scholarly journals Integrative multi-omics identifies high risk Multiple Myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

2021 ◽  
Author(s):  
María Ortiz-Estévez ◽  
Mehmet Samur ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
...  

AbstractDespite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features.Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval<1×10−6)], which uniquely presents a broad genomic loss (>9% of entire genome, t.test pval<1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways.Statement of SignificanceUsing multi-omics unsupervised clustering we discovered a new high-risk multiple myeloma patient segment. We linked its diverse genetic markers (previously known, and new including genomic loss) to transcriptional dysregulation (cell cycle, DNA repair and DNA damage) and identified master regulators that control these key biological pathways.

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
María Ortiz-Estévez ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
In Sock Jang ◽  
...  

Abstract Background Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10−6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Conclusion Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.


2021 ◽  
Author(s):  
María Ortiz-Estévez ◽  
Mehmet Samur ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
...  

Abstract Background Despite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval < 1x10− 6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t.test pval < 1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Conclusion Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.


2019 ◽  
Vol 3 (5) ◽  
pp. 744-750 ◽  
Author(s):  
Nidhi Tandon ◽  
Surbhi Sidana ◽  
S. Vincent Rajkumar ◽  
Morie A. Gertz ◽  
Francis K. Buadi ◽  
...  

Abstract We evaluated the impact of achieving a rapid response in 840 newly diagnosed multiple myeloma patients from 2004 to 2015. Rates of very good partial response (VGPR) or better were 29% (240/840) after 2 cycles of treatment, 42% (350/840) after 4 cycles of treatment, and 66% (552/840) as best response. Early responders after 2 cycles of treatment had higher rates of light chain disease, anemia, renal failure, International Staging System (ISS) stage III disease, and high-risk cytogenetics, especially t(4;14), and were more likely to have received triplet therapy and undergo transplant. Median progression-free survival (PFS) and overall survival (OS) were not different among patients with ≥VGPR and &lt;VGPR after 2 cycles (PFS, 28 vs 30 months, P = .6; OS, 78 vs 96 months, P = .1) and 4 cycles (PFS, 31 vs 29 months; OS, 89 vs 91 months, P = .9), although both were improved, with ≥VGPR as best response (PFS, 33 vs 22 months, P &lt; .001; OS, 102 vs 77 months, P = .003). On multivariate analysis stratified by transplant status, achievement of ≥VGPR after 2 cycles was not associated with improved PFS (hazard ratio [95% confidence interval]; transplant cohort, 1.1 [0.7-1.6]; nontransplant cohort, 1.2 [0.8-1.7]) or OS (transplant cohort, 1.6 [0.9-2.9]; nontransplant cohort, 1.5 [1.0-2.4]). Covariates in the model included high-risk cytogenetics, ISS stage III, triplet therapy, creatinine ≥2 mg/dL, light chain disease, and age. Although patients with high-risk disease are more likely to achieve early response, a rapid achievement of a deep response by itself does not affect long-term outcomes.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3944-3944
Author(s):  
Patricia Maiso ◽  
Yosra Aljawai ◽  
Antonio Sacco ◽  
Susanne B Breitkopf ◽  
Ilyas Sahin ◽  
...  

