Matti Nykter

Circulating Plasma MiR-141 Is a Novel Biomarker for Metastatic Colon Cancer and Predicts Poor Prognosis

Background Colorectal cancer (CRC) remains one of the major cancer types and cancer related death worldwide. Sensitive, non-invasive biomarkers that can facilitate disease detection, staging and prediction of therapeutic outcome are highly desirable to improve survival rate and help to determine optimized treatment for CRC. The small non-coding RNAs, microRNAs (miRNAs), have recently been identified as critical regulators for various diseases including cancer and may represent a novel class of cancer biomarkers. The purpose of this study was to identify and validate circulating microRNAs in human plasma for use as such biomarkers in colon cancer. Methodology/Principal Findings By using quantitative reverse transcription-polymerase chain reaction, we found that circulating miR-141 was significantly associated with stage IV colon cancer in a cohort of 102 plasma samples. Receiver operating characteristic (ROC) analysis was used to evaluate the sensitivity and specificity of candidate plasma microRNA markers. We observed that combination of miR-141 and carcinoembryonic antigen (CEA), a widely used marker for CRC, further improved the accuracy of detection. These findings were validated in an independent cohort of 156 plasma samples collected at Tianjin, China. Furthermore, our analysis showed that high levels of plasma miR-141 predicted poor survival in both cohorts and that miR-141 was an independent prognostic factor for advanced colon cancer. Conclusions/Significance We propose that plasma miR-141 may represent a novel biomarker that complements CEA in detecting colon cancer with distant metastasis and that high levels of miR-141 in plasma were associated with poor prognosis.

Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals.

The innate immune system is like a double-edged sword: it is absolutely required for host defense against infection, but when uncontrolled, it can trigger a plethora of inflammatory diseases. Here we use systems-biology approaches to predict and confirm the existence of a gene-regulatory network involving dynamic interaction among the transcription factors NF-κB, C/EBPδ and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of the genes encoding interleukin 6 and C/EBPδ and experimentally confirmed the prediction that the combination of an initiator (NF-κB), an amplifier (C/EBPδ) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4–induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately.

Integrated Analyses Identify a Master MicroRNA Regulatory Network for the Mesenchymal Subtype in Serous Ovarian Cancer

Integrated genomic analyses revealed a miRNA-regulatory network that further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141, and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.

Evaluation of methods for modeling transcription factor sequence specificity

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a proteins DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Gene expression dynamics in the macrophage exhibit criticality

Cells are dynamical systems of biomolecular interactions that process information from their environment to mount diverse yet specific responses. A key property of many self-organized systems is that of criticality: a state of a system in which, on average, perturbations are neither dampened nor amplified, but are propagated over long temporal or spatial scales. Criticality enables the coordination of complex macroscopic behaviors that strike an optimal balance between stability and adaptability. It has long been hypothesized that biological systems are critical. Here, we address this hypothesis experimentally for system-wide gene expression dynamics in the macrophage. To this end, we have developed a method, based on algorithmic information theory, to assess macrophage criticality, and we have validated the method on networks with known properties. Using global gene expression data from macrophages stimulated with a variety of Toll-like receptor agonists, we found that macrophage dynamics are indeed critical, providing the most compelling evidence to date for this general principle of dynamics in biological systems.

A prostate cancer susceptibility allele at 6q22 increases RFX6 expression by modulating HOXB13 chromatin binding

Genome-wide association studies have identified thousands of SNPs associated with predisposition to various diseases, including prostate cancer. However, the mechanistic roles of these SNPs remain poorly defined, particularly for noncoding polymorphisms. Here we find that the prostate cancer risk-associated SNP rs339331 at 6q22 lies within a functional HOXB13-binding site. The risk-associated T allele at rs339331 increases binding of HOXB13 to a transcriptional enhancer, conferring allele-specific upregulation of the rs339331-associated gene RFX6. Suppression of RFX6 diminishes prostate cancer cell proliferation, migration and invasion. Clinical data indicate that RFX6 upregulation in human prostate cancers correlates with tumor progression, metastasis and risk of biochemical relapse. Finally, we observe a significant association between the risk-associated T allele at rs339331 and increased RFX6 mRNA levels in human prostate tumors. Together, our results suggest that rs339331 affects prostate cancer risk by altering RFX6 expression through a functional interaction with the prostate cancer susceptibility gene HOXB13.

