Clinical Proteomics

Liotta, Lance A.; Kohn, Elise C.; Petricoin, Emanuel F.

doi:10.1001/jama.286.18.2211

Cited by 203 publications

(37 citation statements)

References 23 publications

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“…Deciphering the molecular pathogenesis of deadly diseases, such as cancer, is fundamental for understanding disease mechanisms and for the rational design of targeted therapy regimens [1, 2]. This new diagnosis and treatment paradigm has several designations - individualized therapy, molecular medicine, or personalized medicine – all of which indicate the need to design therapies based on known/predictive biomarkers, prognostic factors, and a patient’s genomic and/or proteomic disease profile [3].…”

Section: Introductionmentioning

confidence: 99%

“…Personalized medicine, the term used herein, aims to improve disease detection, predict treatment response, and reduce adverse therapy events by combining common prognostic criteria such as tumor stage, grade, age, etc. with an individual patient’s genomic/proteomic profile [1–3]. The ability to quantify phosphoprotein levels in small amounts of human biopsy material provides a new class of analytes that factor into treatment decisions [5].…”

Section: Introductionmentioning

confidence: 99%

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Reverse Phase Protein Arrays: Mapping the Path Towards Personalized Medicine

Gallagher

Espina

2014

Mol Diagn Ther

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Reverse phase protein array (RPPA) technology evolved from the advent of miniaturized immunoassays and gene microarray technology. Reverse phase protein arrays provide either a low throughput or high throughput methodology for quantifying proteins and their post-translationally modified forms in both cellular and non-cellular samples. As the demand for patient tailored therapies increases so does the need for precise and sensitive technology to accurately profile the molecular circuitry driving an individual patient’s disease. RPPAs are currently utilized in clinical trials for profiling and comparing the functional state of protein signaling pathways, either temporally within tumors, between patients, or within the same patients before/after treatment. RPPAs are generally employed for quantifying large numbers of samples on one array, under identical experimental conditions. However, the goal of personalized cancer medicine is to design therapies based on the molecular portrait of a patient’s tumor, which in turn result in more efficacious treatments with less toxicity. Therefore, RPPAs are also being validated for low throughput assays of individual patient samples. This review explores reverse phase protein array technology in the cancer research field, concentrating on its role as a fundamental tool for deciphering protein signaling networks and its emerging role in personalized medicine.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reverse Phase Protein Arrays: Mapping the Path Towards Personalized Medicine

Gallagher

Espina

2014

Mol Diagn Ther

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“…Using tissue samples, researchers can either analyse the entire multicellular unit or select specific cell types in which they are interested [19]. Some years ago, we [20] and others [21], developed a laser-based tissue microdissection method that allows one to procure and study individual cells from a histopathologically-verified tissue section.…”

Section: Basic Science Applications: Cell Linesmentioning

confidence: 99%

“…One goal for clinical proteomics will be to characterise the information flow within single cells, tissues or entire organisms [19] under normal or disease conditions. DNA is the information archive, while proteins are the actors in the cell performing all the tasks and ultimately direct biological processes and biological fates.…”

Section: Formalin Fixed Tissuesmentioning

confidence: 99%

Clinical Proteomics: New Trends for Protein Microarrays

Becker¹,

Metzger²,

Hipp³

et al. 2006

CMC

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Protein microarrays are an emerging class of nanotechnology for tracking many different proteins simultaneously. Much progress has been made for applications in basic sciences. Translation of these methods for the treatment of patients, however, is slow, because the realities in the clinic are rarely taken into account, and proteomic changes in cultured cell lines might not fully reflect human diseases due to the lack of the tissue microenvironment. In this review, we summarise current protein microarray approaches that are being developed for profiling tissues and histopathologically defined cell populations from cancer patients. We provide an overview of clinical applications for protein lysate microarrays and discuss the power of this technology for the discovery of disease markers for cancer diagnosis and individualised treatment.

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“…Following processing by mass spectroscopy, the low molecular weight serum proteome is rendered an archive of metabolic and physiologic information, which has been shown to reflect the changes within an organ system in response to a pathologic process. This new information archive is just beginning to be explored and exploited for early disease diagnosis strategies [10]. Within defined feasibility study sets, experimentalists have used serum proteomic information content to detect the onset of disease at a very early stage, thus maximizing the likelihood of successful outcomes [3][4][5][6][7].…”

Section: The Challenge Of Proteomicsmentioning

confidence: 99%

Toward a systems biology software toolkit

Johann

McGuigan

Tomov

et al.

Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems

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Insight to complex problems may be revealed when domain data sets are viewed or structured in new and innovative ways. Systems approaches to biomedical problems fundamentally involve forecasting, and a deliberate integration of diverse data sources. Adding a clinical proteomic dimension to these developing efforts affirms the ultimate aims of enhanced diagnostic prediction, maximizing the possibility for a rational therapy individualized to a patient's pathologic process, and thus, improving patient outcomes. Consistent with these evolving goals, computer-aided diagnostic systems are rapidly approaching a new paradigm involving the integration of medical imaging with high throughput molecular medicine tests. As medical imaging systems become more sensitive in finding anatomic anomalies, their lack of specificity becomes much more of a clinical dilemma. Proteomic tests may be used to help resolve the lack of specificity of imaging findings. A synergistic test composed of a targeted imaging study correlated with a genomic or proteomic test(s), offers the potential of a tremendous medical advancement. Bioinformatic software toolkits are crucial components of these systems. Open source software provides a mechanism for leveraging existing toolkits, sharing expertise, accelerating development, and furthering biomedical, software, and systems sciences in new and complex multi-disciplinary fields. Our evolving toolkit utilizes components from several existing open source projects. It will initially be customized for serum proteomic pattern diagnostics, which will be used in the upcoming NCI/CCR Clinical Trial involving the monitoring for ovarian cancer recurrence.

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Clinical Proteomics

Cited by 203 publications

References 23 publications

Reverse Phase Protein Arrays: Mapping the Path Towards Personalized Medicine

Reverse Phase Protein Arrays: Mapping the Path Towards Personalized Medicine

Clinical Proteomics: New Trends for Protein Microarrays

Toward a systems biology software toolkit

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