SPECIAL EDITORIAL
Proteomics for Early Diagnostics Shrikant B. Mali, MDS Abstract: Advances in molecular biology over the past decade have helped to enhance understanding of the complex interplay between genetic, transcriptional, and translational alterations in human cancers. These molecular changes are the basis for an evolving field of high-throughput cancer discovery techniques using microscopic amounts of patient-based materials. In recent years, there has been an explosion in the development of new tools to analyze the proteome of cells, blood, and other body fluids. The information that is expected from such technologies may soon exert a dramatic change in the pace of cancer research and impact dramatically on the care of cancer patients. (J Craniofac Surg 2015;26: 1013–1014)
O
wing to various cellular mechanisms including alternative splicing and post-translational modifications of proteins (eg, phosphorylation, glycosylation, acetylation, and proteolytic cleavage), it is estimated that the human proteome comprises more than 500,000 proteins in comparison with about 22,000 protein-coding genes. This discrepancy implies that protein diversity cannot be fully characterized by gene expression analysis alone, making proteomics a promising tool for characterizing cells and tissues of interest and for biomarker discovery. Proteomics is the large-scale study of proteins, particularly their structure and functions, including detection, identification, measurement of their concentration, characterization of modification, characterization of protein–protein interaction and regulation. The proteome is a rather dynamic entity and differs from cell to cell. The unique advantage is that, in contrast with the genome, the inherent dynamic nature of the proteome allows us to monitor changes in the state of a cell, tissue, or organism over time. Only dynamic information will allow us to follow the course of a disease and track its pathogenetic mechanisms as well as its response to therapy. This may be of particular importance for diseases such as cancer, which evolve dynamically and affect many heterogeneous cell populations, either as part of the cancer or as part of the host’s reaction to the tumour. What Is This Box? A QR Code is a matrix barcode readable by QR scanners, mobile phones with cameras, and smartphones. The QR Code links to the online version of the article.
From the Department of Oral and Maxillofacial Surgery, MGV KBH Dental College, Nashik, Maharashtra, India. Received October 21, 2014. Accepted for publication January 31, 2015. Address correspondence and reprint requests to Dr Shrikant Balasaheb Mali, MDS, Oral and Maxillofacial Surgery, Associate professor, MGV KBH Dental College, Flat No 2, Jyoti Savitri Apartment, Ashoka Marg, Nashik, Maharashtra, India; Email: [email protected] The author reports no conflicts of interest. Copyright # 2015 by Mutaz B. Habal, MD ISSN: 1049-2275 DOI: 10.1097/SCS.0000000000001705
The Journal of Craniofacial Surgery
Generally, proteomics can be characterized as qualitative proteomics and quantitative proteomics. Qualitative proteomics experiments aim to study changes in protein expression. Qualitative proteomics aims to monitor changes in protein mixture composition under different physiologically relevant conditions. Quantitative proteomics provides quantitative information for all proteins in a sample instead of only providing lists of identified proteins. It aims to discover differences between samples (eg, healthy and diseased patients). This enables the identification of state- and stage-specific proteins. Structural proteomics attempts to uncover the structure of proteins and to unravel and map protein–protein interactions. The experimental design of a diagnostic proteomic investigation aims to scrutinize clinical samples from healthy and afflicted individuals in a high throughput manner, allowing for the relative abundance of thousands of proteins from the two histologically distinct samples to be visualized. Proteins that are found to be differentially abundant between the samples are then selected for identification with the hope that knowledge of their identity will provide the basis for defining a diagnostic biomarker. In proteomics research for biomarker discovery, at the time of writing there are two main approaches used in cancer: protein identification and pattern recognition. Both approaches require high-capacity computing and bioinformatics systems to process the enormous amount of data that are produced by proteomic studies. Identification and quantitation of proteins and peptides by mass spectrometry (MS) are usually carried out using ‘‘bottom-up’’ approach or ‘‘top-down’’ approach. Identification of protein targets will immediately facilitate their quantification and validation as well as evaluating their potential clinical value for further development of the discovery of a panel of biomarker called ‘‘protein signatures’’ or ‘‘protein pattern’’ comprising several proteins which is thought to provide higher sensitivity and specificity. 