Kooperativer Bibliotheksverbund

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 371-371

Abstract: Introduction Multiple myeloma (MM) is characterised by the malignant expansion of clonal plasma cells in the bone marrow (BM). We and others have used massive parallel sequencing to describe the somatic aberrations acquired in different subclones in newly diagnosed MM (NDMM). These studies have showed that chemotherapy has an impact on intra-clonal heterogeneity, but more analyses are required in paired presentation/relapse samples and samples from multiple sites at the same and different time points. Materials and methods We have studied 49 paired presentation/relapse patients from a series of 463 NDMM patients entered into the Myeloma XI trial (NCT01554852). To understand the impact of spatial separation within the MM clone and the consideration that MM is a metastatic disease, we examined BM aspirates and compared them to targeted biopsies from extramedullary disease sites in 9 MM patients. These cases were 1 patient with samples bilaterally collected from the hip during the course of the disease, 4 MM cases with plasma cell leukemia (PCL), 3 MM cases with plasmacytomas, 1 MM patient with ascites, and 1 MM case with pleural effusion. DNA from both BM and peripheral blood samples were used for whole exome sequencing plus a pull down of the MYC, IGH, IGL and IGK loci following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants, indels, translocations, and copy number aberrations. Mean sequencing depth was 59.3x. The proportion of mutant tumor cells carrying a mutation was inferred. The presence and proportion of subclones will be defined using bioinformatics tools. Results For the 463 NDMM samples, the following 15 significantly mutated genes are seen KRAS (n=103 mutations), NRAS (n=88), LTB (n=53), DIS3 (n=49), BRAF (n=37), EGR1 (n=22), FAM46C (n=20), IRF4 (n=19), TRAF3 (n=17), HIST1H1E (n=16), TP53 and FGFR3 (n=14), CYLD (n=13), MAX (n=12), and RB1 (n=5). These mutations were seen within all clonal cells and at subclonal levels, consistent with the mutations being acquired at different time points and being associated with different subclonal fitness. We show that NDMM have a mean number of exonic mutations of 61.1±13.0, in contrast to samples taken at the time of relapse, which show an average of 80.6±25.4, Figure 1A. We report diverse patterns of subclonal evolution: no change, subclonal tiding, and subclonal tiding with new subclones arising. We are currently examining samples taken during clinical remission to track subclones at the time of response. For patient with multiple samples taken at different timepoints, 77 mutations were shared across all samples but, of note, specific mutations were seen at the same timepoint in different sites (13/1662 R2R vs 13/1662 R2L), which illustrates the impact of sampling differences in reporting mutation calls and differential response to therapy, Figure 1B. This is also observed in a plasmacytoma case with both a BM aspirate sample containing 11 mutations (including NRAS c.183A 〉 T and BRAF c.1783T 〉 C), and a femur plasmacytoma with 18 mutations, of which only 2 are shared with the BM sample, Figure 3. One of these shared lesions is BRAF c.1783T 〉 C, the cancer clonal fraction of which increases ten-fold, suggesting that the sub-clone with this mutation disseminated from the BM and founded the plasmacytoma. Conclusion Our preliminary data demonstrate that MM subclones not only respond differently to clinical treatment, but also have different biological properties leading to cause extramedullary disease. To our knowledge, this is the first comprehensive genetic analysis of the spatio-temporal heterogeneity in myeloma and reveals genetic differences due to sampling bias. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉 0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉 0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Disclosures Jones: Celgene: Other: Travel support, Research Funding. Peterson:University of Arkansas for Medical Sciences: Employment. Brioli:Celgene: Honoraria; Janssen: Honoraria. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Membership on an entity's Board of Directors or advisory committees; Array-Biopharma: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.371.371

