Dianne van Strijp

Human PDE4D isoform composition is deregulated in primary prostate cancer and indicative for disease progression and development of distant metastases

Phosphodiesterase 4D7 was recently shown to be specifically over-expressed in localized prostate cancer, raising the question as to which regulatory mechanisms are involved and whether other isoforms of this gene family (PDE4D) are affected under the same conditions. We investigated PDE4D isoform composition in prostatic tissues using a total of seven independent expression datasets and also included data on DNA methylation, copy number and AR and ERG binding in PDE4D promoters to gain insight into their effect on PDE4D transcription. We show that expression of PDE4D isoforms is consistently altered in primary human prostate cancer compared to benign tissue, with PDE4D7 being up-regulated while PDE4D5 and PDE4D9 are down-regulated. Disease progression is marked by an overall down-regulation of long PDE4D isoforms, while short isoforms (PDE4D1/2) appear to be relatively unaffected. While these alterations seem to be independent of copy number alterations in the PDE4D locus and driven by AR and ERG binding, we also observed increased DNA methylation in the promoter region of PDE4D5, indicating a long lasting alteration of the isoform composition in prostate cancer tissues. We propose two independent metrics that may serve as diagnostic and prognostic markers for prostate disease: (PDE4D7 - PDE4D5) provides an effective means for distinguishing PCa from normal adjacent prostate, whereas PDE4D1/2 - (PDE4D5 + PDE4D7 + PDE4D9) offers strong prognostic potential to detect aggressive forms of PCa and is associated with metastasis free survival. Overall, our findings highlight the relevance of PDE4D as prostate cancer biomarker and potential drug target.

Complete sequence-based pathway analysis by differential on-chip DNA and RNA extraction from a single cell

We demonstrate on-chip, differential DNA and RNA extraction from a single cell using a microfluidic chip and a two-stage lysis protocol. This method enables direct use of the whole extract, without additional washing steps, reducing sample loss. Using this method, the tumor driving pathway in individual cells from a colorectal cancer cell line was determined by applying a Bayesian computational pathway model to sequences obtained from the RNA fraction of a single cell and, the mutations driving the pathway were determined by analyzing sequences obtained from the DNA fraction of the same single cell. This combined functional and mutational pathway assessment of a single cell could be of significant value for dissecting cellular heterogeneity in tumors and analyzing single circulating tumor cells.

Sequencing of human genomes extracted from single cancer cells isolated in a valveless microfluidic device.

Sequencing the genomes of individual cells enables the direct determination of genetic heterogeneity amongst cells within a population. We have developed an injection-moulded valveless microfluidic device in which single cells from colorectal cancer derived cell lines (LS174T, LS180 and RKO) and fresh colorectal tumors have been individually trapped, their genomes extracted and prepared for sequencing using multiple displacement amplification (MDA). Ninety nine percent of the DNA sequences obtained mapped to a reference human genome, indicating that there was effectively no contamination of these samples from non-human sources. In addition, most of the reads are correctly paired, with a low percentage of singletons (0.17 ± 0.06%) and we obtain genome coverages approaching 90%. To achieve this high quality, our device design and process shows that amplification can be conducted in microliter volumes as long as the lysis is in sub-nanoliter volumes. Our data thus demonstrates that high quality whole genome sequencing of single cells can be achieved using a relatively simple, inexpensive and scalable device. Detection of genetic heterogeneity at the single cell level, as we have demonstrated for freshly obtained single cancer cells, could soon become available as a clinical tool to precisely match treatment with the properties of a patients own tumor.

Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes

BACKGROUND The clinical metrics used to date to assess the progression risk of newly diagnosed prostate cancer patients only partly represent the true biological aggressiveness of the underlying disease. OBJECTIVE Validation of the prognostic biomarker phosphodiesterase-4D7 (PDE4D7) in predicting longitudinal biological outcomes in a historical surgery cohort to improve postsurgical risk stratification. DESIGN, PATIENTS, AND METHODS RNA was extracted from biopsy punches of resected tumors from 550 patients. PDE4D7 was quantified using one-step quantitative reverse transcription-polymerase chain reaction. PDE4D7 scores were calculated by normalization of PDE4D7 to reference genes. Multivariate analyses were adjusted for clinical prognostic variables. Outcomes tested were: prostate-specific antigen relapse, start of salvage treatment, progression to metastases, overall mortality, and prostate cancer-specific mortality. The PDE4D7 score was combined with the clinical risk model Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S) using multivariate regression modeling; the combined score was tested in post-treatment progression free survival prediction. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Correlations with outcomes were analyzed using multivariate Cox regression and logistic regression statistics. RESULTS AND LIMITATIONS The PDE4D7 score was significantly associated with time-to-prostate specific antigen failure after prostatectomy (hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.41-0.67 for each unit increase, p<0.0001). After adjustment for postsurgical prognostic variables the HR was 0.56 (95% CI: 0.43-0.73, p<0.0001). The PDE4D7 score remained significant after adjusting the multi-variate analysis for the CAPRA-S model categories (HR=0.54, 95% CI=0.42-0.69, p<0.0001). Combination of the PDE4D7 score with the CAPRA-S demonstrated a significant incremental value of 4-6% in 2-yr (p=0.004) or 5-yr (p=0.003) prediction of progression free survival after surgery. The combined model of PDE4D7 and CAPRA-S improves patient selection with very high risk of fast disease relapse after primary intervention. CONCLUSIONS The PDE4D7 score has the potential to provide independent risk information and to restratify patients with clinical intermediate- to high-risk characteristics to a very low-risk profile. PATIENT SUMMARY In this report, we studied the potential of a novel biomarker to predict outcomes of a cohort of prostate cancer patients who underwent surgery more than 10 yr ago. We found that a gene called phosphodiesterase-4D7 added extra information to the available clinical data. We conclude that the measurement of this gene in tumor tissue may contribute to more effective treatment decisions.

Sequencing Metrics of Human Genomes Extracted from Single Cancer Cells Individually Isolated in a Valveless Microfluidic Device

Sequencing the genomes of individual cells enables the direct determination of genetic heterogeneity amongst cells within a population. We have developed an injection-moulded valveless microfluidic device in which single cells from colorectal cell (LS174T, LS180 and RKO) lines and fresh colorectal cancers are individually trapped, their genomes extracted and prepared for sequencing, using multiple displacement amplification (MDA). Ninety nine percent of the DNA sequences obtained mapped to a reference human genome, indicating that there was effectively no contamination of these samples from non-human sources. In addition, most of the reads are correctly paired, with a low percentage of singletons (0.17 ± 0.06 %) and we obtain genome coverages approaching 90%. To achieve this high quality, our device design and process shows that amplification can be conducted in microliter volumes as long as extraction is in sub-nanoliter volumes. Our data also demonstrates that high quality single cell sequencing can be achieved using a relatively simple, inexpensive and scalable device.

Statins Promote Cardiac Infarct Healing by Modulating Endothelial Barrier Function Revealed by Contrast-Enhanced Magnetic Resonance Imaging

Objective— The endothelium has a crucial role in wound healing, acting as a barrier to control transit of leukocytes. Endothelial barrier function is impaired in atherosclerosis preceding myocardial infarction (MI). Besides lowering lipids, statins modulate endothelial function. Here, we noninvasively tested whether statins affect permeability at the inflammatory (day 3) and the reparative (day 7) phase of infarct healing post-MI using contrast-enhanced cardiac magnetic resonance imaging (MRI). Approach and Results— Noninvasive permeability mapping by MRI after MI in C57BL/6, atherosclerotic ApoE−/−, and statin-treated ApoE−/− mice was correlated to subsequent left ventricular outcome by structural and functional cardiac MRI. Ex vivo histology, flow cytometry, and quantitative polymerase chain reaction were performed on infarct regions. Increased vascular permeability at ApoE−/− infarcts was observed compared with C57BL/6 infarcts, predicting enhanced left ventricular dilation at day 21 post-MI by MRI volumetry. Statin treatment improved vascular barrier function at ApoE−/− infarcts, indicated by reduced permeability. The infarcted tissue of ApoE−/− mice 3 days post-MI displayed an unbalanced Vegfa(vascular endothelial growth factor A)/Angpt1 (angiopoetin-1) expression ratio (explaining leakage-prone vessels), associated with higher amounts of CD45+ leukocytes and inflammatory LY6Chi monocytes. Statins reversed the unbalanced Vegfa/Angpt1 expression, normalizing endothelial barrier function at the infarct and blocking the augmented recruitment of inflammatory leukocytes in statin-treated ApoE−/− mice. Conclusions— Statins lowered permeability and reduced the transit of unfavorable inflammatory leukocytes into the infarcted tissue, consequently improving left ventricular outcome.

