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Supplementary Table 2. Mutational signature profiles for 5 environmental agents detected in the WTC debris (Kucab et al, Cell 2019).

Purpose:Duration of first remission is important for the survival of patients with multiple myeloma.Experimental Design:From the CoMMpass study (NCT01454297), 926 patients with newly diagnosed multiple myeloma, characterized by next-generation sequencing, were analyzed to evaluate those who experienced early progressive disease (PD; time to progression, TTP ≤18 months).Results:After a median follow-up of 39 months, early PD was detected in 191/926 (20.6%) patients, 228/926 (24.6%) patients had late PD (TTP >18 months), while 507/926 (54.8%) did not have PD at the current follow-up. Compared with patients with late PD, patients with early PD had a lower at least very good partial response rate (47% vs. 82%, P < 0.001) and more frequently acquired double refractoriness to immunomodulatory drugs (IMiD) and proteasome inhibitors (PI; 21% vs. 8%, P < 0.001). Patients with early PD were at higher risk of death compared with patients with late PD and no PD (HR, 3.65; 95% CI, 2.7–4.93; P < 0.001), showing a dismal median overall survival (32.8 months). In a multivariate logistic regression model, independent factors increasing the early PD risk were TP53 mutation (OR, 3.78, P < 0.001), high lactate dehydrogenase levels (OR, 3.15, P = 0.006), λ-chain translocation (OR, 2.25, P = 0.033), and IGLL5 mutation (OR, 2.15, P = 0.007). Carfilzomib-based induction (OR, 0.15, P = 0.014), autologous stem-cell transplantation (OR, 0.27, P < 0.001), and continuous therapy with PIs and IMiDs (OR, 0.34, P = 0.024) mitigated the risk of early PD.Conclusions:Early PD identifies a high-risk multiple myeloma population. Further research is needed to better identify baseline features predicting early PD and the optimal treatment approaches for patients at risk.

Supplementary Appendix: Additional methods. Carfilzomib-treated patients - Trial design; Table S1. List and classification method of the analyzed variables; Table S2. Induction treatment classification; Table S3. List of the 21 genes analyzed and mutation frequency; Table S4. Distribution of upfront treatment and ASCT in CT subgroups; Table S5. Distribution of variables in Early PD vs reference population; Figure S1. Multivariate logistic regression model evaluating risk factors associated with death within 24 months; Figure S2. Sub-analysis on patients with or without baseline del(17p) and/or TP53 mutation; Figure S3. TP53 and IGLL5 mutations at diagnosis and at first relapse in available longitudinal samples; Figure S4. Epanechnikov kernel-smoothed estimated hazard rates of progressive disease (PD) over time; Figure S5. Comparison of Seq-FISH and conventional FISH in a subgroup of patients enrolled in clinical trials and analyzed in the same centralized laboratory.

Supplementary Table 1. Baseline patient and disease characteristics with response details in male first responders and recovery workers followed at MSKCC.

Purpose:The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. High incidence of multiple myeloma and precursor conditions has been reported among first responders to the WTC disaster. To expand on our 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 developed a plasma cell disorder after the attack.Experimental Design:We performed WGS of nine CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after the WTC disaster.Results:No significant differences were observed in comparing the post-WTC driver and mutational signature landscapes with 110 previously published WGSs from 56 patients with multiple myeloma and the CoMMpass WGS cohort (n = 752). Leveraging constant activity of the single-base substitution mutational signatures 1 and 5 over time, we estimated that tumor-initiating chromosomal gains were windowed to both pre- and post-WTC exposure.Conclusions: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.

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.

Purpose: Minimal residual disease (MRD) negativity is a strong predictor for outcome in multiple myeloma. To assess V(D)J clonotype capture using the updated Adaptive next-generation sequencing (NGS) MRD assay in a clinical setting, we analyzed baseline and follow-up samples from patients with multiple myeloma who achieved deep clinical responses. Experimental Design: A total of 159 baseline and 31 follow-up samples from patients with multiple myeloma were sequenced using the NGS MRD assay. Baseline samples were also sequenced using a targeted multiple myeloma panel (myTYPE). We estimated ORs with 95% confidence intervals (CI) for clonotypes detection using logistic regression. Results: The V(D)J clonotype capture rate was 93% in baseline samples with detectable genomic aberrations, indicating presence of tumor DNA, assessed through myTYPE. myTYPE-positive samples had significantly higher V(D)J clonotype detection rates in univariate (OR, 7.3; 95% CI, 2.8–22.6) and multivariate analysis (OR, 4.4; 95% CI, 1.4–16.9; P = 0.016). Higher disease burden was associated with higher probability of V(D)J clonotype capture, meanwhile no such association was found for age, gender, or type of heavy or light immunoglobulin chain. All V(D)J clonotypes detected at baseline were detected in MRD-positive samples indicating that the V(D)J clonotypes remained stable and did not undergo further rearrangements during follow-up. Of the 31 posttreatment samples, 12 were MRD-negative using the NGS MRD assay. Conclusions: NGS for V(D)J rearrangements in multiple myeloma offers a reliable and sensitive method for MRD tracking with high detection rates in the clinical setting.

Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic., Rustad et al. present a software package for the R statistical environment for the accurately quantify of somatic mutational signatures in hematological malignancies

The 2016 International Myeloma Working Group consensus recommendations emphasize high-sensitivity methods for minimal residual disease (MRD) detection, treatment response assessment, and prognostication. Next-generation sequencing (NGS) of IGH gene rearrangements is highly specific and sensitive, but its description in routine clinical practice and performance comparison with high-sensitivity flow cytometry (hsFC) remain limited. In this large, single-institution study including 438 samples from 251 patients, the use of NGS targeting the IGH and IGK genes for clonal characterization and monitoring, with comparison to hsFC, is described. The index clone characterization success rate was 93.6% (235/251), which depended on plasma cell (PC) cellularity, reaching 98% when PC ≥10% and below 80% when PC

Purpose: The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. High incidence of multiple myeloma and precursor conditions has been reported among first responders to the WTC disaster. To expand on our 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 developed a plasma cell disorder after the attack. Experimental Design: We performed WGS of nine CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after the WTC disaster. Results: No significant differences were observed in comparing the post-WTC driver and mutational signature landscapes with 110 previously published WGSs from 56 patients with multiple myeloma and the CoMMpass WGS cohort (n = 752). Leveraging constant activity of the single-base substitution mutational signatures 1 and 5 over time, we estimated that tumor-initiating chromosomal gains were windowed to both pre- and post-WTC exposure. Conclusions: 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.

Recent genomic research efforts in multiple myeloma have revealed clinically relevant molecular subgroups beyond conventional cytogenetic classifications. Implementing these advances in clinical trial design and in routine patient care requires a new generation of molecular diagnostic tools. Here, we present a custom capture next-generation sequencing (NGS) panel designed to identify rearrangements involving the IGH locus, arm level, and focal copy number aberrations, as well as frequently mutated genes in multiple myeloma in a single assay. We sequenced 154 patients with plasma cell disorders and performed a head-to-head comparison with the results from conventional clinical assays, i.e., fluorescent in situ hybridization (FISH) and single-nucleotide polymorphism (SNP) microarray. Our custom capture NGS panel had high sensitivity (>99%) and specificity (>99%) for detection of IGH translocations and relevant chromosomal gains and losses in multiple myeloma. In addition, the assay was able to capture novel genomic markers associated with poor outcome such as bi-allelic events involving TP53. In summary, we show that a multiple myeloma designed custom capture NGS panel can detect IGH translocations and CNAs with very high concordance in relation to FISH and SNP microarrays and importantly captures the most relevant and recurrent somatic mutations in multiple myeloma rendering this approach highly suitable for clinical application in the modern era.

