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  • 1
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 2189-2189
    Abstract: Introduction Multiple myeloma is a clinically highly heterogeneous disease, which is reflected by both a complex genome and epigenome. Dynamic epigenetic changes are involved at several stages of myeloma biology, such as transformation and disease progression. Our previous genome wide epigenetic analyses identified prognostically relevant DNA hypermethylation at specific tumor suppressor genes (Kaiser MF et al., Blood 2013), indicating that specific epigenetic programming influences clinical behavior. This clinically relevant finding prompted further investigation of the epigenomic structure of myeloma and its interaction with genetic aberrations. Material and Methods Genome wide DNA methylation of CD138-purified myeloma cells from 464 patients enrolled in the NCRI Myeloma XI trial at presentation were analyzed using the high resolution 450k DNA methylation array platform (Illumina). In addition, 4 plasma cell leukemia (PCL) cases (two t(11;14) and two (4;14)) and 7 myeloma cell lines (HMCL) carrying different translocations were analysed. Analyses were performed in R Bioconductor packages after filtering and removal of low quality and non-uniquely mapping probes. Results Variation in genome wide DNA methylation was analyzed using unsupervised hierarchical clustering of the 10,000 most variable probes, which revealed epigenetically defined subgroups of disease. Presence of recurrent IGH translocations was strongly associated with specific epigenetic profiles. All 60 cases with t(4;14) clustered into two highly similar sub-clusters, confirming that overexpression of the H3K36 methyltransferase MMSET in t(4;14) has a defined and specific effect on the myeloma epigenome. Interestingly, HMCLs KMS-11 and LP-1, which carry t(4;14), MM1.S, a t(14;16) cell line with an E1099K MMSET activating mutation as well as two PCLs with t(4;14) all clustered in one sub-clade. The majority (59/85) of t(11;14) cases showed global DNA hypomethylation compared to t(4;14) cases and clustered in one subclade, indicating a epigenetic programming effect associated with CCND1, with a subgroup of t(11;14) cases showing a variable DNA methylation pattern. In addition to translocation-defined subgroups, a small cluster of samples with a distinct epigenetic profile was identified. In total 7 cases with a shared specific DNA methylation pattern (median inter-sample correlation 0.4) were identified. The group was characterized by DNA hypermethylation (4,341 hypermethylated regions vs. 750 hypomethylated regions) in comparison to all other cases. Intersection of regions hypermethylated in this subgroups with ENCODE datasets revealed mapping to poised enhancers and promoters in H1-hESC, indicating functionally relevant epigenetic changes. Gene set enrichment analysis (KEGG) demonstrated enrichment of developmental pathway genes, e.g. Hedgehog signaling (adj p=5x10exp-13), amongst others and all four HOX clusters were differentially methylated in this group. Of note, three of seven cases in this subgroup carried a t(11;14) and all t(11;14) or t(11;14)-like HMCLs clustered closely together with these patient cases, but not with the cluster carrying the majority of t(11;14) myeloma or t(11;14) PCLs. This potentially indicates that t(11;14) HMCL could be derived from a subgroup of patients with specific epigenetic characteristics. Conclusion Our results indicate that the recurrent IGH translocations are fundamentally involved in shaping the myeloma epigenome through either direct upregulation of epigenetic modifiers (e.g. MMSET) or through insufficiently understood mechanisms. However, developmental epigenetic processes seem to independently contribute to the complexity of the epigenome in some cases. This work provides important insights into the spectrum of epigenetic subgroups of myeloma and helps identify subgroups of disease that may benefit from specific epigenetic therapies currently being developed. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 2
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 172-172
    Abstract: Introduction Co-segregation of two or more adverse structural genetic aberrations in myeloma is associated with a particularly bad outcome and defines a molecular high risk subgroup of patients that is in urgent need of innovative treatment approaches (Boyd, Leukemia 2012). Interphase in situ fluorescence hybridization(iFISH) is the current clinical standard for detecting structural genetic aberrations in myeloma. However, iFISH is labor-intensive, slow and dependent on investigator expertise, which makes standardization difficult. There is an urgent need to develop a standardized and easily accessible all-molecular diagnostic test to enable the design of risk-stratified trials and, finally, risk-adapted precision medicine treatments for high risk patients. Material and Methods Bone marrow material from 1596 