Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning

This study presents a multi-faceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based Generalized Additive Models (GAM), whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular molecular tumor characteristics. This retrospective study enrolled ten treatment-naive patients with radiologically confirmed glioma (5 WHO grade II, 5 WHO grade IV). Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery (27.9+/-34.0 days). During standard craniotomy procedure, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target MR contrast combination. The mean AUC for the five gene targets 31 MR contrast combinations was 0.75+/-0.11; individual AUCs were as high as 0.96 for both IDH1 TP53 with T2W-FLAIR ADC 0.99 for EGFR with T2W ADC. An average AUC of 0.85 across the five mutations was achieved using the combination of T1W, T2W-FLAIR, ADC. These results suggest the possibility of predicting exome-wide mutation events from non-invasive, in vivo imaging by combining stereotactic localization of glioma samples a semi-parametric deep learning method. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.