Genomic Alterations in Cell-Free DNA and Enzalutamide Resistance in Castration-Resistant Prostate Cancer

Importance The molecular landscape underpinning response to the androgen receptor (AR) antagonist enzalutamide in patients with metastatic castration-resistant prostate cancer (mCRPC) is undefined. Consequently, there is an urgent need for practical biomarkers to guide therapy selection and elucidate resistance. Although tissue biopsies are impractical to perform routinely in the majority of patients with mCRPC, the analysis of plasma cell-free DNA (cfDNA) has recently emerged as a minimally invasive method to explore tumor characteristics. Objective To reveal genomic characteristics from cfDNA associated with clinical outcomes during enzalutamide treatment. Design, Setting, and Participants Plasma samples were obtained from August 4, 2013, to July 31, 2015, at a single academic institution (British Columbia Cancer Agency) from 65 patients with mCRPC. We collected temporal plasma samples (at baseline, 12 weeks, end of treatment) for circulating cfDNA and performed array comparative genomic hybridization copy number profiling and deep AR gene sequencing. Samples collected at end of treatment were also subjected to targeted sequencing of 19 prostate cancer-associated genes. Exposure Enzalutamide, 160 mg, daily orally. Main Outcomes and Measures Prostate-specific antigen response rate (decline ≥50% from baseline confirmed ≥3 weeks later). Radiographic (as per Prostate Cancer Working Group 2 Criteria) and/or clinical progression (defined as worsening disease-related symptoms necessitating a change in anticancer therapy and/or deterioration in Eastern Cooperative Group performance status ≥2 levels). Results The 65 patients had a median (interquartile range) age of 74 (68-79) years. Prostate-specific antigen response rate to enzalutamide treatment was 38% (25 of 65), while median clinical/radiographic progression-free survival was 3.5 (95% CI, 2.1-5.0) months. Cell-free DNA was isolated from 122 of 125 plasma samples, and targeted sequencing was successful in 119 of 122. AR mutations and/or copy number alterations were robustly detected in 48% (31 of 65) and 60% (18 of 30) of baseline and progression samples, respectively. Detection of AR amplification, heavily mutated AR (≥2 mutations), and RB1 loss were associated with worse progression-free survival, with hazard ratios of 2.92 (95% CI, 1.59-5.37), 3.94 (95% CI, 1.46-10.64), and 4.46 (95% CI, 2.28-8.74), respectively. AR mutations exhibited clonal selection during treatment, including an increase in glucocorticoid-sensitive AR L702H and promiscuous AR T878A in patients with prior abiraterone treatment. At the time of progression, cfDNA sequencing revealed mutations or copy number changes in all patients tested, including clinically actionable alterations in DNA damage repair genes and PI3K pathway genes, and a high frequency (4 of 14) of activating CTNNB1 mutations. Conclusions and Relevance Clinically informative genomic profiling of cfDNA was feasible in nearly all patients with mCRPC and can provide important insights into enzalutamide response and resistance.

Loss of PTEN is associated with aggressive behavior in ERG positive prostate cancer

Background: The associations of ERG overexpression with clinical behavior and molecular pathways of prostate cancer are incompletely known. We assessed the association of ERG expression with AR, PTEN, SPINK1, Ki-67, and EZH2 expression levels, deletion, and mutations of chromosomal region 3p14 and TP53, and clinicopathologic variables. Methods: The material consisted of 326 prostatectomies, 166 needle biopsies from men treated primarily with endocrine therapy, 177 transurethral resections of castration-resistant prostate cancers (CRPC), and 114 CRPC metastases obtained from 32 men. Immunohistochemistry, FISH, and sequencing was used for the measurements. Results: ERG expression was found in about 45% of all patient cohorts. In a multivariate analysis, ERG expression showed independent value of favorable prognosis (P = 0.019). ERG positivity was significantly associated with loss of PTEN expression in prostatectomy (P = 0.0348), and locally recurrent CRPCs (P = 0.0042). Loss of PTEN expression was associated (P = 0.0085) with shorter progression-free survival in ERG-positive, but not in negative cases. When metastases in each subject were compared, consistent ERG, PTEN, and AR expression as well as TP53 mutations were found in a majority of subjects. Conclusions: A similar frequency of ERG positivity from early to late stage of the disease suggests lack of selection of ERG expression during disease progression. The prognostic significance of PTEN loss solely in ERG-positive cases indicates interaction of these pathways. The finding of consistent genetic alterations in different metastases suggests that the major genetic alterations take place in the primary tumor. Impact: Interaction of PTEN and ERG pathways warrants further studies. Cancer Epidemiol Biomarkers Prev; 22(12); 2333–44. ©2013 AACR.

Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and show that these relationships are most diverse in the critical regime.

Simulation of microarray data with realistic characteristics

BackgroundMicroarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed.ResultsWe present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples.ConclusionThe proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.