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Lightbody ED, Firer DT, Sklavenitis-Pistofidis R, Agius M, Dutta AK, Aranha M, Alberge JB, Hevenor L, Su NK, Boehner C, Horowitz E, Perry J, Cowan A, Barr H, Justis A, Auclair D, Marinac CR, Getz G, Ghobrial I. Abstract 641: Single-cell RNA sequencing of rare circulating tumor cells in precursor myeloma patients reveals molecular underpinnings of tumor cell circulation. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Multiple Myeloma (MM) is a hematological malignancy characterized by abnormal proliferation of terminally differentiated plasma cells (PCs) in the bone marrow (BM). MM is almost always preceded by the precursor stage smoldering multiple myeloma (SMM). BM biopsies are useful to monitor disease progression, but they are invasive and not routinely collected from patients for disease monitoring during precursor stages. Profiling circulating tumor cells (CTCs) from peripheral blood (PB) could aid early detection, disease monitoring, and biomarker identification to predict patients at high risk of progression that may benefit from early therapeutic intervention.
Methods: Paired PB and BM aspirates were collected from 40 SMM patients enrolled in the PCROWD study (IRB #14-174) at Dana-Farber Cancer Institute. Malignant PCs were enriched by magnetic bead-based methods and underwent 5’ single-cell RNA sequencing (scRNA-seq) and single-cell B-cell receptor sequencing (scBCR-seq) (10x Genomics).
Results: We analyzed 105,246 BM PCs and 33,234 PB PCs from 15 patients. To differentiate malignant from normal PCs, we used clonal V(D)J rearrangements, assessed by concurrent scBCR-seq. A total of 86,986 BM tumor cells and 8,718 CTCs were captured. A median of 5, 26, and 47 CTCs were present per mL of blood from low, intermediate, and high-risk SMM patients as defined by the International Myeloma Working Group (IMWG) “20/2/20” criteria, suggesting sequencing-based CTC enumeration corresponds to prognosis. High levels of driver genes commonly upregulated in patients with specific translocations, including CCND1 and MAF, were detected in both BM tumor and CTC clusters in 3 patients with t(11;14) and t(14;16) confirmed by fluorescence in situ hybridization (FISH) clinical testing, and 2 additional patients with inconclusive FISH results (Wilcoxon, q <10-3), supporting the idea of CTC-based prognostication. Differential expression (DE) analysis revealed 8 genes that were significantly upregulated and 3 genes that were significantly downregulated in CTCs compared to BM tumor cells robustly across 15 paired samples. Gene set enrichment analysis (GSEA) revealed genes DE in CTCs are associated with TNF-α and NF-κB signaling, which are commonly induced by extrinsic factors in the bone marrow milieu, providing insight into the biology of tumor cell circulation.
Conclusions: This study highlights the utility of scRNA-seq for molecular profiling of CTCs, even in asymptomatic low tumor burden disease. Additional analyses are ongoing in the expanded cohort of 40 patients with paired samples to help gain further insight into CTC heterogeneity. Overall, this study will help enable the design of new molecular liquid biopsy-based approaches to diagnosis, disease monitoring, and biological insights to improve treatment strategies for precursor myeloma patients.
Citation Format: Elizabeth D. Lightbody, Danielle T. Firer, Romanos Sklavenitis-Pistofidis, Michael Agius, Ankit K. Dutta, Michelle Aranha, Jean-Baptiste Alberge, Laura Hevenor, Nang Kham Su, Cody Boehner, Erica Horowitz, Jacqueline Perry, Anna Cowan, Hadley Barr, Anna Justis, Daniel Auclair, Catherine R. Marinac, Gad Getz, Irene Ghobrial. Single-cell RNA sequencing of rare circulating tumor cells in precursor myeloma patients reveals molecular underpinnings of tumor cell circulation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 641.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Anna Cowan
- 1Dana-Farber Cancer Insitute, Boston, MA
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Bustoros M, Anand S, Sklavenitis-Pistofidis R, Redd R, Boyle EM, Zhitomirsky B, Dunford AJ, Tai YT, Chavda SJ, Boehner C, Neuse CJ, Rahmat M, Dutta A, Casneuf T, Verona R, Kastritis E, Trippa L, Stewart C, Walker BA, Davies FE, Dimopoulos MA, Bergsagel PL, Yong K, Morgan GJ, Aguet F, Getz G, Ghobrial IM. Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes. Nat Commun 2022; 13:3449. [PMID: 35705541 PMCID: PMC9200804 DOI: 10.1038/s41467-022-30694-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models.
