Abstract
Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
Highlights
Using a limited initial sample set and validating it with a homogenous specimen set that likely fails to capture the large-scale variance expected at the population level constitutes an important misstep that has led to the failure of many protein biomarkers to reach the clinic
We provided six recommendations to encourage robust and early careful study design that takes into considerations pitfalls of promising protein biomarkers that failed to reach utility at the bedside, to facilitate clinical translation of rapid lipid profiling for accelerated cancer diagnosis in the clinical domain
We hope that our recommendations will save time and effort in the early evaluation of promising leads towards successful translation in a manner that reduces the discordance between initial phase promise and late-stage performance
Summary
Introduction and ProblemStatement published maps and institutional affil-Based on the close relationship between lipid metabolism and cancer formation/progression [1], tissue pathology determinations (cancer versus healthy or differentiation between various types of the same cancer) through lipid profiling with ambient mass spectrometry (MS) has received much traction [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. A variety of aerosolization and liquid extraction methods [3] (some utilizing handheld sampling probes capable of desorbing tissue molecules under ambient conditions) are coupled to MS analysis after ionization, generating a lipid profile signature from the target tissue specimen. Through comparing the overall mass to charge (m/z) pattern of this “crude” tissue lipid profile (or signature) generated in the absence of chromatographic separation, to a library of previously collected lipid profile signatures characteristic to various tissue pathologies, rapid identification of said pathologies (cancer, infection, or inflammation) has been made possible within a few seconds of data collection and analysis [4].
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