Publications by authors named "Teemu D Laajala"

26 Publications

  • Page 1 of 1

A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma.

Sci Rep 2021 Apr 21;11(1):8650. Epub 2021 Apr 21.

Department of Oncology and Radiotherapy, Fican West Cancer Centre, University of Turku and Turku University Hospital, Hämeentie 11, Post Box 52, 20521, Turku, Finland.

After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4-8.6) and 5.4 years (4.0-7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-021-88177-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060273PMC
April 2021

Exploratory Analysis of CA125-MGL and -STn Glycoforms in the Differential Diagnostics of Pelvic Masses.

J Appl Lab Med 2020 03;5(2):263-272

Institute of Biomedicine, Research Center for Cancer, Infections and Immunity, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.

Background: The cancer antigen 125 (CA125) immunoassay (IA) does not distinguish epithelial ovarian cancer (EOC) from benign disease with the sensitivity needed in clinical practice. In recent studies, glycoforms of CA125 have shown potential as biomarkers in EOC. Here, we assessed the diagnostic abilities of two recently developed CA125 glycoform assays for patients with a pelvic mass. Detailed analysis was further conducted for postmenopausal patients with marginally elevated conventionally measured CA125 levels, as this subgroup presents a diagnostic challenge in the clinical setting.

Methods: Our study population contained 549 patients diagnosed with EOC, benign ovarian tumors, and endometriosis. Of these, 288 patients were postmenopausal, and 98 of them presented with marginally elevated serum levels of conventionally measured CA125 at diagnosis. Preoperative serum levels of conventionally measured CA125 and its glycoforms (CA125-MGL and CA125-STn) were determined.

Results: The CA125-STn assay identified EOC significantly better than the conventional CA125-IA in postmenopausal patients (85% vs. 74% sensitivity at a fixed specificity of 90%, P = 0.0009). Further, both glycoform assays had superior AUCs compared to the conventional CA125-IA in postmenopausal patients with marginally elevated CA125. Importantly, the glycoform assays reduced the false positive rate of the conventional CA125-IA.

Conclusions: The results indicate that the CA125 glycoform assays markedly improve the performance of the conventional CA125-IA in the differential diagnosis of pelvic masses. This result is especially valuable when CA125 is marginally elevated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jalm/jfz012DOI Listing
March 2020

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets.

Int J Med Inform 2020 01 17;133:104014. Epub 2019 Nov 17.

Department of Future Technologies, University of Turku, Turku, Finland. Electronic address:

Introduction: Predictive survival modeling offers systematic tools for clinical decision-making and individualized tailoring of treatment strategies to improve patient outcomes while reducing overall healthcare costs. In 2015, a number of machine learning and statistical models were benchmarked in the DREAM 9.5 Prostate Cancer Challenge, based on open clinical trial data for metastatic castration resistant prostate cancer (mCRPC). However, applying these models into clinical practice poses a practical challenge due to the inclusion of a large number of model variables, some of which are not routinely monitored or are expensive to measure.

Objectives: To develop cost-specified variable selection algorithms for constructing cost-effective prognostic models of overall survival that still preserve sufficient model performance for clinical decision making.

Methods: Penalized Cox regression models were used for the survival prediction. For the variable selection, we implemented two algorithms: (i) LASSO regularization approach; and (ii) a greedy cost-specified variable selection algorithm. The models were compared in three cohorts of mCRPC patients from randomized clinical trials (RCT), as well as in a real-world cohort (RWC) of advanced prostate cancer patients treated at the Turku University Hospital. Hospital laboratory expenses were utilized as a reference for computing the costs of introducing new variables into the models.

Results: Compared to measuring the full set of clinical variables, economic costs could be reduced by half without a significant loss of model performance. The greedy algorithm outperformed the LASSO-based variable selection with the lowest tested budgets. The overall top performance was higher with the LASSO algorithm.

