Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection

Multiplex plasma protein assays as a diagnostic tool for lung cancer

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Conducting polymer composite-based biosensing materials for the diagnosis of lung cancer: A review

The development of a simple and fast cancer detection method is crucial since early diagnosis is a key factor in increasing survival rates for lung cancer patients. Among several diagnosis methods, the electrochemical sensor is the most promising one due to its outstanding performance, portability, real-time analysis, robustness, amenability, and cost-effectiveness. Conducting polymer (CP) composites have been frequently used to fabricate a robust sensor device, owing to their excellent physical and electrochemical properties as well as biocompatibility with nontoxic effects on the biological system. This review brings up a brief overview of the importance of electrochemical biosensors for the early detection of lung cancer, with a detailed discussion on the design and development of CP composite materials for biosensor applications. The review covers the electrochemical sensing of numerous lung cancer markers employing composite electrodes based on the conducting polyterthiophene, poly(3,4-ethylenedioxythiophene), polyaniline, polypyrrole, molecularly imprinted polymers, and others. In addition, a hybrid of the electrochemical biosensors and other techniques was highlighted. The outlook was also briefly discussed for the development of CP composite-based electrochemical biosensors for POC diagnostic devices.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BACKGROUND

For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.

METHODS

TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.

RESULTS

Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.

CONCLUSIONS

Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

Multiplexed electrochemical assays for clinical applications

Rapid, accurate diagnoses are central to future efficient healthcare to identify diseases at early stages, avoid unnecessary treatment, and improve outcomes. Electrochemical techniques have been applied in many ways to support clinical applications by enabling the analysis of relevant disease biomarkers in user-friendly, sensitive, low-cost assays. Electrochemistry offers a launchpad for multiplexed biomarker assays that offer more accurate and precise diagnostics compared to single biomarker assays. In this short review, we underpin the importance of multiplexed analyses and provide a universal overview of current electrochemical assay strategies for multiple biomarkers. We highlight relevant examples of electrochemical methods that successfully quantify important disease biomarkers. Finally, we offer a future outlook on possible strategies that can be employed to increase throughput, sensitivity, and specificity of multiplexed electrochemical assays.

Quantification of 782 Plasma Peptides by Multiplexed Targeted Proteomics

Blood analysis is one of the foundations of clinical diagnostics. In recent years, the analysis of proteins in blood samples by mass spectrometry has taken a jump forward in terms of sensitivity and the number of identified proteins. The recent development of parallel reaction monitoring with parallel accumulation and serial fragmentation (prm-PASEF) combines ion mobility as an additional separation dimension. This increases the proteome coverage while allowing the use of shorter chromatographic gradients. To demonstrate the method's full potential, we used an isotope-labeled synthetic peptide mix of 782 peptides, derived from 579 plasma proteins, spiked into blood plasma samples with a prm-PASEF measurement allowing the quantification of 565 plasma proteins by targeted proteomics. As a less time-consuming alternative to the prm-PASEF method, we describe guided data independent acquisition (dia)-PASEF (g-dia-PASEF) and compare its application to prm-PASEF for measuring blood plasma. To demonstrate both methods' performance in clinical samples, 20 patient plasma samples from a colorectal cancer (CRC) cohort were analyzed. The analysis identified 14 differentially regulated proteins between the CRC patient and control individual plasma samples. This shows the technique's potential for the rapid and unbiased screening of blood proteins, abolishing the need for the preselection of potential biomarker proteins.

