Päivi Östling

Screening out irrelevant cell-based models of disease.

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell- and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates.

Systematic knockdown of epigenetic enzymes identifies a novel histone demethylase PHF8 overexpressed in prostate cancer with an impact on cell proliferation, migration and invasion

Our understanding of key epigenetic regulators involved in specific biological processes and cancers is still incomplete, despite great progress in genome-wide studies of the epigenome. Here, we carried out a systematic, genome-wide analysis of the functional significance of 615 epigenetic proteins in prostate cancer (PrCa) cells. We used the high-content cell-spot microarray technology and siRNA silencing of PrCa cell lines for functional screening of cell proliferation, survival, androgen receptor (AR) expression, histone methylation and acetylation. Our study highlights subsets of epigenetic enzymes influencing different cancer cell phenotypes. Plant homeo domain (PHD) finger proteins have a key role in cell survival and histone methylation, whereas histone deacetylases were primarily involved in regulating AR expression. In contrast, JumonjiC-domain (JmjC) containing histone lysine demethylases (KDMs) mainly had an impact on cell proliferation. Our results show that the KDMs JARID1B, PHF8, KDM3A, KDM3B and KDM4A were highly expressed in clinical PrCa samples. The PHD-finger protein 8 (PHF8), a transcriptional coactivator with both PHD- and JmjC-domains, was moderately to strongly expressed in 80% of clinical PrCa samples, whereas 76% of normal and benign samples were negative or only showed weak PHF8 expression. Strong PHF8 expression correlated significantly with high Gleason grade and was borderline significant for poor prognosis. The results of functional PHF8 knockdown implicate a role in cell migration and invasion, as shown by cell motility and 3-D invasion assays. Our study suggests that various cellular phenotypes are regulated by distinct subsets of epigenetic enzymes. Proteins interpreting and modifying histone methylation, such as JmjC-domain and particularly PHD-finger proteins like PHF8, are activated in subsets of PrCas and promote cancer relevant phenotypes.

Non-canonical notch signaling activates IL-6/JAK/STAT signaling in breast tumor cells and is controlled by p53 and IKKα/IKKβ

Notch signaling is frequently hyperactivated in breast cancer, but how the enhanced signaling contributes to the tumor process is less well understood. In this report, we identify the proinflammatory cytokine interleukin-6 (IL-6) as a novel Notch target in breast tumor cells. Enhanced Notch signaling upregulated IL-6 expression, leading to activation of autocrine and paracrine Janus kinase/signal transducers and activators of transcription signaling. IL-6 upregulation was mediated by non-canonical Notch signaling, as it could be effectuated by a cytoplasmically localized Notch intracellular domain and was independent of the DNA-binding protein CSL. Instead, Notch-mediated IL-6 upregulation was controlled by two proteins in the nuclear factor (NF)-κB signaling cascade, IKKα and IKKβ (inhibitor of nuclear factor kappa-B kinase subunit alpha and beta, respectively), as well as by p53. Activation of IL-6 by Notch required IKKα/IKKβ function, but interestingly, did not engage canonical NF-κB signaling, in contrast to IL-6 activation by inflammatory agents such as lipopolysaccharide. With regard to p53 status, IL-6 expression was upregulated by Notch when p53 was mutated or lost, and restoring wild-type p53 into p53-mutated or -deficient cells abrogated the IL-6 upregulation. Furthermore, Notch-induced transcriptomes from p53 wild-type and -mutated breast tumor cell lines differed extensively, and for a subset of genes upregulated by Notch in a p53-mutant cell line, this upregulation was reduced by wild-type p53. In conclusion, we identify IL-6 as a novel non-canonical Notch target gene, and reveal roles for p53 and IKKα/IKKβ in non-canonical Notch signaling in breast cancer and in the generation of cell context-dependent diversity in the Notch signaling output.

Identification of miR-193b Targets in Breast Cancer Cells and Systems Biological Analysis of Their Functional Impact

Identification of protein targets for microRNAs (miRNAs) is a significant challenge due to the complexity of miRNA-mediated regulation. We have previously demonstrated that miR-193b targets estrogen receptor-α (ERα) and inhibits estrogen-induced growth of breast cancer cells. Here, we applied a high-throughput strategy using quantitative iTRAQ (isobaric tag for relative and absolute quantitation) reagents to identify other target proteins regulated by miR-193b in breast cancer cells. iTRAQ analysis of pre-miR-193b transfected MCF-7 cells resulted in identification of 743 unique proteins, of which 39 were down-regulated and 44 up-regulated as compared with negative control transfected cells. Computationally predicted targets of miR-193b were highly enriched (sevenfold) among the proteins whose level of expression decreased after miR-193b transfection. Only a minority of these (13%) showed similar effect at the mRNA level illustrating the importance of post-transcriptional regulation. The most significantly repressed proteins were selected for validation experiments. These data confirmed 14–3-3ζ (YWHAZ), serine hydroxyl transferase (SHMT2), and aldo-keto reductase family 1, member C2 (AKR1C2) as direct, previously uncharacterized, targets of miR-193b. Functional RNAi assays demonstrated that specific combinations of knockdowns of these target genes by siRNAs inhibited growth of MCF-7 cells, mimicking the effects of the miR-193b overexpression. Interestingly, the data imply that besides targeting ERα, the miR-193b effects include suppression of the local production of estrogens and other steroid hormones mediated by the AKR1C2 gene, thus provoking two separate molecular mechanisms inhibiting steroid-dependent growth of breast cancer cells. In conclusion, we present here a proteomic screen to identify targets of miR-193b, and a systems biological approach to mimic its effects at the level of cellular phenotypes. This led to the identification of multiple genes whose combinatorial knock-down likely mediates the strong anti-cancer effects observed for miR-193b in breast cancer cells.