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Affiliation(s)
- Mark Bustoros
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
- Division of Hematology & Medical Oncology, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Shankara Anand
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | | | - Robert Redd
- Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eileen M Boyle
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | | | - Yu-Tzu Tai
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
| | - Selina J Chavda
- Division of Hematology, University College London, London, UK
| | - Cody Boehner
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
| | - Carl Jannes Neuse
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
- University of Münster Medical School, Münster, Germany
| | - Mahshid Rahmat
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
| | - Ankit Dutta
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA
| | | | - Raluca Verona
- Janssen Research and Development, Spring House, PA, USA
| | - Efstathis Kastritis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, Athens, Greece
| | - Lorenzo Trippa
- Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Chip Stewart
- Broad Institute of MIT & Harvard, Cambridge, MA, USA
| | - Brian A Walker
- Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Indianapolis, IN, USA
| | - Faith E Davies
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | | | - Kwee Yong
- Division of Hematology, University College London, London, UK
| | - Gareth J Morgan
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | | | - Gad Getz
- Broad Institute of MIT & Harvard, Cambridge, MA, USA.
- Department of Pathology, Massachusetts General Hospital Cancer Center, Boston, MA, USA.
| | - Irene M Ghobrial
- Medical Oncology, Dana-Farber Cancer Center, Boston, MA, USA.
- Broad Institute of MIT & Harvard, Cambridge, MA, USA.
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Anand S, Bustoros M, Aguet F, Sklavenitis-Pistofidis R, Redd R, Zhitomirsky B, Dunford AJ, Tai YT, Chavda SJ, Boehner C, Neuse CJ, Casneuf T, Trippa L, Stewart C, Yong K, Ghobrial I, Getz G. Abstract 2240: Genomic profiling of smoldering multiple myeloma classifies distinct molecular groups. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Multiple Myeloma (MM) is an incurable plasma cell malignancy with significant genomic heterogeneity. It is usually preceded by the asymptomatic stage known as smoldering multiple myeloma (SMM). SMM patients have a 10% annual risk of progression to MM. Genomic alterations that are observed in SMM patients include chromosomal gains and losses, translocations, and point mutations. However, current SMM risk models rely solely on clinical markers that do not accurately capture the progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase the precision of risk models.
Methods: We obtained a total of 214 patients' samples at SMM diagnosis in the US and Europe. We performed whole exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture with a MM gene panel was done on an additional 48 tumors. We identified subgroups using binarized DNA features and performing consensus binary non-negative matrix factorization.
Results: We identified six clusters (C1-C6) with the following features: four with a hyperdiploidy (HD) (>48 chromosomes) and two with IgH translocations. These subgroups have unique transcriptomic profiles overlapping with known MM signatures and biological pathways. One of the clusters harboring translocation (11;14), which we call C4-CCND1, was enriched with the previously defined CD-2 MM signature that uniquely expresses B cell markers CD20 and CD79A; shows upregulation of CCND1 and E2F7; and is enriched with pathways like DNA replication, heme metabolism, and NFkB signaling. The C3-MS_MF cluster with the IgH translocations (4;14) and (14;16) shows downregulation of ribosomal genes, TRAF2, and DUSP2. The MYC oncogene was highly expressed in the four HD clusters: C1-HD_NRAS, C2-HD_MAFB, C5-HD_KRAS, and C6-HD_1q (BH-P = 0.037). The clusters also showed different outcomes in terms of time to progression (TTP) to active MM (P = 0.005). Median TTP for patients in C2-HD_MAFB, C3-MS_MF, and C5-HD_KRAS was 3.7, 2.6, and 2.2 years, respectively; TTP for C1-HD_NRAS, C4-CCND1, and C6-HD_1q was 4.3, 11, and not reached, respectively. In multivariate analysis, C2-HD_MAFB, C3-MS_MF, and C5-HD_KRAS were independent predictors of progression after accounting for the clinical risk stage. Moreover, the odds of having evolving hemoglobin and monoclonal protein levels in these three clusters were 3.5 and 12.3 times higher than the other clusters, respectively (P = 0.01 and 0.002).