patients was received by a central laboratory for patients enrolled in the NCRI Myeloma XI trial (NCT01554852) at diagnosis from over 80 centers throughout the UK. Myeloma cells were purified to a purity of 〉 98% (median across samples) using an AutoMACS (Miltenyi Biotech) system and DNA and RNA were extracted using AllPrep columns (QIAGEN). Recurrent translocations were predicted by gene expression using a sensitive and specific TC-classification based multiplex qRT-PCR assay on a standard TaqMan (Life Technologies) real-time cycler (Kaiser et al., Leukemia 2013). Myeloma specific copy number alterations were assayed using the sensitive and specific multiplex ligation-dependent probe amplification assay (MLPA P425; MRC Holland; Alpar et al, Gen Chrom Cancer 2013) on a standard thermocycler and a standard ABI 3730 capillary electrophoresis Genetic Analyzer. Analysis of qRT-PCR and MLPA results was performed on a desktop computers using standard software without need for bioinformatics expertise or infrastructure. Results Translocation status was successfully analyzed for 1201 cases and copy number aberrations were successfully analyzed for 1232 cases. Matched translocation and copy number aberration data was available for 1044 cases. Genetic lesions associated with an adverse prognosis were detected with the following frequencies among the 1044 cases: t(4;14): 13%; t(14;16): 4%; t(14;20): 1%; del(1p32): 9%; gain(1q): 27%; amp(1q): 8%; del(17p): 9%. Non-high risk recurrent IGH translocations as well as copy number aberrations were assayed through both tests as well. Co-segregation analysis of all detected abnormalities using Fisher’s exact test, corrected for multiple testing, revealed co-occurrence more than expected by chance of the following lesions: t(4;14) and gain(1q): q=6.2x10-4; t(4;14) and amp(1q): q=2.1x10-7; del(1p32) and gain(1q): 1.1x10-3. Statistically significant co-occurrence was also observed for del(12p) and del(17p): q=2.1x10-5 as well as del(12p) and t(4;14): q=1.8x10-5. Survival data at the timepoint of analysis was available for 450 patients with a median follow-up of 25 months. Patients were classified as previously described (Boyd et al, Leukemia 2013) into molecular risk groups with standard risk defined by absence of adverse genetic lesions (n=224), intermediate risk with presence of one adverse genetic lesion (n=161) and high risk with presence of two adverse lesions (n=65). On Cox analysis, there was a significant difference in terms of PFS between these groups with a median PFS of 31.3 months (95% CI 28.5-35.2), 25.8 months (CI 22.1-27.6) and 16.2 months (CI 10.6-23.7) for groups with none, one, two or more genetic lesions, respectively. The 2-year OS was also significantly different between the groups with 84% (CI 79-89%) in standard risk, 78% (CI 71-85%) in intermediate risk and 65% (CI 53-78%) in high risk patients. Conclusion This all-molecular diagnostic approach for recurrent structural aberrations in myeloma offers a fast, robust and high throughput alternative to iFISH that can be run in any molecular diagnostic laboratory on standard equipment. The methods described here enable standardized and specific identification of a high risk subgroup of patients without the need for a bioinformatics infrastructure or expertise. The clinical applicability of this method makes it an ideal candidate method for prospective molecular risk-stratified clinical trials. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria. Savola:MRC-Holland: Employment.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 3
    In: Blood, American Society of Hematology, Vol. 125, No. 5 ( 2015-01-29), p. 831-840
    Abstract: Coexistent hyperdiploidy or t(11;14) does not abrogate the poor prognosis associated with adverse cytogenetics in myeloma patients. Single-cell analysis reveals that hyperdiploidy may precede IGH translocation in the clonal history of a proportion of patients with both.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2015
    detail.hit.zdb_id: 1468538-3
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  • 4
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 640-640
    Abstract: Multiple myeloma (MM) is a disease characterized by the abnormal proliferation of plasma cells in the bone marrow. We and others have recently demonstrated the existence of different myeloma subclones phylogenetically related to the founding clone. This intra-clonal heterogeneity is the basis for disease progression, treatment resistance, and relapse. However, the clinical and biological relevance of the presence and diversity of different myeloma subclones has not been fully established. In this study, we used whole exome sequencing (WES) plus a pull down of the MYC, IGH, IGL and IGK loci as a tool to analyze the largest series of presenting cases of myeloma (463 patients) to date, which were entered into the Myeloma XI trial (NCT01554852). DNA from both tumor and peripheral blood samples were used in the exome capture protocol