Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use

Abstract As molecular profiling technologies have evolved, our understanding of multiple myeloma heterogeneity and the relative effectiveness of treatment options have increased dramatically. Most recently, next generation sequencing (NGS) studies have provided a new degree of molecular resolution into this disease, however it remains a challenge to translate these methodologies and insights from research tools to widely-available clinical assays which can be performed as part of routine patient care. MyPRS® is a clinically and scientifically validated high-throughput gene-expression (Affymetrix) based assay available in all 50 US states. The CLIA and CAP-accredited laboratory workflow is able to isolate sufficient RNA from small amounts of fresh bone marrow aspirate, up to 72 hours post collection[1]. In order to expand the content of the MyPRS assay to include DNA-sequencing based variant, copy number and mutation detection, we have validated highly-scalable methods for isolating RNA and DNA separately and in parallel from individual patient specimens of varying quality and quantity. Yet another challenge of translational NGS profiling is performing complex laboratory procedures, developed primarily for research use only, with the requisite reproducibility and accuracy required for submission to regulatory agencies and ultimately for clinical use. Coupled with the protocols we developed for isolation of both DNA and RNA from small amounts of patient bone marrow aspirate, we performed an investigation of the Illumina NextSeq 500 and "on-site" BaseSpace data processing server. With the multiplexing and parallel processing capability of this platform we estimate being able to sequence, align and variant-call up to 150 myeloma-relevant genes at 1000x coverage per run. Results from our MM cell-line-, normal human- and NIST reference-DNA whole-exome-profiling studies show extremely high levels of technical reproducibility and agreement with results generated from 3rd party laboratories. In addition, we describe associations between the (RNA-expression based) prognostic, molecular subtyping and virtual karyotyping currently included in the MyPRS assay, with results from DNA-based exome profiling, performed on matched specimens. We believe findings underscore the need for comprehensive DNA and RNA based molecular profiling in order to make the most informed patient management decisions. 1. van Laar R, Flinchum R, Brown N, Ramsey J, Riccitelli S, Heuck C, Barlogie B, Shaughnessy Jr J: Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use. BMC Medical Genomics 2014, 7 (1):25. Disclosures Zielinska: Signal Genetics: Employment. Leigh:Signal Genetics: Employment. Gomez:Signal Genetics: Employment. Van Laar:Signal Genetics: Employment.

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Twist-1 is upregulated by NSD2 and contributes to tumour dissemination and an epithelial-mesenchymal transition-like gene expression signature in t(4;14)-positive multiple myeloma

Cancer Letters ◽

10.1016/j.canlet.2020.01.040 ◽

2020 ◽

Vol 475 ◽

pp. 99-108 ◽

Cited By ~ 6

Author(s):

Chee Man Cheong ◽

Krzysztof M. Mrozik ◽

Duncan R. Hewett ◽

Elyse Bell ◽

Vasilios Panagopoulos ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Epithelial Mesenchymal Transition ◽

Gene Expression Signature ◽

Mesenchymal Transition ◽

Expression Signature ◽

Positive Multiple ◽

Twist 1

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A robust gene expression signature to predict proteasome inhibitor benefit in Multiple Myeloma

10.1101/2019.12.16.19015024 ◽

2019 ◽

Author(s):

Joske Ubels ◽

Pieter Sonneveld ◽

Martin H. van Vliet ◽

Jeroen de Ridder

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Proteasome Inhibitor ◽

Expression Profiles ◽

Specific Treatment ◽

Gene Expression Signature ◽

Hazard Ratio ◽

Treatment Benefit ◽

Expression Signature ◽

Gene Expression Signatures

AbstractMany cancer drugs only benefit a subset of the patients that receive them, but are often associated with serious side effects. Predictive classification methods that can identify which patients will benefit from a specific treatment are therefore of great clinical utility. We here introduce a novel machine learning method to identify predictive gene expression signatures, based on the idea that patients who received different treatments but exhibit similar expression profiles can be used to model response to the alternative treatment. We use this method to predict proteasome inhibitor benefit in Multiple Myeloma (MM). In a dataset of 910 MM patients we identify a 14-gene expression signature that can successfully predict benefit to the proteasome inhibitor bortezomib, with a hazard ratio of 0.47 (p = 0.04) in class ‘benefit’, while in class ‘no benefit’ the hazard ratio is 0.91 (p = 0.68). Importantly, we observe a similar classification performance (HR class benefit = 0.46, p = 0.04) in an independent patient cohort which was moreover measured on a different platform, demonstrating the robustness of the signature. Moreover, we find that the genes in the discovered signature are essential, as no equivalent signature can be found when they are excluded from the analysis. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or MM disease progression. In conclusion, our method allows for identification of gene expression signatures that can aid in treatment decisions for MM patients and provide insight into the biological mechanism behind treatment benefit.

