scholarly journals Whole-Genome Sequencing Reveals Evidence of Two Biologically and Clinically Distinct Entities: Progressive Versus Stable Myeloma Precursor Disease

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 47-48
Author(s):  
Bénedith Oben ◽  
Guy Froyen ◽  
Kylee H Maclachlan ◽  
Binbin Zheng-Lin ◽  
Venkata Yellapantula ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Trials ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Research Funding ◽  
Data Monitoring ◽  
Whole Genome ◽  
Independent Data ◽  
Genetics Research ◽  
Complex Events

Introduction Multiple myeloma (MM) is consistently preceded by an asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). Here, we present the first comprehensive whole-genome sequencing (WGS) analysis of patients with MGUS and SMM. Methods To characterize the genomic landscape of myeloma precursor disease (i.e. SMM and MGUS) we performed WGS of CD138-positive bone marrow mononuclear samples from 32 patients with MGUS (N=18) and SMM (N=14), respectively. For cases with low cellularity resulting in low amounts of extracted DNA (N=15), we used the low-input enzymatic fragmentation-based library preparation method (Lee-Six et al, Nature 2019). Myeloma precursor disease samples were compared with 80 WGS of patients with MM. All WGSs (N=112) were investigated using computational tools available at the Wellcome Sanger Institute. Results After a median follow up of 29 months (range: 2-177), 17 (53%) patients with myeloma precursor disease progressed to MM (13 SMM and 4 MGUS). To interrogate the genomic differences between progressive versus stable myeloma precursor disease we first characterized the single base substitution (SBS) signature landscape. Across the entire cohort of plasma cell disorders, all main MM mutational signatures were identified: aging (SBS1 and SBS5), AID (SBS9), SBS8, SBS18, and APOBEC (SBS2 and SBS13). Interestingly, only 2/15 (13%) stable myeloma precursor disease cases showed evidence of APOBEC activity, while 14/17 (82%) and 68/80 (85%) patients with progressive myeloma precursor disease (p=0.0058) and MM (p=0.004), respectively, had APOBEC mutational activity. The two stable cases with detectable APOBEC were characterized by a high APOBEC3A:3B ratio, a feature which defines a group of MAF-translocated MM patients whose pathogenesis is characterized by intense and early APOBEC activity (Rustad et al Nat Comm 2020) and is distinct from the canonical ~1:1 APOBEC3A:3B mutational activity observed in most cases. When exploring the cytogenetic landscape, no differences were found between progressive myeloma precursor disease and MM cases. Compared to progressors and to MM, patients with stable myeloma precursor disease were characterized by a significantly lower prevalence of known recurrent MM aneuploidies (i.e. gain1q, del6q, del8p, gain 8q24, del16q) (p<0.001). This observation was validated using SNP array copy number data from 78 and 161 stable myeloma precursor disease and MM patients, respectively. To further characterize differences between progressive versus stable myeloma precursor disease, we leveraged the comprehensive WGS resolution to explore the distribution and prevalence of structural variants (SV). Interestingly, stable cases were characterized by low prevalence of SV, SV hotspots, and complex events, in particular chromothripsis and templated insertions (both p<0.01). In contrast, progressors showed a genome wide distribution and high prevalence of SV and complex events similar to the one observed in MM. To rule out that the absence of key WGS-MM defining events among stable cases would reflect a sample collection time bias, we leveraged our recently developed molecular-clock approach (Rustad et al. Nat Comm 2020). Notably, this approach is based on pre- and post-chromosomal gain SBS5 and SBS1 mutational burden, designed to estimate the time of cancer initiation. Stable myeloma precursor disease showed a significantly different temporal pattern, where multi-gain events were acquired later in life compared to progressive myeloma precursor disease and MM cases. Conclusions In summary, we were able to comprehensively interrogate for the first time the whole genome landscape of myeloma precursor disease. We provide novel evidence of two biologically and clinically distinct entities: (1) progressive myeloma precursor disease, which represents a clonal entity where most of the genomic drivers have been already acquired, conferring an extremely high risk of progression to MM; and (2) stable myeloma precursor disease, which does not harbor most of the key genomic MM hallmarks and follows an indolent clinical outcome. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; National Cancer Institute: Research Funding; AbbVie: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Merck: Other; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.

scholarly journals Copy Number Signatures Predict Chromothripsis and Poor Clinical Outcome in Newly Diagnosed Multiple Myeloma Patients

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 52-53
Author(s):  
Kylee H Maclachlan ◽  
Binbin Zheng-Lin ◽  
Venkata Yellapantula ◽  
Andriy Derkach ◽  
Even H Rustad ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Trials ◽  
Research Funding ◽  
De Novo ◽  
Data Monitoring ◽  
Signature Analysis ◽  
Independent Data ◽  
Genetics Research ◽  
Manual Curation ◽  
Low Coverage