Systematic Identification of MicroRNAs That Impact on Proliferation of Prostate Cancer Cells and Display Changed Expression in Tumor Tissue

BACKGROUND Systematic approaches to functionally identify key players in microRNA (miRNA)-target networks regulating prostate cancer (PCa) proliferation are still missing. OBJECTIVE To comprehensively map miRNA regulation of genes relevant for PCa proliferation through phenotypic screening and tumor expression data. DESIGN, SETTING, AND PARTICIPANTS Gain-of-function screening with 1129 miRNA molecules was performed in five PCa cell lines, measuring proliferation, viability, and apoptosis. These results were integrated with changes in miRNA expression from two cohorts of human PCa (188 tumors in total). For resulting miRNAs, the predicted targets were collected and analyzed for patterns with gene set enrichment analysis, and for their association with biochemical recurrence free survival. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Rank product statistical analysis was used to evaluate miRNA effects in phenotypic screening and for expression differences in the prostate tumor cohorts. Expression data were analyzed using the significance analysis of microarrays (SAM) method and the patient material was subjected to Kaplan-Meier statistics. RESULTS AND LIMITATIONS Functional screening identified 25 miRNAs increasing and 48 miRNAs decreasing cell viability. Data integration resulted in 14 miRNAs, with aberrant expression and effect on proliferation. These miRNAs are predicted to regulate >3700 genes, of which 28 were found up-regulated and 127 down-regulated in PCa compared with benign tissue. Seven genes, FLNC, MSRB3, PARVA, PCDH7, PRNP, RAB34, and SORBS1, showed an inverse association to their predicted miRNA, and were identified to significantly correlate with biochemical recurrence free survival in PCa patients. CONCLUSIONS A systematic in vitro screening approach combined with in vivo expression and gene set enrichment analysis provide unbiased means for revealing novel miRNA-target links, possibly driving the oncogenic processes in PCa. PATIENT SUMMARY This study identified novel regulatory molecules, which impact on PCa proliferation and are aberrantly expressed in clinical tumors. Thus, our study reveals regulatory nodes with potential for therapy.

Consistency in drug response profiling

The comparative analysis by Haibe-Kains et al.1 concluded that data from two large-scale studies of cancer cell lines2,3 showed highly discordant results for drug sensitivity measurements, whereas gene expression data were reasonably concordant. Here, we crosscompared the two original datasets2,3 against our own data of drug response profiles in overlapping cancer cell line panels. Our results indicate that it is possible to achieve concordance between different laboratories for drug response measurements by paying attention to the harmonization of assays and experimental procedures. There is a Reply to this Comment by Safikhani, Z. et al. Nature 540, http://dx.doi.org/10.1038/ nature20172 (2016). Haibe-Kains et al.1 reported on a comparative evaluation of two drug sensitivity and molecular profiling datasets, one from the Cancer Genome Project (CGP)2 and the other from the Cancer Cell Line Encyclopedia (CCLE)3. In their analyses, gene expression profiles between hundreds of common cancer cell lines across all genes showed high consistency between the two studies (median rank correlation (MRC) = 0.85), whereas the drug response data for 15 common compounds were highly discordant (MRC = 0.28 for halfmaximum inhibitory concentration (IC50) values). This report1 and the accompanying commentary4 suggested that differences in laboratory protocols, compounds and their tested concentration ranges, and computational methods may account for the differences, but these reports did not elaborate which of these factors are important and whether they can be controlled for. Here, we reanalysed the dose–response data from both CGP and CCLE using a standardized area under the curve (AUC) response metric, which we call the drug sensitivity score (DSS)5. We then compared the CGP and CCLE data with a new dataset of drug responses profiled using the Institute for Molecular Medicine Finland (FIMM) compound testing assay6, covering 308 drugs across 106 cancer cell lines. The FIMM data included 45 compounds in common with CGP and 14 with the CCLE in 50 cell lines (Supplementary Data 1). In the AUC calculation, we unified the drug concentration ranges across the CGP, CCLE and FIMM assays. We observed a significantly higher level of consistency (P = 4.2 × 10−5), especially between the CCLE and FIMM drug response data (MRC = 0.74), as compared to the consistency between FIMM and CGP data (MRC = 0.54) (Fig. 1a). Similar experimental protocols were applied at FIMM and CCLE, including the same readout (CellTiter-Glo, Promega), similar controls (vehicle as negative control and positive controls of toxic compounds 100 μ M benzethonium chloride or 1 μ M MG132). However, there were also differences, such as the plate format used (1,536 versus 384 wells). Importantly, there was no effort made to standardize cell numbers used or any other parameters between the three laboratories, such as the source, passage number and media used for cells, nor the origin and handling of drugs. Therefore, this observed level of drug response agreement could be substantially improved by further standardization of the laboratory protocols. The CGP experimental protocol differed from the two others in terms of the readout (fluorescent nucleic acid stain Syto 60, Life Technologies), in the use of controls (drug-free cells as negative and no cells as positive controls), and the plate format used (96or 384-well plates). We compared the drug response profiles between the same cell lines from different laboratories, in line with the approach of Haibe-Kains et al.1, in which they showed consistency in gene expression profiles from CGP and CCLE (MRC = 0.85)1. The Haibe-Kains et al.1 approach, in which the correlation is calculated for each drug separately across the cell lines, showed more variability (Fig. 1b), owing to the fact that some drugs show minimal efficacy in all the tested cell lines. Analogously, gene expression correlations vary more widely when analysed at the level of genes across cell lines (MRC = 0.58 between CGP and CCLE), as certain genes are not expressed above technical noise. Although both ways to compare the data are relevant to the overall goal of personalized

Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer

BACKGROUND Technology development to enable the culture of human prostate cancer (PCa) progenitor cells is required for the identification of new, potentially curative therapies for PCa. OBJECTIVE We established and characterized patient-derived conditionally reprogrammed cells (CRCs) to assess their biological properties and to apply these to test the efficacies of drugs. DESIGN, SETTING, AND PARTICIPANTS CRCs were established from seven patient samples with disease ranging from primary PCa to advanced castration-resistant PCa (CRPC). The CRCs were characterized by genomic, transcriptomic, protein expression, and drug profiling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The phenotypic quantification of the CRCs was done based on immunostaining followed by image analysis with Advanced Cell Classifier using Random Forest supervised machine learning. Copy number aberrations (CNAs) were called from whole-exome sequencing and transcriptomics using in-house pipelines. Dose-response measurements were used to generate multiparameter drug sensitivity scores using R-statistical language. RESULTS AND LIMITATIONS We generated six benign CRC cultures which all had an androgen receptor-negative, basal/transit-amplifying phenotype with few CNAs. In three-dimensional cell culture, these cells could re-express the androgen receptor. The CRCs from a CRPC patient (HUB.5) displayed multiple CNAs, many of which were shared with the parental tumor. We carried out high-throughput drug-response studies with 306 emerging and clinical cancer drugs. Using the benign CRCs as controls, we identified the Bcl-2 family inhibitor navitoclax as the most potent cancer-specific drug for the CRCs from a CRPC patient. Other drug efficacies included taxanes, mepacrine, and retinoids. CONCLUSIONS Comprehensive cancer pharmacopeia-wide drug testing of CRCs from a CRPC patient highlighted both known and novel drug sensitivities in PCa, including navitoclax, which is currently being tested in clinical trials of CRPC. PATIENT SUMMARY We describe an approach to generate patient-derived cancer cells from advanced prostate cancer and apply such cells to discover drugs that could be applied in clinical trials for castration-resistant prostate cancer.

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose–response data

Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose–response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose–response curves. Contact: john.mpindi@helsinki.fi Availability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

MicroRNA-135b regulates ERα, AR and HIF1AN and affects breast and prostate cancer cell growth

MicroRNAs (miRNAs) regulate a wide range of cellular signaling pathways and biological processes in both physiological and pathological states such as cancer. We have previously identified miR‐135b as a direct regulator of androgen receptor (AR) protein level in prostate cancer (PCa). We wanted to further explore the relationship of miR‐135b to hormonal receptors, particularly estrogen receptor α (ERα). Here we show that miR‐135b expression is lower in ERα‐positive breast tumors as compared to ERα‐negative samples in two independent breast cancer (BCa) patient cohorts (101 and 1302 samples). Additionally, the miR‐135b expression is higher in AR‐low PCa patient samples (47 samples). We identify ERα as a novel miR‐135b target by demonstrating miR‐135b binding to the 3′UTR of the ERα and decreased ERα protein and mRNA level upon miR‐135b overexpression in BCa cells. MiR‐135b reduces proliferation of ERα‐positive BCa cells MCF‐7 and BT‐474 as well as AR‐positive PCa cells LNCaP and 22Rv1 when grown in 2D. To identify other genes regulated by miR‐135b we performed gene expression studies and found a link to the hypoxia inducible factor 1α (HIF1α) pathway. We show that miR‐135b influences the protein level of the inhibitor for hypoxia inducible factor 1α (HIF1AN) and is able to bind to HIF1AN 3′UTR. Our study demonstrates that miR‐135b regulates ERα, AR and HIF1AN protein levels through interaction with their 3′UTR regions, and proliferation in ERα‐positive BCa and AR‐positive PCa cells.