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Chromothripsis as a pathogenic driver of multiple myeloma

Maura, Francesco; Boyle, Eileen M; Rustad, Even H; Ashby, Cody; Kaminetzky, David; Bruno, Benedetto; Braunstein, Marc; Bauer, Michael; Blaney, Patrick; Wang, Yubao; Ghamlouch, Hussein; Williams, Louis; Stoeckle, James; Davies, Faith E; Walker, Brian A; Maclachlan, Kylee; Diamond, Ben; Landgren, Ola; Morgan, Gareth J
Analysis of the genetic basis for multiple myeloma (MM) has informed many of our current concepts of the biology that underlies disease initiation and progression. Studying these events in further detail is predicted to deliver important insights into its pathogenesis, prognosis and treatment. Information from whole genome sequencing of structural variation is revealing the role of these events as drivers of MM. In particular, we discuss how the insights we have gained from studying chromothripsis suggest that it can be used to provide information on disease initiation and that, as a consequence, it can be used for the clinical classification of myeloma precursor diseases allowing for early intervention and prognostic determination. For newly diagnosed MM, the integration of information on the presence of chromothripsis has the potential to significantly enhance current risk prediction strategies and to better characterize patients with high-risk disease biology. In this article we summarize the genetic basis for MM and the role played by chromothripsis as a critical pathogenic factor active at early disease phases.
PMID: 33958284
ISSN: 1096-3634
CID: 4866742

Inflammation and infection in plasma cell disorders: how pathogens shape the fate of patients

Caro, Jessica; Braunstein, Marc; Williams, Louis; Bruno, Benedetto; Kaminetzky, David; Siegel, Ariel; Razzo, Beatrice; Alfandari, Serge; Morgan, Gareth J; Davies, Faith E; Boyle, Eileen M
The role of infection and chronic inflammation in plasma cell disorders (PCD) has been well-described. Despite not being a diagnostic criterion, infection is a common complication of most PCD and represents a significant cause of morbidity and mortality in this population. As immune-based therapeutic agents are being increasingly used in multiple myeloma, it is important to recognize their impact on the epidemiology of infections and to identify preventive measures to improve outcomes. This review outlines the multiple factors attributed to the high infectious risk in PCD (e.g. the underlying disease status, patient age and comorbidities, and myeloma-directed treatment), with the aim of highlighting future prophylactic and preventive strategies that could be implemented in the clinic. Beyond this, infection and pathogens as an entity are believed to also influence disease biology from initiation to response to treatment and progression through a complex interplay involving pathogen exposure, chronic inflammation, and immune response. This review will outline both the direct and indirect role played by oncogenic pathogens in PCD, highlight the requirement for large-scale studies to decipher the precise implication of the microbiome and direct pathogens in the natural history of myeloma and its precursor disease states, and understand how, in turn, pathogens shape plasma cell biology.
PMCID:8809233
PMID: 35110727
ISSN: 1476-5551
CID: 5153682

Improving prognostic assignment in older adults with multiple myeloma using acquired genetic features, clonal hemopoiesis and telomere length

Boyle, Eileen M; Williams, Louis; Blaney, Patrick; Ashby, Cody; Bauer, Michael; Walker, Brian A; Ghamlouch, Hussein; Choi, Jinyoung; Perrial, Emeline; Wang, Yubao; Caro, Jessica; Stoeckle, James H; Arbini, Arnaldo; Kaminetzky, David; Braunstein, Marc; Bruno, Benedetto; Razzo, Beatrice; Diamond, Benjamin; Maclachlan, Kylee; Maura, Francesco; Landgren, Ola; Litke, Rachel; Fegan, Christopher D; Keats, Johnathan; Auclair, Daniel; Davies, Faith E; Morgan, Gareth J
PMID: 34148053
ISSN: 1476-5551
CID: 4918002

Multiomic Mapping of Copy Number and Structural Variation on Chromosome 1 (Chr1) Highlights Multiple Recurrent Disease Drivers [Meeting Abstract]