Biomarkers in Lung Cancer Screening: a Narrative Review

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals

As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath using electronic noses (E-noses), which have become emerging tools for analyzing breath because of their potential and promising technology for diagnosing. However, the signal processing of the E-Nose sensor becomes vital in exposing information about the subject condition, which most researchers strive to accomplish. We proposed a Convolutional Neural Network (CNN) architecture to classify COPD in smokers and non-smokers, healthy subjects, and smokers from E-Nose signals to contribute to this field. Two models were constructed following E-Nose signal processing state-of-the-arts. One was by combined feature extraction and classifier, and the second was by CNN, which directly processed the raw signal. In addition, various feature extraction and classifier (Machine Learning and CNN) used in prior research were investigated. Using 3K and 5K Fold cross-validation results demonstrated that our proposed models outperformed in Kernel Principal Component Analysis (KPCA) with Fx-ConvNet and Pure-ConvNet. They all reached maximum F1-Score with zero standard deviation values indicating a consistent result. Further experiments also showed that KPCA contributed to the increasing performance of some classifiers with average F1-Score 0.933 and 0.068 as standard deviation values.

Proteomic Analysis of Lung Cancer Types—A Pilot Study

Lung cancer is the leading cause of tumor-related mortality, therefore significant effort is directed towards understanding molecular alterations occurring at the origin of the disease to improve current treatment options. The aim of our pilot-scale study was to carry out a detailed proteomic analysis of formalin-fixed paraffin-embedded tissue sections from patients with small cell or non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). Tissue surface digestion was performed on relatively small cancerous and tumor-adjacent normal regions and differentially expressed proteins were identified using label-free quantitative mass spectrometry and subsequent statistical analysis. Principal component analysis clearly distinguished cancerous and cancer adjacent normal samples, while the four lung cancer types investigated had distinct molecular profiles and gene set enrichment analysis revealed specific dysregulated biological processes as well. Furthermore, proteins with altered expression unique to a specific lung cancer type were identified and could be the targets of future studies.

The Parallel Reaction Monitoring-Parallel Accumulation-Serial Fragmentation (prm-PASEF) Approach for Multiplexed Absolute Quantitation of Proteins in Human Plasma

Mass spectrometry (MS)-based quantitative proteomic methods have become some of the major tools for protein biomarker discovery and validation. The recently developed parallel reaction monitoring-parallel accumulation-serial fragmentation (prm-PASEF) approach on a Bruker timsTOF Pro mass spectrometer allows the addition of ion mobility as a new dimension to LC-MS-based proteomics and increases proteome coverage at a reduced analysis time. In this study, a prm-PASEF approach was used for the multiplexed absolute quantitation of proteins in human plasma using isotope-labeled peptide standards for 125 plasma proteins, over a broad (10⁴-10⁶) dynamic range. Optimization of LC and MS parameters, such as accumulation time and collision energy, resulted in improved sensitivity for more than half of the targets (73 out of 125 peptides) by increasing the signal-to-noise ratio by a factor of up to 10. Overall, 41 peptides showed up to a 2-fold increase in sensitivity, 25 peptides showed up to a 5-fold increase in sensitivity, and 7 peptides showed up to a 10-fold increase in sensitivity. Implementation of the prm-PASEF method allowed absolute protein quantitation (down to 1.13 fmol) in human plasma samples. A comparison of the concentration values of plasma proteins determined by MRM on a QTRAP instrument and by prm-PASEF on a timsTOF Pro revealed an excellent correlation (R² = 0.97) with a slope of close to 1 (0.99), demonstrating that prm-PASEF is well suited for "absolute" quantitative proteomics.

Liquid biopsy of extracellular vesicle biomarkers for prostate cancer personalized treatment decision

Liquid biopsy of tumor-derived extracellular vesicles (EVs) has great potential as a biomarker source for prostate cancer (CaP) early diagnosis and predicting the stages of cancer. The contents of EVs play an important role in intercellular communication and have specific expression in blood and urine samples from CaP patients. Powered by high-throughput, next-generation sequencing and proteomic technologies, novel EV biomarkers are easily detected in a non-invasive manner in different stages of CaP patients. These identified potential biomarkers can be further validated with a large sample size, machine learning model, and other different methods to improve the sensitivity and specificity of CaP diagnosis. The EV-based liquid biopsy is a novel and less-invasive alternative to surgical biopsies which would enable clinicians to potentially discover a whole picture of tumor through a simple blood or urine sample. In summary, this approach holds promise for developing personalized medicine to guide treatment decisions precisely for CaP patients.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.