A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry.

Picosecond infrared laser mass spectrometry (PIRL-MS) is shown, through a retrospective patient tissue study, to differentiate medulloblastoma cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma (PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with PIRL-MS in 10 s of sampling and analysis time. The average sensitivity and specificity values for this classification, taking genomic profiling data as standard, were 96.41 and 99.54%, and this classification used many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis and liquid chromatography coupled with tandem high-resolution mass spectrometry (LC-MS/MS) further allowed us to reduce the molecular feature list to only 18 metabolic lipid markers most strongly involved in this classification. The identified 'metabolite array' was comprised of a variety of phosphatidic and fatty acids, ceramides, and phosphatidylcholine/ethanolamine and could mediate the above-mentioned classification with average sensitivity and specificity values of 94.39 and 98.78%, respectively, at a 95% confidence in prediction probability threshold. Therefore, a rapid and accurate pathology classification of select pediatric brain cancer types from 10 s PIRL-MS analysis using known metabolic biomarkers can now be available to the neurosurgeon. Based on retrospective mining of 'survival' versus 'extent-of-resection' data, we further identified pediatric cancer types that may benefit from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness of the surgical resection can be optimized in a manner that is expected to benefit the patient's overall or progression-free survival. PIRL-MS is a promising tool to drive such personalized decision-making in the operating theater.

Metabolic Lipids in Melanoma Enable Rapid Determination of Actionable BRAF-V600E Mutation with Picosecond Infrared Laser Mass Spectrometry in 10 s.

Rapid molecular profiling of biological tissues with picosecond infrared laser mass spectrometry (PIRL-MS) has enabled the detection of clinically important histologic types and molecular subtypes of human cancers in as little as 10 s of data collection and analysis time. Utilizing an engineered cell line model of actionable BRAF-V600E mutation, we observed statistically significant differences in 10 s PIRL-MS molecular profiles between BRAF-V600E and BRAF-wt cells. Multivariate statistical analyses revealed a list of mass-to-charge (m/z) values most significantly responsible for the identification of BRAF-V600E mutation status in this engineered cell line that provided a highly controlled testbed for this observation. These metabolites predicted BRAF-V600E expression in human melanoma cell lines with greater than 98% accuracy. Through chromatography and tandem mass spectrometry analysis of cell line extracts, a 30-member "metabolite array" was characterized for determination of BRAF-V600E expression levels in subcutaneous melanoma xenografts with an average sensitivity and specificity of 95.6% with 10 s PIRL-MS analysis. This proof-of-principle work warrants a future large-scale study to identify a metabolite array for 10 s determination of actionable BRAF-V600E mutation in human tissue to guide patient care.