Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.

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A recent publication from Vasilopoulou et al. 1 reports on full lipidome profiling by a combination of trapped ion mobility spectrometry (TIMS), parallel accumulation serial fragmentation (PASEF) and nano HPLC 1 . While this represents an impressive technological advance with the potential to increase lipidome coverage and lower detection limits for individual lipids, the interpretation of the acquired spectra is a matter of concern. Specifically, the authors relied exclusively on software-assisted lipid assignments that were not confirmed by an independent inspection of matched spectra to recognize abundant structurally unique lipid fragments. Further, no attempts were made to correlate the retention times of identified species with available lipid standards, which constitutes the gold standard typically employed in lipidomics to reduce false-positive assignments. Manual inspection of the dataset performed by us suggested that the identification of at least 510 out of 1108 features reported as unique lipids would require additional experimental evidence. This, in turn, compromises the assignment of collision cross section (CCS) values for 1856 features, potentially misguiding other lipidomics laboratories that may use these CCS data for identifying lipids.Automated lipid species annotation based on fragment ion mass spectra (MS n spectra) faces three major challenges: (i) Isobaric or isomeric lipid species from different classes often yield similar fragments and cannot be unambigiously matched; (ii) the abundance of lipid fragments strongly depends on the experimental conditions 2 which compromises their similarity to reference spectra; (iii) fragmentation of co-isolated precursors often originating from different classes yields highly convoluted spectra. Consequently, further, inspection is indispensable for spectra that were matched to lipid structures by software tools. Rule-based or decision tree-based approaches are more suitable for automated spectral annotation, such as lipid data analyzer (LDA) 2,3 , LipidHunter 4 , LipidXplorer 5 , LipidMatch 6 , and MS-DIAL 7 , to mention only a few common tools. These algorithms scout spectra for fragmentation patterns characteristic to each lipid class according to established fragmentation pathways and peak intensity relationships. Nonetheless, the key for correct unequivocal lipid species annotation lies in two other peculiarities of lipids that do not pertain to the interpretation of MS n spectra: (a) lipids often form more than one adduct ion in electrospray ionization; (b) all