Chromothripsis is emerging as a strong and independent prognostic factor in multiple myeloma (MM), predicting shorter progression-free (PFS) and overall survival (Rustad BioRxiv 2019). Reliable detection requires whole genome sequencing (WGS), with 24% prevalence in 752 newly diagnosed multiple myeloma (NDMM) from CoMMpass (NCT01454297, Rustad BioRxiv 2019) compared with 1.3% by array-based techniques (Magrangeas Blood 2011). In MM, chromothripsis presents differently to solid cancers. Although the biological impact is similar across malignancies, in MM the structural complexity of chromothriptic events is typically lower. In addition, chromothripsis can occur early in MM development and remain stable over time (Maura Nat Comm 2019). Computational algorithms for chromothripsis detection (e.g. ShatterSeek; Cortes-Ciriano Nat Gen 2018) were developed in solid cancers and are accurate in that setting. Running ShatterSeek on 752 NDMM patients with low coverage WGS from CoMMpass, we observed a high specificity for chromothripsis (98.3%) but poor sensitivity (30.2%). ShatterSeek detected chromothripsis in 64/752 samples (8.5%), with 85% confirmed on manual curation; however, missed 114 cases located by manual curation. This indicates that MM-specific computational methods are required. We hypothesized that a signature analysis approach using copy number variation (CNV) may provide an accurate estimation of chromothripsis. We adapted CNV signature analysis, developed in ovarian cancer (Macintyre Nat Gen 2018), to now detect MM-specific CNV and structural features. The analysis utilizes 6 fundamental CN features: i) absolute CN of segments, ii) difference in CN in adjacent segments, iii) breakpoints per 10 Mb, iv) breakpoints per chromosome arm, v) lengths of oscillating CN segment chains, and vi) the size of segments. The optimal number of categories in each CNV feature was established using a mixed effect model (mclust R package). Using CoMMpass low-coverage WGS, de novo extraction using the hierarchical dirichlet process defined 5 signatures, 2 of which (CNV-SIG 4 and CNV-SIG 5) contain features associated with chromothripsis: longer lengths of oscillating CN states, higher numbers of breakpoints / chromosome arm, and higher total numbers of small segments of CN change. Next, we demonstrate that CNV signatures are highly predictive of chromothripsis (average area-under-the-curve /AUC = 0.9, based on 10-fold cross validation). Chromothripsis-associated CNV signatures are correlated with biallelic TP53 inactivation (p= 0.01) and gain1q21 (p<0.001) and show negative association with t(11;14) (p<0.001). Chromothriptic signatures were associated with shorter PFS, with multivariate analysis after correction for ISS, age, biallelic TP53 inactivation, t(4;14) and gain1q21 producing a hazard ratio of 2.9 (95% CI 1.07-7.7, p = 0.036). A validation set of 29 NDMM WGS confirmed the ability of CNV signatures to predict chromothripsis (AUC 0.87). As WGS is currently too expensive and computationally intensive to employ in routine practice, we investigated if CNV signatures can predict chromothripsis without using WGS. First, we performed de novo signature extraction using whole exome data from 865 CoMMpass samples. CNV signatures extracted without reference to WGS produced an AUC = 0.81 for predicting chromothripsis (in those with WGS to confirm; n =752), and the chromothriptic-signatures confirmed the association with a shorter PFS (HR=7.2, 95%CI 1.32-39.4, p = 0.022). Second, we applied CNV signature analysis to NDMM having either the myTYPE targeted sequencing panel (n= 113; Yellapantula, Blood Can J 2019) or a single nucleotide polymorphism (SNP) array (n= 217). CNV signature assessment by each technology was predictive of clinical outcome, likely due to the detection of chromothripsis. As with WGS, multivariate analysis confirmed CNV signatures to be independently prognostic (myTYPE; p = 0.003, SNP; p = 0.004). Overall, we demonstrate that CNV signature analysis in NDMM provides a highly accurate prediction of chromothripsis. CNV signature assessment remains reliable by multiple surrogate measures, without requiring WGS. Chromothripsis-associated CNV signatures are an independent and adverse prognostic factor, potentially allowing refinement of standard prognostic scores for NDMM patients and providing a more accurate risk stratification for clinical trials. Disclosures Hultcrantz: Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding; Intellisphere LLC: Consultancy. Dogan:Takeda: Consultancy; National Cancer Institute: Research Funding; Roche: Consultancy, Research Funding; Seattle Genetics: Consultancy; AbbVie: Consultancy; EUSA Pharma: Consultancy; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Karyopharm: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Cellectis: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria.

scholarly journals The Genomic Complexity of Multiple Myeloma Precursor Disease Can be Predicted Using Copy Number Signatures on Targeted Sequencing and SNP Array Data

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 10-10
Author(s):  
Kylee H Maclachlan ◽  
Binbin Zheng-Lin ◽  
Venkata Yellapantula ◽  
Even H Rustad ◽  
Benjamin Diamond ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Trials ◽  
Copy Number ◽  
Research Funding ◽  
Snp Array ◽  
Targeted Sequencing ◽  
Data Monitoring ◽  
Independent Data ◽  
Genetics Research ◽  
Chromosome Arm

