Age, not therapy intensity, determines outcomes of adults with acute myeloid leukemia

Büchner, Thomas; Krug, Utz; Gale, Robert Peter; Heinecke, Achim; Sauerland, Maria Cristina; Haferlach, Claudia; Schnittger, Susanne; Haferlach, Torsten; Müller‐Tidow, Carsten; Stelljes, Matthias; Mesters, Rolf M.; Braess, Jan; Spiekermann, Karsten; Staib, Peter; Grüneisen, Andreas; Reichle, Albrecht; Balleisen, Leopold; Eimermacher, Hartmut; Giagounidis, Aristoteles; Rasche, H; Lengfelder, Eva; Görlich, Dennis; Faldum, Andreas; Köpcke, Wolfgang; Hehlmann, Rüdiger; Wörmann, Bernhard; Berdel, Wolfgang E.; Hiddemann, Wolfgang

doi:10.1038/leu.2016.54

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…Training set 1 consisted of 407 patients randomized and treated in the multicenter phase III AMLCG-1999 trial ( clinicaltrials.gov identifier 00266136 ) between 1999 and 2005. 9 , 10 The patients are part of a previously published gene expression data set (GSE37642) and samples were analyzed on Affymetrix arrays. 11 , 12 Patient selection was based on the availability of information on response to induction treatment.…”

Section: Methodsmentioning

confidence: 99%

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

et al. 2017

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Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10−9, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10−9); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10−10). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.

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Section: Methodsmentioning

confidence: 99%

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

et al. 2017

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“…All patients included in the analysis received cytarabine and anthracycline based induction treatment. The training set consists of 447 patients randomized and treated in the multicenter phase III AMLCG-1999 trial (clinicaltrials.gov identifier NCT00266136) between 1999 and 2005 [ 25 , 26 ]. The patients are part of a previously published gene expression dataset (GSE37642) analyzed with Affymetrix arrays [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data

et al. 2018

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BackgroundThe inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind.ResultsIn this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models.ConclusionThe results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2344-6) contains supplementary material, which is available to authorized users.

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“…In order to perform the analyses for the random forests scenario (scenario 2), we consider a data set consisting of patients who were randomized and treated in the multicenter phase III AMLCG‐1999 trial (clinicaltrials.gov identifier NCT00266136) between 1999 and 2005 (Büchner et al., , ). It consists of gene expression data analyzed with Affymetrix arrays (Herold et al., ).…”

Section: Quantifying Uncertainty In Omics Biomarker Selection and Ranmentioning

confidence: 99%

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

et al. 2019

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Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared. K E Y W O R D Shigh-dimensional data, resampling, stability, variable ranking, variable selection INTRODUCTIONStatistical results are variable. On the one hand, results are affected by sampling uncertainty: if we draw different samples from a considered distribution, we obtain different results on each of these samples. Most researchers are familiar with this kind of variability and classical probability models account for it.On the other hand, one also obtains different results when applying different analysis strategies to address the same research question. These possible analysis strategies result from the combination of every choice that has to be made for an analysis, 670

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Age, not therapy intensity, determines outcomes of adults with acute myeloid leukemia

Cited by 14 publications

References 15 publications

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

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