Minimal residual disease (MRD) tracking, by next generation sequencing of immunoglobulin sequences, is moving towards clinical implementation in multiple myeloma. However, there is only sparse information available to address whether clonal sequences remain stable for tracking over time, and to what extent light chain sequences are sufficiently unique for tracking. Here, we analyzed immunoglobulin repertoires from 905 plasma cell myeloma and healthy control samples, focusing on the third complementarity determining region (CDR3). Clonal heavy and/or light chain expression was identified in all patients at baseline, with one or more subclones related to the main clone in 3.2%. In 45 patients with 101 sequential samples, the dominant clonal CDR3 sequences remained identical over time, despite differential clonal evolution by whole exome sequencing in 49% of patients. The low frequency of subclonal CDR3 variants, and absence of evolution over time in active multiple myeloma, indicates that tumor cells at this stage are not under selective pressure to undergo antibody affinity maturation. Next, we establish somatic hypermutation and non-templated insertions as the most important determinants of light chain clonal uniqueness, identifying a potentially trackable sequence in the majority of patients. Taken together, we show that dominant clonal sequences identified at baseline are reliable biomarkers for long-term tracking of the malignant clone, including both IGH and the majority of light chain clones.

T-cell epitopes with broad population coverage may form the basis for a new generation of SARS-CoV-2 vaccines. However, published studies on immunoprevalence are limited by small test cohorts, low frequencies of antigen-specific cells and lack of data correlating eluted HLA ligands with T-cell responsiveness. As the protective role of pre-existing cross-reactivity to homologous peptides is unclear, we aimed to identify SARS-CoV-2-specific minimal epitopes recognized by CD8 T-cells among 48 peptides eluted from prevalent HLA alleles, and an additional 84 predicted binders, in a large cohort of convalescents (n=83) and pre-pandemic control samples (n=19). We identified nine conserved SARS-CoV-2-specific epitopes restricted by four of the most prevalent HLA class I alleles in the Norwegian study cohort, to which responding CD8 T cells were detected in 70-100% of convalescents expressing the relevant HLA allele. Only two of these were derived from the Spike protein, included in current vaccines. We found a strong correlation between immunoprevalence and immunodominance. Thus, the CD8 T-cell response to SARS-CoV-2 is more focused than previously believed. Using a new algorithm, we predict that a vaccine including these epitopes could induce a T-cell response in 83% of Caucasians.

Background Lenalidomide maintenance improves progression-free survival for patients with multiple myeloma, although its optimal duration is unknown. Clearance of minimal residual disease (MRD) in the bone marrow results in superior outcomes, although its attainment or sustainment does not alter clinical decision-making. Studies that have evaluated MRD serially are limited in length. We therefore aimed to evaluate longitudinal changes in MRD-status (dynamics) and their association with progression-free survival in patients with multiple myeloma.In this single-centre, single-arm, phase 2 study, we enrolled patients aged 18 years and older from the Memorial Sloan Kettering Cancer Center (New York, NY, USA) who had newly diagnosed multiple myeloma following unrestricted frontline therapy and an Eastern Cooperative Oncology Group Performance Status of 2 or lower, including patients who started maintenance before study enrolment. All participants received lenalidomide maintenance at 10 mg for 21 days of 28-day cycles until progression or unacceptable toxic effects for up to 5 years on protocol. The primary endpoint was progression-free survival at 60 months per protocol and key secondary endpoints were MRD rates after completion of the 12th, 24th, and 36th cycle of maintenance and the association between progression-free survival and annual measurement of MRD status. MRD was assessed from first-pull bone marrow aspirates at baseline and annually by flow cytometry per International Myeloma Working Group criteria, (limit of detection of at least 1 × 10Between Sept 8, 2015, and Jan 25, 2019, 108 patients (100 evaluable for the primary endpoint) were enrolled. Median follow-up was 40·7 months (95% CI 38·7-45·0). At 60 months, progression-free survival was 64% (95% CI 52-79). Median progression-free survival was unreached (95% CI unreached-unreached). MRD dynamics were assessed using 340 MRD assessments done over 5 years for 103 evaluable patients. Patients who sustained MRD negativity for 2 years (n=34) had no recorded disease progression at median 19·8 months (95% CI 15·8-22·3) past the 2-year maintenance landmark. By contrast, patients who lost their MRD-negative responses (n=10) were more likely to progress than those with sustained MRD negativity (HR infinite; p0·0001) and those with persistent MRD positivity (HR 5·88, 95% CI 1·18-33·33; p=0·015) at the 2-year landmark. Haematological and non-haematological serious adverse events occurred in 19 patients (18%). The most common adverse events of grade 3 or worse were decreased lymphocyte count in 48 (44%) patients and decreased neutrophil count in 47 (44%) patients. One death occurred on study due to sepsis and heart failure and was considered unrelated to the study drug.Serial measurements of MRD allow for dynamic assessment of risk for disease progression. Early intervention should be investigated for patients with loss of MRD negativity. Sustained MRD positivity is not categorically an unfavourable outcome and might portend prolonged stability of low-level disease.Memorial Sloan Kettering and Celgene.

Multiple myeloma (MM) progression is characterized by the seeding of cancer cells in different anatomic sites. To characterize this evolutionary process, we interrogated, by whole genome sequencing, 25 samples collected at autopsy from 4 patients with relapsed MM and an additional set of 125 whole exomes collected from 51 patients. Mutational signatures analysis showed how cytotoxic agents introduce hundreds of unique mutations in each surviving cancer cell, detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature. Thus, a unique, single-cell genomic barcode can link chemotherapy exposure to a discrete time window in a patient's life. We leveraged this concept to show that MM systemic seeding is accelerated at relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high-dose melphalan therapy and autologous stem cell transplant.

Chromothripsis is detectable in 20–30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study we interrogate 752 NDMM patients using whole genome sequencing (WGS) to investigate the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC = 0.90) and can be used identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis is confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC = 0.97) and an independent series of 34 NDMM (AUC = 0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we are able to predict both the presence of chromothripsis (AUC = 0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data., Chromothripsis is associated with unfavourable outcomes in multiple myeloma (MM), but its detection usually requires whole genome sequencing. Here the authors develop an approach to detect chromothripsis in MM based on copy-number signatures that also works with whole exome sequencing data.

The landscape of structural variants (SV) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 patients with multiple myeloma by low-coverage long-insert whole-genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAM7, and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients, two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis. Significance: Previous genomic studies in multiple myeloma have largely focused on single-nucleotide variants, recurrent copy-number alterations, and recurrent translocations. Here, we demonstrate the crucial role of SVs and complex events in the development of multiple myeloma and highlight the importance of whole-genome sequencing to decipher its genomic complexity. See related commentary by Bergsagel and Kuehl, p. 221. This article is highlighted in the In This Issue feature, p. 215

Multiple myeloma (MM) progression is characterized by the seeding of cancer cells in different anatomic sites. To characterize this evolutionary process, we interrogated, by whole genome sequencing, 25 samples collected at autopsy from 4 patients with relapsed MM and an additional set of 125 whole exomes collected from 51 patients. Mutational signatures analysis showed how cytotoxic agents introduce hundreds of unique mutations in each surviving cancer cell, detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature. Thus, a unique, single-cell genomic barcode can link chemotherapy exposure to a discrete time window in a patient′s life. We leveraged this concept to show that MM systemic seeding is accelerated at relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high-dose melphalan therapy and autologous stem cell transplant., In multiple myeloma, disease progresses via seeding to different anatomic sites and clonal expansion. Here, utilising autopsy material, the authors show that systemic seeding accelerates at relapse following treatment.

