Revealing the impact of recurrent and rare structural variants in multiple myeloma

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.

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Practical application of whole genome and transcriptome tumour analysis to guide chemotherapy decision-making for patients with advanced cancers.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e22020 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e22020-e22020

Author(s):

Janessa J. Laskin ◽

Howard John Lim ◽

Karen A. Gelmon ◽

Cheryl Ho ◽

Daniel John Renouf ◽

...

Keyword(s):

Decision Making ◽

Drug Targets ◽

Cell Cancer ◽

Copy Number Variations ◽

Whole Genome ◽

Incurable Cancer ◽

Dna And Rna ◽

Drug Databases ◽

Tumour Biopsy ◽

The Impact

e22020 Background: We propose that applying personal genomic information prospectively, in a clinically realistic timeframe can aid chemotherapy decision-making and result in more effective cancer treatment. We are investigating this approach in a variety of cancers to examine timeliness, deliverability, and rate of actionable targets identified. Methods: Eligible subjects with incurable cancer and limited chemo options have a tumour biopsy and “normal” blood taken for analysis. Archival specimens are concurrently analyzed to look for changes with time and treatment. Samples are subject to both an Ampliseq amplicon panel and in-depth whole genome DNA and RNA sequencing (WGS). Bioinformatics approaches identity genes with somatic and copy number variations, and expression changes. Variants are integrated into a pathway analysis to identify tumour specific processes that may drive the tumour, these are then matched to drug databases, with manual literature reviews, to indentify drugs that may be useful or even contra-indicated. Results: Between July 2012 -Jan 2013, 9 subjects (of 30 planned) are enrolled: 2 cases each of: colorectal and breast and 1 each of: squamous skin, squamous ethmoid sinus, nasopharyngeal, lung, and CLL-peripheral mantle cell cancer. 5 have completed analyses. Cancer panel results correlated well with WGS; although the panel is more rapid, it provides less comprehensive information and has not been as informative for identifying candidate druggable drivers. Extensive pathway mapping uncovered potential drug targets in each case that would not have necessarily been considered without this analyses. To date, 4 subjects have started chemo based on the analyses and 1 patient has had his diagnosis radically changed. There are significant genomic differences between archival and fresh tumour samples. Conclusions: This approach is feasible and yields actionable targets that can inform real-time chemotherapy decision-making. Archival samples do not appear to adequately represent post-treatment cancers. The impact of WGS vs. panel sequencing will require more subjects but it appears a panel may be insufficient for detailed treatment guidance.

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Revealing Transcriptome Deregulation upon Genomic Complexity in Multiple Myeloma

Blood ◽

10.1182/blood-2020-136211 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 3-4

Author(s):

Matteo Claudio Da Via' ◽

Bachisio Ziccheddu ◽

Matteo Dugo ◽

Marta Lionetti ◽

Katia Todoerti ◽

...

Keyword(s):

