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.
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.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
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.
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.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
INTRODUCTION: Multiple myeloma (MM) evolution is complex and heterogeneous. Hyperdiploidy (HRD) and translocations affecting the immunoglobulin heavy chain (IGH) locus are historically considered ...initiating genomic events of MM. The potential impact of genomic events acquired before known MM initiating events has never been addressed. METHODS: To investigate whether genomic deletions are acquired before and after known MM initiating events we interrogated whole-genome sequencing (WGS) data from patients with newly diagnosed MM (NDMM, n=319) and relapsed MM (RRMM, n=59). Our newly developed analytical and chronological workflow integrating single nucleotide variants (SNV), structural variants (SV), and copy number variants (CNV) comprises two main steps. First, we estimated the molecular time (i.e. corrected ratio between duplicated and non-duplicated clonal SNV) of large clonal chromosomal duplications and identified the earliest set of chromosomal gains in each patient. Second, within each early large chromosomal gain we identified clonal SV mediating CNV loss. A deletion on a gain can generate three possible scenarios: 1) one of the duplicated alleles is lost after the gain (i.e. post-gain) causing a CNV jump from 3:1 to 2:1 (total alleles : minor alleles); 2) there is a deletion before the duplication (i.e., pre-gain) causing a CNV jump from 3:1 to 1:0. 3) the deletion occurs on the minor, non-duplicated allele, causing a CNV jump from 3:1 to 2:0. Timing the deletion in relation with the chromosomal duplication is impossible in this scenario. RESULTS: Molecular time data were successfully generated for 249/319 (78%) NDMM and 51/59 (86%) RRMM patients. Restricting our analysis to NDMM with HRD without canonical IGH translocations, 16/170 (9%) of patients acquired deletions before the earliest multi-chromosomal gains, suggesting MM precursors can acquire deletions before HRD. Investigating the entire series, post-gain deletions were observed in 126/417 (30%) samples considering both early and late time windows. Leveraging a background model, we demonstrated that pre-gain events involved more tumor-suppressor genes (TSG) than expected by chance, including TCF3, ATM and TRAF3. In contrast, oncogenes were involved less than expected. In post-gain deletions, both oncogenes and TSG were involved more than expected by chance . To validate and assess the impact of deletions on driver gene expression we investigated WGS and RNAsequencing data from the MMRF CoMMpass study (n=752 NDMM). Because of the low coverage not allowing for molecular time estimation, we limited our analysis to HRD without IGH translocations. Pre-gain and post-gain deletions were observed in 47/431 (11%) and 225/431 (52%) of patients, respectively. We defined loss and gain of function events based on whether the expression level of a gene affected by the deletion was in the first or fourth quartile, respectively. Pre-gain deletions were mostly associated with downregulation of TSG expression n=31 events in 13/431 (3%) patients. In contrast,post-gain deletions had a more heterogenous impact with loss- and gain-of-function events. Gain-of-function events were driven by two main mechanisms: 1) the deletion joined an oncogene with a distal regulatory region inducing its overexpression n=44 events in 6/431 (1.3%) patients; 2) the deletion caused a new and expressed fusion n=231 events in 109/431 (25%) patients, resulting in either loss- or gain-of-function. Surprisingly, post-gain deletions had also a major impact on TSG expression. In 60 patients (14%), we observed 146 post-gain deletions where the affected tumor suppressor gene (TSG) expression was downregulated to the level of cases with monoallelic and biallelic deletions, even though two alleles were retained. Finally, to validate these findings, we investigated 16 RRMM patients with available WGS, scATAC-seq and scRNA-seq and observed additional evidence of TSG downregulation after both pre- and post-gain deletions. CONCLUSION: Leveraging a large cohort of NDMM we show that somatic deletions can be acquired before HRD trisomies that are assumed to be initiating events. Furthermore, post-gain deletions emerged as a new mechanism inducing TSG down-regulation, despite an apparently diploid gene status.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
