Loss of KDM6A/UTX Function Is a Common Event in a Mouse Model of Multiple Myeloma, Human Cell Lines and Patients.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 606-606
Author(s):  
Jonathan J Keats ◽  
Marta Chesi ◽  
Esteban Braggio ◽  
Stephan Palmer ◽  
Angela Baker ◽  
...  

Abstract Abstract 606 Multiple myeloma is a complex malignancy with multiple underlying genetic events. Our group has spent considerable effort over the last 15 years to elucidate the genetic underpinnings of myeloma. Most recently, we used array-based comparative genomic hybridization (aCGH) as a discovery tool in 62 myeloma patients and 46 myeloma cell lines. In that preliminary screen using the Agilent 44B aCGH platform (∼70kb resolution) we identified a diverse array of abnormalities, which resulted in constitutive activation of the NF-kB pathways. That initial analysis concentrated on the 43 genes we identified as potential targets of the 13 homozygous deletion events detected in the patient samples. A pathway analysis of these genes revealed a single pathway involving TRAF3, TRAF2, BIRC2, BIRC3, and CYLD. This first analysis focused exclusively on abnormalities present in the patient samples as we worried some abnormalities identified exclusively in the cell lines might not be relevant to the pathogenesis of myeloma in patients. However, several abnormalities were equally or more frequent overall but occurred exclusively in cell lines including CDKN2C (14 samples), CDKN1B (4 samples), KDM6A/UTX (4 samples), RB1 (3 samples), TP53 (3 samples). Given the fact that KDM6A/UTX deletions were as frequent as many of the best-described tumor suppressors it seemed like a good candidate but in the absence of patient events or a known function at the time it was not prioritized for further study. Recently, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, we have completed the analysis of a cohort of 250 myeloma patient samples by aCGH using the Agilent 244A aCGH platform (∼15kb resolution) and gene expression profiling using the Affymetrix U133Plus2.0 genechip. In this cohort with a significantly improved aCGH platform we identified 17 genes that are recurrently inactivated by homozygous deletions including DIAPH2 (15 samples), CDKN2C (14 samples), TRAF3 (11 samples), CYLD (8 samples), BIRC2/3 (7 samples), KDM6A/UTX (6 samples), and RB1 (5 samples). Based on the significant improvement in resolution and data quality achieved with the Agilent 244A aCGH platform we rescreen all of the cell lines on this improved platform. This significantly changed the frequency of several homozygous deletions in this population with the most frequently targeted genes now being CDKN2C (20 samples), KDM6A/UTX (13 samples), DIAPH2 (7 samples), RB1 (4 samples), TP53 (4 samples), CDKN1B (4 samples), and TRAF3 (4 samples). Moreover, as part of the genomic characterization of a spontaneous myeloma mouse model that we have developed, Vk*-Myc, we have identified recurrent (∼50%) homozygous deletions of Kdm6a/Utx. Therefore, one of the genes most commonly targeted by a homozygous deletion in human and mouse myeloma is KDM6A/UTX. In late 2007 after we had identified the first patients with KDM6A/UTX deletions it was shown that UTX is a functional histone demethylase that removes methyl groups from histone H3 lysine 27 (H3K27me). Given the high incidence of deletions and the fact that MMSET, the overexpressed target gene of t(4;14) in myeloma, is predicted to methylate H3K27, H3K36, and/or H4K20 by evolutionary conservation we developed the hypothesis that myeloma is characterized by abnormalities that result in excessive H3K27me (typically a repressive chromatin mark). Given the extensive whole genome sequencing occurring in the MMRC genomics initiative we elected to focus our resequencing efforts on the cell lines exclusively. These studies identified an additional 4 samples with LOH and an inactivating mutation bringing the total percentage of inactivated cell lines to 33%. Clearly, in the expanded patient and cell line cohorts the inactivation of KDM6A/UTX is not independent of MMSET overexpression suggesting they may act independently or synergistically. We are currently attempting to identify the genes controlled by KDM6A/UTX inactivation to better understand the functional consequences of this highly recurrent event. However, in the mouse model unlike the patient or cell lines, the gene expression profiling has identified a gene expression signature that differentiates UTX inactivated and functional samples suggesting an oncogenic function of inactivation. Disclosures: No relevant conflicts of interest to declare.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1833-1833
Author(s):  
Sascha Tuchman ◽  
Chaitanya Acharya ◽  
William Mostertz ◽  
William Barry ◽  
Cristina Gasparetto ◽  
...  

