Proteomics and personalized medicine: a focus on kidney disease

ABSTRACT

Introduction: Inter-individual variability in response to drug treatment has induced an increased demand for decisions via personalize medicine. Also, the contribution of proteomics to the era of personalized medicine would seem to be vital in improving therapeutic outcomes.

Areas covered: We review validated biomarkers discovered by proteomics techniques and their use in personalized medicine with the focus on kidney diseases. We discuss this topic with a special emphasis on recent publications and relevant initiatives and depict some limitations that remain for personalized medicine.

Expert opinion: The development of highly accurate biomarkers is essential for optimizing the management of kidney diseases. Various biomarkers of kidney diseases have been identified using proteomic techniques. However, only a few of these biomarkers showed the potential to be used in clinical practice concerning personalized medicine. Therefore, it becomes evident that the combination of multiple biomarkers confers higher accuracy and the ability to depict complex pathophysiological conditions, a prerequisite for personalized treatment. CKD273, a multimarker panel for early CKD detection may serve as a first example for personalized medicine in nephrology. Based on this successful example, proteomics is expected to develop into the key technology to guide personalized intervention.     

Pathology,proteomics and the pathway to personalised medicine

Introduction: As we move from a discovery to a translational phase in proteomics, with a focus on developing validated clinical assays to assist personalized medicine, there is a growing need for strong bidirectional interactions with the clinical pathology community. Thus, while on one hand the proteomics community will provide candidate biomarkers to assist in diagnosis, prognosis, surveillance, identification of individualized patient medication, and development and validation of new assays for diagnostic use, on the other the pathology community will assist with specific tissue identification and selection (e.g. laser capture microdissection, tissue sections for MS imaging), biobanking, validation of emerging automated histopathology techniques, preparation and classification of relevant patient medical reports, and assisting with the optimization of experimental design for clinical trials.

Areas covered: Here we discuss these topics with a particular emphasis on recent publications and relevant initiatives and outline some of the hurdles that still remain for personalized medicine.

Expert commentary: It is clear that effective crosstalk between the proteomics and pathology communities will greatly accelerate crossover of candidate biomarkers to personalized medicine, which will have significant benefits not only for patient wellbeing, but also the global healthcare budget. However, analysis of the big data generated may become rate-limiting.     

从文献计量看个性化医学国际发展态势

该文选取当前生物医学发展的前沿领域——个性化医学领域,以美国科技信息所科学引文指标数据库(SCI-Expanded)为信息源,德温特数据分析家(Thomson Data Analyzer)为工具,对1999?2009年间,全球个性化医学研究领域发表的论文进行了文献计量学分析,以了解世界个性化医学的发展态势,为我国提升个性化医学的研究提供参考依据。     

Personalized diagnostics and biosensors: a review of the biology and technology needed for personalized medicine

Abstract

Exploiting the burgeoning fields of genomics, proteomics and metabolomics improves understanding of human physiology and, critically, the mutations that signal disease susceptibility. Through these emerging fields, rational design approaches to diagnosis, drug development and ultimately personalized medicine are possible. Personalized medicine and point-of-care testing techniques must fulfill a host of constraints for real-world applicability. Point-of-care devices (POCDs) must ultimately provide a cost-effective alternative to expensive and time-consuming laboratory tests in order to assist health care personnel with disease diagnosis and treatment decisions. Sensor technologies are also expanding beyond the more traditional classes of biomarkers – nucleic acids and proteins – to metabolites and direct detection of pathogens, ultimately increasing the palette of available techniques for the use of personalized medicine. The technologies needed to perform such diagnostics have also been rapidly evolving, with each generation being increasingly sensitive and selective while being more resource conscious. Ultimately, the final hurdle for all such technologies is to be able to drive consumer adoption and achieve a meaningful medical outcome for the patient.     

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics

Triple negative breast cancer accounts for 15%–20% of all breast carcinomas and is clinically characterized by an aggressive phenotype and poor prognosis. Triple negative tumors do not benefit from targeted therapies, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of 125 formalin-fixed paraffin-embedded samples from patients diagnosed with non-metastatic triple negative breast cancer were analyzed using data-independent acquisition + in a LTQ-Orbitrap Fusion Lumos mass spectrometer coupled to an EASY-nLC 1000. 1206 proteins were identified in at least 66% of the samples. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were combined to characterize proteomics-based molecular groups. Two molecular groups were defined with differences in biological processes such as glycolysis, translation and immune response. These two molecular groups showed also several differentially expressed proteins. This clinically homogenous dataset may serve to design new therapeutic strategies in the future.     

Direct tissue proteomics in human diseases: potential applications to melanoma research

Although the rates of cancer are stabilizing, the number of new invasive melanoma continues to rise. Melanoma represents only 4% of all skin cancers, but nearly 80% of skin cancer deaths. In loss of potential productive life-years, it is second only to adult leukemia. Once melanoma spreads to regional and distant sites, the chance of cure decreases significantly. Unfortunately, current diagnostic and prognostic methods are often inadequate. More precise staging and disease characterization will lead to new and more rational approaches to treatment. Proteomics is a fast-growing discipline in biomedicine that can be defined as the global characterization and differential expression of the entire protein complement of a cell, tissue or organism. Despite major advances in molecular approaches to the diagnosis and prognostication of human diseases such as melanoma, there remain significant obstacles in applying the proteomic technologies to clinical samples to extract important biological information. The application of a shotgun-based technique termed direct tissue proteomics with improved extraction protocol of proteins from formalin-fixed paraffin-embedded tissue would enable retrospective biomarker investigations of the vast archive of pathologically characterized clinical samples that exist worldwide. Combination of this direct tissue proteomics method with laser-capture microdissection may assist in the discovery of new biomarkers and may lead to new diagnostic tests, risk assessment and staging tools as well as improvement in therapeutics. In addition, these tools can provide a molecular characterization of melanoma, which may enable individualized molecular therapy.     

