(SST) Rapid High-throughput Intraoperative Diagnosis of Gliomas Using Automated Desorption Electrospray Ionization Mass Spectrometry

Introduction: Current intra-operative diagnosis relies on frozen section pathology with turnaround times of up to 30 minutes per sample and need for specialized professionals. Metabolic and lipidomic profiling by mass-spectrometry (MS) has emerged as a novel rapid method for tumor diagnosis augmenting frozen pathology.

Methods: Our groups have developed a novel tool for the analysis of tissue microarrays using automated high-throughput desorption electrospray ionization MS (DESI-MS) for rapid analysis of high-density arrays. Tissue biopsies were spotted on a slide using a pin-tool. The slides were automatically transferred to a DESI stage and analyzed by MS in a spot-to-spot fashion, thereby producing large data volumes. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample.

Results: A total of 66 unique tumor samples, subdivided into two microarray sets (36 and 30 samples) including normal brain parenchyma, gliomas, meningiomas and pituitary tumors were included. Principal component analysis (PCA) of all samples in the first set showed unsupervised clustering by tissue (68% variance explained). Using estimated principal components as input features, high accuracy (>90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Unsupervised analysis of the results for the second set, which contains only glioma samples, shows two main clusters in the two-dimensional PCA space (65% explained variance), one of low and low-moderate tumor cell percentage (TCP) samples and the other corresponding to biopsies identified with high and moderate-high TCP.

Conclusion : We have developed a new high-throughput method employing DESI-MS that is capable of rapidly analyzing a large number of specimens with low sample requirement, which will enable identification of diagnostic molecular markers for use in intraoperative applications (e.g., molecular guided tumor resections) and potentially improve patient outcomes.