Michal Pešta

Oncogenic MicroRNAs: miR-155, miR-19a, miR-181b, and miR-24 enable monitoring of early breast cancer in serum

BackgroundMicroRNAs (miRs) represent a distinct class of posttranscriptional modulators of gene expression with remarkable stability in sera. Several miRs are oncogenic (oncomiRs) and are deregulated in the pathogenesis of breast cancer and function to inhibit tumor suppressors. Routine blood monitoring of these circulating tumor-derived products could be of significant benefit to the diagnosis and relapse detection of early-stage breast cancer (EBC) patients.MethodsAim of this project was to determine expression of miR-155, miR-19a, miR-181b, miR-24, relative to let-7a in sera of 63 patients with EBC and 21 healthy controls. Longitudinal multivariate data analysis was performed to stochastically model the serum levels of each of the oncomiRs during disease phases: from diagnosis, after surgery, and following chemo/radiotherapy. Moreover, this analysis was utilized to evaluate oncomiR levels in EBC patients subgrouped using current clinical prognostic factors including HER2, Ki-67, and grade III.ResultsEBC patients significantly over-express the oncomiRs at the time of diagnosis. Following surgical resection the serum levels of miR-155, miR-181b, and miR-24 significantly decreased (p = 1.89e-05, 5.41e-06, and 0.00638, respectively) whereas the miR-19a decreased significantly after the therapy (p = 0.00869). Furthermore, in case of high-risk patients serum levels of miR-155, miR-19a, miR-181b, and miR-24 are significantly more abundant in comparison to low-risk group (p = 0.026, 0.02567, 0.0250, and 0.00990) and show a decreasing trend upon therapy.ConclusionsOncomiRs are significantly more abundant in the sera of EBC patients compared to controls at diagnosis. Differences in oncomiR levels reflecting EBC risk were also observed. Testing the oncomiRs may be useful for diagnostic purpose and possibly also for relapse detection in follow-up studies of EBC.

Total least squares and bootstrapping with applications in calibration

The solution to the errors-in-variables problem computed through total least squares is highly nonlinear. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied. One possible solution to this dilemma is bootstrapping. A nonparametric bootstrap technique could fail. Here, the proper nonparametric bootstrap procedure is provided and its correctness is proved. On the other hand, a residual bootstrap is not valid and suitable in this case. The results are illustrated through a simulation study. An application of this approach to calibration data is presented.

Plasma miR-155, miR-203, and miR-205 are Biomarkers for Monitoring of Primary Cutaneous T-Cell Lymphomas

Primary cutaneous T-cell lymphomas (CTCL) affect the skin and tend to transform and spread. CTCL involves primarily the Mycosis fungoides (MF) and more aggressive Sezary syndrome (SS). Oncogenic microRNAs (miRs) are stable epigenetic inhibitors often deregulated in the tumour and detectable as biomarkers in non-cellular fractions of peripheral blood. The tumour-specific expression of miR-155, miR-203, and miR-205 was shown to correctly diagnose CTCL. We herein asked whether these microRNAs can be used as plasma biomarkers for clinical CTCL monitoring. Patients with CTCL (n = 10) and controls with non-malignant conditions (n = 11) repeatedly donated plasma samples every ca. five months. MicroRNAs were detected in the plasma samples by specifically-primed RT-PCR followed by multivariate analyses of the miR expression dynamics. We herein established the plasma miR-classifier for detecting CTCL based on the miR-155 upregulation and miR-203/miR-205 downregulation with 100% specificity and 94% sensitivity. The 3-miR-score in the consecutive samples coincided with the clinical outcome of MF and SS patients such as the therapy response or changes in the clinical stage or tumor size. Quantitation of the selected microRNAs in plasma is a specific and straightforward approach for evaluating CTCL outcome representing, thus, a valuable tool for CTCL diagnostics and therapy response monitoring.

Prediction Potential of Serum miR-155 and miR-24 for Relapsing Early Breast Cancer

Oncogenic microRNAs (oncomiRs) accumulate in serum due to their increased stability and thus serve as biomarkers in breast cancer (BC) pathogenesis. Four oncogenic microRNAs (miR-155, miR-19a, miR-181b, and miR-24) and one tumor suppressor microRNA (let-7a) were shown to differentiate between high- and low-risk early breast cancer (EBC) and reflect the surgical tumor removal and adjuvant therapy. Here we applied the longitudinal multivariate data analyses to stochastically model the serum levels of each of the oncomiRs using the RT-PCR measurements in the EBC patients (N = 133) that were followed up 4 years after diagnosis. This study identifies that two of the studied oncomiRs, miR-155 and miR-24, are highly predictive of EBC relapse. Furthermore, combining the oncomiR level with Ki-67 expression further specifies the relapse probability. Our data move further the notion that oncomiRs in serum enable not only monitoring of EBC but also are a very useful tool for predicting relapse independently of any other currently analyzed characteristics in EBC patients. Our approach can be translated into medical practice to estimate individual relapse risk of EBC patients.

