Bakgrund: Prostatacancer är den näst vanligaste cancerdiagnosen bland män och den femte mest aggressiva cancern bland män över hela världen. En vanlig behandlingsmetod av prostatacancer är radikal prostatektomi. Tidigare forskning visar att behandlingsmetoden associeras med en rad komplikationer samt försämrad livskvalitet.  Syfte: Att belysa mäns erfarenheter efter radikal prostatektomiMetod: Kvalitativ litteraturstudie där resultatet bygger på åtta vetenskapliga artiklar.  Resultat: Mäns upplevelser av radikal prostatektomi kunde beskrivas i tre kategorier; Fysiska konsekvenser av ingreppet, Stöd respektive bristande stöd från omgivningen och Existentiella tankar.  Konklusion: En ökad förståelse för mäns erfarenheter av radikal prostatektomi behövs för att vårdpersonal ska kunna tillgodose patienternas komplexa vårdbehov samt främja hälsa och livskvalitet. / Background: Prostate cancer is the second most common cancer diagnosis among men and the fifth most aggressive cancer among men worldwide. A common treatment method for prostate cancer is radical prostatectomy. Previous research shows that the treatment method is associated with a number of complications and reduced quality of life.  Aim: To illuminate men's experiences after radical prostatectomyMethods: Qualitative literature study where the result is based on eight scientific studies.  Results: Men's experiences of radical prostatectomy could be described in three categories; Physical consequences of the intervention, Support or lack of support from the environment and Existential thoughts.  Conclusion: An increased understanding of mens’ experience of radical prostatectomy is needed so that health-care professionals can meet patients' complex care needs and promote health and quality of life.

Prostate cancer is one of the most common types of cancer occurring in men. Several types of research have been done using deep learning methods for the classification/prediction of cancer grades. In this thesis, the results of prostate cancer risk prediction, based only on the images of cells from the prostate tissues, have been analyzed. Cell images from the prostate tissues were extracted using a deep learning based segmentation model. These cell images were then used in a Multiple Instance Learning model for cancer risk prediction. An attention mechanism was used to visualize the regions in the tissue to which the model paid more attention. The results suggest that the Multiple Instance Learning (MIL) model achieves an Area Under the receiver Operating Characteristics (AUROC) of 0.641 ± 0.013, which is better than a random model for low-risk vs. high-risk cancer prediction. The model’s prediction was made on cell images, with the glandular information destroyed. The MIL model, however, performs worse than a model which gets to see the glandular architecture of the cells in the prostate tissues. / Prostatacancer är en av de vanligaste typerna av cancer som förekommer ho smän. Flera typer av forskning har gjorts med metoder för djupinlärning förklassificering/förutsägelse av cancerns malignitetsgrad. I detta examensarbete harresultaten av prostatacancerriskprediktion, baserad enbart på bilder av celler från prostatavävnaderna, analyserats. Cellbilder från prostatavävnaderna extraherades med hjälp av en djupinlärningsbaserad segmenteringsmodell. Dessa cellbilder användes sedan i en Multiple Instance Learning-modell för förutsägelse av cancerrisk. En uppmärksamhetsmekanism användes för att visualisera de regioner i vävnaden som modellen ägnade mer uppmärksamhet åt. Resultaten tyder på att Multiple Instance Learning-modellen uppnår en AUROC på 0.641 ± 0.013, vilket är bättre än en slumpmässig modell för förutsägelse av lågrisk kontra högrisk cancer. Modellens förutsägelse gjordes på cellbilder, med körtelinformationen förstörd. MIL-modellen presterar dock sämre än en modell som får se körtelarkitekturen hos cellerna i prostatavävnaderna.

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The genotoxicological effects of five intra-peritoneal administered oestrogens (17β- oestradiol, daidzein, diethylstilboestrol, genistein, and equol), were examined. Male hooded- Lister rats were used to examine to what extent DNA damage occurred. The alkaline Comet assay was the chosen method used to assess double-strand DNA breakage by examining the Olive tail moment and %age tail DNA. Tissues from the testis, bone marrow, liver and blood were analysed after an 8-day duration of exposure. Statistically significant increases in DNA damage were observed in the testis with daidzein and in the blood with diethylstilboestrol. In addition, a further study was carried out to examine the effects of bulk and nanotised forms of non-steroidal anti-inflammatory drugs (NSAIDs), aspirin and ibuprofen, in the Comet and micronucleus assays, on whole blood taken from prostate cancer patients or volunteers. These were used because it is known that the sensitivity of DNA to genotoxins can be heightened in patients with cancer. Patients’ and volunteers’ blood was cultured with either the bulk or nano-forms for 44 hours at 37°C, 5% CO2. Data were obtained for the Comet assay as above and the number of binucleated cells scored for the micronucelus assay. The results show the nanotised forms of the NSAIDs decreased the levels of strand breakage and lowered the numbers of micronuclei generated compared with their bulk forms. There was no clear difference between the sensitivity of the healthy controls and the prostate cancer patients, with only one individual showing evidence of heightened sensitivity.

Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, mathematical modeling provides a quantitative framework for testing in silico the efficacy of new treatments and combination therapies, as well as understanding unknown biological mechanisms. In this dissertation we present two mathematical models of prostate cancer immunotherapy defined as systems of ordinary differential equations. The first work, introduced in Chapter 2, provides a mathematical model of prostate cancer immunotherapy which has been calibrated using data from pre-clinical experiments in mice. This model describes the evolution of prostate cancer, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, this model serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models. The Chapter 3 is devoted to the description of a human prostate cancer mathematical model. The potential effect of immunotherapies on castration-resistant form has been analyzed. In particular, the model includes the dendritic vaccine sipuleucel-T, the only currently available immunotherapy option for advanced prostate cancer, and the ipilimumab, a drug targeting the cytotoxic T-lymphocyte antigen 4 , exposed on the CTLs membrane, currently under Phase II clinical trial. From a mathematical analysis of a simplified model, it seems likely that, under continuous administration of ipilimumab, the system lies in a bistable situation where both the no-tumor equilibrium and the high-tumor equilibrium are attractive. The schedule of periodic treatments could then determine the outcome, and mathematical models could help in deciding an optimal schedule.

In search of new biomarkers suitable for the diagnosis and treatment of prostate cancer, genome-wide transcriptome sequencing was carried out with tissue specimens from 40 prostate cancer (PCa) and 8 benign prostate hyperplasia patients. We identified two intergenic long non-coding transcripts, located in close genomic proximity, which are highly expressed in PCa. Microarray studies on a larger cohort comprising 155 patients showed a profound diagnostic potential of these transcripts (AUC~0.94), which we designated as tumor associated prostate cancer increased lncRNA (TAPIR-1 and -2). To test their therapeutic potential, knockdown experiments with siRNA were carried out. The knockdown caused an increase in the p53/TP53 tumor suppressor protein level followed by downregulation of a large number of cell cycle- and DNA-damage repair key regulators. Furthermore, in radiation therapy resistant tumor cells, the knockdown leads to a renewed sensitization of these cells to radiation treatment. Accordingly, in a preclinical PCa xenograft model in mice, the systemic application of nanoparticles loaded with siRNA targeting TAPIR-1 significantly reduced tumor growth. These findings point to a crucial role of TAPIR-1 and -2 in PCa.

Purpose: Platinum chemotherapy can be considered to treat metastatic castration-resistant prostate cancer (mCRPC) with features of neuroendocrine differentiation. However, platinum compounds are generally only applied after the failure of multiple prior-line treatment options. This study investigated whether acquired resistance against ionizing radiation or docetaxel chemotherapy—two commonly applied treatment modalities in prostate cancer—influences the cisplatin (CDDP) tolerance in mCRPC cell line models. Methods: Age-matched parental as well as radio- or docetaxel-resistant DU145 and PC-3 cell lines were treated with CDDP and their sensitivity was assessed by measurements of growth rates, viability, apoptosis, metabolic activity and colony formation ability. Results: The data suggest that docetaxel resistance does not influence CDDP tolerance in all tested docetaxel-resistant cell lines. Radio-resistance was associated with sensitization to CDDP in PC-3, but not in DU145 cells. In general, DU145 cells tolerated higher CDDP concentrations than PC-3 cells regardless of acquired resistances. Furthermore, non-age-matched treatment-naïve PC-3 cells exhibited significantly different CDDP tolerances. Conclusion: Like patients, different mCRPC cell lines exhibit significant variability regarding CDDP tolerance. The presented in vitro data suggest that previous radiation treatment may be associated with a moderate sensitization to CDDP in an isogenic and age-matched setting. Therefore, previous radiotherapy or docetaxel chemotherapy might be no contraindication against initiation of platinum chemotherapy in selected mCRPC patients.

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