Veronika Rockova

Mutant DNMT3A: a marker of poor prognosis in acute myeloid leukemia

The prevalence, the prognostic effect, and interaction with other molecular markers of DNMT3A mutations was studied in 415 patients with acute myeloid leukemia (AML) younger than 60 years. We show mutations in DNMT3A in 96 of 415 patients with newly diagnosed AML (23.1%). Univariate Cox regression analysis showed that patients with DNMT3A(mutant) AML show significantly worse overall survival (OS; P = .022; hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.04-1.81), and relapse-free survival (RFS; P = .005; HR, 1.52; 95% CI, 1.13-2.05) than DNMT3A(wild-type) AMLs. In a multivariable analysis, DNMT3A mutations express independent unfavorable prognostic value for OS (P = .003; HR, 1.82; 95% CI, 1.2-2.7) and RFS (P < .001; HR, 2.2; 95% CI, 1.4-3.3). In a composite genotypic subset of cytogenetic intermediate-risk AML without FLT3-ITD and NPM1 mutations, this association is particularly evident (OS: P = .013; HR, 2.09; 95% CI, 1.16-3.77; RFS: P = .001; HR, 2.65; 95% CI, 1.48-4.89). The effect of DNMT3A mutations in human AML remains elusive, because DNMT3A(mutant) AMLs did not express a methylation or gene expression signature that discriminates them from patients with DNMT3A(wild-type) AML. We conclude that DNMT3A mutation status is an important factor to consider for risk stratification of patients with AML.

Risk stratification of intermediate-risk acute myeloid leukemia: integrative analysis of a multitude of gene mutation and gene expression markers

Numerous molecular markers have been recently discovered as potential prognostic factors in acute myeloid leukemia (AML). It has become of critical importance to thoroughly evaluate their interrelationships and relative prognostic importance. Gene expression profiling was conducted in a well-characterized cohort of 439 AML patients (age < 60 years) to determine expression levels of EVI1, WT1, BCL2, ABCB1, BAALC, FLT3, CD34, INDO, ERG and MN1. A variety of AML-specific mutations were evaluated, that is, FLT3, NPM1, N-RAS, K-RAS, IDH1, IDH2, and CEBPA(DM/SM) (double/single). Univariable survival analysis shows that (1) patients with FLT3(ITD) mutations have inferior overall survival (OS) and event-free survival (EFS), whereas CEBPA(DM) and NPM1 mutations indicate favorable OS and EFS in intermediate-risk AML, and (2) high transcript levels of BAALC, CD34, MN1, EVl1, and ERG predict inferior OS and EFS. In multivariable survival analysis, CD34, ERG, and CEBPA(DM) remain significant. Using survival tree and regression methodologies, we show that CEBPA(DM), CD34, and IDH2 mutations are capable of separating the intermediate group into 2 AML subgroups with highly distinctive survival characteristics (OS at 60 months: 51.9% vs 14.9%). The integrated statistical approach demonstrates that from the multitude of biomarkers a greatly condensed subset can be selected for improved stratification of intermediate-risk AML.

EMVS: The EM Approach to Bayesian Variable Selection

Despite rapid developments in stochastic search algorithms, the practicality of Bayesian variable selection methods has continued to pose challenges. High-dimensional data are now routinely analyzed, typically with many more covariates than observations. To broaden the applicability of Bayesian variable selection for such high-dimensional linear regression contexts, we propose EMVS, a deterministic alternative to stochastic search based on an EM algorithm which exploits a conjugate mixture prior formulation to quickly find posterior modes. Combining a spike-and-slab regularization diagram for the discovery of active predictor sets with subsequent rigorous evaluation of posterior model probabilities, EMVS rapidly identifies promising sparse high posterior probability submodels. External structural information such as likely covariate groupings or network topologies is easily incorporated into the EMVS framework. Deterministic annealing variants are seen to improve the effectiveness of our algorithms by mitigating the posterior multimodality associated with variable selection priors. The usefulness of the EMVS approach is demonstrated on real high-dimensional data, where computational complexity renders stochastic search to be less practical.

