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Tumor Immunotherapy

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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Predictive Immune Modeling of Solid Tumors

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Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy.

Yu Gui^1,2, Xiujing He¹, Jing Yu¹

¹Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu 610041, China.

Journal of Clinical Medicine

|February 25, 2023

Summary

Artificial intelligence (AI) enhances cancer immunotherapy by analyzing transcriptomics data. AI-assisted analysis improves understanding of tumor heterogeneity, microenvironment, and adverse events, paving the way for better cancer treatments.

Keywords:

artificial intelligence drug resistance drug target discovery immune-related adverse events immunotherapy transcriptomic analysis tumor microenvironment

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Area of Science:

Cancer Research
Immunotherapy
Genomics
Artificial Intelligence

Background:

Immunotherapy has revolutionized cancer treatment but faces challenges like low response rates and adverse events.
Transcriptomics, especially single-cell RNA sequencing (scRNA-seq), offers insights into treatment response and toxicity.
Artificial intelligence (AI) is crucial for efficient analysis of complex transcriptomic data in cancer research.

Purpose of the Study:

To review emerging AI-assisted transcriptomic technologies for cancer research.
To highlight advancements in understanding cancer immunotherapy using AI-driven transcriptomic analysis.
To provide insights into tumor heterogeneity, microenvironment, and immunotherapy challenges.

Main Methods:

Review of current literature on AI-assisted transcriptomic technologies.
Analysis of scRNA-seq data applications in cancer immunotherapy.
Focus on AI's role in deciphering molecular mechanisms of immunotherapy response and toxicity.

Main Results:

AI significantly improves the analysis of transcriptomic data for cancer immunotherapy.
AI-assisted transcriptomics provides deeper insights into tumor heterogeneity and the tumor microenvironment.
AI aids in understanding drug resistance, immune-related adverse events, and identifying new therapeutic targets.

Conclusions:

AI-assisted transcriptomic analysis is a powerful tool for advancing cancer immunotherapy.
This approach offers solutions to overcome current limitations in immunotherapy efficacy and safety.
The review provides evidence to support the integration of AI in future cancer immunotherapy research and development.