Blaney, P; Boyle, E M; Wang, Y; Ghamlouch, H; Choi, J; Williams, L; James, S; Siegel, A; Razzo, B; Braunstein, M; Kaminetzky, D; Arbini, A A; Bruno, B; Corre, J; Montes, L; Auclair, D; Davies, F E; Tsirigos, A; Rustad, E H; Maura, F; Landgren, O; Bauer, M A; Walker, B; Morgan, G
Introduction Copy number abnormalities (CNA) and structural variants (SV) are crucial to driving cancer progression and in multiple myeloma (MM). Chr1 CNA are seen in up to 40% of cases and associate with poor prognosis. Variants include deletions, gains, translocations and complex SV events such as chromothripsis (CT), chromoplexy (CP) and templated insertions (TI) which result in aberrant transcriptional patterns. Abnormal expression of genes on chr1 lead to the adverse clinical outcome and studies focussed on 1p12, 1p32.3 and 1q12-21 identified potential causal genes including TENT5C, CDKN2C, CKS1B, PDZK1, BCL9, ANP32E, ILF2, ADAR, MDM2 and MCL1 but none fully explain the clinical behavior. To address this deficiency and to relate chromatin structure to gene deregulation we present a multiomic bioinformatic analysis of SV, CNA, mutation and expression changes in relation to the chromatin structure of chr1. Methods We analysed data derived from 1,154 CoMMpass trial patients. We analyzed 972 NDMM patients with whole exome for mutations, and 752 whole genomes for copy number, translocations, complex rearrangements such as CP, CT and TI as previously described. Using GISTIC 2.0, we identified hotspots of CNA. This information was then analyzed in conjunction to the RNA-seq data derived from 643 patients to determine the aberrant transcriptional landscape of chr1. Using HiC data derived from U266 MM cell line, we associated these changes with TAD structures, A/B compartments, and histone marks along chr1, to gene expression changes, and recurrent SV. Using the cell line dependency map for CRISPR knockdown of the gene set on chr1 derived from 20 MM cell lines we related cell viability to chr1 copy number status. Results * We identified 7 hotspots of deletion, 9 of gain, 3 of CT and 2 of templated-insertion across chr1. We mapped these regions to epigenetic plots and show that gained regions are hypomethylated compared to the rest of chr1 (Wilcoxon, p=0.0002). Overall 69% of gain(1q) and 45% of the non-gained hotspots were in A compartments (chi 2=11, p=0.0009) and had an overall higher compartment score (p=0.01). * The recurrent regions of loss on 1p confirm the clinical relevance of this region. The critical importance of TENT5C, CDKN2C and RPL5 is identified by the impact of deletion, mutation and the rearrangement of superenhancers. Further this convergence of multiple oncogeneic mechanisms to a single locus points to a number of novel candidate drivers including FUB1 and NTRK1. * We provide important new information on 1q21.1-1q25.2 encompassing 145-180Mb a transcriptionally dense region containing 6 GISTIC 2.0 hotspots of gain (G2-G7). The hotspots occur within TAD structures that correlate upregulation of known drivers listed above and also identified novel potential upregulated drivers including POU2F1, a transcription factor, CREG1, an adenovirus E1A protein that both activates and represses gene expression promoting proliferation and inhibiting differentiation (G6) and BTG2 a G1/S transition regulator (G8). These data for copy number gain provides strong evidence for the prognostic relevance of of multiple drivers within deregulated TADs rather than single candidate genes. It also highlights the importance of the chromatin structure of Chr1 in the generation of these events. * Using dependency map CRISPR data we identified 320 essential genes for at least one cell line (>1). A common set of 31 genes were identified including 3 proteasome subunits (PSMA5, PSMB2, PSMB4), three regulators of ubiquitin-protein transferase activity (RPL5, RPL11, CDC20), splicing (SF3B4, SF3A3, SFPQ, RNPC3, SRNPE, PRPF38A, PRPF38B) and DTL. A common dependency for 1q+ or 1p- was not identified but a number of dependencies were identified in more than one cell line including UQCRH, SLCA1, CLSPN in 1p- cell lines and IPO9, PPIAL4G, and MRPS2 in 1q+. Conclusion We present an elegant anatomic map of chr1 at the genetic and epigenetic levels providing an unprecedented level of resolution for the relationships of structural variants to epigenetic, expression and mutation status. The analysis highlights the importance of active chromatin in gene deregulation by SV and CNA where the importance of multiple gene deregulation within TAD structures is critical to MM pathogenesis. The implications are that we could improve prognostic assignment and identify new targets for therapy by further characterizing these relationships. [Formula presented] Disclosures: Braunstein: Jansen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Davies: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees.
Copyright
EMBASE:2016082367
ISSN: 0006-4971
CID: 5098702

