The number of novel cancer therapies targeted at specific signaling and subcellular machinery is increasing. Meanwhile, tumor heterogeneity is an important clinical feature to affect therapeutic decision making, and the need to match these therapies to individual patients is growing. Traditional cancer classification is limited to gross assessments using clinical features, such as age, gender, and tumor anatomic and morphological features. Considering the low accuracy of traditional classification, there is an urgent need to characterize molecular markers that predict the cancer treatment response. Recent breakthroughs in artificial intelligence and abundant public multi-omics data make big health data analysis possible, and the precision medicine era is coming. The response to cancer therapies can be predicted by various molecular markers, generated from DNA sequence, DNA methylation, gene expression, etc. These new technologies and data suggest a promising diagnostic strategy that integrates functional testing with omics data to match therapies to individual cancer patients.
In this Research Topic, we invite authors to submit their Original Research and Review articles. The Original Research articles can be basic, translational, or clinical studies utilizing omics data to improve the treatment response for cancer. Statistical methods and tools with validation will also be considered. Review articles should discuss recent advances in omics techniques and tools used for precision medicine of cancers.
We invite contributions of Original Research, Methods, and Reviews including but not limited to the following topics of interest:
• Identification of molecular markers used to predict the response to cancer treatment, including immunotherapy, radiotherapy, drugs, etc.
• Cancer mRNA vaccines: mRNA technology has been applied to the emerging field of cancer immunotherapy recently and has been proven to be able to boost immunity against cancer. This subtopic could include mRNA vaccine design, immune pathways activated by various mRNA vaccine platforms, new clinical trials against disease targets, etc.
• Functional biomarkers used as potential therapeutic targets.
• Statistical/Bioinformatic methods and tools for cancer multi-omic data analysis to improve treatment response.
• Effect of tumor microenvironment on treatment response.
The number of novel cancer therapies targeted at specific signaling and subcellular machinery is increasing. Meanwhile, tumor heterogeneity is an important clinical feature to affect therapeutic decision making, and the need to match these therapies to individual patients is growing. Traditional cancer classification is limited to gross assessments using clinical features, such as age, gender, and tumor anatomic and morphological features. Considering the low accuracy of traditional classification, there is an urgent need to characterize molecular markers that predict the cancer treatment response. Recent breakthroughs in artificial intelligence and abundant public multi-omics data make big health data analysis possible, and the precision medicine era is coming. The response to cancer therapies can be predicted by various molecular markers, generated from DNA sequence, DNA methylation, gene expression, etc. These new technologies and data suggest a promising diagnostic strategy that integrates functional testing with omics data to match therapies to individual cancer patients.
In this Research Topic, we invite authors to submit their Original Research and Review articles. The Original Research articles can be basic, translational, or clinical studies utilizing omics data to improve the treatment response for cancer. Statistical methods and tools with validation will also be considered. Review articles should discuss recent advances in omics techniques and tools used for precision medicine of cancers.
We invite contributions of Original Research, Methods, and Reviews including but not limited to the following topics of interest:
• Identification of molecular markers used to predict the response to cancer treatment, including immunotherapy, radiotherapy, drugs, etc.
• Cancer mRNA vaccines: mRNA technology has been applied to the emerging field of cancer immunotherapy recently and has been proven to be able to boost immunity against cancer. This subtopic could include mRNA vaccine design, immune pathways activated by various mRNA vaccine platforms, new clinical trials against disease targets, etc.
• Functional biomarkers used as potential therapeutic targets.
• Statistical/Bioinformatic methods and tools for cancer multi-omic data analysis to improve treatment response.
• Effect of tumor microenvironment on treatment response.