2D-PAGE analysis has been the standard procedure for more than 30 years, which has been combined with MS for the detection of aberrantly expressed proteins in tissue and serum of cancer patients. With the help of 2D-PAGE analysis, nanomolar amounts of proteins are separated and identified by MS, especially in the molecular weight ranging from 10 to 150 kDa. The invention of laser capture microdissection greatly improved the specificity of 2D-PAGE for biomarker discovery, as it provided a means of rapidly procuring pure cell populations from the surrounding heterogeneous tissue and also markedly enriched the proteomes of interest. Laser capture microdissection has helped in sampling specific cell populations directly from tissue sections without causing any mechanical disruption while maintaining cell viability. Specific cell populations have also been isolated using the ultraviolet laser microscope system with pressure catapulting. Collection of cells with laser capture microdissection preserves the molecular composition and architecture of the cells so that direct comparisons of transcriptional and translational messages can be made between tissue microcompartments of the same sample, providing a snapshot of a tumor’s in vivo biological and physiological properties. Matrix-assisted laser desorption and ionization with time-of-flight detection (MALDI-TOF) MS and surface-enhanced laser desorption and ionization with time-of-flight spectrometry (SELDI-TOF) are the two methods employed in pattern recognition. MALDI techniques immobilize protein samples in an energy absorbing matrix (chemical) on a chip or plate. The entire repertoire of proteins in the sample interacts with the matrix from which a selected subset of proteins is bound to, a function of the composition of the selected matrix. MALDI
Volume 26, Number 4, June 2015
1013
Copyright © 2015 Mutaz B. Habal, MD. Unauthorized reproduction of this article is prohibited.
Special Editorial
The Journal of Craniofacial Surgery
analysis is well suited for resolution of proteins less than 20 kDa, the low molecular weight proteome, a heretofore poorly dissected information reserve. Conversely, SELDI technology uses selective surfaces for binding a subset of proteins based on absorption, partition, electrostatic interaction, or affinity chromatography on a solid-phase protein chip surface. SELDI technology is potentially an important tool for the rapid identification of cancer specific biomarkers and proteomic patterns in the proteomes of both tissues and body fluids. SELDI is useful in high-throughput proteomic fingerprinting of cell lysates and body fluids that uses on-chip protein fractionation coupled to time-of-flight separation. Within minutes, sub-proteomes of a complex milieu, such as serum, can be visualized as a proteomic fingerprint or ‘‘bar-code.’’ SELDI technology has significant advantages over other proteomic technologies, in that the amounts of input material required for analysis are miniscule compared with more traditional 2D-PAGE approaches. SELDI analysis is also very high throughput—data can be generated in minutes or hours for large study sets, as opposed to days for 2D-PAGE analyses. In pattern recognition approach, MS combined with adequate bioinformatics tools are used to measure the mass and relative quantity of all proteins or peptides in biological sample without proteolysis or deep fractionation. By comparing profiles (protein signatures) between samples taken from patients with those taken for their matched healthy controls, a list of differentially expressed proteins is generated and used for further validation. Independent identification of those proteins is consequently carried out on MALDI-TOF-MS/MS or LCMS/MS or even with gold standard assays to rollout the likelihood that differences in protein signatures observed between those biological samples are due to experimental bias. Types of bioinformatics data-mining systems are supervised and unsupervised. Supervised systems require knowledge or data in which the outcome or classification is known ahead of time, so that the system can be trained to recognize and distinguish outcomes. Unsupervised systems cluster or group records without previous knowledge of outcome or classification. Application of these artificial intelligence (AI) systems to mass spectral data derived from the serum proteome has given rise to a new analytical model: proteomic pattern diagnostics. As each new patient is validated through pathological diagnosis using retrospective or prospective study sets, its input can be added to an ever-expanding training set. The AI tool learns, adapts, and gains experience through constant vigilant retraining—meaning that it can start to recognize a unique and new phenotype even though the system had not been trained or seen it beforehand. This is extremely important when clinical applications are considered in which hundreds of thousands of patients might be screened for a particular cancer. An important aspect of cancer proteomics is the ability to target and analyze subsets of proteins. Protein subsets include those with a specific modification, the ability to bind a specific sequence or site, multimeric complexes, and so forth. In a parallel development there is a focus on subcellular proteomes to target cellular organelles. This is an attempt to reduce the complexity of the eukaryotic cell to ascertain meaningful information related to cancer biology. Specific compartments under scrutiny include proteome analysis of mitochondria, lysosomes, peroxisomes, endoplasmic reticulum, Golgi apparatus, endocytic vesicles, and the nucleus. Proteomic biomarker research focuses on the following main areas: The identification of new targets for therapeutic intervention. The identification of markers that permit early detection of disease, better stratification and are of prognostic value. Markers for the monitoring of response to therapy. Proteomic technologies can be used to identify markers for cancer diagnosis, to monitor disease progression, and to identify therapeutic targets. Proteomics is valuable in the discovery of biomarkers because
