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Personalized medicine in oncology
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Shahram Nemati   |
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Abstract: (132 Views) |
Cancer is one of the leading causes of mortality worldwide, and the incidence of new cases continues to rise. This disease results from uncontrolled cellular proliferation, a process rooted in stable genomic alterations. Such mutations can drive extensive and impactful changes across multiple levels, including proteins, metabolites, transcripts, and signaling pathways. Since multiple factors contribute to the onset of cancer, the disease manifests in diverse forms. In other words, cancer is not a single disease, and therefore a uniform therapeutic approach cannot be applied to all types. In this context, personalized medicine has emerged as a novel strategy. Within this approach, each tumor mass is regarded as a unique entity, and by analyzing the molecular profile of tumor cells, a specific and tailored treatment is designed for that tumor. According to evolutionary theory, tumor cells are constantly competing and even cooperating with other cellular groups within a tumor mass. Consequently, their molecular markers are continuously changing. Thus, effective targeted therapy requires precise knowledge of the molecular mechanisms involved in the initiation and progression of the disease. Thanks to advances in modern technologies such as next-generation sequencing, mass spectrometry, artificial intelligence, and mathematical modeling for predicting drug effects, achieving a complete cure for cancer may not be out of reach. Nevertheless, serious challenges remain, including limitations in monitoring molecular changes during the evolutionary process of tumor masses. In this article, we first examine cancer within the framework of systems biology. We then highlight Darwin’s evolutionary theory and its role in the dynamics of tumor masses, emphasizing the necessity of adopting a dynamic monitoring approach based on molecular markers in personalized cancer therapy.
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| Keywords: Personalized medicine, Systems biology, Mathematical modeling, Evolutionary theory in tumors |
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Full-Text [PDF 674 kb]
(85 Downloads)
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Type of Study: Review |
Subject:
ژنتیک
Received: 2026/04/13 | Accepted: 2026/04/13 | Published: 2026/04/13
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