3 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS TABLE 1 | Demographic and clinical data. (A) Total group (N = 1,240) (B) LS-OCMB group (N = 277) Pml (n = 35) NoT PML P PML (N = 24) NOT PML p (n = 1,205) (N = 253) Sex (M/F) 16/19 358/847 0.04 10/14 78/175 0.28 Age at 1st relapse (y) 30.1 ± 9.5 (23–36) 28.2 ± 8.7 (22–33) 0.33 31.1 ± 9.6 28.1 ± 8.41 0.14 Disease duration at 11.2 ± 7.4 8.3 ± 6.3 (3.4–11.9) 0.02 12.7 ± 7.8 6.7 ± 5.7 0.0002 NTZ onset (y) (4.7–17.9) Age at NTZ onset (y) 41.3 ± 8.9 36.5 ± 9.4 0.004 43.8 ± 8.7 34.8 ± 8.9 <0.0001 (33.2–49.2) (29.9–42.7) Duration of NTZ 3.4 ± 1.5 (1.1–7.7) 3.8 ± 2.1 (1.0–13.4) 0.77 3.3 ± 1.6 3.47 ± 2.0 0.82 treatment (y) EDSS at NTZ onset 3.3 ± 1.4 (2–4) 3.2 ± 1.6 (2–4) 0.68 3.6 ± 1.4 3.1 ± 1.6 0.07 MSSS at NTZ onset 4.3 ± 2.5 (2.2–6.8) 4.8 ± 2.4 (2.8–6.6) 0.24 4.4 ± 2.6 5.1 ± 2.4 0.23 Relapse rate before 0.8 ± 0.95 1.4 ± 1.4 <0.0001 0.6 ± 0.5 1.6 ± 1.7 <0.0001 NTZ onset (0.25–0.93) (0.53–1.56) Prior IS (yes/no) 7/28 139/1066 0.13 5/19 34/219 0.32 Anti-JCV Abs 32/2 844/331 0.004 21/2 162/87 0.010 (pos/neg)* Anti-JCV Ab levels* 2.2 ± 1.2 0.9 ± 1.1 <0.0001 1.9 ± 1.3 1.0 ± 1.1 0.0047 (1.23–3.18) (0.09–1.45) OCGB (pos/neg) 30/2 651/73 0.48 22/2 234/19 0.88 LS-OCMB (pos/neg) 1/23 162/91 <0.0001 For continuous variables values are expressed as mean ± standard deviation (interquartile range). *The last measure before study completion; 1st , first; Anti-JCV Ab, anti-John Cunningham virus antibodies; EDSS, expanded disability status scale; F, female; IS, immunosuppression; LS-OCMB, lipid-specific oligoclonal IgM bands; M, male; MSSS, multiple sclerosis severity score; neg, negative; NOT PML, not progressive multifocal leukoencephalopathy; NTZ, Natalizumab; OCGB, oligoclonal IgG bands; PML, progressive multifocal leukoencephalopathy; pos, positive; y, years. 2.01–5.03 years) or lower (median = 3.26, range: 1.00–13.40, antibody positivity (OR = 6.04, p = 0.014), annualized relapse IQR: 2.08–4.3 years) than 0.9. rate before natalizumab <0.5 (OR = 4.25, p < 0.001), and age To better define associations of the different variables at natalizumab initiation ≥45 years (OR = 2.33, p = 0.022) with PML onset, we first performed univariate analyses significantly impacted on PML appearance. In this model area (Table 2A). Cutoff values were established using receiver under the ROC curve was 0.78. operating characteristic (ROC) curves in case of age, time until The second multivariate analysis included anti-JCV natalizumab initiation, and relapse rate before treatment or pre- antibodies classified using the level of 0.9 as cutoff value. established cutoffs for anti-JCV antibody levels and EDSS and In the minimal model, only anti-JCV antibody levels ≥0.9 (OR = MSSS scores. The strongest association was found with high anti- 18.72, p < 0.001) and annualized relapse rate before natalizumab JCV index values, being the clearest one obtained for anti-JCV initiation <0.5 (OR = 4.66, p < 0.001) had an effect on PML risk. indices higher than 0.9 (OR = 18.29, p < 0.001). Additionally, Although only these two factors were significant in this model, having anti-JCV index levels >1.5 (OR = 8.58, p < 0.001) and the area under ROC curve was higher (0.85). the presence of anti-JCV antibodies (OR = 6.27, p = 0.012) also Finally, we performed a multivariate analysis using 1.5 as associated with PML onset. Age at natalizumab initiation ≥45 cutoff value for anti-JC antibody levels. Anti-JCV antibody levels years also increased PML risk (OR = 3.20, p = 0.001). A disease ≥1.5 (OR = 7.85, p < 0.001), annualized relapse rate before duration higher than 10 years at natalizumab initiation (OR = natalizumab initiation <0.5 (OR = 3.73, p = 0.001), and age 2.37, p = 0.012) and an MSSS score lower than 3 (OR = 2.25, p at natalizumab initiation ≥45 years (OR = 2.31, p = 0.048) = 0.019) was also associated with PML onset. Finally, male sex significantly increased PML risk in this model. The area under associated modestly with an increased