Kooperativer Bibliotheksverbund

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 189-189

Abstract: Background: Maximising response in myeloma (MM) patients with effective induction regimens prior to autologous stem cell transplant (ASCT) improves progression-free and overall survival. Triplet regimens combining an immunomodulatory agent (IMiD) and/or proteasome inhibitor (PI) are standard of care, however a more personalised approach is achieved by sequential triplet combinations based on an individual's response. Alternatively, quadruplet regimens may be more effective and new generation PIs such as carfilzomib, with less off-target activity, provide the opportunity to investigate this whilst minimising the risk of increased toxicity. The UK NCRI Myeloma XI trial is a large, phase III study aiming to answer these questions in transplant eligible (TE) patients comparing the quadruplet carfilzomib, cyclophosphamide, lenalidomide and dexamethasone to the sequential strategy of triplet IMiD combinations (with thalidomide or lenalidomide) followed by additional PI triplet therapy for those with a suboptimal response ( 〈 VGPR) prior to ASCT. Methods: In 2013, the TE pathway was amended to include KCRD: carfilzomib 36mg/m2 IV d1-2,8-9,15-16 (20mg/m2 #1d1-2), cyclophosphamide (cyclo) 500mg PO d1,8, lenalidomide (len) 25mg PO d1-21, dexamethasone (dex) 40mg PO d1-4,8-9,15-16). Patients are randomised to this up-front quadruplet or the sequential strategy of CRD: cyclo 500mg PO d1,8, len 25mg PO d1-21 PO daily, dex 40mg PO d1-4, 12-15 or CTD: cyclo 500mg PO d1,8,15 thalidomide 100-200mg PO daily, dex 40mg PO d1-4,12-15 given to max. response - patients with VGPR/CR proceed straight to ASCT, PR/MR are randomised to sequential CVD: cyclo 500mg d1,8,15, bortezomib 1.3mg/m2 IV/SC d1,4,8,11, dex 20mg PO d1,2,4,5,8,9,11,12 or nothing and SD/PD all receive sequential CVD. All treatments are given to max. response prior to ASCT, after which there is a maintenance randomisation. Patients: 1512 patients entered the TE pathway prior to amendment (756 CRD, 756 CTD). Of these, 201 patients with a suboptimal initial response went on to receive CVD, 142 following randomisation (initial response PR/MR) and 59 with NC/PD. 788 (of target n=1036) patients have been randomised post-amendment to date (394 KCRD, 197 CRD, 197 CTD). Results: TE patients receiving treatment prior to the amendment had response rates ≥VGPR: CRD 58% vs CTD 52%. For patients receiving the sequential triplet CVD due to a suboptimal response this was upgraded to ≥VGPR in 49% of those with initial MR/PR, 27% with NC/PD. This suggests the overall ≥VGPR rate to this treatment approach prior to ASCT would be approx. 