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 372-372

Abstract: Introduction: Next generation sequencing of over 800 newly diagnosed multiple myeloma (NDMM) cases has established the mutational landscape and key cancer driver pathways. The mutational basis of relapse has not been systematically studied. Two previous studies (Keats et al.; Bolli et al.) identified 4 patterns of clonal evolution. Neither study included uniformly treated patients and looked at the impact of therapy on clonal structure at relapse. Understanding the mutational patterns underlying relapse and how they relate to specific therapies is crucial in order to improve MM outcomes, especially for high-risk (HR) MM. In this study we compare the clonal structure at presentation (PRES) and at relapse (REL), after exposure to Total Therapy (TT). Materials and Methods: We studied 33 pairs of tumor samples collected at PRES and REL. 9 patients were treated on TT2, 13 on TT3, 10 on TT4 and 1 on TT5-like regimen. Eleven patients had HR disease at PRES. DNA was extracted from CD138+ selected cells from random bone marrow aspirates. Germline controls were obtained from leukapheresis products. Whole exome sequencing libraries were prepared using the Agilent qXT kit and the Agilent SureSelect Clinical Research Exome kit with additional baits covering the Ig and MYC loci. All samples were sequenced on an Illumina HiSeq2500 to a median depth of 120x. Sequencing data were aligned to the Ensembl GRCh37/hg19 human reference using BWA. Somatic variants were called using MuTect. Translocations were identified using MANTA. Copy number variations were inferred using TITAN. Gene expression profiles (GEP), generated using the Affymetrix U133plus2 microarray, were available for all tumor samples. Nonnegative matrix factorization (NMF) was used to define mutation signatures. Results: The median time to progression was 30 months with a median follow up of 9.5 years. 22 cases achieved a complete remission (CR) or near CR. There were 11 cases of HR at PRES. Of the 22 cases with low risk (LR) MM, 7 relapsed with HR disease. There were on average 478 SNVs per sample at PRES and 422 at REL. All but 2 cases had evidence of new mutations at REL. There were no consistent patterns or number of mutation associated with REL or GEP-defined risk. Patients of the MF molecular subgroup had more mutations compared to other molecular subgroups (657 vs. 379) and were enriched for mutations with an APOBEC signature. We did not detect any mutation signature consistent with chemotherapy-induced alterations, providing evidence that TT itself does not cause additional mutations. Primary recurrent IgH translocations called by MANTA were confirmed by GEP data. A number of new translocations were identified , several only at REL. In particular we demonstrate a case with a newly acquired MYC translocation at relapse, indicating that it contributed