A correction to this paper has been published and can be accessed via a link at the top of the paper.

Achieving minimal residual disease (MRD) negativity in the bone marrow is one of the strongest prognostic factors in multiple myeloma. Consequently, MRD testing is routinely performed in clinical trials and moving towards standard of care. This review focuses on the role of next generation sequencing (NGS) of tumor-specific immunoglobulin V(D)J sequences for MRD tracking. The immunoglobulin variable regions are ideal targets for tracking, because every tumor cell shares an identical gene sequence, which is stable over time and generally distinct from the immunoglobulin sequences of normal B-cells. Several excellent assays for NGS-based MRD testing are available, both commercial and community-based, including one that is FDA-approved. These assays can achieve the gold standard analytical sensitivity of one tumor cell per million (10(−6)), requiring a minimum input of 3 million bone marrow cells. On-going clinical trials will outline how MRD testing should be used to inform dynamic risk-adopted therapy.

In multiple myeloma, novel treatments with proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) have prolonged survival but the disease remains incurable. At relapse, next-generation sequencing has shown occasional mutations of drug targets but has failed to identify unifying features that underlie chemotherapy resistance. We studied 42 patients refractory to both PIs and IMiDs. Whole-exome sequencing was performed in 40 patients, and RNA sequencing (RNA-seq) was performed in 27. We found more mutations than were reported at diagnosis and more subclonal mutations, which implies ongoing evolution of the genome of myeloma cells during treatment. The mutational landscape was different from that described in published studies on samples taken at diagnosis. The TP53 pathway was the most frequently inactivated (in 45% of patients). Conversely, point mutations of genes associated with resistance to IMiDs were rare and were always subclonal. Refractory patients were uniquely characterized by having a mutational signature linked to exposure to alkylating agents, whose role in chemotherapy resistance and disease progression remains to be elucidated. RNA-seq analysis showed that treatment or mutations had no influence on clustering, which was instead influenced by karyotypic events. We describe a cluster with both amp(1q) and del(13) characterized by CCND2 upregulation and also overexpression of MCL1, which represents a novel target for experimental treatments. Overall, high-risk features were found in 65% of patients. However, only amp(1q) predicted survival. Gene mutations of IMiD and PI targets are not a preferred mode of drug resistance in myeloma. Chemotherapy resistance of the bulk tumor population is likely attained through differential, yet converging evolution of subclones that are overall variable from patient to patient and within the same patient.

Purpose: Duration of first remission is important for the survival of patients with multiple myeloma. Experimental Design: From the CoMMpass study (NCT01454297), 926 patients with newly diagnosed multiple myeloma, characterized by next-generation sequencing, were analyzed to evaluate those who experienced early progressive disease (PD; time to progression, TTP ≤18 months). Results: After a median follow-up of 39 months, early PD was detected in 191/926 (20.6%) patients, 228/926 (24.6%) patients had late PD (TTP >18 months), while 507/926 (54.8%) did not have PD at the current follow-up. Compared with patients with late PD, patients with early PD had a lower at least very good partial response rate (47% vs. 82%, P < 0.001) and more frequently acquired double refractoriness to immunomodulatory drugs (IMiD) and proteasome inhibitors (PI; 21% vs. 8%, P < 0.001). Patients with early PD were at higher risk of death compared with patients with late PD and no PD (HR, 3.65; 95% CI, 2.7–4.93; P < 0.001), showing a dismal median overall survival (32.8 months). In a multivariate logistic regression model, independent factors increasing the early PD risk were TP53 mutation (OR, 3.78, P < 0.001), high lactate dehydrogenase levels (OR, 3.15, P = 0.006), λ-chain translocation (OR, 2.25, P = 0.033), and IGLL5 mutation (OR, 2.15, P = 0.007). Carfilzomib-based induction (OR, 0.15, P = 0.014), autologous stem-cell transplantation (OR, 0.27, P < 0.001), and continuous therapy with PIs and IMiDs (OR, 0.34, P = 0.024) mitigated the risk of early PD. Conclusions: Early PD identifies a high-risk multiple myeloma population. Further research is needed to better identify baseline features predicting early PD and the optimal treatment approaches for patients at risk.

International audience; Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) may soon replace routine electrophoretic methods for monitoring monoclonal proteins in patients with multiple myeloma. To further evaluate the clinical utility of this assay, we compared the performance of MALDI-TOF-MS head-to-head with an established bone marrow-based measurable residual disease assay by flow cytometry (Flow-BM-MRD), using Memorial Sloan Kettering Cancer Center's 10-color, single-tube method. Our results suggest that MALDI-TOF-MS adds value to bone marrow-based MRD testing and may be most useful for early detection of relapse in peripheral blood compared to current electrophoretic methods.

SummaryThe landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive classification and analysis of SVs in multiple myeloma, interrogating a large cohort of 762 patients with whole genome and RNA sequencing. We identified 100 SV hotspots involving 31 new candidate driver genes, including drug targets BCMA (TNFRSF17) and SLAMF7. Complex SVs, including chromothripsis and templated insertions, were present in 61 % of patients and frequently resulted in the simultaneous acquisition of multiple drivers. After accounting for all recurrent events, 63 % of SVs remained unexplained. Intriguingly, these rare SVs were associated with up to 7-fold enrichment for outlier gene expression, indicating that many rare driver SVs remain unrecognized and are likely important in the biology of individual tumors.

IMPORTANCE: All patients who develop multiple myeloma have a preceding asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). During the past decade, significant progresses have been made in the smoldering multiple myeloma (SMM) classification and risk stratification. OBSERVATIONS: Here, we summarize and discuss current clinical challenges and available models for risk stratification in the setting of SMM. Thanks to several novel more effective and less toxic drugs, these aspects are increasingly becoming important to identify patients eligible for early treatment. However, all proposed criteria were built around indirect markers of disease burden, and therefore they are generally able to identify a fraction of SMM patients where both the transformation into multiple myeloma and the genomic subclonal diversification are already happening. In contrast, next generation sequencing approaches have the potential to identify myeloma precursor disease that will progress even before the major clonal expansion and progression, providing a potential base for a more effective and better precision regarding the optimal timing of treatment initiation. CONCLUSIONS AND RELEVANCE: Thanks to modern technologies, in the near coming future, prognostic models derived from genomic signatures independent from the disease burden will allow to better identify the optimal timing to treat a plasma cell clonal disorder at the very early stages, when the chances of eradication are higher.

Characterization of CD8+ T cells in the tumor microenvironment (TME) is important to predict responses to checkpoint therapy. The TME in multiple myeloma is the bone marrow, which also is an immune organ where immune responses are generated and memory cells stored. The presence of T cells with other specificities than the tumor in the bone marrow may affect the search for biomarkers to predict responses to immunotherapy in myeloma. Here, we found similar proportions of PD1+ CD8+ T cells and similar levels of PD1 expression on CD8+ T cells in the bone marrow of myeloma patients and healthy controls. PD1 expression on CD8+ T cells did not correlate with tumor load suggesting that at least some of the PD1+ CD8+ T cells were specific for non-myeloma antigens. Indeed, PD1+ EBV-specific CD8+ T cells were detected it the bone marrow of patients. Terminal effectors (Teff), effector memory (Tem) and central memory (Tcm) cells as well as exhausted T cells were all found in the myeloma bone marrow. However, myeloma patients had more terminal effectors and fewer memory cells than healthy controls suggesting that the tumor generate an immune response against myeloma cells in the bone marrow. The presence of CD8 EOMEShigh Tbetlow T cells with intermediate levels of PD1 in myeloma patients suggests that T cell types, that are known to be responsive to checkpoint therapy, are found at the tumor site.