75%. This now needs to be compared to the alternative approach of an upfront quadruplet. Comparing patients contemporaneously randomised to initial induction the patients receiving KCRD have completed a median 4 cycles (range 1-7), CRD 5 (range 1-10) and CTD 6 (range 1-9). Dose modifications have been required in 62% of patients receiving KCRD (56% to carfilzomib, 42% to lenalidomide) 44% CRD (40% to lenalidomide) and 65% CTD (59% to thalidomide). Data for study drug related toxicity in patients who have completed at least one cycle of initial induction are shown in table 1. Serious adverse events suspected to be due to trial medications have occurred in 37% on KCRD, 32% CRD and 35% CTD. Updated toxicity and preliminary response analysis on 23/09/15 will be presented at the meeting. This will include a response comparison at the end of initial induction regimen i.e. KCRD vs CRD vs CTD for an anticipated 700 contemporaneous patients who will have completed treatment. Updated response to the sequencing approach (with 250 patients having received sequential CVD) will also be presented and compared. Conclusions: In our study KCRD, an outpatient delivered 4-drug regimen combining second generation IMiD and PI drugs, is well-tolerated in TE NDMM patients, comparable to 3-drug regimens. Data will be presented at the meeting to compare the response rates achieved with the different regimens and treatment approaches. On behalf of the NCRI Haemato-oncology CSG Table 1. Comparative toxicities KCRD n=261 CRD n=143 CTD n=142 % (no. of patients) Peripheral neuropathy Sensory Gr II-IV 1.9 (5) 1.4 (2) 8.5 (12) Motor Gr II-IV 3.1 (8) 1 (1) 5.6 (8) VTE all grades 4.2 (11) 4.9 (7) 5.6 (8) Anaemia Gr III-IV 9.2 (24) 4.2 (6) 5.6 (8) Neutropenia Gr III-IV 14.9 (39) 16.1 (22) 13.3 (19) Thrombocytopenia Gr III-IV 8.4 (22) 1.4 (2) 1.4 (2) Infusion reaction Gr III-IV 0.4 (1) - - Disclosures Pawlyn: Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Off Label Use: Carfilzomib as induction treatment for myeloma Lenalidomide and vorinostat as maintenance treatments for myeloma. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Honoraria; Onyx-Amgen: Honoraria; Takeda-Milenium: Honoraria. Jones:Celgene: Other: Travel support, Research Funding. Kaiser:Janssen: Honoraria; Chugai: Consultancy; Amgen: Consultancy, Honoraria; BristolMyerSquibb: Consultancy; Celgene: Consultancy, Honoraria, Research Funding. Jenner:Takeda: Honoraria; Amgen: Honoraria. Cook:Jazz Pharma: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Chugai: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Russell:Therakos: Other: shares. Owen:Celgene: Honoraria, Research Funding; Janssen: Honoraria. Gregory:Janssen: Honoraria; Celgene: Honoraria. Jackson:Celgene: Honoraria; Amgen: Honoraria; Takeda: Honoraria. Morgan:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; 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; CancerNet: Honoraria; Weisman Institute: Honoraria; MMRF: Honoraria; MMRF: Honoraria; University of Arkansas for Medical Sciences: Employment; Weisman Institute: Honoraria; CancerNet: Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.189.189

