Building network for biomarker interaction analysis
You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease.

Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't show a constant relationship. To do so you can use WGCNA (although with just 10 variables is too low to expect a scale free network you can use to correlate the variables and group them by correlation profile)

Then the question is which variables are more important, those who kept the same relationship or those which change? 
Use the eigengenes of those variables (separately by those that have a constant relationship and those which don't) to estimate the dependency with the parametric variables. Or you simply can do an ANOVA of all the variables with each non-parametric variable.