A PEG-assisted membrane coating to prepare biomimetic mesoporous silicon for PET/CT imaging of triple-negative breast cancer

Triple-negative breast cancer (TNBC) diagnosis remains challenging without expressing critical receptors. Cancer cell membrane (CCm) coating has been extensively studied for targeted cancer diagnostics due to attractive features such as good biocompatibility and homotypic tumor-targeting. However, the present study found that widely used CCm coating approaches, such as extrusion, were not applicable for functionalizing irregularly shaped nanoparticles (NPs), such as porous silicon (PSi). To tackle this challenge, we proposed a novel approach that employs polyethylene glycol (PEG)-assisted membrane coating, wherein PEG and CCm are respectively functionalized on PSi NPs through chemical conjugation and physical absorption. Meanwhile, the PSi NPs were grafted with the bisphosphonate (BP) molecules for radiolabeling. Thanks to the good chelating ability of BP and homotypic tumor targeting of cancer CCm coating, a novel PSi-based contrast agent (CCm-PEG-89Zr-BP-PSi) was developed for targeted positron emission tomography (PET)/computed tomography (CT) imaging of TNBC. The novel imaging agent showed good radiochemical purity (∼99 %) and stability (∼95 % in PBS and ∼99 % in cell medium after 48 h). Furthermore, the CCm-PEG-89Zr-BP-PSi NPs had efficient homotypic targeting ability in vitro and in vivo for TNBC. These findings demonstrate a versatile biomimetic coating method to prepare novel NPs for tumor-targeted diagnosis.

Platinum-based drugs induce phenotypic alterations in nucleoli and Cajal bodies in prostate cancer cells

PURPOSE

Platinum-based drugs are cytotoxic drugs commonly used in cancer treatment. They cause DNA damage, effects of which on chromatin and cellular responses are relatively well described. Yet, the nuclear stress responses related to RNA processing are incompletely known and may be relevant for the heterogeneity with which cancer cells respond to these drugs. Here, we determine the type and extent of nuclear stress responses of prostate cancer cells to clinically relevant platinum drugs.

METHODS

We study nucleolar and Cajal body (CB) responses to cisplatin, carboplatin, and oxaliplatin with immunofluorescence-based methods in prostate cancer cells. We utilize organelle-specific markers NPM, Fibrillarin, Coilin, and SMN1, and study CB-regulatory proteins FUS and TDP-43 using siRNA-mediated downregulation.

RESULTS

Different types of prostate cancer cells have different sensitivities to platinum drugs. With equally cytotoxic doses, cisplatin, and oxaliplatin induce prominent nucleolar and CB stress responses while the nuclear stress phenotypes to carboplatin are milder. We find that Coilin is a stress-specific marker for platinum drug response heterogeneity. We also find that CB-associated, stress-responsive RNA binding proteins FUS and TDP-43 control Coilin and CB biology in prostate cancer cells and, further, that TDP-43 is associated with stress-responsive CBs in prostate cancer cells.

CONCLUSION

Our findings provide insight into the heterologous responses of prostate cancer cells to different platinum drug treatments and indicate Coilin and TDP-43 as stress mediators in the varied outcomes. These results help understand cancer drug responses at a cellular level and have implications in tackling heterogeneity in cancer treatment outcomes.

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.

Biomimetic Inorganic Nanovectors as Tumor-Targeting Theranostic Platform against Triple-Negative Breast Cancer

Mesoporous silicon nanoparticles (PSi NPs) are promising platforms of nanomedicine because of their good compatibility, high payload capacities of anticancer drugs, and easy chemical modification. Here, PSi surfaces were functionalized with bisphosphonates (BP) for radiolabeling, loaded with doxorubicin (DOX) for chemotherapy, and the NPs were coated with cancer cell membrane (CCm) for homotypic cancer targeting. To enhance the CCm coating, the NP surfaces were covered with polyethylene glycol prior to the CCm coating. The effects of the BP amount and pH conditions on the radiolabeling efficacy were studied. The maximum BP was (2.27 wt%) on the PSi surfaces, and higher radiochemical yields were obtained for 99mTc (97% ± 2%) and 68Ga (94.6% ± 0.2%) under optimized pH conditions (pH = 5). The biomimetic NPs exhibited a good radiochemical and colloidal stability in phosphate-buffered saline and cell medium. In vitro studies demonstrated that the biomimetic NPs exhibited an enhanced cellular uptake and increased delivery of DOX to cancer cells, resulting in better chemotherapy than free DOX or pure NPs. Altogether, these findings indicate the potential of the developed platform for cancer treatment and diagnosis.

Complementary analysis of proteome-wide proteomics reveals changes in RNA binding protein-profiles during prostate cancer progression

BACKGROUND

Accumulating evidence indicates importance of RNA regulation in cancer. This includes events such as splicing, translation, and regulation of noncoding RNAs, functions which are governed by RNA binding proteins (RBPs).

AIMS

To find which RBPs could be relevant for prostate cancer, we performed systematic screening of RBP expression in clinical prostate cancer.

METHODS AND RESULTS

We interrogated four proteome-wide proteomics datasets including tumor samples of primary, castration resistant, and metastatic prostate cancer. We found that, while the majority of RBPs are expressed but not significantly altered during prostate cancer development and progression, expression of several RBPs increases in advanced disease. Interestingly, most of the differentially expressed RBPs are not targets of differential posttranscriptional phosphorylation during disease progression. The RBPs undergoing expression changes have functions in, especially, poly(A)-RNA binding, nucleocytoplasmic transport, and cellular stress responses, suggesting that these may play a role in formation of castration resistance. Pathway analyzes indicate that increased ribosome production and chromatin-related functions of RBPs are also linked to castration resistant and metastatic prostate cancers. We selected a group of differentially expressed RBPs and studied their role in cultured prostate cancer cells. With siRNA screens, several of these were indicated in survival (DDX6, EIF4A3, PABPN1), growth (e.g., EIF5A, HNRNPH2, LRRC47, and NVL), and migration (e.g., NOL3 and SLTM) of prostate cancer cells. Our analyzes further show that RRP9, a U3 small nucleolar protein essential for ribosome formation, undergoes changes at protein level during metastasis in prostate cancer.

CONCLUSION

In this work, we recognized significant molecular alterations in RBP profiles during development and evolution of prostate cancer. Our study further indicates several functionally significant RBPs warranting further investigation for their functions and possible targetability in prostate cancer.

Deep learning transforms colorectal cancer biomarker prediction from histopathology images

Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.

The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demonstrated highly accurate reproduction of histology, especially with increased network capacity, and demonstrated applicability to several tissues. We show that network architecture optimization can improve the image translation accuracy of virtual H&E staining, highlighting the potential of virtual staining in streamlining histopathological analysis.