Introduction: the complex structural variant (SV) chromothripsis is an independent predictor of progression-free (PFS) and overall survival (OS) in newly diagnosed multiple myeloma (NDMM); predictive ...in multivariate analysis when considering ISS, IgH translocations and TP53 status (Rustad BCD 2020). Chromothripsis is not included in current prognostic scores, despite a prevalence of 24% in NDMM. While gold-standard definition requires whole genome sequencing (WGS), we've previously shown that copy-number (CN) signatures are also able to accurately define chromothripsis in whole exome sequencing (WES, Maclachlan Nat Com 2021). As neither WGS nor WES are currently used in clinical medicine, we applied CN signatures to targeted sequencing, aiming for direct clinical translation of this key biological feature into clinical practice. Memorial Sloan Kettering (MSK) now offers the MSK-Heme-IMPACT targeted sequencing panel to all MM patients having a bone marrow biopsy (BMBx), as well as offering retrospective analysis for current patients having leftover DNA from prior BMBx. Methods: We analyzed 420 samples having either myTYPE or MSK-Heme-IMPACT targeted sequencing at MSK prior to July 2023. We excluded those with whole genome duplication, applied CN signatures, then for samples from NDMM performed multivariate analysis for PFS prediction by backwards stepwise regression. In samples with CN signatures predictive for chromothripsis and with DNA available from the same BMBx, we performed parallel WGS in order to check for chromothripsis. WGS sequencing was at 60-80x for tumor, 30-40x for normal, with analysis including Battenberg for CN analysis, with SvABA, BRASS and GRIDSS for SV calling. Additional parallel WGS is in progress for 45 samples with NDMM prior to DVRd or DKRd, in order to examine chromothripsis prediction and PFS impact in the context of highly effective induction. Results: From 420 samples, 48 were collected at the precursor stage, 205 with NDMM, and 167 with relapsed disease. 39 patients had 2 assays performed, while the remainder were unique patient samples. Within NDMM, the range of induction regimens received included lenalidomide and dexamethasone with bortezomib (VRd), carfilzomib (KRd) or daratumumab (DRd), along with quadruplet therapy (DVRd or DKRd). Targeted sequencing data detected all 6 key CN features observed in WGS and WES analysis. Similar to WES, there was a lower contribution from very high breakpoint counts per 10mB and longer lengths of oscillating CN segments, due to the lower resolution of targeted sequencing data. De novo extraction of CN signatures defined 1 signature containing multiple features consistent with chromothripsis; high breakpoint count per 10mB, more jumps between adjacent CN segments, longer lengths of oscillating CN segments and a predominance of small CN segments. When examining samples from NDMM, contribution from this signature was predictive of PFS in multivariate analysis when considering age, ISS, t(4;14), TP53 status and gain1q21 (hazard ratio > 4, p=0.003). 33 samples had a predictive probability for chromothripsis > 0.6 (the cut-off in WGS / WES having the highest sensitivity and specificity for prediction), with also having further DNA available. 30/33 (91%) were confirmed to harbor chromothripsis on manual inspection of WGS data. 19/30 (63%) with chromothripsis were BMBx from NDMM, while 11/30 (37%) were from relapsed disease. Discussion: the complex SV chromothripsis is a key genomic risk factor predicting for poor PFS and OS in MM, which is currently not being captured in routine practice or in clinical trials. Here, we demonstrate that chromothripsis can be predicted from targeted sequencing data, confirmed by gold-standard WGS analysis. Ongoing studies are expanding our cohort of NDMM with concurrent targeting sequencing and WGS, to define the sensitivity and specificity of targeted-sequencing-based CN signature prediction of chromothripsis. The additional cohort largely comprises patients treated with daratumumab-quadruplet induction therapy, aiming to confirm that chromothripsis and CN signatures retain prognostic significance in the context of this therapy. With this data is in hand, we will develop a CN signature fitting tool, similar to mmsig for mutational signatures (Rustad Comm Biol 2021), to allow easily accessible incorporation of chromothripsis assessment into clinical practice.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
