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Combining epigenetic and clinicopathological variables improves prognostic prediction in clear cell Renal Cell Carcinoma
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
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(English)Manuscript (preprint) (Other academic)
Keywords [en]
DNA methylation, cancer, cluster analysis, classification, clear cell renal cell carcinoma
National Category
Cancer and Oncology Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-167269OAI: oai:DiVA.org:umu-167269DiVA, id: diva2:1385517
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-01-31Bibliographically approved
In thesis
1. cancer subtype identification using cluster analysis on high-dimensional omics data
Open this publication in new window or tab >>cancer subtype identification using cluster analysis on high-dimensional omics data
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Identification and prediction of cancer subtypes are important parts in the development towards personalized medicine. By tailoring treatments, it is possible to decrease unnecessary suffering and reduce costs. Since the introduction of next generation sequencing techniques, the amount of data available for medical research has increased rapidly. The high dimensional omics data produced by various techniques requires statistical methods to transform data into information and knowledge.

All papers in this thesis are related to distinguishing of disease subtypes in patients with cancer using omics data. The high dimension and the complexity of sequencing data from tumor samples makes it necessary to pre—process the data.  We carry out comparisons of feature selection methods and clustering methods used for identification of cancer subtypes. In addition, we evaluate the effect that certain characteristics of the data have on the ability to identify cancer subtypes. The results show that no method outperforms the others in all cases and the relative ranking of methods is very dependent on the data. We also show that the benefit of receiving a more homogeneous data by analyzing genders separately can outweigh the possible drawbacks caused by smaller sample sizes. One of the major challenges when dealing with omics data from tumor samples is that the patients are generally a very heterogeneous group. Factors that lead to heterogeneity include age, gender, ethnicity and stage of disease. How big the effect size is for each of these factors might affect the ability to identify the subgroups of interest.

In omics data, the feature space is often large and how many of the features that are informative for the factors of interest will also affect the complexity of the problem. We present a novel clustering approach that can identify different clusters in different subsets of the feature space, which is applied on methylation data to create new potential biomarkers. It is shown that by combining clinical data with methylation data for patients with clear cell renal carcinoma, it is possible to improve the currently used prediction model for disease progression.  

Using unsupervised clustering techniques, we identify three molecular subtypes of prostate cancer bone metastases based on gene expression profiles. The robustness of the identified subtypes is confirmed by applying several clustering algorithms with very similar results.

 

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2020. p. 22
Series
Research report in mathematical statistics, ISSN 1653-0829 ; 70/20
Keywords
cluster analysis, cancer, classification
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-167275 (URN)978-91-7855-172-9 (ISBN)978-91-7855-173-6 (ISBN)
Public defence
2020-02-07, N460, Naturvetarhuset, Umeå, 09:15 (English)
Opponent
Supervisors
Available from: 2020-01-17 Created: 2020-01-14 Last updated: 2020-01-15Bibliographically approved
2. DNA methylation as a prognostic marker in clear cell Renal Cell Carcinoma
Open this publication in new window or tab >>DNA methylation as a prognostic marker in clear cell Renal Cell Carcinoma
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma worldwide. Metastatic ccRCC is correlated to poor prognosis whereas non-metastatic disease has a 5-year survival rate up to 90%. Due to increased accessibility to different types of diagnostic imaging the frequency of metastatic ccRCC at diagnosis has decreased since the beginning of the 21st century. This has led to an earlier detection of primary tumors before patients present symptoms. However, 20-30% of the non-metastatic patients at diagnosis will progress and metastasize within five years of primary nephrectomy. Identifying patients at high risk of tumor progression at an early stage after diagnosis is of importance to improve outcome and survival. Currently, in Sweden, the Mayo scoring system is used to divide tumors into low, intermediate or high risk for tumor progression.

DNA methylation has been associated with tumor development and progression in different malignancies. In this thesis, Illumina Infinium HumanMeth27 BeadChip Arrays and Human Meth450K BeadChip Arrays have been used to evaluate the relationship between methylation and clinicopathological variables as well as ccRCC outcome in 45 and 115 patients.