Introduction Current clinical models for predicting the progression from myeloma precursor disease (smoldering multiple myeloma (SMM) and monoclonal gammopathy of undetermined significance (MGUS)) to multiple myeloma (MM) are based on tumor burden, and not designed to capture heterogeneity in tumor biology. With the advent of whole genome sequencing (WGS), complex genomic change including the catastrophic event of chromothripsis has been detected in a significant fraction of MM patients. Chromothripsis is associated with other features of aggressive biology (i.e. biallelic TP53 deletion and increased APOBEC activity), and in newly diagnosed MM (NDMM), patients harboring chromothripsis have a shorter progression free survival (PFS) (Rustad BioRxiv 2019). Chromothripsis has also been demonstrated in SMM which later progressed to MM (Maura Nat Comm 2019) and our preliminary results indicate that the absence of chromothripsis is associated with stable precursor disease (Oben ASH 2020). We have demonstrated that chromothripsis can be accurately predicted in NDMM using copy-number variation (CNV) signatures on both WGS and whole exome sequencing (Maclachlan ASH 2020). As with WGS, CNV signature analysis in less comprehensive assays (e.g. targeted sequencing panels and single nucleotide polymorphism (SNP) arrays) demonstrated that chromothripsis-associated CNV signatures are associated with shorter PFS. The aim of this study was to define the landscape of CNV signatures in myeloma precursor disease, and to compare the results with CNV signatures in NDMM. Methods CNV signature analysis uses 6 fundamental features: i) breakpoint count per 10 Mb, ii) absolute CN of segments, iii) difference in CN between adjacent segments, iv) breakpoint count per chromosome arm, v) lengths of oscillating CN segments, and vi) the size of segments (Macintyre Nat Gen 2018). The number of subcategories for each feature (which may differ between cancer and assay types) was established using a mixed effect model (mclust R package). For both targeted sequencing (myTYPE panel; (n=19, 4 MGUS, 15 SMM) and SNP array (n=78, 16 MGUS, 62 SMM), de novo CNV signature extraction was performed by hierarchical dirichlet process, running the analysis together with NDMM samples for reliable signature detection. Results Our analysis identified 4 and 6 CNV signatures from myTYPE and SNP array data respectively, with the extracted signatures being analogous to those from WGS, which are highly predictive of chromothripsis (Maclachlan ASH 2020). Compared with NDMM (myTYPE; n=113; SNP array; n=217), precursor samples contained significantly fewer breakpoints / chromosome arm (myTYPE; p= 0.0003, SNP; p <0.0001), fewer breakpoints / 10 Mb (both; p <0.0001), shorter lengths of oscillating CN (myTYPE; p= 0.013, SNP; p= 0.018), fewer jumps between CN states (myTYPE; p= 0.0043, SNP; p < 0.0001), lower absolute CN (myTYPE; p= 0.0059, SNP; p < 0.0001) and fewer small segments of CN change (myTYPE; p= 0.0007, SNP; p= 0.0008). Chromothripsis-associated CNV signatures were significantly enriched in NDMM compared to precursor disease (p<0.0001), with only 8.2% of precursors having a significant contribution from these signatures (NDMM; 38.7%). Overall, every CNV feature consistent with chromothripsis was measured at a significantly lower level in precursors than NDMM. As <5% of the precursors have progressed to MM, and given that we see heterogeneity in the pattern of CNV abnormalities both between MM and precursor disease, and within patients with precursor disease, we are currently investigating the role of CNV abnormalities in relation to clinical progression. As an interim measure; restricting analysis to patients with clinical stability >5 years (n=11), we observed chromothripsis-associated signatures to be absent in all samples. Conclusion All individual CN features comprising chromothripsis-associated CNV signatures are significantly lower in stable myeloma precursor disease compared with NDMM when assessed by targeted sequencing and SNP array, along with a lower contribution from chromothripsis-associated signatures. Given the adverse impact of chromothripsis in MM, these data show great promise towards the future refinement of risk prediction estimation in myeloma precursor disease. Our ongoing work involves extending CNV analysis into larger datasets, including precursor patients who subsequently progressed to MM. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; AbbVie: Consultancy; National Cancer Institute: Research Funding. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Merck: Other; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.

scholarly journals The Role of 18f-FDG-PET/CT in Characterizing Depth of Response in High Risk Smoldering Multiple Myeloma Patients Treated with Carfilzomib, Lenalidomide, and Dexamethasone (KRd)

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 11-12
Author(s):  
Elizabeth Hill ◽  
Neha Korde ◽  
Candis Morrison ◽  
Alexander Dew ◽  
Ashley Carpenter ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Flow Cytometry ◽  
Clinical Trials ◽  
Maintenance Therapy ◽  
Research Funding ◽  
Data Monitoring ◽  
Independent Data ◽  
Genetics Research ◽  
Pet Ct

Introduction A direct association exists between minimal residual disease (MRD) negativity and prolonged survival in multiple myeloma (MM) (Landgren et al, BMT 2016). 18F-fluoro-deoxy-glucose (FDG) positron emission tomography-computed tomography (PET/CT) is a recommended monitoring technique for patients with MM as persistence of FDG uptake after induction therapy, prior to maintenance, is an independent risk factor for progression. Therefore PET/CT and MRD detection in the bone marrow are complementary prognostic tools prior to initiation of maintenance therapy. In patients with smoldering multiple myeloma (SMM), the presence of a focal FDG-avid lesion without underlying osteolytic lesion on PET/CT is associated with rapid progression to MM. However, little is known about the prognostic value of PET/CT for SMM patients receiving treatment. Herein, we show that treatment of high risk (HR)-SMM with carfilzomib, lenalidomide, and dexamethasone with lenalidomide maintenance (KRd-R) leads to sustained remissions detected on PET/CT imaging. Methods Trial design including key results for KRd-R in HR-SMM (NCT01572480) has been submitted to the meeting separately (abstract ID: 136148). As part of the study design, all eligible patients had bone marrow biopsies with multicolor flow cytometry (MRD sensitivity, 10-5) and whole-body PET/CT performed at baseline and at key time points, including achievement of complete response (CR) or completion of KRd induction (8 cycles), after 1 and 2 years of -R maintenance, and annually thereafter. PET/CTs were evaluated by nuclear medicine radiologists blinded to flow cytometry and considered positive if at least one focal hypermetabolic (above background reference) lesion and/or heterogenous bone marrow involvement were present, as defined by the IMWG (Hillengass et al. Lancet Oncol 2019). Results As of data cutoff, 46 patients had completed at least 8 cycles of therapy and had 2 sequential PET/CTs performed. By the end of induction therapy, no patient developed progressive disease and the overall response rate was 100%. Approximately 72% of patients with baseline negative PET/CTs remained negative, 11% of patients had resolution of previous focal/heterogenous FDG avidity, 15% of patients had decrease or stable focal/ heterogenous lesions, and 2% developed new focal lesions. Table 1 shows the results at subsequent time points of one and two years of maintenance therapy. Throughout this time period, one patient developed a lytic lesion after 1 year of maintenance therapy. However, 3 patients had either resolution or decrease in focal/heterogenous lesions. Specifically, after 8 cycles of combination therapy, 33 patients (70.2%, 95% CI 55.9 - 81.4%) had a response of MRD negative CR based on bone marrow flow cytometry and 26 patients (55.3%; 95% CI 41.2-68.6%) had a negative PET/CT in addition to MRD negative CR (Table 2). Conclusions It is important to evaluate the tools used in MM response assessment specifically in the SMM population as more studies report results of treatment in this population. MRD information can be used as a biomarker to evaluate the efficacy of different treatment strategies. This study demonstrates an exceptionally high rate of concordance between MRD negativity by flow cytometry and negative PET/CT after 8 cycles of KRd. However, 15% of patients were MRD negative yet had positive findings on PET/CT. While these lesions were not biopsy proven, some resolved during maintenance therapy. Further follow-up is needed to determine whether early MRD negativity in bone marrow with negative PET/CT correlates to longer overall survival and decreased progression to MM compared to those patients with a positive PET/CT. The use of PET/CT imaging may increase our understanding in assessing depth response to treatment in HR-SMM patients and be an important outcome predictor. Disclosures Korde: Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Landgren:Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Juno: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other.