Identification of long noncoding RNAs with aberrant expression in prostate cancer metastases

Prostate cancer (PCa) is the second-most common cause of male cancer-related death in western industrialized countries, and the emergence of metastases is a key challenge in the treatment of PCa. Accumulating studies have shown that long noncoding RNAs (lncRNAs) play an important role in the regulation of diverse cellular and molecular processes during the development and progression of cancer. Here, we utilized a unique cohort of castration-resistant prostate cancer metastases (mCRPC) and corresponding localized tumors and RNA sequencing (RNA-seq). First, we showed that patient-to-patient variability accounted for most of the variance in lncRNA expression between the samples, suggesting that genomic alterations in the samples are the main drivers of lncRNA expression in PCa metastasis. Subsequently, we identified 27 lncRNAs with differential expression (DE-lncRNAs) between metastases and corresponding primary tumors, suggesting that they are mCRPC-specific lncRNAs. Analyses of potential regulation by transcription factors (TFs) revealed that approximately half of the DE-lncRNAs have at least one binding site for the androgen receptor (AR) in their regulatory regions. In addition, transcription factor enrichment analysis revealed the enrichment of binding sites for PCa-associated TFs, such as FOXA1 and HOXB13, in the regulatory regions of the DE-lncRNAs. In a cohort of prostatectomy-treated prostate tumors, four of the DE-lncRNAs showed association with progression-free time, and two of them (lnc-SCFD2-2, and lnc-R3HCC1L-8) were independent prognostic markers. Our study highlights several mCRPC-specific lncRNAs that might be important in the progression of the disease to the metastatic stage and may also serve as potential biomarkers for aggressive PCa.

Nonmalignant AR-positive prostate epithelial cells and cancer cells respond differently to androgen

Prostate cancer research suffers from the lack of suitable models to study the role of normal cells in prostate carcinogenesis. To address this challenge, we developed a cell line model mimicking luminal prostate epithelial cells by modifying the immortalized prostate epithelial cell line RWPE-1 to constitutively express the androgen receptor (AR). RWPE-1-AR cells express known AR target genes, and exhibit coexpression of luminal and basal markers characteristic of transient amplifying cells, and an RNA signature resembling prostate luminal progenitor cells. Under unstimulated conditions, constitutive AR expression does not have a biologically significant effect on the proliferation of RWPE-1 cells, but when stimulated by androgens, growth is retarded. The transcriptional response of RWPE-1-AR cells to androgen stimulation involves suppression of the growth-related KRAS pathway and is thus markedly different from that of the prostate cancer cell line LNCaP and its derivative AR-overexpressing LNCaP-ARhi cells, in which growth- and cancer-related pathways are upregulated. Hence, the nonmalignant AR-positive RWPE-1-AR cell line model could be used to study the transformation of the prostate epithelium.

Nuclear size rectification: A potential new therapeutic approach to reduce metastasis in cancer

Research on metastasis has recently regained considerable interest with the hope that single cell technologies might reveal the most critical changes that support tumor spread. However, it is possible that part of the answer has been visible through the microscope for close to 200 years. Changes in nuclear size characteristically occur in many cancer types when the cells metastasize. This was initially discarded as contributing to the metastatic spread because, depending on tumor types, both increases and decreases in nuclear size could correlate with increased metastasis. However, recent work on nuclear mechanics and the connectivity between chromatin, the nucleoskeleton, and the cytoskeleton indicate that changes in this connectivity can have profound impacts on cell mobility and invasiveness. Critically, a recent study found that reversing tumor type-dependent nuclear size changes correlated with reduced cell migration and invasion. Accordingly, it seems appropriate to now revisit possible contributory roles of nuclear size changes to metastasis.

Chemical Interrogation of Nuclear Size Identifies Compounds with Cancer Cell Line-Specific Effects on Migration and Invasion

Background: Lower survival rates for many cancer types correlate with changes in nuclear size/scaling in a tumor-type/tissue-specific manner. Hypothesizing that such changes might confer an advantage to tumor cells, we aimed at the identification of commercially available compounds to guide further mechanistic studies. We therefore screened for Food and Drug Administration (FDA)/European Medicines Agency (EMA)-approved compounds that reverse the direction of characteristic tumor nuclear size changes in PC3, HCT116, and H1299 cell lines reflecting, respectively, prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. Results: We found distinct, largely nonoverlapping sets of compounds that rectify nuclear size changes for each tumor cell line. Several classes of compounds including, e.g., serotonin uptake inhibitors, cyclo-oxygenase inhibitors, β-adrenergic receptor agonists, and Na⁺/K⁺ ATPase inhibitors, displayed coherent nuclear size phenotypes focused on a particular cell line or across cell lines and treatment conditions. Several compounds from classes far afield from current chemotherapy regimens were also identified. Seven nuclear size-rectifying compounds selected for further investigation all inhibited cell migration and/or invasion. Conclusions: Our study provides (a) proof of concept that nuclear size might be a valuable target to reduce cell migration/invasion in cancer treatment and (b) the most thorough collection of tool compounds to date reversing nuclear size changes specific to individual cancer-type cell lines. Although these compounds still need to be tested in primary cancer cells, the cell line-specific nuclear size and migration/invasion responses to particular drug classes suggest that cancer type-specific nuclear size rectifiers may help reduce metastatic spread.

Nuclear Organization in Response to Stress: A Special Focus on Nucleoli

In this chapter, we discuss the nuclear organization and how it responds to different types of stress. A key component in these responses is molecular traffic between the different sub-nucleolar compartments, such as nucleoplasm, chromatin, nucleoli, and various speckle and body compartments. This allows specific repair and response activities in locations where they normally are not active and serve to halt sensitive functions until the stress insult passes and inflicted damage has been repaired. We focus on mammalian cells and their nuclear organization, especially describing the central role of the nucleolus in nuclear stress responses. We describe events after multiple stress types, including DNA damage, various drugs, and toxic compounds, and discuss the involvement of macromolecular traffic between dynamic, phase-separated nuclear organelles and foci. We delineate the key proteins and non-coding RNA in the formation of stress-responsive, non-membranous nuclear organelles, many of which are relevant to the formation of and utilization in cancer treatment.

Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment

Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological assessments are performed from individual tissue sections, leading to the loss of three-dimensional context of the tissue. Yet, the tissue context and spatial determinants are critical in several pathologies, such as in understanding growth patterns of cancer in its local environment. Here, we develop computational methods for visualization and quantitative assessment of histopathological alterations in three dimensions. First, we reconstruct the 3D representation of the whole organ from serial sectioned tissue. Then, we proceed to analyze the histological characteristics and regions of interest in 3D. As our example cases, we use whole slide images representing hematoxylin-eosin stained whole mouse prostates in a Pten+/- mouse prostate tumor model. We show that quantitative assessment of tumor sizes, shapes, and separation between spatial locations within the organ enable characterizing and grouping tumors. Further, we show that 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology. Our results underline the heterogeneity in composition and cellular organization within individual tumors. As an example, we show how prostate tumors have nuclear density gradients indicating areas of tumor growth directions and reflecting varying pressure from the surrounding tissue. The methods presented here are applicable to any tissue and different types of pathologies. This work provides a proof-of-principle for gaining a comprehensive view from histology by studying it quantitatively in 3D.

Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Background

Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology to aid cancer research.

Methods

Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology.

Results

In our interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects, which is not possible in the traditional 2D-image view used in digital pathology. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model.

Conclusions

Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08542-9.

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

Expression and ERG regulation of PIM kinases in prostate cancer

The three oncogenic PIM family kinases have been implicated in the development of prostate cancer (PCa). The aim of this study was to examine the mRNA and protein expression levels of PIM1, PIM2, and PIM3 in PCa and their associations with the MYC and ERG oncogenes. We utilized prostate tissue specimens of normal, benign prostatic hyperplasia (BPH), prostatic intraepithelial neoplasia (PIN), untreated PCa, and castration‐resistant prostate cancer (CRPC) for immunohistochemical (IHC) analysis. In addition, we analyzed data from publicly available mRNA expression and chromatin immunoprecipitation sequencing (ChIP‐Seq) datasets. Our data demonstrated that PIM expression levels are significantly elevated in PCa compared to benign samples. Strikingly, the expression of both PIM1 and PIM2 was further increased in CRPC compared to PCa. We also demonstrated a significant association between upregulated PIM family members and both the ERG and MYC oncoproteins. Interestingly, ERG directly binds to the regulatory regions of all PIM genes and upregulates their expression. Furthermore, ERG suppression with siRNA reduced the expression of PIM in PCa cells. These results provide evidence for cooperation of PIM and the MYC and ERG oncoproteins in PCa development and progression and may help to stratify suitable patients for PIM‐targeted therapies.

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

Androgen-Driven Fusion Genes and Chimeric Transcripts in Prostate Cancer

Androgens are steroid hormones governing the male reproductive development and function. As such, androgens and the key mediator of their effects, androgen receptor (AR), have a leading role in many diseases. Prostate cancer is a major disease where AR and its transcription factor function affect a significant number of patients worldwide. While disease-related AR-driven transcriptional programs are connected to the presence and activity of the receptor itself, also novel modes of transcriptional regulation by androgens are exploited by cancer cells. One of the most intriguing and ingenious mechanisms is to bring previously unconnected genes under the control of AR. Most often this occurs through genetic rearrangements resulting in fusion genes where an androgen-regulated promoter area is combined to a protein-coding area of a previously androgen-unaffected gene. These gene fusions are distinctly frequent in prostate cancer compared to other common solid tumors, a phenomenon still requiring an explanation. Interestingly, also another mode of connecting androgen regulation to a previously unaffected gene product exists via transcriptional read-through mechanisms. Furthermore, androgen regulation of fusion genes and transcripts is not linked to only protein-coding genes. Pseudogenes and non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) can also be affected by androgens and de novo functions produced. In this review, we discuss the prevalence, molecular mechanisms, and functional evidence for androgen-regulated prostate cancer fusion genes and transcripts. We also discuss the clinical relevance of especially the most common prostate cancer fusion gene TMPRSS2-ERG, as well as present open questions of prostate cancer fusions requiring further investigation.

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

AR and ERG drive the expression of prostate cancer specific long noncoding RNAs

Long noncoding RNAs (lncRNAs) play pivotal roles in cancer development and progression, and some function in a highly cancer-specific manner. However, whether the cause of their expression is an outcome of a specific regulatory mechanism or nonspecific transcription induced by genome reorganization in cancer remains largely unknown. Here, we investigated a group of lncRNAs that we previously identified to be aberrantly expressed in prostate cancer (PC), called TPCATs. Our high-throughput real-time PCR experiments were integrated with publicly available RNA-seq and ChIP-seq data and revealed that the expression of a subset of TPCATs is driven by PC-specific transcription factors (TFs), especially androgen receptor (AR) and ETS-related gene (ERG). Our in vitro validations confirmed that AR and ERG regulated a subset of TPCATs, most notably for EPCART. Knockout of EPCART was found to reduce migration and proliferation of the PC cells in vitro. The high expression of EPCART and two other TPCATs (TPCAT-3-174133 and TPCAT-18-31849) were also associated with the biochemical recurrence of PC in prostatectomy patients and were independent prognostic markers. Our findings suggest that the expression of numerous PC-associated lncRNAs is driven by PC-specific mechanisms and not by random cellular events that occur during cancer development. Furthermore, we report three prospective prognostic markers for the early detection of advanced PC and show EPCART to be a functionally relevant lncRNA in PC.

3D-Printed Whole Prostate Models with Tumor Hotspots Using Dual-Extruder Printer

3D printing has emerged as a popular technology in various biomedical applications. Physical models of anatomical structures concretize the digital representations and can be used for teaching and analysis. In this study we combine 3D histology with 3D printing, creating realistic physical models of tissues with hotspots of interest. As an example we use mouse prostates containing tumors. Surface meshes are created from binary masks of HE-stained serial sections of mouse prostates and manually annotated tumor areas. Sections are interpolated to expand sparse image stacks for smoother results. Fiji, Meshlab and Tinkercad are used for mesh creation and processing. Objects are printed with Prusa-based dual-extruder printer enabling different colors for tumors and the surrounding prostate tissue. Our 3D-printed mouse prostates appear realistic and tumors located at the edges of the organ are clearly visible. When transparent filament is used, the tumor hotspots are visible even when they are inside the prostate.

Phosphorylation of NFATC1 at PIM1 target sites is essential for its ability to promote prostate cancer cell migration and invasion

Background

Progression of prostate cancer from benign local tumors to metastatic carcinomas is a multistep process. Here we have investigated the signaling pathways that support migration and invasion of prostate cancer cells, focusing on the role of the NFATC1 transcription factor and its post-translational modifications. We have previously identified NFATC1 as a substrate for the PIM1 kinase and shown that PIM1-dependent phosphorylation increases NFATC1 activity without affecting its subcellular localization. Both PIM kinases and NFATC1 have been reported to promote cancer cell migration, invasion and angiogenesis, but it has remained unclear whether the effects of NFATC1 are phosphorylation-dependent and which downstream targets are involved.

Methods

We used mass spectrometry to identify PIM1 phosphorylation target sites in NFATC1, and analysed their functional roles in three prostate cancer cell lines by comparing phosphodeficient mutants to wild-type NFATC1. We used luciferase assays to determine effects of phosphorylation on NFAT-dependent transcriptional activity, and migration and invasion assays to evaluate effects on cell motility. We also performed a microarray analysis to identify novel PIM1/NFATC1 targets, and validated one of them with both cellular expression analyses and in silico in clinical prostate cancer data sets.