to progression. We identified 5 patterns of clonal evolution (Figure 1): A) genetically distinct relapse in 3 patients, B) linear evolution in 8 patients, C) clonal selection in 9 patients, D) branching evolution in 11 patients, and E) stable clone(s) in 2 patients. Patterns A (distinct) and B (linear) were associated with low risk and longer survival, whereas patterns D (branching) and E (stable) were associated with high risk and shorter time to relapse and overall survival (Table 1). Conclusion: This is the first study to systematically analyze the pattern of clonal evolution using NGS in patients treated with combination chemotherapy and tandem ASCT. We identified 5 patterns of evolution, which correlate with survival. We identified 3 cases with a loss of the original clone and emergence of a new clone, suggesting high effectiveness of Total Therapy for those patients. The persistence of major clones despite multi agent chemotherapy in most other cases supports a concept of a tumor-initiating cell population that persist in a protective niche, for which new therapies are needed. Table 1. Pattern of Evolution GEP70 Pres.(high risk: ≥0.66) Proliferation Index Pres. GEP70 Rel.(high risk: ≥0.66) Proliferation Index Rel Mean OS Mean TTR A: distinct (n=3) -0.690 -3.34 -0.015 2.04 8.18 5.00 B: linear (n=8) -0.171 -0.34 0.618 9.22 5.70 4.05 C: selection (n=9) 0.366 3.20 0.569 6.97 3.95 2.64 D: branching (n=11) 0.710 5.17 1.173 11.15 3.84 2.21 E: stable (n=2) 1.532 7.42 1.124 2.54 0.96 0.35 Pres.: Presentation; Rel.: Relapse; OS: Overall Survival; TTR: Time to Relapse Figure 1. Patterns of Relapse Figure 1. Patterns of Relapse Disclosures Heuck: Foundation Medicine: Honoraria; Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment. Weinhold:Janssen Cilag: Other: Advisory Board; University of Arkansas for Medical Sciences: Employment. Peterson:University of Arkansas for Medical Sciences: Employment. Bauer:University of Arkansas for Medical Sciences: Employment. Stein:University of Arkansas for Medical Sciences: Employment. Ashby:University of Arkansas for Medical Sciences: Employment. Chavan:University of Arkansas for Medical Sciences: Employment. Stephens:University of Arkansas for Medical Sciences: Employment. Johann:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Waheed:University of Arkansas for Medical Sciences: Employment. Johnson:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Millennium: Research Funding; Onyx: Research Funding; Novartis: Research Funding. Matin:University of Arkansas for Medical Sciences: Employment. Petty:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Millenium: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Celgene: Consultancy. Epstein:University of Arkansas for Medical Sciences: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Weismann Institute: Honoraria; MMRF: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.372.372