scholarly journals Initial Whole Genome Sequencing of Plasma Cell Neoplasms in First Responders and Recovery Workers Exposed to the World Trade Center Attack of September 11, 2001

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 50-51
Author(s):  
Benjamin Diamond ◽  
Kylee H Maclachlan ◽  
Andriy Derkach ◽  
Venkata Yellapantula ◽  
Even H Rustad ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Trials ◽  
Plasma Cell ◽  
Research Funding ◽  
First Responders ◽  
Data Monitoring ◽  
Independent Data ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Plasma Cell Neoplasms

PURPOSE : The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. A higher incidence of multiple myeloma (MM) and precursor disease has been reported among first responders to the WTC disaster compared to the unexposed population (Landgren, 2018). To expand on prior screening studies, and to characterize the genomic impact of the exposure to known and potential carcinogens in the WTC debris, we were motivated to perform whole genome sequencing (WGS) of WTC first responders and recovery workers who were diagnosed with a plasma cell disorder after the attack. PATIENTS AND METHODS: We performed WGS of 9 CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after exposure to the WTC disaster: 4 monoclonal gammopathy of undetermined significance (MGUS), 2 smoldering multiple myeloma (SMM), 2 MMs, and 1 patient with plasma cell leukemia (PCL). Eight patients (88%) were first responders and one was a recovery worker. Peripheral blood mononuclear cells were used as normal match. Median coverage for tumor and normal samples was 50.9X (range 47-76) and 37X (range 35-41), respectively. The landscape of genomic drivers and complex structural events was compared to 752 MM patients enrolled in the CoMMpass trial with available whole exome and low-coverage long-insert WGS data (IA15; NCT01454297). To characterize the mutational signature landscape we combined the WTC cohort with 110 whole genomes from 56 patients with multiple myeloma and myeloma precursor disease (Rustad et al. 2020; Landau et al. 2020) and we ran our three-step workflow: de novo extraction (i.e. sigprofiler), assignment, and fitting (i.e. mmsig). To exclude contribution of any environmental agents in the WTC debris with known mutational signatures (Kucab et al., 2019), we ran our fitting algorithm mmsig in each post-WTC case, including and forcing the extraction of these mutational signatures. RESULTS: No significant differences were observed in comparing the post-WTC driver and mutational signatures landscape with 110 previously published WGS from 56 patients with MM and the CoMMpass WGS cohort (n=752). Likewise, we did not observe any new or distinct mutational signatures among WTC-exposed patients. Following forced extraction of 5 mutational signatures associated with environmental agents detected in the WTC debris (e.g. PAHs), we did not find significant contributions from any of these described environmental mutational signatures. To reconstruct the temporal activity of each mutational process we divided all single nucleotide variants into subclonal and clonal. Clonal mutations were further subdivided into duplicated (acquired before a chromosomal gain) and unduplicated (Rustad et al. 2020). WTC-exposed patients had differing patterns in mutational signature timelines of AID and APOBEC activity. Overall, the mutational signature activity over time in post-WTC plasma cell dyscrasia reflects what has been previously observed in multiple myeloma without WTC-exposure (Rustad et al., 2020). Finally, leveraging constant activity of the clock-like single base substitution mutational signatures 1 and 5 over time and our molecular time workflow (Rustad et al., 2020), we estimated the age at which each evaluable patient acquired a tumor-initiating chromosomal gain and found that they were windowed to both pre- and post-WTC exposure across neoplasms (Figure 1). In some cases, clonal multi-chromosomal gain events were acquired decades before both the diagnosis and the WTC exposure. Specifically, of 6 patients with large clonal chromosomal gains, 1 MM case, 1 SMM, and 1 MGUS showed evidence of a pre-existing clone prior to WTC exposure, two MGUS showed evidence of multi-gain events following the exposure, and one MM case had a 1q gain in the same time window as the attack. CONCLUSIONS: Post-WTC plasma cell neoplasms had similar genomic landscapes to non-exposed cases. Although limitations in sample size preclude any definitive conclusions, our findings suggest that the observed increased incidence of plasma cell neoplasms in this population is due to complex and heterogeneous effects of the WTC exposure that may have initiated or contributed to progression of malignancy. The existence of pre-malignant clonal entities at time of WTC exposure may therefore be relevant for future WTC-related study. Figure 1 Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Iacobuzio-Donahue:BMS: Research Funding. Papaemmanuil:Isabl: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Illumina: Consultancy, Honoraria; Kyowa Hakko Kirin: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Prime Oncology: Consultancy, Honoraria; MSKCC: Patents & Royalties. Verma:BMS: Consultancy, Research Funding; acceleron: Consultancy, Honoraria; stelexis: Current equity holder in private company; Janssen: Research Funding; Medpacto: Research Funding. Dogan:National Cancer Institute: Research Funding; EUSA Pharma: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Roche: Consultancy, Research Funding; AbbVie: Consultancy. Landgren:Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.