Unifying the Definition of High-Risk in Multiple Myeloma [Meeting Abstract]

Siegel, A; Boyle, E M; Blaney, P; Wang, Y; Ghamlouch, H; Choi, J; Caro, J; Williams, L; Razzo, B; Arbini, A A; Braunstein, M; Kaminetzky, D; Auclair, D; Pawlyn, C; Cairns, D; Jackson, G; Walker, B; Bruno, B; Morgan, G J; Davies, F E
Introduction: There is considerable heterogeneity in the clinical outcome of newly diagnosed multiple-myeloma (NDMM) with some patients having a good prognosis while others fail to respond or relapse quickly after therapy progressing rapidly to death. Using risk scores based on clinical, biochemical and genetic features it is possible to predict some of this variation giving an ability to segment the disease into risk strata. Clinical studies have suggested that patients with standard-risk disease have benefited more from the recent advances in therapy compared to those with high-risk disease. The development of clinical trials specifically recruiting patients with high-risk disease features offers the potential to improve the outcome of a subgroup of patients with a very poor clinical outcome. To perform such studies is it important to have a unifying definition of high-risk including standard parameters, group size and outcome of individual risk strata so that clinical trial rigor can be achieved (e.g., common entry criteria, statistical power). In order to understand the size and feasibility of such studies we analyzed the Myeloma Genome Project (MGP) dataset to assess multiple risk factors and scores to determine and compare how they perform as risk stratifiers with each other.
Method(s): The MGP dataset is a large set of molecular and clinical data from 1273 patient with NDMM. Data were available on clinical variables (Albumin (Alb), B2-microglobulin (B2M), LDH, age), cytogenetic variables [t(4;14), t(14;16), t(14;20), 17p-, TP53 mutations, 1q+ and 1p-] and gene expression analysis (GEP70). A literature search was used to identify risk models used in clinical studies. Survival analysis was performed in R. The median follow-up at the time of analysis was 54.5 (53.2-56.5) months.
Result(s): The median patient age was 66 years, with 641 (50.4%) patients over age 65. The sex ratio (M:F) was 1:0.66. African American, White, and Asian constituted 17%, 76%, and 2%, of cases respectively. 26.7% received a stem cell transplant. We determined the size of the strata and actual risk (measure by the hazard ratios, HR) compared to standard risk cases for both PFS and OS of the various clinical models available, data are summarized in Figure 1. When looking at individual risk scores, the HR for progression for t(4;14), TP53 inactivation (deletion and mutations), gain(1q), and del(1p) were 1.4, 1.1, 1.3, and 1.1 respectively. When considering overall survival these HR were 1.4, 1.7, 1.5, and 1.4 respectively. We went on to analyze the impact of these events in combination and show that combined, there is increased specificity, especially for OS (HR 2.3-5.1) but they identify small subsets making up <10% of patients. We then analyzed the purely clinical scores (ISS) and combined clinical/genetic scores. We show again, that the more specific risk scores (double hit, Boyd IV, GEP70) identify between 7-13% of cases with HR (2-3.1) for OS. When we looked specifically at the younger patients (=< 65), similar trends were seen with GEP70 by RNA-seq offering one of the most interesting means of identifying HR cases.
Conclusion(s): In this large NDMM dataset, we demonstrate the clear variation in risk groups that occur dependent upon the approach used resulting in heterogeneous levels of risk, strata size, and performance. With the exception of GEP70, none of the single features are sensitive or specific enough to identify all cases. Risk models based on a combination of markers improve the ability to detect true high-risk disease but there remains variability. At a molecular level the inclusion of TP53 inactivation, and 1q+ improve the performance of the ISS. This analysis provides insights into standardizing the definition of high-risk and the generation of consensus definitions for clinical trial entry. Figure 1 [Formula presented] Disclosures: Braunstein: Jansen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Pawlyn: Celgene / BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cairns: Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Jackson: GSK: Consultancy, Honoraria, Speakers Bureau; takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; amgen: Consultancy, Honoraria, Speakers Bureau; celgene BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; J and J: Consultancy, Honoraria, Speakers Bureau; oncopeptides: Consultancy; Sanofi: Honoraria, Speakers Bureau. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Davies: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees.
Copyright
EMBASE:2016062846
ISSN: 0006-4971
CID: 5099092