Results

Here we have identified ten PIM1 target sites in NFATC1 and found that prevention of their phosphorylation significantly decreases the transcriptional activity as well as the pro-migratory and pro-invasive effects of NFATC1 in prostate cancer cells. We observed that also PIM2 and PIM3 can phosphorylate NFATC1, and identified several novel putative PIM1/NFATC1 target genes. These include the ITGA5 integrin, which is differentially expressed in the presence of wild-type versus phosphorylation-deficient NFATC1, and which is coexpressed with PIM1 and NFATC1 in clinical prostate cancer specimens.

Conclusions

Based on our data, phosphorylation of PIM1 target sites stimulates NFATC1 activity and enhances its ability to promote prostate cancer cell migration and invasion. Therefore, inhibition of the interplay between PIM kinases and NFATC1 may have therapeutic implications for patients with metastatic forms of cancer.

Graphical abstract

Comparative analysis of tissue reconstruction algorithms for 3D histology

Motivation

Digital pathology enables new approaches that expand beyond storage, visualization or analysis of histological samples in digital format. One novel opportunity is 3D histology, where a three-dimensional reconstruction of the sample is formed computationally based on serial tissue sections. This allows examining tissue architecture in 3D, for example, for diagnostic purposes. Importantly, 3D histology enables joint mapping of cellular morphology with spatially resolved omics data in the true 3D context of the tissue at microscopic resolution. Several algorithms have been proposed for the reconstruction task, but a quantitative comparison of their accuracy is lacking.

Results

We developed a benchmarking framework to evaluate the accuracy of several free and commercial 3D reconstruction methods using two whole slide image datasets. The results provide a solid basis for further development and application of 3D histology algorithms and indicate that methods capable of compensating for local tissue deformation are superior to simpler approaches.

Availability and implementation

Code: https://github.com/BioimageInformaticsTampere/RegBenchmark. Whole slide image datasets: http://urn.fi/urn: nbn: fi: csc-kata20170705131652639702.

Supplementary information

Supplementary data are available at Bioinformatics online.

Abstract LB-096: Chromatin alterations in human prostate cancer

Whole human genome is packed into chromatin, which is dynamically remodeled. Chromatin structure has been extensively studied with cell lines, but information about chromatin structure in tissue context is lacking. We present genome-wide chromatin accessibility analysis of clinical tissue samples using transposase-accessible chromatin sequencing (ATAC-seq). Our sample cohort consist 11 benign prostatic hyperplasia (BPH), 16 primary prostate cancer (PC), and 11 castration resistant prostate cancer (CRPC) samples. We identified 23,307 to 136,104 regions of accessible chromatin per sample using MACS2 peak calling. Utilizing a peak unification method resulted in a unified set of 178,333 high confidence peaks across sample set. To find out which loci are differentially accessible during disease progression, we further compared normalized ATAC-seq signal between sample groups across the genome. We identified 4747 and 9445 differentially accessible regions (DARs) for BPH to PC and PC to CRPC comparison, respectively. Out of these, in 2961 and 6652 chromatin was opening and in 1786 and 2793 chromatin was closing in respective comparison. Using DARs, we observe clear separation of the sample groups. Earlier, we have characterized this cohort using DNA, RNA and DNA methylation sequencing as well as SWATH proteomics. Using these data and the same analysis approach as with DARs, we identified 2061 and 2723 differentially methylated regions (DMRs) in BPH to PC and PC to CRPC comparisons, respectively. We compared locations of DARs and DMRs and found out that these occur in different loci overlapping only in 27 and 35 loci in respective comparisons. When integrated with gene expression data, the chromatin accessibility correlated (|coefficient| &gt0.5) with the expression of at least one gene located in the same topologically associating domain (TAD) in altogether 2713 DARs. Next, we examined which transcription factors (TFs) are binding to DARs and thus putatively regulating gene-expression. Using HOMER database, we found several TFs with binding motif enrichment in our DARs. In BPH to PC comparison, opening DARs contain binding sites e.g. for AR and FOXA1 and, in PC to CRPC comparison, opening DARs contain binding sites e.g. for HOXB13, as expected. Interestingly, in PC to CRPC comparison closing DARs contain binding sites for AR and FOXA1 indicating that these TFs have smaller role or alternative regulatory programs when disease progresses. We utilized publicly available CHIP-seq datasets to study this more closely in DARs where ATAC-seq signal correlates with gene-expression within a TAD. Here, while the total number of AR binding sites doubles, the number of AR binding sites in closing DARs is ten times higher in CRPC to PC comparison than in PC to BPH comparison. These results suggest that chromatin accessibility is an important regulator of prostate cancer progression and changes occur in specific loci to where several relevant prostate cancer TFs can bind.

Citation Format: Joonas I. Tuominen, Ebrahim Afyounian, Francesco Tabaro, Tomi Häkkinen, Anastasia Shcherban, Matti Annala, Kati Kivinummi, Teuvo Tammela, Juha Kesseli, Leena Latonen, Kirsi Granberg, Tapio Visakorpi, Matti Nykter. Chromatin alterations in human prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-096.

Abstract 4393: Integrative proteomic analysis of prostate cancer reveals distinct regulation of RNA binding proteins during disease progression

To understand the etiology of the disease, and to find novel and more specific drug targets, the driver mutations and expressional changes in prostate cancer have been examined through extensive genomic and transcriptomic characterization. Although significant insight has been gained through these efforts, it is clear that not all molecular alterations influencing the tumor outcome can be captured through these approaches, and that a comprehensive understanding of the molecular events in cancer require thorough investigation of the proteome.

To understand the functional consequences of genetic and transcriptional aberrations in prostate cancer, we aimed to reveal the proteomic changes during disease formation and progression. We performed high throughput mass spectrometry on clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC) and castration resistant prostate cancer (CRPC). We performed an integrative analysis of the proteomic data with gene copy number, DNA methylation, and RNA expression data from the same samples. Furthermore, proteomic events correlating with the androgen receptor (AR) status of the tumors were analysed.

We uncovered previously unrecognized molecular and pathway events and several novel AR-associated events in the prostate cancer proteomes to study further. We found significant changes in expression of RNA-binding proteins during disease formation and progression. Examining the relationship of RNA binding proteins at the RNA and protein expression level reveal that while many RNA binding proteins exhibit correlation between the expression levels, some seem regulated at the posttranslational level. Two RNA binding proteins, TDP-43 and FUS, which regulated at the protein, but not at RNA level during prostate cancer progression, show opposite behavior during disease progression and correlation with AR status of the tumors. In cultured prostate cancer cell models, we show that these proteins have specific, but divergent interactions with AR at the RNA and protein levels, and that they contribute differentially to AR activity-mediated responses. Thus, these proteins may significantly contribute to prostate cancer molecular evolution and may pinpoint possible targetable pathways in future prostate cancer therapy.

