Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

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.

Picosecond Infrared Laser Mass Spectrometry Identifies a Metabolite Array for 10 s Diagnosis of Select Skin Cancer Types: A Proof-of-Concept Feasibility Study

“…Hence, in Figure S6, we assess the capability of PIRL-MS in providing diagnostic information by way of in situ sampling (sacrificed murine models). As shown in Figure S6, the ex vivo murine model of Figure S5 is overlayed with in situ PIRL-MS sampling results of normal skin (106 events), muscle (112), melanotic (80), and amelanotic (47) numbers used). The reasonable concordance between the ex vivo and in situ data judged by juxtaposition of said results in the PCA-LDA space suggests that the correct PIRL-MS tissue classification results are feasible during in situ sampling through utilizing statistical models from ex vivo tissue explorations.…”

“…In addition, parallel studies using actual biopsy specimens as well as in vivo performance evaluation using a point of care device that allows collection of large patient base data are required to further extend this proof-of-principle study. Additional guidelines for evaluation of analytical and safety 46 as well as post-analytical validation 47 put forward by our group can be helpful in this quest. These studies will complement the present report where we suggest PIRL-MS may be an invaluable tool situated to meet the needs of a high speed, sensitive and specific skin cancer diagnostic method, further highlighting the utility of ambient MS 48…”

Authentication of Edible Insects’ Powders by the Combination of DART-HRMS Signatures: The First Application of Ambient Mass Spectrometry to Screening of Novel Food

“…The performance of the random forest model was evaluated on a test set, resulting in a 100% overall accuracy. Further blind-controlled tests, with an independent batch of samples, are still necessary to establish the real and late-stage performances of this non-targeted method [ 31 , 32 ]. It is worth noticing that the chemical fingerprints could be affected by the rearing system, developmental stage, dietary interventions, and exposure to different bacterial strains used as dietary sources.…”

Early prediction of paratuberculosis health status in cattle by integrating NMR analysis and blood indices through the LASSO method

“…The penalty has the effect of forcing some of the variables, i.e., those with a minor contribution to the model, to be equal to zero; this results in an alternative method for selecting relevant variables, with subsequent reduction of the complexity of the model (27,28). Note that this parsimonious method, which applies feature reduction, is less susceptible to the noise linked to the heterogeneity of the samples (29). The LASSO method has been extensively applied to analyze ambient mass spectrometry data in cancer diagnosis and is currently moving toward successful translation to clinical medicine (30,31).…”