Introduction: Regulatory T cells (Tregs), a highly immunosuppressive subset of CD4 + T cells, represent a key challenge in the tumor microenvironment by limiting potent antitumor immune responses. While high densities of tumor-infiltrating Tregs are associated with poor prognosis in patients with various types of solid cancers, their prognostic impact in B-cell non-Hodgkin lymphoma (NHL) remains unclear. Emerging studies suggest substantial heterogeneity in the phenotype and suppressive capacities of Tregs, emphasizing the importance of understanding Treg diversity and the need for additional markers to identify highly suppressive Tregs. Our in-depth characterization of Tregs in NHL tumors could open new paths for rational drug design, facilitating selective therapeutic manipulation of Tregs to reduce immunosuppression and improve anti-tumor immunity. Methods: Single-cell suspensions from NHL patients (diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL) and healthy donors (tonsils and peripheral blood)) were analyzed by fluorescent flow- and mass cytometry to characterize Tregs, focusing on their expression of co-stimulatory and co-inhibitory checkpoint receptors. The immunosuppressive capacity of Tregs was measured by in vitro co-culture of FACS-sorted subsets of Tregs together with autologous CellTrace Violet-labelled T effector cells as responder cells, using samples from FL and tonsils. Live CD4 + T cells were obtained by FACS sorting from DLBCL (n = 3), FL (n = 3) and healthy donor tonsils (n = 3) and subjected to single-cell RNA sequencing (scRNA-seq), Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) and scTCR-seq by the 10X Genomics platform. The computational framework of CIBERSORTx was used to generate unique signature matrices for the three Treg subsets identified by scRNA-seq, to facilitate validation in separate scRNA-seq cohorts (King, Sci Immunol 2021; Roider, Nat Cell Biol 2020), and to impute frequencies of the Treg subsets in cohorts with bulk RNA-seq data (Chapuy, Nat Med 2018; Schmitz, NEJM 2018; Pastore, Lancet Oncol 2015). Results: Immunophenotyping by mass cytometry revealed a subset of activated Tregs identified by co-expression of TIGIT, CTLA-4, PD-1, ICOS and OX40, and higher expression of FOXP3, CD25 and CD45RO, that was present in DLBCL and tonsils, but lacking in peripheral blood. This was validated by fluorescent flow cytometry, demonstrating significantly higher frequencies of activated Tregs in NHL tumors compared to PBMCs and tonsils from healthy donors. The phenotypic heterogeneity of intratumoral Tregs reflected different suppressive capacities as activated Tregs more potently suppressed the proliferation of autologous effector CD4 + and CD8 + T cells than naïve Tregs. For global transcriptomic profiling of CD4 + T cells from FL, DLBCL and tonsillar samples, we integrating scRNA-seq and CITE-seq data from 17,774 cells, revealing 13 distinct cellular states including three states of Tregs: naïve, activated and non-conventional LAG3 +FOXP3 - Tregs. Activated Tregs had higher expression of checkpoint receptors (TNFRSF4, TNFRSF18, ICOS), phosphatases (DUSP2, DUSP4), NF-κB pathway (NFKBIA, TNFAIP3, NFKBIZ, REL), chemokine receptors (CXCR4) and transcription factors (JUNB, IRF1, STAT3) as compared to naïve Tregs. We next used a computational approach to develop unique signature matrices for each Treg subset. This approach demonstrated strong concordance between CIBERSORTx estimated cell abundances of the three Treg subsets and the ground truth, and was validated in two external scRNA-seq cohorts. The development of unique signature matrices for Treg subsets facilitated imputation of their frequencies in bulk RNA-seq datasets. These analyses revealed that higher frequency of activated Tregs was enriched in the germinal B cell subtype of DLBCL and was associated with adverse outcome in FL. Conclusion: This study demonstrates that Tregs infiltrating NHL tumors are transcriptionally and functionally diverse and include highly immunosuppressive activated Tregs co-expressing several checkpoint receptors, which distinguish them from peripheral blood Tregs. Activated intratumoral Tregs could hamper clinical responses to checkpoint blockade, and identifying and targeting their vulnerabilities has the potential to improve anti-tumor immune responses. Disclosures Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Alizadeh: Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CAPP Medical: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Forty Seven: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Foresight Diagnostics: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Roche: Consultancy, Honoraria; Janssen Oncology: Honoraria; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Bristol Myers Squibb: Research Funding.

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 (χ 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. Figure 1 Figure 1. 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.

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 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.

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.

Background. Consensus from prior studies shows that the use of maintenance therapy after completion of combination therapy leads to longer progression-free survival (PFS) for patients with multiple myeloma with some studies showing an overall survival (OS) benefit. Currently, lenalidomide is the standard of care; however, there are limited published data on long-term use regarding ability to sustain minimal residual disease (MRD)-negativity and late toxicities. We were motivated to develop a study focusing on continuous, induction-agnostic lenalidomide maintenance with integration of clinical and correlative data. Here, we report formal results of this phase II study with focus on MRD dynamics and tolerability. Methods. This single arm, phase II trial enrolled 100 evaluable patients. Lenalidomide 10 mg is given days 1-21 on a 28-day cycle. Per protocol, patients underwent bone marrow biopsies and aspirates as well as PET/CT at baseline, annually, at progression/end of study; blood work was done every 3 months. The study was statistically powered for the primary endpoint of PFS at 36 months. Correlative assays included MRD testing (10-color single-tube flow cytometry and IGHV sequencing; sensitivity ≤10-5), genomic characterization of detectable disease, and profiling of the bone marrow microenvironment performed on serially banked samples. Results. 100 evaluable patients were enrolled (63% males) between September 2015 and January 2019. Baseline characteristics include median age 63 years (range 38-87 years) and median ECOG score 1 (range 0-1). Prior to enrollment, 22 (30%) patients had high-risk FISH/SNP signature defined as one or more of: 1q+, t(4;14), t(14;16), t(14;20), and 17p- and 48 patients had undergone autologous hematopoietic cell transplantation (AHCT). At abstract submission, median cycles delivered is 39 (range 9-62). 74% of patients have completed ³24 cycles and 55% have completed ³36 cycles. Overall PFS at 36 months was 77% (95% CI: 0.69-0.87) and PFS at 60 months was 63% (95% CI: 0.51-0.78). All patients had MRD testing at least once. 46% were MRD-negative at enrollment. 7 patients who were MRD+ at enrollment converted to MRD-negative. At median follow up 39.4 months (range 7-56 months), 20/100 patients (20%) have progressed. In consideration of the entire follow-up time from initial MRD-negativity, 44 (of 95 tested; 46%) and 37 (of 73 tested; 51%) achieved sustained MRD-negativity at 1 and 2 years, respectively. 22 patients were MRD-negative at 3 years (of 51 tested; 43%). Among those who sustained MRD-negativity for 2 years, with median follow-up of 19 months past the 2-year landmark analysis (max 120 months), there were no progression events. Age, induction regimen, and MRD status at enrollment were the only significant variables associated with PFS regardless of cytogenetic risk or transplant status. At 1 and 2-year landmark analysis, MRD-negativity superseded all else as the most significant factor associated with PFS with HR 0.06(p=0.0004) and HR 1/Inf (p=0.015), respectively. Toxicities (grade 3) included neutrophil count decrease (20%), hypertension (3%), diarrhea (3%), lung infection (2%), and maculo-papular rash (2%), and toxicities (grade 4) include sepsis (2%) and platelet count decrease (7%). The most common non-grade 3/4 toxicities were diarrhea (55%), fatigue (36%), and upper respiratory infection (30%). 7% developed a secondary malignancy on study: 3 adenocarcinoma, 1 squamous cell carcinoma, 1 CMML, 1 MDS, 1 ALL, and 1 glioblastoma. One evaluable patient required dose reduction due to toxicities/tolerability. Conclusions. This prospective study of continuous lenalidomide maintenance, agnostic to induction regimen or AHCT usage, was designed to evaluate the dynamics of MRD-negativity in relation to PFS. It expands on the importance of MRD as a predictor of outcome and illustrates how continuous maintenance therapy can deepen and sustain MRD-negative responses achieved with modern combination therapy. For this cohort, MRD-negativity at each landmark profoundly outweighed the impact of all other variates. Among those who had sustained MRD-negativity at 2 years (37% of the cohort), regardless of MRD status at enrollment, none have had progression events at median 43 months. Our results support cross-sectional MRD testing as a surrogate endpoint for drug approval, and the use of longitudinal MRD tracking in clinical management. Disclosures Korde: Amgen: Research Funding; Astra Zeneca: Other: Advisory Board. Lesokhin:Genentech: Research Funding; Janssen: Research Funding; GenMab: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; BMS: Consultancy, Honoraria, Research Funding. Smith:Precision Biosciences: Consultancy; Bristol Myers Squibb: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics: Consultancy. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Mailankody:Physician Education Resource: Honoraria; PleXus Communications: Honoraria; Takeda Oncology: Research Funding; Janssen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding. Hultcrantz:Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Hassoun:Takeda: Research Funding; Celgene: Research Funding; Novartis: Consultancy. Scordo:McKinsey & Company: Consultancy; Angiocrine Bioscience, Inc.: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Other: Ad-hoc advisory board. Chung:Genentech: Research Funding. Shah:Amgen: Research Funding; Janssen Pharmaceutica: Research Funding. Lahoud:MorphoSys: Other: Advisory Board. Thoren:Sebia: Research Funding; The Binding Site: Research Funding. Ho:Invivoscribe, Inc.: Honoraria. Dogan:AbbVie: Consultancy; EUSA Pharma: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Roche: Consultancy, Research Funding; National Cancer Institute: Research Funding. Giralt:MILTENYI: Consultancy, Research Funding; ACTINUUM: Consultancy, Research Funding; KITE: Consultancy; OMEROS: Consultancy, Honoraria; NOVARTIS: Consultancy, Honoraria, Research Funding; CELGENE: Consultancy, Honoraria, Research Funding; JAZZ: Consultancy, Honoraria; AMGEN: Consultancy, Research Funding; TAKEDA: Research Funding. Landgren:Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: 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; Pfizer: Consultancy, Honoraria; Merck: Other; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Karyopharma: Research Funding; Merck: Other; Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria.

