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Oncologist Theranostic Guideline by Artificial Intelligence:‘Oncoproteomics’ By 3D MALDI with MRI-PET Imaging in Cancer, Review of Clinical Trials on Docetaxel Combination Treatment in Cancer

Rakesh Sharma

Abstract


Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. The proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip along with imaging tumor proteins to improve understanding of cancer pathogenesis, to develop new tumor imaging biomarkers for diagnosis, early detection of tumor staging and chemosensitivity using proteomic portrait of samples. Oncoproteomics has the potential in ‘Oncologist clinical practice’, including cancer stage diagnosis and screening chemotherapy response based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Oncoproteomics is also applied to the discovery and artificial intelligence of new therapeutic targets and to the study of drug effects. Over years of our lab efforts on texol chemosensitivity theranostic imaging biomarker developments, showed progressive apoptosis specific tumor proteins in different stages as visible by MALDI-MRI-PET and visible texol antineoplastic action over tumors. Our study of oncoproteomics provided better understanding of intracellular sodium correlation with apoptosis during neoplasiadevelopment and its treatment by texol. In this paper, the discovery of cancer imaging proteomic biomarkers, MALDI with MRI-PET fusion methods and combination therapies used in clinical trials with machine learning concepts in recent years are reviewed. The challenges ahead and perspectives of integrated MALDI with MRI-PET and oncoproteomics associated with cancer cell tumorigenesis biomarker development are addressed. The paper serves as an oncologist’s guideline on oncoproteomics based theranostic reference for biomarker researchers, cancer scientists working in proteomics research and image bioinformatics to oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.


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Abdelmoula, W.M., Lopez, B.GC., Randall, E.C. et al. Peak learning of mass spectrometry imaging data using artificial neural networks. Nat Commun 12, 5544 (2021). https://doi.org/10.1038/s41467-021-25744-8.

a. A.R.Sharma,R Kline: Flow Cytometry, MRI, PET and NMR Spectroscopy Methods of Non-Invasive Drug Monitoring in Prostate Tumor: Technical Note.16th IEEE Symposium on Computer-Based Medical Systems (CBMS'03) p. 263-267.

b. Sharma R., Katz J. Taxotere Chemosensitivity Evaluation in Rat Breast Tumor by Multimodal Imaging: Quantitative Measurement by Fusion of MRI, PET Imaging with MALDI and Histology. Recent Patents in Medical Imaging. 2011;1(2):152-164.

c. Sharma R, Katz JK. Taxotere Chemosensitivity Evaluation in Mice Prostate Tumor: Validation and Diagnostic Accuracy of Quantitative Measurement of Tumor Characteristics by MRI, PET, and Histology of Mice Tumor. Technology in Cancer Research and Treatment. 2008; (7)3:155-268.

d. Sharma R. New approaches of imaging MALDI, protein markers Part II: Prostate cancer drug targeting chemosensitivity biosensors. Cancer Therapy Oncol. Int Journal. 2021;20(1): 556027. Doi:10.19080/CTOIJ.2021.20.556027.

Schwamborn K, Caprioli RM. MALDI imaging mass spectrometry--painting molecular pictures. Mol Oncol. 2010 Dec;4(6):529-38.

Seeley, E.H., Caprioli, R.M. , 2008. Molecular imaging of proteins in tissues by mass spectrometry. Proc. Natl. Acad. Sci. U.S.A. 105, 18126–18131.

Hanselmann, M., Köthe, U., Kirchner, M., Renard, B.Y., Amstalden, E.R., Glunde, K., Heeren, R.M.A., Hamprecht, F.A., 2009. Toward digital staining using imaging mass spectrometry and random forests. J. Proteome Res. 8, 3558–3567.

Sinha, T.K., Khatib-Shahidi, S., Yankeelov, T.E., Mapara, K., Ehtesham, M., Cornett, D.S., Dawant, B.M. , Caprioli, R.M. , Gore, J.C., 2008. Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging. Nat. Methods. 5, 57–59.

Yanagisawa, K. , Shyr, Y. , Xu, B.J., Massion, P.P. , Larsen, P.H. , White, B.C. , Roberts, J.R. , Edgerton, M., Gonzalez, A. , Nadaf, S. , Moore, J.H. , Caprioli, R.M. , Carbone, D.P., 2003. Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet. 362, 433–439.

Rauser, S., Marquardt, C., Balluff, B., Deininger, S.O., Albers, C., Belau, E., Hartmer, R., Suckau, D. , Specht, K., Ebert, M.P., Schmitt, M., Aubele, M., Hofler, H., Walch, A., 2010. Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry. J. Proteome Res.

Mackay, A., Jones, C., Dexter, T., Silva, R.L., Bulmer, K., Jones, A., Simpson, P., Harris, R.A., Jat, P.S., Neville, A.M., Reis, L.F., Lakhani, S.R., O'Hare, M.J., 2003. C DNA microarray analysis of genes associated with ERBB2 (HER2/neu) over expression in human mammary luminal epithelial cells. Oncogene. 22, 2680–2688. Wilson et al., 2002.

Cazares, L.H., Troyer, D., Mendrinos, S., Lance, R.A., Nyalwidhe, J.O., Beydoun, H.A., Clements, M.A., Drake, R.R., Semmes, O.J., 2009. Imaging mass spectrometry of a specific fragment of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 2 discriminates cancer from uninvolved prostate tissue. Clin. Cancer Res. 15, 5541–5551.


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