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. 2983-2983

Abstract: Introduction Hyperdiploidy (HRD) comprises the largest pathogenetic subgroup of myeloma. However, its clinical and molecular characterisation is incomplete. Here, we investigate HRD using a novel high-throughput molecular analysis method (MyMaP - Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015) in a large cohort of 1,036 patients from the UK NCRI Myeloma XI trial. Materials, Methods and Patients Copy number changes, including gain of chromosomes 5, 9 and 15, as well as translocation status were assayed for 1,036 patients enrolled in the UK NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. HRD was defined by triploidy of at least 2 of analysed chromosomes 5, 9 or 15. Analysis was performed on standard laboratory equipment with MyMaP, a combination of TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). The parallel assessment of multiple loci with copy number alteration (CNA) by MLPA allowed unbiased association studies using a Bayesian approach. Semi-quantitative gene expression data for CCND1 and CCND2 was generated as part of the multiplexed qRT-PCR analysis. Median follow up for the analysis was 24 months. Results Of the 1,036 analysed patients, 475 (46%) were HRD. Of these, 325 (68%) had gain(11q25), 141 (29.7%) gain(1q), 43 (9.1%) del(1p32) and 36 (7.5%) del(17p). Gain(11q25) was significantly associated with HRD (Bayes Factor BF01 〈 0.05) in the entire group of 1,036 cases and occurred in only 17% of non-HRD cases, but frequencies of the other copy number alterations (CNA) were similar to entire group. Although gain(1q) was negatively correlated with gain(11q25) within the HRD group (Corr-0.21, BF=0.0004), the two lesions co-occurred in 73 (15.4%) cases. Analysis of other CNA revealed that del(13q) was significantly less frequent (25%) in HRD cases than in non-HRD (56%) cases (BF 〈 0.0001). Interestingly, del(13q) within HRD was highly associated with gain(1q) (BF 〈 0.0001) and negatively correlated with gain(11q25) (BF 〈 0.0001). Thus, CNA status can help discriminate three distinct molecular subgroups of HRD: gain(11q25), gain(11q25)+gain(1q), gain(1q)[+/-del(13q)]. HRD cases were classified as D1, D2 or D1+D2 according to the TC classification based on qPCR CCND1 and CCND2 expression values and expression was correlated with copy number status. An association of the D1 subtype with gain(11q25) and of D2 with gain(1q) was confirmed. CCND1 expression was significantly (P 〈 0.001) higher in cases with gain(11q) [Mean Relative Quantitative (RQ) value 5,466] than in cases with gain(1q) [Mean RQ value 721] . In contrast, CCND2 expression values were significantly higher in cases with gain(1q) [Mean RQ 8,723] than in cases with gain(11q) [mean RQ 1,087] (P 〈 0.001). Co-occurrence of gain(11q) and gain(1q) was associated with intermediate values with CCND1 mean RQ 5,090 and CCND2 mean RQ 2,776, reminiscent of the D1+D2 subtype. HRD was associated with favourable outcome when compared to non-HRD cases with median PFS 28.8 vs. 21.7 months (P 〈 0.0001) and 24-months OS of 83% vs. 77% (median not reached), respectively. However, cases with t(11;14) had a median PFS of 27.0 months and 24-month OS of 80%, combarable to outcome of the HRD group. Within HRD cases, gain(1q) was associated with shorter PFS (P =0.02) and OS (P =0.009), associating the D2 group with inferior outcome. Presence of del(1p32) was associated with inferior PFS (P =0.01) and OS (P =0.0007) in the HRD subgroup and del(17p) was associated with inferior OS (P =0.04) with a trend for PFS. HRD cases with presence of any of the risk factors gain(1q), del(1p32) or del(17p) in comparison to those without had a median PFS of 25.1 vs 35.1 months (P =0.0001) and 24-month OS of 73.8% vs 89.0% (P 〈 0.0001). Conclusion We describe in a large trial cohort an association between gain(11q25) and the D1 hyperdiploid subtype as well as gain(1q) and the D2 subtype, a finding that has so far only been inferred by gene expression array data in the original TC classification. We also find an association with adverse outcome for the D2/gain(1q) subtype. Our findings demonstrate that the novel molecular approach MyMaP allows precise molecular sub-classification of HRD myeloma. Disclosures Kaiser: BristolMyerSquibb: Consultancy; Chugai: Consultancy; Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Jones:Celgene: Other: Travel support, Research Funding. Savola:MRC Holland: Employment. Owen:Celgene: Honoraria, Research Funding; Janssen: Honoraria. Cook:Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; BMS: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Honoraria; Celgene: Honoraria; University of Arkansas for Medical Sciences: Employment; Takeda-Milenium: Honoraria. Jackson:Amgen: Honoraria; Takeda: Honoraria; Celgene: Honoraria. Morgan: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; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; MMRF: Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.2983.2983

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. 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