Molecular Predictors of Outcome and Drug Response in Multiple Myeloma: An Interim Analysis of the Mmrf CoMMpass Study

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 194-194 ◽  
Author(s):  
Jonathan J Keats ◽  
Gil Speyer ◽  
Austin Christofferson ◽  
Christophe Legendre ◽  
Jessica Aldrich ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Dna Repair ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Peripheral Blood ◽  
Interim Analysis ◽  
Research Funding ◽  
Whole Genome ◽  
Sequencing Data

Abstract The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) is a longitudinal study of 1147 patients with newly-diagnosed multiple myeloma from clinical sites in the United States, Canada, Spain, and Italy. Each patient receives a treatment regimen containing a proteasome inhibitor, immunomodulatory agent, or both. Clinical parameters are collected at study enrollment and every three months through the eight-year observation period. To identify molecular determinants of clinical outcome each baseline and progression tumor specimen is characterized using Whole Genome Sequencing, Exome Sequencing, and RNA sequencing. Data available as of January 1, 2016 is included in this first formal interim analysis, which includes 995 enrolled patients of whom 851 are molecularly characterized. This cohort of patients includes 74 patients with at least two sequential samples, plus 15 patients with characterized tumor samples from the bone marrow and peripheral blood. The median follow-up of the cohort is 66 weeks, which identified a median PFS of 36 months for the cohort. The median OS was not reached but 76% are still alive at 3 years. Although the age at enrollment by gender is uniform, there is a significant difference in PFS and OS, with males performing worse than females, p=0.001 and p=0.0004 respectively. Analysis of the exome sequencing data from the 746 baseline BM localized tumors identified a median of 122 non-immunoglobulin related mutations per patient, with an interquartile range of 96-155. There is a group of highly mutated (>481 mutations [mean+1SD]) patients who frequently have MAF family translocations (66%) and/or mutations in the DNA repair genes MSH2, MSH3, MSH4, MSH6, or ATM (38%). Across the cohort 21/53 of the DNA repair gene mutations reside in these 21 patients compared to 14/47 MAF family translocations. Analysis of the somatic mutations identified 20 significant genes, which are recurrently mutated and the mutated allele is detectably expressed; BRAF, CYLD, DIS3, FAM46C, FCF1, FGFR3, FUBP1, KRAS, MAX, NFKBIA, NRAS, PRKD2, RASA2, RB1, SAMHD1, SP140, TGDS, TP53, TRAF2, and TRAF3. Integration of the copy number data and the mutation data identified an association between TP53 deletion and mutation, suggesting many patients present with homozygous loss of TP53. Patients with one or two functional TP53 alleles had similar PFS and OS but the patients with zero functional alleles had a significantly reduced OS (p<0.05). Therefore, the existing association between 17p deletion and outcome is driven by the subpopulation of patients with bi-allelic TP53 loss. Analysis of the whole genome sequencing data available from the 719 baseline BM localized tumors for immunoglobulin translocations identified the expected canonical translocations along with events targeting MYC in 14.6% of patients. Unlike the canonical translocations the MYC translocations are enriched in hyperdiploid tumors (22.3%) and often involve light chain loci, particularly the lambda locus. Within the hyperdiploid patients, those with a MYC translocation have a reduced PFS. To identify molecular subtypes we performed unsupervised clustering using a consensus clustering approach on the 613 baseline BM tumors with RNA sequencing data, which identified 12 distinct subtypes. This analysis confirmed previous studies identifing distinct subtypes associated with WHSC1, MAF/MAFA/MAFB translocations and two subtypes associated with CCND1/CCND2/CCND3 subtypes, which can be separated by CD20 expression. Five subtypes are associated with hyperdiploid myeloma, one group is nearly devoid of chromosome 11 gains while this event is ubiquitous in the remaining hyperdiploid groups. The genomic profiles of the paired tumors isolated from the peripheral blood and bone marrow share 87% of the observed events with unique events being more common in the PB compartment suggesting the PB compartments are often derived from a closely related progenitor clone of the bulk BM tumor. Applying our bayesian clonal analysis method to the serial samples identified multiple clones in all patients with some showing no changes in clonal populations while the majority show significant shifts in clonal burden. These analyses have identified tumor initiating mutations and new subtypes of myeloma, which are associated with distinct molecular events and clinical outcomes. Disclosures Niesvizky: Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Wolf:Takeda: Honoraria; Telomere Diagnostics: Consultancy; Amgen: Honoraria; Celgene: Honoraria; Pharmacyclics: Honoraria. Lonial:Onyx: Consultancy; BMS: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Janssen: Consultancy; Merck: Consultancy; Onyx: Consultancy; Celgene: Consultancy; Millenium: Consultancy; Novartis: Consultancy; BMS: Consultancy; Celgene: Consultancy.

scholarly journals Linked-read whole-genome sequencing resolves common and private structural variants in multiple myeloma

2021 ◽  
Author(s):  
Lucía Peña Pérez ◽  
Nicolai Frengen ◽  
Julia Hauenstein ◽  
Charlotte Gran ◽  
Charlotte Gustafsson ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Cohort Analysis ◽  
Genomic Medicine ◽  
Molecular Classification ◽  
Regulatory Elements ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Structural Variants