Hispanic or Latin American Ancestry Is Associated with a Similar Genomic Profile and a Trend Toward Inferior Outcomes in Newly Diagnosed Multiple Myeloma As Compared to Non-Hispanic White Patients in the Multiple Myeloma Research Foundation (MMRF) CoMMpassstudy [Meeting Abstract]

Williams, L; Blaney, P; Boyle, E M; Ghamlouch, H; Wang, Y; Choi, J; Bauer, M A; Siegel, A; Stoeckle, J; Razzo, B; Auclair, D; Kaminetzky, D; Braunstein, M; Bruno, B; Arbini, A A; Walker, B A; Davies, F E; Morgan, G J
Introduction Large clinical data sets suggest that the natural history and prognosis of newly diagnosed multiple myeloma (NDMM) differs between patients of European and African ancestry, with the latter group exhibiting an earlier age at onset and poorer overall prognosis in some studies. The use of next generation sequencing (NGS) to characterize the genomic landscape of multiple myeloma (MM) suggests that the observed phenotypic differences between these groups of patients may reflect distinct underlying genomic profiles and mutational processes. Thus far, characterizations of this type have focused principally on patients of African ancestry (AA). Here, we characterize the genomic features and outcomes of a large series of patients of Hispanic or Latin American ancestry (HL) as compared to their Non-Hispanic white (NHW) counterparts. Methods Subjects were selected from the MMRF CoMMpass SM trial, a study that includes 1,154 patients with updated outcome data as of March, 2020. Within this data set, 760 patients had information on race and ethnicity. Among these, 55 HL patients and 478 NHW patients possessed complete clinical and genomic information. We analyzed baseline whole exome sequencing (WES) and long insert whole genome sequencing (WGS) as previously described (Walker, et al. Blood 2019). Our analysis focused on 63 known driver mutations in multiple myeloma and 39 sites of common copy number variation across the study population. Complex structural variants and tumor telomere length were called using previously described bioinformatic tools (Boyle et al. Leukemia 2021). Survival analysis was undertaken using the Kaplan-Meier method with hazard ratios determined by the Cox proportional hazards model. Results In a comparison of clinical features between the Hispanic and NHW population, we did not identify any differences in age of onset, gender, presenting cytogenetics, International Staging System Score (ISS), and IMWG Risk Category. The proportion of patients undergoing autologous stem cell transplantation was similar between groups. We identified no statistically significant differences in the presence of characteristic translocations involving IgH locus or in hyperdiploidy status. No statistically significant differences in tumor mutational burden or loss-of-heterozygosity percentage emerged between HL and NHW patients. We examined non-synonymous variations (NSV) and copy number variations at the loci of known MM driver genes and encountered no statistically significant differences in NSV, copy number, or biallelic status. We further categorized genes into pathways relevant to the pathogenesis of MM and discovered no difference in the proportions of patients harboring mutations in genes related to the MEK/ERK and NF-kappaB pathways, cell cycle regulation, and epigenetic modification. We were unable to the distinguish either population based on the presence of chromothripsis or in the overall preponderance of an APOBEC mutational signature. Tumor telomere length was not significantly different between the populations. An analysis of overall and progression free survival (PFS) with a median duration of follow up of 44 months revealed a trend toward poorer outcomes among the HL population that did not reach statistical significance. Median PFS was 24 months in HL patients and 35 months in the NHW population (p = 0.19). Median OS was not reached in either ethnic subgroup. In terms of overall survival, age, ISS score, overall number of driver mutations, and the presence of chromothripsis emerged with a negative impact on outcome (Figures 1a, 1b). These variables with the exception of chromothripsis retained their significant impact on progression free survival (Figure 2a, 2b). Conclusion The correlation between Hispanic or Latin American ancestry and underlying disease biology in MM has yet to be fully elucidated. In our analysis, which was based on self-declared ancestry as opposed to admixture, no obvious differences in significant measures of genomic variation known to impact prognosis in MM emerged between HL and NHW patients. These results may help to inform the future large-scale studies to ascertain the impact of genomics, disease biology and socioeconomic factors on outcomes in this heterogeneous patient population. [Formula presented] Disclosures: Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees.
Copyright
EMBASE:2016085983
ISSN: 1528-0020
CID: 5104232

