Publications by authors named "Laura L Elo"

101 Publications

Computational strategies for single-cell multi-omics integration.

Comput Struct Biotechnol J 2021 27;19:2588-2596. Epub 2021 Apr 27.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.
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http://dx.doi.org/10.1016/j.csbj.2021.04.060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114078PMC
April 2021

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data.

BMC Genomics 2021 May 17;22(1):357. Epub 2021 May 17.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland.

Background: Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.

Result: Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.

Conclusions: Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.
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http://dx.doi.org/10.1186/s12864-021-07686-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130438PMC
May 2021

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

Protein interactome of the Cancerous Inhibitor of protein phosphatase 2A (CIP2A) in Th17 cells.

Curr Res Immunol 2020 Dec;1:10-22

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Cancerous inhibitor of protein phosphatase 2A (CIP2A) is involved in immune response, cancer progression, and Alzheimer's disease. However, an understanding of the mechanistic basis of its function in this wide spectrum of physiological and pathological processes is limited due to its poorly characterized interaction networks. Here we present the first systematic characterization of the CIP2A interactome by affinity-purification mass spectrometry combined with validation by selected reaction monitoring targeted mass spectrometry (SRM-MS) analysis in T helper (Th) 17 (Th17) cells. In addition to the known regulatory subunits of protein phosphatase 2A (PP2A), the catalytic subunits of protein PP2A were found to be interacting with CIP2A. Furthermore, the regulatory (PPP1R18, and PPP1R12A) and catalytic (PPP1CA) subunits of phosphatase PP1 were identified among the top novel CIP2A interactors. Evaluation of the ontologies associated with the proteins in this interactome revealed that they were linked with RNA metabolic processing and splicing, protein traffic, cytoskeleton regulation and ubiquitin-mediated protein degradation processes. Taken together, this network of protein-protein interactions will be important for understanding and further exploring the biological processes and mechanisms regulated by CIP2A both in physiological and pathological conditions.
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http://dx.doi.org/10.1016/j.crimmu.2020.02.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008788PMC
December 2020

Complex Interplay Between MAZR and Runx3 Regulates the Generation of Cytotoxic T Lymphocyte and Memory T Cells.

Front Immunol 2021 17;12:535039. Epub 2021 Mar 17.

Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria.

The BTB zinc finger transcription factor MAZR (also known as PATZ1) controls, partially in synergy with the transcription factor Runx3, the development of CD8 lineage T cells. Here we explored the role of MAZR as well as combined activities of MAZR/Runx3 during cytotoxic T lymphocyte (CTL) and memory CD8 T cell differentiation. In contrast to the essential role of Runx3 for CTL effector function, the deletion of MAZR had a mild effect on the generation of CTLs . However, a transcriptome analysis demonstrated that the combined deletion of MAZR and Runx3 resulted in much more widespread downregulation of CTL signature genes compared to single Runx3 deletion, indicating that MAZR partially compensates for loss of Runx3 in CTLs. Moreover, in line with the findings made , the analysis of CTL responses to LCMV infection revealed that MAZR and Runx3 cooperatively regulate the expression of CD8α, Granzyme B and perforin . Interestingly, while memory T cell differentiation is severely impaired in Runx3-deficient mice, the deletion of MAZR leads to an enlargement of the long-lived memory subset and also partially restored the differentiation defect caused by loss of Runx3. This indicates distinct functions of MAZR and Runx3 in the generation of memory T cell subsets, which is in contrast to their cooperative roles in CTLs. Together, our study demonstrates complex interplay between MAZR and Runx3 during CTL and memory T cell differentiation, and provides further insight into the molecular mechanisms underlying the establishment of CTL and memory T cell pools.
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http://dx.doi.org/10.3389/fimmu.2021.535039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010151PMC
March 2021

Preoperative Risk Prediction Models for Short-Term Revision and Death After Total Hip Arthroplasty: Data from the Finnish Arthroplasty Register.

JB JS Open Access 2021 Jan-Mar;6(1). Epub 2021 Jan 25.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Because of the increasing number of total hip arthroplasties (THAs), even a small proportion of complications after the operation can lead to substantial individual difficulties and health-care costs. The aim of this study was to develop simple-to-use risk prediction models to assess the risk of the most common reasons for implant failure to facilitate clinical decision-making and to ensure long-term survival of primary THAs.