PML risk (OR = 1.99, p the ROC curve was 0.84. = 0.046). On the other hand, having an annualized relapse rate We made a nomogram analysis of the second multivariate higher than 0.5 before treatment initiation clearly diminished analysis (cutoff: anti-JCV antibody levels of 0.9) to explore the PML risk (OR = 4.47, p < 0.001). contribution of each variable to PML risk. Data are shown Based on univariate analyses, we performed three different in Figure 1A. We adjusted the risk using a correction factor multivariate analyses according to anti-JCV antibody obtained calculating the ratio between the numbers of PML cases classification. First, we included all the significant factors per 1,000 patients reported after commercialization (4.16‰) and and anti-JCV antibodies classified according to the positive that of our cohort (28‰). Patients with anti-JCV antibody levels or negative results (Table 3). In the minimal model, anti-JCV lower than 0.9 and annualized relapse rate higher than 0.5 prior Frontiers in Neurology | www.frontiersin.org 4 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS TABLE 2 | Univariate analysis to explore the ability of different clinical and demographic variables for predicting PML onset during natalizumab treatment. (A) Total patient group (B) Patients with a LS-OCMB (n = 1,240) study (n = 277) OR 95% CI P OR 95% CI p Male sex 1.99 1.06–4.17 0.046 1.60 0.68–3.77 0.28 Age at NTZ onset ≥45 years 3.20 1.60–6.39 0.001 7.36 3.06–17.72 <0.001 Disease duration at NTZ onset ≥10 years 2.37 1.21–4.66 0.012 4.60 1.94–10.90 0.001 NTZ treatment for >2 years 1.02 0.46–2.27 0.96 1.69 0.61–4.70 0.31 NTZ treatment for >3 years 1.25 0.63–2.48 0.53 1.29 0.56–2.99 0.55 NTZ treatment for >4 years 0.99 0.43–1.98 0.97 0.91 0.36–2.27 0.84 NTZ treatment for >5 years 0.43 0.15–1.23 0.12 0.41 0.09–1.80 0.24 Positive anti–JCV Abs 6.27 1.50–26.33 0.012 5.64 1.29–24.61 0.021 Anti-JCV Ab levels ≥0.9 18.29 5.46–61.19 <0.001 9.08 2.54–32.45 0.001 Anti-JC Ab levels ≥1.5 8.58 3.59–20.54 <0.001 4.78 1.71–13.33 0.003 EDSS at NTZ onset <3 0.75 0.38–1.51 0.42 0.43 0.17–1.07 0.07 EDSS at NTZ onset <6 1.15 0.35–3.82 0.82 1.31 0.29–5.90 0.72 MSSS at NTZ onset <3 2.25 1.14–4.43 0.019 2.25 0.95–5.32 0.06 MSSS at NTZ onset <6 0.95 0.47–1.94 0.90 0.86 0.35–2.09 0.74 Relapse rate before NTZ onset <0.5 4.47 2.26–8.86 <0.001 6.77 2.80–16.35 <0.001 Prior immunosuppression 1.92 0.82–4.47 0.13 1.70 0.59–4.84 0.32 LS-OCMB Negative 40.94 5.44–308.20 <0.001 Anti-JCV Abs, anti-John Cunningham virus antibodies; CI, confidence interval; EDSS, expanded disability status scale; LS-OCMB, lipid-specific oligoclonal IgM bands; MSSS, multiple sclerosis severity score; NTZ, natalizumab; OR, odd ratio; PML, progressive multifocal leukoencephalopathy. TABLE 3 | Factors predicting PML onset in the total group of patients. OR 95% CI P Minimal model with anti-JCV antibodies classified as positive/negative Anti-JCV antibodies (positive) 6.04 1.43–25.53 0.014 Relapse rate before natalizumab 4.25 2.08–8.69 <0.001 onset <0.5 2.33 1.13–4.80 0.022 Age at natalizumab onset ≥45 years Area under ROC curve: 0.78 Minimal model with anti-JCV antibodies classified using a level of 0.9 as cut off value Anti-JCV antibody levels ≥0.9 18.7 5.56–63.02 <0.001 Relapse rate before natalizumab 4.66 2.10–10.35 <0.001 onset <0.5 Area under ROC curve: 0.85 Minimal model with anti-JCV antibodies classified using a level of 1.5 as cut off value Anti-JCV antibodies levels ≥1.5 7.85 3.25–19.00 <0.001 Relapse rate before natalizumab 3.73 1.67–8.34 0.001 onset <0.5 2.31 1.01–5.28 0.048 Age at natalizumab onset ≥45 years Area under ROC curve: 0.84 Multivariate analyses. Anti-JC antibodies, anti-John Cunningham virus antibodies; CI, confident interval; OR, odd ratio; PML, progressive multifocal leukoencephalopathy; ROC, Receiver operating characteristic. natalizumab initiation showed a PML risk lower than 0.3‰. If Role of Lipid Specific Oligoclonal IgM the annualized relapse rate was lower than 0.5, the