Cutting-Edge Discoveries in Cancer Biology and Therapeutic Development
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141-152
Digital Object Identifier:
10.58578/ajbmbr.v2i2.5306
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Authors:Muhammad Akram1, Muhammad Abdullah2, Momina Iftikhar3, Isaac John Umaru4
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
Growth in precision medicine, immunotherapy, genetics, and new therapeutic approaches is driving the rapid progression of cancer science and drug discovery. Greater tailor-made treatments attacking specific genetic abnormalities and immune systems have become an alternative to the one-size-fits-all approach, bringing renewed hope to patients for improved outcomes and fewer adverse effects. The most critical growth impacting the face of cancer research and drug discovery is summarized in this brief. Next-generation sequencing (NGS) and other methods enable complete genomic profiling, which identifies actionable mutations and guides targeted therapy. Stepping away from blanket chemotherapy, scientists can develop more effective and less toxic drugs by understanding the specific mutations that create each patient's cancer. By blocking the brakes of the immune system, these drugs allow T cells to better fight cancer cells. However, research is ongoing to maximize these treatments' efficacy and safety, particularly in the case of solid tumors. Targeting some of the TME components, such as the extracellular matrix or TME-dwelling immune cells, could provide new treatment strategies. Huge amounts of genetic information are being evaluated, potential medicine candidates are being identified, and patient outcomes are being forecast by AI algorithms. In addition, machine learning algorithms are being employed to improve patient stratification, optimize clinical trial design, and personalize treatment regimens using real-time data. Combination Treatments: Combination therapies employing multiple mechanisms of action are gaining increasing popularity as a result of the flexibility and complexity of cancer. For maximizing efficacy and overcoming resistance, researchers are incorporating immunotherapy with targeted therapy, chemotherapy, radiation therapy, and other modalities. The goal is to keep tumor cells from evading treatment by attacking cancer from a variety of angles. These combinations are currently under investigation in clinical trials, which have had promising results, particularly in cancers such as breast, lung, and melanoma.
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References
Foulon, V., Schöffski, P., & Wolter, P. (2011). Patient adherence to oral anticancer drugs: An emerging issue in modern oncology. Acta Clinica Belgica, 66(2), 85-96.
Gaj, T., Sirk, S. J., Shui, S. L., & Liu, J. (2016). Genome-editing technologies: Principles and applications. Cold Spring Harbor Perspectives in Biology, 8(12), a023754.
Ignatiadis, M., Sledge, G. W., & Jeffrey, S. S. (2021). Liquid biopsy enters the clinic—implementation issues and future challenges. Nature Reviews Clinical Oncology, 18(5), 297-312.
Little, S. J., Holte, S., Routy, J.-P., Daar, E. S., Markowitz, M., Collier, A. C., Koup, R. A., Mellors, J. W., Connick, E., Conway, B., & Kilby, M. (2002). Antiretroviral-drug resistance among patients recently infected with HIV. New England Journal of Medicine, 347(6), 385-394.
Longley, D. B., & Johnston, P. G. (2005). Molecular mechanisms of drug resistance. The Journal of Pathology, 205(2), 275-292.
Martinez-Outschoorn, U. E., Peiris-Pagés, M., Pestell, R. G., Sotgia, F., & Lisanti, M. P. (2017). Cancer metabolism: A therapeutic perspective. Nature Reviews Clinical Oncology, 14(1), 11-31.
Mirnezami, R., Nicholson, J., & Darzi, A. (2012). Preparing for precision medicine. New England Journal of Medicine, 366(6), 489-491.
Compton, C., & Compton, C. (2020). Understanding the hallmark characteristics of cancer. In Cancer: The enemy from within: A comprehensive textbook of cancer's causes, complexities and consequences (pp. 49-81). Springer.