Methods: We analyzed patient and surgical data reported to the Finnish Arthroplasty Register (FAR) on 25,919 primary THAs performed in Finland between May 2014 and January 2018. For the most frequent adverse outcomes after primary THA, we developed multivariable Lasso regression models based on the data of the randomly selected training cohort (two-thirds of the data). The performances of all models were validated using the remaining, independent test set consisting of 8,640 primary THAs (one-third of the data) not used for building the models.

Results: The most common outcomes within 6 months after the primary THA were revision operations due to periprosthetic joint infection (1.1%), dislocation (0.7%), or periprosthetic fracture (0.5%), and death (0.7%). For each of these outcomes, Lasso regression identified subsets of variables required for accurate risk predictions. The highest discrimination performance, in terms of area under the receiver operating characteristic curve (AUROC), was observed for death (0.84), whereas the performance was lower for revisions due to periprosthetic joint infection (0.68), dislocation (0.64), or periprosthetic fracture (0.65).

Conclusions: Based on the small number of preoperative characteristics of the patient and modifiable surgical parameters, the developed risk prediction models can be easily used to assess the risk of revision or death. All developed models hold the potential to aid clinical decision-making, ultimately leading to improved clinical outcomes.

Level Of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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http://dx.doi.org/10.2106/JBJS.OA.20.00091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963508PMC
January 2021

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

NPJ Digit Med 2021 Mar 19;4(1):53. Epub 2021 Mar 19.

Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
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http://dx.doi.org/10.1038/s41746-021-00414-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979931PMC
March 2021

Discovery of a Novel CIP2A Variant (NOCIVA) with Clinical Relevance in Predicting TKI Resistance in Myeloid Leukemias.

Clin Cancer Res 2021 May 5;27(10):2848-2860. Epub 2021 Mar 5.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Purpose: Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that inhibits the tumor suppressor PP2A-B56α. However, mRNA variants remain uncharacterized. Here, we report the discovery of a splicing variant, novel CIP2A variant ().

Experimental Design: Characterization of CIP2A variants was performed by both 3' and 5' rapid amplification of cDNA ends from cancer cells. The function of NOCIVA was assessed by structural and molecular biology approaches. Its clinical relevance was studied in an acute myeloid leukemia (AML) patient cohort and two independent chronic myeloid leukemia (CML) cohorts.

Results: contains exons 1 to 13 fused to 349 nucleotides from intron 13. Intriguingly, the first 39 nucleotides of the -specific sequence are in the coding frame with exon 13 of and code for a 13-amino acid peptide tail nonhomologous to any known human protein sequence. Therefore, NOCIVA translates to a unique human protein. NOCIVA retains the capacity to bind to B56α, but, whereas CIP2A is predominantly a cytoplasmic protein, NOCIVA translocates to the nucleus. Indicative of prevalent alternative splicing from to in myeloid malignancies, AML and CML patient samples overexpress , but not mRNA. In AML, a high mRNA expression ratio is a marker for adverse overall survival. In CML, high expression is associated with inferior event-free survival among imatinib-treated patients, but not among patients treated with dasatinib or nilotinib.

Conclusions: We discovered a novel variant of the oncoprotein CIP2A and its clinical relevance in predicting tyrosine kinase inhibitor therapy resistance in myeloid leukemias.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-3679DOI Listing
May 2021

Computational deconvolution to estimate cell type-specific gene expression from bulk data.

NAR Genom Bioinform 2021 Mar 12;3(1):lqaa110. Epub 2021 Jan 12.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland.

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.
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http://dx.doi.org/10.1093/nargab/lqaa110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803005PMC
March 2021

Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies.

