A novel, accurate, and non-invasive liquid biopsy test to measure cellular immune responses as a tool to diagnose early-stage lung cancer: a clinical trials study.

INTRODUCTION: Lung cancer remains the leading cause of death from cancer, worldwide. Developing early detection diagnostic methods, especially non-invasive methods, is a critical component to raising the overall survival rate and prognosis for lung cancer. The purpose of this study is to evaluate two protocols of a novel in vitro cellular immune response test to detect lung cancer. The test specifically quantifies the glycolysis metabolism pathway, which is a biomarker for the activation level of immune cells. It summarizes the results of two clinical trials, where each deploys a different protocol's version of this test for the detection of lung cancer. In the later clinical trial, an improved test protocol is applied. METHOD: The test platform is based on changes in the metabolic pathways of the immune cells following their activation by antigenic stimuli associated with Lung cancer. Peripheral Blood Mononuclear Cells are loaded on a multiwell plate together with various lung tumor associated antigens and a fluorescent probe that exhibits a pH-dependent absorption shift. The acidification process in the extracellular fluid is monitored by a commercial fluorescence plate reader device in continuous reading for 3 h at 37 °C to document the fluorescent signal received from each well. RESULTS: In the later clinical trial, an improved test protocol was applied and resulted in increased test accuracy. Specificity of the test increased to 94.0% and test sensitivity increased to 97.3% in lung cancer stage I, by using the improved protocol. CONCLUSION: The improved protocol of the novel cellular immune metabolic response based test detects stage I and stage II of lung cancer with high specificity and sensitivity, with low material costs and fast results.

Novel non-invasive early detection of lung cancer using liquid immunobiopsy metabolic activity profiles.

Lung cancer is the leading cause of cancer death worldwide. Survival is largely dependent on the stage of diagnosis: the localized disease has a 5-year survival greater than 55%, whereas, for spread tumors, this rate is only 4%. Therefore, the early detection of lung cancer is key for improving prognosis. In this study, we present an innovative, non-invasive, cancer detection approach based on measurements of the metabolic activity profiles of immune system cells. For each Liquid ImmunoBiopsy test, a 384 multi-well plate is loaded with freshly separated PBMCs, and each well contains 1 of the 16 selected stimulants in several increasing concentrations. The extracellular acidity is measured in both air-open and hermetically-sealed states, using a commercial fluorescence plate reader, for approximately 1.5 h. Both states enable the measurement of real-time accumulation of 'soluble' versus 'volatile' metabolic products, thereby differentiating between oxidative phosphorylation and aerobic glycolysis. The metabolic activity profiles are analyzed for cancer diagnosis by machine-learning tools. We present a diagnostic accuracy study, using a multivariable prediction model to differentiate between lung cancer and control blood samples. The model was developed and tested using a cohort of 200 subjects (100 lung cancer and 100 control subjects), yielding 91% sensitivity and 80% specificity in a 20-fold cross-validation. Our results clearly indicate that the proposed clinical model is suitable for non-invasive early lung cancer diagnosis, and is indifferent to lung cancer stage and histological type.

MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes--now with human data.

Genome sequencing and transcriptomic profiling are two widely used approaches for the identification of human disease pathways. However, each approach typically provides a limited view of disease pathways: Genome sequencing can identify disease-related mutations but rarely reveals their mode-of-action, while transcriptomic assays do not reveal the series of events that lead to the transcriptomic change. ResponseNet is an integrative network-optimization approach that we developed to fill these gaps by highlighting major signaling and regulatory molecular interaction paths that connect disease-related mutations and genes. The ResponseNet web-server provides a user-friendly interface to ResponseNet. Specifically, users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction subnetwork connecting them, that is biased toward regulatory and signaling pathways. ResponseNet2.0 enhances the functionality of the ResponseNet web-server in two important ways. First, it supports analysis of human data by offering a human interactome composed of proteins, genes and micro-RNAs. Second, it offers a new informative view of the output, including a randomization analysis, to help users assess the biological relevance of the output subnetwork. ResponseNet2.0 is available at http://netbio.bgu.ac.il/respnet .