Multi-Level Molecular Characterisation of Prostate Cancer

Prostate cancer is the most common malignancy in Norwegian men, and represents a substantial health burden. The disease is heterogeneous, ranging from slow growing and indolent, to very aggressive and lethal. One of the major unsolved clinical challenges is to accurately separate indolent from harmful disease at an early time point. This causes substantial overtreatment of patients with harmless cancers, as well as undertreatment of patients with aggressive cancers. To enable improved treatment selection, an increased understanding of the molecular characteristics of prostate cancer progression is needed. In this thesis, multi-level molecular analyses of gene expression and metabolism were performed in an integrated fashion on prostate tissue samples. The aim was to obtain more comprehensive knowledge of prostate cancer aggressiveness, and to identify candidate biomarkers for improved risk stratification of prostate cancer patients. Gene expression analysis is a method that detects active genes; it can indicate which molecular processes occur in cells and tissue. The expression of genes is the instruction for which proteins are produced in the cells. Proteins are components of cellular signalling pathways, where the pathway activity can be altered to favour cancer survival. Activation of the Wnt signalling pathway may increase the cells’ motility, and can therefore be exploited by cancer cells to gain invasive and metastatic properties. The work in this thesis showed increased activation of a subgroup of the Wnt pathway, called the non-canonical Wnt pathway. By using a set of genes representing the non-canonical Wnt pathway (NCWP), combined with markers of increased cell mobility (epithelial-mesenchymal transition (EMT)), a gene signature coined NCWP-EMT was developed. An increased signature score suggests increased activation of this pathway. High signature score, representing increased activation of the pathway, was associated with aggressive cancer, where more patients experienced recurrent and metastatic disease after surgery. The signature may therefore have clinical potential to improve the discrimination of aggressive from indolent prostate cancer at an early time point. One of the signature members, secreted frizzled-related protein 4 (SFRP4), was further investigated on its own. The expression level of SFRP4 was shown to be a predictor for aggressive, recurrent and metastatic disease, and this was validated in several independent patient cohorts, and in a total of 1884 patients. SFRP4 alone, may therefore have potential as a biomarker for prediction of prostate cancer outcome. Changes in the genome can alter gene expression, and an example of this is a fusion of two genes, called TMPRSS2-ERG. This gene fusion is found in approximately half of malignant prostate tumours, however, little is known about its relation to other molecular processes, such as cancer cell metabolism. In this thesis, a distinctive metabolic profile was seen in cancer tissue possessing TMPRSS2-ERG, and this profile was similar to metabolic alterations previously observed in aggressive prostate cancer. Metabolism in tissues and cells can be studied by magnetic resonance (MR) spectroscopy. Cancer cell metabolism differ from healthy cells, as cancer often prioritise growth which require increased energy production and synthesis of new building blocks. Reprogramming of metabolism is therefore regarded as one of the hallmarks of cancer cells. The normal prostate cells produce and excrete high levels of the metabolite citrate for the prostatic fluid. Previously, a reduced levels of citrate have been detected in prostate cancer compared with healthy tissue, and this is likely due to citrate being used for energy and fatty acid production, rather than production and excretion. Furthermore, alterations to polyamine metabolism, and in particular to spermine, are important in prostate cancer, where decreased spermine concentration has been associated with the disease. In this thesis, reduced concentrations of both citrate and spermine were detected in cancer tissue samples containing the TMPRSS2-ERG gene fusion, samples with a high score of the non-canonical Wnt pathway signature, and samples with a high expression level of SFRP4. This suggests that citrate and spermine have great potential as tissue biomarkers of prostate cancer. Importantly, these metabolic alterations were also detected by non-invasive patient MR examination, which is therefore a candidate as a prognostic tool in prostate cancer diagnosis. To summarise, the work presented in this thesis shows that the TMPRSS2-ERG gene fusion, the non-canonical Wnt pathway, and SFRP4 expression are all associated with reprogramming of prostate cancer metabolism. Additionally, activation of the non-canonical Wnt pathway and the expression level of SFRP4 were associated with recurrent and metastatic disease after surgery. Further investigation of these aggressive molecular characteristics may lead to clinical biomarkers for improved early risk stratification in prostate cancer patients

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

BackgroundMagnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment.PurposeTo investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI).Material and methods62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models.ResultsAll three models showed a significant difference (pConclusionSignal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations

SFRP4 gene expression is increased in aggressive prostate cancer

Abstract Increased knowledge of the molecular differences between indolent and aggressive prostate cancer is needed for improved risk stratification and treatment selection. Secreted frizzled-related protein 4 (SFRP4) is a modulator of the cancer-associated Wnt pathway, and previously suggested as a potential marker for prostate cancer aggressiveness. In this study, we investigated and validated the association between SFRP4 gene expression and aggressiveness in nine independent cohorts (n = 2157). By differential expression and combined meta-analysis of all cohorts, we detected significantly higher SFRP4 expression in cancer compared with normal samples, and in high (3–5) compared with low (1–2) Grade Group samples. SFRP4 expression was a significant predictor of biochemical recurrence in six of seven cohorts and in the overall analysis, and was a significant predictor of metastatic event in one cohort. In our study cohort, where metabolic information was available, SFRP4 expression correlated significantly with the concentrations of citrate and spermine, two previously suggested biomarkers for aggressive prostate cancer. SFRP4 immunohistochemistry in an independent cohort (n = 33) was not associated with aggressiveness. To conclude, high SFRP4 gene expression is associated with high Grade Group and recurrent prostate cancer after surgery. Future studies investigating the mechanistic and clinical usefulness of SFRP4 in prostate cancer are warranted

A PET/MRI study towards finding the optimal [18F]Fluciclovine PET protocol for detection and characterisation of primary prostate cancer.

Purpose: [18F]Fluciclovine PET imaging shows promise for the assessment of prostate cancer. The purpose of this PET/MRI study is to optimise the PET imaging protocol for detection and characterisation of primary prostate cancer, by quantitative evaluation of the dynamic uptake of [18F]Fluciclovine in cancerous and benign tissue. Methods: Patients diagnosed with high-risk primary prostate cancer underwent an integrated [18F]Fluciclovine PET/MRI exam before robot-assisted radical prostatectomy with extended pelvic lymph node dissection. Volumes-of-interest (VOIs) of selected organs (prostate, bladder, blood pool) and sub-glandular prostate structures (tumour, benign prostatic hyperplasia (BPH), inflammation, healthy tissue) were delineated on T2-weighted MR images, using whole-mount histology samples as a reference. Three candidate windows for optimal PET imaging were identified based on the dynamic curves of the mean and maximum standardised uptake value (SUVmean and SUVmax, respectively). The statistical significance of differences in SUV between VOIs were analysed using Wilcoxon rank sum tests (p<0.05, adjusted for multiple testing). Results: Twenty-eight (28) patients [median (range) age: 66 (55-72) years] were included. An early (W1: 5-10 minutes post-injection) and two late candidate windows (W2: 18-23; W3: 33-38 minutes post-injection) were selected. Late compared with early imaging was better able to distinguish between malignant and benign tissue [W3, SUVmean: tumour vs. BPH 2.5 vs. 2.0 (p<0.001), tumour vs. inflammation 2.5 vs. 1.7 (p<0.001), tumour vs. healthy tissue 2.5 vs. 2.0 (p<0.001); W1, SUVmean: tumour vs. BPH 3.1 vs. 3.1 (p=0.771), tumour vs inflammation 3.1 vs. 2.2 (p=0.021), tumour vs. healthy tissue 3.1 vs. 2.5 (p<0.001)] as well as between high-grade and low/intermediate-grade tumours (W3, SUVmean: 2.6 vs. 2.1 (p=0.040); W1, SUVmean: 3.1 vs. 2.8 (p=0.173)). These differences were relevant to the peripheral zone, but not the central gland. Conclusion: Late-window [18F]Fluciclovine PET imaging shows promise for distinguishing between prostate tumours and benign tissue and for assessment of tumour aggressiveness

Simultaneous 18F-fluciclovine Positron Emission Tomography and Magnetic Resonance Spectroscopic Imaging of Prostate Cancer

Purpose: To investigate the associations of metabolite levels derived from magnetic resonance spectroscopic imaging (MRSI) and 18F-fluciclovine positron emission tomography (PET) with prostate tissue characteristics. Methods: In a cohort of 19 high-risk prostate cancer patients that underwent simultaneous PET/MRI, we evaluated the diagnostic performance of MRSI and PET for discrimination of aggressive cancer lesions from healthy tissue and benign lesions. Data analysis comprised calculations of correlations of mean standardized uptake values (SUVmean), maximum SUV (SUVmax), and the MRSI-derived ratio of (total choline + spermine + creatine) to citrate (CSC/C). Whole-mount histopathology was used as gold standard. Results: The results showed a moderate significant correlation between both SUVmean and SUVmax with CSC/C ratio. Conclusions: We demonstrated that the simultaneous acquisition of 18F-fluciclovine PET and MRSI with an integrated PET/MRI system is feasible and a combination of these imaging modalities has potential to improve the diagnostic sensitivity and specificity of prostate cancer lesions

Combined 18F-Fluciclovine PET/MRI shows potential for detection and characterization of high-risk prostate cancer

