Integrated proteo-genomic approach for early diagnosis and prognosis of cancer.
Cancer is the leading cause of mortality among men and women worldwide. Despite the availability of numerous diagnostic techniques for various cancers, the overall survival rate remains low and the majority of patients die due to late diagnosis and advanced stage of the disease. Diagnosing and treating cancer at its early stages ideally during the precancerous phase could significantly increase survival rate with the possibility of cure and prolong survival. Cancer is a genetic disease and it is illicitly activated by the acquisition of somatic DNA lesions and aberrations in genome structure and defects in maintenance and repair. These somatic DNA mutations known as driver mutations seem to be the prime cause in initiating tumorigenesis. The advances in genomic technologies have immensely facilitated the understanding of cancer progression and metastasis, and the discovery of novel biomarkers. However, changes in somatic mutational landscape of the oncogenome are translated into aberrantly regulated oncoproteome which drives the cancer initiation. Thus, combination of proteomic and genomic technologies is urgently required to discover biomarkers for early diagnosis. The recent advances in human genome based detection of cancer using advanced genomic technologies like NextGen Sequencing, digital PCR, cfDNA technology have shown promise; for example oncogenic somatic mutation variants, transcriptomic analysis, copy number variant, and methylation data from the Cancer Genome Atlas. Similarly, oncoproteomics has the potential to revolutionize clinical management of the disease, including cancer diagnosis and screening based on new proteomic database which embodies somatic variants and post translational modifications, thus devising proteomic technologies as a complement to histopathology. Further, the use of multiple proteomic and genomic biomarkers rather than a single gene or protein could greatly improve diagnostic accuracy and enhance the predictive power for treatment outcome and may enable adequate monitoring of the response to treatment and could be an important option for personalized medicine. The proteogenomic approach has the promise to identify new biomarkers for radiation therapy (RT) which could reliably predict the tumor radiation resistance and which could also accurately predict normal tissue toxicity, and at the same time radiotherapy effectiveness. In this review we have summarize the recent advances in proteogenomic approaches to develop more sensitive diagnostic and prognostic biomarkers which could be translated into improved clinical care and management of the disease.
Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201, USA. Electronic address: hshukla@ndm.edu.
Hem D
HD
Shukla
Department of Radiation Oncology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA. Electronic address: jmahmood@som.umaryland.edu.
Javed
J
Mahmood
Department of Radiation Oncology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA. Electronic address: zvujaskovic@som.umaryland.edu.
Zeljko
Z
Vujaskovic
D014408:Biomarkers, Tumor / Q000378:metabolism*; D055088:Early Detection of Cancer; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000532:radiotherapy; D057285:Precision Medicine; D011379:Prognosis; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics; D011836:Radiation Tolerance
D014408:Biomarkers, Tumor; D020543:Proteome
Early detection; Proteogenomics; RNAseq database; Radiation resistance; Radiation therapy (RT); cfDNA