Deregulated expression of EVI1 defines a poor prognostic subset of MLL-rearranged acute myeloid leukemias: a study of the German-Austrian Acute Myeloid Leukemia Study Group and the Dutch-Belgian-Swiss HOVON/SAKK Cooperative Group

PURPOSE To evaluate the prognostic value of ecotropic viral integration 1 gene (EVI1) overexpression in acute myeloid leukemia (AML) with MLL gene rearrangements. PATIENTS AND METHODS We identified 286 patients with AML with t(11q23) enrolled onto German-Austrian Acute Myeloid Leukemia Study Group and Dutch-Belgian-Swiss Hemato-Oncology Cooperative Group prospective treatment trials. Material was available from 177 AML patients for EVI1 expression analysis. RESULTS We divided 286 MLL-rearranged AMLs into three subgroups: t(9;11)(p22;q23) (44.8%), t(6;11)(q27;q23) (14.7%), and t(v;11q23) (40.5%). EVI1 overexpression (EVI1(+)) was found in 45.8% of all patients with t(11q23), with t(6;11) showing the highest frequency (83.9%), followed by t(9;11) at 40.0%, and t(v;11q23) at 34.8%. Concurrent gene mutations were rare or absent in all three subgroups. Within all t(11q23) AMLs, EVI1(+) was the sole prognostic factor, predicting for inferior overall survival (OS; hazard ratio [HR], 2.06; P = .003), relapse-free survival (HR, 2.28; P = .002), and event-free survival (HR, 1.79; P = .009). EVI1(+) AMLs with t(11q23) in first complete remission (CR) had a significantly better outcome after allogeneic transplantation compared with other consolidation therapies (5-year OS, 54.7% v 0%; Mantel-Byar, P = .0006). EVI1(-) t(9;11) AMLs had lower WBC counts, more commonly FAB M5 morphology, and frequently had additional trisomy 8 (39.6%; P < .001). Among t(9;11) AMLs, EVI1(+) again was the sole independent adverse prognostic factor for survival. CONCLUSION Deregulated EVI1 expression defines poor prognostic subsets among AML with t(11q23) and AML with t(9;11)(p22;q23). Patients with EVI1(+) MLL-rearranged AML seem to benefit from allogeneic transplantation in first CR.

The prognostic relevance of miR-212 expression with survival in cytogenetically and molecularly heterogeneous AML

Acute myeloid leukemia (AML) is a highly heterogeneous disease, characterized by various cytogenetic and molecular abnormalities, many of which may express prognostic value. MicroRNAs (miRNAs) are a class of small regulatory RNAs. The prognostic value of miRNAs in AML is yet to be determined. Here, we set out to identify miRNAs that are consistent significant prognostic determinants, independent from other known prognostic factors. A discovery cohort (n=167) and validation cohort (n=409) of a heterogeneous AML population were used to reliably identify miRNAs with prognostic value. We report miR-212 as an independent prognostic factor, significantly associated with a prolonged overall survival (OS) and also event-free and relapse-free survival in a discovery cohort (hazard ratio (HR)s=0.77, P=0.015 for OS) that was subsequently confirmed in an independent validation cohort of 409 cases (HR=0.83, P=0.016). The prognostic significance and the prevalence of high miR-212 did not correlate with specific (cyto)genetic subtypes of AML. High miR-212 expression levels are associated with a gene expression profile that is significantly enriched for genes involved in the immune response. MiR-212 may improve the current prognostic risk stratification of mixed AML including normal karyotype AML and AML with cytogenetic and molecular abnormalities.

The Spike-and-Slab LASSO

ABSTRACT Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potential for penalized likelihood estimation has largely been overlooked. In this article, we bridge this gap by cross-fertilizing these two paradigms with the Spike-and-Slab LASSO procedure for variable selection and parameter estimation in linear regression. We introduce a new class of self-adaptive penalty functions that arise from a fully Bayes spike-and-slab formulation, ultimately moving beyond the separable penalty framework. A virtue of these nonseparable penalties is their ability to borrow strength across coordinates, adapt to ensemble sparsity information and exert multiplicity adjustment. The Spike-and-Slab LASSO procedure harvests efficient coordinate-wise implementations with a path-following scheme for dynamic posterior exploration. We show on simulated data that the fully Bayes penalty mimics oracle performance, providing a viable alternative to cross-validation. We develop theory for the separable and nonseparable variants of the penalty, showing rate-optimality of the global mode as well as optimal posterior concentration when p > n. Supplementary materials for this article are available online.

Hierarchical Bayesian formulations for selecting variables in regression models.

The objective of finding a parsimonious representation of the observed data by a statistical model that is also capable of accurate prediction is commonplace in all domains of statistical applications. The parsimony of the solutions obtained by variable selection is usually counterbalanced by a limited prediction capacity. On the other hand, methodologies that assure high prediction accuracy usually lead to models that are neither simple nor easily interpretable. Regularization methodologies have proven to be useful in addressing both prediction and variable selection problems. The Bayesian approach to regularization constitutes a particularly attractive alternative as it is suitable for high-dimensional modeling, offers valid standard errors, and enables simultaneous estimation of regression coefficients and complexity parameters via computationally efficient MCMC techniques. Bayesian regularization falls within the versatile framework of Bayesian hierarchical models, which encompasses a variety of other approaches suited for variable selection such as spike and slab models and the MC(3) approach. In this article, we review these Bayesian developments and evaluate their variable selection performance in a simulation study for the classical small p large n setting. The majority of the existing Bayesian methodology for variable selection deals only with classical linear regression. Here, we present two applications in the contexts of binary and survival regression, where the Bayesian approach was applied to select markers prognostically relevant for the development of rheumatoid arthritis and for overall survival in acute myeloid leukemia patients.