1014
Volume 26, Number 4, June 2015
the proteome reflects both the intrinsic genetic program of the cell and the impact of its immediate environment. More focused approaches within the category of functional proteomics aim to facilitate the analysis of complex proteomes by selecting specific protein functions of interest. This strategy, referred to as activity-based protein profiling, uses active site-directed probes to interrogate, for example, the functional state of enzyme families. The delineation of enzyme activities selectively associated with tumor cells or tissues has the potential to yield a rich source of biomarkers and targets for cancer diagnosis and therapy. Stable isotope labeling of amino acids in cell culture has become a popular approach to study the proteomes of various cell types and microorganisms and how they change in response to various conditions. This approach has been used to study cancer cell spread and cancer cell secretion as well as to identify therapeutic targets. Protein arrays hold considerable promise for functional proteomics and expression profiling for monitoring a disease state. It would allow us to study changes in protein expression or modification on a global scale without much of the specialist and expensive equipment required by MS-based proteomics. There are two basic designs: (i) FPAs (forward phase arrays), where antibodies are arrayed and probed with cell lysates or (ii) RPAs (reverse phase arrays), where cell lysates are arrayed and probed with antibodies. Limitations to overcome include the development of high throughput technologies to express and purify proteins, and the generation of large sets of well-characterized antibodies. Tissue arrays have applications in the simultaneous analysis of tumors from many different patients at different stages of disease. Disadvantages of this technique are that a single core is not representative because of tumor heterogeneity and uncertainty of antigen stability on long-term storage of the array. Proteomics has become an important tool for the discovery of new targets, and the further characterization of known drug targets with unknown function. Proteomics approaches for the analysis of protein expression and posttranslational modifications are also very useful for the investigation of the cellular responses to drugs, and to study their mechanism of action and the basis of resistance. The identification of such a set of target proteins is equally powerful for elucidating the molecular basis for side effects as well as opening new potential applications for a known drug Clinical proteomics can have important direct bedside applications. In the future, the physician and pathologist will use these different proteomic analyses at many points of disease management. Proteomics will help in early detection of the disease using proteomic patterns of body fluid samples, diagnosis based on proteomic signatures as a complement to histopathology, individualized selection of therapeutic combinations, real-time assessment of therapeutic efficacy and toxicity, and rational redirection of therapy based on changes in the diseased protein network associated with drug resistance. Proteins being observed in these analyses are generally of high abundance. Therefore, valuable biomarkers expressed at low abundance remain undetected until current analytical technologies become more sensitive. Discovery of effective biomarkers requires the analysis of hundreds of histologically well-defined samples retrieved from healthy and disease-afflicted individuals. In addition, to be clinically relevant, the biomarker should be present in easily obtainable samples such as serum, plasma, or urine. The presence of a single, definitive biomarker for a particular histological condition, such as human chorionic gonadotropin for pregnancy, is the exception rather than the rule. The complex nature and instability of the human clinical samples during their collection and analysis, due to the degradation of their quality, and content linked to the presence of enzymes proteins make the integrity of those samples during the whole processing steps a key to any analysis of their content. The large dynamic range in protein concentration and the presence of different sates of proteins are other hurdles for proteomics to overcome.
#
2015 Mutaz B. Habal, MD
Copyright © 2015 Mutaz B. Habal, MD. Unauthorized reproduction of this article is prohibited.