Terraflow, a New High Parameter Data Analysis Tool, Reveals Systemic T-Cell Exhaustion and Dysfunctional Cytokine Production in Classical Hodgkin Lymphoma [Meeting Abstract]

Freeman, D; Lam, L; Li, T; Alexandre, J; Raphael, B G; Kaminetzky, D; Ruan, J; Chattopadhyay, P; Diefenbach, C S
Background Classical Hodgkin lymphoma (cHL) is characterized by rare, malignant Hodgkin/Reed Sternberg (HRS) cells that shape their microenvironment (TME) to inhibit anti-tumor immune response. Systemic immune dysregulation may influence treatment response and toxicity, but the systemic influence of the TME is less well described. The wide variety of proteins measured in high-parmater flow cytometry make it a powerful tool for immune monitoring, but presents challenges in immuno-monitoring. Combinatorial expression of these proteins defines cell types that may influence disease. TerraFlow is a fully automated data analysis platform that evaluates millions of phenotypes and selects the populations that best predict clinical variables. The analysis can be performed using classical Boolean gates or a non-gating approach that approximates gates without using manual thresholds, allowing immunophenotypes to be comprehensively surveyed for disease associations. The platform was used to find phenotypes that discriminate healthy versus cHL patients (AUC = 1) and pre versus post treatment patient phenotypes(AUC = 0.79). Methods Human Subjects: Informed consent was obtained from cHL patients (N=44) treated at the Perlmutter Cancer Center (PCC) at NYU Langone Health and New York Presbyterian Weil Cornell (NYP) between 2011 and 2016. Blood samples were drawn at multiple time-points, for this study pre-treatment and 3 month post-treatment samples were used. Age-matched, cryopreserved healthy donor PBMC (n=25) were obtained from STEMCELL Technologies (Cambridge, MA).Patient-derived blood was processed for isolation of PBMC, stained analyzed on a Symphony Flow Cytometer (BD Biosciences, San Jose, CA). Analysis: Data was analyzed using an original platform called terraFlow. Many immune cell subsets are defined by the combinations of proteins they express. TerraFlow systematically evaluates millions of cell types by generating every possible combination of 1 to 5 markers. A network-based algorithm then selects the "best" phenotype from each set of inter-related combinations based on statistical power and ease of interpretation. Each phenotype is defined using a minimal gating strategy that can be replicated in a diagnostic panel or cell sorter. Together, phenotypes describe all the major differences between patient groups. A new platform developed by Epistemic AI was used to mine scientific literature and interpret selected phenotypes. Results We observed clear perturbations in the cHL systemic T-cell compartment pre-treatment as shown in Figure 1. These include higher levels of activated (CD278+), exhausted (CD366+, PD1+, CD152+), and suppressive (GITR+) T-cells compared to healthy donors, and diminished levels of T-cells producing effector cytokines (like IFNgamma and IL4). Subsets of cytokine-producing cells that co-express markers of exhaustion (i.e., TNF+ CD366+ cells) are also elevated in cHL patients. Finally, T-cells expressing CD127 a receptor for IL7 involved in homeostatic renewal of cells and observed on naive and central memory T-cells are reduced. Taken together, these findings suggest that in cHL the systemic T-cell compartment is shifted toward a more exhausted profile, and away from less differentiated cells, with the potential for self-renewal. Our data also demonstrates a shift from T-helper 1 and T-helper 2 type toward T-helper 17 cells suggesting that T-cell effector function may be reduced. Conclusion Using a novel data analysis platform, TerraFlow we demonstrate dysregulation in systemic T cell function in cHL patients pre-treatment that persists within 3 months of completing therapy. Associations of phenotypes with clinical variables, and post-treatment phenotypes will be described in detail at the meeting. Our results detail new immunotherapy and biomarker research targets, and suggest novel strategies for combination therapies. [Formula presented] Disclosures: Li: BD Bioscience: Current Employment. Ruan: Kite Pharma: Consultancy; AstraZeneca: Research Funding; BMS: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seagen: Consultancy. Diefenbach: Incyte: Research Funding; Trillium: Research Funding; Celgene: Research Funding; IGM Biosciences: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Gilead: Current equity holder in publicly-traded company; AbbVie: Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; MEI: Consultancy, Research Funding; Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; IMab: Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding.
Copyright
EMBASE:2016086916
ISSN: 1528-0020
CID: 5104372