following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants (SNVs), indels, translocations, and copy number aberrations. The proportion of tumor cells containing an SNV was inferred. The presence and proportion of subclones were defined in a subset of 437 patients using a genetic algorithm based-tool (GAUCHO), which also calculated different indices of clonal diversity: number of clones, mean pairwise genetic divergence, Shannon and Inverse Simpson diversity index and Berger-Parker dominance index. Based on these results, we aimed to determine the clinical implications of the number of mutations and the subclonal diversity of MM at presentation in progression free (PFS) and overall survival (OS). We found that MM patients with t(14;16) and t(14;20) had more exonic mutations (not including Ig variants) than the rest of samples (median 87 versus 43, p 〈 0.001). Additionally, we found that MM patients with an APOBEC signature or with mutations in ATM/ATR had significantly more mutations than patients without these genetic lesions with a median number of 137 mutations (range 20-569) and 84.5 (range 33-319) respectively (p 〈 0.001). Subsequently, we identified patients with high number of mutations ( 〉 59 mutations) that had a worse outcome in terms of OS (2-year OS rate of 71% (95% CI, 63-80%) vs. 82% (95% CI, 78-87%), p=0.02), but not progression free survival (median 22.5 (95% CI 18.7-30.2) vs. 27.5 (95% CI, 25.8-30.5) months, p=0.1) We reported recurrent mutated genes in myeloma with mutations being present at both clonal and subclonal levels (IRF4, RB1, DIS3, BRAF, KRAS, and NRAS), whereas other genes were mutated only at clonal (HIST1H1E, LTB, TP53 or EGR1), or subclonal levels (CYLD, TRAF3, MAX). These results give insights about the differences in mutation acquisition times and potential subclonal fitness. We inferred that the median number of clones present in this myeloma series was 5, and determined the prognostic value of the number and diversity of subclones in MM patients. The prognostic impact of having high number of clones was unclear as no significant differences were found. On the contrary, there was a significant difference in terms of outcome when calculating distinct measurements of subclonal diversity. Briefly, MM patients with high values of inverse Simpson diversity index had a significantly poorer PFS (median 13.2 (95% CI, 9.4-∞) vs. 26.9 months (95% CI, 24-30.2) months, p=0.02) and OS (66% (95% CI, 52-82%) vs. 81% (95% CI, 77-85%) alive at 2-years, p=0.01); and, alternatively, MM patients who did not have a dominant subclone accounting for 〉 25% of MM cells (low values of Berger-Parker Dominance index, n=56) had a significantly shorter PFS than those with a dominant clone accounting for more than 25% of cells with a median of 22 (95% CI, 12.3-26.3) vs. 27.5 months (95% CI, 23.9-30.9) respectively, p=0.02. Our results show that mutational load and subclonal diversity are poor prognostic factors in myeloma. Previous studies from massive-parallel sequencing and single cell analyses of myeloma plasma cells already revealed that myeloma had the features of an evolutionary ecosystem, where different tumour subclones coexist and have differential response to treatment. We have demonstrated in this study that measures of tumor diversity have important clinical consequences. To our knowledge, this is the first time that the use of clonal diversity indices as predictive biomarkers of progression is proposed in haematological malignancies, and more specifically, myeloma. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 5
    In: Blood, American Society of Hematology, Vol. 122, No. 21 ( 2013-11-15), p. 3097-3097
    Abstract: Obtaining reliable information about the molecular subtype of myeloma is and will become ever more important in a number of clinical settings, such as alternative treatment strategies for high risk or ultra high risk disease (Boyd KD et al., Leukemia 2011), or patient selection for small molecule inhibitors, that are currently under development, targeting myeloma subtype specific proteins (e.g. MMSET or MAF). We report here our experience with a novel, highly applicable and high throughput diagnostic approach in a large sample set of 1016 myeloma presentation cases, using a combination of qRT-PCR and Multiplex Ligation-dependent Probe Amplification (MLPA) for molecular patient characterization of Ig loci translocations and well-defined copy number abnormalities. Material and Methods Recurrent translocations were assessed for 1016 presentation NCRI Myeloma XI trial cases and 41 matched relapse samples, using a previously published and interphase fluorescence in situ hybridization (iFISH)-validated in house qRT-PCR assay on purified bone marrow plasma cell material. The assay measures expression of translocation partner genes and their downstream effectors (e.g. CCND1, MMSET, FGFR3, MAF, MAFB, CCND2) with subsequent interpretation and categorization of results based on the translocation/cyclin D (TC) classification. This allows prediction of presence of the recurrent translocations with high sensitivity and specificity (Kaiser MF et al., Leukemia 2013) and evaluation of overexpression of potential drug targets independent of translocations (e.g. MAF). For selected cases, the myeloma specific SALSA MLPA assay (MRC-Holland) was performed, containing 46 probes that inform about prognostically relevant copy number alterations, such as del(1p), gain(1q), or del(17p). High correlation between MLPA and FISH results for clinically relevant copy number aberrations has been previously reported (Alpar D et al., Genes Chrom Canc 2013). Results The TC classification based translocation qRT-PCR assay worked reliably even for poor quality input RNA, providing results for 〉 96% of analyzed samples. Predicted translocation frequencies among the 1016 evaluable cases were comparable to previously reported results [t(11;14): 16.6%; t(4;14): 12.6%, of which 21.1% lacked FGFR3 expression; t(14;16): 2.6%; t(14;20): 0.5%; t(6;14): 0.7%]. Relapse samples showed consistent results with matched presentation samples, with one t(4;14) case losing initial high FGFR3 expression at relapse. Correlation with clinical data will be available for presentation at the meeting. Measurement and analysis of the samples was performed by a single lab technician in a short time, demonstrating the high throughput capability of the method. This makes rapid analysis of very large sample collections possible, revealing novel findings. When the assayed group was split by median age, the younger group (22-66 years) contained relatively more t(4;14) [15.7% vs. 9.4%; p=0.003] cases than the older group (67-88 years), consistent with recent reports on iFISH data (Avet-Loiseau H, 2013). We also found a lower frequency of t(11;14) [13.6% vs. 19%;p=0.022] in the younger vs. the older group, which has not been reported. MLPA results were generated for a subset of 30 samples for which iFISH and copy number array data were available. The previously reported high level of correlation with iFISH results (Alpar D et al., Genes Chrom Canc 2013) was confirmed and extended for copy number array data, with 〉 85% sensitivity and 〉 95% specificity for del(1p), gain(1q), del(13p) and del(17p). MLPA assessments will be extended in the coming months to include a large group of Myeloma XI cases, and results and their associations with qRT-PCR results and clinical features will be presented at the meeting. Conclusion Precision medicine approaches in myeloma require fast, robust and practicable molecular diagnostic tools. The current diagnostic standard iFISH doesn’t fulfill any of these criteria. Other approaches such as microarray analyses have never found acceptance outside of highly specialized centers due to practicability issues. With the approach presented here, clinically relevant molecular features can be assessed within 48 hours with standard molecular laboratory equipment. This approach is a suitable candidate for a novel standard for routine clinical molecular analysis of multiple myeloma. Disclosures: Savola: MRC-Holland: Employment.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2013
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  • 6
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 22, No. 23 ( 2016-12-01), p. 5783-5794
    Abstract: Purpose: Epigenetic dysregulation is known to be an important contributor to myeloma pathogenesis but, unlike other B-cell malignancies, the full spectrum of somatic mutations in epigenetic modifiers has not been reported previously. We sought to address this using the results from whole-exome sequencing in the context of a large prospective clinical trial of newly diagnosed patients and targeted sequencing in a cohort of previously treated patients for comparison. Experimental Design: Whole-exome sequencing analysis of 463 presenting myeloma cases entered in the UK NCRI Myeloma XI study and targeted sequencing analysis of 156 previously treated cases from the University of Arkansas for Medical Sciences (Little Rock, AR). We correlated the presence of mutations with clinical outcome from diagnosis and compared the mutations found at diagnosis with later stages of disease. Results: In diagnostic myeloma patient samples, we identify significant mutations in genes encoding the histone 1 linker protein, previously identified in other B-cell malignancies. Our data suggest an adverse prognostic impact from the presence of lesions in genes encoding DNA methylation modifiers and the histone demethylase KDM6A/UTX. The frequency of mutations in epigenetic modifiers appears to increase following treatment most notably in genes encoding histone methyltransferases and DNA methylation modifiers. Conclusions: Numerous mutations identified raise the possibility of targeted treatment strategies for patients either at diagnosis or relapse supporting the use of sequencing-based diagnostics in myeloma to help guide therapy as more epigenetic targeted agents become available. Clin Cancer Res; 22(23); 5783–94. ©2016 AACR.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