The objective of this study is to investigate if quantitative imaging features derived from combined 18F-Fluciclovine Positron Emission Tomograpy (PET) / multiparametric Magnetic Resonance Imaging (MRI) show potential for detection and characterization of primary prostate cancer. Methods: Twenty-eight (28) patients diagnosed with high-risk prostate cancer underwent simultaneous 18F-Fluciclovine PET/MRI before radical prostatectomy. Volumes-of-interest (VOIs) of prostate tumors, benign prostatic hyperplasia (BPH) nodules, prostatitis, and healthy tissue were delineated on T2-weighted images using histology as a reference. Tumor VOIs were marked as high-grade (≥ Gleason Grade group 3) or not. MRI and PET features were extracted on the voxel and VOI-level. Partial least-squared discriminant analysis (PLS-DA) with double leave-one-patient-out cross validation was performed to classify tumor from benign tissue (BPH, prostatitis, healthy tissue) and high-grade tumor from other tissue (low-grade tumor, benign tissue). The performances of PET, MRI, and combined PET/MRI features were compared using the area under the receiver operating characteristic curve (AUC). Results: Voxel and VOI features were extracted from 40 tumor (26 high-grade), 36 BPH, 6 prostatitis, and 37 healthy tissue VOIs. PET/MRI performed better than MRI and PET for classification of tumor vs benign tissue (voxel: AUC 87%, 81%, and 83%; VOI: AUC 96%, 93%, and 93%, respectively) and high-grade tumor vs other tissue (voxel: AUC 85%, 79%, and 81%; VOI: AUC 93%, 93%, and 91%, respectively). T2-weighted MRI, diffusion-weighted MRI and PET features were most important for classification. Conclusion: Combined 18F-Fluciclovine PET/multiparametric MRI shows potential for improving detection and characterization of high-risk prostate cancer, in comparison to MRI and PET alone

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI

Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). Discussion All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s

Lærerstudenters forventninger til arbeidet som profesjonelle lærere i skolen: Resultater fra en spørreundersøkelse i regi av NFR-prosjektet STEP: Partnerskap for bærekraftig overgang fra lærerutdanning til yrke

Formålet med undersøkelsen var å kartlegge hvilke forventninger det første kullet i den femårige integrerte masterutdanningen for grunnskolelærere har til arbeidet som profesjonelle lærere i skolen. Det empiriske grunnlaget for rapporten består av svar fra studenter som nettopp hadde startet i det siste året i grunnskolelærerutdanningen. Spørreskjemaet omfattet i hovedsak spørsmål med faste svaralternativer, men med anledning for respondentene til å kommentere sine svar. Datainnsamlingen ble gjennomført høsten 2021, i en periode preget av Covid19-pandemien med nedstengning og utstrakt nettbasert undervisning som erstatning for planlagt undervisning på campus. Det er også verdt å merke seg at undersøkelsen ble gjennomført før studentene hadde startet arbeidet med masteroppgaven, og deres vurderinger av utdanningen og betydningen av masteroppgaven må forstås i lys av det. Resultatene viser at studentene generelt sett ser fram til yrkesstarten med forventning og spenning. De føler seg godt forberedt til å undervise i fagene de har fordypning i fra utdanningen. Samtidig viser kommentarene at mange er usikre på om de i tilstrekkelig grad er rustet for bredden og kompleksiteten i utfordringene de forventer å møte i skolen. Denne usikkerheten handler i første rekke om to forhold: For det første er de urolige med tanke på eventuelt å undervise i fag de ikke har fordypning i. Videre indikerer svar og kommentarer at de mener de er lite forberedt til å håndtere en del av de oppgavene som understøtter møtet med elevene i klasserommet. Det gjelder i første rekke samarbeid med foresatte, kontaktlærerrollen, samarbeid med skolens hjelpetjenester og tilrettelegging for elever med særskilte behov. Studentene forventer at overgangen fra utdanning til arbeidet i skolen blir krevende. De har en offensiv og læringsorientert holdning til møtet med disse utfordringene. Når det gjelder deres eget behov for tilrettelegging og lærings-støttende tiltak, peker de i første rekke på forventninger om samarbeid med erfarne kolleger, tilpasning fra ledelsen i form av arbeids- og undervisningsplaner som de kan mestre og deltakelse i en kvalitetssikret veiledningsordning i tråd med de nasjonale prinsippene for veiledning av nyutdannede lærere. På spørsmål om hva studentene mener de vil kunne tilføre det profesjonelle fellesskapet i skolen, peker de på entusiasme og glød for elevenes læring og skolemiljø og vilje til å stimulere til elevmedvirkning og demokratiforståelse. Studentene gir også uttrykk for at de tror de kan bidra med en kritisk-analytisk og utforskende tilnærming til undervisning og læring. Både svar og kommentarer viser at svært mange av studentene primært oppfatter arbeidet med masteroppgaven som en ytterligere fordypning og spesialisering i fag