timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Abstract 6606: Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood

Lung cancer is the leading cause of cancer-related deaths in the United States, with estimates of 236,740 new cases and 118,830 deaths in 2022 secondary to the disease. Blood-based liquid biopsies hold promise to reduce morbidity and mortality from lung cancer by enabling early detection to downstage disease at diagnosis, theragnostic identification of patients most likely to be helped or harmed by therapy, monitoring of therapeutic efficacy, and detection of residual disease. PrognomiQ’s multi-omics platform comprehensively profiles proteins, metabolites, lipids, mRNA, and cfDNA in blood samples which can be used for the development of liquid biopsy tests with high sensitivity and specificity for lung cancer. We conducted a case-control study comprising 1031 subjects: 361 subjects with untreated non-small-cell lung cancer (NSCLC) and 670 matched controls which included 340 subjects with salient pulmonary and gastrointestinal co-morbidities. Blood samples from each subject were processed to provide 7 different `omics readouts. LCMS was used to detect and quantify proteins, metabolites, and lipids. In addition, cfDNA and mRNA were assayed using next-generation sequencing. cfDNA reads were analyzed to estimate fragment-lengths, copy-number variation, and CpG site methylation. All molecular data were normalized using standard methods specific to each assay. Univariate analyses of cases vs controls were performed to identify differentially abundant features on all available samples per assay. We detected 9,868 proteins, 605 lipids, 329 metabolites, and 109,070 mRNA transcripts. Of these, 3,098 proteins, 210 lipids, 57 metabolites, and 30,236 mRNA transcripts were significantly different (FWER &lt; 0.05) in cases versus controls. Gene set enrichment analysis on statistically significant transcripts and proteins identified multiple gene-ontology terms associated with cancer including the Wnt signaling process and IgA immunoglobulin complex, respectively. From cfDNA data, we identified 234 non-contiguous genomic regions associated with the fragment-length disorder, 4,790 with copy-number variation, and 74 differentially methylated genomic regions spanning 184 CpG sites (FWER &lt; 0.05). With the premise that deviations from copy number neutrality are more likely to indicate a tumor contribution, we then focused our examination on those differentially expressed proteins that overlap with differentially expressed mRNA transcripts as well as CNV genomic regions. We identified 52 protein coding genes including E-cadherin (associated with EMT) and related binding proteins such as RAB11B, CAPZB, EPS15, FLNB, MYH9, STK24 and YWHAE. Ongoing machine-learning-based classifier training to distinguish between cancer and non-cancer can serve as the basis for the development of high-sensitivity liquid-biopsy tests for lung cancer.

Citation Format: Jinlyung Choi, Ajinkya Kokate, Ehdieh Khaledian, Manway Liu, Preethi Prasad, John Blume, Jessica Chan, Rea Cuaresma, Kevin Dai, Manoj Khadka, Thidar Khin, Yuya Kodama, Joon-Yong Lee, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Sara Nouri Golmaei, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Dijana Vitko, Kavya Swaminathan, James Yee, Brian Young, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6606.