Key Takeaways:
- Artificial intelligence (AI) can be used to identify drivers of resistance to CAR T-cell therapies in patients with mantle cell lymphoma (MCL)
- An AI-based model identified TCL1A as an oncogene that could enhance cell survival and impair apoptosis, and the S100A4 and S100A6 genes were found to be involved in cellular migration and metastatic behavior
- The AI model can handle patient heterogeneity and learn how genes interact with one another in resistant tumors
- The next steps include conducting laboratory experiments to validate the potential treatment targets and designing compounds to overcome CAR T-cell therapy resistance
- CAR T-cell therapy is an important treatment option for patients with relapsed or refractory MCL, but some patients develop resistance, and understanding the mechanisms of resistance is crucial to improving treatment outcomes
Introduction to CAR T-Cell Therapy in MCL
CAR T-cell therapy is a promising treatment option for patients with relapsed or refractory mantle cell lymphoma (MCL). According to Fangfang Yan, MD, "CAR T-cell therapy is an important treatment option for patients with relapsed or refractory MCL, especially for those who have progressed on BTK inhibitors." However, despite its effectiveness, some patients do not respond to CAR T-cell therapy or eventually relapse after an initial response. To address this issue, researchers are exploring the use of artificial intelligence (AI) to identify drivers of resistance to CAR T-cell therapies in MCL. As Yan explained, "Our key findings revealed a list of candidate targets. By inhibiting these targets, we may be able to reverse or overcome CAR T-[cell therapy] resistance."
The Role of AI in Addressing CAR T-Cell Therapy Resistance
The application of AI in biology and cancer research has the potential to revolutionize the field. AI models can analyze complex, high-dimensional genomic datasets in an efficient way, uncovering hidden connections and patterns within patient samples. As Yan noted, "When AI is applied to biology and cancer research, it allows us to analyze complex, high-dimensional genomic datasets in an efficient way. Instead of focusing on single genes or individual pathways, AI models help us uncover hidden connections and patterns within patient samples." This is particularly useful when trying to understand resistance mechanisms, which are often driven by multiple genes. By using AI-based approaches, researchers can identify and take down these genes, ultimately overcoming cancer.
The AI Analysis in MCL
The AI analysis presented by Yan during the 2025 ASH Annual Meeting used single-cell RNA sequencing of 38 samples from 15 patients with MCL who received CD19-targeted CAR-T therapy. The model identified a strong overrepresentation of immune-related processes, such as MHC protein complex assembly, antigen processing and presentation, interferon gamma signaling, B-cell activation, and type II interferon production. Additionally, TCL1A was identified as an oncogene that could enhance cell survival and impair apoptosis, and the S100A4 and S100A6 genes were found to be involved in cellular migration and metastatic behavior. As Yan explained, "We trained the model on millions of cells to recognize these hidden connections. One core component of our approach was running virtual experiments. We computationally knocked out each gene one by one and observed what happened. If knocking out a gene caused resistant cells to behave more like sensitive ones, then we identified that gene as a key driver."
Next Steps for Using AI Models to Study CAR T-Cell Therapy Resistance in MCL
The next steps for using AI models to study CAR T-cell therapy resistance in MCL include conducting laboratory experiments to validate the potential treatment targets identified by the AI model. As Yan noted, "We’re currently conducting laboratory experiments to validate whether these targets truly work. If they do, the clinical effect could be significant. We could design compounds to overcome CAR T-[cell therapy] resistance, giving patients who become resistant to CAR T-cell therapy another option to improve survival." The use of AI models has the potential to significantly improve our understanding of resistance mechanisms and ultimately lead to the development of more effective treatments for patients with MCL.
Conclusion
In conclusion, the use of AI-based approaches has the potential to revolutionize the field of cancer research, particularly in the context of CAR T-cell therapy resistance in MCL. By identifying drivers of resistance and understanding the mechanisms underlying this phenomenon, researchers can develop more effective treatments and improve patient outcomes. As Yan emphasized, "That helps explain, at least in part, why some patients develop resistance." The application of AI in biology and cancer research is a rapidly evolving field, and further studies are needed to fully explore its potential. However, the preliminary findings presented by Yan and her team are promising, and the use of AI models is likely to play an increasingly important role in the development of more effective treatments for patients with MCL.
https://www.onclive.com/view/artificial-intelligence-based-model-identifies-potential-resistance-drivers-to-car-t-cell-therapy-in-mcl