Proteomics for Early Diagnostics Shrikant B. Mali, MDS Abstract: Advances in molecular biology over the past decade have helped to enhance understanding of the complex interplay between genetic, transcriptional, and translational alterations in human cancers. These molecular changes are the basis for an evolving field of high-throughput cancer discovery techniques using microscopic amounts of patient-based materials. In recent years, there has been an explosion in the development of new tools to analyze the proteome of cells, blood, and other body fluids. The information that is expected from such technologies may soon exert a dramatic change in the pace of cancer research and impact dramatically on the care of cancer patients. (J Craniofac Surg 2015;26: 1013–1014)
O
wing to various cellular mechanisms including alternative splicing and post-translational modifications of proteins (eg, phosphorylation, glycosylation, acetylation, and proteolytic cleavage), it is estimated that the human proteome comprises more than 500,000 proteins in comparison with about 22,000 protein-coding genes. This discrepancy implies that protein diversity cannot be fully characterized by gene expression analysis alone, making proteomics a promising tool for characterizing cells and tissues of interest and for biomarker discovery. Proteomics is the large-scale study of proteins, particularly their structure and functions, including detection, identification, measurement of their concentration, characterization of modification, characterization of protein–protein interaction and regulation. The proteome is a rather dynamic entity and differs from cell to cell. The unique advantage is that, in contrast with the genome, the inherent dynamic nature of the proteome allows us to monitor changes in the state of a cell, tissue, or organism over time. Only dynamic information will allow us to follow the course of a disease and track its pathogenetic mechanisms as well as its response to therapy. This may be of particular importance for diseases such as cancer, which evolve dynamically and affect many heterogeneous cell populations, either as part of the cancer or as part of the host’s reaction to the tumour. What Is This Box? A QR Code is a matrix barcode readable by QR scanners, mobile phones with cameras, and smartphones. The QR Code links to the online version of the article.
From the Department of Oral and Maxillofacial Surgery, MGV KBH Dental College, Nashik, Maharashtra, India. Received October 21, 2014. Accepted for publication January 31, 2015. Address correspondence and reprint requests to Dr Shrikant Balasaheb Mali, MDS, Oral and Maxillofacial Surgery, Associate professor, MGV KBH Dental College, Flat No 2, Jyoti Savitri Apartment, Ashoka Marg, Nashik, Maharashtra, India; Email: [email protected] The author reports no conflicts of interest. Copyright # 2015 by Mutaz B. Habal, MD ISSN: 1049-2275 DOI: 10.1097/SCS.0000000000001705
The Journal of Craniofacial Surgery
Generally, proteomics can be characterized as qualitative proteomics and quantitative proteomics. Qualitative proteomics experiments aim to study changes in protein expression. Qualitative proteomics aims to monitor changes in protein mixture composition under different physiologically relevant conditions. Quantitative proteomics provides quantitative information for all proteins in a sample instead of only providing lists of identified proteins. It aims to discover differences between samples (eg, healthy and diseased patients). This enables the identification of state- and stage-specific proteins. Structural proteomics attempts to uncover the structure of proteins and to unravel and map protein–protein interactions. The experimental design of a diagnostic proteomic investigation aims to scrutinize clinical samples from healthy and afflicted individuals in a high throughput manner, allowing for the relative abundance of thousands of proteins from the two histologically distinct samples to be visualized. Proteins that are found to be differentially abundant between the samples are then selected for identification with the hope that knowledge of their identity will provide the basis for defining a diagnostic biomarker. In proteomics research for biomarker discovery, at the time of writing there are two main approaches used in cancer: protein identification and pattern recognition. Both approaches require high-capacity computing and bioinformatics systems to process the enormous amount of data that are produced by proteomic studies. Identification and quantitation of proteins and peptides by mass spectrometry (MS) are usually carried out using ‘‘bottom-up’’ approach or ‘‘top-down’’ approach. Identification of protein targets will immediately facilitate their quantification and validation as well as evaluating their potential clinical value for further development of the discovery of a panel of biomarker called ‘‘protein signatures’’ or ‘‘protein pattern’’ comprising several proteins which is thought to provide higher sensitivity and specificity. 