Abstract Abstract 3944 Introduction: Multiple Myeloma (MM) is the second most prevalent hematological malignancy and remains incurable, with a median survival of 3–7 years. However, despite the success of the new treatments, most patients still succumb to their disease. In about 20–25% of high-risk patients, MM progresses rapidly and does not respond to conventional therapies leading to rapid extramedullary disease and demise of these patients. One such regulator of dissemination and drug resistance is the dynamic process of oxygen deprivation or hypoxia. A number of studies show that hypoxia promotes neo-angiogenesis, cancer progression, epithelial-mesenchymal transition (EMT), acquisition of metastasis potential and stem-cell features, as well as resistance to therapy by activating adaptive transcriptional programs. Targeting hypoxia, and the metabolic pathways regulated by hypoxia in the tumor cells, could lead to novel opportunities for cancer therapy. Rapidly proliferating hypoxic cancer cells undergo a “metabolic switch” to anaerobic glycolysis. This altered energy metabolism has been shown to be associated with activated oncogenes and mutant tumor suppressors, which are more prevalent in patients with high-risk MM. Methods: The effect of hypoxia was analyzed in different MM cell lines (MM1S, RPMI8226, U266 and H929) in basal conditions and after the treatment with bortezomib, dexamethasone or melphalan. The cytotoxicity was analyzed by means of MTT assay. Cell cycle and apoptosis studies were performed by flow cytometry. Proteomic changes induced after treatment were analyzed under normoxic and hypoxic conditions by western-blotting. Gene expression profile of MM1S cells treated with bortezomib was compared in normoxia vs hypoxia using D-chip software. Genes with expression changes greater or lower than 2 fold in either direction were selected. HIF1A and HIF2A knockdowns were performed in MM1S using lentiviral vectors. For metabolite collection, samples were re-suspended using HPLC grade water for mass spectrometry and analyzed using a 5500 QTRAP hybrid triple quadrupole mass spectrometer (AB/SCIEX) coupled to a Prominence UFLC HPLC system (Shimadzu). A total of 254 endogenous water soluble metabolites were analyzed. Results: We observed that hypoxic conditions (12 hours at 0.7% of oxygen levels) suppressed the effect of melphalan and more significantly the effect of bortezomib. At the transcriptional level and protein level, we observed that cells treated with bortezomib in hypoxic conditions affected a large number of genes/proteins involved in cell cycle, cell death, glucose metabolism and the Wnt signaling pathway. Hypoxia blocked cell cycle progression, which was accompanied by p21, p53 and p57 up-regulation. In addition, apoptosis pathways were inhibited after exposure to hypoxia including inactivation of caspases 3, 8 and 9 and PARP cleavage. HIF1A and HIF2A knockdowns restore the effect of bortezomib in MM1S and increased the percentage of apoptosis in cells treated with bortezomib under hypoxic conditions. To further explore the role of hypoxia in the regulation of tumor metabolism, metabolomic studies were performed to characterize metabolic alterations following bortezomib treatment. This analysis revealed that hypoxic tumor cells treated with bortezomib show significant metabolic changes involving multiple pathways, the most significant of which are intermediates in glucose and, sucrose metabolism. Bortezomib treatment under hypoxic conditions was accompanied by a significant decrease in UDP-D-glucose, UDP-D-glucuronate, and glutathione disulfide. Conclusion: Hypoxic conditions are essential for drug resistance and glucose utilization. These data provide new therapeutic targets and associated biomarkers for the treatment of Multiple Myeloma. Disclosures: Ghobrial: Millennium: Advisory Board Other; Novartis: Advisory Board, Advisory Board Other.


2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Douaa Sayed ◽  
Mohamed K. Al-Sadoon ◽  
Gamal Badr

Background. Multiple myeloma (MM), an almost incurable disease, is the second most common blood cancer. Initial chemotherapeutic treatment could be successful; however, resistance development urges the use of higher toxic doses accompanied by hematopoietic stem cell transplantation. The establishment of more effective treatments that can overcome or circumvent chemoresistance has become a priority. We recently demonstrated that venom extracted fromWalterinnesia aegyptia(WEV) either alone or in combination with silica nanoparticles (WEV+NPs) mediated the growth arrest and apoptosis of prostate cancer cells. In the present study, we evaluated the impact of WEV alone and WEV+NP on proliferation and apoptosis of MM cells.Methods. The impacts of WEV alone and WEV+NP were monitored in MM cells from 70 diagnosed patients. The influences of WEV and WEV+NP were assessed with flow cytometry analysis.Results. WEV alone and WEV+NP decreased the viability of MM cells. Using a CFSE proliferation assay, we found that WEV+NP strongly inhibited MM cell proliferation. Furthermore, analysis of the cell cycle using the propidium iodide (PI) staining method indicated that WEV+NP strongly altered the cell cycle of MM cells and enhanced the induction of apoptosis.Conclusions. Our data reveal the biological effects of WEV and WEV+NP on MM cells that enable these compounds to function as effective treatments for MM.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2909-2909
Author(s):  
Guldane Cengiz Seval ◽  
Klara Dalva ◽  
Dilek Oz ◽  
Sule Mine Bakanay ◽  
Ender Soydan ◽  
...  