Circulating tumor DNA is a promising biomarker to monitor tumor load and genome alterations. We explored the presence of circulating tumor DNA in multiple myeloma patients and its relation to disease activity during long-term follow-up. We used digital droplet polymerase chain reaction analysis to monitor recurrent mutations, mainly in mitogen activated protein kinase pathway genes NRAS, KRAS and BRAF. Mutations were identified by next-generation sequencing or polymerase chain reaction analysis of bone marrow plasma cells, and their presence analyzed in 251 archived serum samples obtained from 20 patients during a period of up to 7 years. In 17 of 18 patients, mutations identified in bone marrow during active disease were also found in a time-matched serum sample. The concentration of mutated alleles in serum correlated with the fraction in bone marrow plasma cells (r=0.507, n=34, P

INTRODUCTION: Cancer pathogenesis is usually characterized by a long evolutionary process where genomic driver events accumulate over time, conferring advantage to distinct subclones, allowing their expansion and progression. METHODS: To investigate the multiple myeloma (MM) evolutionary history, we characterized the mutational processes' landscape and activity over time utilizing a large cohort of 89 whole genomes and 973 exomes. To improve the accuracy of mutational signatures analysis, we analyzed both the 3' and 5' nucleotide context of each mutation and we developed the novel fitting algorithm mmSig, which fits the entire mutational catalogue of each patient with the mutational signatures involved in MM pathogenesis. The contribution of each mutational signature was then corrected based on the cosine similarity between the original 96-mutational profile and the reconstructed profile generated without that signature. To reconstruct the genetic evolutionary history of each patient's cancer, we integrated two approaches. First dividing all mutations into clonal (early) or subclonal (late), then subdivided the clonal mutations into duplicated mutations (present on two alleles and therefore acquired before the duplication) or non-duplicated mutations (detected on a single allele), reflecting either pre-gain and post-gain mutations on the minor allele, or post-gain mutations acquired on one of the duplicated alleles. RESULTS Eight mutational signatures were identified, seven of which showed significant similarity with the most recent mutational signature catalogue (i.e SBS1, SBS2, SBS5, SBS8, SBS9, SBS13 and SBS18). The new mutational signature (named SBS-MM1) was observed only among relapsed patients exposed to alkylating agents (i.e melphalan). The etiology of this specific signature was further confirmed by analyzing recent whole genomes public data from human-induced pluripotent stem cells exposed to melphalan (Kucab et al, Cell 2019). Reconstructing the chronological activity of each mutational signature, we identified four different routes to acquire the full mutational spectrum in MM based on the differential temporal activity of AID (SBS9) and APOBEC (SBS2 and SBS13). Our data indicate that AID activity is not limited to the first contact with the GC, but persists in the majority of patients, behaving similarly to a B-memory cells, capable of re-entering the germinal center upon antigen stimulation to undergo clonal expansion several times before MM diagnosis. Next, we confirmed the clock-like nature (i.e constant mutation rate) of SBS5 in MM and other post-germinal center disorders such as chronic lymphocytic leukemia and B-cell lymphomas. Based on the SBS5 mutation rates and the corrected ratio between duplicated and non-duplicated mutations within large chromosomal gains, we could time the acquisition of the first copy number gain during the life history of each MM patient. Intriguingly, the first MM chromosomal duplication was acquired on average 38 years (ranges 11-64) before sample collection. In 23/27 (85%) cases the first multi gain event occurred before 30 years of age, and in 13/27 (48%) before 20 years reflecting a long and slow process potentially influenced and accelerated by extrinsic and intrinsic factors. DISCUSSION Our analysis provides a glimpse into the early stages of myelomagenesis, where acquisition of the first key drivers precedes cancer diagnosis by decades. Defining the time window when transformation occurs opens up for new avenues of research: to identify causal mechanisms of disease initiation and evolution, to better define the optimal time to start therapy, and ultimately develop early prevention strategies. Disclosures Bolli: CELGENE: Honoraria; JANSSEN: Honoraria; GILEAD: Other: Travel expenses. Corradini:Janssen: Honoraria, Other: Travel Costs; Jazz Pharmaceutics: Honoraria; KiowaKirin: Honoraria; Servier: Honoraria; Takeda: Honoraria, Other: Travel Costs; Kite: Honoraria; Novartis: Honoraria, Other: Travel Costs; Gilead: Honoraria, Other: Travel Costs; Roche: Honoraria; Sanofi: Honoraria; BMS: Other: Travel Costs. Anderson:Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Moreau:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Papaemmanuil:Celgene: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Munshi:Adaptive: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Abbvie: Consultancy. Landgren:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Multiple Myeloma, the second most prevalent hematologic malignancy, yet lacks an established curative therapy. However, overall response rate to modern four-drug regimens approaches 100%. Major efforts have thus focused on the measurement of minute quantities of residual disease (minimal residual disease or MRD) for prognostic metrics and therapeutic response evaluation. Currently, MRD is assessed by flow cytometry or by next generation sequencing to track tumor-specific immunoglobulin V(D)J rearrangements. These bone marrow-based methods can reach sensitivity thresholds of the identification of one neoplastic cell in 1,000,000 (10(−6)). New technologies are being developed to be used alone or in conjunction with established methods, including peripheral blood-based assays, mass spectrometry, and targeted imaging. Data is also building for MRD as a surrogate endpoint for overall survival. Here, we will address the currently utilized MRD assays, challenges in validation across labs and clinical trials, techniques in development, and future directions for successful clinical application of MRD in multiple myeloma.