2D-PAGE analysis has been the standard procedure for more than 30 years, which has been combined with MS for the detection of aberrantly expressed proteins in tissue and serum of cancer patients. With the help of 2D-PAGE analysis, nanomolar amounts of proteins are separated and identified by MS, especially in the molecular weight ranging from 10 to 150 kDa. The invention of laser capture microdissection greatly improved the specificity of 2D-PAGE for biomarker discovery, as it provided a means of rapidly procuring pure cell populations from the surrounding heterogeneous tissue and also markedly enriched the proteomes of interest. Laser capture microdissection has helped in sampling specific cell populations directly from tissue sections without causing any mechanical disruption while maintaining cell viability. Specific cell populations have also been isolated using the ultraviolet laser microscope system with pressure catapulting. Collection of cells with laser capture microdissection preserves the molecular composition and architecture of the cells so that direct comparisons of transcriptional and translational messages can be made between tissue microcompartments of the same sample, providing a snapshot of a tumor’s in vivo biological and physiological properties. Matrix-assisted laser desorption and ionization with time-of-flight detection (MALDI-TOF) MS and surface-enhanced laser desorption and ionization with time-of-flight spectrometry (SELDI-TOF) are the two methods employed in pattern recognition. MALDI techniques immobilize protein samples in an energy absorbing matrix (chemical) on a chip or plate. The entire repertoire of proteins in the sample interacts with the matrix from which a selected subset of proteins is bound to, a function of the composition of the selected matrix. MALDI
Volume 26, Number 4, June 2015
1013
Copyright © 2015 Mutaz B. Habal, MD. Unauthorized reproduction of this article is prohibited.
Special Editorial
The Journal of Craniofacial Surgery
analysis is well suited for resolution of proteins less than 20 kDa, the low molecular weight proteome, a heretofore poorly dissected information reserve. Conversely, SELDI technology uses selective surfaces for binding a subset of proteins based on absorption, partition, electrostatic interaction, or affinity chromatography on a solid-phase protein chip surface. SELDI technology is potentially an important tool for the rapid identification of cancer specific biomarkers and proteomic patterns in the proteomes of both tissues and body fluids. SELDI is useful in high-throughput proteomic fingerprinting of cell lysates and body fluids that uses on-chip protein fractionation coupled to time-of-flight separation. Within minutes, sub-proteomes of a complex milieu, such as serum, can be visualized as a proteomic fingerprint or ‘‘bar-code.’’ SELDI technology has significant advantages over other proteomic technologies, in that the amounts of input material required for analysis are miniscule compared with more traditional 2D-PAGE approaches. SELDI analysis is also very high throughput—data can be generated in minutes or hours for large study sets, as opposed to days for 2D-PAGE analyses. In pattern recognition approach, MS combined with adequate bioinformatics tools are used to measure the mass and relative quantity of all proteins or peptides in biological sample without proteolysis or deep fractionation. By comparing profiles (protein signatures) between samples taken from patients with those taken for their matched healthy controls, a list of differentially expressed proteins is generated and used for further validation. Independent identification of those proteins is consequently carried out on MALDI-TOF-MS/MS or LCMS/MS or even with gold standard assays to rollout the likelihood that differences in protein signatures observed between those biological samples are due to experimental bias. Types of bioinformatics data-mining systems are supervised and unsupervised. Supervised systems require knowledge or data in which the outcome or classification is known ahead of time, so that the system can be trained to recognize and distinguish outcomes. Unsupervised systems cluster or group records without previous knowledge of outcome or classification. Application of these artificial intelligence (AI) systems to mass spectral data derived from the serum proteome has given rise to a new analytical model: proteomic pattern diagnostics. As each new patient is validated through pathological diagnosis using retrospective or prospective study sets, its input can be added to an ever-expanding training set. The AI tool learns, adapts, and gains experience through constant vigilant retraining—meaning that it can start to recognize a unique and new phenotype even though the system had not been trained or seen it beforehand. This is extremely important when clinical applications are considered in which hundreds of thousands of patients might be screened for a particular cancer. An important aspect of cancer proteomics is the ability to target and analyze subsets of proteins. Protein subsets include those with a specific modification, the ability to bind a specific sequence or site, multimeric complexes, and so forth. In a parallel development there is a focus on subcellular proteomes to target cellular organelles. This is an attempt to reduce the complexity of the eukaryotic cell to ascertain meaningful information related to cancer biology. Specific compartments under scrutiny include proteome analysis of mitochondria, lysosomes, peroxisomes, endoplasmic reticulum, Golgi apparatus, endocytic vesicles, and the nucleus. Proteomic biomarker research focuses on the following main areas: The identification of new targets for therapeutic intervention. The identification of markers that permit early detection of disease, better stratification and are of prognostic value. Markers for the monitoring of response to therapy. Proteomic technologies can be used to identify markers for cancer diagnosis, to monitor disease progression, and to identify therapeutic targets. Proteomics is valuable in the discovery of biomarkers because
