In recent years, the intersection of artificial intelligence (AI) and precision medicine has sparked remarkable advancements, particularly in the domain of cancer treatment. Predictive medicine, empowered by computational tools, enables clinicians to forecast tumor progression and therapeutic responses with unprecedented specificity. However, a critical dialogue is emerging among researchers challenging the predominant reliance on AI alone, advocating instead for an integrated approach that harmonizes AI methodologies with classical mathematical modeling to propel predictive immunotherapy forward.
Elana Fertig, PhD, Director of the Institute for Genome Sciences (IGS) and Professor of Medicine at the University of Maryland School of Medicine (UMSOM), alongside colleagues including Daniel Bergman, PhD, posits that while AI excels at pattern recognition and data-driven predictions, it lacks the mechanistic interpretability essential for unraveling the biological underpinnings of cancer dynamics. In their April 14 commentary published in Nature Biotechnology, Fertig and Bergman emphasize that mathematical models imbue computational endeavors with biological context, explicitly incorporating known cellular behaviors and molecular interactions, thereby providing a more transparent framework for hypothesis testing and therapeutic design.
The core components that underpin effective computational models in oncology comprise comprehensive datasets, precise mathematical formulations, and sophisticated software implementations. These elements synergize to simulate cancer cell behavior, immune response, and drug interactions, allowing researchers to generate virtual experiments that can guide real-world clinical decision-making. This hybrid modeling approach is particularly critical in scenarios where empirical data are limited, such as emerging immunotherapies, where AI’s dependence on vast training data sets may lead to overfitting or bias.
Dr. Bergman elaborates on the distinctive advantages of mechanistic models by illustrating how virtual cancer cells and healthy tissue can be computationally instantiated to mimic their dynamic interactions within a tumor microenvironment under various treatment regimens. Such models afford unprecedented granularity, capturing the complexity of tumor evolution and immune evasion mechanisms that remain elusive to AI algorithms, which primarily detect correlations without explicating causality.
Complementing this perspective, a second commentary published on April 15 in Cell Reports Medicine by Dr. Fertig and colleagues Dmitrijs Lvovs, Anup Mahurkar, and Owen White explores the ethical imperatives and practical challenges inherent in health data sharing. They advocate for rigorous standards in data governance that balance transparency and reproducibility with patient confidentiality. This includes obtaining detailed informed consent, harmonizing heterogeneous datasets, and employing standardized, vetted analytical pipelines, all of which ensure that computational results are both robust and broadly accessible.
The issue of reproducibility in biomedical data science is particularly pressing. Surveys reveal that a significant proportion of scientific experiments prove irreproducible, undermining the trustworthiness of findings and the pace of innovation. Lvovs underscores that reproducibility is not merely a procedural nicety but foundational to validating models that inform clinical interventions. Open science practices—sharing code, data, and protocols—facilitate independent verification and refinement, vital steps towards reliable predictive oncology.
A salient concern in this context is protecting patient privacy while enabling data openness. Genomic datasets, when coupled with personal health information, risk patient re-identification, posing ethical and legal challenges. The Institute for Genome Sciences advocates employing anonymization techniques alongside secure data platforms that permit controlled access. Such frameworks serve dual goals: empowering broad research collaboration and safeguarding individual rights.
Integrating AI with mechanistic mathematical modeling, supported by transparent and ethical data sharing, constitutes a comprehensive strategy for advancing predictive immunotherapy. This approach acknowledges AI’s remarkable capacity for pattern discovery but tempers it with the rigor and interpretability of biologically grounded models. As therapeutic options expand and data proliferate, such hybrid methodologies promise to enhance treatment specificity, reduce bias, and accelerate translational breakthroughs.
UMSOM’s Institute for Genome Sciences, at the forefront of genomic technology and systems biology, exemplifies this multidisciplinary convergence. Their research spans diverse health domains, from vulnerable neonatal populations to cancer genomics, underpinned by a cutting-edge genomics core that services global collaborators. The collaborative environment fosters the kind of integrative science essential for addressing the complexities of cancer and other multifactorial diseases.
This vision intersects with broader trends in biomedical research, where computational immunotherapy emerges as a transformative field. Leveraging virtual cellular models to predict immune responses to tumors could enable personalized treatment regimens that adapt in real time, minimizing adverse effects while maximizing efficacy. Incorporating data from diverse patient populations strengthens model generalizability, addressing historical gaps in clinical trial demographics and fostering health equity.
Ultimately, the call is for a paradigm shift where open, reproducible science coupled with sophisticated, biologically faithful modeling reshapes cancer care. Ethical stewardship of data, combined with innovations in AI and computational biology, will underpin the next generation of precision oncology, promising profound impacts on patient outcomes worldwide. As Professor Fertig notes, the future belongs not to AI in isolation but to an orchestrated alliance of computational intelligence, mathematical insight, and ethical responsibility.
Subject of Research: People
Article Title: Virtual cells for predictive immunotherapy
News Publication Date: April 15, 2025
Web References:
Nature Biotechnology article: https://www.nature.com/articles/s41587-025-02583-2
Cell Reports Medicine article: https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(25)00153-3
References:
Fertig, E., Bergman, D. et al. “Virtual cells for predictive immunotherapy.” Nature Biotechnology, April 14, 2025. DOI: 10.1038/s41587-025-02583-2
Fertig, E., Lvovs, D., Mahurkar, A., White, O. “Ethical and reproducible data sharing in computational oncology.” Cell Reports Medicine, April 15, 2025.
Image Credits: University of Maryland School of Medicine
Keywords:
Generative AI, Bioinformatics, Applied research, Research ethics, Scientific method, Cancer, Cancer genomics, Cancer research, Computer science, Genetic algorithms
Tags: AI in predictive medicinebiological context in computational modelscancer treatment technologieschallenges in AI reliance for health outcomescomprehensive datasets for oncologyintegrating AI with mathematical modelingmathematical formulations in cancer researchmechanistic interpretability in AIPrecision Medicine Advancementspredictive immunotherapy strategiestherapeutic design in immunotherapytumor progression forecasting