Background and Significance: Patients with multiple myeloma (MM) harboring translocation t(11;14) have been shown to benefit from the apoptosis-inducing drug venetoclax; however, the drug lacks FDA ...approval for the treatment of multiple myeloma thus far. Selinexor is an inhibitor of nuclear export that is FDA-approved for patients with multiple myeloma refractory to multiple lines of therapy. We recently reported that in four patients with multiple myeloma with t(11;14), the concomitant administration of venetoclax and selinexor was safe and associated with disease response (Nguyen et al. Nature Precision Oncology 2022). Moreover, the combination was synergistic in t(11;14) multiple myeloma cell lines and caused decreased levels of Cyclin D1 (which is overexpressed due to the CCND1-IGH fusion) when given in combination as compared to single agents ( Figure 1). These data suggested that the combination of venetoclax and selinexor is effective and t(11;14) may serve as a therapeutic marker for response and target for future clinical trials. We have therefore developed an investigator-initiated study of selinexor and venetoclax in patients with relapsed, refractory multiple myeloma harboring translocation t(11;14)- the SELVEDge study. Methods and Study Design:This is an investigator-initiated, Phase 2 clinical study (NCT05530421) that is being conducted at the Sylvester Comprehensive Cancer Center at the University of Miami of adult (≥18 years) patients with relapsed refractory MM (RRMM) harboring t(11;14) in which approximately 24 patients will be treated with selinexor, venetoclax, and dexamethasone (SELVEDge) to determine the primary objective of response rate (ORR). Therapy will be given in 28-day cycles. Patients will receive venetoclax orally for cycle 1 only at a dosage of 400 mg daily for the first 7 days followed by 800 mg daily for the remainder of the cycle with oral dexamethasone given weekly. From cycle 2 and beyond, patients will receive oral selinexor 80 mg weekly; venetoclax daily, and dexamethasone weekly. Using a Simon Optimal 2-stage design, in the first stage, 9 patients will be enrolled and if ≥2 clinical responses occur (≥PR), the study will continue to the second stage to enroll a total of 24 patients. Early termination for excessive toxicity has been incorporated. Patients will undergo BM biopsy prior to cycle 2 and between cycle 3-4 and/or at CR and/or PD. PET/CT imaging will be performed at the same time points as bone marrow biopsies. MM serum markers for response will be performed at baseline and the start of every cycle. Exploratory corollary research will be performed on research samples collected at clinical timepoints including BCL2/MCL1 biomarkers. The study design is shown in Figure 2. The primary objective of the study is to determine the overall response rate (ORR) of SELVEDge in t(11;14)-positive RRMM with secondary objectives of durability of response (DoR), measurable residual disease (MRD) negative remission rate, progression-free and overall survival, and safety and tolerability. For patients to be included in the study they must have documented evidence of receiving two prior lines of therapy and be refractory to, not a candidate for (ineligible), or intolerant of at least one immunomodulatory (IMiD), one proteasome inhibitor, and one anti-CD38 monoclonal antibody-based treatments. Patients with prior therapy with selinexor or another specific inhibitor of nuclear exporter (SINE) compound are excluded. Statistical Hypothesis: Historically, in patients who have RRMM and have progressed after receiving IMiD, PI, and anti-CD38 -based regimens, approved experimental agents have been associated with an ORR of ~25%. It would be desirable to demonstrate that XVenD has a substantially higher response rate with a clinically meaningful ORR of 50%. As such, the study would rule out an unacceptably low ORR of 25% (p0=0.25) in favor of an improved response rate of 50% (p1=0.50), with a one sided alpha = 0.05 (probability of accepting a poor treatment=0.05) and beta = 0.20 (probability of rejecting a good treatment=0.20). Current Status: This clinical trial is currently open to enrollment at the Sylvester Myeloma Institute, University of Miami and to date 5 patients have been thus far enrolled and treatment initiated.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
