The proteomics field has experienced rapid growth with technologies achieving ever increasing accuracy, sensitivity, and throughput, and with availability of computational tools to address particular applications. Given that the proteome represents the most functional component encoded for in the genome, a systems approach to disease investigations and biomarker identification benefits substantially from integration of proteome level studies. Here we present proteomic approaches that have allowed systematic searches for potential cancer markers by integrating cancer cell profiling with additional sources of data, as illustrated with recent studies of ovarian cancer.

Sharon Pitteri

Sam Hanash

A Systems Approach to the Proteomic Identification of Novel Cancer Biomarkers

Systems biology is an approach to the science that views biology as an information science, studies biological systems as a whole and their interactions with the environment. This approach, for the reasons described here, has particular power in the search for informative diagnostic biomarkers of diseases because it focuses on the fundamental causes and keys on the identification and understanding of disease- perturbed molecular networks. In this review, we describe some recent developments that have used systems biology to address complex diseases &#x2013; prion disease and drug induced liver injury- and use these as examples to illustrate the importance of understanding network structure and dynamics. The knowledge of network dynamics through in vitro experimental perturbation and modeling allows us to determine the state of the networks, to identify molecular correlates, and to derive new disease treatment approaches to reverse the pathology or prevent its progress into a more severe state through the manipulation of network states. This general approach, including diagnostics and therapeutics, is becoming known as systems medicine.

Kai Wang

Inyoul Lee

George Carlson

Leroy Hood

David Galas

Systems Biology and the Discovery of Diagnostic Biomarkers

Our understanding of human disease and potential therapeutics is improving rapidly. In order to take advantage of these developments it is important to be able to identify disease markers. Many new high-throughput genomics and proteomics technologies are being implemented to identify candidate disease markers. These technologies include protein microarrays, next-generation DNA sequencing and mass spectrometry platforms. Such methods are particularly important for elucidating the repertoire of molecular markers in the genome, transcriptome, proteome and metabolome of patients with diseases such as cancer, autoimmune diseases, and viral infections, resulting from the disruption of many biological pathways. These new technologies have identified many potential disease markers. These markers are expected to be valuable to achieve the promise of truly personalized medicine.

Donald Sharon

Rui Chen

Michael Snyder

Systems Biology Approaches to Disease Marker Discovery

Karin D. Rodland

Systems Biology and Biomarker Discovery

Recent advances in high throughput, high content &#x201c;omic&#x201d; technologies coupled with clinical information has lead to the expectation that the complexity of the molecular information generated will lead to more robust scientific research as well as the expectation that overarching therapeutic approaches will be patient-tailored to the underlying specific molecular defects of the disease. As disease understanding progresses and more therapeutics, which predominately target proteins, are developed there is a need to more confidently determine the protein signaling events that can be correlated with drug response since the deranged protein signaling networks are often the drug target itself. In this environment, the Reverse Phase Protein Microarray (RPMA) can be utilized to address the needs of both clinical screening and disease understanding through its ability to provide an unmatched functional and highly multiplexed signaling network level mapping of ongoing signaling activation, coupled with the ability of the platform to provide this information reproducibly from a tiny needle biopsy specimen or fine needle aspirate. This platform has now been utilized for biomarker discovery/validation and advancements in disease understanding both in the clinic and at the bench in the fields of cancer, liver disease, immunological disorders, and bacterial infection.

Bridget Wilson

Lance A. Liotta

Emanuel Petricoin III

Monitoring Proteins and Protein Networks Using Reverse Phase Protein Arrays

The recent advances in high-throughput data acquisition have driven a revolution in the study of human disease and determination of molecular biomarkers of disease states. It has become increasingly clear that many of the most important human diseases arise as the result of a complex interplay between several factors including environmental factors, such as exposure to toxins or pathogens, diet, lifestyle, and the genetics of the individual patient. Recent research has begun to describe these factors in the context of networks which describe relationships between biological components, such as genes, proteins and metabolites, and have made progress towards the understanding of disease as a dysfunction of the entire system, rather than, for example, mutations in single genes. We provide a summary of some of the recent work in this area, focusing on how the integration of different kinds of complementary data, and analysis of biological networks and pathways can lead to discovery of robust, specific and useful biomarkers of disease and how these methods can help shed light on the mechanisms and etiology of the diseases being studied.

Jason E. McDermott

Michelle Costa

Derek Janszen

Mudita Singhal

Susan C. Tilton

Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data

Alternative splicing plays an important role in protein diversity without increasing genome size. Earlier thought to be uncommon, splicing appears to affect the majority of genes. Alternative splice variants have been detected at the mRNA level in many diseases. We have designed and demonstrated a discovery pipeline for alternative splice variant (ASV) proteins from tandem MS/MS datasets. We created a modified ECgene database with entries from exhaustive three-frame translation of Ensembl transcripts and gene models from ECgene, with periodic updates. The human database has 14 million entries; the mouse database, 10 million entries. We match MS/MS findings against these potential translation products to identify and quantify known and novel ASVs. In this review, we summarize findings and systems biology implications of biomarker candidates from a mouse model of human pancreatic ductal adenocarcinoma [28] and a mouse model of human Her2/neu-induced breast cancer [27]. The same approach is being applied to human tumors, plasma, and cell line studies of other cancers.

Gilbert S. Omenn

Anastasia K. Yocum

Rajasree Menon

Alternative Splice Variants, a New Class of Protein Cancer Biomarker Candidates: Findings in Pancreatic Cancer and Breast Cancer with Systems Biology Implications

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Disease Markers publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.

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Chief Editor Paola Gazzaniga is an Associate Professor in the Department of Molecular Medicine at Sapienza University of Rome, Italy. Her core research focuses on liquid biopsies in patients with solid tumors.

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Disease Markers

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