Figure 1.
SMA plasma biomarker discovery campaign and confirmation schematic.
Analyte markers were identified in different discovery campaigns in two platforms. BforSMA samples were screened in LC/MS using iTRAQ technology, generating 84 markers that regressed with SMA motor function (MHFMS). Samples from the same study were screened in commercially available Luminex panels, yielding an additional 64 markers that regressed to motor function. There were 14 markers in the MAP panels that were hits in the LC/MS campaign, and 11 of these were repeat hits. New Luminex assays were created to represent the top 8 analytes from the LC/MS analysis. Filtering was performed by evaluation of statistical strength and assay performance, and 35 top analytes were selected for further MAP testing in a new sample set from the PNCRN natural history study. An additional 91 analytes were present in the panels for testing, allowing discovery based on non-motor outcome data that was collected in the PNCRN study. 13 analytes were repeat motor regressors, while 15 were new non-motor analytes. A total of 27 analytes were selected for inclusion to the final SMA-MAP panel, which was validated for reproducibility using unthawed samples from BforSMA.
Table 1.
SMA plasma protein marker that regress to motor function (MHFMS).
Table 2.
BforSMA LC/MS and MAP Repeat Hit Rankings.
Figure 2.
Classification of SMA types by the top 13 biomarker analytes Receiver-Operator curves.
(ROCs) and (area under the curves (AUCs) were generated for the top 13 markers to differentiate between SMA types within the PNCRN’s natural history study dataset. Both sensitivity (True positive rate) and specificity (1-False positive rate) of the SMA type classifications were very high across several thresholds. A: Type 1 versus Type 2 AUC was 0.98. B: Type 1 versus Type 3 AUC was 1. C: Type 3 versus Type 3 AUC was 0.94.
Table 3.
Top 13 SMA motor function regressors are markers in two SMA populations.
Table 4.
SMA non-motor outcome regressors.
Table 5.
SMA-MAP analytes and their correlated outcomes.
Figure 3.
SMA-MAP motor function score prediction model.
Using Tobit linear regression models SMA motor scores were predicted from SMA-MAP analytes values with age of onset as a covariable. Pearson correlations between actual and predicted motor scores for the top 6 combinations from BforSMA were plotted. A: Graph of actual and predicted motor scores of a 6 analyte model uncensored model. Type 1 SMA patients and ambulatory Type 3 subjects can be represented in the analysis and given a score below 0 or over 40 respectively. B: Graph of 6 analyte motor scores using values censored between 0 and 40. Note that the Type 1 datapoints have been moved arbitrarily to the right to allow visualization, and these points still represent values of 0.
Figure 4.
Several types of biomarkers for SMA-MAP analytes.
SMA biomarkers were identified for their regression to motor function and non-motor outcome measures. The analytes have been confirmed and validated to different degrees and will require more validation in prospective, longitudinal studies to determine their utility as biomarkers for disease progression and pharmacodynamic response.