A challenge of proteogenomics in clinical oncology is to understand systems biology of tumor subtypes by integrating proteomics data with genomics and transcriptomics data, which will ultimately lead to the identification of better clinical biomarkers in tumor stratification, development of treatment strategy, drug discovery & repurposing, treatment response, and resistance to therapy. Cancer diseases are often characterized by high mutation rates that are closely related to the physiological and pathological characteristics of individual patients. A variety of driver mutations have been discovered, therapeutic agents targeting them have been developed, and since then the construction of treatment strategies has changed dramatically. Transcriptome data includes thousands of new or unassessed novel splice junctions, sequence variants, chimeric transcripts, non-coding RNAs, gene fusions, RNA editing events, and more. Clinical proteomics studies are indispensable to studying how genomic changes reflect the functional interaction of protein-protein networks. Mass spectrometry-based proteogenomics enables deeper analysis with integrated analysis that detects and quantifies novel peptides that are expressed in specific samples but are overlooked when using standard reference protein sequence databases.
A challenge of clinical proteogenomics is to understand the systems biology of tumor subtypes by integrating proteomics data with genomics and transcriptomics data. Cancer diseases are often characterized by high mutation rates that are closely related to the physiological and pathological characteristics of individual patients. A variety of driver mutations have been discovered, therapeutic agents targeting them have been developed, and since then the construction of treatment strategies has changed dramatically. Transcriptome data includes thousands of new or unassessed novel splice junctions, sequence variants, chimeric transcripts, non-coding RNAs, gene fusions, RNA editing events, and more. However, the success and impact of clinical proteogenomics in cancer research depend on several following concerns.
1) How many peptide sequences (and types) can be confidently identified from a sample-specific proteogenomics database?
2) What new biological/clinical information (or usefulness) can be obtained from the identification of such peptide sequences?
3) Whether peptide identification (and quantification) by clinical proteogenomics functions as a more sensitive biomarker?
In this Special Issue, we seek contributions regarding MS-based proteogenomics i.e. mutant proteomics and its development in clinical oncology, which potentially improves our understanding of tumor biology and implicates for patient care. Results of measurements performed on human tissue or clinical data. Original reports, review articles, meta-analyses, and clinical trial reports which contain mutant protein-level investigation with a direct implication for patient care are welcome.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
A challenge of proteogenomics in clinical oncology is to understand systems biology of tumor subtypes by integrating proteomics data with genomics and transcriptomics data, which will ultimately lead to the identification of better clinical biomarkers in tumor stratification, development of treatment strategy, drug discovery & repurposing, treatment response, and resistance to therapy. Cancer diseases are often characterized by high mutation rates that are closely related to the physiological and pathological characteristics of individual patients. A variety of driver mutations have been discovered, therapeutic agents targeting them have been developed, and since then the construction of treatment strategies has changed dramatically. Transcriptome data includes thousands of new or unassessed novel splice junctions, sequence variants, chimeric transcripts, non-coding RNAs, gene fusions, RNA editing events, and more. Clinical proteomics studies are indispensable to studying how genomic changes reflect the functional interaction of protein-protein networks. Mass spectrometry-based proteogenomics enables deeper analysis with integrated analysis that detects and quantifies novel peptides that are expressed in specific samples but are overlooked when using standard reference protein sequence databases.
A challenge of clinical proteogenomics is to understand the systems biology of tumor subtypes by integrating proteomics data with genomics and transcriptomics data. Cancer diseases are often characterized by high mutation rates that are closely related to the physiological and pathological characteristics of individual patients. A variety of driver mutations have been discovered, therapeutic agents targeting them have been developed, and since then the construction of treatment strategies has changed dramatically. Transcriptome data includes thousands of new or unassessed novel splice junctions, sequence variants, chimeric transcripts, non-coding RNAs, gene fusions, RNA editing events, and more. However, the success and impact of clinical proteogenomics in cancer research depend on several following concerns.
1) How many peptide sequences (and types) can be confidently identified from a sample-specific proteogenomics database?
2) What new biological/clinical information (or usefulness) can be obtained from the identification of such peptide sequences?
3) Whether peptide identification (and quantification) by clinical proteogenomics functions as a more sensitive biomarker?
In this Special Issue, we seek contributions regarding MS-based proteogenomics i.e. mutant proteomics and its development in clinical oncology, which potentially improves our understanding of tumor biology and implicates for patient care. Results of measurements performed on human tissue or clinical data. Original reports, review articles, meta-analyses, and clinical trial reports which contain mutant protein-level investigation with a direct implication for patient care are welcome.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.