scholarly journals Unsupervised Clustering of DNA Copy Number Profiles Identifies a High-Risk Subtype of Hyperdiploid Multiple Myeloma: An Mmrf Commpass Analysis

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1805-1805 ◽  
Author(s):  
Austin Christofferson ◽  
Sheri Skerget ◽  
Jessica Aldrich ◽  
Christophe Legendre ◽  
Sara Nasser ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Copy Number ◽  
Research Funding ◽  
Clustering Algorithm ◽  
Dynamic Range ◽  
Risk Group ◽  
Consensus Clustering ◽  
Dna Copy Number ◽  
Data Set

Multiple myeloma (MM) is a malignancy of the antibody producing plasma cell, which exhibits a high degree of genetic diversity between patients. As genetic analysis technologies have improved so has our understanding of the diverse genetic phenotypes underlying the disease. The MMRF CoMMpass study (NCT01454297) is using whole genome (WGS), exome (WES), and RNA (RNAseq) sequencing to provide a precise characterization of each patient before and after therapy. However, these advanced assays are not widely available to patients today limiting the utility of many observations to a small population of patients. To expand the utility of the data set to a broader patient population we focused on DNA copy number (CN) phenotypes that can be identified by the standard FISH assays widely used in the field. To discover potential underlying phenotypes of myeloma beyond the known dichotomy of hyperdiploid (HRD) and non-hyperdiploid (NHRD) karyotypes, unsupervised consensus clustering was performed on 871 patients with CN profiles from WGS. Given the limited dynamic range of CN values, a Monte Carlo reference-based consensus clustering algorithm, M3C, was used to limit potential overfitting issues. Three independent replicates of this procedure identified an optimal solution of eight subtypes with no more than 6 patients having different class assignments between replicates. The eight CN subtypes consisted of five HRD and three NHRD subtypes and were annotated based on common CN features. The HRD classic subtype had ubiquitous CN gains, trisomies, of classic HRD chromosomes, 3, 5, 7, 9, 11, 15, and 19. The remaining HRD subtypes were annotated based on deviations from the classic HRD phenotype. The HRD, ++15 subtype phenocopies classic HRD except tetrasomy, not trisomy, is observed on chr15. Two groups of HRD patients were identified lacking CN gains of chr7 which are split into two distinct subtypes: the HRD, diploid 7 subtype, which lacked gains of chr7; and the HRD diploid 3, 7 subtype lacking trisomies of both chr3 and chr7. This suggest some relationship between chromosomes 3 and 7 where trisomy 7 is not tolerated in the absence of trisomy 3. Finally, the HRD, +1q, diploid 11, -13 subtype had gains of the classic HRD chromosomes except chr11 with gains of chr1q and loss of chr13. This subtype suggests trisomy 11 is essential for an HRD phenotype but it can be phenocopied by the combination of 1q gains and 13 loss. Within the NHRD subtypes, the diploid subtype is almost devoid of CN abnormalities less a common gain of 11q initiating at the breakpoint the t(11;14) event, which is almost universally observed in this subtype. Unlike the diploid subtype, the remaining NHRD subtypes have more complex CN profiles with the -13 subtype defined by monosomy 13, and the +1q/-13 subtype defined by gains of 1q and monosomy 13. Outcome analyses of the CN subtypes identified in CoMMpass revealed that both HRD and NHRD patients with gains of chr1q and loss of chr13 exhibited poor PFS and OS outcomes as compared to patients in other CN subtypes. Interestingly, the PFS curves split into three groups with a good risk group defined by the HRD classic and HRD ++15 subtypes. a high-risk group defined by 1q gain and monosomy 13 regardless of ploidy phenotype, and an intermediate group with all other subtypes. The distribution of HRD patients into these three outcome groups highlights the danger of assuming all HRD myeloma patients will have similar outcomes. Patients in the HRD, +1q, diploid 11, -13 subtype exhibited poor OS outcomes (median = 56 months) as compared to patients in the HRD, ++15 (p<0.01), HRD, classic (median = 65 months, p<0.05), diploid (p<0.01), and -13 (p<0.05) subtypes. Patients in the +1q, -13 subtype also exhibited poor OS outcomes (median = 57 months) as compared to patients in the diploid (p<0.01), -13 (p<0.05), HRD classic (p<0.05), and HRD, ++15 (p<0.01) subtypes. Overall, both HRD and NHRD patients with gain of 1q and loss of chr13 exhibit poor outcome as compared to patients with other genetic backgrounds (HR = 1.928, 95% CI = 1.435 - 2.59, p<0.001). Further, the observation that NHRD patients in the +1q, -13 subtype exhibit poor OS outcomes as compared to NHRD patients in the -13 subtype highlights the importance of 1q gains in determining patient prognosis. These results can easily be translated into clinics around the world by matching existing FISH data to each of these groups until more advanced testing is common practice. Disclosures Lonial: BMS: Consultancy; GSK: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy.