Somatic mutation dynamics in MDS patients treated with azacitidine indicate clonal selection in patients-responders

Azacitidine (AZA) for higher risk MDS patients is a standard therapy with limited durability. To monitor mutation dynamics during AZA therapy we utilized massive parallel sequencing of 54 genes previously associated with MDS/AML pathogenesis. Serial sampling before and during AZA therapy of 38 patients (reaching median overall survival 24 months (Mo) with 60% clinical responses) identified 116 somatic pathogenic variants with allele frequency (VAF) exceeding 5%. High accuracy of data was achieved via duplicate libraries from myeloid cells and T-cell controls. We observed that nearly half of the variants were stable while other variants were highly dynamic. Patients with marked decrease of allelic burden upon AZA therapy achieved clinical responses. In contrast, early-progressing patients on AZA displayed minimal changes of the mutation pattern. We modeled the VAF dynamics on AZA and utilized a joint model for the overall survival and response duration. While the presence of certain variants associated with clinical outcomes, such as the mutations of CDKN2A were adverse predictors while KDM6A mutations yield lower risk of dying, the data also indicate that allelic burden volatility represents additional important prognostic variable. In addition, preceding 5q- syndrome represents strong positive predictor of longer overall survival and response duration in high risk MDS patients treated with AZA. In conclusion, variants dynamics detected via serial sampling represents another parameter to consider when evaluating AZA efficacy and predicting outcome.

Asymptotic and Bootstrap Tests for a Change in Autoregression Omitting Variability Estimation

A sequence of time-ordered observations follows an autoregressive model of order one and its parameter is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. A change-point method presented here rely on a ratio type test statistic based on the maxima of cumulative sums. The main advantage of the developed approach is that the variance of the observations neither has to be known nor estimated. Asymptotic distribution of the test statistic under the no-change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternative. A bootstrap procedure is proposed in the way of a completely data-driven technique without any tuning parameters. The results are illustrated through a simulation study, which demonstrates the computational efficiency of the procedure. A practical application to real data is presented as well.

Modelling prescription behaviour of general practitioners

Abstract The health care costs have been rapidly rising in recent years and the public health insurance companies are highly interested in prescription policies of general practitioners. In our contribution, a complex model for prescription behaviour of Belgian general practitioners is built using the structural equation modelling (SEM) framework. The model involves a large number of prescribed medicament groups as well as doctors’ and patients’ characteristics. As one of the results, a relatively small number of medicament groups, which effectively describe the prescription behaviour of a given doctor, is obtained. These indicators are consequently used in a generalized linear model for predicting the drug expenses per patient. Such a model can be used as a useful guideline for the expenses’ assessment of a particular practitioner.

Block bootstrap for dependent errors-in-variables

ABSTRACT A linear errors-in-variables (EIV) model that contains measurement errors in the input and output data is considered. Weakly dependent (α- and ϕ-mixing) errors, not necessarily stationary nor identically distributed, are taken into account within the EIV model. Parameters of the EIV model are estimated by the total least squares approach, which provides highly non linear estimates. Because of this, many statistical procedures for constructing confidence intervals and testing hypotheses cannot be applied. One possible solution to this dilemma is a block bootstrap. An appropriate moving block bootstrap procedure is provided and its correctness proved. The results are illustrated through a simulation study and applied on real data as well.

Change Point in Panel Data with Small Fixed Panel Size: Ratio and Non-Ratio Test Statistics

The main goal is to develop and, consequently, compare stochastic methods for detecting whether a structural change in panel data occurred at some unknown time or not. Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. Testing procedures to detect a possible common change in means of the panels are established. Ratio and non-ratio type test statistics are considered. Their asymptotic distributions under the no change null hypothesis are derived. Moreover, we prove the consistency of the tests under the alternative. The advantage of the ratio statistics compared to the non-ratio ones is that the variance of the observations neither has to be known nor estimated. A simulation study reveals that the proposed ratio statistic outperforms the non-ratio one by keeping the significance level under the null, mainly when stronger dependence within the panel is present. However, the non-ratio statistic incorrectly rejects the null in the simulations more often than it should, which yields higher power compared to the ratio statistic.

Ratio Tests of a Change in Panel Means with Small Fixed Panel Size

The aim of this paper is to develop stochastic methods for detection whether a change in panel data occurred at some unknown time or not. Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. Testing procedures to detect a possible common change in means of the panels are established. To this end, we consider several competing ratio type test statistics and derive their asymptotic distributions under the no change null hypothesis. Moreover, we prove the consistency of the tests under the alternative. The main advantage of the proposed approaches is that the variance of the observations neither has to be known nor estimated. The results are illustrated through a simulation study. An application of the procedure to actuarial data is presented.