Biomarker Signature for Individualized Treatment Response

We assessed cytokines at baseline and 3, 6, 9, and 12 months by multiplex electrochemiluminescence. Hematological parameters were assessed at the same timepoints. CD34⁺ and CD34⁺CD38⁻ hematopoietic stem and progenitor cells (HSPC) were analyzed in peripheral blood by flow cytometry as previously described (10). Structural MRI was conducted at two 1.5-Tesla scanners. We used the model-generated filgrastim response groups and evaluated blood cytokines by an area under the curve (AUC) approach. We used the baseline value of the analyzed biomarker as bottom border and a line connecting the measured values at 3, 6, and 9 months as upper border.

Several biomarkers with a multitude of dimensionality were detected in filgrastim treated patients over time. Non-linear principal component analyses were applied in the evaluation of hematological parameters, stem cells, cytokines, and structural changes in brain architecture (diffusion tensor imaging, DTI-MRI) as a robust descriptive tool to reduce the dimensionality of large numbers of variables, handling various data types (ordinal, scalar, etc.) and missing values (14). A non-linear principal component analysis (NLPCA) was used to determine covariance among variables within each biomarker package. NLPCA was performed for each biomarker package separately at three time points: all available measures up to 3 months, up to 6 months, and up to 12 months. The goal of these analyses is to (1) determine which variables at each time point are most highly correlated with the variance explained by a particular principal component, (2) identify the emergent “identity” of each principal component, and (3) use the normalized principal component scores (PC scores) to run specific hypothesis tests of group differences. This approach allowed us to reduce a large number of variables into a single composite outcome score for each PC and then perform a single hypothesis test, rather than run multiple tests on individual outcome measures, increasing our probability of committing a Type I error. For the following analyses, only those variables that were over an absolute loading threshold of 0.5 are shown; this thresholding step allows domain experts to focus only on those variables that are most strongly correlated with the variance explained by their respective PCs, in order to best identify the identity of that PC. The statistical significance level for all analyses was set to α = 0.05. Data for DTI–ROI determinations, cell mobilization, and hematology were treated accordingly.

A linear mixed model was conducted, with the previously determined responder categories serving as independent variables (Further information in Supplementary Material).