Multiple myeloma (MM) is an incurable and aggressive plasma cell malignancy characterized by a complex karyotype with multiple structural variants (SVs) and copy number variations (CNVs). Linked-read whole-genome sequencing (lrWGS) allows for refined detection and reconstruction of SVs by providing long-range genetic information from standard short-read sequencing. This makes lrWGS an attractive solution for capturing the full genomic complexity of MM. Here we show that high-quality lrWGS data can be generated from low numbers of FACS sorted cells without DNA purification. Using this protocol, we analyzed FACS sorted MM cells from 37 MM patients with lrWGS. We found high concordance between lrWGS and FISH for the detection of recurrent translocations and CNVs. Outside of the regions investigated by FISH, we identified >150 additional SVs and CNVs across the cohort. Analysis of the lrWGS data allowed for resolving the structure of diverse SVs affecting the MYC and t(11;14) loci causing the duplication of genes and gene regulatory elements. In addition, we identified private SVs causing the dysregulation of genes recurrently involved in translocations with the IGH locus and show that these can alter the molecular classification of the MM. Overall, we conclude that lrWGS allows for the detection of aberrations critical for MM prognostics and provides a feasible route for providing comprehensive genetics. Implementing lrWGS could provide more accurate clinical prognostics, facilitate genomic medicine initiatives, and greatly improve the stratification of patients included in clinical trials.

scholarly journals Genome-Wide Analysis of Non-Coding Alterations in Pan-Myeloid Cancers Using Whole Genome Sequencing

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 103-103
Author(s):  
Yasuhito Nannya ◽  
Kenichi Yoshida ◽  
Lanying Zhao ◽  
June Takeda ◽  
Hiroo Ueno ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Research Funding ◽  
Large Cohort ◽  
Whole Genome ◽  
Structural Variations ◽  
Driver Genes ◽  
Complex Structural ◽  
Short Indels ◽  
Tandem Duplications

Abstract Background Intensive efforts of genome sequencing studies during the past decade identified >100 driver genes recurrently mutated in one or more subtypes of myeloid neoplasms, which collectively account for the pathogenesis of >90% of the cases. However, approximately 10% of the cases have no alterations in known drivers and their pathogenesis is still unclear. A possible explanation might be the presence of alterations in non-coding regions that are not detected by conventional exome/panel sequencing; mutations and complex structural variations (SVs) affecting these regions have been shown to deregulate expression of relevant genes in a variety of solid cancers. Unfortunately, however, no large studies have ever been performed, in which a large cohort of myeloid malignancies were analyzed using whole genome sequencing (WGS) in an attempt to identify a full spectrum of non-coding alterations, even though its efficacy have been demonstrated in many solid cancers. In this study, we performed WGS in a large cohort of pan-myeloid cancers, in which both coding and non-coding lesions were comprehensively analyzed. Patients and methods A total of 338 cases of myeloid malignancies, including 212 with MDS, 70 with AML, 17 with MDS/MPN, 23 with t-AML/MDS, and 16 with MPN were analyzed with WGS, of which 173 were also analyzed by transcriptome sequencing. Tumor samples were obtained from patients' bone marrow (N=269) or peripheral blood (N=69), while normal controls were derived from buccal smear (N=263) or peripheral T cells (N=75). Sequencing of target panel of 86 genes were performed for all samples. Sequencing data were processed using in-house pipelines, which were optimized for detection of complex structural variations (SVs) and abnormalities in non-coding sequences. Results WGS identified a median of 586,612 single nucleotide variants (SNVs) and 124,863 short indels per genome. NMF-based decomposition of the variants disclosed three major mutational signatures, which were characterized by age-related C>T transitions at CpG sites (Sig. A), C>T transitions at CpT sites (Sig. B), and T>C transitions at ApTpN context (Sig. C). Among these, Sig. C showed a prominent strand bias and corresponds to COSMIC signature 16, which has recently been implicated in alcohol drinking. Significant clustering of SNVs and short indels were interrogated across the genome divided into different window sizes (1Kbp, 10Kbp, 100Kbp) or confining the targets to coding exons and known regulatory regions, such as promoters, enhancers/super enhances, and DNase I hypersensitive sites. Recapitulating previous findings, SNVs in the coding exons were significantly enriched in known drivers, including TP53, TET2, ASXL1, DNMT3A, SF3B1, RUNX1, EZH2, and STAG2. We detected significant enrichment of SNVs in CpG islands, and promoters/enhancers. We also detected a total of 8,242 SVs with a median of 15 SVs/sample, which is more prevalent than expected from conventional karyotype analysis. Focal clusters of complex rearrangements compatible with chromothripsis were found in 8 cases, of which 7 carried biallelic TP53 alterations. NMF-based signature analysis of SVs revealed that large (>1Mb) deletions, inversions, and tandem duplications and translocations are clustered together and were strongly associated with TP53 mutations, while smaller deletions and tandem duplications, but not inversions, constitute another cluster. As expected, FLT3-ITD (N=15) and MLL-PTD (N=12) were among the most frequent SVs. Unexpectedly, in addition to known SVs associated with t(8;21) (RUNX1-RUNX1T1) (N=6) and t(3;21) (RUNX1-MECOM) (n=1) as well as non-synonymous SNVs within the coding exons (N=30), we detected frequent non-coding alterations affecting RUNX1, including SVs (N=15) and SNVs around splicing acceptor sites (N=5), suggesting that RUNX1 was affected by multiple mechanism, where as many as 38% of RUNX1 lesions were explained by non-coding alterations. Other recurrent targets of non-coding lesions included ASXL1, NF1, and ETV6. Conclusions WGS was successfully used to reveal a comprehensive registry of genetic alterations in pan-myeloid cancers. Non-coding alterations affecting known driver genes were more common than expected, suggesting the importance of detecting non-coding abnormalities in diagnostic sequencing. Disclosures Nakagawa: Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Usuki:Mochida Pharmaceutical: Speakers Bureau; Astellas Pharma Inc.: Research Funding; Sanofi K.K.: Research Funding; GlaxoSmithKline K.K.: Research Funding; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Kyowa Hakko Kirin Co., Ltd.: Research Funding; Daiichi Sankyo: Research Funding; Celgene Corporation: Research Funding, Speakers Bureau; SymBio Pharmaceuticals Limited.: Research Funding; Shire Japan: Research Funding; Janssen Pharmaceutical K.K: Research Funding; Boehringer-Ingelheim Japan: Research Funding; Sumitomo Dainippon Pharma: Research Funding, Speakers Bureau; Pfizer Japan: Research Funding, Speakers Bureau; Novartis: Speakers Bureau; Nippon Shinyaku: Speakers Bureau; Chugai Pharmaceutical: Speakers Bureau; Takeda Pharmaceutical: Speakers Bureau; Ono Pharmaceutical: Speakers Bureau; MSD K.K.: Speakers Bureau. Chiba:Bristol Myers Squibb, Astellas Pharma, Kyowa Hakko Kirin: Research Funding. Miyawaki:Otsuka Pharmaceutical Co., Ltd.: Consultancy; Novartis Pharma KK: Consultancy; Astellas Pharma Inc.: Consultancy.