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 369-369

Abstract: Introduction: Molecular assessment using conventional karyotyping, interphase FISH and gene expression profiling (GEP) has revealed multiple subgroups of myeloma with distinct pathogenesis and clinical course. While these technologies have tremendously impacted risk assessment they have had little contribution to the identification of therapeutic targets. Next generation sequencing (NGS) technology can identify mutations in genes of key cancer pathways, which impact outcome and are targetable by new drugs. Targeted gene panels can analyze clinical samples in sufficient depth affording the opportunity to incorporate NGS into clinical decision making in a meaningful way. Using the FoundationOne Heme test (F1H), we aimed to determine the mutational spectra of cancer-associated genes in multiple myeloma (MM), their association with disease risk and their effect on clinical outcome. Methods: DNA and RNA were extracted from CD138-selected MM cells. Comprehensive genomic profiling (CGP) using F1H was performed by Foundation Medicine, Inc (Cambridge, MA). Sequencing to an average depth of 470x (range: 5-3781) was performed on a HiSeq2500 sequencer. Sequences were analyzed for base substitutions, insertions, deletions, copy number alterations, and rearrangements in frequently altered genes. Annotated germline variants (dbSNP135) were removed. Somatic alterations in COSMIC (v62) and inactivating variants in tumor suppressor genes were called as biologically significant. GEP of CD138-selected MM cells using Affymetrix U133 2.0 plus arrays was performed as described. Overall survival analysis was done using log-rank tests. Results: CGP was performed on a total of 630 patients (3.4% MGUS, 6.5% SMM, 24.9% newly diagnosed MM, 24.9% relapsed MM, 18.8% MM in remission). We found increasing mutation load in from MGUS to relapsed MM. Later stages of the disease had an increased frequency of mutations in genes coding for epigenetic modulators and proteins involved in DNA repair. Alterations of TP53 and RB1 among others weresignificantly more frequent in GEP-defined high-risk (HR) disease and after relapse. Patients of the GEP-defined MF molecular subgroup carried a significantly greater mutation load. While there was no difference in the frequency of altered RAS/MAPK pathway genes between newly diagnosed and relapsed patients, we found an increased average mutant allele frequency in relapsed patients, indicating clonal selection. Using paired GEP data we identified gene expression signatures for patients with RAS/MAPK activation and patients with loss-of-function mutations in the DNA repair pathway, suggesting a functional relevance of these mutations. Mutations in either of these pathways were associated with significantly worse overall survival (OS) (Figure 1). Presence of DNA repair gene mutations resulted in significantly worse OS within the GEP-defined low-risk subgroup. Among the 630 patients who underwent CGP, 396 had clinically relevant alterations, which were associated with either an FDA approved drug or a clinical trial. For example, 316 patients had alterations of the RAS/MAPK pathway. Recently we have shown clinical benefit of MEK directed therapy in this patient population. 39 patients had alterations in the mTOR pathway, suggesting benefit from mTOR inhibitors. 426 patients with MM had mutations in epigenetic modulators. For 37 of them therapy with demethylating agents was recommended. Many more epigenetic targeted drugs, such as EZH2 or Bromodomain inhibitors are currently in development. Conclusion: Using the F1H test we demonstrate a negative impact of somatic mutations of the MAPK and DNA repair pathways on outcome. In tandem, for 396 patients we identified genomic alterations, which suggest benefit from targeted treatment. Thus targeted therapy, guided by comprehensive genomic profiling, may be applied to the majority of MM patients, with the potential of significantly improving clinical outcomes. Comprehensive genomic profiling should therefore be considered in the routine work-up, especially for HR patients where outcomes remain poor. Figure 1. Inferior outcome of patients with mutations in the MAPK or DNA repair pathway. Panels A) and C) mutation of MAPK pathway; Panels B) and D) mutation of the DNA repair pathway. Overall survival is measured from time of disease diagnosis in panels A) and B) and is shown from sample date in panels C) and D) Figure 1. Inferior outcome of patients with mutations in the MAPK or DNA repair pathway. Panels A) and C) mutation of MAPK pathway; Panels B) and D) mutation of the DNA repair pathway. Overall survival is measured from time of disease diagnosis in panels A) and B) and is shown from sample date in panels C) and D) Disclosures Heuck: Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Foundation Medicine: Honoraria. Chavan:University of Arkansas for Medical Sciences: Employment. Stein:University of Arkansas for Medical Sciences: Employment. Tytarenko:University of Arkansas for Medical Sciences: Employment. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Ali:Foundation Medicine, Inc.: Employment, Equity Ownership. Miller:Foundation Medicine, Inc.: Employment, Equity Ownership. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Schinke:University of Arkansas for Medical Sciences: Employment. Mohan:University of Arkansas for Medical Sciences: Employment. Sawyer:University of Arkansas for Medical Sciences: Employment. Peterson:University of Arkansas for Medical Sciences: Employment. Bauer:University of Arkansas for Medical Sciences: Employment. Ashby:University of Arkansas for Medical Sciences: Employment. Johann:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Waheed:University of Arkansas for Medical Sciences: Employment. Davies:Millenium: Consultancy; Onyx: Consultancy; Janssen: Consultancy; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; Weismann Institute: Honoraria; MMRF: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.369.369