Introduction Early intervention for High-Risk Smoldering Multiple Myeloma (HR-SMM) achieves deeper and more prolonged responses compared to Newly Diagnosed (ND) MM. Various clinical risk models ...estimate the risk of SMM progression to MM but there is significant discordance between them (Hill et al., JAMA Onc. 2021). It is unclear if beneficial outcomes of interventional studies in HR-SMM are due to treatment of less complex, susceptible disease or inaccuracy in clinical definition of cases entered. Methods To gain greater biologic insight into treatment outcomes, we performed the first whole genome sequencing (WGS) analysis of treated HR-SMM for 27 patients treated with carfilzomib, lenalidomide, and dexamethasone (KRd) and R maintenance (NCT01572480, presented in parallel). We pooled genomic features from 27 patients with HR-SMM treated with Elotuzumab (Elo)R+/-d; (E-PRISM). Genomic features were compared to those of 701 patients with NDMM from CoMMpass (NCT01454297). Results After a median follow-up of 52.8 months, median PFS was not reached with KRd/R. After 8 cycles of KRd, 19 (70.3%) achieved minimal residual disease (MRD) negativity (LOD 10 -5). At data cutoff, June 13, 2023, 14 patients (51.9%) achieved sustained MRD-negativity, 6 patients (22.2%) lost an initial MRD-negative response and 5 patients clinically progressed (18.5%). Overall, there was discordance between risk models: 3 patients (11.1%) were HR by Mayo2008 criteria, 14 (51.9%) by Mayo 20/2/20, 18 (66.7%) by PETHEMA, and 21 (77.8%) by Rajkumar/Landgren/Mateos criteria (Rajkumar et al., Blood. 2015). Eighteen (66.7%) met criteria for 2 or more scores. The estimated 5-year risk of progression ranged 4.8 to 82.1% (Pangea, median 18.6%). We compared the pooled HR-SMM to NDMM from CoMMpass. The frequency of HR translocations was similar (t(4;14), t(14;16), t(14;20); p>0.05). Consistent with the early disease stage of SMM, mutations of NRAS were lower in SMM (p = <0.001) as were events at the MYC locus (8q24; p <0.001) and gains of 1q (p = 0.039). Next, tumor suppressor genes (TSG) were interrogated together with copy number loss at their loci. Consistent with their late onset in tumor evolution, aberrations at key TSG were less common in HR-SMM (p < 0.05): CDKN2C, CYLD, TENT5C, FUBP1, MAX, NCOR1, NF1, NFKBIA, PRDM1, RB1, RPL5,and TRAF3 (p < 0.05). In a genome-corrected comparison, APOBEC (SBS2+SBS13) mutational signatures were diminished in KRd WGS compared to 60 Dara-KRd-treated NDMM (Maura et al., ASH. 2021; 48% vs 87%, p < 0.001) and in E-PRISM vs CoMMpass (15% vs 45%, p = 0.001). We next related genomic features associated with HR-SMM to treatment outcomes. Patients treated with KRd/R had yearly MRD testing and gain1q, MYC dysregulation via loss of MAX, and t(4:14) were all associated with failure to sustain MRD-negativity. Across pooled HR-SMM, inactivation of CYLD, CREBBP, MAX, and HIST1H2BK; t(4;14), APOBEC expression, loss at select GISTIC peaks ( Fig 1A) and chromothripsis all were associated with HR-SMM progression in the face of triplet therapy (p<0.05). Presence of any one or more of these features was associated with progression (p = 0.005; Fig1B). Conversely, no clinical risk score was able to discern those with this molecular high risk. Conclusion In patients treated on 2 parallel clinical trials for HR-SMM, we found a uniform and relative genomic simplicity. Moreover, non-progressors appear genomically similar to patients with non-progressive/stable MGUS and SMM under observation (Oben et al., Nat Comm 2021). However, within clinical HR-SMM, a set of high-risk genomic features portends progression despite intervention. These results suggest that clinical risk scores do not effectively discriminate between genomically indolent and aggressive disease. Though possible that results reflect treatment before the acquisition of key drivers, prior data suggest that many of these high-risk features are not acquired within the 5 years preceding clinical diagnosis of MM (Bolli et al., Nat Comm. 2018; Bustoros et al., J Clin Onc. 2020). Altogether, these results support the use of genomics to contextualize the advantage of early intervention in SMM (i.e., to avoid overtreatment of non-progressors and to better identify cases likely to progress without therapy). Fig1A: Heatmap of High-Risk Features in Patients treated with KRD/R and EPRISM. Fig1B: Kaplan-Meier curve for time to progression by presence of HR features for KRD/R.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
