New Cancer Detection Tool using Big Data Analytics developed

A specially designed scorecard to detect cancer is a new finding in the quest for diagnostics of cancer. Featured as a personalized one, the tool finds use with standard blood test for detection of cancer. Meanwhile, the finding is the effort of a team of two researchers at Department of Biomedical Engineering at the National University of Singapore. In addition, prediction of patient survivability and patient response to immunotherapy are the other features of the tool.

 Termed Tumor Matrisome (TMI), the scorecard is a panel of 29 selected genes. The genes are produced in the extracellular matrix of the human body. Anatomically, extracellular matrix behaves like a scaffolding. It provides structural and biochemical support to surrounding cells.

Appearance Consistency of Selected Genes Strengthens finding

 The selected 29 genes displayed consistency of appearance. The genes repeatedly appeared as a consistent factor among patients with non-small-cell lung cancer. The cancer type accounts for almost 85% of all lung cancers.

 The development and validation of TMI scorecard involved computer algorithms. The team employed big data and predictive analysis for more than 30,00 patient-derived biopsies. Following this, a comparison of TMI score of healthy individuals from public datasets and cancer patients carried out. The latter displayed a higher set of TMI scores. Therefore, this revealed testing of TMI signature is an index to determine if an individual has cancer.

 The study did not end here. The team examined 29-gene TMI for 11 major types of cancer. These are lung, prostate, stomach, ovary, bladder, pancreas, kidney, colon, breast, and melanoma. This revealed TMI scores distinguishes cancers from normal tissues, with each cancer type bearing a specific TMI signature.

 Following this, TMI score is now a measure for diagnosis of lung cancer with certainty. The index requires further validation for 10 other cancer types. Besides this, the score could find use to know response of patients for cancer treatments.

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