Targeted Drugs and Treatment Response: How AI-Guided Therapy Enhances Precision Oncology

Precision oncology has revolutionized cancer treatment by enabling the personalization of therapies based on the genetic and molecular characteristics of each patient. In this context, targeted drugs have emerged as a crucial tool, allowing for the specific targeting of genetic alterations that drive tumor growth. However, treatment response remains variable, leading to the integration of artificial intelligence (AI) as an ally to optimize AI-guided therapy and improve clinical outcomes.

AI in Precision Oncology

AI is transforming the way we approach cancer, from diagnosis to treatment. An example of this is the use of machine learning models that predict immunotherapy response based on the cancer's "stemness" signature, allowing for the identification of patients who will benefit most from these therapies. Additionally, tumor transcriptomics is being utilized to guide patient treatment, expanding treatment opportunities beyond actionable mutations in cancer driver genes.

The prediction of treatment response using AI models that integrate multimodal data, such as radiology, pathology, and clinical information, is proving to be highly effective. These models not only accurately predict response to anti-HER2 therapy but also improve progression-free survival and overall survival in patients with HER2-positive gastric cancer.

Conclusions

The integration of AI in precision oncology is redefining the landscape of cancer treatment. By combining genetic testing with advanced data analysis, AI enables a more precise and personalized treatment response. As we continue to explore these technologies, it is crucial to develop robust predictive models that can be implemented in daily clinical practice, thereby improving outcomes for cancer patients.

Referencias

• [1] Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response.
• [2] Synthetic lethality-mediated precision oncology via the tumor transcriptome.
• [3] Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data.

Created 20/1/2025