Abstract
During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein–protein interaction, and pharmacogenomics data, are indispensable to glean into the cancer genome and proteome and these approaches have generated multidimensional universal studies of genes and proteins (OMICS) data which has the potential to facilitate precision medicine. However, due to slow progress in computational technologies, the translation of big omics data into their clinical aspects have been slow. In this review, attempts have been made to describe the role of high-throughput genomic and proteomic technologies in identifying a panel of biomarkers which could be used for the early diagnosis and prognosis of cancer.
Highlights
The heterogeneous nature of cancer has tremendously stalled progress in understanding the underpinnings of cancer signaling and its phenotypic manifestation
Attempts have been made to describe the role of high-throughput genomic and proteomic technologies in identifying a panel of biomarkers which could be used for the early diagnosis and prognosis of cancer
The high-throughput proteomic analysis of human tissue samples has shown us the expression of a tissue-specific proteome and long noncoding RNAs, which play an important role in tumorigenesis and aggression [3,4,5]
Summary
The heterogeneous nature of cancer has tremendously stalled progress in understanding the underpinnings of cancer signaling and its phenotypic manifestation. Recent investigations on human colon cancer have shown that there is a poor correlation between expressed proteome and protein abundance and DNA mutation in the colon cancer genome [8,10] These studies have shown that tumor-associated somatic mutations or copy number variations might be accountable for these outcomes. Oncoproteo-genomics technologies dealing with cancer diagnosis and therapeutics have immense promise to transform clinical practice, including cancer diagnosis and prognosis, and could serve as an alternative to histopathology and the personalized selection of therapeutic combinations that could target a cancer-specific protein network [17,18] Employing this approach, therapeutic efficacy, toxicity, and drug response could be monitored in cancer patients, and it delineates deeper insight into neoplasia. By employing genomic and proteomic tools in a specific cancer subtype, promising biomarkers could be successfully identified as a specific signature pattern associated with it, which could be used either for the early diagnosis or recurrence of cancer [24]
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