我国蛋白质组学研究现状及展望

蛋白质组学是系统研究分子机器、亚细胞器、细胞、组织、器官乃至整体等生物体系内蛋白质全组成及其活动规律的科学,已成为21世纪生命科学的焦点之一。本文简要介绍了蛋白质组学研究背景,我国蛋白质组学研究现状、存在问题和前景展望。     

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.     

LncRNAs as potential diagnostic and prognostic biomarkers in gastric cancer: A novel approach to personalized medicine

Gastric cancer is the third leading cause of cancer death with 5-year survival rate of about 30–35%. Since early detection is associated with decreased mortality, identification of novel biomarkers for early diagnosis and proper management of patients with the best response to therapy is urgently needed. Long noncoding RNAs (lncRNAs) due to their high specificity, easy accessibility in a noninvasive manner, as well as their aberrant expression under different pathological and physiological conditions, have received a great attention as potential diagnostic, prognostic, or predictive biomarkers. They may also serve as targets for treating gastric cancer. In this review, we highlighted the role of lncRNAs as tumor suppressors or oncogenes that make them potential biomarkers for the diagnosis and prognosis of gastric cancer. Relatively, lncRNAs such as H19, HOTAIR, UCA1, PVT1, tissue differentiation-inducing nonprotein coding, and LINC00152 could be potential diagnostic and prognostic markers in patients with gastric cancer. Also, the impact of lncRNAs such as ecCEBPA, MLK7-AS1, TUG1, HOXA11-AS, GAPLINC, LEIGC, multidrug resistance-related and upregulated lncRNA, PVT1 on gastric cancer epigenetic and drug resistance as well as their potential as therapeutic targets for personalized medicine was discussed.     

Role of oncoproteomics in the personalized management of cancer

Oncoproteomics is the term used to describe the application of proteomic technologies in oncology and parallels the related field of oncogenomics. It is now contributing to the development of personalized management of cancer. Proteomic technologies are used for the identification of biomarkers in cancer, which will facilitate the integration of diagnosis and therapy of cancer. Molecular diagnostics, laser capture microdissection and protein biochips are among the technologies that are having an important impact on oncoproteomics. The discovery of protein patterns developed by the US Food and Drug Administration/National Cancer Institute Clinical Proteomics Program is capable of distinguishing cancer and disease-free states with high sensitivity and specificity and will also facilitate the development of personalized therapy of cancer. Examples of application are given for breast and prostate cancer and a selection of companies and their collaborations that are developing application of proteomics to personalized treatment of cancer are discussed. Continued refinement of techniques and methods to determine the abundance and status of proteins in vivo holds great promise for the future study of normal cells and the pathology of associated neoplasms. Personalized cancer therapy is expected to be in the clinic by the end of the first decade of the 21st century.     

Introduction to proteomics: tools for the new biology

Towards revolutionary biomarkers, a considerable amount of research funds and time have been dedicated to proteomics. Although the discovery of novel biomarkers at the dawn of proteomics was a promising development, only a few identified biomarkers seemed to be beneficial for cancer patients. We may need to approach this issue differently, instead of only extending the conventional approaches that have been used historically. The study of biomarkers is essentially a study of diseases and the biochemistry relating to peptide, protein and post-translational modifications is only a tool. A problem-oriented approach should be needed in biomarker development. Clinician participation in the study of biomarkers will lead to realistic, practical and interesting biomarker candidates, which justify the time and expense involved in validation studies. Although discussion in this article is focused on cancer biomarkers, it can generally be applied to biomarker studies for other diseases.     

Human mesenchymal stromal cell proteomics: contribution for identification of new markers and targets for medicine intervention

Mesenchymal stem or stromal cells (MSCs) have become of great interest for cell-based therapy owing to their roles in tissue repair and immune suppression. MSCs have the ability to differentiate into specialized tissues, including bone, cartilage and muscle, among several others. Furthermore, it has been found that MSCs can also serve as cellular factories that secrete mediators to stimulate in situ regeneration of injured tissues. Proteomics has contributed significantly to the identification of new proteins to improve cellular characterization of MSCs, to identify new targets for therapeutic intervention and to elucidate important pathways utilized by MSCs to differentiate into distinct tissues. As proteomics technology advances, several studies can be revisited and analyzed in depth, employing state-of-the-art approaches, helping to uncover the cellular mechanisms utilized by MSCs to exert their regenerative functionalities. In this article, we will review the progress made so far and discuss further opportunities for proteomics to contribute to the clinical applications of MSCs.     

Clinical applications of mass spectrometry-based proteomics in cancer: Where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic.     

The need to revisit published data: A concept and framework for complementary proteomics

Tandem proteomic strategies based on large‐scale and high‐resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.