Genome-Wide Copy Number Analyses Correlated with Outcomes in Untreated Multiple Myeloma Patients

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3991-3991
Author(s):  
Vishwanathan Hucthagowder ◽  
Mark Fiala ◽  
Doug Cox ◽  
Keith E. Stockerl-Goldstein ◽  
Michael H. Tomasson ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Serum Creatinine ◽  
Copy Number ◽  
Clinical Characteristics ◽  
Plasma Cells ◽  
Risk Groups ◽  
Data Set ◽  
Genomic Changes ◽  
Clinical Annotation

Abstract Abstract 3991 Multiple myeloma (MM) is an incurable hematalogic malignancy characterized by the clonal proliferation and uncontrolled accumulation of malignant plasma cells in the bone marrow. In recent years a breath of new data has been reported on the genomics of MM. Nearly all MM patients studied to date show numerous genomic changes; however most studies to date have not included clinical annotation to correlate these markers to clinical outcomes. Methods: We reviewed the “Multiple Myeloma Research Consortium's (MMRC) copy number” data set and corresponding clinical annotation available from the Multiple Myeloma Research Foundation (MMRF) Genomics Portal in attempt to identify clinically relevant mutations. Agilent 244k aCGH on DNA from CD138-selected plasma cells were performed on 254 MM patients. We identified 105 untreated patients from this data set for further analysis that had at least partial clinical annotation available. Results: Median age of the population at diagnosis was 63-years-old (range 40–89), 84% were Caucasian, and 63% were male. Fifty-eight percent had IgG isotype, 18% had IgA, 10% had no heavy chain, and 2% had IgD. Eighty-five patients had albumin and beta-2 available to calculate ISS stage. Fifty- three percent were ISS stage I, 26% were stage II, and 21% were stage III. Median M-Spike was 3.0 g/dL, 78% had elevated free light chains, and 56% had lytic bone disease. Patients received a variety of therapies for MM. A univariate analysis of the clinical annotation found several previously discovered high-risk groups that were prognostic for survival including: age > 65 years at diagnosis (HR 3.424; p = 0.012), serum creatinine > 2.0 mg/dL (HR 3.197; p = 0.028), and ISS stage 3 compared to 1 and 2 (HR 2.701; p = 0.077). Genome-wide copy number analysis yielded several genomic aberrations that were significantly more common in these high risk sub-groups. Patients 65-years-old or older at diagnosis were more likely to have deletions at chromosome 1p12, 8p21, 10p12, 13q34, 14q24, 16p13, 22q13 and amplifications at 5q35 and 15q15. Deletion of chromosome 10p12 (HR 3.618; p < 0.01) involving genes ANKRD26, MEG4 and amplification of 5q35 involving GRK6, DBN1, DOK3, DDX41, ABS, PDLIM7, F12 and SLC34A1 (HR 2.358; p = 0.07) both correlated with survival. Patients with serum creatinine > 2g/dL were more likely to have deletions of chromosome 1p21, 2p11, and amplifications at 8q24, 14q32.2 and 16p11 (p< 0.05). Interestingly gain of chromosome 14q32.2 (EVL and RNB6) correlated with survival (HR 5.539; p < 0.001). Our analysis revealed that patients with ISS stage III had higher percent of deletion on chromosome 12q23, 13q32, 16q and amplification of 1q (p < 0.01), although none of these genomic aberrations correlated with survival. Interestingly, the isotype of MM (IgA vs. IgG) did not correlate with survival in this data set, however, deletion of chromosome 8p21 correlated with survival (HR 2.760; p = 0.02) and occurred more frequently in IgA patients (p < 0.01).We also analyzed previously identified high-risk groups: LDH > 300 units/L, CRP > 6.0 mg/dL and patients with lytic bone disease. Several genetic aberrations were more frequent in these groups (p < 0.01), but neither the genomic changes nor the clinical characteristics correlated with survival. Conclusion: Several high-risk groups have been previously identified using clinical characteristics or genetic data, but are rarely analyzed together. In this data set, we found several factors that correlated with survival including: Age >65, serum creatinine >2.0mg/dL, ISS stage 3, amplification of chromosomes 5q35, 14q32, deletions of chromosomes 8p21, and 10p12. Additional multivariate analysis would determine if these genetic aberrations or the clinical characteristics are independently significant for survival. The heterogeneity of treatments limits the ability of retrospective studies to draw firm conclusions; however, this study is illustrative of the power of combing clinical and genomic data to narrow the multitude of genomic changes to those of clinical relevance. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Evolving Real-World Treatment Patterns in Patients with Newly-Diagnosed Multiple Myeloma (NDMM) in the United States (U.S.)