INTRODUCTION: Improving health outcomes for patients with African ancestry (AA) is a key healthcare aim, but there is uncertainty in whether disparities arise predominantly from socioeconomic ...differences or from genetic differences in tumor biology. It remains an open question as to whether multiple myeloma (MM) occurring in AA patients has a similar or different spectrum of genomic abberations when compared to patients having European ancestry. To date, studies have suggested that AA have an excess of t(11;14) and a deficiency of TP53 mutations. We have established a series of 302 cases of MM precursor and newly diagnosed MM with whole-genome sequencing (WGS) available that is enriched for self-declared AA or diverse ethnic background. METHODS: In collaboration with the NYGC and Polyethnic-1000 consortium, we sequenced tumor samples to a depth of 60-80X and normal tissue to 30-40X. We a employed bioinformatics pipeline with a consensus mechanism for somatic variant calling, including Mutect2, Strelka2, and VarScan2 for SNVs; Mutect2, Strelka2, VarScan2, and SvABA for InDels; Battenberg and FACETS for CNVs; Manta, SvABA, DELLY2, and IgCaller for SVs. Additionally, an admixture workflow was used to estimate each individual's ancestral lineage using continentally-distinct references, comprising 23 regional populations within 5 super-populations from the 1000 Genomes Project (https://github.com/pblaney/mgp1000). Mutational signature were calculated using the R package mmsig (Rustad et al. Comm. Bio. 2021). RESULTS: Using admixture estimations from 302 patients with high-coverage WGS together with 941 patients from the CoMMpass trial, we identified five clusters corresponding to single dominant genetic ancestries (median proportion >75% assignment to reference super-population), together with a cluster characterized by highly admixed individuals with no dominant genetic ancestry (median proportion <50%). Of the total, 53.0% are in the European dominant (EUR) cluster, 26.5% African dominant (AFR), 8.6% American dominant (AMR), 7.9% highly admixed, 2.0% East Asian dominant (EAS) and South-East Asian dominant (SAS) clusters, respectively. Stratifying patients by their cluster assignment and calculating the frequency of subtype translocations, we show that t(11;14) occurred in 25.8% of EUR patients while in only 14.6% of AFR (p=0.045) and 7.7% of AMR (p=0.05) patients. The frequency of the t(4;14) was more closely distributed, with 15.7% in EUR, 11.5% in AMR, and 11% in AFR clusters. For the acquired somatic mutations, the tumor mutational burden (TMB) was lowest in the AFR cluster 2.21 (median, somatic mutations per Mb), significantly lower by comparison to the EUR cluster at 2.94 (FDR adj. P=4.3x10 -6). The most striking genomic difference was observed when comparing the mutational signatures landscape between AA and the other racial groups. Using WGS, AA had lower SBS1 and SBS5 absolute contribution compared to EUR, and this was largely responsible for the difference in TMB. SBS1 and SBS5 are known to be clock-like signatures, accumulating at a constant rate over time. Because no differences in age, cancer cell fraction, and coverage were observed between AA and EUR, this finding suggest different mutational clock-like rate between AA and EUR. The higher TMB observed in EUR was also driven by the higher APOBEC-mutational activity (SBS2 and SBS13) compared to AA (p=0.05). Interestingly, 72% of all EUR had APOBEC-activity evident, in contrast to 45% of the AA (p=0.001). This difference was confirmed after excluding the MM precursor patients, previously demonstrated to have lower APOBEC-activity (Oben et al. Natur Comm. 2021), and was validated on CoMMpass whole-exomes. CONCLUSIONS: Leveraging one of the largest series of diverse patients with WGS, and integrating genomic data with comprehensive ancestry information, AA MM emerged as biologically different in term of genomic drivers and mutational signatures, suggesting potential differences in etiology and genomic evolution over time. Further analysis will include molecular timing of clonal copy number gains, and reconstruction of phylogenetic trees, with view to improving our understanding of the etiology of MM development across patient genetic backgrounds. FIGURE: Genomic characteristics of myeloma across ancestries. A) Translocation percentage (number per cluster) and B) Tumor mutational burden across clusters.