Abstract Introduction: Post-induction minimal residual disease (MRD) within but not outside (peripheral blood/stem cell graft) of marrow among transplant eligible patients with multiple myeloma (MM) is currently recognized as poor-prognostic. Emerging number of studies are evaluating MRD within the context of cytogenetic risk. In this study we aimed to quantify circulating plasma cells (PCs) by flow in apheresis products (graft=gMRD) and compare with marrow MRD(mMRD) and outcome according to cytogenetics. Patients & Methods: Four hundred eleven subsequent newly diagnosed multiple myeloma (NDMM) patients transplanted (AHCT) between September 2006 - June 2021 were included prospectively. Standard-risk cytogenetics(SR) is defined as t(11;14), t(6;14), or a normal karyotype , whereas del(17p13), t(4;14), t(14;16), t(14;20), + 1q21 and complex findings are high-risk cytogenetics (HR). In the sample drawn for HPSC quantification of the graft and bone marrow, the number of clonal PCs were quantified by Flow. CD27 PC7 orCD27 A750, CD56 A700, CD19 ECD, CD38 FITC orCD38 A750, CD138 APC, CD45 KO, CD81 PE, CD117 PC7, polyclonal Rabbit Anti-Human Kappa or Lambda Chains /FITC antibodies and acquisition of at least 10 5 cells per tube Analysis was performed using the Navios Flow Cytometer (3L10C, Beckman Coulter) using the Kaluza software (Beckman Coulter, USA) according to the criteria defined by Montero et al and also abnormal distribution of kappa vs. Lambda expression. Undetectable MRD was defined as absence of clonal PCs at a sensitivity of 10 -4 prior to 2017(n=217) and 10 -5 after 2017(n=131). MRD assessment is similar in the graft and marrow. Impact of postinduction MRD analysis was performed in 131 patients with MRD data of 10 -5 sensitivity level. Results were reported in the intention-to-treat (ITT) population for mMRD. Results: Median follow-up after AHCT was 61.5 months (range:3.2-168) (prior to 2017) and 17.7 months (range: 3-47.4) (after 2017). Induction regimen consisted of bortezomib without or with immunomodulatory drug (IMID) 78.8%, 2.8% (prior to 2017) and 74.1%, 22.9% (after 2017). Consolidation 18% (n=39/217), 22.1% (n=29/131) (prior and after 2017) and maintenance 21.2% (n=46/217), 35.1% (n=46/131) (prior and after 2017) were administered based on the response to AHCT. Cytogenetically HR was observed 14.1% (n=47) (among total cohort) and 15.8% (n=19) (after 2017 cohort). Post-induction biochemical response distribution among patients with undetectable MRD are shown in Table-1. MRD assessments were performed at a sensitivity of 10 -4 and 10 -5 in graft (n=147 and 76), marrow (n=18 and 4) or both (n=52 and 51). A statistically significant correlation was detected between marrow and graft MRD only at sensitivity level 10 -5 (SE: 0.638, p&lt;0.001). Additionally, correlations between CR and gMRD (Kappa coefficient (SE): -0.284, p=0.03); CR and mMRD (SE: -0.452, p:0.001) were found. Since marrow and graft MRD results are correlated, all graft and marrow results were merged for the multivariate analysis (MVA) (Table-2). Having undetectable vs detectable MRD in either graft or marrow estimates a 2 years-PFS of 83.6% vs 46.5% (p=0.007). Among 42 MRD(-) patients, only four (two with HR)have relapsed. There is a tendency for better two year probability of PFS with undetectable mMRD vs gMRD at 10-5 ( not reached vs 84.7% ; ns)(Figure 1). The patients (after 2017) are divided into four groups according to MRD status and cytogenetic risk stratification: MRD(-)SR (n=35; 29.2%), MRD(-)HR (n=7; 5.8%), MRD(+)SR (n=66; 55%), MRD(+)HR (n=12; 10%). Kaplan-Meier curves revealed significant differences in PFS among these groups (p=0.03) (Figure-2). Conclusion: Our real-world triplet drug induction-based experience shows for the first-time post-induction mMRD and MRD to be correlated with each other and with PFS. PFS with MRD(-) at 10 -5 results have displayed a better outcome compared to 10 -4. MVA showed MRD and age to determine PFS, independent from post-induction CR, ISS and cytogenetic risk. Although observed less frequently, achieving post-induction MRD(-) either in graft or marrow may ameliorate the poor prognosis of HR. With improvement in induction it may be possible to achieve more frequent MRD(-) and thus analyze the impact of each cytogenetics risk group ie 1q amplification separately. Furthermore, MRD in graft may be a non-invasive therapeutic efficacy tool which is subject to less sampling variation. Figure 1 Figure 1. Disclosures Beksac: Amgen,Celgene,Janssen,Takeda,Oncopeptides,Sanofi: Consultancy, Speakers Bureau.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2658-2658
Author(s):  
Aarif Ahsan ◽  
Ann Polonskaia ◽  
Chih-Chao Hsu ◽  
Chad C Bjorklund ◽  
Maria Ortiz Estevez ◽  
...  