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3164-3164
Author(s):  
Sikander Ailawadhi ◽  
Dorothy Romanus ◽  
Dasha Cherepanov ◽  
Yu Yin ◽  
Meng-Ru Cheng ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Research Funding ◽  
Risk Group ◽  
Treatment Patterns ◽  
Frontline Therapy ◽  
Early Cohort ◽  
Recent Cohort ◽  
The U.S ◽  
Over Time

Background Multiple myeloma (MM), a malignant neoplasm of plasma cells in the bone marrow, accounts for up to 1.8% of all cancers in the U.S., most frequently affecting people 65-74 years old. A variety of therapies are available to manage MM, including stem cell transplantation (SCT), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), monoclonal antibodies (mAB), and alkylating agents (alk). Given the heterogeneity of MM and the rapidly evolving therapeutic landscape, MM contemporaneous real-world treatment patterns are not well described. We examined the patient characteristics and first-line (LT1) treatment patterns in NDMM patients. Methods MM patients (≥18 years), diagnosed in April 30, 2015 - April 29, 2017 (early cohort) or in April 30, 2017 - April 30, 2019 (recent cohort), were followed retrospectively from MM diagnosis to last patient activity in the Flatiron Health database - a geographically-diverse, longitudinal electronic health record spanning over 280 community and academic cancer clinics in the U.S. LT1 regimens were described as: 1) containing an IMiD (thalidomide, lenalidomide [R], or pomalidomide), PI (bortezomib [V], carfilzomib, or ixazomib), alk (melphalan, cyclophosphamide [C], bendamustine), mAB (daratumumab, elotuzumab), or combinations of these; and 2) doublet/monotherapy (doublets-) vs. triplet or greater agent (triplets+) combinations. Treatment patterns were examined by SCT status and by cytogenetic risk (high: del17p, t(4;14) and/or t(14;16); standard: ≥1 cytogenetic tests without high cytogenetic risk) and age groups (<65, 65-74, ≥75). Duration of therapy (DOT) and time to next therapy (TTNT) were estimated using Kaplan-Meier methods in the early cohort with longer follow-up. Results Of 4,070 NDMM patients, 3,433 were non-SCT (nSCT: early cohort: n=1,736; recent cohort: n=1,697) and 637 had SCT (early cohort: n=407; recent cohort: n=230). In nSCT patients, mean age at diagnosis was 70 years (SD: 10); 46% were female; 36% had stage III (699/1916, among non-missing), and 15% (392/2574, among non-missing) had high risk MM (25% had unknown cytogenetics). SCT patients were younger at diagnosis (mean [SD]: 61 years [9]); 44% were female; 25% (117/470, in non-missing) had stage III, and 19% (102/547, in non-missing) had high risk MM (14% had unknown cytogenetics). Overall, proportions with known cytogenetic risk were similar within SCT status cohorts over time but were lower in the SCT group (nSCT early vs. recent cohort: 26% vs. 24% had unknown cytogenetics; and in SCT: 15% vs. 13%, respectively). In nSCT and SCT patients, respectively, most common regimens were VRd (d: dexamethasone; 44% and 58%), Rd (16% and 7%), Vd (13% and 1%), and VCd (12% and 4%). In nSCT patients, the use of VRd increased over time (37% [early cohort] to 51% [recent cohort]), while frontline therapy with Rd/Vd doublets (19% to 14%/16% to 9%) and with VCd (13% to 11%) decreased. In the nSCT recent cohort, VRd (51%) frontline therapy dominated, with a slightly higher proportion of patients in the high-risk group vs. standard and unknown risk receiving VRd (56% vs. 53% and 46%); use of doublet therapy with Rd/Vd was lower in the high risk (12%/5%) vs. standard risk group (14%/9%). Irrespective of age, VRd was the most common frontline regimen in the nSCT recent cohort, albeit its use was lower among patients 75+ years of age (43%) vs. younger patients (54% [<65 years] and 59% [65-74 years]); 75+ year old patients had a higher use of Rd/Vd doublets (19%/15%) vs. <65 (10%/5%) or 65-74 (10%/6%) years of age. Triplets+ were more commonly used than doublets- across all cohorts: 59% vs. 41% (nSCT early cohort); 74% vs. 26% (nSCT recent cohort); and 89% vs. 11% (SCT early cohort); 95% vs. 5% (SCT recent cohort). mAB use in the recent cohort was low: 1.4% nSCT and 2.2% SCT patients. In the nSCT early cohort, the median (95% CI) LT1 DOT was 10 months (9-11) and for TTNT was 14 months (13-16). Conclusions PI/IMiD treatment combinations were most commonly observed in both nSCT and SCT patients, with an increase in use from early (40%) to recent (56%) cohort in nSCT patients. Use of triplets, generally, is on the rise from early (60%) to recent cohorts (74%). LT1 TTNT was lower than has been shown in clinical trials. These findings indicate a notable change in treatment patterns over time in nSCT NDMM patients, highlighting the changing landscape of MM management. Disclosures Ailawadhi: Celgene: Consultancy; Takeda: Consultancy; Cellectar: Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding. Romanus:Takeda: Employment. Cherepanov:Takeda: Employment. Yin:Takeda: Employment. Cheng:Takeda: Employment. Hari:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria.