Conclusion: We identified six distinct SMM molecular groups with corresponding transcription profiles and dysregulated pathways. These groups have different progression risks to active MM, with three groups being independent predictors of progression. Our results underscore the importance of molecular classification in MM to better understand and target various tumor vulnerabilities.
Citation Format: Shankara Anand, Mark Bustoros, François Aguet, Romanos Sklavenitis-Pistofidis, Robert Redd, Benny Zhitomirsky, Andrew J. Dunford, Yu-Tzu Tai, Selina J. Chavda, Cody Boehner, Carl J. Neuse, Tineke Casneuf, Lorenzo Trippa, Chip Stewart, Kwee Yong, Irene Ghobrial, Gad Getz. Genomic profiling of smoldering multiple myeloma classifies distinct molecular groups [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2240.
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Affiliation(s)
| | | | | | | | - Robert Redd
- 2Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | | | | | - Yu-Tzu Tai
- 2Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | | | - Cody Boehner
- 2Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Carl J. Neuse
- 4University of Münster Medical School, Münster, Germany
| | | | - Lorenzo Trippa
- 2Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Chip Stewart
- 1Broad Institute of MIT & Harvard, Cambridge, MA
| | - Kwee Yong
- 3University College London, London, United Kingdom
| | - Irene Ghobrial
- 2Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Gad Getz
- 1Broad Institute of MIT & Harvard, Cambridge, MA
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Bustoros M, Sklavenitis-Pistofidis R, Park J, Redd R, Zhitomirsky B, Dunford AJ, Salem K, Tai YT, Anand S, Mouhieddine TH, Chavda SJ, Boehner C, Elagina L, Neuse CJ, Cha J, Rahmat M, Taylor-Weiner A, Van Allen E, Kumar S, Kastritis E, Leshchiner I, Morgan EA, Laubach J, Casneuf T, Richardson P, Munshi NC, Anderson KC, Trippa L, Aguet F, Stewart C, Dimopoulos MA, Yong K, Bergsagel PL, Manier S, Getz G, Ghobrial IM. Genomic Profiling of Smoldering Multiple Myeloma Identifies Patients at a High Risk of Disease Progression. J Clin Oncol 2020; 38:2380-2389. [PMID: 32442065 PMCID: PMC7367550 DOI: 10.1200/jco.20.00437] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2020] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models. METHODS We used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors. RESULTS We observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway (KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort. CONCLUSION SMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.
