On Statistical Methods in Prostate Cancer Genomics

Event start date
Event start time
12.00
Place

Arvo building, Jarmo Visakorpi auditorium, address: Lääkärinkatu 1.

Doctoral defence of Dipl.-Stat. Daniel Fischer

On Statistical Methods in Prostate Cancer Genomics

The field of science of the dissertation is Biometry.

The opponent is docent Patrik Ryden (University of Umeå, Sweden). Professor Jaakko Nevalainen acts as the custos.

The language of the dissertation defence is English.

On Statistical Methods in Prostate Cancer Genomics

With the introduction of the PSA test for Prostate Cancer (PrCa) using Prostate Specific Antigen (PSA) values, the number of diagnosed PrCa cases has been growing tremendously. However, the growing number of PrCa cases is not only due to an increased PrCa risk, but also due to an overly sensitive but not cancer specific PSA test. Consequently, the number of false positive test results also grows tremendously and creates pressure to the health care system in terms of increased expenses for treatments. Even worth, many men suffer from the side effects of a possibly unnecessary PrCa treatment, like e.g. incontinence or impotence. This motivates the development of other, more sensitive tests that can better differentiate between false and true positive tests, that are cancer specific on the one hand as well as having on the other hand an ability to detect aggressive PrCa in early stages of the disease. The vast majority of PrCa cases are indolent and do not necessarily require instant active treatment. However, it would be utterly important to be able to detect aggressive cases already on early, curable phases of the disease. Identification of high-risk patients would enable targeted screening and treatment to be offered. Thereby focusing resources upon those patients is of prime importance.

Whereas aggressive PrCa requires immediate treatment to increase the chances of survival, the indolent respective non-aggressive cases often do not require any drastic treatments and the side-effects of the treatments are often expected to be worse than the symptoms of the disease itself. Clinicians have urgent needs for tests that help to identify such cases within the group of PrCa patients.

One aim of this study was to evaluate the use of microRNA (miRNA) expression values as possible biomarkers. The underlying RNA was extracted from lymphoblastoid cell lines (LCL), what means that the expression profile was measured in the blood and not in the cancer tissue. It was hypothesized that measuring the miRNA expression in the LCL could lead to a possible clinical application that is less invasive for the patient.

In terms of a research hypothesis, the interest was to identify those miRNA for which a directional hypothesis holds. In other words, for a set of miRNAs the expression values were measured for a set of aggressive PrCa (aPrCa) and less or non-aggressive PrCa (naPrCa) cases as well as miRNAs from healthy controls. The focus was then on identifying those miRNAs for which the healthy individuals had a lower(higher) expression value than the naPrCa patients and they again also had then lower(higher) expression values than the aPrCa cases.

For this scenario a generalized Mann-Whitney test (gMWT) for directional alternatives was derived. The software implementation of this test was made publicly available in the R-package gMWT. Further, the same gMWT was applied to improve the testing methods for quantitative trait loci (QTL) respective expression quantitative trait loci (eQTL) analysis. These methods were also made available in an R-package called GenomicTools.

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The dissertation is published in the self-publishing, Turku 2017. ISBN 978-952-93-8212-5. The dissertation is also published in the e-series Acta Electronica Universitatis Tamperensis; 1750, Tampere University Press 2017. ISBN 978-952-03-0325-9, ISSN 1456-954X.

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