Our studies identified an association between higher level of promoter-associated DNA methylation and clinicopathological variables in ccRCC. There was a significant stepwise increase of average methylation from tumor-free tissue, via non-metastatic tumors to metastatic disease. Cluster analysis divided patients into two distinct groups that differed in average methylation levels, TNM stage, Fuhrman nuclear grade, tumor size, survival and tumor progression. We also presented two prognostic classifiers for non-metastatic tumors; the promoter methylation classifier (PMC) panel and the triple classifier. The PMC panel divided tumors depending on the methylation level, PMC low or PMC high, with significantly worse prognosis in the PMC high group. This data was verified in an independent, publically available cohort. The triple classifier was created using a combination of clinicopathological variables, previously identified CpGs biomarkers and a novel cluster analysis approach (Directed Cluster Analysis). The triple classifier had a higher specificity compared to the clinically used Mayo scoring system and predicted tumor progression with higher accuracy at a fixed sensitivity.

The identification of two epigenetic classifiers that predicted outcome in non-metastatic ccRCC further establishes the role of DNA methylation as a prognostic marker. This knowledge can contribute to identification of patients with a high risk of tumor progression and can be of importance in the decision regarding adjuvant treatment post-nephrectomy.

Abstract [sv]

Populärvetenskaplig sammanfattning

Klarcellig njurcancer är den vanligaste form av njurcancer. I Sverige diagnostiseras ca 1 000 individer årligen med sjukdomen. Idag upptäcks ofta njurcancer när patienter undersöks med bilddiagnostik av buken av andra anledningar, exempelvis vid oklar buksmärta och vid trauma. Detta gör att tumörerna upptäcks innan de hunnit ge symtom och andelen patienter med spridd sjukdom vid diagnos är under 20%, jämfört med över 30% i början av milleniet.

Den enda botande behandling som finns för njurcancer är total eller partiell nefrektomi, vilket innebär att hela njuren eller delen av njuren med tumör opereras bort. Om sjukdomen upptäcks tidigt, innan den hunnit sprida sig till kringliggande organ, är prognosen god och 90% av patienterna lever minst fem år efter diagnos. Är sjukdomen spridd vid diagnos finns det idag behandlingar som förlänger överlevnadstiden, och ny immunbehandling som också kan bota sjukdomen.

Även om klarcellig njurcancer som är begränsad till njuren botas när tumören avlägsnas är det ungefär en tredjedel av dessa patienter som drabbas av en ny njurtumör eller spridning till andra organ inom fem år efter diagnos. Behovet av att identifiera dessa patienter är stort när de är i behov av tilläggsbehandling. Idag utgår klinikerna från hur tumören ser ut när de bestämmer hur patienterna ska följas efter kirurgi, vid karakteristika som talar för en hög risk för spridning följs patienterna tätare och under längre tid.

Genom att analysera hur ccRCC tumörer ser ut på cellnivå har skillnader i genuttryck och DNA-sekvenser kunna identifieras. Att utnyttja dessa skillnader är viktiga för att identifiera tumörer med ökad risk för spridning.

I min avhandling har vi analyserat DNA metylering i klarcellig njurcancer vid diagnos. DNA metylering är en epigenetisk förändring, en förändring på DNA nivå som inte påverkar DNA sekvensen men kan påverkar vilka gener som uttrycks i cellerna. Nivåerna av DNA metylering skiljer sig mellan olika prognostiska grupper ccRCC. De tumörer som vid diagnos redan spridit sig till kringliggande organ har en högre grad av metylering jämfört med de tumörer som växer endast i njuren. Vi har även kunnat visa på skillnader mellan de lokala tumörer som senare sprids. Detta gör att DNA metylering kan användas som prognostisk markör i ccRCC.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2020. p. 50
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 2066
Keywords
Clear cell renal cell carcinoma, DNA methylation, Prognosis, Clinical outcome, Epigenetic classifiers
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-167591 (URN)978-91-7855-166-8 (ISBN)978-91-7855-165-1 (ISBN)
Public defence
2020-02-21, Stora Hörsalen, byggnad 5B, Norrlands universitetssjukhus, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2020-01-31 Created: 2020-01-31 Last updated: 2020-02-04Bibliographically approved

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Andersson-Evelönn, EmmaVidman, LindaKällberg, DavidLandfors, MattiasLiu, XijiaLjungberg, BörjeHultdin, MagnusDegerman, SofieRydén, Patrik

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PathologyDepartment of Mathematics and Mathematical StatisticsStatisticsUrology and AndrologyDepartment of Clinical Microbiology
Cancer and OncologyProbability Theory and Statistics

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