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 20-20

Abstract: Introduction: Recent next generation sequencing studies have defined the mutation spectrum in multiple myeloma (MM) and uncovered significant intra-clonal heterogeneity, showing that clinically relevant mutations are often only present in sub-clones. Longitudinal analyses demonstrated that tumor clones under therapeutic pressure behave in a "Darwinian" fashion, with shifting dominance of tumor clones over time. Recently, stratification of clonal substructures in distinct areas of the tumor bulk has been shown for multiple cancer types. So far, spatial genomic heterogeneity has not been systematically analyzed in MM. This stratification in space is becoming increasingly important as we begin to understand the contribution of Focal Lesions (FL) to tumor progression and emergence of drug resistance in MM. We have recently shown that high numbers of FL are associated with gene expression profiling (GEP) defined high risk (HR). A comparison of GEP data of 170 paired random bone marrow (RBM) and FL aspirates showed differences in risk signatures, supporting the concept of spatial clonal heterogeneity. In this study we have extended the analysis by performing whole exome sequencing (WES) and genotyping on paired RBM and FL in order to gain further insight into spatial clonal heterogeneity in MM and to find site-specific single nucleotide variant (SNV) spectra and copy number alterations (CNA), which contribute to disease progression and could form the basis of adaptation of the tumor to therapeutic pressure. Materials and Methods: We included 50 Total Therapy MM patients for whom paired CD138-enriched RBMA and FL samples were available. Leukapheresis products were used as controls. For WES we applied the Agilent qXT kit and a modified Agilent SureSelect Clinical Research Exome bait design additionally covering the immunoglobulin heavy chain locus and sequences located within 1Mb of the MYC locus. Paired-End sequencing to a minimum average coverage of 120x was performed on an Illumina HiSeq 2500. Sequencing data were aligned to the Ensembl GRCh37/hg19 human reference using BWA. Somatic variants were identified using MuTect. For detection of CNA we analyzed Illumina HumanOmni 2.5 bead chip data with GenomeStudio. Subclonal reconstruction was performed using PhyloWGS. Mutational signatures were investigated using SomaticSignatures. The GEP70 risk signature was calculated as described previously. Informed consent in accordance with the Declaration of Helsinki was obtained for all cases included in this study. Results: Analyzing RBM and FL WES data, we detected between 100 and 200 somatic SNVs in covered regions, with approximately 30% of them being non-synonymous, and less than 5% stop gained or splice site variants. A comparison of paired RBM and FL WES data showed different extents of spatial heterogeneity. Some pairs had very similar mutation profiles with up to 90% shared variants, whereas others demonstrated marked heterogeneity of point mutations. We did not detect differences in mutational signatures between RBM and FL using the 'SomaticSignatures' package. We found site-specific driver mutations with high variant allele frequencies, indicating replacement of other clones in these areas. For example we observed a clonal KRAS mutation exclusively in the RBM, whereas a NRAS variant was only identified in the paired FL. The same holds true for large-scale CNAs ( 〉 1 Mb). We identified a case in which the high risk CNAs gain(1q) and del(17p) were only detectable in the FL. Further examples for site-specific CNAs were a del(10q21) and a gain(4q13) detected in FLs only. As a prominent pattern, we observed outgrowth of sub-clonal RBM CNAs as clonal events in the FL. Based on mutation and CNA data we identified different forms of spatial evolution, including parallel, linear and branching patterns. Of note, a stratified analysis by GEP70-defined risk showed that a more pronounced spatial genomic heterogeneity of SNVs and CNAs was associated with HR disease. Conclusion: We show that spatial heterogeneity in clonal substructure exists in MM and that it is more pronounced in HR. The existence of site-specific HR CNAs and driver mutations highlights the importance of heterogeneity analyses for targeted treatment strategies, thereby facilitating optimal personalized MM medicine. Disclosures Weinhold: University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Heuck:Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Foundation Medicine: Honoraria. Stephens:University of Arkansas for Medical Sciences: Employment. Tytarenko:University of Arkansas for Medical Sciences: Employment. Bauer:University of Arkansas for Medical Sciences: Employment. Peterson:University of Arkansas for Medical Sciences: Employment. Ashby:University of Arkansas for Medical Sciences: Employment. Stein:University of Arkansas for Medical Sciences: Employment. Johann:University of Arkansas for Medical Sciences: Employment. Johnson:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Zangari:Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding; University of Arkansas for Medical Sciences: Employment. Schinke:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Davies:Millenium: Consultancy; Onyx: Consultancy; Celgene: Consultancy; University of Arkansas for Medical Sciences: Employment; Janssen: Consultancy. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Weismann Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.20.20

