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Serum proteomics in prostate cancer patients vs. healthy and benign prostate hyperplasia

ABSTRACT: The study aim was to identify novel serum markers of prostate cancer diagnosis compared to benign prostate hyperplasia and healthy controls

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood

SUBMITTER: Antigoni Manousopoulou

LAB HEAD: Spiro Garbis

PROVIDER: PXD004575 | Pride | 2016-10-04

REPOSITORIES: Pride

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			Action	DRS
	05Jul12_prostate_HILIC_seg03_101.raw	Raw
	05Jul12_prostate_HILIC_seg03_103.raw	Raw
	05Jul12_prostate_HILIC_seg03_104.raw	Raw
	05Jul12_prostate_HILIC_seg03_105.raw	Raw
	05Jul12_prostate_HILIC_seg03_108.raw	Raw

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Publications

Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study.

Larkin S E T SE Johnston H E HE Jackson T R TR Jamieson D G DG Roumeliotis T I TI Mockridge C I CI Michael A A Manousopoulou A A Papachristou E K EK Brown M D MD Clarke N W NW Pandha H H Aukim-Hastie C L CL Cragg M S MS Garbis S D SD Townsend P A PA

British journal of cancer 20160929 9

<h4>Background</h4>Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease.<h4>Methods</h4>We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa.<h4>Results</h4>We identified >1000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which tw ...[more]

PMID: 27685442

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Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia

Project description:There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants with a diagnosis of PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model was able to separate PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.

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