Quality of life analyses in patients with multiple myeloma: results from the Selinexor (KPT-330) Treatment of Refractory Myeloma (STORM) phase 2b study

Tremblay, Gabriel; Daniele, Patrick; Breeze, Janis; Li, Lingling; Shah, Jatin; Shacham, Sharon; Kauffman, Michael; Engelhardt, Monika; Chari, Ajaj; Nooka, Ajay; Vogl, Dan; Gavriatopoulou, Maria; Dimopoulos, Meletios-Athanasios; Richardson, Paul; Biran, Noa; Siegel, David; Vlummens, Philip; Doyen, Chantal; Facon, Thierry; Mohty, Mohamad; Meuleman, Nathalie; Levy, Moshe; Costa, Luciano; Hoffman, James E; Delforge, Michel; Kaminetzky, David; Weisel, Katja; Raab, Marc; Dingli, David; Tuchman, Sascha; Laurent, Frenzel; Vij, Ravi; Schiller, Gary; Moreau, Philippe; Richter, Joshua; Schreder, Martin; Podar, Klaus; Parker, Terri; Cornell, Robert Frank; Lionel, Karlin; Choquet, Sylvain; Sundar, Jagannath
BACKGROUND:Selinexor is an oral, selective nuclear export inhibitor. STORM was a phase 2b, single-arm, open-label, multicenter trial of selinexor with low dose dexamethasone in patients with penta-exposed relapsed/refractory multiple myeloma (RRMM) that met its primary endpoint, with overall response of 26% (95% confidence interval [CI], 19 to 35%). Health-related quality of life (HRQoL) was a secondary endpoint measured using the Functional Assessment of Cancer Therapy - Multiple Myeloma (FACT-MM). This study examines impact of selinexor treatment on HRQoL of patients treated in STORM and reports two approaches to calculate minimal clinically important differences for the FACT-MM. METHODS:FACT-MM data were collected at baseline, on day 1 of each 4-week treatment cycle, and at end of treatment (EOT). Changes from baseline were analyzed for the FACT-MM total score, FACT-trial outcome index (TOI), FACT-General (FACT-G), and the MM-specific domain using mixed-effects regression models. Two approaches for evaluating minimal clinically important differences were explored: the first defined as 10% of the instrument range, and the second based on estimated mean baseline differences between Eastern Cooperative Oncology Group performance status (ECOG PS) scores. Post-hoc difference analysis compared change in scores from baseline to EOT for treatment responders and non-responders. RESULTS:Eighty patients were included in the analysis; the mean number of prior therapies was 7.9 (standard deviation [SD] 3.1), and mean duration of myeloma was 7.6 years (SD 3.4). Each exploratory minimal clinically important difference threshold yielded consistent results whereby most patients did not experience HRQoL decline during the first six cycles of treatment (range: 53.9 to 75.7% for the first approach; range: 52.6 to 72.9% for the second). Treatment responders experienced less decline in HRQoL from baseline to EOT than non-responders, which was significant for the FACT-G, but not for other scores. CONCLUSION/CONCLUSIONS:The majority of patients did not experience decline in HRQoL based on minimal clinically important differences during early cycles of treatment with selinexor and dexamethasone in the STORM trial. An anchor-based approach utilizing patient-level data (ECOG PS score) to define minimal clinically important differences for the FACT-MM gave consistent results with a distribution-based approach. TRIAL REGISTRATION/BACKGROUND:This trial was registered on ClinicalTrials.gov under the trial-ID NCT02336815 on January 8, 2015.
PMCID:8419947
PMID: 34488662
ISSN: 1471-2407
CID: 5011382

