Development of a Serum Metabolome-Based Test for Early-Stage Detection of Multiple Cancers.

Background: Detection of cancer at the early stage currently offers the only viable strategy for reducing disease-related morbidity and mortality. Various approaches for multi-cancer early detection are being explored, which largely rely on capturing signals from circulating analytes shed by tumors into the blood. The fact that biomarker concentrations are limiting in the early stages of cancer, however, compromises the accuracy of these tests.

The High Sensitivity of the Multi-Cancer Detection Test ONCOVERYX-F Offers a Promising Platform for Ovarian Cancer Screening.

We evaluated the potential relevance of our multi-cancer detection test, OncoVeryx-F, for ovarian cancer screening. For this, we compared its accuracy with that of CA125-based screening. We demonstrate here that, in contrast to CA125-based detection, OncoVeryx-F detected ovarian cancer with very high sensitivity and specificity.

A non-invasive method for concurrent detection of multiple early-stage cancers in women.

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%.

Untargeted metabolomics reveals altered branch chain amino acids, glucose and fat metabolism contributing to coronary artery disease among Indian diabetic patients.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterised by increased blood glucose levels. Patients with T2DM have a high risk of developing atherosclerotic coronary artery disease (CAD). CAD with T2DM has a complex etiology and the understanding of the pathophysiology of coronary artery disease (CAD) in the presence of diabetes is poor.

A non-invasive method for concurrent detection of early-stage women-specific cancers.

We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across diverse age-groups and ethnicities. A two-step approach was employed wherein cancer-positive samples were first identified as a group. A second multi-class algorithm then helped to distinguish between the individual cancers of the group.

Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism.

Objective: Signal transduction not only initiates entry into the cell cycle, but also reprograms the cell's metabolism. To control abnormalities in cell proliferation, both the aspects should be taken care of, thus pleiotropic signaling molecules are considered as crucial modulators. Considering this, we investigated the role of AKT1 in central carbon metabolism.

Pathogenicity of Mycobacterium tuberculosis is expressed by regulating metabolic thresholds of the host macrophage.

The success of Mycobacterium tuberculosis as a pathogen derives from its facile adaptation to the intracellular milieu of human macrophages. To explore this process, we asked whether adaptation also required interference with the metabolic machinery of the host cell. Temporal profiling of the metabolic flux, in cells infected with differently virulent mycobacterial strains, confirmed that this was indeed the case.