Tumor Hypoxia Diagnosis using Deep CNN Learning strategy a theranostic pharmacogenomic approach

Abstract

Tumor hypoxia results in most of the anticancer drugs becoming ineffective. However, due to lack of proper signaling in the hypoxic micro environment, the condition cannot be detected in advance, leading into unnecessary delay in the diagnosis and treatment. The main objective of the work is to identify the hypoxia prone SNPs to help the patients to predict their possibility of hypoxia formation and to Design and develop a machine helping in diagnosing the hypoxia from pathological images using deep learning with 'convolution neural network. The genetic signatures corresponding to 'tumor hypoxia development' have been identified by pharmacogenomic method, comprising of genomics, epigenomics, metagenomics and environmental genomics. All the common hypoxia related mutations have been included in the study. The formation of the hypoxia condition has to be carefully identified and monitored during the process of treatment to ensure that the right drug is being administered. In the present manuscript, a novel method of elucidating the condition using deep convolution network from simple pathological image has been suggested. The efficiency of the suggested machine is found to be 92.8% making it as a potential device for prediction of hypoxia mutation and thereby helping us to monitor the hypoxic conditions effectively. Thus, the hypoxia prone SNPs corresponding to common mutations have been identified. The patients having the hypoxia prone SNPs are advised to guard against hypoxia formation with the help of diagnostic tests using the machine. The machine helps to warn the patients against the respective mutations from simple pathological image of the tumor cells.