scholarly journals Novel Structural Variants Originating in F8 Non-Coding Regions in Previously Unresolved Cases of Severe Hemophilia A

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 379-379
Author(s):  
Marsha M Wheeler ◽  
Barbara A Konkle ◽  
Crystal Watson ◽  
Glenn F. Pierce ◽  
Deborah A Nickerson ◽  
...  
Keyword(s):  
Factor Viii ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Research Funding ◽  
Hemophilia A ◽  
Whole Genome ◽  
Severe Hemophilia ◽  
Structural Variants ◽  
Coding Regions ◽  
Intron 1

Abstract Background. Hemophilia A is a rare X-linked bleeding disorder resulting from deficiency in coagulation factor VIII. Numerous genetic variants (>2000) affecting the F8 gene have been implicated as causative of hemophilia A, including structural variants (SVs) such as copy number variants (CNVs) and large intra-chromosomal inversions caused by recombination between distant regions with high homology to sequences within F8 intron 1 or intron 22. SVs detected in patients with hemophilia are associated with more severe disease, and different types of SVs may inform inhibitor risk. For the vast majority of patients, causative variants can be identified using targeted DNA sequencing of F8 coding regions and/or the use of methods which detect known SVs (e.g. inverse shifting PCR, long-range PCR, MLPA). However, these approaches fail to explain 1-3% of hemophilia A cases. We hypothesized that a dedicated structural variant analysis at the F8 locus using whole genome sequencing data could identify previously undetected deleterious F8 gene variants in unsolved cases of hemophilia A. Methods. Cases were selected from the My Life, Our Future (MLOF) hemophilia study cohort recently whole genome sequenced by the NHLBI TOPMed program. In this study, we performed a custom SV analyses using whole genome sequencing (WGS) data from 11 cases of severe hemophilia A (factor VIII activity level < 1%) that remained genetically unexplained after exhausting all available laboratory testing methods. Two of the eleven unsolved severe hemophilia A cases (18%) were reported to have had an inhibitor. Results. SV analyses of the F8 genomic region revealed previously undetected deletions and inversions in 6 out of the 11 cases. In these 6 samples, SV calls were supported by multiple sequencing reads (> 25 reads) and multiple types of read evidence (read depth, paired-end and/or split read evidence). Two deletions within intron 6 were detected in a single hemophilia A case, a finding which suggests F8 intron 6 may contain one or more regulatory elements critical for F8 expression. Three distinct large inversions predicted to disrupt the F8 structural gene were detected in five other cases; a 720Kb inversion with breakpoints in F8 intron 6 and SPRY3 intron 1 (n=1), a 20Mb inversion with breakpoints in F8 intron 1 and INTS6L intron 8 (n=1), and a 7.4Kb inversion with breakpoints in F8 intron 25 and the SMIM9 intron 1 (n=3). These events are novel in hemophilia and were also not present in the larger, sequenced My Life, Our Future dataset (N=2186), supporting these SVs as likely causative of severe hemophilia A. Both cases with inhibitors had the F8 intron 25-SMIM9 inversion. Conclusions. This work demonstrates that dedicated analyses of WGS for SVs originating in non-coding regions can identify novel variants in previously unsolved cases of hemophilia A. We conclude that any genetic studies of diseases caused by loss-of-function variants should consider dedicated analyses for SVs. We predict additional deleterious SVs remain to be discovered in rare unexplained cases of hemophilia. Disclosures Konkle: BioMarin: Consultancy; Bioverativ: Research Funding; CSL Behring: Consultancy; Genentech: Consultancy; Spark: Consultancy, Research Funding; Pfizer: Research Funding; Gilead: Consultancy; Sangamo: Research Funding; Shire: Research Funding. Johnsen:CSL Behring: Consultancy; Octapharma: Consultancy.