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Identification of a Gene Signature Associated to Treatment Response to Palbociclib in Multiple Myeloma

Blood ◽

10.1182/blood.v128.22.5666.5666 ◽

2016 ◽

Vol 128 (22) ◽

pp. 5666-5666

Author(s):

Angelique Bruyer ◽

Alboukadel Kassambara ◽

Paul Anziani ◽

Donia El Bahlagui ◽

Nicolas Robert ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Multiple Myeloma ◽

Rna Sequencing ◽

Cell Lines ◽

Molecular Mechanisms ◽

Conflicts Of Interest ◽

Gene Expression Signature ◽

G1 Arrest ◽

Expression Signature

Abstract Background: Inpatients with relaspsed/refractoryMultiple Myeloma (MM), outcomes are far from optimal, especially in patients refractory to current treatments Recent studies and clinical trials have highlighted the therapeutic potential of Palbociclib, a CDK4/6 inhibitor, in various cancers including MM. Deregulation of CDK4/6 is involved in the loss of cell cycle control in MM. Response to Palbociclib combined with bortezomib and dexamethasone was acquired in 20% of the relapsed/refractory MM patients, suggesting that biomarkers to identify patients that could benefit from this treatment are needed. Additional studies are required to understand the biological pathways associated with sensitivity or resistance of MM cells to Palbociclib. Methods: 14 human MM cell lines and 12 primary MM samples were tested for response to Palbociclib treatment. The concentration required to inhibit growth by 50% (IC50) was calculated. Gene expression signature associated with multiple myeloma response to Palbociclib, as well as, genes deregulated by the treatment have been analyzed using microarray and RNA-sequencing methods. Results: Palbociclib had an heterogeneous in vitro activity among the 14 human myeloma cell lines tested, which aggregated into three groups based on the distribution of the IC50 values: sensitive (n = 5, IC50: 0.2 - 0.3µM), intermediate (n = 3, IC50: 0.5 - 0.7µM) or more resistant group (n = 6, IC50: 0.9 - 2.4µM). The same holds true when testing the Palbociclib on primary multiple myeloma samples. The evaluation of the Palbociclib effect on cell cycle progression and the induction of the apoptosis, reveals that Palbociclib is essentially cytostatic, inducing prolonged G1 arrest in sensitive cell lines with a strong reduction of the percentage of cells in S phase. To better understand the molecular mechanisms associated with Palbociclib response, we identified a gene expression signature correlated with the response in both MM cell lines and primary myeloma cells from patients. Additionally, we have analyzed differentially expressed genes after Palbociclib treatment in human MM cell lines using RNA sequencing (n = 4). The physiological role of the downregulated genes after Palbociclib treatment is associated with cell cycle, mitosis and E2F mediated regulation of DNA replication. Significantly upregulated genes, after Palbociclib treatment, were enriched in genes encoding proteins involved in glutathione synthesis and recycling, and biological oxidations. Conclusion: Altogether, our data demonstrated a high heterogeneity in the response of MM cells to Palbociclib. We identified a gene expression signature associated with Palbociclib response in MM. These genes could help to identify MM patients that could benefit from Palbociclib treatment and provide novel targets for efficient combination therapy. Disclosures No relevant conflicts of interest to declare.

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Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma

Medical Science Monitor ◽

10.12659/msm.913970 ◽

2019 ◽

Vol 25 ◽

pp. 3247-3255 ◽

Cited By ~ 2

Author(s):

Fang-xiao Zhu ◽

Yu-chan He ◽

Jun-yan Zhang ◽

Hang-fei Wang ◽

Chen Zhong ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Drug Repositioning ◽

Gene Expression Signature ◽

Related Gene ◽

Connectivity Map ◽

Expression Signature

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A gene expression signature for high-risk multiple myeloma

Leukemia ◽

10.1038/leu.2012.127 ◽

2012 ◽

Vol 26 (11) ◽

pp. 2406-2413 ◽

Cited By ~ 123

Author(s):

R Kuiper ◽

A Broyl ◽

Y de Knegt ◽

M H van Vliet ◽

E H van Beers ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Gene Expression Signature ◽

Expression Signature

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Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data

Blood ◽

10.1182/blood-2007-11-126003 ◽

2008 ◽

Vol 111 (12) ◽

pp. 5654-5662 ◽

Cited By ~ 128

Author(s):

Duane C. Hassane ◽

Monica L. Guzman ◽

Cheryl Corbett ◽

Xiaojie Li ◽

Ramzi Abboud ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

High Throughput ◽

In Silico ◽

High Throughput Screening ◽

Drug Targets ◽

Expression Profiles ◽

Gene Expression Signature ◽

Myelogenous Leukemia ◽

Expression Signature

Abstract Increasing evidence indicates that malignant stem cells are important for the pathogenesis of acute myelogenous leukemia (AML) and represent a reservoir of cells that drive the development of AML and relapse. Therefore, new treatment regimens are necessary to prevent relapse and improve therapeutic outcomes. Previous studies have shown that the sesquiterpene lactone, parthenolide (PTL), ablates bulk, progenitor, and stem AML cells while causing no appreciable toxicity to normal hematopoietic cells. Thus, PTL must evoke cellular responses capable of mediating AML selective cell death. Given recent advances in chemical genomics such as gene expression-based high-throughput screening (GE-HTS) and the Connectivity Map, we hypothesized that the gene expression signature resulting from treatment of primary AML with PTL could be used to search for similar signatures in publicly available gene expression profiles deposited into the Gene Expression Omnibus (GEO). We therefore devised a broad in silico screen of the GEO database using the PTL gene expression signature as a template and discovered 2 new agents, celastrol and 4-hydroxy-2-nonenal, that effectively eradicate AML at the bulk, progenitor, and stem cell level. These findings suggest the use of multicenter collections of high-throughput data to facilitate discovery of leukemia drugs and drug targets.

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