PML risk Bands in Risk Stratification increased to 1.5‰, and if, in addition, anti-JCV antibody levels Two hundred seventy-seven patients (22.3% of the whole cohort) were higher than 0.9, the risk was augmented to 2%. These values were examined for LS-OCMB. Twenty-four of them (8.7%) are independent of the sex, disease duration, time on natalizumab developed PML. Clinical and demographic data of these patients treatment, or previous treatment with anti-suppressive drugs. are shown in Table 1B. One hundred and sixty-two of the 253 Frontiers in Neurology | www.frontiersin.org 5 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS FIGURE 1 | Nomogram for predicting progressive multifocal leukoencephalopathy (PML) onset in individual MS patients. The multivariate logistic regression analysis assigns a score to every variable included in the minimal model. The sum of the scores obtained by a patient is interpolated in the total score point-probability line at (Continued) Frontiers in Neurology | www.frontiersin.org 6 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS FIGURE 1 | the bottom of each nomogram and gives the individual PML risk. (A) PML risk in the total cohort. Having a relapse rate lower than 0.5 gives a score of 5 and showing anti-John Cunningham virus antibody levels (anti-JC levels) higher than 0.9 provides a score of 10. Individual patient scores range from 0 to 15 and their PML risk from <1/3,300 to 1/50, respectively. (B) PML risk in the patients with lipid-specific oligoclonal IgM band (LS-OCMB) detection. Being negative (Neg) for LS-OCMB gives a score of 10. Showing anti-JC levels higher than 0.9 provides a score of 5.75. Being older than 45 years gives a score of 5.75. Individual patient scores range from 0 to 21.5 and their PML risk from <1/10,000 to 1/30, respectively. TABLE 4 | Factors predicting PML onset in the group of patients with LS-OCMB detection. OR 95% CI P Minimal model with anti-JCV antibodies classified as positive/negative LS-OCMB negative 30.44 3.94–234.91 <0.001 Age at natalizumab initiation ≥45 4.80 1.76–13.14 0.002 years 3.21 1.19–8.66 0.022 Relapse rate before natalizumab initiation <0.5 Area under ROC curve: 0.90 Minimal model with anti-JCV antibodies classified using a level of 0.9 as cut off value LS-OCMB negative 26.83 3.33–216.29 0.002 Age at natalizumab initiation ≥45 6.74 2.00–22.73 0.002 years 6.52 1.64–25.85 0.008 Anti-JCV antibodies levels ≥0.9 Area under ROC curve: 0.92 Minimal model with anti-JCV antibodies classified using a level of 1.5 as cut off value LS-OCMB negative 31.18 3.81–255.16 0.001 Age at natalizumab initiation ≥45 8.85 2.64–29.60 <0.001 years 4.38 1.33–14.43 0.015 Anti-JCV antibodies levels ≥1.5 Area under ROC curve: 0.92 Multivariate analyses. Anti-JCV antibodies, anti-John Cunningham virus antibodies; CI, confident interval; LS-OCMB, lipid-specific oligoclonal IgM bands; OR, odd ratio; PML, progressive multifocal leukoencephalopathy; ROC, Receiver operating characteristic. patients not developing PML (64.0%) were LS-OCMB positive. relapse rate before natalizumab initiation <0.5 (OR = 3.21, p = By contrast, only one of the 24 PML patients (4.2%) displayed 0.022) had an effect on PML risk. The area under the ROC curve these antibodies (p < 0.0001). Similarly to the whole cohort, was 0.90. patients suffering PML were older (p < 0.0001), had a longer In the second multivariate analysis we used an anti-JCV index disease duration (p = 0.0002) at natalizumab initiation, and of 0.9 as cutoff value. In the minimal model the variables that had a lower relapse rate before natalizumab (p < 0.0001). A significantly impacted PML development were absence of LS- higher percentage of these patients were anti-JCV positive (p = OCMB (OR = 26.83, p = 0.002), age at natalizumab initiation 0.010), and they also displayed higher anti-JCV antibody levels ≥45 years (OR = 6.74, p = 0.002), and anti-JCV antibody index (p = 0.0047). ≥0.9 (OR = 6.52, p = 0.008). In this model, the