How to Personalize Cancer Treatment with AI in 2025

Personalized cancer treatment, also known as precision oncology, tailors therapies to an individual's unique genetic, molecular, and clinical profile rather than using a one-size-fits-all approach. By 2025, AI has become integral to this process, enabling faster analysis of vast datasets, more accurate predictions, and innovative treatment designs. AI tools help integrate genomics, imaging, multi-omics data, and patient history to optimize outcomes while minimizing side effects. Advances include AI-driven drug matching, custom protein design, and predictive models for immunotherapy and radiation. Here's a high-level guide to how this is achieved, based on current practices and research.

Step 1: Collect Comprehensive Patient Data

The foundation of personalization is gathering detailed, multimodal data from the patient. This typically includes:

• Genomic Sequencing: Analyzing the tumor's DNA and RNA to identify mutations, biomarkers, and gene expressions. Affordable sequencing technologies make this routine. 
• Imaging and Diagnostics: Using CT, PET scans, MRIs, or pathology slides to assess tumor size, location, and response patterns. 
• Additional Data Sources: Clinical notes, medical history, microbiome analysis (e.g., for oral cancers), and even lifestyle factors.

In 2025, AI streamlines data integration from electronic health records and biobanks, ensuring privacy through federated learning techniques.

Step 2: Apply AI for Data Analysis and Prediction

AI algorithms process the collected data to uncover patterns and predict treatment responses. Key methods include:

• Machine Learning and Deep Learning Models**: These analyze genetic profiles and drug molecular structures to forecast efficacy. For instance, models trained on thousands of cell-drug pairs can achieve 98% accuracy in predicting how a patient's DNA will respond to specific anti-cancer compounds. 
• Multimodal AI Integration**: Combining imaging, genomics, and clinical data to predict outcomes like immunotherapy success or radiation needs. AI can adjust radiation doses based on real-time patient data, improving efficacy. 
• Biomarker Discovery**: AI identifies novel biomarkers from multi-omics data, enabling early detection and targeted therapies. This includes paradoxical patterns in datasets that humans might miss.

Tools like these reduce analysis time from months to days, making personalization scalable.

Step 3: Generate Personalized Treatment Recommendations

Based on AI insights, clinicians select or design tailored interventions:

• Drug Matching and Targeted Therapies**: AI recommends drugs that align with the tumor's genetic vulnerabilities, such as matching inhibitors to specific mutations. 
• Custom Immunotherapies**: AI designs proteins or vaccines, like mRNA-based ones, personalized to the patient's tumor profile. For example, Russia's Enteromix vaccine uses AI to customize doses, achieving 60-80% tumor reduction in trials for cancers like melanoma and colorectal. Similarly, AI platforms create "minibinders" to engineer T cells (IMPAC-T cells) that target specific cancer antigens, with built-in safety checks to avoid off-target effects. 
• Predictive Modeling for Combinations**: AI simulates treatment combinations, such as immunotherapy with radiation, to optimize plans.

Step 4: Implement, Monitor, and Iterate

• Treatment Delivery**: Administer the AI-recommended therapy, often in clinical settings. For cell-based therapies, patient immune cells are modified in labs and reinfused. 
• Real-Time Monitoring**: Use AI for ongoing surveillance via wearable devices or follow-up scans to detect resistance or side effects early, allowing adjustments. 
• Ethical and Accessibility Considerations**: AI helps reduce disparities by broadening access to trials and diagnostics, but human oversight remains crucial.

How AI Simulates Cancer Treatment Combinations: Focus on Immunotherapy with Repurposed Drugs

In 2025, AI plays a pivotal role in simulating cancer treatment combinations by leveraging computational models to predict how therapies interact at molecular, cellular, and systemic levels. This is particularly useful for combining immunotherapy—which harnesses the immune system to target cancer cells—with repurposed drugs (existing medications approved for other conditions but redeployed for oncology). These simulations help optimize efficacy, minimize toxicity, and personalize plans without exhaustive real-world trials. AI achieves this through data-driven predictions, reducing the time and cost of drug development while addressing resistance issues. Below is a high-level overview of the process, with examples centered on immunotherapy and repurposed drugs.