scholarly journals A Novel Early Relapse Prediction Score Based on Age, ISS and Disease Status at the Time of Transplant in Patients with Newly Diagnosed Multiple Myeloma. a Study of the EBMT Chronic Malignancies Working Party

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3937-3937
Author(s):  
Meral Beksac ◽  
Simona Iacobelli ◽  
Linda Koster ◽  
Didier Blaise ◽  
Jan J. Cornelissen ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Scoring System ◽  
Research Funding ◽  
Risk Group ◽  
Disease Status ◽  
Early Relapse ◽  
Bone Marrow Plasma ◽  
Additional Point

Abstract Rationale and Aim: In patients with Myeloma, early relapse following Autologous Haematopoietic Cell Transplantation (Auto-HCT) is a poor prognostic marker. Two groups have published scoring systems to predict early relapse. The CIBMTR score is based on cytogenetics, the bone marrow plasma cell percentage at the time of Auto-HCT and serum albumin. The GIMEMA Simplified early relapse in multiple myeloma (S-ERMM) score is a cumulative score based on a raised serum lactate dehydrogenase (LDH), t(4;14), del17p, low albumin, bone marrow plasma percentage &gt;60%, and lambda light chain. The aim of the current study was to develop a scoring system to predict early relapse post-Auto-HSCT-1 using readily available variables. Study design and statistics: Within the EBMT database, there were 8,206 patients meeting the following eligibility criteria: First auto transplant 2014-2019, Known sex, ISS at diagnosis, cytogenetics analysis at diagnosis, disease status at Auto-HCT, Interval diagnosis-Auto-HCT &gt; 1 month and &lt;= 12 months, conditioning with Melphalan 200 mg/m2 and known information on relapse; tandem auto-allo patients were excluded. The analysis consisted of two steps: (1) Training: modeling based on 4,389 patients (611 events for PFS12) transplanted between 2014 and 2017, with internal validation carried out by bootstrapping; and (2) Testing: the models obtained were applied to 3,817 patients (346 events for PFS12) transplanted in 2018 and 2019 for external validation. The characteristics of the two cohorts are first reported separately and then together (Table 1). Possible adjustment factors analyzed for the prognostic model included Age at Auto-HCT, Known sex, ISS at diagnosis, disease status at Auto-HCT, and time from diagnosis to Auto-HCT. Complete cytogenetic information was not available at the time of this analysis and will be included in the later analysis. The shape of the effect of age and of time from diagnosis to Auto-HCT was investigated both by the analysis of residuals and by applying boot-strap backward selection among different alternatives. The final results were confirmed in a robustness analysis excluding patients undergoing tandem Auto-HCT. Results: Comparison of the training and validation cohorts revealed no relevant differences (Table 1). Importantly, OS and PFS of both cohorts were overlapping with the probability of PFS at 12 months being 83.3% and 86.8%, respectively. The cumulative incidence of relapse at 12 month was 15.7% and 12.1%, respectively. Among patients who relapsed early, this occurred at a median of 6.64 months (0.56-11.99) in the first cohort, and at 5.85 months (0.1- 11.99) in the second cohort. The final model included (1) disease status at Auto-HCT, (2) age at Auto-HCT, and (3) ISS at diagnosis. Considering the order of magnitude of the coefficients, the points attributed in the risk score were: 0 for CR or VGPR; 1 for PR or SD/MR; 3 for Rel/Prog; 0 / 1/ 2 respectively for ISS I / II / III and -1 for Age&lt;=55 yrs; -2 for Age (55-75 yrs]; -3 for Age&gt;=75 yrs. The Hazard Ratio for a +1 point is 1.52 i.e. the risk of early relapse/death increased on average by 