Version 1
: Received: 13 August 2021 / Approved: 16 August 2021 / Online: 16 August 2021 (11:15:11 CEST)
How to cite:
Dhanjal, J.K.; Kalra, R.S. Protein Structure Readouts of Cancer Drivers for Precision Medicine. Preprints2021, 2021080321. https://doi.org/10.20944/preprints202108.0321.v1
Dhanjal, J.K.; Kalra, R.S. Protein Structure Readouts of Cancer Drivers for Precision Medicine. Preprints 2021, 2021080321. https://doi.org/10.20944/preprints202108.0321.v1
Dhanjal, J.K.; Kalra, R.S. Protein Structure Readouts of Cancer Drivers for Precision Medicine. Preprints2021, 2021080321. https://doi.org/10.20944/preprints202108.0321.v1
APA Style
Dhanjal, J.K., & Kalra, R.S. (2021). Protein Structure Readouts of Cancer Drivers for Precision Medicine. Preprints. https://doi.org/10.20944/preprints202108.0321.v1
Chicago/Turabian Style
Dhanjal, J.K. and Rajkumar Singh Kalra. 2021 "Protein Structure Readouts of Cancer Drivers for Precision Medicine" Preprints. https://doi.org/10.20944/preprints202108.0321.v1
Abstract
Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:
Keywords
cancer; mutations; cancer drivers; precision medicine; protein structure; personalized medicine; cancer therapies; genetic signatures
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
