Erik H. van Beers

Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System

Patients with multiple myeloma have variable survival and require reliable prognostic and predictive scoring systems. Currently, clinical and biological risk markers are used independently. Here, International Staging System (ISS), fluorescence in situ hybridization (FISH) markers, and gene expression (GEP) classifiers were combined to identify novel risk classifications in a discovery/validation setting. We used the datasets of the Dutch-Belgium Hemato-Oncology Group and German-speaking Myeloma Multicenter Group (HO65/GMMG-HD4), University of Arkansas for Medical Sciences-TT2 (UAMS-TT2), UAMS-TT3, Medical Research Council-IX, Assessment of Proteasome Inhibition for Extending Remissions, and Intergroupe Francophone du Myelome (IFM-G) (total number of patients: 4750). Twenty risk markers were evaluated, including t(4;14) and deletion of 17p (FISH), EMC92, and UAMS70 (GEP classifiers), and ISS. The novel risk classifications demonstrated that ISS is a valuable partner to GEP classifiers and FISH. Ranking all novel and existing risk classifications showed that the EMC92-ISS combination is the strongest predictor for overall survival, resulting in a 4-group risk classification. The median survival was 24 months for the highest risk group, 47 and 61 months for the intermediate risk groups, and the median was not reached after 96 months for the lowest risk group. The EMC92-ISS classification is a novel prognostic tool, based on biological and clinical parameters, which is superior to current markers and offers a robust, clinically relevant 4-group model.

Detection of Mutant NPM1 mRNA in Acute Myeloid Leukemia Using Custom Gene Expression Arrays

Mutations in the gene encoding nucleophosmin (NPM1) carry a prognostic value for patients with acute myeloid leukemia (AML). Various techniques are currently being used to detect these mutations in routine molecular diagnostics. Incorporation of accurate NPM1 mutation detection on a gene expression platform would enable simultaneous detection with various other expression biomarkers. Here we present an array-based mutation detection using custom probes for NPM1 WT mRNA and NPM1 type A, B, and D mutant mRNA. This method was 100% accurate on a training cohort of 505 newly diagnosed unselected AML cases. Validation on an independent cohort of 143 normal-karyotype AML cases revealed no false-negative results, and one false positive (sensitivity 100.0% and specificity 98.7%). Based on this, we conclude that this method provides a reliable method for NPM1 mutation detection. The method can be applied to other genes/mutations as long as the mutant alleles are sufficiently highly expressed.

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

High levels of BAALC, ERG, EVI1 and MN1 expression have been associated with shorter overall survival in AML but standardized and clinically validated assays are lacking. We have therefore developed and optimized an assay for standardized detection of these prognostic genes for patients with intermediate cytogenetic risk AML. In a training set of 147 intermediate cytogenetic risk cases we performed cross validations at 5 percentile steps of expression level and observed a bimodal significance profile for BAALC expression level and unimodal significance profiles for ERG and MN1 levels with no statistically significant cutoff points near the median expression level of BAALC, ERG or MN1. Of the possible cutoff points for expression levels of BAALC, ERG and MN1, just the 30th and 75th percentile of BAALC expression level and the 30th percentile of MN1 expression level cutoff points showed clinical significance. Of these only the 30th percentile of BAALC expression level reproduced in an independent verification (extended training) data set of 242 cytogenetically normal AML cases and successfully validated in an external cohort of 215 intermediate cytogenetic risk AML cases. Finally, we show independent prognostic value for high EVI1 and low BAALC in multivariate analysis with other clinically relevant molecular AML markers. We have developed a highly standardized molecular assay for the independent gene expression markers EVI1 and BAALC.

Detection of CEBPA Double Mutants in Acute Myeloid Leukemia Using a Custom Gene Expression Array

Double (bi-allelic) mutations in the gene encoding the CCAAT/enhancer-binding protein-alpha (CEBPA) transcription factor have a favorable prognostic impact in acute myeloid leukemia (AML). Double mutations in CEBPA can be detected using various techniques, but it is a notoriously difficult gene to sequence due to its high GC-content. Here we developed a two-step gene expression classifier for accurate and standardized detection of CEBPA double mutations. The key feature of the two-step classifier is that it explicitly removes cases with low CEBPA expression, thereby excluding CEBPA hypermethylated cases that have similar gene expression profiles as a CEBPA double mutant, which would result in false-positive predictions. In the second step, we have developed a 55 gene signature to identity the true CEBPA double-mutation cases. This two-step classifier was tested on a cohort of 505 unselected AML cases, including 26 CEBPA double mutants, 12 CEBPA single mutants, and seven CEBPA promoter hypermethylated cases, on which its performance was estimated by a double-loop cross-validation protocol. The two-step classifier achieves a sensitivity of 96.2% (95% confidence interval [CI] 81.1 to 99.3) and specificity of 100.0% (95% CI 99.2 to 100.0). There are no false-positive detections. This two-step CEBPA double-mutation classifier has been incorporated on a microarray platform that can simultaneously detect other relevant molecular biomarkers, which allows for a standardized comprehensive diagnostic assay. In conclusion, gene expression profiling provides a reliable method for CEBPA double-mutation detection in patients with AML for clinical use.

Prognostic Validation of SKY92 and Its Combination With ISS in an Independent Cohort of Patients With Multiple Myeloma

&NA; An independent dataset of 91 multiple myeloma patients were tested with eight prognostic mRNA expression signatures. SKY92 best predicted survival (HR = 8.2) and classified the largest fraction (21%) as high risk. Finally 38/91 (42%) cases were low risk by the recently proposed combination SKY92 + ISS achieving a HR 10 for low versus high risk. Background: High risk and low risk multiple myeloma patients follow a very different clinical course as reflected in their PFS and OS. To be clinically useful, methodologies used to identify high and low risk disease must be validated in representative independent clinical data and available so that patients can be managed appropriately. A recent analysis has indicated that SKY92 combined with the International Staging System (ISS) identifies patients with different risk disease with high sensitivity. Patients and Methods: Here we computed the performance of eight gene expression based classifiers SKY92, UAMS70, UAMS80, IFM15, Proliferation Index, Centrosome Index, Cancer Testis Antigen and HM19 as well as the combination of SKY92/ISS in an independent cohort of 91 newly diagnosed MM patients. Results: The classifiers identified between 9%‐21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. Conclusion: Among the eight signatures, SKY92 identified the largest proportion of patients (21%) also with the highest HR (8.2). Our analysis also validated the combination SKY92/ISS for identification of three classes; low risk (42%), intermediate risk (37%) and high risk (21%). Between low risk and high risk classes the HR is >10.

Potential therapeutic and economic value of risk-stratified treatment as initial treatment of multiple myeloma in Europe

Biomarkers associated with prognosis in multiple myeloma (MM) can be used to stratify patients into risk categories. An attractive alternative to uniform treatment (UT), risk-stratified treatment (RST) is proposed where high-risk patients receive bortezomib-based regimens while standard-risk patients receive alternative less costly regimens. An early Markov-type decision analytic model evaluated the potential therapeutic and economic value of different RST strategies compared with UT in MM patients in key European countries. Results suggest RST strategies were both cheaper and more effective than UT across all countries, with the molecular marker-only strategy RST-SKY92 producing maximum health gains (0.031-0.039 QALYs). The conclusions remained consistent in the univariate sensitivity analyses. These findings should encourage stakeholders to support the adoption of RST approaches in MM.

Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient’s treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.Selection of the right cancer treatment is still a challenge. Here, the authors introduce a framework to analyze treatment benefits, using the idea that patients with similar genetic tumor profiles receiving different treatments can be used to model their responses to the alternative treatment.