Conclusion: The cost-specified variable selection offers significant budget optimization capability for the real-world survival prediction without compromising the predictive power of the model.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ijmedinf.2019.104014DOI Listing
January 2020

Cumulative Cancer Locations is a Novel Metric for Predicting Active Surveillance Outcomes: A Multicenter Study.

Eur Urol Oncol 2018 09 15;1(4):268-275. Epub 2018 May 15.

Department of Urology, Helsinki University Hospital, Helsinki, Finland; University of Helsinki, Helsinki, Finland.

Background: Active surveillance (AS) of prostate cancer (PC) has increased in popularity to address overtreatment.

Objective: To determine whether a novel metric, cumulative cancer locations (CCLO), can predict AS outcomes in a group of AS patients with low and very low risk.

Design, Setting, And Participants: CCLO is obtained by summing the total number of histological cancer-positive locations in both diagnostic and confirmatory biopsies (Bx). The retrospective study cohort comprised three prospective AS cohorts (Helsinki University Hospital: n=316; European Institute of Oncology: n=204; and University of Münster: n=89).

Outcome Measurements And Statistical Analysis: We analyzed whether risk stratification based on CCLO predicts different AS outcomes: protocol-based discontinuation (PBD), Gleason upgrading (GU) during AS, and adverse findings in radical prostatectomy (RP) specimens.

Results: In Kaplan Meier analyses, patients in the CCLO high-risk group experienced significantly shorter event-free survival for all outcomes (PBD, GU, and adverse RP findings; all p<0.002). In multivariable Cox regression analysis, patients in the CCLO high-risk group had a significantly higher risk of experiencing PBD (hazard ratio [HR] 12.15, 95% confidence interval [CI] 6.18-23.9; p<0.001), GU (HR 6.01, 95% CI 2.16-16.8; p=0.002), and adverse RP findings (HR 9.144, 95% CI 2.27-36.9; p=0.006). In receiver operating characteristic analyses, the area under the curve for CCLO outperformed the number of cancer-positive Bxs in confirmatory Bx in predicting PBD (0.734 vs 0.682), GU (0.655 vs 0.576) and adverse RP findings (0.662 vs 0.561) and the added value was supported by decision curve analysis.

Conclusions: CCLO is distinct from the number of positive Bx cores. Higher CCLO predicts AS outcomes and may aid in selection of patients for AS.

Patient Summary: For patients on active surveillance for prostate cancer, the cumulative number of cancer-positive locations in diagnostic and confirmatory biopsies is a predictor of active surveillance outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.euo.2018.04.006DOI Listing
September 2018

Publisher Correction: Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

Nat Commun 2019 02 20;10(1):932. Epub 2019 Feb 20.

Cancer Cell Circuitry Laboratory, Research Programs Unit/Translational Cancer Biology and Medicum, University of Helsinki, Street address: Haartmaninkatu 8, P.O. Box 63, 00014, Helsinki, Finland.

The original version of this Article contained an error in Fig. 7. In panel b, the survival curves were shifted relative to the y axis. This error has been corrected in both the PDF and HTML versions of the Article.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-019-08956-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382943PMC
February 2019

Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

Nat Commun 2019 02 6;10(1):620. Epub 2019 Feb 6.

Cancer Cell Circuitry Laboratory, Research Programs Unit/Translational Cancer Biology and Medicum, University of Helsinki, P.O. Box 63, Street address: Haartmaninkatu 8, 00014, Helsinki, Finland.

