Julie Støve Bødker

Plasminogen activator inhibitor-1 and tumour growth, invasion, and metastasis

In recent decades, evidence has been accumulating showing the important role of urokinase-type plasminogen activator (uPA) in growth, invasion, and metastasis of malignant tumours. The evidence comes from results with animal tumour models and from the observation that a high level of uPA in human tumours is associated with a poor patient prognosis. It therefore initially came as a surprise that a high tumour level of the uPA inhibitor plasminogen activator inhibitor-1 (PAI-1) is also associated with a poor prognosis, the PAI-1 level in fact being one of the most informative biochemical prognostic markers. We review here recent investigations into the possible tumour biological role of PAI-1, performed by animal tumour models, histological examination of human tumours, and new knowledge about the molecular interactions of PAI-1 possibly underlying its tumour biological functions. The exact tumour biological functions of PAI-1 remain uncertain but PAI-1 seems to be multifunctional as PAI-1 is expressed by multiple cell types and has multiple molecular interactions. The potential utilisation of PAI-1 as a target for anti-cancer therapy depends on further mapping of these functions.

Biochemical properties of plasminogen activator inhibitor-1

PAI-1 is a Mr ~50,000 glycoprotein, which is the primary physiological inhibitor of the two plasminogen activators uPA and tPA. PAI-1 belongs to the serpin protein family. Studies of PAI-1 have contributed significantly to the elucidation of the protease inhibitory mechanism of serpins, which is based on a metastable native state becoming stabilised by insertion of the RCL into the central beta-sheet A and formation of covalent complexes with target proteases. In PAI-1, this insertion can occur in the absence of the protease, resulting in generation of a so-called latent, inactive form of the protein. PAI-1, in its active state, also binds to the extracellular protein vitronectin. When in complex with its target proteases, it binds with high affinity to endocytosis receptors of the low density receptor family.

Biochemical mechanism of action of a diketopiperazine inactivator of plasminogen activator inhibitor-1.

XR5118 [(3 Z,6 Z )-6-benzylidine-3-(5-(2-dimethylaminoethyl-thio-))-2-(thienyl)methylene-2,5-dipiperazinedione hydrochloride] can inactivate the anti-proteolytic activity of the serpin plasminogen activator inhibitor-1 (PAI-1), a potential therapeutic target in cancer and cardiovascular diseases. Serpins inhibit their target proteases by the P(1) residue of their reactive centre loop (RCL) forming an ester bond with the active-site serine residue of the protease, followed by insertion of the RCL into the serpins large central beta-sheet A. In the present study, we show that the RCL of XR5118-inactivated PAI-1 is inert to reaction with its target proteases and has a decreased susceptibility to non-target proteases, in spite of a generally increased proteolytic susceptibility of specific peptide bonds elsewhere in PAI-1. The properties of XR5118-inactivated PAI-1 were different from those of the so-called latent form of PAI-1. Alanine substitution of several individual residues decreased the susceptibility of PAI-1 to XR5118. The localization of these residues in the three-dimensional structure of PAI-1 suggested that the XR5118-induced inactivating conformational change requires mobility of alpha-helix F, situated above beta-sheet A, and is in agreement with the hypothesis that XR5118 binds laterally to beta-sheet A. These results improve our understanding of the unique conformational flexibility of serpins and the biochemical basis for using PAI-1 as a therapeutic target.

Diffuse Large B-Cell Lymphoma Classification System That Associates Normal B-Cell Subset Phenotypes With Prognosis

PURPOSE Current diagnostic tests for diffuse large B-cell lymphoma use the updated WHO criteria based on biologic, morphologic, and clinical heterogeneity. We propose a refined classification system based on subset-specific B-cell-associated gene signatures (BAGS) in the normal B-cell hierarchy, hypothesizing that it can provide new biologic insight and diagnostic and prognostic value. PATIENTS AND METHODS We combined fluorescence-activated cell sorting, gene expression profiling, and statistical modeling to generate BAGS for naive, centrocyte, centroblast, memory, and plasmablast B cells from normal human tonsils. The impact of BAGS-assigned subtyping was analyzed using five clinical cohorts (treated with cyclophosphamide, doxorubicin, vincristine, and prednisone [CHOP], n = 270; treated with rituximab plus CHOP [R-CHOP], n = 869) gathered across geographic regions, time eras, and sampling methods. The analysis estimated subtype frequencies and drug-specific resistance and included a prognostic meta-analysis of patients treated with first-line R-CHOP therapy. RESULTS Similar BAGS subtype frequencies were assigned across 1,139 samples from five different cohorts. Among R-CHOP-treated patients, BAGS assignment was significantly associated with overall survival and progression-free survival within the germinal center B-cell-like subclass; the centrocyte subtype had a superior prognosis compared with the centroblast subtype. In agreement with the observed therapeutic outcome, centrocyte subtypes were estimated as being less resistant than the centroblast subtype to doxorubicin and vincristine. The centroblast subtype had a complex genotype, whereas the centrocyte subtype had high TP53 mutation and insertion/deletion frequencies and expressed LMO2, CD58, and stromal-1-signature and major histocompatibility complex class II-signature genes, which are known to have a positive impact on prognosis. CONCLUSION Further development of a diagnostic platform using BAGS-assigned subtypes may allow pathogenetic studies to improve disease management.