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 As 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.

INTRODUCTION. Recent studies show that ~25% of newly diagnosed multiple myeloma patients treated with 8 cycles of bortezomib, lenalidomide and dexamethasone (VRd) achieve minimal residual disease (MRD) negativity. Recently, 42% stringent complete response (sCR) rates were reported with the use of VRd combined with the CD38-targeted monoclonal antibody daratumumab (VRd-D). Here, we present the final results from a phase 2 study using weekly dosing of carfilzomib 56 mg/m2 with lenalidomide and dexamethasone in combination with daratumumab (wKRd-D). The primary endpoint of our study was to demonstrate >60% and to target up to 80% MRD negativity rate with wKRd-D. METHODS. This phase II clinical trial is based on Simon's optimal two-stage design. The wKRd-D dosing schedule is as follows: 8 cycles of treatment; 28-day cycles with IV carfilzomib 20/56 mg/m2 days 1, 8, and 15; PO lenalidomide 25 mg days 1-21; PO/IV dexamethasone 40 mg weekly cycles 1-4, 20 mg after cycle 4; and IV daratumumab 16 mg/kg days 1, 8, 15, and 22 cycles 1-2, days 1 and 15 cycles 3-6, and day 1 cycles 7-8. For fit patients, stem cell collection is recommended after 4 to 6 cycles of therapy; wKRd-D therapy resumed after collection to a total of 8 cycles wKRd-D. Treatment response is being assessed with parallel bone marrow-based MRD assays (10-color single tube flowcytometry and invivoscribe IGHV sequencing); per IMWG guidelines both MRD assays allows detection of 1 myeloma cell in 100,000 cells (10^-5). Baseline bone marrow samples are evaluated with targeted DNA sequencing for FISH-Seq and somatic mutational characteristics (myTYPE). RESULTS. The study is fully enrolled; between October 2018 and November 2019 a total of 41 evaluable patients were enrolled. Baseline characteristics include; median age 59 years (range 30-70 years); 25 (61%) females;16 (39%) males; 20 (49%) patients had high-risk FISH/SNP signature defined as one or more of the following: 1q+, t(4;14), t(14;16), t(14;20), and 17p-. At submission of this abstract, 39 out of 41 patients have completed 8 cycles of treatment and end of treatment evaluations. Of those 39, 29 patients were MRD negative and 10 patients MRD positive. Two patients are pending end of treatment evaluations for response (currently receiving cycle 8 of wKRd-D). Thus, among patients treated on the weekly cohort (wKRd-D) and who were evaluable for the MRD primary endpoint at this analysis, we found 29/39 (74%) to be MRD negative. We further show no added major clinical toxicities with wKRd-D compared to our institution standard of care KRd. At a median follow-up of 10 months, none of the MRD negative patients have progressed. Among 29 patients found to be MRD negative after 8 cycles of wKRd-D, 2 patients have been assessed for MRD at 1 year of follow-up and 2/2 (100%) show 1-year sustained MRD negativity. There are no deaths on the study. CONCLUSIONS. Among patients evaluable for the MRD primary endpoint, in the absence of an autologous bone marrow transplant, here we show a 29/39 (74%) MRD negativity rate among newly diagnosed multiple myeloma patients treated with wKRd-D, including weekly carfilzomib 56 mg/m2 dosing. At a median follow-up of 10 months, none of the MRD negative patients have progressed. These results compare favorably with previously published results with KRd, VRd, or VRd-D. Using optimized IV fluid management (250 ml saline prior to first dose of carfilzomib only, and thereafter no IV fluids) coupled with baseline work-up with EKG/echocardiograms for all patients, we did not observe excess rates of cardiovascular or renal adverse event. The wKRd-D dosing schedule has a total of 27 infusions and offers an attractive treatment modality for newly diagnosed multiple myeloma patients. Based on these promising results, a large randomized multi-center study ("ADVANCE") evaluating wKRd-D in relation to established standard of care has been opened for enrollment. Disclosures Landgren: Adaptive: Consultancy, Honoraria; BMS: 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; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding. Hultcrantz:Intellisphere LLC: Consultancy; Amgen: Research Funding; GSK: Research Funding; Daiichi Sankyo: Research Funding. Lesokhin:BMS: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Janssen: Research Funding; GenMab: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties. Mailankody:Janssen Oncology: Research Funding; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding; Allogene Therapeutics: Research Funding; PleXus Communications: Honoraria; Physician Education Resource: Honoraria; Takeda Oncology: Research Funding. Hassoun:Novartis: Consultancy; Celgene: Research Funding; Takeda: Research Funding. Shah:Celgene/BMS: Research Funding; Physicians Education Resource: Honoraria. Scordo:McKinsey & Company: Consultancy; Angiocrine Bioscience, Inc.: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Other: Ad-hoc advisory board. Chung:Genentech: Research Funding. Shah:Amgen Inc.: Research Funding; Janssen: Research Funding. Lahoud:MorphoSys: Other: Advisory Board. Thoren:The Binding Site: Research Funding; Sebia: Research Funding. Murata:Abbott Laboratories: Research Funding. Ho:Invivoscribe, Inc.: Honoraria. 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. Giralt:CSL Behring: Research Funding; Jazz: Research Funding; Actinuum: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Quintiles: Research Funding; Pfizer: Research Funding; Sanofi: Research Funding; Adienne: Research Funding; Kite: Research Funding. Korde:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. OffLabel Disclosure: Daratumumab, Carfilzomib, Lenalidomide, and Dexamethasone in Newly-Diagnosed Multiple Myeloma. These drugs are included in the current clinical and correlative Phase II study

Multiple myeloma is the second most common lymphoproliferative disorders, characterized by aberrant expansion of monoclonal plasma cells. In the last years, thanks to novel next generation sequencing technologies, multiple myeloma has emerged as one of the most complex hematological cancers, shaped over time by the activity of multiple mutational processes and by the acquisition of key driver events. In this review, we describe how whole genome sequencing is emerging as a key technology to decipher this complexity at every stage of myeloma development: precursors, diagnosis and relapsed/refractory. Defining the time windows when driver events are acquired improves our understanding of cancer etiology and paves the way for early diagnosis and ultimately prevention.