Elevated MYC expression sensitizes tumor cells to apoptosis but the therapeutic potential of this mechanism remains unclear. We find, in a model of MYC-driven breast cancer, that pharmacological activation of AMPK strongly synergizes with BCL-2/BCL-X inhibitors to activate apoptosis. We demonstrate the translational potential of an AMPK and BCL-2/BCL-X co-targeting strategy in ex vivo and in vivo models of MYC-high breast cancer. Metformin combined with navitoclax or venetoclax efficiently inhibited tumor growth, conferred survival benefits and induced tumor infiltration by immune cells. However, withdrawal of the drugs allowed tumor re-growth with presentation of PD-1+/CD8+ T cell infiltrates, suggesting immune escape. A two-step treatment regimen, beginning with neoadjuvant metformin+venetoclax to induce apoptosis and followed by adjuvant metformin+venetoclax+anti-PD-1 treatment to overcome immune escape, led to durable antitumor responses even after drug withdrawal. We demonstrate that pharmacological reactivation of MYC-dependent apoptosis is a powerful antitumor strategy involving both tumor cell depletion and immunosurveillance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-019-08541-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365524PMC
February 2019

Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts.

Am J Pathol 2018 12 28;188(12):2890-2901. Epub 2018 Sep 28.

Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland; Turku Center for Disease Modeling, University of Turku, Turku, Finland; Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Electronic address:

The role of adrenal androgens as drivers for castration-resistant prostate cancer (CRPC) growth in humans is generally accepted; however, the value of preclinical mouse models of CRPC is debatable, because mouse adrenals do not produce steroids activating the androgen receptor. In this study, we confirmed the expression of enzymes essential for de novo synthesis of androgens in mouse adrenals, with high intratissue concentration of progesterone (P) and moderate levels of androgens, such as androstenedione, testosterone, and dihydrotestosterone, in the adrenal glands of both intact and orchectomized (ORX) mice. ORX alone had no effect on serum P concentration, whereas orchectomized and adrenalectomized (ORX + ADX) resulted in a significant decrease in serum P and in a further reduction in the low levels of serum androgens (androstenedione, testosterone, and dihydrotestosterone), measured by mass spectrometry. In line with this, the serum prostate-specific antigen and growth of VCaP xenografts in mice after ORX + ADX were markedly reduced compared with ORX alone, and the growth difference was not abolished by a glucocorticoid treatment. Moreover, ORX + ADX altered the androgen-dependent gene expression in the tumors, similar to that recently shown for the enzalutamide treatment. These data indicate that in contrast to the current view, and similar to humans, mouse adrenals synthesize significant amounts of steroids that contribute to the androgen receptor-dependent growth of CRPC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajpath.2018.07.029DOI Listing
December 2018

Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths.

Eur Heart J 2018 07;39(27):2562-2573

Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, SU Sahlgrenska, 41345 Gothenburg, Sweden.

Aims: Low-density lipoprotein (LDL) particles cause atherosclerotic cardiovascular disease (ASCVD) through their retention, modification, and accumulation within the arterial intima. High plasma concentrations of LDL drive this disease, but LDL quality may also contribute. Here, we focused on the intrinsic propensity of LDL to aggregate upon modification. We examined whether inter-individual differences in this quality are linked with LDL lipid composition and coronary artery disease (CAD) death, and basic mechanisms for plaque growth and destabilization.

Methods And Results: We developed a novel, reproducible method to assess the susceptibility of LDL particles to aggregate during lipolysis induced ex vivo by human recombinant secretory sphingomyelinase. Among patients with an established CAD, we found that the presence of aggregation-prone LDL was predictive of future cardiovascular deaths, independently of conventional risk factors. Aggregation-prone LDL contained more sphingolipids and less phosphatidylcholines than did aggregation-resistant LDL. Three interventions in animal models to rationally alter LDL composition lowered its susceptibility to aggregate and slowed atherosclerosis. Similar compositional changes induced in humans by PCSK9 inhibition or healthy diet also lowered LDL aggregation susceptibility. Aggregated LDL in vitro activated macrophages and T cells, two key cell types involved in plaque progression and rupture.

Conclusion: Our results identify the susceptibility of LDL to aggregate as a novel measurable and modifiable factor in the progression of human ASCVD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/eurheartj/ehy319DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6047440PMC
July 2018

ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts.

Bioinformatics 2018 11;34(22):3957-3959

Department of Mathematics and Statistics, University of Turku, Turku, Finland.

