Gastrointestinės stromos tumorai (GIST) – dažniausi mezenchiminės kilmės virškinamojo trakto navikai, kurie pradėti identifikuoti ir diagnozuoti tik per pastaruosius 25 m. GIST, pagal ligos progresavimo reliatyvią riziką, klasifikuojami į rizikos kategorijas, kurių nustatymas 2002 m. Nacionalinių sveikatos institutų sutarimu (NSI) paremtas dviem kintamaisiais: naviko dydžiu ir navikinių ląstelių mitozių skaičiumi 50 DPRL, kurių apimamas plotas įvairių studijų ir rekomendacijų duomenimis varijuoja nuo 5 mm² iki 11,9 mm². Neaišku, kaip netikslus mitozių skaičius įtakoja rizikos kategorijos nustatymą bei jų sąsajas su ligos progresavimu. Nors atliktose didžiausiose GIST studijose teigiama, kad, esant identiškiems morfologiniams požymiams, skrandžio GIST prognozė geresnė, kiti tyrėjai nustatė morfologinius požymius lemiančius geresnę šių navikų prognozę. Esant prieštaringoms nuomonėms, tyrimo metu nustatėme ir palyginome skirtingų lokalizacijų GIST morfologinius požymius ir prognozę. NSI klasifikacijoje buvo pastebėta, kad ligos prognozę įtakoja ir GIST histologinis fenotipas, tačiau tuomet nebuvo pakankamai atliktų studijų, paneigiančių ar patvirtinančių šiuos teiginius, todėl detaliai nagrinėjome skirtingų histologinių fenotipų GIST morfologinius požymius ir jų įtaką ligos prognozei bei kitų morfologinių požymių: naviko dydžio, mitozių skaičiaus, ląstelingumo, branduolių polimorfizmo ir nekrozės sąsajas su ligos prognoze. / Gastrointestinal stromal tumours (GIST) – the most common mesenchymal tumours of the gastrointestinal tract which have been identified and diagnosed in the past 25 years. GIST, according to relative risk of disease progression, are classified in risk categories which under the agreement of the National Institutes of Health (2002) were based on two variables: tumour size and mitotic count in 50 HPF, which total area varies from 5 mm² to 11.9 mm². There is no consensus what area is sufficient for the counting of mitoses. Thus, we compare number of GIST tumor cell mitoses in different sized areas and evaluated the impact of mitotic rate changes in determination of risk categories and their relation to the progression of the disease. According to the data of largest GIST study, disease prognosis is also influenced by tumour localization, other researchers found morphological features of these tumours leading to a better prognosis. Thus, we identified and compared the different localizations of GIST morphological features and prognosis. It was observed that disease prognosis is also determined by the tumour histological phenotype, but up till now the controversial results of histological phenotypes relationship to disease prognosis are published. We set morphological features of different GIST histological phenotypes and evaluate their relation to disease progression, evaluate the impact of GIST morphological features to disease prognosis.

Aberrant DNA methylation in gene promoters causes gene silencing and is a common event in prostate cancer development and progression. While commonly identified methylated genes have been analyzed for their potential clinical utility in a variety of cancers, few studies have attempted a genome-wide methylation approach to discover new and possibly improved biomarkers for prostate cancer. In order to identify DNA methylation changes associated with aggressive prostate cancer, we performed a genome-wide analysis of 40 prostate cancers using Agilent human CpG island microarrays. Methylation profiles of candidate genes were validated using quantitative MethyLight technology in an independent series of 219 radical prostatectomies and compared to clinicopathological parameters. The effects of methylation on expression of HOXD3 and HOXD8 and the possible role of HOXD8 in progression of PCa were also investigated. We discovered previously unidentified methylation in the HOXD cluster of genes, namely HOXD3 and HOXD8, as well as TGFβ2 and GENE X as potential prognostic biomarkers. Furthermore, unsupervised clustering of samples by methylation signature indicated ERG oncogene expression as significantly different between clusters. Within the independent cohort, we observed strong correlations between Gleason score (GS) and HOXD3 as well as GENE X, while HOXD3 and HOXD8 methylation were associated with ERG expresson. TGFβ2 was an independent predictor of disease recurrence using Cox multivariate regression analysis. In gene expression studies, both HOXD3 and HOXD8 were elevated in cancers with poor prognosis, while DNA methylation did not correlate with expression levels. Both genes were found to contain alternative transcription start sites, explaining the poor correlation between methylation and expression. Finally, knockdown of HOXD8 expression did not have any effect on viable cells or cell motility in an in vitro model. These results indicate that a panel of novel DNA methylation markers distinguish indolent prostate cancers from aggressive ones, and that expression of HOXD3 and HOXD8 is regulated by mechanisms including, but not dependent on, DNA methylation.

This thesis addresses the challenge of prognosis, in terms of survival prediction, for patients with Glioblastoma Multiforme brain tumors. Glioblastoma is the most malignant brain tumor, which has a median survival time of no more than a year. Accurate assessment of prognostic factors is critical in deciding amongst different treatment options and in designing stratified clinical trials. This thesis is motivated by two observations. Firstly, clinicians often refer to properties of glioblastoma tumors based on magnetic resonance images when assessing prognosis. However, clinical data, along with histological and most recently, molecular and gene expression data, have been more widely and systematically studied and used in prognosis assessment than image based information. Secondly, patient survival times are often used along with clinical data to conduct population studies on brain tumor patients. Recursive Partitioning Analysis is typically used in these population studies. However, researchers validate and assess the predictive power of these models by measuring the statistical association between survival groups and survival times. In this thesis, we propose a learning approach that uses historical training data to produce a system that predicts patient survival. We introduce a classification model for predicting patient survival class, which uses texture based features extracted from magnetic resonance images as well as other patient properties. Our prognosis approach is novel as it is the first to use image-extracted textural characteristics of glioblastoma scans, in a classification model whose accuracy can be reliably validated by cross validation. We show that our approach is a promising new direction for prognosis in brain tumor patients.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23810号 / 医博第4856号 / 新制||医||1058(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 高橋 淳, 教授 伊佐 正, 教授 渡邉 大 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Tez (Tıpta Uzmanlık) - Süleyman Demirel Üniversitesi, Tıp Fakültesi, Anesteziyoloji ve Reanimasyon Anabilim Dalı, 2006. / Bibliyografya var.

Diss. (sammanfattning) Stockholm : Karol. inst., 2002. / Härtill 5 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2002. / Härtill 6 uppsatser.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2005. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2007. / Härtill 5 uppsatser.

Diss. (sammanfattning) Göteborg : Univ. , 2009. / Härtill 4 uppsatser.