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 4416-4416

Abstract: Introduction Gene expression and comprehensive genomic profiling (CGP) underscore the importance of multiple myeloma (MM) being driven by diverse genomic abnormalities and are increasingly being integrated into personalized treatment algorithms to optimize clinical outcomes, in particular that of high risk disease. Furthermore, CGP allow for ultra-deep sequencing of various clinically relevant and targetable genomic alterations using a single assay, with an advantage of detection of low frequency variants. Methods Samples from 578 patients (monoclonal gammopathy of undetermined significance, MGUS, (n=19); smoldering multiple myeloma, SMM, (n=42); or multiple myeloma, MM, (n=517; 87 newly diagnosed (NDMM), 107after treatment (TRMM), and 323 at relapse (RLMM)) were analyzed using the FoundationOne® Heme (F1H) assay. 50 ng of DNA and RNA from CD138+ selected cells were analyzed for genomic alterations including base substitutions, indels, copy number alterations, and rearrangements. Sequencing was performed to a median depth of 468x in 405 genes, as well as selected introns of 31 genes involved in rearrangements. Additionally, matched Gene Expression Profiling (GEP) was performed using Affymetrix U133 Plus 2 array, and GEP70-defined risk status and molecular subgroups were calculated. Results Results of the F1H assay revealed the most common alterations in MM to be: KRAS (28.8%), NRAS (23.2%), TP53 (17.4%), BRAF (6.8%), CDKN2C (6.0%), RB1 (5.8%), TRAF3 (5.8%), DNMT3A (3.9%), TET2 (3.7%) and ATM (2.5%), including mutations, homozygous loss and rearrangements. When these frequencies were split across GEP70 risk groups, TP53, CDKN2C/FAF1, RB1, and the t(4;14) were significantly different (p 〈 0.05). As the disease progressed from MGUS to relapse, the number of mutations showed an increasing trend. Likewise, there were significant differences in the number of mutations between CCND1/CCND3 (CD-1) and low bone disease, CD-1 and hyperdiploid, and hyperdiploid and proliferation groups. In order to identify independent prognostic genomic alterations, we performed a multivariate Cox regression analysis on all the gene alterations that were present in at least 5% of the patient cohort, resulting in identification of four significant alterations: the t(4;14), mutation/loss of TP53, CDKN2C/FAF1 or RB1. Alterations in CDKN2C and RB1 were associated with the PR group. When the MM samples were split according to type (NDMM, TRMM, RLMM) the effect on survival of each of these alteration was more pronounced at relapse, but still present at diagnosis for CDKN2C and t(4;14). Bi-allelic events in CDKN2C, TP53 and RB1 were examined, by both homozygous deletion and monosomy with accompanying mutation, showing the rate of inactivation increased from 9.2% in NDMM to 17.9% at relapse, indicating that bi-allelic inactivation of these genes are correlated with relapse. CDKN2C and TP53 are known prognostic markers but the prognostic significance of RB1 has been debated. Previous data have shown that the association of t(4;14) with del(13q) results in insignificance of del(13q) as a prognostic marker in multivariate analyses. Here, we confirmed that the prognostic effect of RB1 is not due to association with t(4;14), and show that patients with either the t(4;14) or alteration of RB1 have a poor prognosis, which is worse when both lesions are present. Conclusions Using the F1H assay, we establish the mutational spectrum in MM, identifying lesions associated with high risk. This is the first study in MM to identify and confirm the poor prognostic effect of RB1 driven by bi-allelic inactivation, which is more prevalent at relapse. Furthermore, we determined the gene alterations that are independent prognostic markers in relapsed MM, thereby identifying novel therapeutic targets. Disclosures He: Foundation Medicine, Inc: Employment, Equity Ownership. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Ashby:University of Arkansas for Medical Sciences: Employment. Zhong:foundation medicine: Employment. Nahas:Foundation medicine: Employment. Ali:Foundation Medicine: Employment, Equity Ownership. Vergillo:Foundation Medicine, Inc: Employment. Ross:Foundation Medicine, Inc: Employment. Miller:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine: Employment, Equity Ownership. Barlogie:Signal Genetics: Patents & Royalties. Mughal:Foundation Medicine: Employment, Equity Ownership. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding; Univ of AR for Medical Sciences: Employment.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.4416.4416

Language: English

Publisher: American Society of Hematology

Publication Date: 2016

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Nature Genetics, Springer Science and Business Media LLC, Vol. 54, No. 4 ( 2022-04), p. 412-436

Abstract: Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.