Motivation: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data.

Results: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients.

Availability And Implementation: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage.

Supplementary Information: Supplementary data are available at Bioinformatics online.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/bty477DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223370PMC
November 2018

Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma.

Gynecol Oncol 2018 04 2;149(1):173-180. Epub 2018 Mar 2.

Department of Gynaecology and Obstetrics, University of Tampere, Tampere University Hospital, PL2000, 33521 Tampere, Finland.

Objective: In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information.

Methods: A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability.

Results: A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (29.5%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P<0.001) of dying of EEC compared to the low-risk group.

Conclusions: P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ygyno.2018.02.016DOI Listing
April 2018

Community mining of open clinical trial data.

Oncotarget 2017 Oct 13;8(47):81721-81722. Epub 2017 Sep 13.

James C. Costello: Department of Pharmacology, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.18632/oncotarget.20853DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5669837PMC
October 2017

Antiandrogens Reduce Intratumoral Androgen Concentrations and Induce Androgen Receptor Expression in Castration-Resistant Prostate Cancer Xenografts.

Am J Pathol 2018 01 7;188(1):216-228. Epub 2017 Nov 7.

Department of Physiology, Institute of Biomedicine, University of Turku, Turku, Finland; Turku Center for Disease Modeling, Institute of Biomedicine, University of Turku, Turku, Finland; Center for Bone and Arthritis Research, The Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden. Electronic address:

The development of castration-resistant prostate cancer (CRPC) is associated with the activation of intratumoral androgen biosynthesis and an increase in androgen receptor (AR) expression. We recently demonstrated that, similarly to the clinical CRPC, orthotopically grown castration-resistant VCaP (CR-VCaP) xenografts express high levels of AR and retain intratumoral androgen concentrations similar to tumors grown in intact mice. Herein, we show that antiandrogen treatment (enzalutamide or ARN-509) significantly reduced (10-fold, P < 0.01) intratumoral testosterone and dihydrotestosterone concentrations in the CR-VCaP tumors, indicating that the reduction in intratumoral androgens is a novel mechanism by which antiandrogens mediate their effects in CRPC. Antiandrogen treatment also altered the expression of multiple enzymes potentially involved in steroid metabolism. Identical to clinical CRPC, the expression levels of the full-length AR (twofold, P < 0.05) and the AR splice variants 1 (threefold, P < 0.05) and 7 (threefold, P < 0.01) were further increased in the antiandrogen-treated tumors. Nonsignificant effects were observed in the expression of certain classic androgen-regulated genes, such as TMPRSS2 and KLK3, despite the low levels of testosterone and dihydrotestosterone. However, other genes recently identified to be highly sensitive to androgen-regulated AR action, such as NOV and ST6GalNAc1, were markedly altered, which indicated reduced androgen action. Taken together, the data indicate that, besides blocking AR, antiandrogens modify androgen signaling in CR-VCaP xenografts at multiple levels.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajpath.2017.08.036DOI Listing
January 2018

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

Lancet Oncol 2017 01 16;18(1):132-142. Epub 2016 Nov 16.

Department of Pharmacology and Computational Biosciences Program, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA; University of Colorado Comprehensive Cancer Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA. Electronic address:

Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease.

Methods: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone.

Findings: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker.

Interpretation: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.

Funding: Sanofi US Services, Project Data Sphere.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/S1470-2045(16)30560-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217180PMC
January 2017

Longitudinal modeling of ultrasensitive and traditional prostate-specific antigen and prediction of biochemical recurrence after radical prostatectomy.

Sci Rep 2016 11 2;6:36161. Epub 2016 Nov 2.

Computational Biomedicine Group, Turku Centre for Biotechnology, University of Turku, Turku, Finland.