Citation Format: Mauro Scaravilli, Annika Kohvakka, Pekka Ruusuvuori, Ebrahim Afyounian, Matti Nykter, Tapio Visakorpi, Leena Latonen. Integrative proteomic analysis of prostate cancer reveals distinct regulation of RNA binding proteins during disease progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4393.

Phase-to-Phase With Nucleoli - Stress Responses, Protein Aggregation and Novel Roles of RNA

Protein- and RNA-containing foci and aggregates are a hallmark of many age- and mutation-related neurodegenerative diseases. This article focuses on the role the nucleolus has as a hub in macromolecule regulation in the mammalian nucleus. The nucleolus has a well-established role in ribosome biogenesis and functions in several types of cellular stress responses. In addition to known reactions to DNA damaging and transcription inhibiting stresses, there is an emerging role of the nucleolus especially in responses to proteotoxic stress such as heat shock and inhibition of proteasome function. The nucleolus serves as an active regulatory site for detention of extranucleolar proteins. This takes place in nucleolar cavities and manifests in protein and RNA collections referred to as intranucleolar bodies (INBs), nucleolar aggresomes or amyloid bodies (A-bodies), depending on stress type, severity of accumulation, and material propensities of the macromolecular collections. These indicate a relevance of nucleolar function and regulation in neurodegeneration-related cellular events, but also provide surprising connections with cancer-related pathways. Yet, the molecular mechanisms governing these processes remain largely undefined. In this article, the nucleolus as the site of protein and RNA accumulation and as a possible protective organelle for nuclear proteins during stress is viewed. In addition, recent evidence of liquid-liquid phase separation (LLPS) and liquid-solid phase transition in the formation of nucleoli and its stress responses, respectively, are discussed, along with the increasingly indicated role and open questions for noncoding RNA species in these events.

Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks

BACKGROUND

Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. We propose a method for cell detection that requires annotated training data for one cell line only, and generalizes to other, unseen cell lines.

RESULTS

Training a deep learning model with one cell line only can provide accurate detections for similar unseen cell lines (domains). However, if the new domain is very dissimilar from training domain, high precision but lower recall is achieved. Generalization capabilities of the model can be improved with training data transformations, but only to a certain degree. To further improve the detection accuracy of unseen domains, we propose iterative unsupervised domain adaptation method. Predictions of unseen cell lines with high precision enable automatic generation of training data, which is used to train the model together with parts of the previously used annotated training data. We used U-Net-based model, and three consecutive focal planes from brightfield image z-stacks. We trained the model initially with PC-3 cell line, and used LNCaP, BT-474 and 22Rv1 cell lines as target domains for domain adaptation. Highest improvement in accuracy was achieved for 22Rv1 cells. F1-score after supervised training was only 0.65, but after unsupervised domain adaptation we achieved a score of 0.84. Mean accuracy for target domains was 0.87, with mean improvement of 16 percent.

CONCLUSIONS

With our method for generalized cell detection, we can train a model that accurately detects different cell lines from brightfield images. A new cell line can be introduced to the model without a single manual annotation, and after iterative domain adaptation the model is ready to detect these cells with high accuracy.

Abstract A077: Chromatin state alterations in human prostate cancer progression

Understanding the mechanisms of prostate cancer (PC) development into castration-resistant prostate cancer (CRPC) is a key factor in finding better diagnostic and treatment tools. Although a number of studies have tried to explain the molecular evolution of CRPC, more refined understanding on molecular mechanisms is still needed for improved patient care. While increasing amounts of genomic aberrations are accumulating in prostate cancer genome during the disease progression, we still lack understanding on the functional impact of the majority of these aberrations or their combinations. Identification of the regulatory regions from tissue samples enables in-depth analysis of regulatory elements and upstream regulators that are driving tumor development. Thus, we can gain detailed understanding of how the genomic alterations reorganize the chromatin and enable the emergence of cancer phenotype.

We have previously characterized a cohort of 60 clinical prostate tissue samples including benign prostatic hyperplasia (BPH), PC, and CRPC with RNA-seq, MeDIP-seq, DNA-seq, small-RNA-seq, and mass spectrometry. To gain insight into the epigenetic regulation during the disease progression, we decided to use assay for transposase-accessible chromatin using sequencing (ATAC-seq). We first developed a method that allows us to use freshly frozen prostate samples as starting material, and performed ATAC-seq for BPH, PC, and CRPC samples from the above-mentioned cohort.

High quality peaks were identified from all the samples, ranging from tens of thousands to over one hundred thousand peaks per sample. Large variation in the chromatin structure was observed across the cohort. Unsupervised clustering based on peak intensities was able to separate the three different sample types into separate clusters with distinct chromatin state profile for each sample cluster. Next we extracted nucleosome signals from the data. This signal is able to illustrate nucleosome occupancy and positioning with high resolution in the areas of open chromatin. We observed organized nucleosome patterns in our BPH samples, but this organization start to fall apart in PC and especially in CRPC samples where nucleosome localization is no longer uniform inside the group. The results imply increased heterogeneity in chromatin structure as a result of disease progression.

Our data show that ATAC-seq data from clinical prostate material are sufficient to separate different sample groups to their own clusters, and it is possible to have detailed information about nucleosome binding in tumor tissues. These data allow us to reveal novel changes in chromatin state and integrate those changes to other features such as gene expression. With these data we aim to connect specific regulatory elements and upstream regulators to cancer phenotypes and genomic alterations. This new layer of information will bring us closer to understanding the mechanisms that drive molecular evolution of CRPC with potential implications in clinical practice for patients suffering from this devastating disease.

Citation Format: Joonas Tuominen, Tomi Häkkinen, Matti Annala, Kati Kivinummi, Teuvo Tammela, Leena Latonen, Kirsi Granberg, Tapio Visakorpi, Matti Nykter. Chromatin state alterations in human prostate cancer progression [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A077.

Abstract B077: 3D reconstruction and machine learning-based analysis of prostate cancer from histologic images

Prostate cancer is multifocal in nature, and histologic grading is the key clinical prognostic factor. Imaging-based tools are required for decreasing the subjectivity of histologic grading, providing quantitative information of the tissue and the pathologic changes in it, and to allow quantitative associations of histologic alterations with other types of information collected from the tissues, such as genomic data.

To enable imaging-based diagnosis, methods for quantifying both the naturally occurring heterogeneity of normal tissue and morphologic changes due to pathology need to be developed. On the other hand, accurate distinction of early pathologic changes from natural variation could provide novel information about development of tumors. Furthermore, the 3-dimensional evolution and growth patterns of tumors should be considered as, traditionally, histologic scoring mostly relies on individual tissue sections extracted from their spatial context.