Heterogeneity of MYC Abnormalities in Multiple Myeloma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 2-3
Author(s):  
Neeraj Sharma ◽  
James B Smadbeck ◽  
Nadine Abdallah ◽  
Kathryn E. Pearce ◽  
Yan Asmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Overall Survival ◽  
Clinical Trials ◽  
Genome Sequencing ◽  
Mayo Clinic ◽  
Research Funding ◽  
Advisory Board ◽  
Expression Levels ◽  
Myc Gene

Background: Multiple myeloma (MM) is an incurable plasma cell malignancy and genetic abnormalities contribute to disease heterogeneity and outcome. Primary abnormalities, namely recurrent immunoglobulin (Ig) heavy chain translocations and hyperdiploidy, occur early in disease course. Secondary events, such as MYC abnormalities occur upon progression. Earlier studies showed MYC abnormalities detected by FISH or by capture sequencing were independently associated with poor outcome (Walker, et al., BCJ, 2014), while recent studies using WGS did not support this finding (excluding MYC/IGL) (Mikulasova, et al. Haematologica, 2020; Misund, et al. Leukemia, 2020). We hypothesize these discrepancies are due to differences in methods and sensitivities of detection of MYC abnormalities by FISH vs. WGS. Given that MYC abnormalities often display remarkable genomic heterogeneity with numerous gene partners, reduced detection of MYC abnormalities by FISH is not unexpected. This hypothesis is supported by lower frequencies of MYC abnormalities found by FISH (15%) vs. NGS (30-35%) consistent with ~50% false-negative rate of the MYC FISH probe (Smadbeck, et al. BCJ, 2019). To better understand the role of MYC in myeloma disease outcome, we compared the MYC abnormality subtype identified by FISH or NGS vs. MYC gene expression levels and overall survival. Methods: We performed a retrospective study of newly diagnosed MM patients seen at Mayo Clinic or enrolled in the MMRF CoMMpass trial. For Mayo cases, MYC FISH results (breakapart probe, Abbott) were obtained from the Mayo Clinic Genomics database (N=1342) and mate pair sequencing (MPseq) was performed on 140 cases. For CoMMpass cases, we obtained tumor long-insert whole genome sequencing (WGS), RNA sequencing (RNAseq) for gene expression and clinical outcome data. Overall survival (OS) was defined as time from diagnosis to death from any cause or to last follow up. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical analyses performed using SPSS and JMP with significance determined when P &lt;0.05. Results: We first evaluated the impact of MYC abnormalities on OS when detected by FISH or NGS. In Mayo cases, OS was significantly shorter in patients with MYC abnormalities compared to patients without MYC abnormalities using FISH (5.3 vs. 8.0 years, P&lt;0.001, N=1342). In contrast, there was no significant difference in OS between patients with or without MYC or abnormalities using MPseq or WGS in both the Mayo and CoMMpass cohorts (Mayo: 6.4 vs. and 6.9 years P=0.78, N=140; CoMMpass: 4.9 vs. and 5.1 years P=0.74, N=546). Since FISH-detected MYC abnormalities were associated with poor outcome, we evaluated differences in the types of MYC abnormalities identified FISH and genome sequencing; 270 of 658 CoMMpass cases had a MYC abnormality and 12 abnormality subgroups were identified. In the Mayo cases, FISH preferentially detected translocations and complex abnormalities and missed insertions with flanking duplicating sequences or terminal tandem duplications (TTD) that occur telomeric to MYC. Since the level of MYC expression should be a consequence of the various genomic abnormalities altering the MYC gene region, we compared MYC expression levels in relation to MYC abnormality subgroups. Highest expression was seen with MYC amplification, followed by Ig abnormalities, non-Ig abnormalities, complex deletion/duplications, proximal deletions, non-Ig insertions, terminal deletions, TTD, trisomy 8, no MYC structural variation, monosomy 8 and cases with MAX mutations had the lowest expression. Abnormalities identified by FISH had higher MYC expression (83.5 TPM) compared to cases predicted to be missed by FISH (63.2 TPM). We tested if high MYC expression, irrespective of MYC structural abnormality, was associated with differences in OS. Boxplot analysis was used to categorize MYC expression in 631 CoMMpass patients as top quartile/high MYC expression (Q4≥ 75 TPM, n=159) and bottom quartile/low MYC expression (Q1≤ 16.5 TPM, n= 158) (see Figure). OS was significantly shorter in patients with high MYC expression compared to patients with low MYC expression (4.6 vs. 5.3 years, P &lt;0.038). Conclusion: We show that FISH detects only a subset of the MYC abnormalities detected by genome sequencing, and that FISH-detected MYC abnormalities are associated with higher MYC gene expression and decreased survival. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Dr. Reddy's Laboratories: Honoraria; Cellectar: Other; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Tenebio: Other, Research Funding; Carsgen: Other, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Merck: Consultancy, Research Funding; Genecentrix: Consultancy; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Adaptive Biotechnologies: Consultancy.

scholarly journals Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bénedith Oben ◽  
Guy Froyen ◽  
Kylee H. Maclachlan ◽  
Daniel Leongamornlert ◽  
Federico Abascal ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genome Sequence ◽  
Monoclonal Gammopathy ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Driver Genes ◽  
Smoldering Myeloma ◽  
Undetermined Significance

AbstractMultiple myeloma (MM) is consistently preceded by precursor conditions recognized clinically as monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM). We interrogate the whole genome sequence (WGS) profile of 18 MGUS and compare them with those from 14 SMMs and 80 MMs. We show that cases with a non-progressing, clinically stable myeloma precursor condition (n = 15) are characterized by later initiation in the patient’s life and by the absence of myeloma defining genomic events including: chromothripsis, templated insertions, mutations in driver genes, aneuploidy, and canonical APOBEC mutational activity. This data provides evidence that WGS can be used to recognize two biologically and clinically distinct myeloma precursor entities that are either progressive or stable.

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