Background: Pre-operative triage of adnexal masses with serum CA-125, HE4, and ultrasound (O-RADS) has limited accuracy, contributing to unnecessary surgery. We develop and validate a first of its kind, biomarker panel-free plasma proteomic classifier that distinguishes malignant from benign adnexal masses from a single blood draw, operating directly on discovery-mode mass spectrometry. Methods: In this multicenter prospective observational study, plasma from women with adnexal masses and from healthy controls at a large urban academic center (discovery and four validation cohorts) and an external academic center from another Urban-suburban area (external validation) was analyzed by data-independent-acquisition mass spectrometry (Orbitrap Astral), quantifying >1,000 proteins per sample. Cancerous (malignant, borderline adnexal mass, or metastasis to ovary) versus benign adnexal masses were classified with ADAPT-MS (Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry), which retrains an ElasticNet model for each sample based on the proteins measured. The primary outcome was discrimination (area under the receiver operating characteristic curve [AUC]). Pre-operative O-RADS scores and serum CA-125 were compared and combined with proteomics on matched subsets. Findings: Using a discovery cohort (n=1062), an ADAPT-MS classifier, validated across five independent cohorts (n=623), achieved a summary AUC of 0.835 (cohort range 0.779-0.904). Among patients with pre-operative O-RADS (n=231), proteomics (AUC 0.852) was comparable to O-RADS alone (0.863) and combining them raised discrimination to 0.922. Proteomics outperformed serum CA-125 (0.915 versus 0.777; n=125) and proteome-derived ROMA-like and two-marker surrogates. Biologically, the classification-relevant proteins are enriched for host response proteins rather than tumor cell associated protein changes, comprising ECM remodeling, complement and innate immune response pathways. This explains good detection of tumor with heterogenous pathology and also metastasis to ovary. Interpretation: A single plasma proteomic measurement interpreted using adaptive machine learning discriminates malignant from benign adnexal masses with good internal and external validation, and is complementary to ultrasound, warranting prospective evaluation as a panel-free diagnostic adjunct to imaging. ### Competing Interest Statement MM is an investor in Evosep. All other authors declare no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Institutional Review Board at The University of Chicago and Roswell Park Cancer Institute, and informed written consent was obtained from all women participating in the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. Max Planck Society for the Advancement of Sciences NIH R35 grant Arthur and Nicole Herbst

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thous...

This Perspective highlights key research areas within mass spectrometry-based proteomics where AI is poised to drive significant advances.

Vascular smooth muscle cells (VSMCs) drive atherosclerosis through phenotypic switching, yet their spatial organization and protein signatures within plaques remain poorly characterized. Here, we appl...

Deep proteome profiling of over 5,000 cerebrospinal fluid samples by mass spectrometry maps protein alterations across major neurological disorders, resolving key sources of variation as well as share...

The genome and proteome of the same cells are rarely measured together, which is especially consequential in cancer, where somatic mutations vary across clones and drive disease. We show that a single...

We applied an ultra-sensitive, histomorphology-guided spatial proteomics workflow to advanced atherosclerotic carotid plaques to define molecular changes associated with minimum fibrous-cap thickness....

Atherosclerotic plaque rupture underlies many cerebrovascular events, yet the molecular determinants of vulnerable plaque morphology remain poorly defined. Using histomorphology-guided spatial proteom...

Sample preparation increasingly sets the throughput and reproducibility of mass spectrometry (MS)-based proteomics. StageTips (stop-and-go extraction tips) and variants thereof have long been common i...

Although protein-protein interactions govern virtually all cellular processes, systematic interactome mapping by affinity enrichment mass spectrometry (AE-MS) is constrained by manual sample preparati...

This Comment discusses depth, robustness and bias in plasma proteomics, concluding that increasing the depth of coverage does not necessarily translate to quantitative robustness.

This Comment discusses depth, robustness and bias in plasma proteomics, concluding that increasing the depth of coverage does not necessarily translate to quantitative robustness.

2026 Canada Gairdner Awards recognize world-leading scientists for breakthrough discoveries advancing human health.

Vacancy at Department of Biomedicine - Forsk. & udd., Skou-bygningen, Aarhus University

One of <em>Literary Hub</em>'s Most Anticipated Books of 2024<br /><br />&#8220;Lively and entertaining.&hellip; Biology will never be the same.&#8221; &#8212;Adrian Woolfson, <em>Wall Street Journal<...

Mass spectrometry-based proteomics increasingly demands platforms that combine quantitative rigor with the discovery capabilities of accurate mass systems. Here we present the ZenoTOF 8600 system, a compact mass spectrometry system that integrates enhanced ion capture and transmission optics with an optical detection system, Zeno trap-enhanced MS/MS, electron-activated dissociation, and scanning quadrupole data-independent acquisition (ZT Scan DIA). We show that ZT Scan DIA outperforms conventional variable-window DIA (Zeno SWATH DIA) in both identifications and quantitative reproducibility, and demonstrate the platform's versatility across proteomics applications: thousands of protein groups from bulk samples at up to 500 samples per day, single-cell proteomics yielding up to 4,700 proteins, accurate ratio recovery in mixed-species quantitative benchmarks, low-attomole targeted quantitation, and detection of disease-relevant phosphorylation in a Parkinson's disease cellular model using complementary CID and EAD fragmentation. The instrument's compact footprint makes it attractive for settings where both analytical breadth and operational robustness are required. ### Competing Interest Statement B.L., Y.L.B., N. B., I.B., J.C., A.C., G.I., K.T., T.T., B.S., and J.C.-P. are employees of SCIEX. M.M. is an indirect investor in Evosep Biosystems. The remaining authors declare no competing interests.