In the past decade, the role of cancer immunotherapy in oncology has significantly grown, establishing itself as a potent treatment method against tumors. It now stands comparable in efficacy to traditional treatments such as chemotherapy, radiotherapy, and surgery. Despite its promise, a challenge in current clinical practice arises from tumor heterogeneity and phenotypic diversity, leading to limited accuracy in predicting immunotherapy responses based on single or multiple biomarkers. Currently, cancer treatment is evolving towards precision medicine, multi-omics, multi-dimensional and multi-modalities data can provide more individualized information for assessing therapeutic efficacy, predicting prognosis, and screening for potential beneficiaries of immunotherapy.
Artificial Intelligence (AI) is making strides in extracting valuable insights from the complex, rich biomedical information encapsulated in omics data. This includes radiomics, pathomics, proteomics, genomics, and spatial biology. By analyzing these vast datasets, AI can identify hidden patterns and correlations. This capability is crucial for predicting individual responses to immunotherapy and prognoses with greater precision. AI methods enable the creation of predictive models to determine which patients are more likely to benefit from immune checkpoint inhibitors (ICIs) therapy, facilitating a more personalized approach to immunotherapy. This not only enhances treatment efficacy but also reduces ineffective interventions. Furthermore, AI aids in discovering new immunotherapy targets, contributing to the design of targeted therapeutic strategies. Leveraging techniques like deep learning, AI explores multi-omics data to uncover mechanisms of resistance and recurrence patterns in tumor immunotherapy. This contributes to understanding the molecular mechanisms underlying drug resistance.
The primary aim of this research topic is to delve into and examine the practical implications and potential impact of employing artificial intelligence (AI)-based omics-data analysis techniques in the facilitation and optimization of tumor immunotherapy strategies. Through meticulous exploration and analysis, this study endeavors to elucidate the efficacy, challenges, and innovative advancements associated with the integration of AI methodologies within the domain of tumor immunotherapy.
We warmly welcome submissions of Original Research and Reviews with a primary emphasis on, while not exclusively limited to, the following subtopics:
1) Using omics data combined with AI techniques to establish models for monitoring and predicting immunotherapy response.
2) Prediction of tumor immunotherapy prognosis and survival based on omics data analysis and artificial intelligence.
3) Discovery of novel immunotherapy targets and biomarkers through AI analysis of omics data
4) Omics data employed for predicting immune-related adverse events (irAEs) occurrences and prognosis.
5) AI-based omics data analysis for interpreting tumor immunotherapy resistance mechanisms.
6) AI-based omics data analysis for revealing molecular mechanisms of immunotherapy, such as changes in the expression of immune-related genes and activation status of signaling pathways.
7) AI-based omics analysis for diagnosis and differential diagnosis, such as distinguishing between benign and malignant tumors, differentiating between pseudoprogression and true progression, diagnosing radiation pneumonitis, and identifying immune-related pneumonia.
8) Novel methodologies, algorithms, and software tools for omics data extraction, analysis, and interpretation
In the past decade, the role of cancer immunotherapy in oncology has significantly grown, establishing itself as a potent treatment method against tumors. It now stands comparable in efficacy to traditional treatments such as chemotherapy, radiotherapy, and surgery. Despite its promise, a challenge in current clinical practice arises from tumor heterogeneity and phenotypic diversity, leading to limited accuracy in predicting immunotherapy responses based on single or multiple biomarkers. Currently, cancer treatment is evolving towards precision medicine, multi-omics, multi-dimensional and multi-modalities data can provide more individualized information for assessing therapeutic efficacy, predicting prognosis, and screening for potential beneficiaries of immunotherapy.
Artificial Intelligence (AI) is making strides in extracting valuable insights from the complex, rich biomedical information encapsulated in omics data. This includes radiomics, pathomics, proteomics, genomics, and spatial biology. By analyzing these vast datasets, AI can identify hidden patterns and correlations. This capability is crucial for predicting individual responses to immunotherapy and prognoses with greater precision. AI methods enable the creation of predictive models to determine which patients are more likely to benefit from immune checkpoint inhibitors (ICIs) therapy, facilitating a more personalized approach to immunotherapy. This not only enhances treatment efficacy but also reduces ineffective interventions. Furthermore, AI aids in discovering new immunotherapy targets, contributing to the design of targeted therapeutic strategies. Leveraging techniques like deep learning, AI explores multi-omics data to uncover mechanisms of resistance and recurrence patterns in tumor immunotherapy. This contributes to understanding the molecular mechanisms underlying drug resistance.
The primary aim of this research topic is to delve into and examine the practical implications and potential impact of employing artificial intelligence (AI)-based omics-data analysis techniques in the facilitation and optimization of tumor immunotherapy strategies. Through meticulous exploration and analysis, this study endeavors to elucidate the efficacy, challenges, and innovative advancements associated with the integration of AI methodologies within the domain of tumor immunotherapy.
We warmly welcome submissions of Original Research and Reviews with a primary emphasis on, while not exclusively limited to, the following subtopics:
1) Using omics data combined with AI techniques to establish models for monitoring and predicting immunotherapy response.
2) Prediction of tumor immunotherapy prognosis and survival based on omics data analysis and artificial intelligence.
3) Discovery of novel immunotherapy targets and biomarkers through AI analysis of omics data
4) Omics data employed for predicting immune-related adverse events (irAEs) occurrences and prognosis.
5) AI-based omics data analysis for interpreting tumor immunotherapy resistance mechanisms.
6) AI-based omics data analysis for revealing molecular mechanisms of immunotherapy, such as changes in the expression of immune-related genes and activation status of signaling pathways.
7) AI-based omics analysis for diagnosis and differential diagnosis, such as distinguishing between benign and malignant tumors, differentiating between pseudoprogression and true progression, diagnosing radiation pneumonitis, and identifying immune-related pneumonia.
8) Novel methodologies, algorithms, and software tools for omics data extraction, analysis, and interpretation