To build nonsubjective histologic analysis tools, and to model the multifocality of prostate cancer within the organ, we use analysis of histologic images to quantitatively describe prostate cancer. We present an approach for 1) imaging the whole organ into digital pathology slides, 2) reconstruction of the 3D structure of the organ based on the histologic images, and 3) both feature-based and deep learning-based quantitative analysis of the digital images. Our approach enables characterization of tissue morphology with numerical descriptors, enabling subsequent analysis, such as determining the likelihood of pathologic changes. Our current efforts show how heterogeneity in prostate tissue due to spatial location or cancer can be quantified with image-derived features. In addition, we use mouse prostate as a model to visualize and reconstruct a whole organ from high-resolution whole-slide images, and combine tissue type classification in the 3D reconstruction. Further development of these methods and their application for human samples will improve our understanding of human prostate pathologies and cancer evolution within the 3D environment of the organ.

Citation Format: Pekka Ruusuvuori, Mira Valkonen, Kimmo Kartasalo, Tapio Visakorpi, Matti Nykter, Leena Latonen. 3D reconstruction and machine learning-based analysis of prostate cancer from histologic images [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B077.

Abstract A020: Integrative analysis of the proteome in primary and advanced prostate cancer

To fully understand the output of alterations in cancer genomes and transcriptomes, we need to know how these aberrations are translated into the functional protein units in cells. We assessed proteomic changes during disease formation and progression in prostate cancer by performing high-throughput mass spectrometry on clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC), and castration-resistant prostate cancer (CRPC). With SWATH-MS quantitation-based proteomics we found that each of these sample groups show a distinct protein profile. By integrative analysis of this mass spectrometry dataset with genetic, epigenetic, and transcriptional data from the same samples, we show that, especially in CRPC, gene copy number, DNA methylation, and RNA expression levels do not reliably predict proteomic changes. From our analysis, we have identified sets of novel expression changes occurring primarily at the protein level, in addition to identification of several miRNA-target correlations present at protein but not at mRNA level. We find novel expression changes in previously unrecognized pathways in prostate cancer that are likely to affect disease development and progression. For example, we identify two metabolic shifts in the citric acid cycle (TCA cycle), one occurring during primary cancer development and the second during castration resistance, having implications on drug targeting against cancer metabolism. Our proteogenomic analysis of prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression, reveals new disease mechanisms, and significantly extends understanding of prostate cancer biology.

Citation Format: Leena Latonen, Ebrahim Afyounian, Antti Jylhä, Janika Nättinen, Ulla Aapola, Matti Annala, Kati Kivinummi, Teuvo Tammela, Roger W. Beuerman, Hannu Uusitalo, Matti Nykter, Tapio Visakorpi. Integrative analysis of the proteome in primary and advanced prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A020.

Constitutively active androgen receptor splice variants AR-V3, AR-V7 and AR-V9 are co-expressed in castration-resistant prostate cancer metastases

BACKGROUND

A significant subset of prostate cancer (PC) patients with a castration-resistant form of the disease (CRPC) show primary resistance to androgen receptor (AR)-targeting drugs developed against CRPC. As one explanation could be the expression of constitutively active androgen receptor splice variants (AR-Vs), our current objectives were to study AR-Vs and other AR aberrations to better understand the emergence of CRPC.

METHODS

We analysed specimens from different stages of prostate cancer by next-generation sequencing and immunohistochemistry.

RESULTS

AR mutations and copy number variations were detected only in CRPC specimens. Genomic structural rearrangements of AR were observed in 5/30 metastatic CRPC patients, but they were not associated with expression of previously known AR-Vs. The predominant AR-Vs detected were AR-V3, AR-V7 and AR-V9, with the expression levels being significantly higher in CRPC cases compared to prostatectomy samples. Out of 25 CRPC metastases that expressed any AR variant, 17 cases harboured expression of all three of these AR-Vs. AR-V7 protein expression was highly heterogeneous and higher in CRPC compared to hormone-naïve tumours.

CONCLUSIONS

AR-V3, AR-V7 and AR-V9 are co-expressed in CRPC metastases highlighting the fact that inhibiting AR function via regions common to all AR-Vs is likely to provide additional benefit to patients with CRPC.

Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression

To understand functional consequences of genetic and transcriptional aberrations in prostate cancer, the proteomic changes during disease formation and progression need to be revealed. Here we report high-throughput mass spectrometry on clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC) and castration resistant prostate cancer (CRPC). Each sample group shows a distinct protein profile. By integrative analysis we show that, especially in CRPC, gene copy number, DNA methylation, and RNA expression levels do not reliably predict proteomic changes. Instead, we uncover previously unrecognized molecular and pathway events, for example, several miRNA target correlations present at protein but not at mRNA level. Notably, we identify two metabolic shifts in the citric acid cycle (TCA cycle) during prostate cancer development and progression. Our proteogenomic analysis uncovers robustness against genomic and transcriptomic aberrations during prostate cancer progression, and significantly extends understanding of prostate cancer disease mechanisms.

Understanding of molecular events in cancer requires proteome-level characterisation. Here, proteome profiling of patient samples representing primary and progressed prostate cancer enables the authors to identify pathway alterations that are not reflected at the genomic and transcriptomic levels.

Metastasis detection from whole slide images using local features and random forests

Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. © 2017 International Society for Advancement of Cytometry.

Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models

Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and Pten+/- mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.

CIP2A is a candidate therapeutic target in clinically challenging prostate cancer cell populations

Residual androgen receptor (AR)-signaling and presence of cancer stem-like cells (SCs) are the two emerging paradigms for clinically challenging castration-resistant prostate cancer (CRPC). Therefore, identification of AR-target proteins that are also overexpressed in the cancer SC population would be an attractive therapeutic approach.

Our analysis of over three hundred clinical samples and patient-derived prostate epithelial cultures (PPECs), revealed Cancerous inhibitor of protein phosphatase 2A (CIP2A) as one such target. CIP2A is significantly overexpressed in both hormone-naïve prostate cancer (HN-PC) and CRPC patients. CIP2A is also overexpressed, by 3- and 30-fold, in HN-PC and CRPC SCs respectively. In vivo binding of the AR to the intronic region of CIP2A and its functionality in the AR-moderate and AR-high expressing LNCaP cell-model systems is also demonstrated. Further, we show that AR positively regulates CIP2A expression, both at the mRNA and protein level. Finally, CIP2A depletion reduced cell viability and colony forming efficiency of AR-independent PPECs as well as AR-responsive LNCaP cells, in which anchorage-independent growth is also impaired.

These findings identify CIP2A as a common denominator for AR-signaling and cancer SC functionality, highlighting its potential therapeutic significance in the most clinically challenging prostate pathology: castration-resistant prostate cancer.