Step 1: Data Integration and Modeling

AI starts by aggregating diverse datasets, including:

• Genomic and proteomic data from tumors to identify vulnerabilities. - 
• Pharmacological profiles of repurposed drugs (e.g., from databases like DrugBank or PubChem). -
• Clinical trial outcomes, patient responses, and immune system biomarkers (e.g., PD-L1 expression or tumor mutational burden).

Machine learning models, such as deep neural networks or graph-based algorithms, simulate drug-tumor-immune interactions. For instance, network-based ML can model how a repurposed drug (like a cholesterol-lowering statin) might enhance immunotherapy by modulating immune pathways or reducing inflammation. Tools like AlphaFold2 predict protein structures to simulate how repurposed compounds bind to cancer-related targets, enabling virtual screening of combinations.

Step 2: Predictive Simulation Techniques

AI employs various algorithms to run "in silico" trials:

• Machine Learning for Response Prediction**: Models like LORIS or SCORPIO use routine clinical data (e.g., blood tests) to forecast if a patient's cancer will respond to immune checkpoint inhibitors (e.g., anti-PD-1 drugs like nivolumab) combined with repurposed agents. These outperform traditional biomarkers by achieving higher accuracy in predicting survival and tumor shrinkage. 
• Reinforcement Learning and Optimization**: This simulates iterative dosing scenarios, optimizing combos like immunotherapy with repurposed drugs (e.g., disulfiram for alcohol dependence repurposed to boost anti-tumor immunity). It balances efficacy against side effects, such as immune-related adverse events. 
• Multitarget Modeling**: AI identifies synergistic effects, such as repurposed anti-inflammatory drugs enhancing CAR-T cell therapy or checkpoint inhibitors by altering the tumor microenvironment. For example, AI has suggested combinations of affordable, non-cancer drugs (e.g., for high cholesterol and alcohol dependence) that kill breast cancer cells in lab models, prioritizing safe, regulator-approved options.

Step 3: Validation and Iteration

Simulations are validated against real-world data:

• Retrospective analysis compares predictions to clinical trial results. 
• Prospective simulations guide trial design, e.g., testing immunotherapy with repurposed metformin (a diabetes drug) to overcome resistance in cancers like melanoma or lung cancer. 
• AI iterates by incorporating new data, improving predictions for resistance mechanisms and long-term outcomes.

Key Examples in 2025

Combination Type: Immunotherapy + Anti-inflammatory Repurposed Drugs

• AI Simulation Approach**: Deep learning models simulate immune modulation 
• Potential Benefits**: Reduces resistance, enhances T-cell activity 
• Example Drugs/Applications**: Nivolumab + aspirin-like drugs for colorectal cancer 

Combination Type**: Checkpoint Inhibitors + Metabolic Repurposed Drugs

• AI Simulation Approach**: Network ML predicts metabolic pathway synergies 
• Potential Benefits**: Improves response in high-mutation tumors 
• Example Drugs/Applications**: Pembrolizumab + metformin for lung cancer 

Combination Type**: CAR-T Therapy + Repurposed Antimicrobials

• AI Simulation Approach**: Reinforcement learning optimizes dosing 
• Potential Benefits**: Minimizes cytokine storms, boosts efficacy 
• Example Drugs/Applications**: Engineered T-cells + disulfiram for lymphomas

Conclusion

These advancements accelerate precision oncology, with AI potentially identifying treatments for rare cancers where traditional development is uneconomical. However, simulations are tools for clinicians—not replacements—and require validation through trials. Consult healthcare professionals for any application, as individual responses vary. Always consult oncologists for individual cases, as AI supports but does not replace medical expertise.