52% for each additional point in the score. The distribution of risk scores was as follows: Score= -2 (n=757), -1 (n=1,481), 0 (n=1,358), 1 (n=647), and 2 (n=146). The score allows separation of the PFS12 curves (Figure 1), with the lowest risk group (N=757) having a PFS at 12 months of 91%, and the highest risk group (N=146) having a PFS at 12 months of 65%. Despite some minor differences in the risk factors between the training and validation cohorts, the score has a similar average effect (HR=1.48 i.e. + 48% hazard for each additional point) and worked well in separating the curves, in particular in identifying the patients at high risk of early relapse. Discussion and conclusion: The new EBMT score to predict early relapse post-Auto-HCT uses the easily available variables of age and ISS stage at diagnosis as well as the dynamic variable of response to induction. With this simple approach, we were able to clearly identify patients at high risk of early relapse. To our surprise, older age emerged as a protective factor against relapse. This may reflect a relative selection bias in that older patients with higher risk disease may not have been selected for transplant. Impact of cytogenetics will be presented at the Congress. In conclusion, this novel scoring system is robust and easy to use in routine daily practice. Figure 1 Figure 1. Disclosures Beksac: Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Oncopeptides: Consultancy. Blaise: Jazz Pharmaceuticals: Honoraria. Leleu: Karyopharm Therapeutics: Honoraria; AbbVie: Honoraria; Bristol-Myers Squibb: Honoraria; Amgen: Honoraria; Merck: Honoraria; Mundipharma: Honoraria; Novartis: Honoraria; Carsgen Therapeutics Ltd: Honoraria; Oncopeptides: Honoraria; Janssen-Cilag: Honoraria; Gilead Sciences: Honoraria; Celgene: Honoraria; Pierre Fabre: Honoraria; Roche: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Other: Non-financial support. Forcade: Novartis: Consultancy, Other: Travel Support, Speakers Bureau; Gilead: Other: Travel Support, Speakers Bureau; Jazz: Other: Travel Support, Speakers Bureau; MSD: Other: Travel Support. Rabin: Janssen: Consultancy, Honoraria, Other: Travel support for meetings; BMS / Celgene: Consultancy, Honoraria, Other: Travel support for meetings; Takeda: Consultancy, Honoraria, Other: Travel support for meetings. Kobbe: Celgene: Research Funding. Sossa: Amgen: Research Funding. Hayden: Jansen, Takeda: Other: Travel, Accomodation, Expenses; Amgen: Honoraria. Schoenland: Pfizer: Honoraria; sanofi: Research Funding; janssen,Prothena,Takeda,: Consultancy, Honoraria. Yakoub-Agha: Jazz Pharmaceuticals: Honoraria.

Generation of An Automated Tool for the Identification of Genetics Markers and Signatures in Multiple Myeloma Risk-Stratification

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2881-2881
Author(s):  
Esteban Braggio ◽  
Jonathan J Keats ◽  
Shaji Kumar ◽  
Gregory Ahmann ◽  
Jeremy Mantei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Stratification ◽  
Research Funding ◽  
Plasma Cells ◽  
Risk Group ◽  
High Risk Group ◽  
Proliferation Index ◽  
Gene Index

Abstract Abstract 2881 Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures: Stewart: Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Genomic Changes in Chromosomes 10, 16, and X in Malignant Peripheral Nerve Sheath Tumors Identify a High-Risk Patient Group

2010 ◽  
Vol 28 (9) ◽  
pp. 1573-1582 ◽  
Author(s):  
Helge R. Brekke ◽  
Franclim R. Ribeiro ◽  
Matthias Kolberg ◽  
Trude H. Ågesen ◽  
Guro E. Lind ◽  
...  
Keyword(s):  
High Risk ◽  
Copy Number ◽  
Risk Group ◽  
High Risk Group ◽  
Poor Survival ◽  
Dna Copy Number ◽  
Nerve Sheath Tumors ◽  
Peripheral Nerve Sheath Tumors ◽  
Nerve Sheath ◽  
Copy Number Changes