Abstract Introduction: The Myeloma Genome Project (MGP) characterized the genomic landscape of patients with newly diagnosed multiple myeloma (NDMM) (Walker BA, et al. Blood 2018; 132[6]:587-597). Using a multi-omics unsupervised clustering approach, 12 molecularly-defined disease segments were identified (Ortiz M, et al. Blood 2018; 132[suppl 1]:3165). Here, we performed experimental validation of CDC28 Protein Kinase Regulatory Subunit 1B (CSK1B) that was identified as a putative target from the disease segment with poorest clinical outcome. CKS1B was selected for in-depth validation due to their role in cell cycle pathways associated with high-risk disease, biological mechanisms of chromosome 1q amplification and druggability. Methods: Association of CKS1B with outcomes was analyzed in NDMM patients, across relapses and with clinical outcome datasets from MGP and Mayo clinic. Inducible shRNAs of CKS1B and bromodomain containing protein 4 (BRD4, a member of the BET [bromodomain and extra terminal domain] family) were generated in MM cell lines. BRD4 and Aiolos ChIP-seq datasets were analyzed for binding on CKS1B gene. BRD4 inhibitors JQ1 and CC-90010 were utilized for inhibition studies in MM cell lines. Results: Higher expression of CKS1B was associated with significantly poorer PFS, OS, disease severity and relapse. Knock-down of CKS1B in MM cells led to a significant decrease in proliferation (P&lt;0.001) and enhanced apoptosis in MM cell lines. BRD4-ChIP sequencing studies revealed that the expression of CKS1B was regulated by super-enhancer (SE) associated elements. As expected, two BRD4 inhibitors, JQ1 and CC-90010 and inducible BRD4 shRNAs downregulated the expression of CKS1B resulting in decreased proliferation, cell cycle arrest and apoptosis in MM cell lines. Furthermore, MM cell lines harboring chromosome 1q gain/amp showed higher sensitivity to BRD4 inhibition compared to cell lines with normal 1q copy number. Mechanistic studies revealed that BRD4inh and BRD4 shRNAs downregulated the expression of Aiolos and Ikaros in MM cell lines. Interestingly, Aiolos ChIP-sequencing studies demonstrated the binding of Aiolos at the transcriptional start sites of CKS1B with the transcriptional activation mark. The immunomodulatory agent (IMiD ®) pomalidomide (Pom) transcriptionally downregulated CKS1B in Pom-sensitive cells downstream of Aiolos, Ikaros degradation. Based on these mechanisms, IMiD agents, lenalidomide, Pom and the novel Cereblon E3 ligase modulating degrader (CELMoD ®) agent CC-92480 in combination with BRD4inh promoted a synergistic decrease in proliferation, cell cycle arrest and increase in apoptosis in both Pom-sensitive and -resistant cell lines. The combination of IMiD or novel CELMoD agent with BRD4inh also promoted deeper downregulation of CKS1B, Aiolos, Ikaros, c-Myc and survivin proteins with enhanced levels of apoptotic marker cleaved Caspase 3 as compared to single agents alone. Conclusions: In summary, we have identified CKS1B as a key target associated with poor outcome in MM patients. Translational studies suggest a profound downregulation of CKS1B and key pro-survival effector proteins following combination treatment with BRD4inh and IMiD agents/novel CELMoD agents resulting in synergistic anti-tumor effects. These data provide rationale for testing these agents in the clinic for high-risk and IMiD-relapsed patients. Figure: Changes in cell proliferation and protein levels of key signaling mediators were studied in K12PE cell line treated with increasing doses of Lenalidomide, Pomalidomide and CC-92480 in combination with JQ1. Figure 1 Figure 1. Disclosures Ahsan: BMS: Current Employment, Current equity holder in publicly-traded company. Polonskaia: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Hsu: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Bjorklund: BMS: Current Employment, Current equity holder in publicly-traded company. Ortiz Estevez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Bahlis: Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Genentech: Consultancy; Pfizer: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria. Pourdehnad: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties: No royalty. Flynt: BMS: Current Employment, Current equity holder in publicly-traded company. Ahsan: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3473-3473
Author(s):  
Adam Bryant ◽  
Patrick Hilden ◽  
Sergio Giralt ◽  
Miguel-Angel Perales ◽  
Guenther Koehne