Impaired Humoral Immunity to SARS-CoV-2 Vaccination in Non-Hodgkin Lymphoma and CLL Patients

Diefenbach, Catherine; Caro, Jessica; Koide, Akiko; Grossbard, Michael; Goldberg, Judith D; Raphael, Bruce; Hymes, Kenneth; Moskovits, Tibor; Kreditor, Maxim; Kaminetzky, David; Fleur-Lominy, Shella Saint; Choi, Jun; Thannickal, Sara A; Stapleford, Kenneth A; Koide, Shohei
Patients with hematologic malignancies are a high priority for SARS-CoV-2 vaccination, yet the benefit they will derive is uncertain. We investigated the humoral response to vaccination in 53 non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), or CLL patients. Peripheral blood was obtained 2 weeks after first vaccination and 6 weeks after second vaccination for antibody profiling using the multiplex bead-binding assay. Serum IgG, IgA, and IgM antibody levels to the spike specific receptor binding domain (RBD) were evaluated as a measure of response. Subsequently, antibody-positive serum were assayed for neutralization capacity against authentic SARS-CoV-2. Histology was 68% lymphoma and 32% CLL; groups were: patients receiving anti-CD20-based therapy (45%), monitored with disease (28%), receiving BTK inhibitors (19%), or chemotherapy (all HL) (8%). SARS-CoV-2 specific RBD IgG antibody response was decreased across all NHL and CLL groups: 25%, 73%, and 40%, respectively. Antibody IgG titers were significantly reduced (p < 0.001) for CD20 treated and targeted therapy patients, and (p = 0.003) for monitored patients. In 94% of patients evaluated after first and second vaccination, antibody titers did not significantly boost after second vaccination. Only 13% of CD20 treated and 13% of monitored patients generated neutralizing antibodies to SARS-CoV-2 with ICD50s 135 to 1767, and 445 and > 10240. This data has profound implications given the current guidance relaxing masking restrictions and for timing of vaccinations. Unless immunity is confirmed with laboratory testing, these patients should continue to mask, socially distance, and to avoid close contact with non-vaccinated individuals.
PMCID:8183024
PMID: 34100025
ISSN: n/a
CID: 4899722

Influence of Aging Processes on the Biology and Outcome of Multiple Myeloma [Meeting Abstract]

