Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

https://www.mdpi.com/1424-8220/19/17/3722/pdf

Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Clinical potential of mass spectrometry-based proteogenomics.

Risk-Based lung cancer screening: A systematic review.

Immunoaffinity-mass spectrometry (IA-MS) is an emerging analytical genre with several advantages for profiling and determination of protein biomarkers. Because IA-MS combines affinity capture, analogous to ligand binding assays (LBAs), with mass spectrometry (MS) detection, this platform is often described using the term hybrid methods. The purpose of this report is to provide an overview of the principles of IA-MS and to demonstrate, through application, the unique power and potential of this technology. By combining target immunoaffinity enrichment with the use of stable isotope-labeled internal standards and MS detection, IA-MS achieves high sensitivity while providing unparalleled specificity for the quantification of protein biomarkers in fluids and tissues. In recent years, significant uptake of IA-MS has occurred in the pharmaceutical industry, particularly in the early stages of clinical development, enabling biomarker measurement previously considered unattainable. By comparison, IA-MS adoption by CLIA laboratories has occurred more slowly. Current barriers to IA-MS use and opportunities for expanded adoption are discussed. The path forward involves identifying applications for which IA-MS is the best option compared with LBA or MS technologies alone. IA-MS will continue to benefit from advances in reagent generation, more sensitive and higher throughput MS technologies, and continued growth in use by the broader analytical community. Collectively, the pursuit of these opportunities will secure expanded long-term use of IA-MS for clinical applications.

/pdf/protein-biomarker-quantification-by-immunoaffinity-liquid-31tsxu0b57.pdf

Protein Biomarker Quantification by Immunoaffinity Liquid Chromatography-Tandem Mass Spectrometry: Current State and Future Vision.

Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

The World Health Organization’s (WHO) 2018 blueprint of priority diseases, based upon their potential to cause epidemics and public health emergencies included two respiratory illnesses, severe acute respiratory syndrome (SARS‐CoV) and middle east respiratory syndrome (MERS‐CoV), both caused by members of the Coronaviridae family (BioDefense, 2018). Now three years later, we are amid an ongoing worldwide pandemic caused by another Coronavirus, SARS‐Cov‐2. This virus has rapidly spread across the globe causing over 5.4 million deaths throughout the world. Symptoms of the viral infection include shortness of breath, fever, fatigue, muscle and body aches, headaches and most importantly acute lung injury (ALI) caused by dysregulation of pulmonary RAS (Renin‐Angiotensin System). RAS, the main network controlling blood pressure is regulated by ACE (Angiotensin Converting Enzyme) and its homolog ACE2. It is now known that the SARS‐CoV‐2, uses ACE2 as the cell receptor to enter and infiltrate the host cell. While ACE promotes atrophy, fibrosis, inflammation, oxidants, and vasoconstriction, ACE2 through its metabolism of angiotensin I, restricts excessive atrophy, fibrosis and causes vasodilation. Furthermore, ACE2 with its anti‐inflammatory and antioxidant role protects lung tissues and therefore a downregulation of ACE2 by SARS‐Cov‐2 infection can result in development of acute respiratory distress syndrome (ARDS) (Imai, Kuba, & Penninger, 2008). The Spike (S) protein of SARS‐Cov‐2 binds to ACE2, and recent studies have identified several residues in Exon 1 of ACE‐2, that mediate this binding (Rehman & Tabish, 2020). Our study aims to sequence ACE2 Exon 1 from Bos taurus and then compare it to human ACE2 Exon 1 using a variety of bioinformatics tools to understand and possibly identify polymorphisms that could determine species susceptibility to Covid‐19 infection. This investigation began with genomic DNA extraction from calf thymus. The genomic DNA was then analyzed using spectrophotometry (Biophotometer and Nanodrop) and gel electrophoresis. Thereafter, ACE2 target primers were designed using Primer BLAST and Primer Quest. We are now getting ready to run PCR to amplify our ACE2 Exon 1 target from Bos taurus, and after purifying the PCR products, we will sequence the ACE2 Exon 1 using our SeqStudio genetic analyzer. The sequence will then be analyzed and compared to its human counterpart using a variety of bioinformatics tools to gain a better understanding of polymorphisms that might influence SARS‐Cov‐2’s initial interaction with its host as well as pathogenesis of COVID‐19 in general.

L. Jacques

Papers

Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

Sequencing ACE2 in Bos Taurus: A Comparison of Bovine and Human ACE 2’s interaction with SARS‐CoV‐2