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Affiliation(s)
- Mark Bustoros
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Romanos Sklavenitis-Pistofidis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Jihye Park
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Robert Redd
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
| | | | | | - Karma Salem
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Yu-Tzu Tai
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | | | - Tarek H. Mouhieddine
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Selina J. Chavda
- Department of Hematology, University College London, London, United Kingdom
| | - Cody Boehner
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
| | | | - Carl Jannes Neuse
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Faculty of Medicine, University of Münster, Münster, Germany
| | - Justin Cha
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Mahshid Rahmat
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Eliezer Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Shaji Kumar
- Division of Hematology, Mayo Clinic, Rochester, MN
| | - Efstathis Kastritis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens School of Medicine, Athens, Greece
| | | | - Elizabeth A. Morgan
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jacob Laubach
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | | | - Paul Richardson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Nikhil C. Munshi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Kenneth C. Anderson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Lorenzo Trippa
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
- Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Chip Stewart
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | - Kwee Yong
- Department of Hematology, University College London, London, United Kingdom
| | | | - Salomon Manier
- Department of Hematology, CHU Lille, University of Lille, Lille, France
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Irene M. Ghobrial
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
- Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
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Bustoros MW, Cibulskis C, Dowdell T, Gavrilov S, Boehner C, Yesil J, Labkoff SE, Mehr S, Park J, Pistofidis RS, Manier S, Kim AS, Ligon KL, Lennon N, Adalsteinsson V, Wilkinson J, Ghobrial IM, Auclair D. Abstract A38: A novel clinical-grade liquid biopsy platform for multiple myeloma. Clin Cancer Res 2020. [DOI: 10.1158/1557-3265.liqbiop20-a38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Direct-to-patient (DTP) multiple myeloma (MM) research studies have been launched recently, including PCROWD (NCT02269592), PROMISE (NCT03689595), and the MMRF CureCloud Research Initiative (NCT03657251), aimed at enrolling thousands of individuals from whom comprehensive molecular and immune analyses will be generated from blood specimens and the resulting data aggregated with the correlating clinical information. To support the molecular characterization of liquid biopsies for such DTP efforts, a myeloma-specific hybrid selection panel was developed that captures 70 commonly altered genes. The assay detects somatic point mutations and indels present in a patient’s circulating-free DNA (cfDNA). For this MM 70-Gene cfDNA Assay, samples are received as blood in a Streck’s tube and DNA is extracted from buffy coat using magnetic bead-based chemistry. Coverage sequencing (80,000x depth) is performed and duplex BAM files are generated with UMI alignment and de-duplication. As will be presented, MM blood specimens present a unique challenge as circulating MM cells are often present at significant levels in the buffy coat blood fraction used as the source of normal genomic DNA. The performance of the 70-Gene cfDNA Assay was thoroughly validated in order to establish the sensitivity, specificity, and reproducibility of the technical approach. First, the reference genomic DNA from unrelated healthy individuals was sequenced in replicate at deep coverage. Next, two cohorts were used, one from Dana-Farber and one from the MMRF CureCloud pilot. For both cohorts, tumor DNA samples from bone marrow aspirates (BMAs) with matched normal DNA from blood were sequenced on an orthogonal platform and compared to results from the MM 70-Gene Assay on cfDNA extracted from the same individuals. The yield of extracted cfDNA ranged from 6 ng to 80 ng, and about two third of cases yielded enough material to attempt sequencing, with failures coming mostly from individuals in remission. As will be presented, there was a very strong correlation between BMA and cfDNA and additional events could actually be detected in the blood that were not seen in the BMAs. Because this MM 70-Gene cfDNA Assay may potentially be used by treating physicians for management of care, a clinical-grade (CLIA) pipeline was established. For that CLIA pipeline, the variants reported are a subset of all the events detected by the MM 70-Gene Assay. The events detected in the assay are reviewed by a Genomic Tumor Board within the appropriate subset of territory predefined for reporting. The territory limitations are defined by the Genomic Tumor Board knowledgebase of actionable genomic territory available. In summary, we have developed a robust and very sensitive clinical-grade next-gen liquid biopsy sequencing platform allowing for less invasive monitoring of MM disease genomics that can be used to complement other more classical approaches and to help support direct-to-patient Initiatives.
Citation Format: Mark W. Bustoros, Carrie Cibulskis, Teni Dowdell, Svetlana Gavrilov, Cody Boehner, Jennifer Yesil, Steven E. Labkoff, Shaadi Mehr, Jihye Park, Romanos Sklavenitis Pistofidis, Salomon Manier, Annette S. Kim, Keith L. Ligon, Niall Lennon, Viktor Adalsteinsson, Jane Wilkinson, Irene M. Ghobrial, Daniel Auclair. A novel clinical-grade liquid biopsy platform for multiple myeloma [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A38.