Abstract Introduction Despite recent therapeutic advances Multiple Myeloma (MM) remains largely incurable, and outcomes in patients who develop resistance to imid or proteasome inhibitor therapies are universally dismal.1 Allogeneic hematopoietic cell transplant (alloHCT) remains the only curative MM treatment but has been associated with historically high rates of GVHD and of non-relapse mortality (NRM), exceeding 40% in some series.2 Although these rates have decreased in recent years, the potential morbidity and mortality associated with alloHCT and the increasing availability of alternative non-transplant therapies demands a thoroughly informed pre-alloHCT assessment. Here we assess the impact of pre-alloHCT variables on clinical outcomes in a large cohort of relapsed/refractory (RR) MM patients who underwent CD34+ selected alloHCT at our institution. Methods This retrospective study included all MM patients who had CD34+ selected alloHCT from Jun 2010 to Dec 2015. Patients were conditioned with targeted dose busulfan (0.8 mg/kg x 10), melphalan (70 mg/m2 x 2) and fludarabine (25mg/m2 x 5) followed by infusion of a CD34+ selected peripheral blood stem cell graft, without post alloHCT GVHD prophylaxis. Estimates were given using the Kaplan-Meier and cumulative incidence methods. Competing risks for relapse, NRM, and GVHD were death, relapse, and relapse or death respectively. The log-rank and Gray's test were used to assess univariable associations. GVHD by 6 months was assessed via a landmark analysis. Results Our 73 patient cohort had a median age of 55 (37-66) and was mostly male (74%). Most patients had low risk MM by ISS (50/66, 76%) and intermediate risk MM by R-ISS (45/66, 68%) at pre-salvage assessment. Patients had a median of 4 (2-9) pre-alloHCT lines of therapy and were evenly split between patients in PR and in VGPR or CR at time of alloHCT (50% and 49%). Median HCT-CI score was 2 (range 0-6) with the majority of patients graded as intermediate or high risk (score ≥1; 55/73, 75%). At a median follow-up in survivors of 35 months (12-84) OS and PFS rates were 70% and 53% at 1 year (95% CI 58-79, 41-64) and 50% and 30% at 3 years, respectively (38-62, 19-41). The cumulative incidences of relapse were 25% and 47% at 1 and 3 years, respectively (16-35, 35-58), and 1 year NRM was 22% (13-32). Deaths were balanced between relapse and non-relapse causes (54% and 46% respectively). Incidence of grade II-IV acute GVHD was 7% at 100 days (3-14), and of chronic GVHD was 8% at 1 year (3-16). In univariable analysis, intermediate-high risk ISS assessed prior pre-alloHCT salvage therapy was associated with lower OS (3 year 30 v 54%, p=0.037), lower PFS (3 year 9 v 33%, p=0.013), and greater relapse incidence (3 year 72 v 39%, p=0.004). Older age and GVHD prior to 6 months were also associated with lower OS; older age, more heavily pre-treated disease, and worse disease status at alloHCT were associated with lower PFS; and heavier pre-alloHCT treatment was also associated with higher relapse (Table 1). Higher HCTCI was not associated with increased NRM (1 year 22 v 16 v 27% for HCTCT 0, 1-2, ≥3 respectively; p = 0.863). Discussion We describe a cohort of high-risk heavily pretreated RRMM patients with durable OS (50% at 3 years), comparatively low PFS (30% at 3 years), and historically improved rates of NRM (22% at 1 year). We also importantly identified numerous pre-alloHCT variables that were associated with survival, PFS, and relapse. Amongst these, poor ISS measured prior to pre-alloHCT salvage was consistently associated with worse survival and relapse outcomes and may speak to this score's utility as a dynamic measure of disease risk in patients exposed to multiple lines and therapy. Conclusions Our report reinforces that CD34+ selected alloHCT can achieve prolonged disease control and long term survival in high risk, heavily treated refractory MM populations, and newly describes certain pre-transplant variables that may help identify patients with better potential survival and relapse outcomes. Given the dismal prognosis and lack of established alternate therapies for RRMM patients, we advocate that identification of favorable or adverse pre-transplant variables during pre-alloHCT assessment be used to inform alloHCT decision-making rather than to exclude certain patient cohorts from this potentially effective and curative treatment option. Disclosures Perales: Abbvie: Other: Personal fees; Merck: Other: Personal fees; Incyte: Membership on an entity's Board of Directors or advisory committees, Other: Personal fees and Clinical trial support; Takeda: Other: Personal fees; Novartis: Other: Personal fees.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 2-2
Author(s):  
Anil Aktas-Samur ◽  
Mariateresa Fulciniti ◽  
Sanika Derebail ◽  
Raphael Szalat ◽  
Giovanni Parmigiani ◽  
...  