Retroviral Integration Mutagenesis in Mice and Comparative Analysis in Human AML Identify Reduced PTP4A3 Expression as a Prognostic Indicator

Acute myeloid leukemia (AML) results from multiple genetic and epigenetic aberrations, many of which remain unidentified. Frequent loss of large chromosomal regions marks haplo-insufficiency as one of the major mechanisms contributing to leukemogenesis. However, which haplo-insufficient genes (HIGs) are involved in leukemogenesis is largely unknown and powerful experimental strategies aimed at their identification are currently lacking. Here, we present a new approach to discover HIGs, using retroviral integration mutagenesis in mice in which methylated viral integration sites and neighbouring genes were identified. In total we mapped 6 genes which are flanked by methylated viral integration sites (mVIS). Three of these, i.e., Lrmp, Hcls1 and Prkrir, were up regulated and one, i.e., Ptp4a3, was down regulated in the affected tumor. Next, we investigated the role of PTP4A3 in human AML and we show that PTP4A3 expression is a negative prognostic indicator, independent of other prognostic parameters. In conclusion, our novel strategy has identified PTP4A3 to potentially have a role in AML, on one hand as a candidate HIG contributing to leukemogenesis in mice and on the other hand as a prognostic indicator in human AML.

Bayesian estimation of sparse signals with a continuous spike-and-slab prior

We introduce a new framework for estimation of sparse normal means, bridging the gap between popular frequentist strategies (LASSO) and popular Bayesian strategies (spike-and-slab). The main thrust of this paper is to introduce the family of Spike-andSlab LASSO (SS-LASSO) priors, which form a continuum between the Laplace prior and the point-mass spike-and-slab prior. We establish several appealing frequentist properties of SS-LASSO priors, contrasting them with these two limiting cases. First, we adopt the penalized likelihood perspective on Bayesian modal estimation and introduce the framework of Bayesian penalty mixing with spike-andslab priors. We show that the SS-LASSO global posterior mode is (near) minimax rate-optimal under squared error loss, similarly as the LASSO. Going further, we introduce an adaptive two-step estimator which can achieve provably sharper performance than the LASSO. Second, we show that the whole posterior keeps pace with the global mode and concentrates at the (near) minimax rate, a property that is known not to hold for the single Laplace prior. The minimax-rate optimality is obtained with a suitable class of independent product priors (for known levels of sparsity) as well as with dependent mixing priors (adapting to the unknown levels of sparsity). Up to now, the rate-optimal posterior concentration has been established only for spike-and-slab priors with a point mass at zero. Thus, the SS-LASSO priors, despite being continuous, possess similar optimality properties as the “theoretically ideal” point-mass mixtures. These results provide valuable theoretical justification for our proposed class of priors, underpinning their intuitive appeal and practical potential.

Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity

ABSTRACT Rotational post hoc transformations have traditionally played a key role in enhancing the interpretability of factor analysis. Regularization methods also serve to achieve this goal by prioritizing sparse loading matrices. In this work, we bridge these two paradigms with a unifying Bayesian framework. Our approach deploys intermediate factor rotations throughout the learning process, greatly enhancing the effectiveness of sparsity inducing priors. These automatic rotations to sparsity are embedded within a PXL-EM algorithm, a Bayesian variant of parameter-expanded EM for posterior mode detection. By iterating between soft-thresholding of small factor loadings and transformations of the factor basis, we obtain (a) dramatic accelerations, (b) robustness against poor initializations, and (c) better oriented sparse solutions. To avoid the prespecification of the factor cardinality, we extend the loading matrix to have infinitely many columns with the Indian buffet process (IBP) prior. The factor dimensionality is learned from the posterior, which is shown to concentrate on sparse matrices. Our deployment of PXL-EM performs a dynamic posterior exploration, outputting a solution path indexed by a sequence of spike-and-slab priors. For accurate recovery of the factor loadings, we deploy the spike-and-slab LASSO prior, a two-component refinement of the Laplace prior. A companion criterion, motivated as an integral lower bound, is provided to effectively select the best recovery. The potential of the proposed procedure is demonstrated on both simulated and real high-dimensional data, which would render posterior simulation impractical. Supplementary materials for this article are available online.