Purpose The purpose of this study was to identify genetic aberrations contributing to clinical aggressiveness of malignant peripheral nerve sheath tumors (MPNSTs). Patients and Methods Samples from 48 MPNSTs and 10 neurofibromas were collected from 51 patients with (n = 31) or without (n = 20) neurofibromatosis type 1 (NF1). Genome-wide DNA copy number changes were assessed by chromosomal and array-based comparative genomic hybridization (CGH) and examined for prognostic significance. For a subset of 20 samples, RNA microarray data were integrated with the genome data to identify potential target genes. Results Forty-four (92%) MPNSTs displayed DNA copy number changes (median, 18 changes per tumor; range, 2 to 35 changes). Known frequent chromosomal gains at chromosome arms 8q (69%), 17q (67%), and 7p (52%) and losses from 9p (50%), 11q (48%), and 17p (44%) were confirmed. Additionally, gains at 16p or losses from 10q or Xq identified a high-risk group with only 11% 10-year disease-specific survival (P = .00005). Multivariate analyses including NF1 status, tumor location, size, grade, sex, complete remission, and initial metastatic status showed that the genomic high-risk group was the most significant predictor of poor survival. Several genes whose expression was affected by the DNA copy number aberrations were identified. Conclusion The presence of specific genetic aberrations was strongly associated with poor survival independent of known clinical risk factors. Conversely, within the total patient cohort with 34% 10-year disease-specific survival, a low-risk group was identified: without changes at chromosomes 10q, 16p, or Xq in their MPNSTs, the patients had 74% 10-year survival.

scholarly journals Multiple Myeloma Patients Undergoing Carfilzomib: Development and Validation of a Risk Score for Cardiovascular Adverse Events Prediction

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1631
Author(s):  
Anna Astarita ◽  
Giulia Mingrone ◽  
Lorenzo Airale ◽  
Fabrizio Vallelonga ◽  
Michele Covella ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Risk Assessment ◽  
Multiple Myeloma ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Adverse Events ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Cardiovascular Risk Assessment

Cardiovascular adverse events (CVAEs) are linked to Carfilzomib (CFZ) therapy in multiple myeloma (MM); however, no validated protocols on cardiovascular risk assessment are available. In this prospective study, the effectiveness of the European Myeloma Network protocol (EMN) in cardiovascular risk assessment was investigated, identifying major predictors of CVAEs. From January 2015 to March 2020, 116 MM patients who had indication for CFZ therapy underwent a baseline evaluation (including blood pressure measurements, echocardiography and arterial stiffness estimation) and were prospectively followed. The median age was 64.53 ± 8.42 years old, 56% male. Five baseline independent predictors of CVAEs were identified: office systolic blood pressure, 24-h blood pressure variability, left ventricular hypertrophy, pulse wave velocity value and global longitudinal strain. The resulting ‘CVAEs risk score’ distinguished a low- and a high-risk group, obtaining a negative predicting value for the high-risk group of 90%. 52 patients (44.9%) experienced one or more CVAEs: 17 (14.7%) had major and 45 (38.7%) had hypertension-related events. In conclusion, CVAEs are frequent and a specific management protocol is crucial. The EMN protocol and the risk score proved to be useful to estimate the baseline risk for CVAEs during CFZ therapy, allowing the identification of higher-risk patients.

scholarly journals Whole-genome optical mapping of bone-marrow myeloma cells reveals association of extramedullary multiple myeloma with chromosome 1 abnormalities

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eva Kriegova ◽  
Regina Fillerova ◽  
Jiri Minarik ◽  
Jakub Savara ◽  
Jirina Manakova ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Copy Number ◽  
Optical Mapping ◽  
Chromosome 1 ◽  
Gene Copy ◽  
Whole Genome ◽  
Myeloma Cells ◽  
Genomic Architecture

AbstractExtramedullary disease (EMM) represents a rare, aggressive and mostly resistant phenotype of multiple myeloma (MM). EMM is frequently associated with high-risk cytogenetics, but their complex genomic architecture is largely unexplored. We used whole-genome optical mapping (Saphyr, Bionano Genomics) to analyse the genomic architecture of CD138+ cells isolated from bone-marrow aspirates from an unselected cohort of newly diagnosed patients with EMM (n = 4) and intramedullary MM (n = 7). Large intrachromosomal rearrangements (> 5 Mbp) within chromosome 1 were detected in all EMM samples. These rearrangements, predominantly deletions with/without inversions, encompassed hundreds of genes and led to changes in the gene copy number on large regions of chromosome 1. Compared with intramedullary MM, EMM was characterised by more deletions (size range of 500 bp–50 kbp) and fewer interchromosomal translocations, and two EMM samples had copy number loss in the 17p13 region. Widespread genomic heterogeneity and novel aberrations in the high-risk IGH/IGK/IGL, 8q24 and 13q14 regions were detected in individual patients but were not specific to EMM/MM. Our pilot study revealed an association of chromosome 1 abnormalities in bone marrow myeloma cells with extramedullary progression. Optical mapping showed the potential for refining the complex genomic architecture in MM and its phenotypes.