URL: Article

DOI: 10.1182/blood.V124.21.640.640

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

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. 2981-2981

Abstract: Introduction Identifying molecular high risk myeloma remains a diagnostic challenge. We previously reported co-segregation of 〉 1 adverse lesion [t(4;14), t(14;16), t(14;20), gain(1q), del(17p)] by iFISH to specifically characterise a group of high risk patients (Boyd et al., Leukemia 2012). However, implementation of this approach is difficult using FISH because of its technical limitations. We recently developed and validated a novel high-throughput all-molecular testing strategy against FISH (MyMaP- Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015). Here, we molecularly characterised 1,036 patients from the NCRI Myeloma XI trial using MyMaP and validated the co-segregation approach. Materials, Methods and Patients Recurrent translocations and copy number changes were assayed for 1,036 patients enrolled in the NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. The trial included an intensive therapy arm for younger and fitter and a non-intense treatment arm for elderly and frail patients. Analysis was performed using MyMaP, which comprises TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). Median follow up for the analysis was 24 months. Results Adverse translocations [t(4;14), t(14;16), t(14;20)] were present in 18.2% of cases, del(17p) in 9.3%, gain(1q) in 34.5% and del(1p32) in 9.4% of cases. All adverse lesions were associated with significantly shorter PFS and OS by univariate analysis (P 〈 0.05 for all). Of the 1,036 analysed cases, 13.5% carried 〉 1 adverse lesion, 33.9% had one isolated adverse lesion and 52.6% had no adverse lesion. Presence of 〉 1, 1 or no adverse lesion was associated with a median PFS of 17.0, 23.9 and 30.6 months (P =3.0x10-9) and OS at 24 months of 67.9%, 75.0% and 86.0% (P =1.8x10-7), respectively. Del(1p) was associated with shorter PFS and OS for the intensive, but not for the non-intensive therapy arm and was independent of the co-segregation model by multivariate analysis regarding OS (P =0.006). We thus included del(1p) as an additional adverse lesion in the model for younger patients. The groups with 〉 1 (19.4% of cases), 1 (31.1%) and no adverse lesions (49.5%) were characterised by median PFS of 19.4, 29.4 and 39.1 months (P =1.2x10-10) and median 24-months survival of 73.8%, 86.4% and 91.5% (P =1.4x10-6), respectively. Hazard Ratio for 〉 1 adverse lesion was 3.0 (95% CI 2.1-4.1) for PFS and 3.8 (95% CI 2.2-6.5) for OS. By multivariate analysis, co-segregation of adverse lesions was independent of ISS for PFS/OS in the entire group of 1,036 cases and in the intensive treatment arm. We integrated adverse lesions and ISS into a combined model defining High Risk ( 〉 1 adv les + ISS 2 or 3; 1 adv les + ISS 3) and Low Risk (no adv les + ISS 1 or 2; 1 adv les + ISS 1) and the remainder as Intermediate Risk. The High Risk, Intermediate Risk and Low Risk groups of the total cohort included 11.2%, 41.2% and 41.6% of cases with median PFS of 15.8, 19.8 and 35.2 months (P 〈 2.2x10-16) and median OS at 24 months of 62.9%, 73.7%, and 90.7% (P =4.0x10-14), respectively. Integration of ISS into the model for younger patients resulted in highly specific identification of a High Risk group (15.6% of cases) with HR 3.8 (CI 2.6-5.4) for PFS and 6.2 (CI 3.3-11.6) for OS. Conclusions Co-segregation analysis of adverse genetic lesions is a specific molecular risk stratification tool which has now been validated in two large independent trials including a real-world population of all age groups (UK MRC Myeloma IX; NCRI Myeloma XI; total 1,905 patients). MyMaP is a validated all-molecular analysis approach that makes the otherwise technically challenging assessment of multiple genetic regions by FISH accessible using standard laboratory equipment without bioinformatics requirements. Disclosures Kaiser: BristolMyerSquibb: Consultancy; Chugai: Consultancy; Janssen: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Jones:Celgene: Other: Travel support, Research Funding. Savola:MRC Holland: Employment. Owen:Celgene: Honoraria, Research Funding; Janssen: Honoraria. Cook:Celgene: Consultancy, Research Funding, Speakers Bureau; BMS: Consultancy; Sanofi: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. Gregory:Celgene: Honoraria; Janssen: Honoraria. Davies:Takeda-Milenium: Honoraria; Onyx-Amgen: Honoraria; Celgene: Honoraria; University of Arkansas for Medical Sciences: Employment. Jackson:Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria. Morgan:Weisman Institute: Honoraria; Takeda-Millennium: 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, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; MMRF: Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.2981.2981

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. 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