Amplification of the 9p13.3 chromosomal region in prostate cancer

Amplification of the 9p13.3 chromosomal region occurs in a subset of prostate cancers (PCs); however, the target gene or genes of this amplification have remained unidentified. The aim of this study was to investigate the 9p13.3 amplification in more detail to identify genes that are potentially advantageous for cancer cells. We narrowed down the minimally amplified area and assessed the frequency of the 9p13.3 amplification. Of the clinical samples from untreated PCs that were examined (n = 134), 9.7% showed high-level amplification, and 32.1% showed low-level amplification. Additionally, in clinical samples from castration-resistant PCs (n = 70), high- and low-level amplification was seen in 14.3% and 44.3% of the samples, respectively. We next analyzed the protein-coding genes in this chromosomal region for both their expression in clinical PC samples as well as their potential as growth regulators in PC cells. We found that the 9p13.3 amplification harbors several genes that are able to affect the growth of PC cells when downregulated using siRNA. Of these, UBAP2 was the most prominently upregulated gene in the clinical prostate tumor samples. © 2016 Wiley Periodicals, Inc.

Flow Cytometry-Based Classification in Cancer Research: A View on Feature Selection

In this paper, we study the problem of feature selection in cancer-related machine learning tasks. In particular, we study the accuracy and stability of different feature selection approaches within simplistic machine learning pipelines. Earlier studies have shown that for certain cases, the accuracy of detection can easily reach 100% given enough training data. Here, however, we concentrate on simplifying the classification models with and seek for feature selection approaches that are reliable even with extremely small sample sizes. We show that as much as 50% of features can be discarded without compromising the prediction accuracy. Moreover, we study the model selection problem among the ℓ 1 regularization path of logistic regression classifiers. To this aim, we compare a more traditional cross-validation approach with a recently proposed Bayesian error estimator.

Feature-based analysis of mouse prostatic intraepithelial neoplasia in histological tissue sections

This paper describes work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Prostatic intraepithelial neoplasia (PIN) represents premalignant tissue involving epithelial growth confined in the lumen of prostatic acini. In the attempts to understand oncogenesis in the human prostate, early neoplastic changes can be modeled in the mouse with genetic manipulation of certain tumor suppressor genes or oncogenes. As with many early pathological changes, the PIN lesions in the mouse prostate are macroscopically small, but microscopically spanning areas often larger than single high magnification focus fields in microscopy. This poses a challenge to utilize full potential of the data acquired in histological specimens. We use whole prostates fixed in molecular fixative PAXgene™, embedded in paraffin, sectioned through and stained with H&E. To visualize and analyze the microscopic information spanning whole mouse PIN (mPIN) lesions, we utilize automated whole slide scanning and stacked sections through the tissue. The region of interests is masked, and the masked areas are processed using a cascade of automated image analysis steps. The images are normalized in color space, after which exclusion of secretion areas and feature extraction is performed. Machine learning is utilized to build a model of early PIN lesions for determining the probability for histological changes based on the calculated features. We performed a feature-based analysis to mPIN lesions. First, a quantitative representation of over 100 features was built, including several features representing pathological changes in PIN, especially describing the spatial growth pattern of lesions in the prostate tissue. Furthermore, we built a classification model, which is able to align PIN lesions corresponding to grading by visual inspection to more advanced and mild lesions. The classifier allowed both determining the probability of early histological changes for uncategorized tissue samples and interpretation of the model parameters. Here, we develop quantitative image analysis pipeline to describe morphological changes in histological images. Even subtle changes in mPIN lesion characteristics can be described with feature analysis and machine learning. Constructing and using multidimensional feature data to represent histological changes enables richer analysis and interpretation of early pathological lesions.

Incidence of Mucinous Metaplasia in the Prostate of FVB/N Mice (Mus musculus)

Prostate epithelium in mice is considered to be relatively resistant to aged-related changes, as compared with human prostate epithelium, which is prone to spontaneous hyperplasia and cancer, for example. In addition, the incidence of metaplasia in mouse prostate typically is considered to be low. Here we report the incidence of mucinous metaplasia in the prostates of wild-type FVB/N mice. Our histologic study shows that mucinous metaplasia involving goblet cells occurs much more frequently (incidence as high as 50%) in the prostates of aged mice (17-24 mo) than has been reported previously. Mucinous metaplasia in the prostates of laboratory mice may be considerably more frequent than previously appreciated.

Transcriptome Sequencing Reveals PCAT5 as a Novel ERG-Regulated Long Noncoding RNA in Prostate Cancer

Castration-resistant prostate cancers (CRPC) that arise after the failure of androgen-blocking therapies cause most of the deaths from prostate cancer, intensifying the need to fully understand CRPC pathophysiology. In this study, we characterized the transcriptomic differences between untreated prostate cancer and locally recurrent CRPC. Here, we report the identification of 145 previously unannotated intergenic long noncoding RNA transcripts (lncRNA) or isoforms that are associated with prostate cancer or CRPC. Of the one third of these transcripts that were specific for CRPC, we defined a novel lncRNA termed PCAT5 as a regulatory target for the transcription factor ERG, which is activated in approximately 50% of human prostate cancer. Genome-wide expression analysis of a PCAT5-positive prostate cancer after PCAT5 silencing highlighted alterations in cell proliferation pathways. Strikingly, an in vitro validation of these alterations revealed a complex integrated phenotype affecting cell growth, migration, invasion, colony-forming potential, and apoptosis. Our findings reveal a key molecular determinant of differences between prostate cancer and CRPC at the level of the transcriptome. Furthermore, they establish PCAT5 as a novel oncogenic lncRNA in ERG-positive prostate cancers, with implications for defining CRPC biomarkers and new therapeutic interventions.

Abstract 148: Transcriptome sequencing reveals PCAT5 - new ERG-regulated non-coding transcript in prostate cancer

Background: Prostate cancer (PC) is the second most common cancer among men. Most PC-related deaths are due to invasive tumors that are treated with therapies inhibiting androgen production or androgen receptor (AR) activity. After an initial response, tumors invariably progress to castration resistant prostate cancers (CRPCs) for which no effective cure exists.

Methods and Results: We used transcriptome sequencing to study fresh-frozen tissue specimens from 12 benign prostatic hyperplasias (BPHs), 28 PCs, and 13 CRPCs. Reference-based transcriptome assembly uncovered 145 previously unannotated intergenic PC and CRPC associated long non-coding transcripts (lncRNAs) or isoforms. One third of the transcripts were CRPC-specific. We showed that one of the novel transcripts, Prostate Cancer Associated Transcript 5 (PCAT5), expressed in half of the tumors, was likely regulated by ERG, the key transcription factor in ∼50% of prostate cancers. Genome-wide expression analysis of a PCAT5-positive prostate cancer cell line after PCAT5 knockdown suggested significant alterations in proliferation pathways. In vitro validation of the pathway alterations revealed concordantly dramatic effects in phenotype: stalling of cell growth, migration, invasion, and colony forming potential, and increase in the rate of apoptosis.