Type of Medium: Online Resource

ISSN: 1061-4036 , 1546-1718

URL: Article

DOI: 10.1038/s41588-022-01024-z

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2022

detail.hit.zdb_id: 1494946-5

SSG: 12

In: Circulation, Ovid Technologies (Wolters Kluwer Health), Vol. 144, No. 23 ( 2021-12-07), p. 1845-1855

Abstract: Despite advances in surgery and pharmacotherapy, there remains significant residual ischemic risk after coronary artery bypass grafting surgery. Methods: In REDUCE-IT (Reduction of Cardiovascular Events With Icosapent Ethyl–Intervention Trial), a multicenter, placebo-controlled, double-blind trial, statin-treated patients with controlled low-density lipoprotein cholesterol and mild to moderate hypertriglyceridemia were randomized to 4 g daily of icosapent ethyl or placebo. They experienced a 25% reduction in risk of a primary efficacy end point (composite of cardiovascular death, myocardial infarction, stroke, coronary revascularization, or hospitalization for unstable angina) and a 26% reduction in risk of a key secondary efficacy end point (composite of cardiovascular death, myocardial infarction, or stroke) when compared with placebo. The current analysis reports on the subgroup of patients from the trial with a history of coronary artery bypass grafting. Results: Of the 8179 patients randomized in REDUCE-IT, a total of 1837 (22.5%) had a history of coronary artery bypass grafting, with 897 patients randomized to icosapent ethyl and 940 to placebo. Baseline characteristics were similar between treatment groups. Randomization to icosapent ethyl was associated with a significant reduction in the primary end point (hazard ratio [HR], 0.76 [95% CI, 0.63–0.92] ; P =0.004), in the key secondary end point (HR, 0.69 [95% CI, 0.56–0.87]; P =0.001), and in total (first plus subsequent or recurrent) ischemic events (rate ratio, 0.64 [95% CI, 0.50–0.81]; P =0.0002) compared with placebo. This yielded an absolute risk reduction of 6.2% (95% CI, 2.3%–10.2%) in first events, with a number needed to treat of 16 (95% CI, 10–44) during a median follow-up time of 4.8 years. Safety findings were similar to the overall study: beyond an increased rate of atrial fibrillation/flutter requiring hospitalization for at least 24 hours (5.0% vs 3.1%; P =0.03) and a nonsignificant increase in bleeding, occurrences of adverse events were comparable between groups. Conclusions: In REDUCE-IT patients with a history of coronary artery bypass grafting, treatment with icosapent ethyl was associated with significant reductions in first and recurrent ischemic events. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01492361.

Type of Medium: Online Resource

ISSN: 0009-7322 , 1524-4539

URL: Article

DOI: 10.1161/CIRCULATIONAHA.121.056290

Language: English

Publisher: Ovid Technologies (Wolters Kluwer Health)

Publication Date: 2021

detail.hit.zdb_id: 1466401-X

In: Nature Communications, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2021-02-24)

Abstract: Primary open-angle glaucoma (POAG), is a heritable common cause of blindness world-wide. To identify risk loci, we conduct a large multi-ethnic meta-analysis of genome-wide association studies on a total of 34,179 cases and 349,321 controls, identifying 44 previously unreported risk loci and confirming 83 loci that were previously known. The majority of loci have broadly consistent effects across European, Asian and African ancestries. Cross-ancestry data improve fine-mapping of causal variants for several loci. Integration of multiple lines of genetic evidence support the functional relevance of the identified POAG risk loci and highlight potential contributions of several genes to POAG pathogenesis, including SVEP1, RERE, VCAM1, ZNF638 , CLIC5, SLC2A12, YAP1, MXRA5 , and SMAD6 . Several drug compounds targeting POAG risk genes may be potential glaucoma therapeutic candidates.

Type of Medium: Online Resource

ISSN: 2041-1723

URL: Article

DOI: 10.1038/s41467-020-20851-4

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2021

detail.hit.zdb_id: 2553671-0

In: American Potato Journal, Springer Science and Business Media LLC, Vol. 74, No. 6 ( 1997-11), p. 415-477

Type of Medium: Online Resource

ISSN: 0003-0589 , 1874-9380

URL: Article

DOI: 10.1007/BF02852782

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 1997

detail.hit.zdb_id: 2395546-6