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Radiogenomics
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
At the moment it is still an open problem which omics and thus which level of molecular biology is the most relevant for capturing the genomics state of the cell or even if a multi-omics strategy is appropriate. Future efforts relevant for radiogenomics are currently focusing heavily on proteogenomics, an integrated genomics and proteomics analysis in the context of radiogenomics [41].
Precision medicine for brain gliomas
Published in Debmalya Barh, Precision Medicine in Cancers and Non-Communicable Diseases, 2018
Progress in glioma therapy could be attained by improved comprehension of glioma biology, identification of relevant targets and signaling pathways for treatment interventions, development of personalized medicine, optimization of surgery and radiotherapy, and innovative neuroimaging modalities (Grant et al., 2014; Rajesh et al., 2017). Proteogenomic characterization is a potential strategy that could lead to identification of molecular drivers, molecular classification of disease subgroups, and glioma treatment (Grant et al., 2014). The ultimate goal of targeted therapy should be “selectivity” inhibiting only tumor cells (Adair et al., 2014; Oh et al., 2014; Morokoff et al., 2015). The targeted approaches currently in clinical trials or in laboratory development include drugs, monoclonal antibodies, immunotherapy, small molecules inhibiting specific proteins, and specific targeting of glioma stem cells (Oh et al., 2014; Prados et al., 2015; Miller and Wen, 2016). Some of these targets for glial tumors are summarized in Table 4.1. Thus, there is a need for unconventional treatment strategies to curb glioma. Strategies such as gene therapy, microRNA (miRNA) therapy, stem cells, and immunotherapy may potentially lead to effective treatments (Kouri et al., 2015; Morokoff et al., 2015; Hodges et al., 2016; Chandran et al., 2017).
Radiobiology and Hadron Therapy
Published in Manjit Dosanjh, Jacques Bernier, Advances in Particle Therapy, 2018
Eleanor A. Blakely, Manjit Dosanjh
The U.S. National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is collaborating with the U.S. Department of Defense (DoD) and the Veteran Affairs (VA) Veterans Health System to incorporate proteogenomics as part of cancer patient treatment regimes. These three organisations have announced the formation of the Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) Network which aims to build a system in which VA and DoD cancer patients routinely undergo genomic and proteomic profiling with the goal of matching their tumour types to targeted therapies. Applied proteogenomics is a developing new weapon in the war against cancer and is considered the keystone to everything the VA and DoD will be doing in precision oncology. APOLLO will characterise and compare tumours made available through the APOLLO network to develop a deeper understanding of cancer biology, identify potential therapeutic targets and identify pathways important for cancer detection and intervention.
The role of proteomics in the multiplexed analysis of gene alterations in human cancer
Published in Expert Review of Proteomics, 2021
Niraj Babu, Mohd Younis Bhat, Arivusudar Everad John, Aditi Chatterjee
Proteogenomics is widely used to study different cancer types at the molecular level; however, there are many challenges that come along. Development of customized protein databases involves translation of genomic and transcriptomic data sets into multiple frames. The resulting databases are quite large and occupy a lot of search space with chances of redundancy [148,175]. Furthermore, the management of such large databases is quite challenging and increases false discovery rates. Alternatively, patient specific databases are used to increase the accuracy of identified variants. In addition to that customized pipelines are used for robust identification of fusion proteins, mutant proteins and accurate genome annotations. However, the field is still evolving with novel approaches and computational methods being developed to facilitate accurate identification of novel events.
Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade
Published in Expert Review of Proteomics, 2021
Jennie R. Lill, William R. Mathews, Christopher M. Rose, Markus Schirle
Proteogenomics utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of peptides and proteins and pathways evolved a number of years ago [41]. With rapid advancements in the RNA sequencing field, proteogenomics has been shown to be a power tool allowing the generation of customized protein sequence databases using genomic and transcriptomic information. This has allowed easier identification of point mutations, splice variants and other peptides that are not typically represented in reference protein sequence databases. Although it is still not a common practice by most laboratories, proteogenomic analysis has allowed certain biological questions to be answered that would be very time consuming using de novo sequencing or wild card searching approaches. For example, in the rapidly growing field of cancer immunotherapy where neo-antigens are often the targets for various modalities, the identification of these tumor specific point mutations that occur due to the inherent genetic instability of a malignancy is often required. These point mutations can be easily identified using RNA-sequencing and Exome-seq, and translating these into a protein based FASTA file allows easy peptide characterization [42].
Proteogenomic interrogation of cancer cell lines: an overview of the field
Published in Expert Review of Proteomics, 2021
The integrative analysis of genomic and proteomic data, commonly known as proteogenomics, is an emerging field of research (Figure 1). Originally, proteogenomics was a field where proteomics was applied to improve the annotation of genomes [11,12]. Since, the term has been broadened to cover all aspects of analyses that integrate genomics and proteomics data, from the interpretation of MS/MS spectra [13–15] and post-transcriptional regulation of protein abundance [9,16]. In the context of cancer, oncoproteogenomics has been established as a term referring to the use of somatic mutation data from DNA and RNA-sequencing (RNA-seq) to help identify mutation-containing peptides from mass spectrometry-based proteomics data [17,18]. This review will discuss the current state of proteogenomics in CCLs. We will first highlight the proteomic methods that have been used to analyze CCLs, and then discuss different strategies used to perform proteogenomic analysis. Finally, we will highlight some key findings that have emerged through proteogenomic investigations of CCLs.