Ultrasensitive prostate-specific antigen (u-PSA) remains controversial for follow-up after radical prostatectomy (RP). The aim of this study was to model PSA doubling times (PSADT) for predicting biochemical recurrence (BCR) and to capture possible discrepancies between u-PSA and traditional PSA (t-PSA) by utilizing advanced statistical modeling. 555 RP patients without neoadjuvant/adjuvant androgen deprivation from the Turku University Hospital were included in the study. BCR was defined as two consecutive PSA values >0.2 ng/mL and the PSA measurements were log-transformed. One third of the data was reserved for independent validation. Models were first fitted to the post-surgery PSA measurements using cross-validation. Major trends were then captured using linear mixed-effect models and a predictive generalized linear model effectively identified early trends connected to BCR. The model generalized for BCR prediction to the validation set with ROC-AUC of 83.6% and 95.1% for the 1 and 3 year follow-up censoring, respectively. A web-based tool was developed to facilitate its use. Longitudinal trends of u-PSA did not display major discrepancies from those of t-PSA. The results support that u-PSA provides useful information for predicting BCR after RP. This can be beneficial to avoid unnecessary adjuvant treatments or to start them earlier for selected patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/srep36161DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5090356PMC
November 2016

Orphan G protein-coupled receptor GPRC5A modulates integrin β1-mediated epithelial cell adhesion.

Cell Adh Migr 2017 Sep 6;11(5-6):434-446. Epub 2017 Feb 6.

a Institute for Molecular Medicine Finland (FIMM), University of Helsinki , Helsinki , Finland.

G-Protein Coupled Receptor (GPCR), Class C, Group 5, Member A (GPRC5A) has been implicated in several malignancies. The underlying mechanisms, however, remain poorly understood. Using a panel of human cell lines, we demonstrate that CRISPR/Cas9-mediated knockout and RNAi-mediated depletion of GPRC5A impairs cell adhesion to integrin substrates: collagens I and IV, fibronectin, as well as to extracellular matrix proteins derived from the Engelbreth-Holm-Swarm (EHS) mouse sarcoma (Matrigel). Consistent with the phenotype, knock-out of GPRC5A correlated with a reduced integrin β1 (ITGB1) protein expression, impaired phosphorylation of the focal adhesion kinase (FAK), and lower activity of small GTPases RhoA and Rac1. Furthermore, we provide the first evidence for a direct interaction between GPRC5A and a receptor tyrosine kinase EphA2, an upstream regulator of FAK, although its contribution to the observed adhesion phenotype is unclear. Our findings reveal an unprecedented role for GPRC5A in regulation of the ITGB1-mediated cell adhesion and it's downstream signaling, thus indicating a potential novel role for GPRC5A in human epithelial cancers.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/19336918.2016.1245264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810789PMC
September 2017

The Hydroxysteroid (17β) Dehydrogenase Family Gene HSD17B12 Is Involved in the Prostaglandin Synthesis Pathway, the Ovarian Function, and Regulation of Fertility.

Endocrinology 2016 Oct 4;157(10):3719-3730. Epub 2016 Aug 4.

Department of Physiology and Turku Center for Disease Modeling (H.K., M.A., J.M.-J., T.D.L., L.S., M.P.), Institute of Biomedicine, University of Turku, FI-20540 Turku, Finland; Department of Clinical Science, Intervention and Technology (P.D., O.H.), Karolinska Institute, 141 52 Huddinge, Sweden; Swedish Toxicology Sciences Research Center (P.D.), Karolinska Institutet, 141 86 Stockholm, Sweden; Department of Biosciences and Nutrition (A.E.D., J.K.), Karolinska Institutet, 171 77 Stockholm, Sweden; Department of Mathematics and Statistics (T.D.L., T.A.), University of Turku, FI-20014 Turku, Finland; Institute for Molecular Medicine Finland (T.A.), University of Helsinki, FI-00014 Helsinki, Finland; Experimental Genetics (J.A.), Center of Life and Food Sciences, Weihenstephan, 85354 Freising, Germany; Institute of experimental Genetics (J.A.), Helmholtz Zentrum, 81377 München, Germany; Genome Analysis Center (J.A.), German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Neuroscience and Physiology (H.R.), Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Institute of Medicine (C.O., M.P.), The Sahlgrenska Academy, University of Gothenburg, SE-413 46 Gothenburg, Sweden.

