Srivastava M, Garg M. Artificial intelligence-driven decoding of trajectories of cancer cell plasticity: A paradigm shift in oncological assessment and treatment. Artif Intell Cancer 2026; 7(1): 122429 [DOI: 10.35713/aic.122429]
Corresponding Author of This Article
Minal Garg, Full Professor, Department of Biochemistry, University of Lucknow, University Road, Lucknow 226007, Uttar Pradesh, India. garg_minal@lkouniv.ac.in
Research Domain of This Article
Oncology
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review-article
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Srivastava M, Garg M. Artificial intelligence-driven decoding of trajectories of cancer cell plasticity: A paradigm shift in oncological assessment and treatment. Artif Intell Cancer 2026; 7(1): 122429 [DOI: 10.35713/aic.122429]
Artificial intelligence-driven decoding of trajectories of cancer cell plasticity: A paradigm shift in oncological assessment and treatment
Mansi Srivastava, Minal Garg
Mansi Srivastava, Minal Garg, Department of Biochemistry, University of Lucknow, Lucknow 226007, Uttar Pradesh, India
Author contributions: Srivastava M was responsible for material preparation, data collection, and manuscript first drafting; Garg M undertook study conception, experimental supervision, resource provision, and final manuscript revision; All authors approved the submitted version of the manuscript.
AI contribution statement: The authors take full responsibility and accountability for all content of this manuscript. AI tools were not used for language refinement, to generate original scientific data, perform independent scientific analyses, or draw scientific conclusions. Figures 1, 2, and 3 were prepared on power point and Canva.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Corresponding author: Minal Garg, Full Professor, Department of Biochemistry, University of Lucknow, University Road, Lucknow 226007, Uttar Pradesh, India. garg_minal@lkouniv.ac.in
Received: April 20, 2026 Revised: July 2, 2026 Accepted: August 14, 2026 Published online: September 8, 2026 Processing time: 137 Days and 13.4 Hours
Abstract
Cellular plasticity characterized by changes in the fate and identity of cells, substantially fuels the process of tumorigenesis by enhancing growth, metastases, and resistance to therapy under selective pressures. PubMed, Scopus, and Web of Science databases were comprehensively searched for the full papers published during 2013-2026 using the keywords such as artificial intelligence (AI), cellular plasticity, genomic, (epi)genomic, transcriptomic factors, oncological assessment, and therapies. The present mini-review discusses the applications of AI-driven deep learning and machine learning models integrated with advanced next-generation sequencing and single-cell RNA analysis in elucidating the changes in the trajectories of plasticity in tumor ecosystem. It facilitates the deciphering of the molecular landscape associated with normal, metaplastic, and pre-malignant tissues in animal models and human clinical specimens. This would help in establishing new predictive signatures for risk stratification of tumors and predicting the response to drugs. AI plays an important role in drug development, boosts three-dimensional structure of protein based drug discovery, analyzes the pharmacokinetic properties of drugs, and widens the scope of repurposed drugs. Recent trends foresee its application in providing tailored diagnosis, risk assessments, accelerating drug discovery, and individualizing treatment regimens.
Core Tip: Dynamic switching between cellular states generates tumoral heterogeneity as explained by stochastic and hierarchy models, and is driven by genetic and epigenetic alterations in a tumor ecosystem. Recent advancements in the applications of artificial intelligence-driven deep learning and machine learning models integrated with next-generation sequencing and single-cell RNA analysis lead to the elucidation of the changes in the trajectories of cellular plasticity. artificial intelligence-powered approaches decipher the (epi)genomic and transcriptomic landscape associated with normal, metaplastic, and pre-malignant tissues and thus help in establishing the new predictive signatures for tailored diagnosis, risk assessments, accelerating drug discovery, and individualizing treatment regimens.