Background. Prior studies consistently show that the use of maintenance therapy after completion of combination therapy translates into longer progression-free survival (PFS) in patients with multiple myeloma. Some studies show that maintenance therapy prolongs overall survival (OS). Typically, maintenance therapy is used in the setting of newly diagnosed multiple myeloma; however, emerging data suggest that (at least a subset of) patients in the early relapse setting, for example those who achieve MRD negativity, may also be candidates for maintenance therapy integrated with careful disease monitoring. Currently, lenalidomide is considered the standard of care for maintenance; however, there is only limited published data on long-term use, with respect to the ability to sustain MRD negativity, mechanisms of relapse, and late toxicities. We were motivated to develop a study focusing on long-term lenalidomide maintenance therapy and to study clinical and correlative data. Here, we report on sustained MRD negativity and clinical tolerability. Methods. This single arm, phase 2 was designed to enroll 100 evaluable patients. Per protocol, maintenance therapy with lenalidomide 10 mg is given days 1-21 on a 28-day cycle. The initial study design had a total duration of 36 months; it was subsequently extended with additional 24 months (i.e., total of 60 months). Per standard procedures for protocol amendments, patients were offered to re-consent for the extension. Per protocol, patients underwent bone marrow biopsies and aspirates as well as PET/CT exams at baseline, annually, at progression/end of treatment; blood work was done every 3 months. Bone marrow and blood samples were banked longitudinally per the research protocols. Based on practical considerations, the study was statistically powered for the primary end-point progression-free survival, which provided sufficient numbers of samples for the planned correlative assays focusing on MRD testing, genomic characterization of detectable disease, and profiling of the bone marrow microenvironment. All these assays were conducted in serial samples collected over time and assessed in relation to clinical outcomes. Results. A total of 100 evaluable patients meeting eligibility criteria were enrolled (63% males) between September 2015 and January 2019. Baseline characteristics include median age 63 years (range 38-86 years) and median ECOG score 1 (range 0-1). At the submission of this abstract, the median number of cycles delivered is currently 26 (range 1 to 48); 86 patients have completed 12 or more cycles, 57 patients have completed 24 or more cycles, and 29 patients have completed 36 or more cycles. MRD testing had been completed at least once in all patients. Thirty-four patients were MRD negative at enrollment. At median followup time of 28 months (range 3.4 to 45.6), 15/100 (15%) patients have progressed. Considering the entire follow-up time from initial MRD negativity to last follow-up on study, we found 39 (of 85 tested; 46%) and 25 (of 57 tested; 44%) to have evidence of 1 and 2 years sustained MRD negativity, respectively. Only 19 patients were tested for MRD at 3 years and 16 (84%) had sustained MRD negativity. Toxicities (grade 3) include neutrophil count decrease (N=9), hypertension (N=3), diarrhea (N=2), lung infection (N=2), and rash maculo-papular (N=2), and toxicities (grade 4) include sepsis (N=2) and platelet count decrease (N=1). The most common 1/2 toxicities were diarrhea (N=51), fatigue (N=33), and upper respiratory infection (N=23). Among evaluable patients, dose reductions of lenalidomide due to toxicities and tolerability issues were done in 6 (6%) patient. Conclusions. Among evaluable patients who were treated with lenalidomide 10 mg maintenance therapy days 1-21 on a 28-day cycle on this study, at a median followup of 28 months, we found 46% and 44% to have evidence of 1 and 2 years sustained MRD negativity, respectively. Currently, 19 patients have been tested for MRD at 3 years; 16 (84%) show evidence of 3 years sustained MRD negativity. The toxicity profile was in accord with prior studies and tolerability was quite good reflected in only 6 patients requiring dose reductions due to toxicities. Correlative assays focusing on mechanisms of sustained MRD negativity in this study are presented in a separate abstract at this meeting. Disclosures Landgren: Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Theradex: Other: IDMC; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC. Lesokhin:Genentech: Research Funding; GenMab: Consultancy, Honoraria; Janssen: Research Funding; Serametrix Inc.: Patents & Royalties; Juno: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria. Smith:Celgene: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics and Precision Biosciences: Consultancy. Mailankody:Juno: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Takeda Oncology: Research Funding; CME activity by Physician Education Resource: Honoraria. Hassoun:Janssen: Research Funding; Celgene: Research Funding; Novartis: Consultancy. Landau:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding; Caelum: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Scordo:Angiocrine Bioscience, Inc.: Consultancy; McKinsey & Company: Consultancy. Arcila:Invivoscribe, Inc.: Consultancy, Honoraria. Ho:Invivoscribe, Inc.: Honoraria. Roshal:Physicians' Education Resource: Other: Provision of services; Celgene: Other: Provision of Services; Auron Therapeutics: Equity Ownership, Other: Provision of services. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Novartis: Consultancy.

Whole genome sequencing (WGS) studies have started to reveal the critical role of structural variants (SVs) in multiple myeloma (MM) pathogenesis and evolution. We have recently revealed the existence of three main classes of complex events in 30 MM patients: chromothripsis, chromoplexy and templated insertions (Maura F et al, Nat Comm, 2019). Here, drawing on a large cohort of 768 MM patients enrolled in the MMRF CoMMpass study (NCT01454297), we comprehensively characterized the landscape of SVs and their functional implications. Low coverage long-insert WGS (median 4-8X) was available from all patients, of whom 591 also had RNAseq data. Overall, we identified a median of 15 total SVs (range 1-253). Fifty-one percent of SVs (n = 8766) were defined as part of complex events, with a median of one per patient (range 0-14). Chromothripsis, chromoplexy and templated insertions involving >2 chromosomes were observed in 21%, 11% and 21 %, respectively. Chromothripsis was the only SV class with clear prognostic implications after adjustment for molecular and clinical features, resulting in adverse PFS (adjusted HR = 1.57; 95% CI 1.13-2.22; p = 0.008) and OS (adjusted HR = 2.4; 95% CI 1.5-3.83; p < 0.001). Templated insertions emerged as the cause of CCND1-IGH and MYC translocations in 34 % and 73 % of cases, respectively. This is particularly important given the capability of templated insertions to connect and amplify multiple regions of the genome, involving several oncogenes and regulatory regions (e.g. super enhancers). Twenty-four patients (3.1 %) had translocation between an immunoglobulin locus and a non-canonical driver gene (e.g. PAX5, CD40 and MAP3K14), showing outlier expression by RNAseq where available. SV hotspot analysis was carried out using the Piecewise Constant Fitting algorithm, comparing the local SV breakpoint density to an empirical background model (Glodzik et al, Nat Genet, 2017). To identify functionally important hotspots, we integrated: 1) local cumulative copy number data, 2) amplification and deletion peaks identified by GISTIC v2 (q < 0.1), 3) gene fusion data and 4) differential expression analysis with adjustment for main molecular subgroups (limma; Bonferroni-Holm adjusted p-values < 0.01). Ninety-eight hotspots were identified (Figure 1), of which 71 (72%) have not previously been reported. Among these novel hotspots, 23 (33 %) contained a known or suspected driver gene, including TNFRSF17 (encoding CAR-T target BCMA), SYK (BCR signal transduction) and KLF2 (key myeloma transcription factor and germline predisposition locus). Active enhancer regions were present in 29 of the novel hotspots (41 %), including 65 % of those with a concurrent putative driver gene involved. For 34 hotspots (48 %) no clear target gene or regulatory region emerged. SV hotspots and GISTIC peaks covered 13 % of the genome. Overall 38 % of simple and complex SVs had at least one breakpoint falling within a recurrently involved region. The majority of chromoplexy, chromothripsis and templated insertions involved recurrent regions (64, 76 and 86 %, respectively). Simple events were most commonly rare, ranging from 74 % of deletions to 45 % for translocations. Quantifying the global functional impact of the remaining 72 % of non-recurrent or rare SVs, we observed that genes involved by a rare SV were significantly enriched for outlier expression (z-score +/- 2) compared to a permutation background model. Rare deletions and duplications exerted their effects within 10 Kb of the gene body. Translocations and templated insertions were associated with overexpression up to 1 Mb from the gene, but had no effect when involving the gene body, consistent with a major enhancer hijacking mechanism. Finally, we sought to understand the role of recurrent and rare SVs in evolutionary dynamics, analyzing 27 patients that progressed with branching evolution. Seventy-two acquired SVs involved a hotspot region (42 driver and/or enhancer; 48 unknown), while 328 were rare. In conclusion, the SV landscape in multiple myeloma is characterized by multiple recurrently involved genes and regulatory regions. These regions account for the majority of complex SVs, indicating strong positive selection of these events. Nonetheless, the majority of SVs remain unaccounted for. Rare SVs were associated with outlier gene expression and may contribute to the tumor evolutionary trajectory of individual patients. Disclosures Papaemmanuil: Celgene: Research Funding. Landgren:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees.