Multiple Myeloma is preceded by precursor states of monoclonal gammopathy of undermined significance (MGUS) and smoldering multiple myeloma (SMM). Studies have shown that progression to symptomatic MM five years after diagnosis is 1% for MGUS and 10% for SMM. However, based on the genomic background, this rate is highly variable, especially for SMM patients. Recent studies have evaluated the high-risk genomic features for SMM, but the genomic background of SMM patients who do not progress to MM after long-term follow-up (&gt;= 5 years) has not been described. Here, we evaluated genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within a short time (N=71) as well as newly diagnosed Myeloma (N=192). We also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to Myeloma. We report a similar large-scale CN alteration in non-progressor SMM compared to progressor SMMs or MM at diagnosis. However, whole-genome sequencing data showed that the overall mutational load for non-progressor SMM samples was lower than Progressor MGUS/SMM (median SNV 5460 vs. 7018). This difference significantly increased for mutations affecting the coding regions. NP samples at diagnosis had 26% and 53% less coding mutations (missense, nonsense, and frameshift mutations) compared to progressor MGUS/SMM (p=0.008) and newly diagnosed MM (p &lt; 0.001) respectively. We observed very low NRAS (3%, OR=8.86) and BRAF (3%, OR=2.17), mutation frequency in non-progressor SMM samples compared to newly diagnosed MM. We did not observe driver mutations in FAM46C, TTN, CYLD, TP53, KMT2C, IRF4, HIST1H1E that are otherwise frequently mutated in high-risk SMM or symptomatic MM. None of the non-progressor SMM samples had MYC alteration. We observed t(11;14), t(4;14), and t(14;16) translocations at similar frequency compared to newly diagnosed MM samples. We also observed a significant difference in non-recurrent focal deletions. Based on our recent data in newly-diagnosed MM, we quantified genomic scar score, and observed that non-progressor SMM have lower GSS (median=3,IQR=[1-9]) compared to progressor MGUS/SMM (median=11,IQR=[5-15] / median=9,IQR=[9-15], respectively) as well as MM samples at diagnosis (median=9, IQR= [5-16],p=0.002). We further validated this observation in an independent cohort with 69 SMM samples in whom progressor SMM patients had high GSS (median =4, IQR=[2-7.75]), compared to delayed progressor (&gt; four years) or non-progressor SMM (median =1.5, IQR= [0-3.5]; p=0.029). Moreover, non-progressor SMM had significantly low utilization of APOBEC and DNA repair mutational processes. Next, we compared non- progressor SMM with progressor SMM using RNAseq data. We identified 1653 differentially expressed genes (DEG) (762 up-regulated and 891 down-regulated). Genes that were upregulated in non-progressor SMM samples were enriched in IL6/JAK/STAT3 and IL2/STAT5 signaling and the regulation of cytokine secretion. Whereas genes up-regulated in progressor SMM were enriched in MYC targets, DNA repair, and mTOR pathways. Moreover, genes that control the translational initiation, translational elongation, mitochondrial translation, and ATP control were among the top highly expressed genes in progressor SMM. We used our MGUS/SMM to MM paired samples and showed that the E2F target, MYC target, and G2/M checkpoint pathways are more active at MM. We measured the distance between progressor and non-progressor SMM as well as MM and found that non-progressor SMM is less similar to MM compared to progressor SMM. In conclusion, the global CNA and translocations are similar between progressor and non-progressor SMM and symptomatic MM and confirm their role in the development of precursor condition but not adequate for progression to MM, which requires additional hits. On the other hand, lower GSS score reflecting genomic stability along with lower SNVs, low DNA damage and APOBEC mutational processes, down-regulated MYC target genes, and low DNA repair activation define low-risk SMM. These results now provide the basis to develop a genomic definition of SMM. Disclosures Fulciniti: NIH: Research Funding. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; C4: Current equity holder in private company; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.