RNA Biol 2021 Jan 30:1-8. Epub 2021 Jan 30.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Detection of differentially expressed genes (DEGs) between different biological conditions is a key data analysis step of most RNA-sequencing studies. Conventionally, computational tools have used gene-level read counts as input to test for differential gene expression between sample condition groups. Recently, it has been suggested that statistical testing could be performed with increased power at a lower feature level prior to aggregating the results to the gene level. In this study, we systematically compared the performance of calling the DEGs when using read count data at different levels (gene, transcript, and exon) as input, in the context of two publicly available data sets. Additionally, we tested two different methods for aggregating the lower feature-level p-values to gene-level: Lancaster and empirical Brown's method. Our results show that detection of DEGs is improved compared to the conventional gene-level approach regardless of the lower feature-level used for statistical testing. The overall best balance between accuracy and false discovery rate was obtained using the exon-level approach with empirical Brown's aggregation method, which we provide as a freely available Bioconductor package EBSEA (https://bioconductor.org/packages/release/bioc/html/EBSEA.html).
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http://dx.doi.org/10.1080/15476286.2020.1868151DOI Listing
January 2021

Adult-Onset Anti-Citrullinated Peptide Antibody-Negative Destructive Rheumatoid Arthritis Is Characterized by a Disease-Specific CD8+ T Lymphocyte Signature.

Front Immunol 2020 19;11:578848. Epub 2020 Nov 19.

Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.

Rheumatoid arthritis (RA) is a complex autoimmune disease targeting synovial joints. Traditionally, RA is divided into seropositive (SP) and seronegative (SN) disease forms, the latter consisting of an array of unrelated diseases with joint involvement. Recently, we described a severe form of SN-RA that associates with characteristic joint destruction. Here, we sought biological characteristics to differentiate this rare but aggressive anti-citrullinated peptide antibody-negative destructive RA (CND-RA) from early seropositive (SP-RA) and seronegative rheumatoid arthritis (SN-RA). We also aimed to study cytotoxic CD8+ lymphocytes in autoimmune arthritis. CND-RA, SP-RA and SN-RA were compared to healthy controls to reveal differences in T-cell receptor beta (TCRβ) repertoire, cytokine levels and autoantibody repertoires. Whole-exome sequencing (WES) followed by single-cell RNA-sequencing (sc-RNA-seq) was performed to study somatic mutations in a clonally expanded CD8+ lymphocyte population in an index patient. A unique TCRβ signature was detected in CND-RA patients. In addition, CND-RA patients expressed higher levels of the bone destruction-associated TNFSF14 cytokine. Blood IgG repertoire from CND-RA patients recognized fewer endogenous proteins than SP-RA patients' repertoires. Using WES, we detected a stable mutation profile in the clonally expanded CD8+ T-cell population characterized by cytotoxic gene expression signature discovered by sc-RNA-sequencing. Our results identify CND-RA as an independent RA subset and reveal a CND-RA specific TCR signature in the CD8+ lymphocytes. Improved classification of seronegative RA patients underlines the heterogeneity of RA and also, facilitates development of improved therapeutic options for the treatment resistant patients.
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http://dx.doi.org/10.3389/fimmu.2020.578848DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732449PMC
November 2020

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data.

Bioinformatics 2021 05;37(8):1107-1114

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland.

Motivation: Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging.

Results: We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods.

Availability And Implementation: ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa919DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150131PMC
May 2021

Protein synthesis is suppressed in sporadic and familial Parkinson's disease by LRRK2.

FASEB J 2020 11 14;34(11):14217-14233. Epub 2020 Sep 14.

Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland.

Gain of function LRRK2-G2019S is the most frequent mutation found in familial and sporadic Parkinson's disease. It is expected therefore that understanding the cellular function of LRRK2 will provide insight on the pathological mechanism not only of inherited Parkinson's, but also of sporadic Parkinson's, the more common form. Here, we show that constitutive LRRK2 activity controls nascent protein synthesis in rodent neurons. Specifically, pharmacological inhibition of LRRK2, Lrrk2 knockdown or Lrrk2 knockout, all lead to increased translation. In the rotenone model for sporadic Parkinson's, LRRK2 activity increases, dopaminergic neuron translation decreases, and the neurites atrophy. All are prevented by LRRK2 inhibitors. Moreover, in striatum and substantia nigra of rotenone treated rats, phosphorylation changes are observed on eIF2α-S52(↑), eIF2s2-S2(↓), and eEF2-T57(↑) in directions that signify protein synthesis arrest. Significantly, translation is reduced by 40% in fibroblasts from Parkinson's patients (G2019S and sporadic cases alike) and this is reversed upon LRRK2 inhibitor treatment. In cells from multiple system atrophy patients, translation is unchanged suggesting that repression of translation is specific to Parkinson's disease. These findings indicate that repression of translation is a proximal function of LRRK2 in Parkinson's pathology.
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http://dx.doi.org/10.1096/fj.202001046RDOI Listing
November 2020

Metagenomics analysis of gut microbiota in response to diet intervention and gestational diabetes in overweight and obese women: a randomised, double-blind, placebo-controlled clinical trial.