area under the We followed the same approach described for the entire ROC curve was 0.92. cohort. First, we performed univariate analyses (Table 2B). The Finally, we performed a multivariate analysis using 1.5 as conditions associated with PML risk were the following: absence cutoff value for anti-JCV antibody levels. Again, absence of LS- of LS-OCMB (OR = 40.94; p < 0.001); levels of anti-JCV index OCMB (OR = 31.18, p = 0.001), age at natalizumab initiation ≥0.9 (OR = 9.08, p = 0.001) or ≥1.5 (OR = 4.78, p = 0.003); ≥45 years (OR = 8.85, p < 0.001), and anti-JCV antibody levels or positive anti-JCV antibodies (OR = 5.64, p = 0.021); age ≥1.5 (OR = 4.38, p = 0.015) significantly increased PML risk. at natalizumab initiation ≥45 years (OR = 7.36, p < 0.001); The area under the ROC curve was 0.92. annualized relapse rate before natalizumab ≤0.5 (OR = 6.77, p < Finally, we repeated a nomogram analysis of the second 0.001), and disease duration at natalizumab initiation ≥10 years multivariate analysis (cutoff: anti-JCV antibody levels of 0.9). (OR = 4.60, p = 0.001). Data are shown in Figure 1B. We adjusted the risk using Again, we made three different multivariate models according a correction factor obtained calculating the ratio between to anti-JCV antibody classification (Table 4). First, we included the numbers of PML cases per 1,000 patients reported after all the significant variables and anti-JCV antibodies classified commercialization (4.16‰) and that of our cohort (87‰). according to having positive or negative results. In the minimal Patients with LS-OCMB, anti-JCV antibody levels lower than 0.9, model, only absence of LS-OCMB (OR = 30.44, p < 0.001), age and age at natalizumab initiation younger than 45 years showed a at natalizumab initiation ≥45 years (OR = 4.80, p = 0.002), and PML risk lower than 0.1‰. When anti-JCV antibody levels were Frontiers in Neurology | www.frontiersin.org 7 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS FIGURE 2 | Illustration of predicting progressive multifocal leukoencephalopathy (PML) risk depending on the results of the nomograms. (A) In the whole cohort PML risk associates with the anti-John Cunningham virus antibody levels (JC) and the relapse rate (RR). (B) In patients with lipid-specific oligoclonal IgM band (LS-OCMB) detection, PML risk associated with the LS-OCMB, and JC status, and the age at natalizumab onset (ANO). higher than 0.9 or patients were older than 45 at natalizumab TABLE 5 | Value of OCMB for predicting PML onset in patients with anti JC onset, the risk was augmented to 0.5‰. If both conditions were antibody levels >1.5 and treated with natalizumab for more than 2 years. present, it rose to 3.5‰. If LS-OCMB were negative too, the risk PML+ PML- increased to 3%. Again, these values were independent of sex, disease duration, prior immunosuppression, or the duration of LS-OCMB+ (n, %) 1, 4.35% 22, 95.65% natalizumab treatment. LS-OCMB- (n, %) 10, 41.67% 14, 58.33% A graphic representation of PML risk in the two cohorts Total (n, %) 11, 23.40% 36, 76.60% depending of the results of the nomograms is shown in Figure 2. Pearson chi2 = 9.12 p = 0.003 Finally, we studied if OCMB could add some advantage to LS-OCMB, lipid-specific oligoclonal IgM bands; PML, progressive previous risk factors in the 47 patients (29 female/19 male) multifocal leukoencephalopathy. with anti-JCV antibody levels >1.5, who were treated with natalizumab for more than 2 years (2.32, 2.01–4.32 years; median, interquartile range). Results are shown in Table 5. Only one of 23 24 OCMB negative patients suffered this opportunistic infection patients showing OCMB developed a PML. By contrast, 10 out of (Pearson chi square = 9.12, p = 0.003). Frontiers in Neurology | www.frontiersin.org 8 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS DISCUSSION natalizumab onset also identified patients at higher PML risk. When including all factors giving significant results in the total The appearance of