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Proteogenomic convergence for understanding cancer pathways and networks.

Boja ES, Rodriguez H - Clin Proteomics (2014)

Bottom Line: However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems.It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years.However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, 31 Center Drive, MSC 2580, 20892 Bethesda, MD, USA.

ABSTRACT
During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

No MeSH data available.


Related in: MedlinePlus

Climbing up a proteogenomic data ladder. Integrative omics experiments generate tiers of data and knowledge to improve cancer systems biology. Proteogenomic data accumulate at the lower tiers of the data ladder (proteogenomic mapping of linear sequences and protein expression and PTM changes due to genomic alterations), and compress as data analyses become more labor-intensive, complex, and multi-dimensional at the network and pathway level (upper tiers).
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Figure 3: Climbing up a proteogenomic data ladder. Integrative omics experiments generate tiers of data and knowledge to improve cancer systems biology. Proteogenomic data accumulate at the lower tiers of the data ladder (proteogenomic mapping of linear sequences and protein expression and PTM changes due to genomic alterations), and compress as data analyses become more labor-intensive, complex, and multi-dimensional at the network and pathway level (upper tiers).

Mentions: As high throughput approaches generate mountains of data, the global research community is coalescing around the overwhelming realization to improve data management and interpretation in order to obtain better insights into molecular mechanisms and biological principles from these data. The fundamental understanding of biology is, in large part, based upon the understanding of genes and their encoded protein products. Hence, the mapping of cancer genomes and proteomes arising from the cancer genomes can provide valuable information on the effects of genomic aberrations on the functional units of a cell. In the case of TCGA-CPTAC proteogenomics data and other similar datasets, the primary information gleaned from the convergence of both types of data is the proteogenomic mapping against a human reference genome (e.g., HG19) to better define genome annotation [84,85], to confirm and discover peptide-level detection of genomic aberrations such as single mutations [78] and splice variants, and to assess the effect of genomic aberrations on global protein expression and PTM alteration [86] (Figure 3, lower tiers). Currently, identifying unannotated genes and verifying gene calls, defining translational start/stop sites as well as reading frames, and describing signal peptide processing events and PTMs, are feasible tasks that can be carried out at a full-genomic scale once the genomic sequence becomes available [87,88]. This approach has demonstrated its efficiency in discovering existing misannotations and enriching genome annotation in organisms, such as Yersinia[89] and Candida glabrata[90]. Furthermore, proteomic data has been utilized to examine the translation of RNAs to proteins on a genome-wide scale using computational tools to map peptide-based MS data to their encoding genomic loci (genome-based peptide fingerprint scanning) [91] and PTMs by protein inference engine [92]. Through the melding of several large and heterogeneous data sources including MS-based proteomics data, genome sequences, gene annotation sets, and single nucleotide polymorphism sets, this approach revealed alternatively spliced or frameshifted translation products that could not be easily discovered by standard proteomics database search strategies.Establishing the most comprehensive protein list is an essential prerequisite prior to analysis of the quantitative cellular dynamics of proteins, their PTMs and complex network interactions and interpretation of these data as a whole, which constitutes the basis for systems biology. Through network inference, analysis and modeling, it will ultimately provide biological insight by identifying dysregulated networks and pathways as a result of genomic alterations in cancer (Figure 3, upper tier). This will further facilitate new hypothesis generation and experimental validation studies. As more data accumulates through genomic, transcriptomic and proteomic characterization, data integration and analysis become more labor-intensive and complex requiring more sophisticated computational tools while climbing up the data ladder.


Proteogenomic convergence for understanding cancer pathways and networks.

Boja ES, Rodriguez H - Clin Proteomics (2014)

Climbing up a proteogenomic data ladder. Integrative omics experiments generate tiers of data and knowledge to improve cancer systems biology. Proteogenomic data accumulate at the lower tiers of the data ladder (proteogenomic mapping of linear sequences and protein expression and PTM changes due to genomic alterations), and compress as data analyses become more labor-intensive, complex, and multi-dimensional at the network and pathway level (upper tiers).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4067069&req=5

Figure 3: Climbing up a proteogenomic data ladder. Integrative omics experiments generate tiers of data and knowledge to improve cancer systems biology. Proteogenomic data accumulate at the lower tiers of the data ladder (proteogenomic mapping of linear sequences and protein expression and PTM changes due to genomic alterations), and compress as data analyses become more labor-intensive, complex, and multi-dimensional at the network and pathway level (upper tiers).
Mentions: As high throughput approaches generate mountains of data, the global research community is coalescing around the overwhelming realization to improve data management and interpretation in order to obtain better insights into molecular mechanisms and biological principles from these data. The fundamental understanding of biology is, in large part, based upon the understanding of genes and their encoded protein products. Hence, the mapping of cancer genomes and proteomes arising from the cancer genomes can provide valuable information on the effects of genomic aberrations on the functional units of a cell. In the case of TCGA-CPTAC proteogenomics data and other similar datasets, the primary information gleaned from the convergence of both types of data is the proteogenomic mapping against a human reference genome (e.g., HG19) to better define genome annotation [84,85], to confirm and discover peptide-level detection of genomic aberrations such as single mutations [78] and splice variants, and to assess the effect of genomic aberrations on global protein expression and PTM alteration [86] (Figure 3, lower tiers). Currently, identifying unannotated genes and verifying gene calls, defining translational start/stop sites as well as reading frames, and describing signal peptide processing events and PTMs, are feasible tasks that can be carried out at a full-genomic scale once the genomic sequence becomes available [87,88]. This approach has demonstrated its efficiency in discovering existing misannotations and enriching genome annotation in organisms, such as Yersinia[89] and Candida glabrata[90]. Furthermore, proteomic data has been utilized to examine the translation of RNAs to proteins on a genome-wide scale using computational tools to map peptide-based MS data to their encoding genomic loci (genome-based peptide fingerprint scanning) [91] and PTMs by protein inference engine [92]. Through the melding of several large and heterogeneous data sources including MS-based proteomics data, genome sequences, gene annotation sets, and single nucleotide polymorphism sets, this approach revealed alternatively spliced or frameshifted translation products that could not be easily discovered by standard proteomics database search strategies.Establishing the most comprehensive protein list is an essential prerequisite prior to analysis of the quantitative cellular dynamics of proteins, their PTMs and complex network interactions and interpretation of these data as a whole, which constitutes the basis for systems biology. Through network inference, analysis and modeling, it will ultimately provide biological insight by identifying dysregulated networks and pathways as a result of genomic alterations in cancer (Figure 3, upper tier). This will further facilitate new hypothesis generation and experimental validation studies. As more data accumulates through genomic, transcriptomic and proteomic characterization, data integration and analysis become more labor-intensive and complex requiring more sophisticated computational tools while climbing up the data ladder.

Bottom Line: However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems.It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years.However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, 31 Center Drive, MSC 2580, 20892 Bethesda, MD, USA.

ABSTRACT
During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

No MeSH data available.


Related in: MedlinePlus