Gut 2021 Feb 24;70(2):309-318. Epub 2020 Aug 24.

Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.

Objective: Gut microbiota and diet are known to contribute to human metabolism. We investigated whether the metagenomic gut microbiota composition and function changes over pregnancy are related to gestational diabetes mellitus (GDM) and can be modified by dietary supplements, fish oil and/or probiotics.

Design: The gut microbiota of 270 overweight/obese women participating in a mother-infant clinical study were analysed with metagenomics approach in early (mean gestational weeks 13.9) and late (gestational weeks 35.2) pregnancy. GDM was diagnosed with a 2 hour 75 g oral glucose tolerance test.

Results: Unlike women with GDM, women without GDM manifested changes in relative abundance of bacterial species over the pregnancy, particularly those receiving the fish oil + probiotics combination. The specific bacterial species or function did not predict the onset of GDM nor did it differ according to GDM status, except for the higher abundance of in late pregnancy in the combination group in women with GDM compared with women without GDM. In the combination group, weak decreases over the pregnancy were observed in basic bacterial housekeeping functions.

Conclusions: The specific gut microbiota species do not contribute to GDM in overweight/obese women. Nevertheless, the GDM status may disturb maternal gut microbiota flexibility and thus limit the capacity of women with GDM to respond to diet, as evidenced by alterations in gut microbiota observed only in women without GDM. These findings may be important when considering the metabolic complications during pregnancy, but further studies with larger populations are called for to verify the findings.
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http://dx.doi.org/10.1136/gutjnl-2020-321643DOI Listing
February 2021

Critical evaluation of the subcutaneous engraftments of hormone naïve primary prostate cancer.

Transl Androl Urol 2020 Jun;9(3):1120-1134

Institute of Biomedicine, University of Turku, Turku, Finland.

Background: Patient-derived xenografts (PDXs) are considered to better recapitulate the histopathological and molecular heterogeneity of human cancer than other preclinical models. Despite technological advances, PDX models from hormone naïve primary prostate cancer are scarce. We performed a detailed analysis of PDX methodology using a robust subcutaneous model and fresh tissues from patients with primary hormone naïve prostate cancer.

Methods: Clinical prostate tumor specimens (n=26, Gleason score 6-10) were collected from robotic-assisted laparoscopic radical prostatectomies at Turku University Hospital (Turku, Finland), cut into pieces, and implanted subcutaneously into 84 immunodeficient mice. Engraftments and the adjacent material from prostatic surgical specimens were compared using histology, immunohistochemistry and DNA sequencing.

Results: The probability of a successful engraftment correlated with the presence of carcinoma in the implanted tissue. Tumor take rate was 41%. Surprisingly, mouse hormone supplementation inhibited tumor take rate, whereas the degree of mouse immunodeficiency did not have an effect. Histologically, the engrafted tumors closely mimicked their parental tumors, and the Gleason grades and copy number variants of the engraftments were similar to those of their primary tumors. Expression levels of androgen receptor, prostate-specific antigen, and keratins were retained in engraftments, and a detailed genomic analysis revealed high fidelity of the engraftments with their corresponding primary tumors. However, in the second or third passage of tumors, the carcinoma areas were almost completely replaced by benign tissue with frequent degenerative or metaplastic changes.

Conclusions: Subcutaneous primary prostate engraftments preserve the phenotypic and genotypic landscape. Thus, they serve a potential model for personalized medicine and preclinical research but their use may be limited to the first passage.
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http://dx.doi.org/10.21037/tau.2020.03.38DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354344PMC
June 2020

Reproducibility-optimized detection of differential DNA methylation.