    detail.hit.zdb_id: 1225457-5
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  • 7
    In: Blood, American Society of Hematology, Vol. 121, No. 17 ( 2013-04-25), p. 3413-3419
    Abstract: IGH translocations in myeloma can occur through at least 5 mechanisms. t(11;14) and t(14;20) DH-JH rearrangement-mediated translocations occur indicating these appear to occur in a pregerminal center cell.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2013
    detail.hit.zdb_id: 1468538-3
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  • 8
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 723-723
    Abstract: Aberrant chromosomal translocations are seen in ~40% of presenting patients and predominantly involve the IGH locus at 14q32. The five main translocations involving the IGH locus are t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20), which result in over-expression of MMSET/FGFR3, CCND3, CCND1, MAF and MAFB, respectively. In previous clinical trials we have shown that the t(4;14), t(14;16) and t(14;20) are associated with a poor prognosis. In initial sequencing studies of myeloma it has been noted that the spectrum of mutations fall into two groups, one of which is characterised by an APOBEC signature. This signature comprises of C 〉 T, C 〉 G and C 〉 A mutations in a TpC context and comprises only a subset of samples, with the rest having a rather generic mutation signature representing an intrinsic mutational process occurring as a result of the spontaneous deamination of methylated cytosine to thymine. Whole exome sequencing was performed on 463 presentation patients enrolled into the UK Myeloma XI trial. DNA was extracted from germline DNA and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYCloci in order to determine the breakpoints associated with translocations in these genes. Tumor and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumor. Progression-free and overall survival was measured from initial randomization and median follow up for this analysis was 25 months. These combined data allow us to examine the effect of translocations on the mutational spectra in myeloma and determine any associations with progression-free or overall survival. Translocations were detected in 232 (50.1%) patients of which 59 patients (12.7%) had a t(4;14), 86 patients (18.6%) a t(11;14), 17 patients (3.7%) a t(14;16), 5 patients (1%) a t(6;14) and 4 patients (0.9%) a t(14;20). MYC translocations were found in 85 patients (18.4%). Using the tiled regions we were able to detect a mutational signature, kataegis, where regional clustering of mutations can be indicative of somatic genomic rearrangements. We found the hallmarks of kataegis in 15 samples (3.2%), where there was enrichment for TpCpH mutations with an inter-mutational distance 〈 1 kb. Where we detected kataegis surrounding MYC there was also an inter-chromosomal translocation involving either IGK or IGL. Interestingly, the partner chromosomes also showed signs of kataegis e.g. in the t(2;8) kataegis was found at IGK and MYC and in the t(8;22) kataegis was found at MYC and IGL. APOBECs are thought to be involved in the generation of kataegis and as such this co-localisation is indicative of APOBEC involvement in the generation of MYCbreakpoints. We found mutation of translocation partner oncogenes, in particular CCND1 was mutated in 10 patients with the t(11;14). There was an association of mutated CCND1 with a poor prognosis when compared with non-mutated t(11;14) patients (OS median of 20.2 months vs. not reached, p=0.005). Mutations were also seen in FGFR3, MAF and MAFB but only in the samples with the respective translocations. The mutations are likely due to somatic hypermutation mediated by AID, an APOBEC family member. We found that t(14;16) and t(14;20) samples have a significantly higher number of mutations compare to the other translocation groups (p=1.65x10-5). These mutations were enriched for those with an APOBEC signature (T(C 〉 T)A, p=9.1x10-5; T(C 〉 T)T, p=0.0014; T(C 〉 G)A, p=0.001; T(C 〉 G)T, p=0.0064), indicating that the ‘maf’ translocation groups are characterized by APOBEC signature mutations, specifically APOBEC3B. When samples are assigned to either an APOBEC or non-APOBEC group the ‘maf’ translocations account for 66.6% of samples in the APOBEC group but only 1.3% of the non-APOBEC group. Here we show three different mutational signatures mediated by the APOBEC family: translocation partner mutation by AID, APOBEC signature mediated by APOBEC3B, and kataegis mediated by an unknown APOBEC family member. We also show for the first time a clinical impact of APOBEC mutations and their association with a poor prognosis. The poor prognosis of this mutational signature is inextricably linked to a high mutation load and the adverse t(14;16) and t(14;20) translocation subgroups. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.