1014
Volume 26, Number 4, June 2015
the proteome reflects both the intrinsic genetic program of the cell and the impact of its immediate environment. More focused approaches within the category of functional proteomics aim to facilitate the analysis of complex proteomes by selecting specific protein functions of interest. This strategy, referred to as activity-based protein profiling, uses active site-directed probes to interrogate, for example, the functional state of enzyme families. The delineation of enzyme activities selectively associated with tumor cells or tissues has the potential to yield a rich source of biomarkers and targets for cancer diagnosis and therapy. Stable isotope labeling of amino acids in cell culture has become a popular approach to study the proteomes of various cell types and microorganisms and how they change in response to various conditions. This approach has been used to study cancer cell spread and cancer cell secretion as well as to identify therapeutic targets. Protein arrays hold considerable promise for functional proteomics and expression profiling for monitoring a disease state. It would allow us to study changes in protein expression or modification on a global scale without much of the specialist and expensive equipment required by MS-based proteomics. There are two basic designs: (i) FPAs (forward phase arrays), where antibodies are arrayed and probed with cell lysates or (ii) RPAs (reverse phase arrays), where cell lysates are arrayed and probed with antibodies. Limitations to overcome include the development of high throughput technologies to express and purify proteins, and the generation of large sets of well-characterized antibodies. Tissue arrays have applications in the simultaneous analysis of tumors from many different patients at different stages of disease. Disadvantages of this technique are that a single core is not representative because of tumor heterogeneity and uncertainty of antigen stability on long-term storage of the array. Proteomics has become an important tool for the discovery of new targets, and the further characterization of known drug targets with unknown function. Proteomics approaches for the analysis of protein expression and posttranslational modifications are also very useful for the investigation of the cellular responses to drugs, and to study their mechanism of action and the basis of resistance. The identification of such a set of target proteins is equally powerful for elucidating the molecular basis for side effects as well as opening new potential applications for a known drug Clinical proteomics can have important direct bedside applications. In the future, the physician and pathologist will use these different proteomic analyses at many points of disease management. Proteomics will help in early detection of the disease using proteomic patterns of body fluid samples, diagnosis based on proteomic signatures as a complement to histopathology, individualized selection of therapeutic combinations, real-time assessment of therapeutic efficacy and toxicity, and rational redirection of therapy based on changes in the diseased protein network associated with drug resistance. Proteins being observed in these analyses are generally of high abundance. Therefore, valuable biomarkers expressed at low abundance remain undetected until current analytical technologies become more sensitive. Discovery of effective biomarkers requires the analysis of hundreds of histologically well-defined samples retrieved from healthy and disease-afflicted individuals. In addition, to be clinically relevant, the biomarker should be present in easily obtainable samples such as serum, plasma, or urine. The presence of a single, definitive biomarker for a particular histological condition, such as human chorionic gonadotropin for pregnancy, is the exception rather than the rule. The complex nature and instability of the human clinical samples during their collection and analysis, due to the degradation of their quality, and content linked to the presence of enzymes proteins make the integrity of those samples during the whole processing steps a key to any analysis of their content. The large dynamic range in protein concentration and the presence of different sates of proteins are other hurdles for proteomics to overcome.
#
2015 Mutaz B. Habal, MD
Copyright © 2015 Mutaz B. Habal, MD. Unauthorized reproduction of this article is prohibited.