URL: Article

DOI: 10.1182/blood.V124.21.172.172

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

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

URL: Article

DOI: 10.1182/blood.V124.21.2189.2189

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 2194-2194

Abstract: Dysregulation of the epigenome plays an important role in the pathogenesis of the plasma cell malignancy myeloma (MM). For example the H3K36 methyltransferase, MMSET, is overexpressed as a result of t(4;14) in 15% of patients and associated with a distinct DNA methylation pattern and shorter survival. Epigenetic modifiers may also be deregulated due to somatic mutations, seen in the histone demethylase, KDM6A/UTX (Van Haaften et al, Nat.Genet. 2009) and histone methyltransferase, MLL(Chapman et al, Nature 2011). We analysed the spectrum and clinical implications of epigenetic gene mutations in the largest series of newly diagnosed MM patients sequenced to date. Whole exome sequencing was performed on DNA extracted from tumour (CD138+) and peripheral blood samples from patients entering the NCRI Myeloma XI trial (n=463) using SureSelect (Agilent) with extra baits to cover IGH, IGK, IGL and MYC loci, median depth 60x. Data were processed to identify acquired variants, copy number, indels and translocation breakpoints and annotated for potentially deleterious mutations. Significantly mutated genes were detected using MutSigCV (v1.4) inputting all SNV and short indels (q-value threshold 0.1). Survival from initial randomization had median follow up of 25 months. The gene encoding the linker histone protein Histone 1.4, HIST1H1E was mutated in 2.8% of samples and one of the most significantly mutated genes in myeloma (p 〈 1x10-10, q 〈 1x10-10). Average cancer cell fraction in HIST1H1E mutated samples was close to 100%, suggesting these mutations may be an early event in MM pathogenesis. There were also recurrent mutations in genes encoding variants of the Histone 1 protein, HIST1H1B (0.22%), HIST1H1C (2.59%), HIST1H1D (0.65%); the percentage of patients with a mutation in any variant totals 6% (28/463). Mutations clustered in the globular domain and multiple sequence alignment revealed sites of recurrent mutation across variants. Along with the absence of mutations in the fifth common protein variant HIST1H1A,this suggests that these are not passenger mutations and may carry some significance to MM pathogenesis. Histone 1 mutations have been demonstrated to play an important role in other haematological malignancies but have not been previously characterized in MM. Potentially deleterious mutations in histone methyltransferase/demethylase enzymes were also seen in 24% of patients, though the percentage of patients with each gene mutated was low. The most frequently mutated gene family in the methyltransferases was MLL/2/3/4/5 (7% of patients). There were no mutations in EZH2, recurrently mutated in other B cell malignancies, and none of the MMSET activating mutations p.E1099K described in the MM1.S myeloma cell line were seen. The most frequently mutated demethylase gene was KDM3B in 1.5% of patients. KDM6A/UTX mutations occurred in 1.3% of patients and targeted analysis for deletion of whole exons increased the number of patients affected by a potentially inactivating lesion to 3%. Patients carrying a KDM6A mutation or deletion appear to have a shorter OS at current follow up than wild type (medians NR, log-rank p=0.0498, % alive at 2 years 51% CI 30-85 vs 80% CI 77-84). Data suggest that EZH2 inhibitors, currently in development for lymphoma, could be investigated for these patients as inhibiting the H3K27 methyltransferase may counteract the increased H3K27 methylation resulting from inactivation of the demethylase. DNA methylation modifiers were found to be mutated in 4% of patients. These include mutations previously reported in glioma (p.R132C in IDH1) and AML/MDS (p.R140W in IDH2 and p.C1378Y in TET2). Collectively, mutations in any DNA methylation modifier (TET1/2/3 n=9, IDH1/2 n=2 or DNMT1/3A/B n=6) are associated with a shorter OS (medians NR, p=0.045, % alive at 2 years 58% CI 39-88 vs 80% CI 76-84). Patients with these mutations might be amenable to demethylating agents such as azacytidine and newer agents such as IDH inhibitors currently in early stages of development. This is the first extensive analysis of the spectrum of mutations in epigenetic modifiers in a uniformly treated population in MM. An association with clinical outcome is suggested in our dataset but will need validation due to the low overall frequency of the mutations. This data further emphasises the importance of epigenetics in MM and provides new potential targets for personalised therapeutic strategies for patients. Disclosures Pawlyn: Celgene: Honoraria. Walker:Onyx Pharmaceuticals: Consultancy, Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.2194.2194

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

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. 804-804

Abstract: Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified ( 〉 95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P 〈 0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P 〈 0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P 〈 0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.804.804