The hydroxysteroid (17beta) dehydrogenase (HSD17B)12 gene belongs to the hydroxysteroid (17β) dehydrogenase superfamily, and it has been implicated in the conversion of estrone to estradiol as well as in the synthesis of arachidonic acid (AA). AA is a precursor of prostaglandins, which are involved in the regulation of female reproduction, prompting us to study the role of HSD17B12 enzyme in the ovarian function. We found a broad expression of HSD17B12 enzyme in both human and mouse ovaries. The enzyme was localized in the theca interna, corpus luteum, granulosa cells, oocytes, and surface epithelium. Interestingly, haploinsufficiency of the HSD17B12 gene in female mice resulted in subfertility, indicating an important role for HSD17B12 enzyme in the ovarian function. In line with significantly increased length of the diestrous phase, the HSD17B females gave birth less frequently than wild-type females, and the litter size of HSD17B12 females was significantly reduced. Interestingly, we observed meiotic spindle formation in immature follicles, suggesting defective meiotic arrest in HSD17B12 ovaries. The finding was further supported by transcriptome analysis showing differential expression of several genes related to the meiosis. In addition, polyovular follicles and oocytes trapped inside the corpus luteum were observed, indicating a failure in the oogenesis and ovulation, respectively. Intraovarian concentrations of steroid hormones were normal in HSD17B12 females, whereas the levels of AA and its metabolites (6-keto prostaglandin F1alpha, prostaglandin D, prostaglandin E, prostaglandin F, and thromboxane B) were decreased. In conclusion, our study demonstrates that HSD17B12 enzyme plays an important role in female fertility through its role in AA metabolism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1210/en.2016-1252DOI Listing
October 2016

Optimized design and analysis of preclinical intervention studies in vivo.

Sci Rep 2016 08 2;6:30723. Epub 2016 Aug 2.

Department of Mathematics and Statistics, University of Turku, Turku, Finland.

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/srep30723DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4969752PMC
August 2016

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse.

J Proteomics 2016 Feb 9;133:66-75. Epub 2015 Dec 9.

Department of Physiology, Institute of Biomedicine, University of Turku, Turku, Finland; Turku Center for Disease Modeling, University of Turku, Turku, Finland. Electronic address:

Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM+ mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal β-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal β-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jprot.2015.12.009DOI Listing
February 2016

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis.

J Proteome Res 2014 Nov 20;13(11):4983-94. Epub 2014 Aug 20.

Turku Centre for Biotechnology, ‡Department of Physiology, Institute of Biomedicine, ⊥Department of Mathematics and Statistics, and ¶Turku Center for Disease Modeling, University of Turku , Turku, Finland.

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/pr500384nDOI Listing
November 2014

Castration induces up-regulation of intratumoral androgen biosynthesis and androgen receptor expression in an orthotopic VCaP human prostate cancer xenograft model.

Am J Pathol 2014 Aug 17;184(8):2163-73. Epub 2014 Jun 17.

Turku Center for Disease Modeling, Institute of Biomedicine, University of Turku, Turku, Finland; Functional Foods Forum, University of Turku, Turku, Finland. Electronic address:

Androgens are key factors involved in the development and progression of prostate cancer (PCa), and PCa growth can be suppressed by androgen deprivation therapy. In a considerable proportion of men receiving androgen deprivation therapy, however, PCa progresses to castration-resistant PCa (CRPC), making the development of efficient therapies challenging. We used an orthotopic VCaP human PCa xenograft model to study cellular and molecular changes in tumors after androgen deprivation therapy (castration). Tumor growth was monitored through weekly serum prostate-specific antigen measurements, and mice with recurrent tumors after castration were randomized to treatment groups. Serum prostate-specific antigen concentrations showed significant correlation with tumor volume. Castration-resistant tumors retained concentrations of intratumoral androgen (androstenedione, testosterone, and 5α-dihydrotestosterone) at levels similar to tumors growing in intact hosts. Accordingly, castration induced up-regulation of enzymes involved in androgen synthesis (CYP17A1, AKR1C3, and HSD17B6), as well as expression of full-length androgen receptor (AR) and AR splice variants (AR-V1 and AR-V7). Furthermore, AR target gene expression was maintained in castration-resistant xenografts. The AR antagonists enzalutamide (MDV3100) and ARN-509 suppressed PSA production of castration-resistant tumors, confirming the androgen dependency of these tumors. Taken together, the findings demonstrate that our VCaP xenograft model exhibits the key characteristics of clinical CRPC and thus provides a valuable tool for identifying druggable targets and for testing therapeutic strategies targeting AR signaling in CRPC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajpath.2014.04.010DOI Listing
August 2014

Novel Lignan and stilbenoid mixture shows anticarcinogenic efficacy in preclinical PC-3M-luc2 prostate cancer model.

PLoS One 2014 3;9(4):e93764. Epub 2014 Apr 3.

Functional Foods Forum, University of Turku, Turku, Finland; Turku Center for Disease Modeling (TCDM), Institute of Biomedicine, University of Turku, Turku, Finland.

Prostate cancer is the most common cancer of men in the Western world, and novel approaches for prostate cancer risk reduction are needed. Plant-derived phenolic compounds attenuate prostate cancer growth in preclinical models by several mechanisms, which is in line with epidemiological findings suggesting that consumption of plant-based diets is associated with low risk of prostate cancer. The objective of this study was to assess the effects of a novel lignan-stilbenoid mixture in PC-3M-luc2 human prostate cancer cells in vitro and in orthotopic xenografts. Lignan and stilbenoid -rich extract was obtained from Scots pine (Pinus sylvestris) knots. Pine knot extract as well as stilbenoids (methyl pinosylvin and pinosylvin), and lignans (matairesinol and nortrachelogenin) present in pine knot extract showed antiproliferative and proapoptotic efficacy at ≥ 40 μM concentration in vitro. Furthermore, pine knot extract derived stilbenoids enhanced tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induced apoptosis already at ≥ 10 μM concentrations. In orthotopic PC-3M-luc2 xenograft bearing immunocompromized mice, three-week peroral exposure to pine knot extract (52 mg of lignans and stilbenoids per kg of body weight) was well tolerated and showed anti-tumorigenic efficacy, demonstrated by multivariate analysis combining essential markers of tumor growth (i.e. tumor volume, vascularization, and cell proliferation). Methyl pinosylvin, pinosylvin, matairesinol, nortrachelogenin, as well as resveratrol, a metabolite of pinosylvin, were detected in serum at total concentration of 7-73 μM, confirming the bioavailability of pine knot extract derived lignans and stilbenoids. In summary, our data indicates that pine knot extract is a novel and cost-effective source of resveratrol, methyl pinosylvin and other bioactive lignans and stilbenoids. Pine knot extract shows anticarcinogenic efficacy in preclinical prostate cancer model, and our in vitro data suggests that compounds derived from the extract may have potential as novel chemosensitizers to TRAIL. These findings promote further research on health-related applications of wood biochemicals.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0093764PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974786PMC
January 2015

Innate immune activity is detected prior to seroconversion in children with HLA-conferred type 1 diabetes susceptibility.

Diabetes 2014 Jul 18;63(7):2402-14. Epub 2014 Feb 18.

Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FinlandThe Finnish Centre of Excellence in Molecular Systems Immunology and Physiology Research, Helsinki, Finland

The insult leading to autoantibody development in children who will progress to develop type 1 diabetes (T1D) has remained elusive. To investigate the genes and molecular pathways in the pathogenesis of this disease, we performed genome-wide transcriptomics analysis on a unique series of prospective whole-blood RNA samples from at-risk children collected in the Finnish Type 1 Diabetes Prediction and Prevention study. We studied 28 autoantibody-positive children, out of which 22 progressed to clinical disease. Collectively, the samples covered the time span from before the development of autoantibodies (seroconversion) through the diagnosis of diabetes. Healthy control subjects matched for date and place of birth, sex, and HLA-DQB1 susceptibility were selected for each case. Additionally, we genotyped the study subjects with Immunochip to identify potential genetic variants associated with the observed transcriptional signatures. Genes and pathways related to innate immunity functions, such as the type 1 interferon (IFN) response, were active, and IFN response factors were identified as central mediators of the IFN-related transcriptional changes. Importantly, this signature was detected already before the T1D-associated autoantibodies were detected. Together, these data provide a unique resource for new hypotheses explaining T1D biology.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2337/db13-1775DOI Listing
July 2014

Improved statistical modeling of tumor growth and treatment effect in preclinical animal studies with highly heterogeneous responses in vivo.

Clin Cancer Res 2012 Aug 27;18(16):4385-96. Epub 2012 Jun 27.

Department of Mathematics, University of Turku; Turku, Finland.

Purpose: Preclinical tumor growth experiments often result in heterogeneous datasets that include growing, regressing, or stable growth profiles in the treatment and control groups. Such confounding intertumor variability may mask the true treatment effects especially when less aggressive treatment alternatives are being evaluated.

Experimental Design: We developed a statistical modeling approach in which the growing and poorly growing tumor categories were automatically detected by means of an expectation-maximization algorithm coupled within a mixed-effects modeling framework. The framework is implemented and distributed as an R package, which enables model estimation and statistical inference, as well as statistical power and precision analyses.

Results: When applied to four tumor growth experiments, the modeling framework was shown to (i) improve the detection of subtle treatment effects in the presence of high within-group tumor variability; (ii) reveal hidden tumor subgroups associated with established or novel biomarkers, such as ERβ expression in a MCF-7 breast cancer model, which remained undetected with standard statistical analysis; (iii) provide guidance on the selection of sufficient sample sizes and most informative treatment periods; and (iv) offer flexibility to various cancer models, experimental designs, and treatment options. Model-based testing of treatment effect on the tumor growth rate (or slope) was shown as particularly informative in the preclinical assessment of treatment alternatives based on dietary interventions.

Conclusions: In general, the modeling framework enables identification of such biologically significant differences in tumor growth profiles that would have gone undetected or had required considerably higher number of animals when using traditional statistical methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1078-0432.CCR-11-3215DOI Listing
August 2012

Optimized detection of transcription factor-binding sites in ChIP-seq experiments.

Nucleic Acids Res 2012 Jan 18;40(1):e1. Epub 2011 Oct 18.

Department of Mathematics, University of Turku, FI-20014 Turku, Finland.

We developed a computational procedure for optimizing the binding site detections in a given ChIP-seq experiment by maximizing their reproducibility under bootstrap sampling. We demonstrate how the procedure can improve the detection accuracies beyond those obtained with the default settings of popular peak calling software, or inform the user whether the peak detection results are compromised, circumventing the need for arbitrary re-iterative peak calling under varying parameter settings. The generic, open-source implementation is easily extendable to accommodate additional features and to promote its widespread application in future ChIP-seq studies. The peakROTS R-package and user guide are freely available at http://www.nic.funet.fi/pub/sci/molbio/peakROTS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkr839DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245948PMC
January 2012

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments.

BMC Genomics 2009 Dec 18;10:618. Epub 2009 Dec 18.

Turku Centre for Biotechnology, FI-20521 Turku, Finland.

Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.

Results: The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.

Conclusions: In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2164-10-618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804666PMC
December 2009