highly effective immunotherapies has changed cohort, in a multivariate logistic analysis to identify variables that disease course of patients with aggressive multiple sclerosis were statistically independent, the best predictive model to assess (1, 16–18). However, efficacy associates with higher risk of PML risk included anti-JCV levels higher than 0.9 and annualized deleterious side effects (19–22). Finding biomarkers that allow relapse rate below 0.5. We assessed individual PML risk by a the best balance between efficacy and safety for individual nomogram analysis. When anti-JCV levels where below 0.9 and patients has become a challenge of the most clinical relevance in relapse rate over 0.5, PML risk was below 1 in every 3,300 treated multiple sclerosis research. patients. If the results were the opposite, it rose to 1/50. In case of natalizumab, the most important side effect is By contrast, natalizumab treatment duration did not associate the appearance of PML, an opportunistic infection of the brain with PML risk in our study. The divergence of these results appearing in about one of every 250 treated patients (3, 23). It with those previously published can be partly due to the absence may cause patient death or considerable increase of disability. of patients treated for less than a year, who have extremely This has limited the use of this drug. Safety concerns, in both low PML risk, in our cohort. The relatively low number of patient and neurologist sides, often make it difficult to administer patients included in this study (1,306) compared to other cohorts this treatment for long. This is unfortunate, since the clinical with more than 5,000 patients (5) may also contribute to efficacy of this drug in the long term was demonstrated (24). the loss of significance of treatment duration for PML risk Additional factors reflecting patient inflammatory status can stratification. In addition, the particular characteristics of our contribute to further stratify PML risk. Decreased CD4+ T cohort which includes mainly active (median relapse rate 0.88 cell expression of L-Selectin (CD62L), a molecule implicated with a low interquartile range of 0.51) and relatively young in leukocyte adhesion to the endothelium, during natalizumab patients (median age at natalizumab onset = 36.5 years, with a treatment was found to associate with an increase of PML risk high interquartile range of 42.8 years) also can contribute to the (25). Although validation studies gave no uniform results (26, loss of significance in this variable. If these data are confirmed 27), probably due to the difficulty of measuring this biomarker in larger cohorts, they could indicate that treatment duration in cryopreserved cells, these data may reflect that a decrease in impact on PML risk could be modulated by younger age and high cells migrating to the central nervous system may increase PML disease activity. risk. Another factor indicating that patient inflammatory status The presence of LS-OCMB further contributed to stratify may contribute to stratifying PML risk is one of the actual risk PML risk. When we performed a multivariate logistic analysis factors, prior immunosuppression. Previous treatments inducing in the sub-cohort of patients in which these antibodies were a strong immunosuppression increase PML risk (4, 6). assessed, the best predictive model to assess PML risk changed. Age, another factor associated with inhibition of the adaptive It included LS-OCMB as best individual predictor and anti- immune response in multiple sclerosis and with reduced JCV levels higher than 0.9 and age older than 45 years as lymphocyte migration into the central nervous system (CNS), factors that equally contributed to PML risk. Nomogram analysis also relates to a higher PML risk in multiple sclerosis patients showed that patients with CSF restricted LS-OCMB, anti- treated with different biological drugs (10–12, 28). By contrast, JCV antibodies below 0.9, and age younger