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Serum proteome profiling of patients with non-small cell lung cancer treated with immune checkpoint blockade monotherapy

ABSTRACT: Shotgun proteomic profiling of serum from patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint blockade (ICB) monotherapy. Top-14 abundant proteins were depleted (Pierce High-Select, Thermo Fisher Scientific), and tryptic digests were spiked with the Pierce Peptide Retention Time Calibration Mixture as an internal standard. Peptides were analyzed by LC-MS/MS on an EASY-nLC 1200 coupled to an Orbitrap Eclipse Tribrid mass spectrometer with a FAIMS Pro interface (compensation voltages −40, −60, −80 V), using data-dependent acquisition with Orbitrap MS1 and ion-trap-detected HCD MS2. Raw files were searched in Proteome Discoverer 2.4 (SEQUEST HT with Percolator validation) against UniProt Swiss-Prot human (release 2017-10-25), and label-free protein abundances (Minora Feature Detector) were normalized to internal standard peptides. See the associated publication for full experimental details and clinical information.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Tetsunari Hase

PROVIDER: PXD078247 | JPOST Repository | Wed Jun 10 00:00:00 BST 2026

REPOSITORIES: jPOST

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Source:

			Action	DRS
	Homo%20sapiens%20(SwissProt%20TaxID=9606).fasta	Fasta
	nn015_hase_139samples.pdResult	Other
	raw.zip	Other

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Project description:BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.RESULTS: LMN uses a ranking system (1-3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

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Serum proteome profiling of patients with non-small cell lung cancer treated with immune checkpoint blockade monotherapy

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