scholarly journals Jumping translocations of 1q12 in multiple myeloma: a novel mechanism for deletion of 17p in cytogenetically defined high-risk disease

Blood ◽  
2014 ◽  
Vol 123 (16) ◽  
pp. 2504-2512 ◽  
Author(s):  
Jeffrey R. Sawyer ◽  
Erming Tian ◽  
Christoph J. Heuck ◽  
Joshua Epstein ◽  
Donald J. Johann ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Copy Number ◽  
High Risk Disease ◽  
Key Points ◽  
Gains And Losses

Key Points Jumping translocations of 1q12 (JT1q12) provide a mechanism for the deletion of 17p in cytogenetically defined high-risk myeloma. Sequential JT1q12s introduce unexpected copy number gains and losses in receptor chromosomes during subclonal evolution.

scholarly journals Phase II Trial of the Combination of Ixazomib, Lenalidomide, and Dexamethasone in High-Risk Smoldering Multiple Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 804-804 ◽  
Author(s):  
Mark Bustoros ◽  
Chia-jen Liu ◽  
Kaitlen Reyes ◽  
Kalvis Hornburg ◽  
Kathleen Guimond ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Disease Progression ◽  
Board Of Directors ◽  
Rna Sequencing ◽  
Research Funding ◽  
Response Rate ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Background. This study aimed to determine the progression-free survival and response rate using early therapeutic intervention in patients with high-risk smoldering multiple myeloma (SMM) using the combination of ixazomib, lenalidomide, and dexamethasone. Methods. Patients enrolled on study met eligibility for high-risk SMM based on the newly defined criteria proposed by Rajkumar et al., Blood 2014. The treatment plan was designed to be administered on an outpatient basis where patients receive 9 cycles of induction therapy of ixazomib (4mg) at days 1, 8, and 15, in combination with lenalidomide (25mg) at days 1-21 and Dexamethasone at days 1, 8, 15, and 22. This induction phase is followed by ixazomib (4mg) and lenalidomide (15mg) maintenance for another 15 cycles. A treatment cycle is defined as 28 consecutive days, and therapy is administered for a total of 24 cycles total. Bone marrow samples from all patients were obtained before starting therapy for baseline assessment, whole exome sequencing (WES), and RNA sequencing of plasma and bone marrow microenvironment cells. Moreover, blood samples were obtained at screening and before each cycle to isolate cell-free DNA (cfDNA) and circulating tumor cells (CTCs). Stem cell collection is planned for all eligible patients. Results. In total, 26 of the planned 56 patients were enrolled in this study from February 2017 to April 2018. The median age of the patients enrolled was 63 years (range, 41 to 73) with 12 males (46.2%). Interphase fluorescence in situ hybridization (iFISH) was successful in 18 patients. High-risk cytogenetics (defined as the presence of t(4;14), 17p deletion, and 1q gain) were found in 11 patients (61.1%). The median number of cycles completed was 8 cycles (3-15). The most common toxicities were fatigue (69.6%), followed by rash (56.5%), and neutropenia (56.5%). The most common grade 3 adverse events were hypophosphatemia (13%), leukopenia (13%), and neutropenia (8.7%). One patient had grade 4 neutropenia during treatment. Additionally, grade 4 hyperglycemia occurred in another patient. As of this abstract date, the overall response rate (partial response or better) in participants who had at least 3 cycles of treatment was 89% (23/26), with 5 Complete Responses (CR, 19.2%), 9 very good partial responses (VGPR, 34.6%), 9 partial responses (34.6%), and 3 Minimal Responses (MR, 11.5%). None of the patients have shown progression to overt MM to date. Correlative studies including WES of plasma cells and single-cell RNA sequencing of the bone microenvironment cells are ongoing to identify the genomic and transcriptomic predictors for the differential response to therapy as well as for disease evolution. Furthermore, we are analyzing the cfDNA and CTCs of the patients at different time points to investigate their use in monitoring minimal residual disease and disease progression. Conclusion. The combination of ixazomib, lenalidomide, and dexamethasone is an effective and well-tolerated intervention in high-risk smoldering myeloma. The high response rate, convenient schedule with minimal toxicity observed to date are promising in this patient population at high risk of progression to symptomatic disease. Further studies and longer follow up for disease progression are warranted. Disclosures Bustoros: Dava Oncology: Honoraria. Munshi:OncoPep: Other: Board of director. Anderson:C4 Therapeutics: Equity Ownership; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Takeda Millennium: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Oncopep: Equity Ownership. Richardson:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ghobrial:Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.