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    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
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  • 9
    In: Genes, Chromosomes and Cancer, Wiley, Vol. 54, No. 2 ( 2015-02), p. 91-98
    Abstract: Risk stratification in myeloma requires an accurate assessment of the presence of a range of molecular abnormalities including the differing IGH translocations and the recurrent copy number abnormalities that can impact clinical behavior. Currently, interphase fluorescence in situ hybridization is used to detect these abnormalities. High failure rates, slow turnaround, cost, and labor intensiveness make it difficult and expensive to use in routine clinical practice. Multiplex ligation‐dependent probe amplification (MLPA), a molecular approach based on a multiplex polymerase chain reaction method, offers an alternative for the assessment of copy number changes present in the myeloma genome. Here, we provide evidence showing that MLPA is a powerful tool for the efficient detection of copy number abnormalities and when combined with expression assays, MLPA can detect all of the prognostically relevant molecular events which characterize presenting myeloma. This approach opens the way for a molecular diagnostic strategy that is efficient, high throughput, and cost effective. © 2014 The Authors. Genes, Chromosomes & Cancer Published by Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 1045-2257 , 1098-2264
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2015
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  • 10
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 637-637
    Abstract: Background: The main genetic features of myeloma identified so far have been the presence of balanced translocations at the immunoglobulin heavy chain (IGH) region and copy number abnormalities. Novel methodologies such as massively parallel sequencing have begun to describe the pattern of tumour acquired mutations detected at presentation but their biological and clinical relevance has not yet been fully established. Methods: Whole exome sequencing was performed on 463 presentation patients enrolled into the large UK, phase III, open label, Myeloma XI trial. DNA was extracted from germline DNA and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. Progression-free and overall survival was measured from initial randomization and median follow up for this analysis was 25 months. These combined data allow us to examine the effect of translocations on the mutational spectra in myeloma and determine any associations with progression-free or overall survival. Results: We identified 15 significantly mutated genes comprising IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD and FGFR3. By analysing the correlation between mutations and cytogenetic events using a probabilistic approach, we describe the co-segregation of t(11;14) with CCND1 mutations (Corr 0.28,BF=1.5x106 (Bayes Factor)) and t(4;14) with FGFR3 (Corr=0.40, BF=1.12x1014) and PRKD2 mutations (Corr=0.23, BF=3507). The mutational spectrum is dominated by mutations in the RAS (43%) and NF-κB (17%) pathway, however they are prognostically neutral. We describe for the first time in myeloma mutations in genes such as CCND1 and DNA repair pathway alterations (TP53, ATM, ATR and ZFHX4 mutations) that are associated with a negative impact on survival in contrast to those in IRF4 and EGR1 that are associated with a favourable overall-survival. By combining these novel risk factors with the previously described adverse cytogenetic features and ISS we were able to demonstrate in a multivariate analysis the independent prognostic relevance of copy number and structural abnormalities (CNSA) such as del(17p), t(4;14), amp(1q), hyperdiploidy and MYC translocations and mutations in genes such as ATM/ATR, ZFHX4, TP53 and CCND1. We demonstrate that the more adverse features a patient had the worse his outcome was for both PFS (one lesion: HR=1.6, p=0.0012, 2 lesions HR=3.3, p 〈 0.001, 3 lesions HR=15.2, p 〈 0.001) and for OS (one lesion: HR=2.01, p=0.0032, 2 lesions HR=4.79, p 〈 0.001, 3 lesions HR=9.62, p 〈 0.001). When combined with ISS, we identified 3 prognostic groups (Group 1: ISS I/II with no CNSA or mutation, Group 2: ISS III with no CNSA or mutation or ISS I/II/III with one CNSA or mutation, Group 3: Two CNSA or mutation regardless of their ISS) thus identifying three distinct prognostic groups with a high risk population representing 13% of patients that both relapsed [median PFS 10.6 months (95% CI 8.7-17.9) versus 27.7 months (95% CI 25.99-31.1), p 〈 0.001] and died prematurely [median overall survival 23.2 months (95% CI 18.2-35.3.) versus not reached, p 〈 0.001] regardless of their ISS score. Finally, we have also identified a list of potentially actionable mutations for which targeted therapy already exists opening the way into personalized medicine in myeloma. Conclusion: We have refined our understanding of genetic events in myeloma and identified clinically relevant mutations that may be used to better stratify patients at presentation. Identifying high risk populations or patients that may benefit from targeted therapy may open the way into personalized medicine for myeloma. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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