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Affiliation(s)
| | | | | | | | | | | | | | - Shaadi Mehr
- 3Multiple Myeloma Research Foundation, Norwalk, CT,
| | - Jihye Park
- 1Dana-Farber Cancer Institute, Boston, MA,
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6
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Bustoros M, Sklavenitis-Pistofidis R, Kapoor P, Liu CJ, Kastritis E, Zanwar S, Fell G, Abeykoon JP, Hornburg K, Neuse CJ, Marinac CR, Liu D, Soiffer J, Gavriatopoulou M, Boehner C, Cappuccio JM, Dumke H, Reyes K, Soiffer RJ, Kyle RA, Treon SP, Castillo JJ, Dimopoulos MA, Ansell SM, Trippa L, Ghobrial IM. Progression Risk Stratification of Asymptomatic Waldenström Macroglobulinemia. J Clin Oncol 2019; 37:1403-1411. [PMID: 30990729 DOI: 10.1200/jco.19.00394] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Waldenström macroglobulinemia (WM) is preceded by asymptomatic WM (AWM), for which the risk of progression to overt disease is not well defined. METHODS We studied 439 patients with AWM, who were diagnosed and observed at Dana-Farber Cancer Institute between 1992 and 2014. RESULTS During the 23-year study period, with a median follow-up of 7.8 years, 317 patients progressed to symptomatic WM (72%). Immunoglobulin M 4,500 mg/dL or greater, bone marrow lymphoplasmacytic infiltration 70% or greater, β2-microglobulin 4.0 mg/dL or greater, and albumin 3.5 g/dL or less were all identified as independent predictors of disease progression. To assess progression risk in patients with AWM, we trained and cross-validated a proportional hazards model using bone marrow infiltration, immunoglobulin M, albumin, and beta-2 microglobulin values as continuous measures. The model divided the cohort into three distinct risk groups: a high-risk group with a median time to progression (TTP) of 1.8 years, an intermediate-risk group with a median TTP of 4.8 years, and a low-risk group with a median TTP of 9.3 years. We validated this model in two external cohorts, demonstrating robustness and generalizability. For clinical applicability, we made the model available as a Web page application ( www.awmrisk.com ). By combining two cohorts, we were powered to identify wild type MYD88 as an independent predictor of progression (hazard ratio, 2.7). CONCLUSION This classification system is positioned to inform patient monitoring and care and, for the first time to our knowledge, to identify patients with high-risk AWM who may need closer follow-up or benefit from early intervention.
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Affiliation(s)
- Mark Bustoros
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
| | | | | | - Chia-Jen Liu
- 1 Dana-Farber Cancer Institute, Boston, MA.,5 Tapei Veterans General Hospital, Taipei, Taiwan.,6 National Yang-Ming University, Taipei, Taiwan
| | | | | | | | | | | | - Carl Jannes Neuse
- 1 Dana-Farber Cancer Institute, Boston, MA.,8 University of Münster Faculty of Medicine, Münster, Germany
| | - Catherine R Marinac
- 1 Dana-Farber Cancer Institute, Boston, MA.,2 Harvard T.H. Chan School of Public Health, Boston, MA
| | - David Liu
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
| | - Jenny Soiffer
- 1 Dana-Farber Cancer Institute, Boston, MA.,9 University of Miami Miller School of Medicine, Miami, FL
| | | | - Cody Boehner
- 1 Dana-Farber Cancer Institute, Boston, MA.,10 University of Massachusetts, Boston, MA
| | | | | | | | - Robert J Soiffer
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
| | | | - Steven P Treon
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
| | - Jorge J Castillo
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
| | | | | | - Lorenzo Trippa
- 1 Dana-Farber Cancer Institute, Boston, MA.,2 Harvard T.H. Chan School of Public Health, Boston, MA
| | - Irene M Ghobrial
- 1 Dana-Farber Cancer Institute, Boston, MA.,3 Harvard Medical School, Boston, MA
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