Anti-tumor drug resistance and drug-related toxicity are the two major challenges in cancer treatment. The widespread drug resistance leads to poor clinical outcomes and low survival in many patients. The toxic side effects affect the treatment tolerability and quality of life. Lack of treatment efficacy and toxic side effects are the key factors that fail clinical trials.
With the development of high-throughput sequencing technology, researchers have created a large pool of data, multi-omes, which can be used to explore many aspects of anti-tumor drug resistance and toxicity. For example, many single nucleotide polymorphisms (SNP) have been revealed, which provide a basis for determining the difference between the curative and toxic side effects of related drugs. Multi-omics can also help explain the relationship between SNPs and response to disease treatment by locating disease susceptibility genes and potential drug targets, as well as evaluating drug efficacy and side effects. Finally, information such as genetic alterations, copy number variations, chromosome disorders, 3D genome misfolding, DNA methylation, and transcriptome/proteome/metabolome/microbiome profile can also be used to evaluate the toxic side effects that result from tumor treatment.
This Research Topic aims to highlight the emerging role of the multidisciplinary approaches in anti-tumor drug resistance and drug-related toxicity, updating and discussing potential therapeutic resistance and toxicity challenges and reviewing how anti-tumor drug resistance and drug-related toxicity affect the clinical outcomes of cancer patients. And providing new insights into clinical usage and impacting future directions of disease care. We welcome Original Research, Review, Mini Review, Opinion and Perspective articles that describe and covering the recent advances made in the multi-omics in anti-tumor drug resistance and adverse side effects.
The following sub-topics are included in the Research Topic, but are not limited to:
1. Use multi-omics analysis to explore the resistance mechanism of anti-tumor drugs.
2. Use multi-omics to deconvolute molecular mechanisms of drug resistance/toxicity of tumors treatment, such as chemoradiotherapy, chemotherapy, targeted therapy, and immunotherapy.
3. Use multi-omics to identify biomarkers for drug resistance or toxicity.
4. Compare different -omics to predict tumor drug resistance and side effects.
5. Explore the application of multi-omics in preventing drug resistance and adverse side effects.
6. Simulation models to simulate the development of drug resistance using clinical datasets.
7. The multidisciplinary technologies to study the anti-tumor drug resistance and drug-related toxicity affect, e.g., Genetic, genomic, and epigenetic analysis; Proteomic and metabolomic analysis; Transcriptomic analysis; Single-cell analysis
Note: authors are encouraged to provide validation experiments to support in silico predictions/analyses.
Anti-tumor drug resistance and drug-related toxicity are the two major challenges in cancer treatment. The widespread drug resistance leads to poor clinical outcomes and low survival in many patients. The toxic side effects affect the treatment tolerability and quality of life. Lack of treatment efficacy and toxic side effects are the key factors that fail clinical trials.
With the development of high-throughput sequencing technology, researchers have created a large pool of data, multi-omes, which can be used to explore many aspects of anti-tumor drug resistance and toxicity. For example, many single nucleotide polymorphisms (SNP) have been revealed, which provide a basis for determining the difference between the curative and toxic side effects of related drugs. Multi-omics can also help explain the relationship between SNPs and response to disease treatment by locating disease susceptibility genes and potential drug targets, as well as evaluating drug efficacy and side effects. Finally, information such as genetic alterations, copy number variations, chromosome disorders, 3D genome misfolding, DNA methylation, and transcriptome/proteome/metabolome/microbiome profile can also be used to evaluate the toxic side effects that result from tumor treatment.
This Research Topic aims to highlight the emerging role of the multidisciplinary approaches in anti-tumor drug resistance and drug-related toxicity, updating and discussing potential therapeutic resistance and toxicity challenges and reviewing how anti-tumor drug resistance and drug-related toxicity affect the clinical outcomes of cancer patients. And providing new insights into clinical usage and impacting future directions of disease care. We welcome Original Research, Review, Mini Review, Opinion and Perspective articles that describe and covering the recent advances made in the multi-omics in anti-tumor drug resistance and adverse side effects.
The following sub-topics are included in the Research Topic, but are not limited to:
1. Use multi-omics analysis to explore the resistance mechanism of anti-tumor drugs.
2. Use multi-omics to deconvolute molecular mechanisms of drug resistance/toxicity of tumors treatment, such as chemoradiotherapy, chemotherapy, targeted therapy, and immunotherapy.
3. Use multi-omics to identify biomarkers for drug resistance or toxicity.
4. Compare different -omics to predict tumor drug resistance and side effects.
5. Explore the application of multi-omics in preventing drug resistance and adverse side effects.
6. Simulation models to simulate the development of drug resistance using clinical datasets.
7. The multidisciplinary technologies to study the anti-tumor drug resistance and drug-related toxicity affect, e.g., Genetic, genomic, and epigenetic analysis; Proteomic and metabolomic analysis; Transcriptomic analysis; Single-cell analysis
Note: authors are encouraged to provide validation experiments to support in silico predictions/analyses.