Epigenomics 2020 05 4;12(9):747-755. Epub 2020 Jun 4.

Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland.

DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.
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http://dx.doi.org/10.2217/epi-2019-0289DOI Listing
May 2020

MASTL promotes cell contractility and motility through kinase-independent signaling.

J Cell Biol 2020 06;219(6)

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Microtubule-associated serine/threonine-protein kinase-like (MASTL) is a mitosis-accelerating kinase with emerging roles in cancer progression. However, possible cell cycle-independent mechanisms behind its oncogenicity remain ambiguous. Here, we identify MASTL as an activator of cell contractility and MRTF-A/SRF (myocardin-related transcription factor A/serum response factor) signaling. Depletion of MASTL increased cell spreading while reducing contractile actin stress fibers in normal and breast cancer cells and strongly impairing breast cancer cell motility and invasion. Transcriptome and proteome profiling revealed MASTL-regulated genes implicated in cell movement and actomyosin contraction, including Rho guanine nucleotide exchange factor 2 (GEF-H1, ARHGEF2) and MRTF-A target genes tropomyosin 4.2 (TPM4), vinculin (VCL), and nonmuscle myosin IIB (NM-2B, MYH10). Mechanistically, MASTL associated with MRTF-A and increased its nuclear retention and transcriptional activity. Importantly, MASTL kinase activity was not required for regulation of cell spreading or MRTF-A/SRF transcriptional activity. Taken together, we present a previously unknown kinase-independent role for MASTL as a regulator of cell adhesion, contractility, and MRTF-A/SRF activity.
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http://dx.doi.org/10.1083/jcb.201906204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265322PMC
June 2020

Transcriptomic responses to hypoxia in endometrial and decidual stromal cells.

Reproduction 2020 07;160(1):39-51

Yale Systems Biology Institute, West Haven, Connecticut, USA.

Human reproductive success depends on a properly decidualized uterine endometrium that allows implantation and the formation of the placenta. At the core of the decidualization process are endometrial stromal fibroblasts (ESF) that differentiate to decidual stromal cells (DSC). As variations in oxygen levels are functionally relevant in endometrium both upon menstruation and during placentation, we assessed the transcriptomic responses to hypoxia in ESF and DSC. In both cell types, hypoxia-upregulated genes in classical hypoxia pathways such as glycolysis and the epithelial mesenchymal transition. In DSC, hypoxia restored an ESF-like transcriptional state for a subset of transcription factors that are known targets of the progesterone receptor, suggesting that hypoxia partially interferes with progesterone signaling. In both cell types, hypoxia modified transcription of several inflammatory transcription factors that are known regulators of decidualization, including decreased transcription of STATs and increased transcription of CEBPs. We observed that hypoxia-upregulated genes in ESF and DSC had a significant overlap with genes previously detected to be upregulated in endometriotic stromal cells. Promoter analysis of the genes in this overlap suggested the hypoxia-upregulated Jun/Fos and CEBP transcription factors as potential drivers of endometriosis-associated transcription. Using immunohistochemistry, we observed increased expression of JUND and CEBPD in endometriosis lesions compared to healthy endometria. Overall, the findings suggest that hypoxic stress establishes distinct transcriptional states in ESF and DSC and that hypoxia influences the expression of genes that contribute to the core gene regulation of endometriotic stromal cells.
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http://dx.doi.org/10.1530/REP-19-0615DOI Listing
July 2020

Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling.

J Endocr Soc 2020 Apr 11;4(4):bvaa026. Epub 2020 Mar 11.

Turku PET Centre, University of Turku, Turku, Finland.

Purpose: Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and laboratory measures.

Material And Methods: The cross-sectional study population included 259 subjects with normal or impaired fasting glucose or type 2 diabetes in whom skeletal muscle and whole-body insulin sensitivity (M-value) were measured during euglycemic hyperinsulinemic clamp. Muscle glucose uptake (GU) was measured directly using [F]FDG-PET. Serum metabolites were measured using nuclear magnetic resonance (NMR) spectroscopy. We used linear regression to build the models for the muscle GU (Muscle-insulin sensitivity index [ISI]) and M-value (whole-body [WB]-ISI). The models were created and tested using randomly selected training (n = 173) and test groups (n = 86). The models were compared to common fasting indices of insulin sensitivity, homeostatic model assessment-insulin resistance (HOMA-IR) and the revised quantitative insulin sensitivity check index (QUICKI).