Boyle, E M; Williams, L; Blaney, P; Ashby, C; Bauer, M A; Walker, B A; Choi, J; Caro, J; Razzo, B; Arbini, A A; Kaminetzky, D; Braunstein, M; Maura, F; Wang, Y; Landgren, O; Stoeckle, J; Maclachlan, K H; Litke, R; Davies, F E; Morgan, G
Introduction While Multiple myeloma (MM) is a disease of the elderly diagnosed at a median age of 69 years with nearly a third being above the age 75, little is known about the impact of aging processes on either disease biology or clinical outcomes. Treatment decisions are complicated, and it is important to take account three interacting variables: tumor genetics, comorbidities and the efficacy and toxicity of the treatment selected. While frailty scores help stratify elderly MM patients by functional status, quantitative measures of aging could provide biological markers to enhance clinical staging systems, standardize decision making, and guide treatment choices in the elderly MM population. In this work, we characterized the genetics of older MM patients compared to younger patients, and determined the associations of age with clonal hematopoiesis and telomere length (TL), both of which have been shown to be impacted by aging. Methods Using the MMRF CoMMpass IA15 data, we analyzed 972 NDMM patients with whole genome long insert sequencing with matching whole exomes. Using paired samples, we determined mutations (Mutect2 and Strelka), copy number (Control-FREEC v. 11.4), translocations (Manta v. 1.4.0), complex rearrangements (ChainFinder and ShatterSeek), as previously described and TL using Telomerecat. Looking at the germline data, we quantified Clonal Hematopoiesis of Indeterminate Potential (CHIP) and quantified TL using the same approach. Results The overall survival of patients aged over sixty-five is significantly worse than patients younger than this age (HR 1.7 (CI 95% 1.3-2.3), p<0.0001). Using a Bayesian approach, we show that, that del(16p) and del(6q) were more frequent in older patients (Corr=0.10, BF=1.1 and Corr=0.13, BF=11). Similarly, mutational signatures did not substantially differ between age groups with the exception of the proportion of APOBEC (SBS2 and 5) which was higher in the group over age > 80 (chi2=11, p=0.02). We determined both simple and complex structural variants and found that the prevalence of chromothripsis increased with age (chi2=10.8, p=0.001). To determine whether this may be related to chromosomal instability occurring as a consequence of aging we examined the extent of telomere attrition. A significant negative correlation was identified between TL and age (F=9.5, p=0.002) but there were no correlations with complex rearrangements. We did, however, find that TL was significantly shorter in the TP53 (chi2=9, p=0.002) and ATM (chi2=7.2, p=0.007) mutated groups suggesting TL shortening may be associated with DNA instability. To further determine the association of short TL in malignant plasmacells with adverse outcomes we ranked patients based on TL quartile and determined the impact on outcome for the shortest TL. We show that 14%, 29%, 24%, 29%, and 21% of the >50, 50-60, 60-70-70-80, and >80 year old patients were within this short TL group. There was a significant correlation with adverse overall-survival both in the younger and older patients, Figure 1A. To understand and quantify the impact of aging of the normal hematopoietic system on outcomes in MM we quantified CHIP and TL on the germline samples. CHIP was seen in 156 patients (16%) and DNMT3A, ASXL1, and TET2 were the more frequent mutations. Patients with CHIP were significantly older (chi2=3.9, p=0.005), as it was seen in 22% of the over 80. The only signatures identified using a fitting approach for these CHIP mutations were the two age related mutational signatures (SBS1 and SBS5). Interestingly, patients with CHIP did not have significantly adverse clinical outcome. To understand the impact of genetics and markers of aging in the older population we performed a multivariate analysis on the subset of patients over age 65 (n=375). Like others, we found that the well described prognostic genetic risk factors (del(17p), TP53 mutations, t(4;14), t(14;16), and amp1q) did not appear to contribute to the independent assessment of risk when taking into account age, ISS, and performance status (ECOG>=2). We show that in this population of older myeloma that short TL was, however, an independent marker for negative outcome, Figure 1B.
Conclusion(s): We highlight the importance of TL, a composite factor that takes into account both DNA instability, copy number losses, and aging as a potential novel biological marker to assess outcome and aid personalized treatment decisions in older patients with MM. [Formula presented] Disclosures: Bauer: Synthekine: Current Employment. Braunstein: Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Verastem: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Landgren: Amgen: 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; 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; Binding Site: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Glenmark: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Merck: Other. Davies: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotech: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Morgan: Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.
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EMBASE:2013849552
ISSN: 0006-4971
CID: 4978852