Introduction: Next-generation sequencing (NGS)-based IGH clonal rearrangement assays can characterize and subsequently track disease-associated clonal sequences for lymphoid and plasma cell neoplasms, even at very low levels. As IGH PCR primers are used, the detected clonal sequences are usually reported as % of sequencing reads, roughly corresponding to % of B and plasma cells (PC) in samples, rather than % of total cellularity, hampering accurate disease burden assessment. In this study, we evaluated a method for calculating residual disease burden as % of total cellularity, with the aid of adding a known quantity of "spike-in" clonal sequence to the samples, and compared to concurrent 10-color flow cytometry (FC) quantitation of abnormal PC. Methods: DNA was extracted from 40 plasma cell myeloma patient marrow biopsies sent for disease monitoring purposes at Memorial Sloan Kettering Cancer Center (MSKCC), with previously-characterized clonal sequences specific to the patients' myelomas. All samples had concurrent FC analyses and aspirate differential counts performed. 100 cell equivalent of DNA with a known clonal sequence (LymphoQuant®, LQ) was added to 700ng of patient DNA (~100,000 cell equivalent), and testing was performed using LymphotrackTM, a NGS-based assay. Following PCR amplification using IGH FR1 primers, sequencing was performed on the Illumina MiSeqTM instruments at the molecular laboratory of MSKCC. Reproducibility studies were conducted on a subset of samples at the laboratory of Invivoscribe, Inc. using identical methodology. LymphoTrack MRD data analysis tool (MRDDAT) v.1.0.3 was used to search for both the myeloma-specific and LQ clonal sequences. Disease as # of cell equivalent was calculated as: (% reads for myeloma clonal sequence/% reads for LQ) X 100 cells. Disease as % of total cellularity was calculated as: (# of cell equivalent/100,000 cells) X 100%. Results: Disease as % of total cellularity calculated by LQ showed a median of 0.7576% cells (range: 0.000614% to 39.89%), compared to abnormal PC as % of total WBC by FC with a median of 0.355% cells (range: 0.00061% to 44.70%). Overall, a good correlation between disease quantitation by LQ and FC could be observed for cases with ≤10% total PC by aspirate count (r=0.79), while the correlation is lower for cases with >10% total PC (r=0.51). 12/40 samples were tested in two different laboratories, and showed excellent correlation in disease quantitation by LQ (r=0.94). As expected, detectable clonal sequences as % of sequencing reads (rather than as % of total cellularity) showed poor correlation with FC quantitation (r=0.32), due to variability of total B and plasma cell content in different samples. Conclusions: Disease as % of total cellularity calculated with the aid of a known "spike-in" sequence in the NGS-based assay showed good correlation with the quantitation of abnormal PC by FC, when total PC was ≤10% by aspirate count. The correlation between the two declines when total PC was >10%. When patient samples contain a high number of B and/or plasma cells, the PCR amplification efficiency of the very small amount of the admixed "spike-in" clonal sequence may be hampered, affecting accurate quantitation. Furthermore, FR1 primers may not anneal optimally to some patients' clonal sequences due to somatic hypermutations in binding sites, underestimating the % of disease clone. Utilization of a second "spike-in" sequence and other primer sets (FR2, FR3) may improve disease % calculations in some cases. Disclosures Ho: Invivoscribe, Inc.: Honoraria. Roshal:Celgene: Other: Provision of Services; Auron Therapeutics: Equity Ownership, Other: Provision of services; Physicians' Education Resource: Other: Provision of services. Huang:Invivoscribe, Inc.: Employment. Hutt:Invivoscribe, Inc.: Employment. Miller:Invivoscribe, Inc.: Employment. Landgren:Theradex: Other: IDMC; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Merck: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Arcila:Invivoscribe, Inc.: Consultancy, Honoraria.

Introduction In multiple myeloma (MM), the absence of measurable residual disease (MRD) after completed therapy is associated with longer progression free survival. Different techniques are available to detect low levels of plasma cells in bone marrow (BM) either by flow cytometry or by next-generation sequencing as a gold standard of molecular methods. But these techniques are limited because they require a representative bone marrow sample obtained by an invasive procedure. Therefore, detecting low levels of disease in blood would be ideal, because serial sampling is much easier and fully representative, and it would allow for the detection of extramedullary disease. Mass spectrometry-based methods have been shown to be more sensitive for detecting monoclonal proteins (M-protein) in serum. In this study, we were motivated to evaluate MALDI-TOF mass spectrometry (MALDI-TOF MS) head-to-head with an established BM-based MRD assays. Patients and Methods This cohort included 71 patients treated at Memorial Sloan Kettering Cancer Center (MSKCC) who had serum samples available at 2 timepoints including during active disease and within 60 days of MRD results as determined by flow cytometry of bone marrow aspirates (Flow-BM-MRD). The cohort enrolled 26 females and 45 males with a median age of 61 years (range 37-78 years). Twenty-seven patients had high-risk cytogenetics at baseline. The median time between diagnosis and the MRD timepoint was 13.4 months (3.4-91 months). MALDI-TOF MS analysis was performed according to the method published by Mills et al. Immunoglobulins were purified from serum samples using CaptureSelect beads specific of each isotype and were then eluted from the beads. Light chains and heavy chains were separated by the addition of a reducing agent. Purified samples were mixed in matrix and spotted onto a stainless steel MALDI plate and were analyzed using a Microflex LT MALDI-TOF mass spectrometer (Bruker). Samples taken during active disease were used to identify the mass to charge ratio (m/z) of the M-protein and served as a surrogate marker in the analysis of subsequent samples. MALDI-TOF MS results were compared to the Flow-BM-MRD assay, performed using the MSKCC's ten-color, single-tube method. Results MALDI-TOF MS detected an M-protein in all 71 active disease samples and in 25 MRD samples. MALDI-TOF-MS results at the MRD timepoint were concordant with Flow-BM-MRD for 44/71 patients (p=0.342, chi-square test). Eight patients were positive and 36 negative by both techniques. Twenty-seven patients were discordant, including 10 patients detectable only by Flow-BM-MRD and 17 detectable only by MALDI-TOF MS. Among the 10 patients detectable by flow cytometry but not by MALDI, the median MRD level was 0.00092% (+ To determine the clinical utility of more sensitive M-protein detection, we evaluated the clinical outcome for the 48 newly diagnosed MM patients in CR at the MRD timepoint with a median follow-up of 11 months. Of these 48 patients, 2 of the 3 that were positive by both techniques relapsed during follow-up. One out of 27 patients that were negative by both techniques relapsed. None of the 10 patients who were positive only by MALDI-TOF relapsed and 1 of the 8 patients who were positive only by Flow-BM-MRD relapsed. Conclusions This study is important because it is a first step in understanding how to use a more sensitive blood test for the follow-up of MM patients. MALDI-TOF MS analysis may provide complementary results to Flow-BM-MRD especially for the follow-up of patients in CR and during maintenance therapy to detect poor responders that would be positive by both techniques. In summary, our results suggest that MALDI-TOF may be quite useful for early detection of relapse. Disclosures Roshal: Physicians' Education Resource: Other: Provision of services; Celgene: Other: Provision of Services; Auron Therapeutics: Equity Ownership, Other: Provision of services. Hassoun:Celgene: Research Funding; Janssen: Research Funding; Novartis: Consultancy. Smith:Celgene: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics and Precision Biosciences: Consultancy. Lesokhin:Takeda: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; Genentech: Research Funding; GenMab: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Juno: Consultancy, Honoraria. Mailankody:Juno: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Takeda Oncology: Research Funding; CME activity by Physician Education Resource: Honoraria. Landgren:Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Other: IDMC; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.