than 45 years at a highly inflammatory disease course revealed by the presence of natalizumab onset had a PML risk below 1 in every 10,000 LS-OCMB greatly diminishes PML risk (10). We studied here if treated patients. If anti-JCV antibody levels were higher than 0.9 clinical data reflecting disease activity may contribute to stratify or age at natalizumab onset over 45 years, PML risk was only PML risk in a cohort of 1,240 patients treated with natalizumab 1/2,000 in LS-OCMB positive patients. When these two factors in 36 European hospitals. We also studied the value of these coincided in a patient, the risk rose to 1/300 despite LS-OCMB variables in combination with LS-OCMB in a sub-cohort of 277 positivity and even increased to 1/33 in LS-OCMB negative patients in which these antibodies were analyzed. patients. These data are clinically relevant since they show that We did not find any significant association between prior patients with a more inflammatory disease, who get more clinical immunosuppression and PML risk in our cohort, although the benefit of this highly active drug, are at lower PML risk during proportion of patients showing prior immunosuppression was natalizumab treatment. higher in the group of PML patients (20%) than in those not In conclusion, these data allow to introduce a new algorithm developing this opportunistic infection (11%). The lower number in which PML risk can be established for individual patients of immunosuppressed patients in both PML and not PML cases attending to clinical and laboratory data measured prior to compared with previous studies (5) may account for the lack of natalizumab treatment initiation. significance of this variable in our cohort. However, anti-JCV antibodies and mostly anti-JCV indices higher than 0.9 continued to increase the probability of PML in these patients. In addition, clinical data associated with disease activity also contribute to DATA AVAILABILITY STATEMENT identify patients at higher risk. Thus, an MSSS score lower than 3 or relapse rates lower than 0.5 since disease onset is associated The raw data supporting the conclusions of this article will be with increased probability of PML. Age older than 45 years at made available by the authors, without undue reservation. Frontiers in Neurology | www.frontiersin.org 9 December 2020 | Volume 11 | Article 579438 Toboso et al. New PML-Risk Markers in MS ETHICS STATEMENT LR, FPau, IG, YA, EÁ, CR, AC, CC, PE, AB-B, LR-T, EQ, JM-R, AO, CL, LC, LL, JF, GB, PM, MH, JP, DPé, MO, FPad, JG-M, LN, The studies involving human participants were reviewed and AM, LF, and MC: sample collection, collection of clinical data, approved by Ethics Committee of Hospital Ramon y Cajal, and critical review of the manuscript. All authors contributed to Madrid, Spain. The patients/participants provided their written the article and approved the submitted version. informed consent to participate in this study. FUNDING AUTHOR CONTRIBUTIONS This study was supported by grants PI15/00513, PI18/00572, and LV, IT, RA-L, RA, HH, FD, SS, JA-C, GI, DPa, PO, BC, EA-M, DF, RD17/0015 (Red Española de Esclerosis Múltiple) from Instituto MG, OF, PU, JG-D, FR, AL, AU, AP-S, AS, YB, DG, ES, CE, XM, de Salud Carlos III, Ministerio de Economía y Empresa, Spain. REFERENCES 13. Berger JR, Cree BA, Greenberg B, Hemmer B, Ward BJ, Dong VM, et al. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Conflict of Interest: LV received a research grant from Biogen. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original The remaining authors declare that the research was conducted in the absence of publication in this journal is cited, in accordance with accepted academic practice. any commercial or financial relationships that could be construed as a potential No use, distribution or reproduction is permitted which does not comply with these conflict of interest. terms. Frontiers in Neurology | www.frontiersin.org 11 December 2020 | Volume 11 | Article 579438