Abstract

Personalized medicine is the tailoring of treatment to the individual characteristics of patients. Once a treatment has been tested in a clinical trial and its effect overall quantified, it would be of great value to be able to use the baseline patients’ characteristics to identify patients with larger/lower benefits from treatment, for a more personalized approach to therapy.
I will show a previously published statistical method, aimed at identifying patients’ profiles associated
to larger treatment benefits applied to randomized clinical trials in multiple sclerosis, demonstrating the possibility to refine and personalize the treatment effect estimated in randomized studies by using the baseline demographic and clinical characteristics of the included patients. The
method can be applied to any randomized trial in any medical condition to create a treatment-specific score associated to different levels of response to the treatment tested in the trial. This is an easy and affordable method toward therapy personalization, indicating patient profiles related to a larger benefit from a specific drug, which may have implications for taking clinical decisions in everyday clinical practice.

Background

Alemtuzumab has proven to be an effective treatment for patients with highly active multiple sclerosis (MS). However, its use has been limited by adverse events (AEs) as secondary autoimmunity, being the most frequent those involving the thyroid gland, observed in around 40% of patients.

Objectives

To explore whether patient blood lymphocyte profile before alemtuzumab treatment initiation can identify patients with an increased risk of developing later autoimmunity.

Methods

A multicentre prospective longitudinal study was performed, including fifty‐four Relapsing-Remitting MS (RRMS) patients diagnosed in five Spanish hospitals. Patient blood samples were collected before initiating treatment with alemtuzumab. Autoimmune AEs were defined as the development, at any point within 2 years of follow-up, of any autoimmune thyroid-associated event, immune thrombocytopenia and/or autoimmune nephropathy. Differences were assessed using Man-Whitney U tests. Cut-off values were established using ROC curves to predict autoimmune AEs. Odds ratios were calculated by Fisher tests.

Results

Fifty‐four RRMS patients, 36 (66.7%) women, with a median (range) age of 28 (13–67) years and median (range) follow-up of 6 (0-20) years. Fourteen patients (25.9%) experienced autoimmune AEs, and all of them were dysthyroidism. No immune thrombocytopenia or nephropathies were observed. No statistical differences were found in clinical and demographic characteristics between patients who developed autoimmune AEs and those who did not. Patients who experienced autoimmune AEs before treatment onset had a higher percentage of blood CD19+ B cells (p=0.001), with a higher relative percentage of naïve B cells and plasmablasts. When explored total cell numbers, only plasmablast levels remained significant (p=0.02). A lower risk of autoimmune AEs after alemtuzumab was observed among patients with less than 7.6% of blood CD19+ B cells [odds ratio (OR) 16, confidence interval (CI) 3.86–58.95, p<0.0001] or less than 0.13% of plasmablast cells [OR 9.33, CI 2.17–42.65, p=0.002].

Conclusions

A low percentages of blood CD19+ B cells or plasmablasts before Alemtuzumab treatment predicted a lower risk of autoimmune AEs.

Background

Treatment effects could vary from person to person and depend on patient characteristics. Predicting individualized treatment effects is important for clinical decision-making. There are many treatment options for relapsing-remitting multiple sclerosis (RRMS), the most common type of MS; it is crucial to understand whether treatment response and drug safety profiles are heterogeneous.

Objectives

Present a methodological framework that allows personalized predictions for the most likely outcome under different treatment options and demonstrate its utility in RRMS.

Methods

We propose a two-stage prediction model. First, we calculated baseline risk scores (linking the probability of developing the outcome to patient characteristics but not to the treatment) using the least absolute shrinkage and selection operator (LASSO) and ‘pre-specified’ (based on previously identified prognostic factors) models. Second, we estimated the probabilities of post-treatment outcome as a function of baseline risk score using an individual patient data network meta-regression model in a Bayesian framework. We applied this approach to predict relapse at two years in 3,590 patients receiving placebo, natalizumab, dimethyl fumarate, or glatiramer acetate.

Results

Characteristics such as age and disability status at the start of the treatment affected baseline risk of relapse. Both the LASSO and ‘pre-specified’ model estimate a mean baseline risk of 37%, although the ‘pre-specified’ model showed greater estimation variation. For high-risk patients (baseline risk > 50%), the absolute benefit of natalizumab versus dimethyl fumarate was 10% and 15% for LASSO and ‘pre-specified’, respectively. For low-risk patients (baseline risk < 30%), the absolute benefit of dimethyl fumarate versus natalizumab was 2% and 3% for LASSO and ‘pre-specified’, respectively.

Conclusions

Our flexible approach can be extended to any number of RRMS treatments and other outcomes of interest. Importantly, it has the potential to inform patients and their doctors about the most appropriate treatment, thus contributing to the progression of personalized medicine.

Supported by: Writing and editorial support for the preparation of this abstract was provided by Excel Scientific Solutions. Funding was provided by Biogen.

Background

Serum neurofilament light chain (sNfL) reflects neuro-axonal damage and may qualify as a biomarker of suboptimal response to disease modifying therapy (DMT).

Objectives

To investigate the predictive value of sNfL in clinically isolated syndrome (CIS) and relapsing-remitting (RR) MS patients with established DMT for future MS disease activity in the Swiss MS Cohort Study.

Methods

All patients were on DMT for at least 3 months. sNfL was measured 6 or 12-monthly with the NF-light^®assay. The association between sNfL and age was modeled using a generalized additive model for location scale and shape. Z-scores (sNfLz) were derived thereof, reflecting the deviation of a patient sNfL value from the mean value of same age healthy controls (n=8865 samples). We used univariable mixed logistic regression models to investigate the association between sNfLz and the occurrence of clinical events (relapses, EDSS worsening [≥1.5 steps if EDSS 0; ≥1.0 if 1.0-5.5 or ≥0.5 if >5.5] in the following year in all patients, and in those fulfilling NEDA-3 criteria (no relapses, EDSS worsening, contrast enhancing or new/enlarging T2 lesions in brain MRI, based on previous year). We combined sNfLz with clinical and MRI measures of MS disease activity in the previous year (EDA-3) in a multivariable mixed logistic regression model for predicting clinical events in the following year.

Results

sNfL was measured in 1062 patients with 5192 longitudinal samples (median age 39.7 yrs; EDSS 2.0; 4.1% CIS, 95.9% RRMS; median follow-up 5 yrs). sNfLz predicted clinical events in the following year (OR 1.21 [95%CI 1.11-1.36], p<0.001, n=4624). This effect increased in magnitude with increasing sNfLz (sNfLz >1: OR 1.41 [95%CI 1.15-1.73], p=0.001; >1.5: OR 1.80 [95%CI 1.43-2.28], p<0.001; >2: OR 2.33 [95%CI 1.74-3.14], p<0.001). Similar results were found for the prediction of future new/enlarging T2 lesions and brain volume loss. In the multivariable model, new/enlarging T2 lesions (OR 1.88 [95%CI 1.13-3.12], p=0.016) and sNfLz>1.5 (OR 2.18 [95%CI 1.21-3.90], p=0.009) predicted future clinical events (n=853), while previous EDSS worsening, previous relapses and current contrast enhancement did not. In NEDA-3 patients, change of sNfLz (per standard deviation) was associated with a 37% increased risk of clinical events in the subsequent year (OR 1.37 [95%CI 1.04-1.78], p=0.025, n=587).

Conclusions

Our data support the value of sNfL levels, beyond the NEDA3 concept, for treatment monitoring in MS clinical practice.