NAPH-Fluorescence Lifetime Imaging informed machine learning modelling reliably predicts Temozolomide responsiveness in Glioblastoma

Glioblastoma (GBM) is a highly deadly brain tumor. The chemotherapeutic treatment still lacks solid patient stratification, as temozolomide (TMZ) is administered to the majority of GBM patients. In this study, we explored the effectiveness of NAD(P)H-fluorescence lifetime imaging microscopy (NAD(P)H-FLIM) in furnishing clinically relevant insights into GBM responsiveness, a realm constrained by the absence of corresponding clinical outcome data. Using the information obtained by NAD(P)H-FLIM, we conducted a DE analysis on an RNA-seq private dataset, comparing TMZ responder and non-responder tumors. To validate the NAD(P)H-FLIM classification, we conducted a comparable DE analysis on the GBM TCGA (The Cancer Genome Atlas) RNA-seq data using the progression-free interval (PFI) as a responsiveness indicator. We selected the most informative genes shared by both the DE analyses (BIRC3, CBLC, IL6, PTX3, SRD5A1, TNFAIP3) and employed them as transcriptomic signature. Using a different dataset (GBM TCGA Agilent-Microarray), we built a signature-based machine learning model capable of predicting the PFI. We also showed that the performance of our model is similar to that obtained with a well-established biomarker: the methylation status of the MGMT promoter. In conclusion, we assessed the reliability of the NAD(P)H-FLIM in providing clinically relevant drug response information in GBM and provided a new transcriptomic based model for determining patients’ responsiveness to TMZ treatment.