Results: WB-ISI had higher correlation with actual M-value than HOMA-IR or revised QUICKI ( = 0.83 vs -0.67 and 0.66;  < 0.05 for both comparisons), whereas the correlation of Muscle-ISI with the actual skeletal muscle GU was not significantly stronger than HOMA-IR's or revised QUICKI's ( = 0.67 vs -0.58 and 0.59; both nonsignificant) in the test dataset.

Conclusion: Muscle-ISI and WB-ISI based on NMR-metabolomics and common laboratory measurements from fasting serum samples and basic anthropometrics are promising rapid and inexpensive tools for determining insulin sensitivity in at-risk individuals.
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http://dx.doi.org/10.1210/jendso/bvaa026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093091PMC
April 2020

CIP2A Constrains Th17 Differentiation by Modulating STAT3 Signaling.

iScience 2020 Mar 27;23(3):100947. Epub 2020 Feb 27.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6A, Turku, Finland. Electronic address:

Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) is an oncogene and a potential cancer therapy target protein. Accordingly, a better understanding of the physiological function of CIP2A, especially in the context of immune cells, is a prerequisite for its exploitation in cancer therapy. Here, we report that CIP2A negatively regulates interleukin (IL)-17 production by Th17 cells in human and mouse. Interestingly, concomitant with increased IL-17 production, CIP2A-deficient Th17 cells had increased strength and duration of STAT3 phosphorylation. We analyzed the interactome of phosphorylated STAT3 in CIP2A-deficient and CIP2A-sufficient Th17 cells and indicated together with genome-wide gene expression profiling, a role of Acylglycerol Kinase (AGK) in the regulation of Th17 differentiation by CIP2A. We demonstrated that CIP2A regulates the strength of the interaction between AGK and STAT3, and thereby modulates STAT3 phosphorylation and expression of IL-17 in Th17 cells.
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http://dx.doi.org/10.1016/j.isci.2020.100947DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068643PMC
March 2020

Histone deacetylases 1 and 2 restrain CD4+ cytotoxic T lymphocyte differentiation.

JCI Insight 2020 02 27;5(4). Epub 2020 Feb 27.

Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria.

Some effector CD4+ T cell subsets display cytotoxic activity, thus breaking the functional dichotomy of CD4+ helper and CD8+ cytotoxic T lymphocytes. However, molecular mechanisms regulating CD4+ cytotoxic T lymphocyte (CD4+ CTL) differentiation are poorly understood. Here we show that levels of histone deacetylases 1 and 2 (HDAC1-HDAC2) are key determinants of CD4+ CTL differentiation. Deletions of both Hdac1 and 1 Hdac2 alleles (HDAC1cKO-HDAC2HET) in CD4+ T cells induced a T helper cytotoxic program that was controlled by IFN-γ-JAK1/2-STAT1 signaling. In vitro, activated HDAC1cKO-HDAC2HET CD4+ T cells acquired cytolytic activity and displayed enrichment of gene signatures characteristic of effector CD8+ T cells and human CD4+ CTLs. In vivo, murine cytomegalovirus-infected HDAC1cKO-HDAC2HET mice displayed a stronger induction of CD4+ CTL features compared with infected WT mice. Finally, murine and human CD4+ T cells treated with short-chain fatty acids, which are commensal-produced metabolites acting as HDAC inhibitors, upregulated CTL genes. Our data demonstrate that HDAC1-HDAC2 restrain CD4+ CTL differentiation. Thus, HDAC1-HDAC2 might be targets for the therapeutic induction of CD4+ CTLs.
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http://dx.doi.org/10.1172/jci.insight.133393DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101144PMC
February 2020

Easy-to-use tool for evaluating the elevated acute kidney injury risk against reduced cardiovascular disease risk during intensive blood pressure control.

J Hypertens 2020 03;38(3):511-518

Turku Bioscience Centre, University of Turku and Åbo Akademi University.

