A method of diagnosing pancreatic cancer at early stages


Pancreatic cancer is the second leading cause of cancer-related death. It is very hard to be diagnosed at early stages. Neither the imaging tests nor the invasive methods are applicable for large-scale screening and demonstrate sensitivity limitations in the detection. Non-invasive method with high selectivity and specificity is urgently needed.


The mass spectrometry (MS) based analysis of the specific metabolites dysregulation of non-invasively collected samples of pancreatic cancer patients and healthy volunteers enables to build up the statistical models, which may be used to determine the level of probability of the patient suffering from pancreatic cancer. The whole methodology is based on accurate performance of multiple steps, including mainly the sample collection, storage, transport, and processing, followed by analytical qualitative and quantitative analysis using one of three mass spectrometry based methods, followed by multivariate data analysis (MDA) of obtained absolute quantitative data. The selectivity and specificity of this determination is very high, typically 95-100%.


The final methodology provides high selectivity, sensitivity, and sample throughput for pancreatic cancer classification, which is 100% for samples with known classification (292 subjects) and ca. 95% for samples with unknown classification (73 subjects). The sample throughput of our methodology is at least 4000 samples per year and one MS system with possible improvements using automation and multiplexing. The method can be used for population screening of the whole population or selected population groups based on risk factors such as age, gender, body-mass-index, genetic predispositions, risk behavior, etc.


The methodology is fully validated in line with recommendations of the authorities, FDA and EMEA.


Some publications of the research group on the related topic: E. Cífková, M. Lísa, R. Hrstka, D. Vrána, J. Gatěk, B. Melichar, M. Holčapek, Rapid Commun. Mass Spectrom. 31 (2017) 253-263. E. Cífková, M. Holčapek, M. Lísa, D. Vrána, J. Gatěk, B. Melichar, Anal. Bioanal. Chem. 407 (2015) 991-1002.


EP application filed in January 2018.


University of Pardubice

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