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
INTRODUCTION: Little is known about the pattern and function of mutations within the 98% of the genome which is non-coding (nc). Whole-genome sequencing (WGS) can identify the full range of single ...nucleotide variants (SNVs), insertions/deletions (InDels), copy-number variants (CNVs), and structural variants (SVs), which are critical to disease progression. Here, we characterize the non-coding genome to gain significant insight into the role of mutations in gene regulatory elements in the etiology of multiple myeloma (MM) and to models of how it develops. METHODS: We studied 302 of MM precursor and newly diagnosed MM (NDMM) patients with high-coverage WGS, where each SNV/InDel was confirmed by two or more algorithms. Results were validated on an independent cohort of 256 NDMM with 80X WGS data. A pipeline employing a consensus mechanism for determining the final set of somatic events was used, including Mutect2, Strelka2, and VarScan2 for SNVs; Mutect2, Strelka2, VarScan2, and SvABA for InDels; Battenberg and FACETS for CNVs; Manta, SvABA, DELLY2, and IgCaller for SVs (https://github.com/pblaney/mgp1000). The R package fishHook was used toidentify statistically significant enrichment of mutations. To identify nc-variants we partitioned the genome into 10 kb tiles that were iteratively shifted by 500 bp and tested each tile against a regression model built into fishHook. The model includes a series of covariates that inform replication timing, sequencing context, and chromatin states. RESULTS: We identified 2,039,841 SNVs and 492,746 InDels in total. The tumor mutational burden (TMB) varies between molecular subgroups with the t(4;14) being significantly higher at 3.23 (somatic mutations per Mb) in comparison to the t(11;14) at 2.57 (FDR adj. P=0.035), which was closer to patients without a subtype translocation at 2.78. For ncSNVs and ncInDels, we identified 4,374 and 272 tiles respectively with significant mutation enrichment genome-wide (FDR adj. P<0.05). As tiles may overlap, we collapsed contiguous segments into consensus regions assigning the nearest coding gene as an identifier and termed these “mutation-enriched regions” or MERs. We identified 282 MERs associated with 203 genes for ncSNVs and 26 MERs associated with 25 genes for ncInDels. The two types of regions overlap at six loci ( TENT5C, OR2T2, FOXD4L1, BCL6, BLOC1SS- TXNDC5, PLD5P1). Thus, we identified 302 MERs associated with 221 genes, with some of the most highly mutated MERs included BCL6 (76.2% of patients), BLOC1S5- TXNDC5 (28.1%), ZFP36L1 (22.2%), BTG2 (21.2%), IRF8 (16.2%), TENT5C (13.6%), and CCND1 (12.3%). In total 19,743 of the 2,532,587 mutations fall into MERs with 1.3-65.6% of patients having one of these mutations. We evaluated the MERs for functional relevance by intersecting the regions with a list of 8,357 genome-wide enhancer (E) and super-enhancer (SE) elements derived from germinal-center B cells (GCB), DLBCL (Bal et al. Nature 2022) and MM (Lovén et al. Cell 2013). In total, 17.9% (54/302) of the MERs were identified, involving 45 genes. These MERs intersected with 28 Es and 20 SEs, with a non-random distribution of mutations within them. Of the total MER mutations, 21.9% (4,317/19,743) fell into some form of enhancer element. Breaking these down further 41.6% (1,798/4,317) are in Es, and 58.4% (2,519/4,317) are in SEs. All the E mutations were MM specific; of the SE mutations, 6.0% (18/302) of patients had a mutation in an ABC-DLBCL specific SE, 16.6% (50/302) in a GCB specific SE and 64.2% (194/302) in a MM specific SE. We examined the distribution of mutations within the SE regions and found they are non-random