Objective: The Systolic Blood Pressure Intervention Trial (SPRINT) reported that lowering SBP to below 120 mmHg (intensive treatment) reduced cardiovascular morbidity and mortality among adults with hypertension but increased the incidence of adverse events, particularly acute kidney injury (AKI). The goal of this study was to develop an accurate risk estimation tool for comparing the risk of cardiovascular events and adverse kidney-related outcomes between standard and intensive antihypertensive treatment strategies.

Methods: By applying Lasso regression on the baseline characteristics and health outcomes of 8760 participants with complete baseline information in the SPRINT trial, we developed predictive models for primary cardiovascular disease (CVD) outcome and incidence of AKI. Both models were validated against an independent test set of the SPRINT trial (one third of data not used for model building) and externally against the cardiovascular and renal outcomes available in Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial, consisting of 4733 participants with type 2 diabetes mellitus.

Results: Lasso regression identified a subset of variables that accurately predicted the primary CVD outcome and the incidence of AKI (areas under receiver-operating characteristic curves 0.70 and 0.77, respectively). Based on the validated risk models, an easy-to-use risk assessment tool was developed and made available as an easy-to-use online tool.

Conclusion: By predicting the risks of CVD and AKI at baseline, the developed tool can be used to weigh the benefits of intensive versus standard blood pressure control and to identify those who are likely to benefit most from intensive treatment.
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http://dx.doi.org/10.1097/HJH.0000000000002282DOI Listing
March 2020

Likelihood contrasts: a machine learning algorithm for binary classification of longitudinal data.

Sci Rep 2020 01 23;10(1):1016. Epub 2020 Jan 23.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use.
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http://dx.doi.org/10.1038/s41598-020-57924-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6978422PMC
January 2020

Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases.

J Clin Invest 2020 01;130(1):20-28

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
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http://dx.doi.org/10.1172/JCI129202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934214PMC
January 2020

The Transcription Factor MAZR/PATZ1 Regulates the Development of FOXP3 Regulatory T Cells.

Cell Rep 2019 12;29(13):4447-4459.e6

Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria. Electronic address:

Forkhead box protein P3 (FOXP3) regulatory T cells (T cells) play a key role in maintaining tolerance and immune homeostasis. Here, we report that a T cell-specific deletion of the transcription factor MAZR (also known as PATZ1) leads to an increased frequency of T cells, while enforced MAZR expression impairs T cell differentiation. Further, MAZR expression levels are progressively downregulated during thymic T cell development and during in-vitro-induced human T cell differentiation, suggesting that MAZR protein levels are critical for controlling T cell development. However, MAZR-deficient T cells show only minor transcriptional changes ex vivo, indicating that MAZR is not essential for establishing the transcriptional program of peripheral T cells. Finally, the loss of MAZR reduces the clinical score in dextran-sodium sulfate (DSS)-induced colitis, suggesting that MAZR activity in T cells controls the extent of intestinal inflammation. Together, these data indicate that MAZR is part of a T cell-intrinsic transcriptional network that modulates T cell development.
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http://dx.doi.org/10.1016/j.celrep.2019.11.089DOI Listing
December 2019

Systematic evaluation of differential splicing tools for RNA-seq studies.

Brief Bioinform 2020 12;21(6):2052-2065

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.
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http://dx.doi.org/10.1093/bib/bbz126DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711265PMC
December 2020

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota-Implementation and Computational Analysis.

J Proteome Res 2020 01 4;19(1):432-436. Epub 2019 Dec 4.

Turku Bioscience Centre , University of Turku and Åbo Akademi University , Turku 20520 , Finland.

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory-assembled microbial mixtures as well as human fecal samples.
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http://dx.doi.org/10.1021/acs.jproteome.9b00606DOI Listing
January 2020

Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

Scand J Urol 2019 Oct 25;53(5):325-331. Epub 2019 Sep 25.

Department of Urology, Turku University Hospital and University of Turku, Turku, Finland.