Language: English

Publisher: American Society of Hematology

Publication Date: 2016

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. 4407-4407

Abstract: Introduction A significant proportion of myeloma patients relapse early and show short survival with current therapies. Molecular diagnostic tools are needed to identify these high risk patients at diagnosis to stratify treatment and offer the prospect of improving outcomes. Two validated molecular approaches for risk prediction are widely used: 1) molecular genetic risk profiling [e.g. del(17p), t(4;14)] 2) gene expression (GEP) risk profiling, [e.g. EMC92 (Kuiper et al., Leukemia 2012)] . We profiled patients from a large multicentric UK National trial using both approaches for integrated risk stratification. Methods A representative group of 221 newly diagnosed, transplant eligible patients (median age 64 years) treated on the UK NCRI Myeloma XI trial were molecularly profiled. DNA and RNA were extracted from immunomagnetically CD138-sorted bone marrow plasma cells. Molecular genetic profiles, including t(4;14), t(14;16), Del(17p), Gain(1q) were generated using MLPA (MRC Holland) and a TC-classification based qRT-PCR assay (Boyle EM, et al., Gen Chrom Canc 2015, Kaiser MF, et al., Leukemia 2013). GEP risk status as per EMC92 was profiled on a diagnostic Affymetrix platform using the U133plus2.0-based, CE-marked MMprofiler (SkylineDx) which generates a standardised EMC92 risk score, called 'SKY92'. Progression-free (PFS) and overall survival (OS) were measured from initial randomization and median follow-up for the analysed group was 36 months. Statistical analyses were performed using R 3.3.0 and the 'survival' package. Results were confirmed in an independent dataset, MRC Myeloma IX, for which median follow-up was 82.7 months. Results Of the 221 analysed patients, 116 were found to carry an established genetic high risk lesion [t(4;14), t(14;16), del(17p) or gain(1q)]. We and others have recently demonstrated that adverse lesions have an additive effect and that co-occurrence of ≥2 high risk lesions is specifically associated with adverse outcome (Boyd KD et al, Leukemia 2011). 39/221 patients (17.6%) were identified as genetic high risk with ≥2 risk lesions (termed HR2). By GEP, 53/221 patients (24.0%) were identified as SKY92 high risk. Genetic and GEP high risk co-occurred in 22 patients (10.0%), 31 patients (14.0%) were high risk only by GEP and 17 patients (7.7%) by genetics only. SKY92 high risk status was associated with significantly shorter PFS (median 17.1 vs. 34.3 months; P 〈 0.0001; Hazard ratio [HR] 3.2 [95%CI: 2.2-4.7] ) and OS (median 36.0 vs. not reached; P 〈 0.0001; HR 3.9 [2.3-6.9]). Genetic risk by HR2 was similarly associated with adverse outcome: median PFS 17.0 vs. 33.6 months; P 〈 0.0001; HR 2.9 [1.9-4.4]), median OS 33.5 vs. not reached; P 〈 0.0001; HR 4.1 [2.3-7.2]). Importantly, by multivariate analysis GEP and genetic high risk status were independently associated with shorter PFS (P 〈 0.001) and OS (P 〈 0.005). We next investigated interactions between genetic and gene expression high risk status. Three groups were defined: 1) Patients with both SKY92 and genetic (HR2) high risk status (n=22), 2) either GEP or genetic high risk (n=48) or 3) absence of GEP or genetic (HR2) high risk status (n=151). Co-occurring GEP and genetic high risk status was associated with very short PFS (median 12.5 vs. 20.0 vs. 38.3 months; P 〈 0.0001) and OS (median 25.6 vs. 47.3 vs. not reached; P 〈 0.0001) [Figure]. When comparing this ultra-high risk group against the remainder of cases (n=199), their risk of progressing and dying early was significantly elevated (PFS HR 4.4 [2.5-6.7] ; OS HR 5.9 [3.1-11.0]). We confirmed this finding in 116 transplant-eligible patients from the MRC Myeloma IX trial. Patients carrying both EMC92 and genetic high risk status had a median PFS of 7.8 vs. 25.5 months and median OS of 9.5 vs. 62.1 months (both P 〈 0.0001). Moreover, all patients in this ultra-high risk group progressed within 24 months and died within 48 months. Conclusion We demonstrate, for the first time, that combined genetic and gene expression risk profiling identifies a group of patients with ultra-high risk disease behaviour with high fidelity, using molecular features of the disease. Our results indicate that GEP and genetic high risk profiling identify independently relevant, but inter-related features of high risk disease biology. Integrated genetic and gene expression risk profiling could serve as a valuable tool for risk stratified, innovative treatment approaches in myeloma. Figure Figure. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Amgen: Consultancy, Honoraria, Other: Travel support; Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Univ of AR for Medical Sciences: Employment; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Jackson:Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau. Kaiser:Takeda: Consultancy, Other: Travel Support; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Chugai: Consultancy.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.4407.4407

Language: English

Publisher: American Society of Hematology

Publication Date: 2016

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

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

URL: Article

DOI: 10.1182/blood.V124.21.637.637

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7