Increased expression of transcription factor TFAP2α correlates with chemosensitivity in advanced bladder cancer.

BackgroundThe standard treatment for patients with advanced transitional cell carcinoma of the bladder is platin based chemotherapy. Only approximately 50% of the patients respond to chemotherapy. Therefore, molecular predictive markers for identification of chemotherapy sensitive subgroups of patients are highly needed. We selected the transcription factor TFAP2α from a previously identified gene expression signature for chemotherapy response.MethodsTFAP2α expression and localization was assessed by immunohistochemistry using a tissue microarray (TMA) containing 282 bladder cancer tumors from patients with locally advanced (pT2-T4b and N1-3) or metastatic (M1) disease. All patients had received cisplatin containing chemotherapy. Furthermore, QPCR analysis of three TFAP2α isoforms was performed on tumor specimens of advanced muscle invasive bladder cancers (T2-4). Using the bladder cell lines T24 and SW780 the relation of TFAP2α and cisplatin and gemcitabine sensitivity as well as cell proliferation was examined using siRNA directed TFAP2α knockdown.ResultsTFAP2α protein expression was analyzed on a TMA with cores from 282 advanced bladder cancer tumors from patients treated with cisplatin based combinational chemotherapy. TFAP2α was identified as a strong independent predictive marker for a good response and survival after cisplatin-containing chemotherapy in patients with advanced bladder cancer. Strong TFAP2α nuclear and cytoplasmic staining predicted good response to chemotherapy in patients with lymph node metastasis, whereas weak TFAP2α nuclear staining predicted good response in patients without lymph node metastasis. In vitro studies showed that siRNA mediated knockdown of TFAP2α increased the proliferation of SW780 cells and rendered the cells less sensitive to cisplatin and gemcitabine. In contrast to that T24 bladder cells with mutated p53 showed to be more drug sensitive upon TFAP2α depletion.ConclusionsHigh levels of nuclear and cytoplasmic TFAP2α protein were a predictor of increased overall survival and progression free survival in patients with advanced bladder cancer treated with cisplatin based chemotherapy. TFAP2α knockdown increased the proliferation of the SW780 bladder cells and reduced cisplatin and gemcitabine induced cell death. The inverse effect was observed in the TP53 mutated T24 cell line where TFAP2α silencing augmented cisplatin and gemcitabine sensitivity and did not stimulate proliferation.

Construction of a plasminogen activator inhibitor-1 variant without measurable affinity to vitronectin but otherwise normal.

Vitronectin (VN) and plasminogen activator inhibitor‐1 (PAI‐1) have important functional interactions: VN stabilises the protease inhibitory activity of PAI‐1 and PAI‐1 inhibits binding of adhesion receptors to VN. Having previously mapped the PAI‐1 binding area for VN, we have now constructed a PAI‐1 variant, R103A‐M112A‐Q125A, without measurable affinity to VN, but with full protease inhibitory activity and endocytosis receptor binding. As a tool for evaluating the physiological and pathophysiological functions of the PAI‐1–VN interaction, our new variant is far superior to the previously widely used PAI‐1 variant Q125K, which we have found possesses an only about 10‐fold reduced affinity to VN.

High miR-34a expression improves response to doxorubicin in diffuse large B-cell lymphoma

The standard treatment for patients with diffuse large B-cell lymphoma (DLBCL) is the immunochemotherapy-based R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone). Resistance to treatment, intrinsic or acquired, is observed in approximately 40% of patients with DLBCL, who thus require novel interventions to survive. To identify biomarkers for cytotoxic response assessment, microRNAs (miRNAs) associated with doxorubicin sensitivity were determined by combining global miRNA expression profiling with systematic dose-response screens in 15 human DLBCL cell lines. One candidate, miR-34a, was tested in functional in vitro studies and in vivo in a retrospective clinical cohort. High expression of miR-34a was observed in cell lines sensitive to doxorubicin, and upregulation of miR-34a is documented here to increase doxorubicin sensitivity in in vitro lentiviral transduction assays. High expression of miR-34a had a prognostic impact using overall survival as outcome. With risk stratification of DLBCL samples based on resistance gene signatures (REGS), doxorubicin-responsive samples had statistically significant upregulated miR-34a expression. Classification of the DLBCL samples into subset-specific B cell-associated gene signatures (BAGS) revealed differentiation-specific expression of miR-34a. Our data further support FOXP1 as a target of miR-34a, suggesting that downregulation of FOXP1 may sensitize DLBCL cells to doxorubicin. We conclude that miRNAs, in particular miR-34a, may have clinical utility in DLBCL patients as both predictive and prognostic biomarkers.

Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models

BackgroundPatients suffering from cancer are often treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients. Based on recent popularisation of regularised regression models the goal of this study was to establish workflows for pharmacogenomic predictors of response to standard multidrug regimens using baseline gene expression data and origin specific cell lines. The proposed workflows are tested on diffuse large B-cell lymphoma treated with R-CHOP first-line therapy.MethodsFirst, B-cell cancer cell lines were tested successively for resistance towards the chemotherapeutic components of R-CHOP: cyclophosphamide (C), doxorubicin (H), and vincristine (O). Second, baseline gene expression data were obtained for each cell line before treatment. Third, regularised multivariate regression models with cross-validated tuning parameters were used to generate classifier and predictor based resistance gene signatures (REGS) for the combination and individual chemotherapeutic drugs C, H, and O. Fourth, each developed REGS was used to assign resistance levels to individual patients in three clinical cohorts.ResultsBoth classifier and predictor based REGS, for the combination CHO, were of prognostic value. For patients classified as resistant towards CHO the risk of progression was 2.33 (95% CI: 1.6, 3.3) times greater than for those classified as sensitive. Similarly, an increase in the predicted CHO resistance index of 10 was related to a 22% (9%, 36%) increased risk of progression. Furthermore, the REGS classifier performed significantly better than the REGS predictor.ConclusionsThe regularised multivariate regression models provide a flexible workflow for drug resistance studies with promising potential. However, the gene expressions defining the REGSs should be functionally validated and correlated to known biomarkers to improve understanding of molecular mechanisms of drug resistance.

Human B-cell cancer cell lines as a preclinical model for studies of drug effect in diffuse large B-cell lymphoma and multiple myeloma

Drug resistance in cancer refers to recurrent or primary refractory disease following drug therapy. At the cellular level, it is a consequence of molecular functions that ultimately enable the cell to resist cell death-one of the classical hallmarks of cancer. Thus, drug resistance is a fundamental aspect of the cancer cell phenotype, in parallel with sustained proliferation, immortality, angiogenesis, invasion, and metastasis. Here we present a preclinical model of human B-cell cancer cell lines used to identify genes involved in specific drug resistance. This process includes a standardized technical setup for specific drug screening, analysis of global gene expression, and the statistical considerations required to develop resistance gene signatures. The state of the art is illustrated by the first-step classical drug screen (including the CD20 antibody rituximab, the DNA intercalating topoisomerase II inhibitor doxorubicin, the mitotic inhibitor vincristine, and the alkylating agents cyclophosphamide and melphalan) along with the generation of gene lists predicting the chemotherapeutic outcome as validated retrospectively in clinical trial datasets. This B-cell lineage-specific preclinical model will allow us to initiate a range of laboratory studies, with focus on specific gene functions involved in molecular resistance mechanisms.

Long Noncoding RNA Expression during Human B-Cell Development.

Long noncoding RNAs (lncRNAs) have emerged as important regulators of diverse cellular processes, but their roles in the developing immune system are poorly understood. In this study, we analysed lncRNA expression during human B-cell development by array-based expression profiling of eleven distinct flow-sorted B-cell subsets, comprising pre-B1, pre-B2, immature, naive, memory, and plasma cells from bone marrow biopsies (n = 7), and naive, centroblast, centrocyte, memory, and plasmablast cells from tonsil tissue samples (n = 6), respectively. A remapping strategy was used to assign the array probes to 37630 gene-level probe sets, reflecting recent updates in genomic and transcriptomic databases, which enabled expression profiling of 19579 long noncoding RNAs, comprising 3947 antisense RNAs, 5277 lincRNAs, 7625 pseudogenes, and 2730 additional lncRNAs. As a first step towards inferring the functions of the identified lncRNAs in developing B-cells, we analysed their co-expression with well-characterized protein-coding genes, a method known as “guilt by association”. By using weighted gene co-expression network analysis, we identified 272 lincRNAs, 471 antisense RNAs, 376 pseudogene RNAs, and 64 lncRNAs within seven sub-networks associated with distinct stages of B-cell development, such as early B-cell development, B-cell proliferation, affinity maturation of antibody, and terminal differentiation. These data provide an important resource for future studies on the functions of lncRNAs in development of the adaptive immune response, and the pathogenesis of B-cell malignancies that originate from distinct B-cell subpopulations.