Abstract Abstract 1833 Poster Board I-859 Introduction Individualization of therapy for multiple myeloma (MM), based on gene expression profiling, has not yet been achieved. Methods We previously described a “metagene” genomic model that synergizes with standard clinical staging to robustly prognosticate in MM (ASCO 2009 meeting, oral abstract # 8521; submitted for publication). In the current work we queried the Connectivity Map (Lamb J et al., Science 313(5795):1929-35, 2006); http://www.broadinstitute.org/cmap) using the metagene model, to elicit novel therapies that are likely to mitigate the poor-prognosis clinical phenotype we can identify with that model. We then employed gene expression profiling, the metagene model, and probit binary regression analysis to classify 12 untreated MM cell lines as having a similar or dissimilar molecular phenotype to poor-prognosis patients. Lastly, we performed MTT tetrazolium viability assays to survey, in triplicate, the sensitivity of those cell lines to the drug identified as well as melphalan, which we used as a conventional therapeutic control. Results We used Connectivity Map to analyze the metagene model, which we had previously discovered and validated in silico using three patient cohorts with prospectively collected clinical follow-up data (n=624 total patients). In doing so, we elicited 5-azacytidine as a likely agent to reverse the poor-prognosis phenotype. Using the metagene model, we then characterized 8 untreated MM cell lines as having a “good prognosis” molecular phenotype (i.e., a gene expression profile dissimilar to poor-prognosis patients according to the metagene model), and 4 cell lines as having a “poor prognosis” molecular phenotype (i.e., similar to poor-prognosis patients based on the metagene model). “Poor-prognosis” cell lines were susceptible to viability reduction by 5-azacytidine in a dose-independent manner and, importantly, at a physiologically achievable dose in humans (5 μM), whereas ‘good-prognosis‘ cell lines were comparatively less susceptible (p=0.03 by repeated measures 2-way ANOVA). Conversely, melphalan, which we used as a clinically relevant control that was not elicited by our analysis of the metagene model as likely to be effective in poor-prognosis patients, reduced the viability of “good-prognosis” cell lines more than that of “poor-prognosis” cell lines (fig. 1; p=0.02). Conclusions Computational analysis of the extensively validated and clinically relevant metagene prognostic model indicates, and in vitro assays confirm, that 5-azacytidine is more effective in ameliorating a poor-prognosis phenotype than melphalan in a multiple myeloma pre-clinical model. This provides proof-of-concept that gene expression profiling may 1) reveal novel and effective therapeutic approaches for identifiable high-risk subgroups of patients, and 2) enable clinicians to decide prospectively which conventional agents, such as melphalan, are unlikely to be effective in certain patients' myeloma. Clinical trials are needed to study such individualized approaches to the treatment of this molecularly heterogeneous disease. Disclosures No relevant conflicts of interest to declare.


Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 588
Author(s):  
Adam Ustaszewski ◽  
Magdalena Kostrzewska-Poczekaj ◽  
Joanna Janiszewska ◽  
Malgorzata Jarmuz-Szymczak ◽  
Malgorzata Wierzbicka ◽  
...  

Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared the results using multidimensional scaling and hierarchical clustering versus tumor samples and laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the analyzed cohorts separated according to two dominant factors: “malignancy”, which separated controls from malignant samples and “cell culture-microenvironment” which reflected the differences between cultured and non-cultured samples. In conclusion, we advocate the use of cultured epithelial cells as controls for gene expression profiling of cancer cell lines. In contrast, comparisons of gene expression profiles of cancer cell lines versus surgical margin controls should be treated with caution, whereas fresh frozen surgical margins seem to be appropriate for gene expression profiling of tumor samples.


Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.


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