Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma

Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort

Simple Summary

Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival.

Abstract

Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10⁻⁶).

Lipid profiling in malignant mesothelioma reveals promising signatures for diagnosis and prognosis: A plasma-based LC-MS lipidomics study

BACKGROUND AND AIM

Malignant mesothelioma (MM), being a rare and aggressive carcinoma, can barely be cured. Incidence of this cancer will keep climbing up in the next few decades since its major carcinogen, asbestos, is still in use in many countries. Unfortunately, prognosis of MM is unsatisfactory principally due to poor early diagnosis as a result of its long latency period and ambiguous symptoms. Lipids are known to contribute to cellular structure, signaling, and energy storage, and are widely reported to be related with tumorigenesis. Therefore, we aim to discover novel lipid biomarkers by plasma-based lipidomics that may improve MM diagnosis.

METHODS

Plasma samples from 25 MM patients and 32 healthy controls (HCs) were collected and analysed using a high-throughput liquid chromatography-mass spectrometry (LC-MS). Univariate and multivariate analyses were subsequently performed to visualize the separation trend between two groups and to screen for differential feature ions. Ions were annotated using LipidSearch 4.2 and their enriched pathways were detected on LIPEA. Receiver operating characteristic (ROC) curves were used for analysing each annotated lipid's diagnostic value. Survival analyses were performed to investigate each lipid's prognostic value.

RESULTS

In supervised partial least squares discriminant analysis (PLS-DA), clear separation between MM and HC groups was observed. A total of 34 differential lipids were annotated, among which 5 upregulated and 29 downregulated. Levels of plasma triacylglycerols (TGs) were higher in smoking versus non-smoking patients, and lower in female versus male patients. The top six lipids possessing highest diagnostic value included two phosphatidylethanolamines (PEs), two phosphatidylcholines (PCs) and two ceramides. Moreover, elevated circulating TG levels were associated with poorer survival, whereas increased monohexosylceramide (Hex1Cer) might be beneficial.

CONCLUSIONS

Our study revealed differentially expressed lipid patterns in MM compared to HC. PC, PE, and ceramides showed outstanding diagnostic performance, while TG and Hex1Cer exhibited significant prognostic values. Nevertheless, more studies should verify these trends as well as further investigating on underlying mechanisms.