suggesting a selective mechanism. We intersected the SE regions with SV and found an excess at TENT5C, BTG2, BLOC1S5-TXNDC5 and ZFP36L1. A focused analysis of chr1p, chr1q, chr6q and chr14 revealed the importance of mutationally induced breaks within the SE and its translocation to a receptor site often 8q the site of MYC. CONCLUSIONS: We provide evidence for an important contribution of mutations within E and SE regions to the etiology of MM. This may involve either direct selection of mutations within the GC or by the re-entry of a memory B-cell carrying a pattern of mutations it acquired in a pre-MM phase, which then acquires a MM-specific driver. FIGURE: Distribution of mutations across MM genomes. A) Tumor mutational burden across MM subtypes. B) Q-Q plots of fishHook model for SNVs
Full text
Available for:
IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZRSKP
•The highest M−protein detection rates occur with MALDI-TOF MS plus FLC measurement.•MALDI-TOF MS can help accurately determine complete response status in patients.•Daratumumab can be distinguished ...from the M−protein in most samples by MALDI-TOF MS.•Relative concentration and mass difference matters for distinguishing proteins.
Daratumumab-based combination therapies have shown high rates of complete response (CR) and minimal residual disease negativity in patients with multiple myeloma. However, daratumumab, an IgGκ monoclonal antibody, interferes with electrophoretic techniques making it difficult to reliably define residual disease versus CR, especially in patients with IgGκ multiple myeloma.
Enrichment with polyclonal sheep antibody-coated magnetic microparticles combined with MALDI-TOF mass spectrometry (MALDI-TOF MS) analysis was used to detect M−proteins in serial samples from newly diagnosed multiple myeloma patients treated with daratumumab-based therapy. The performance of the MALDI-TOF MS assay was compared to that of a routine test panel (serum protein electrophoresis (SPEP), immunofixation (IFE) and serum free light chain (FLC)).
Comparison of MALDI-TOF MS to SPEP/IFE/FLC showed a concordance of 84.9% (p < 0.001). When MALDI-TOF MS and FLC results were combined, the M−protein detection rate was the same or better than the routine test panel. For the 9 patients who obtained CR during follow-up, MALDI-TOF MS detected an M−protein in 46% of subsequent samples. Daratumumab could be distinguished from the M−protein in 215/222 samples.
MALDI-TOF MS is useful in assessing CR in patients treated with monoclonal antibody-based therapies.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
For patients diagnosed with multiple myeloma (MM) there have been significant treatment advances over the past decade, reflected in an increasing proportion of patients achieving durable remissions. ...Clinical trials repeatedly demonstrate that achieving a deep response to therapy, with a bone marrow assessment proving negative for minimal residual disease (MRD), confers a significant survival advantage. To accurately assess for minute quantities of residual cancer requires highly sensitive methods; either multiparameter flow cytometry or next generation sequencing are currently recommended for MM response assessment. Under optimal conditions, these methods can detect one aberrant cell amongst 1,000,000 normal cells (a sensitivity of 10−6). Here, we will review the practical use of MRD assays in MM, including challenges in implementation for the routine diagnostic laboratory, standardisation across laboratories and clinical trials, the clinical integration of MRD status assessment into MM management and future directions for ongoing research.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