Real-World Experience with Minimal Residual Disease Testing with Next Generation Flow Cytometry and Functional Imaging in Multiple Myeloma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 17-18
Author(s):  
David Böckle ◽  
Paula Tabares Gaviria ◽  
Xiang Zhou ◽  
Janin Messerschmidt ◽  
Lukas Scheller ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Flow Cytometry ◽  
High Risk ◽  
Functional Imaging ◽  
Real World ◽  
Research Funding ◽  
Residual Disease ◽  
Focal Lesions ◽  
High Risk Disease ◽  
Minimal Residual

Background: Minimal residual disease (MRD) diagnostics in multiple myeloma (MM) are gaining increasing importance to determine response depth beyond complete remission (CR) since novel agents have shown to induce high rates of deep clinical responses. Moreover, recent reports indicated combining functional imaging with next generation flow cytometry (NGF) could be beneficial in predicting clinical outcome. This applies in particular to the subset of patients suffering from relapsed/refractory multiple myeloma (RRMM) who tend to show a higher incidence of residual focal lesions despite serological response. Here, we report our institutions experience with implementing both functional imaging and NGF-guided MRD diagnostics in clinical practice. Methods: Our study included patients with newly diagnosed multiple myeloma (NDMM) and RRMM achieving VGPR, CR or sCR. Bone marrow aspirates were obtained for MRD-testing according to IMWG 2016 criteria. Samples were collected between July 2019 and July 2020 and analyzed with NGF (according to EuroFlowTM guidelines) at a sensitivity level of 10-5. Results were compared to functional imaging obtained with positron emission tomography (PET) and diffusion-weighted magnetic resonance imaging (DW-MRI). High-risk disease was defined as presence of deletion 17p, translocation (14;16) or (4;14). Results: We included 66 patients with NDMM (n=39) and RRMM (n=27) who achieved VGPR or better. In patients with RRMM the median number of treatment lines was 2 (range 2-11). Fifteen patients suffered from high-risk disease. Median age at NGF diagnostics was 64 years (range 31-83). Among patients achieving VGPR (n=27), CR (n=10) and sCR (n=29) seventeen (26%) were MRD-negative by NGF testing. CR or better was significantly associated NGF MRD-negativity (p=0.04). Notably, rates of NGF MRD-negativity were similar among patients with NDMM (28%) and RRMM (26%). Even some heavily pretreated patients who underwent ≥ 4 lines of therapy achieved MRD-negativity on NGF (2 of 9). Functional imaging was performed in 46 (70%) patients with DW-MRI (n=22) and PET (n=26). Median time between NGF and imaging assessment was 2 days (range 0-147). Combining results from imaging and NGF, 12 out of 46 (26%) patients were MRD-negative with both methods (neg/neg). Three patients displayed disease activity as measured with both, imaging and NGF (pos/pos). Twenty-nine of the remaining patients were MRD-positive only according to NGF (pos/neg), while two patients were positive on imaging only (neg/pos). More patients demonstrated combined MRD-negativity on NGF and imaging (neg/neg) in the NDMM setting than in RRMM (32% versus 19%). We also observed that 30% of the patients with high-risk genetics showed MRD-negativity on both imaging and NGF. Of note, none of the patients with very advanced disease (≥4 previous lines) was MRD-negative on both techniques. Conclusion In the clinical routine, MRD diagnostics could be used to tailor maintenance and consolidation approaches for patients achieving deep responses by traditional IMWG criteria. Our real-world experience highlights that MRD-negativity can be achieved in patients suffering from high-risk disease and also in late treatment lines, supporting its value as endpoint for clinical trials. However, our data also support MRD diagnostics to be combined with functional imaging at least in the RRMM setting to rule out residual focal lesions. Future studies using MRD for clinical decision-making are highly warranted. Disclosures Einsele: Takeda: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; GlaxoSmithKline: Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Rasche:Celgene/BMS: Honoraria; GlaxoSmithKline: Honoraria; Oncopeptides: Honoraria; Skyline Dx: Research Funding; Janssen: Honoraria; Sanofi: Honoraria.

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