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.
Tumor hypoxia, pharmacogenomics, Deep CNN, Theranostic
Sriraman, S. K., Aryasomayajula, B., & Torchilin, V. P. (2014). Barriers to drug delivery in solid tumors. Tissue barriers, 2(3), e29528.
Fleet, A. (2006). Radiobiology for the Radiologist: Eric J. Hall, Amato J. Giaccia, Lippincott Williams and Wilkins Publishing; ISBN 0-7817-4151-3; 656 pages; 2006; Hardback; £ 53. Journal of Radiotherapy in Practice, 5(4), 237-237.
Brown, J. M., & Wilson, W. R. (2004). Exploiting tumour hypoxia in cancer treatment. Nature Reviews Cancer, 4(6), 437.
Wilson, W. R., & Hay, M. P. (2011). Targeting hypoxia in cancer therapy. Nature Reviews Cancer, 11(6), 393.
Hima Vyshnavi, A.M., Anand, C.L., Deepak, O.M Namboori,
P. K. (2017). Evaluation of Colorectal Cancer (CRC) Epidemiology A Pharmacogenomic Approach. Journal of Young Pharmacists, 9(1), 36.
Iyer, P. M., Karthikeyan, S., Kumar, P. S., & Namboori, P K. (2017). Comprehensive strategy for the design of precision drugs and identification of genetic signature behind proneness of the disease—a pharmacogenomic approach. Functional & integrative genomics, 17(4), 375-385.
M Iyer, P., K Palayat, S., Shanmugam, K., & Namboori, K. (2016). Retrometabolic Approach for Designing Personalized Anti-Cancer Drug Molecules for Controlling Breast Cancer Resulted by BRCA1 Mutations. Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics), 14(1), 56-64.
Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9), 2352-2449.
Bakshi, A. (2017). Deep Learning Tutorial: Artificial Intelligence Using Deep Learning. Deep learning tutorial series.
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018). Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. Ebiomedicine, 27, 317-328.
Komura, D., & Ishikawa, S. (2018). Machine Learning Methods for Histopathological Image Analysis. Computational And Structural Biotechnology Journal, 16, 34-42. doi: 10.1016/j.csbj.2018.01.001
Qu, J., Hiruta, N., Terai, K., Nosato, H., Murakawa, M., & Sakanashi, H. (2018). Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks. Journal Of Healthcare Engineering, 2018, 1-13.
Namboori, P., Vineeth, K., Rohith, V., Hassan, I., Sekhar, L., Sekhar, A., & Nidheesh, M. (2011). The ApoE gene of Alzheimer's disease (AD). Functional & Integrative Genomics, 11(4), 519-522.
Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic acids research, 29(1), 308-311.
Ng, P. C., & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic acids research, 31(13), 3812-3814.
Adzhubei, I., Jordan, D., & Sunyaev, S. (2013). Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Current Protocols in Human Genetics, 76(1), 7.20.1-7.20.41. doi: 10.1002/0471142905.hg0720s76.
Ye, J., Ma, N., Madden, T. L., & Ostell, J. M. (2013). IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic acids research, 41(W1), W34-W40.
Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., King,
B. L., McMorran, R., ... & Mattingly, C. J. (2016). The comparative toxicogenomics database: update 2017. Nucleic acids research, 45(D1), D972-D978.
Bock, C., Walter, J., Paulsen, M., & Lengauer, T. (2007). CpG Island mapping by epigenome prediction. PLoS computational biology, 3(6), e110.
Dworkin, A. M., Huang, T. H. M., & Toland, A. E. (2009, June). Epigenetic alterations in the breast: Implications for breast cancer detection, prognosis and treatment. In Seminars in cancer biology (Vol. 19, No. 3, pp. 165- 171). Academic Press.
Thienpont, B., Steinbacher, J., Zhao, H., D’Anna, F., Kuchnio, A., Ploumakis, A., & Hermans, E. (2016). Tumour hypoxia causes DNA hypermethylation by reducing TET activity. Nature, 537(7618), 63.
The Human Protein Atlas. (2018). Retrieved from https://www.proteinatlas.org/
Banerjee, J., Mishra, N., & Dhas, Y. (2015). Metagenomics: A new horizon in cancer research. Meta gene, 5, 84-89.
Boffetta, P., & Nyberg, F. (2003). Contribution of environmental factors to cancer risk. British medical bulletin, 68(1), 71-94.