Transcriptomic profiling of brain amyloidosis using peripheral blood‐based gene expression

Background In an effort towards pre‐symptomatic risk assessment, precision medicine and biomarker development in AD, we evaluated the efficacy of characterizing and clustering peripheral blood transcriptomic data with respect to brain amyloidosis. Method 356 participants were identified from the ADNI cohorts who had corresponding blood gene expression, Florbetapir SUVR, neurodegeneration measures and CSF measures (Table 1) (CN=120, EMCI=130, LMCI=72 and AD=34). Differentially expressed genes between diagnostic groups from ∼50,000 transcripts(p<0.001) were identified. Pairwise Euclidean distance between genes was used to construct a gene‐gene distance matrix followed by K‐means clustering to identify clusters of genes which shared similar transcriptomic profile (Figure 1). Gene enrichment analysis was performed to identify the most relevant biological process associated with each cluster. Principal component analysis was done to assign the weight of first principal component as the “eigen” gene; a data vector representing the whole cluster. The eigen genes were used to assess the association of the clusters with amyloid SUVR, hippocampal volume, entorhinal thickness, CSF Abeta and p‐tau measures. We defined driver genes, within each cluster, as transcripts whose correlation exceeded the eigen gene’s association with amyloidosis. Finally, classification algorithms (logistic‐regression and SVM) using the driver genes identified from ADNI were used to assess the diagnostic prediction (control vs amnestic MCI) in an external (ImaGene) dataset. Result We identified five clusters with overrepresentation of biological processes highly relevant to AD pathogenesis ‐ mitochondrial function, protein oligomerization, TGF beta signaling, protein autophosphorylation and MAPK activation. All five clusters were significantly associated with Florbetapir SUVR and CSF Abeta measures (p<0.05) (Figure 2). Many of the driver‐genes showed similar correlation with amyloid SUVR in ImaGene (Table 2). The SVM and logistic‐regression classifiers achieved an AUC of 0.81 and 0.82 respectively in predicting amnestic MCI from driver‐gene data in ImaGene (Figure 3). Conclusion We identified five AD‐relevant clusters of transcripts from peripheral blood and validated this association in an independent dataset. Systems level genetic signatures from blood can serve as a highly important risk analysis and screening tool for amyloidosis.