Conclusions: We identified the key differences between PC and CRPC in transcriptome level, and validated the oncogenic potential of a novel lncRNA in ERG-positive prostate cancers, PCAT5. Our study presents a number of putative lncRNA biomarkers for CRPC, and opportunities for therapeutic intervention.

Citation Format: Antti Ylipää, Kati Kivinummi, Matti Annala, Annika Kohvakka, Leena Latonen, Mauro Scaravilli, Kimmo Kartasalo, Simo-Pekka Leppänen, Serdar Karakurt, Janne Seppälä, Olli Yli-Harja, Teuvo L.J. Tammela, Wei Zhang, Tapio Visakorpi, Matti Nykter. Transcriptome sequencing reveals PCAT5 - new ERG-regulated non-coding transcript in prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 148. doi:10.1158/1538-7445.AM2015-148

Abstract 3061: In vivo role of miR-32 in prostate cancer

The androgen receptor (AR) signaling pathway is central to the emergence of castration-resistant prostate cancer (CRPC). We set out to identify androgen-regulated microRNAs (miRNAs) that may contribute to the development of CRPC. We found miR-32 to be an androgen regulated miRNA which is differentially expressed in CRPC compared to benign prostatic hyperplasia (BPH) and able to provide a significant growth advantage to LNCaP cells by reducing apoptosis

To study how increased miR-32 expression contributes to prostate cancer formation and/or progression in vivo, and to search for in vivo targets of miR-32 in the prostate tissue, we have established transgenic mice expressing miR-32 specifically in the prostate. FVB/N mouse strain was used to create transgenic mice with probasin promoter (ARR2PB) driving expression of miR-32 androgen-responsively in prostate epithelium post-puberty. The mice develop and breed normally, and express the transgene specifically in the ventral and dorsolateral lobes of the prostate, with no transgene expression detected in anterior prostate or seminal vesicles up to six months of age. To provoke lesions in prostate epithelium, the miR-32 mice were cross-bred with mice heterozygous for tumor suppressor Pten. Histological analysis of the prostates of ARR2PB-miR-32xPten+/- mice shows increased number of prostatic intraepithelial neoplasia (PIN) lesions in the dorsal prostate compared to Pten+/- mice. In addition, transgenic miR-32 can induce goblet cell metaplasia in prostate epithelium in lateral to ventral prostate, in addition to stromal responses, in the prostate in the Pten+/- background.

In search for in vivo targets of miR-32, we have performed a gene expression microarray analysis comparing the wild type and ARR2PB-miR-32 prostate tissue. Several candidate targets have been identified, and will be discussed further for their potential cancer relevance.

We find that miR-32 is potentially an important gene in the progression of prostate cancer and a putative drug target. We are currently analyzing histology of the miR-32 transgenic mouse prostates further, and assessing tissue targets of miR-32. With the transgenic mouse model, we aim to determine whether miR-32 is an oncomiR for prostate cancer in vivo, and will assess the potency of miR-32 as a therapeutic target.

Citation Format: Leena Latonen, Mauro Scaravilli, Fuping Zhang, Pekka Ruusuvuori, Matti Poutanen, Tapio Visakorpi. In vivo role of miR-32 in prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3061. doi:10.1158/1538-7445.AM2015-3061

Epigenetically altered miR-193b targets cyclin D1 in prostate cancer

Micro-RNAs (miRNA) are important regulators of gene expression and often differentially expressed in cancer and other diseases. We have previously shown that miR-193b is hypermethylated in prostate cancer (PC) and suppresses cell growth. It has been suggested that miR-193b targets cyclin D1 in several malignancies. Here, our aim was to determine if miR-193b targets cyclin D1 in prostate cancer. Our data show that miR-193b is commonly methylated in PC samples compared to benign prostate hyperplasia. We found reduced miR-193b expression (P < 0.05) in stage pT3 tumors compared to pT2 tumors in a cohort of prostatectomy specimens. In 22Rv1 PC cells with low endogenous miR-193b expression, the overexpression of miR-193b reduced CCND1 mRNA levels and cyclin D1 protein levels. In addition, the exogenous expression of miR-193b decreased the phosphorylation level of RB, a target of the cyclin D1-CDK4/6 pathway. Moreover, according to a reporter assay, miR-193b targeted the 3'UTR of CCND1 in PC cells and the CCND1 activity was rescued by expressing CCND1 lacking its 3'UTR. Immunohistochemical analysis of cyclin D1 showed that castration-resistant prostate cancers have significantly (P = 0.0237) higher expression of cyclin D1 compared to hormone-naïve cases. Furthermore, the PC cell lines 22Rv1 and VCaP, which express low levels of miR-193b and high levels of CCND1, showed significant growth retardation when treated with a CDK4/6 inhibitor. In contrast, the inhibitor had no effect on the growth of PC-3 and DU145 cells with high miR-193b and low CCND1 expression. Taken together, our data demonstrate that miR-193b targets cyclin D1 in prostate cancer.

Abstract 5217: Integrative sequencing reveals novel alterations in untreated and castration resistant prostate cancer

Prostate cancer is the third most common source of male cancer deaths in developed countries. The standard of care for aggressive prostate cancer is androgen ablation, which prolongs survival until the tumor acquires a castration resistant phenotype. The molecular pathology underlying prostate cancer progression is not yet fully understood. We used integrative high throughput sequencing to study cancer-associated alterations in 53 prostate cancer related neoplasia at the DNA, RNA and epigenetic levels. The cohort included both hormone-naive and castration resistant prostate cancers, along with two neuroendocrine prostate cancers. We identified two new fusion genes, one of which associated with neuronal differentiation and castration resistance. We also identified a number of novel prostate cancer associated transcripts, including transcripts specific to castration resistant tumors. Based on ChIP-seq data from prostate cancer cell lines, many of the novel transcripts were regulated by known oncogenes such as ERG and AR. Methylation sequencing revealed a near-identical pattern of promoter hypermethylation in both hormone-naive and castration resistant tumors. Enrichment of hypermethylation was observed at EZH2 binding sites, supporting the role of EZH2 in the recruitment of DNA methyltransferase in prostate cancer. Promoter hypermethylation suppressed the expression of hundreds of genes, but a subset of genes characterized by promoter H3K27 trimethylation responded to hypermethylation with increased expression.

Citation Format: Matti J. Annala, Kati K. Waltering, Antti Ylipää, Kimmo Kartasalo, Kirsi Tuppurainen, Serdar Karakurt, Leena Latonen, Outi Saramäki, Simo-Pekka Leppänen, Janne Seppälä, Hanna E. Rauhala, Teuvo LJ Tammela, Olli Yli-Harja, Wei Zhang, Tapio Visakorpi, Matti Nykter. Integrative sequencing reveals novel alterations in untreated and castration resistant prostate cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5217. doi:10.1158/1538-7445.AM2013-5217