To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality. Material consists of 1099 consecutive radical cystectomy (RC) patients operated at 16 different hospitals in Finland 2005-2014. Machine learning methodology was utilized. For model building and testing, the data was randomly divided into training data ( 733, 66.7%) and independent testing data ( 366, 33.3%). To predict the risk of early death after RC from baseline variables, a binary classifier was constructed using logistic regression with lasso regularization. Finally, a user-friendly risk table was constructed for practical use. The model resulted in an area under the receiver operating characteristic curve (AUROC) of 0.73 (95% CI = 0.59-0.87). The strongest risk factors were: American Society of Anesthesiologists physical status classification (ASA), congestive heart failure (CHF), age adjusted Charlson comorbidity index (ACCI) and chronic pulmonary disease. This study with a novel methodological approach adds CHF and chronic pulmonary disease to previously known independent prognostic risk factors for early death after RC. Importantly, the risk prediction tool uses purely pre-operative data and can be used before surgery.
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http://dx.doi.org/10.1080/21681805.2019.1665579DOI Listing
October 2019

Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease.

Clin Epigenetics 2019 09 2;11(1):130. Epub 2019 Sep 2.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, FIN-20520, Turku, Finland.

Background: Alzheimer's disease results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into the disease process would be highly valuable. Environmental and lifestyle factors largely modulate the disease risk and may influence the pathogenesis through epigenetic mechanisms, such as DNA methylation. As environmental and lifestyle factors may affect multiple tissues of the body, we hypothesized that the disease-associated DNA methylation signatures are detectable in the peripheral blood of discordant twin pairs.

Results: Comparison of 23 disease discordant Finnish twin pairs with reduced representation bisulfite sequencing revealed peripheral blood DNA methylation differences in 11 genomic regions with at least 15.0% median methylation difference and FDR adjusted p value ≤ 0.05. Several of the affected genes are primarily associated with neuronal functions and pathologies and do not display disease-associated differences in gene expression in blood. The DNA methylation mark in ADARB2 gene was found to be differentially methylated also in the anterior hippocampus, including entorhinal cortex, of non-twin cases and controls. Targeted bisulfite pyrosequencing of the DNA methylation mark in ADARB2 gene in 62 Finnish and Swedish twin pairs revealed that, in addition to the disease status, DNA methylation of this region is influenced by gender, age, zygosity, APOE genotype, and smoking. Further analysis of 120 Swedish twin pairs indicated that this specific DNA methylation mark is not predictive for Alzheimer's disease and becomes differentially methylated after disease onset.

Conclusions: DNA methylation differences can be detected in the peripheral blood of twin pairs discordant for Alzheimer's disease. These DNA methylation signatures may have value as disease markers and provide insights into the molecular mechanisms of pathogenesis. We found no evidence that the DNA methylation marks would be associated with gene expression in blood. Further studies are needed to elucidate the potential importance of the associated genes in neuronal functions and to validate the prognostic or diagnostic value of the individual marks or marker panels.
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http://dx.doi.org/10.1186/s13148-019-0729-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721173PMC
September 2019

L1TD1 - a prognostic marker for colon cancer.

BMC Cancer 2019 Jul 23;19(1):727. Epub 2019 Jul 23.

Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Background: Prognostic markers specific to a particular cancer type can assist in the evaluation of survival probability of patients and help clinicians to assess the available treatment modalities.

Methods: Gene expression data was analyzed from three independent colon cancer microarray gene expression data sets (N = 1052). Survival analysis was performed for the three data sets, stratified by the expression level of the LINE-1 type transposase domain containing 1 (L1TD1). Correlation analysis was performed to investigate the role of the interactome of L1TD1 in colon cancer patients.

Results: We found L1TD1 as a novel positive prognostic marker for colon cancer. Increased expression of L1TD1 associated with longer disease-free survival in all the three data sets. Our results were in contrast to a previous study on medulloblastoma, where high expression of L1TD1 was linked with poor prognosis. Notably, in medulloblastoma L1TD1 was co-expressed with its interaction partners, whereas our analysis revealed lack of co-expression of L1TD1 with its interaction partners in colon cancer.

Conclusions: Our results identify increased expression of L1TD1 as a prognostic marker predicting longer disease-free survival in colon cancer patients.
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http://dx.doi.org/10.1186/s12885-019-5952-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651905PMC
July 2019