2017 ◽  
Author(s):  
Rosalie G. Waller ◽  
Todd M. Darlington ◽  
Xiaomu Wei ◽  
Michael J. Madsen ◽  
Alun Thomas ◽  
...  

ABSTRACTThe high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance – a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691*, p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly, p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.AUTHOR SUMMARYAlthough family-based studies demonstrate inherited variants play a role in many common and complex diseases, finding the genes responsible remains a challenge. High-risk pedigrees, or families with more disease than expected by chance, have been helpful in the discovery of variants responsible for less complex diseases, but have not reached their potential in complex diseases. Here, we describe a method to utilize high-risk pedigrees to discover risk-genes in complex diseases. Our method is appropriate for complex diseases because it allows for genetic-heterogeneity, or multiple causes of disease, within a pedigree. This method allows us to identify shared segments that likely harbor disease-causing variants in a family. We apply our method in Multiple Myeloma, a heritable and complex cancer of plasma cells. We identified two genes USP45 and ARID1A that fall within shared segments with compelling statistical evidence. Exome sequencing of these genes revealed likely-damaging variants inherited in Myeloma high-risk families, suggesting these genes likely play a role in development of Myeloma. Our Myeloma findings demonstrate our high-risk pedigree method can identify genetic regions of interest in large high-risk pedigrees that are also relevant to smaller nuclear families and overall disease risk. In sum, we offer a strategy, applicable across phenotypes, to revitalize high-risk pedigrees in the discovery of the genetic basis of common and complex disease.


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