Abstract 59: Identifying tumor driving signaling pathways for companion diagnostics using computational pathway models.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Introduction Targeted drug treatment requires reliable companion diagnostics for therapy selection. Genomic and transcriptomic data can provide input for this, provided tools exist to convert this complex data into meaningful clinical information. We develop computational models of oncogenic pathways, to assess which one drives tumor growth in an individual patient and what is the causing (epi)genetic defect. Computational pathway models Based on a selection of experimentally validated direct target genes, we built initial models of the Wnt, ER, AR and Hedgehog pathways, covering their transcriptional program. We have modeled each pathway by a Bayesian network, which interprets the target genes’ mRNA levels (Affymetrix U133Plus2.0), and infers a probability that the respective pathway is active in a certain sample. Model parameters are based on literature insights and experimental data. Results A first Wnt model, calibrated on cell line data, validated perfectly on 32 normal colon samples and 32 colon adenomas from patients ([GSE8671][1]). A second Wnt model, calibrated on these 64 patient samples, correctly predicted no Wnt activity in all 44 normal colon samples, and Wnt activity in 97 of 101 colon cancer samples from [GSE20916][2]. Next, we tested the second Wnt model on other cancer types. On 25 breast cancer cell lines from [GSE12777][3] with known Wnt status, the model correctly identified the two samples with an active pathway. On two patient data sets ([GSE12276][4], n=204; [GSE21653][5], n=266) Wnt activity was predicted in a higher number of basal samples compared to other subtypes (p=0.021 and p=2.7e-5, respectively), in line with increasing evidence for Wnt activity in this subtype. Finally, tests on liver ([GSE9843][6], [GSE6764][7]) and medulloblastoma sets ([GSE10327][8]) confirm the power of these models to predict Wnt pathway activity. A first ER model was calibrated on estrogen-deprived and -stimulated MCF7 cell lines ([GSE8697][9]). Applied on breast cancer cell line data from [GSE21618][10], increased incidence of ER pathway activity was found in tamoxifen-sensitive cell lines compared to resistant ones. On breast cancer patient data ([GSE12276][4], [GSE9195][11], [GSE6532][12]) the model showed no pathway activity in ER- samples, and an active ER pathway in 26-38% of the ER+ samples. Within the latter group, model-predicted ER activity correlated with improved survival. Clinical utility studies to correlate ER activity to hormone therapy response are in progress. Finally, the AR model showed promising results on prostate cancer cell lines ([GSE34211][13], [GSE36133][14]), as did the Hedgehog model on medulloblastoma samples ([GSE10327][8]). Conclusion Our computational pathway models predict functional activity of oncogenic pathways for an individual patient based on mRNA data, complementary to existing molecular and histopathology staining tests. Clinical utility for therapy response prediction is currently being validated with clinical partners. Citation Format: Wim Verhaegh, Henk van Ooijen, Marcia Alves de Inda, Kalyan Dulla, Ralf Hoffmann, Dianne van Strijp, Pantelis Hatzis, Hans Clevers, Anja van de Stolpe. Identifying tumor driving signaling pathways for companion diagnostics using computational pathway models. [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 59. doi:10.1158/1538-7445.AM2013-59 [1]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE8671&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [2]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE20916&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [3]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE12777&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [4]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE12276&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [5]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE21653&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [6]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE9843&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [7]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE6764&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [8]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE10327&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [9]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE8697&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [10]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE21618&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [11]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE9195&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [12]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE6532&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [13]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE34211&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom [14]: /lookup/external-ref?link_type=NCBIGEO&access_num=GSE36133&atom=%2Fcanres%2F73%2F8_Supplement%2F59.atom