Multiple Myeloma ◽

Cell Lines ◽

In Silico ◽

Copy Number ◽

Drug Sensitivity ◽

Genetic Alterations ◽

Prognostic Impact ◽

Immunoglobulin Gene ◽

Driver Genes ◽

The Impact

Introduction Multiple Myeloma (MM) is characterized by hyperdiploidy (HD) or immunoglobulin gene (IgH) rearrangements as initiating events. Clonal heterogeneity is a hallmark of its biology as highlighted by Next Generation Sequencing. In this context, data on the impact of peculiar mutations, copy number aberrations (CNAs), and chromosomal rearrangements (CRs) at the transcriptomic level are still scanty. In this study, we aimed to dissect the transcriptional deregulation promoted by the most recurrent genomic drivers. Based on this geno-trascriptomic link, we also aimed to identify biomarkers that could suggest personalized treatments. Methods We analyzed 517 newly diagnosed patients from the IA12 release of the CoMMpass study, focusing on mutations in MM driver genes, structural variants, copy number segments and raw transcript counts. RNAseq data was processed with the VOOM/LIMMA pipeline. To perform an in-silico drug sensitivity screen, we anchored cell lines to patients samples using the Celligner algorithm and interrogated the DepMap dataset. Results We first analyzed the global impact of genetic aberrations on the transcriptome. Chr(1q)amp/gain, followed by IgH translocations and HD showed the highest number of deregulated transcripts. Individual mutations had much less impact, with the exception of NRAS and chr(13q) genes (DIS3, TGDS, RB1). Next, we investigated differential influence between hotspots (HS) vs nonHS mutations within driver genes. KRAS and NRAS, showed little changes between nonHS and wild type (WT), as the transcriptome was mostly impacted by HS mutations. IRF4 K123 showed a specific transcriptional profile, while nonHS mutations still carried functional relevance although on different genes. For BRAF, the kinase dead D594 mutation surprisingly impacted the most in comparison to V600 and WT cases. Next, we explored the effect of bi-allelic genetic events with known prognostic impact. TP53 double-hits were associated with an upregulation of PHF19, a MM poor prognostic marker, and downregulation of SLAMF7, a new immunotherapy target. CYLD and TRAF3 double-hits correlated with NF-κB pathway activation, and the former showed a significant BCL2 upregulation. Bi-allelic events on chr13 exhibited gene-specific consequences: DIS3 inactivation deregulated mostly lncRNAs, while TGDS impacted on genes involved in cell-cycle regulation. Regarding chromosomal gains, only chr(1q)amp (> 3 copies) showed a gene dosage effect with upregulation of the potential therapeutic targets MCL1 and SLAMF7. Given that the BCL2 axis was perturbated by several genetic alterations, we systematically compared the expression levels of BCL2, NOXA, MCL1 and BCL2L1 in CYLD inactivated, t(11;14) and chr(1q)amp patients. BCL2 levels were higher in the CYLD group, which parallels with the overexpression of the anti-apoptotic gene BCL2L1. NOXA, which promotes MCL1 degradation, was significantly upregulated in t(11;14). Chr(1q)amp patients showed a concomitant MCL1 overexpression and NOXA downregulation. To correlate these results to drug sensitivity, we performed an in-silico screen. We first selected MM and lymphoma cell lines from the DepMap dataset based on a gene expression profile that was most similar to the MM samples, then analyzed candidate drugs. The SKMM2 MM cell line, harboring t(11;14), del(CYLD) e NOXAamp was highly sensitive to Venetoclax. The same was true for the lymphoma ones RI1 and OCI-LY3, both harboring NOXAamp, but negative for t(11;14). On the contrary, the U266 and MOLP8 both with t(11;14) carrying a MCL1amp due to a chr(1q)amp were fully resistant. Of note, these latter resulted sensitive to the pan-BCL2 axis inhibitor Sabutoclax. Conclusions Our study suggests a link between the genomic architecture and transcriptome in MM, where CNAs and CRs had a stronger impact on expression than gene mutations. However, given that not all mutations are identical, HS ones need further testing as they may represent a future treatment target. Moreover, the mutational status is crucial since, while mono-allelic events are often of little transcriptional value, compound heterozygosity carries a huge influence on transcriptomic which provides biological basis for the observed prognostic impact of "double-hit" MM. Finally, we suggest that a comprehensive profiling of the BCL2 pathway may identify biomarkers of sensitivity to BCL2 inhibitors in addition to the t(11;14). Disclosures D'Agostino: GSK: Membership on an entity's Board of Directors or advisory committees. Corradini:Celgene: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Sanofi: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Incyte: Consultancy; Daiichi Sankyo: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other; BMS: Other; F. Hoffman-La Roche Ltd: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Novartis: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Servier: Consultancy, Honoraria; Kite: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Other: Travel and accommodations paid by for; KiowaKirin: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.

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

10.1101/2021.12.09.471893 ◽

2021 ◽

Author(s):

Lucía Peña Pérez ◽

Nicolai Frengen ◽

Julia Hauenstein ◽

Charlotte Gran ◽

Charlotte Gustafsson ◽

...

Keyword(s):

Multiple Myeloma ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Cohort Analysis ◽

Genomic Medicine ◽

Molecular Classification ◽

Regulatory Elements ◽

Copy Number Variations ◽

Whole Genome ◽

Structural Variants

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

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TRIM33 Loss in Multiple Myeloma Impairs the DNA Damage Response Resulting in Sensitivity to PARP and ATR Inhibitors

Blood ◽

10.1182/blood-2021-147728 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1570-1570

Author(s):

Roisin M McAvera ◽

Jonathan J Morgan ◽

Ken I Mills ◽

Lisa J Crawford

Keyword(s):

Multiple Myeloma ◽

Dna Damage ◽

Dna Damage Response ◽

High Throughput ◽

Chromosomal Instability ◽

Conflicts Of Interest ◽

Parp Cleavage ◽

Structural Variants ◽

Damage Response ◽

The Impact

Abstract Introduction Chromosomal instability is a hallmark of Multiple Myeloma (MM), with most patients displaying cytogenetic abnormalities which can arise due to DNA damage response (DDR) defects. TRIM33 is an E3 ligase and transcriptional co-repressor located on chromosome 1p13.2, a region frequently deleted in MM. Previous studies have shown that TRIM33 plays a role in the DDR and can regulate chromosomal stability, but its precise function remains unknown. In this study we investigated the impact of TRIM33 loss in MM on genomic stability and DDR pathways and whether this could be exploited therapeutically. Methods The CoMMpass dataset (IA15 release) was screened to identify patients with copy number (CN) loss of TRIM33 and this was correlated with overall survival (OS) and structural variants. TRIM33 shRNA knockdown models were established in JJN3 and U266 cells. The effect on DDR signalling was determined by western blotting and immunofluorescence. The Selleckchem DNA Damage/Repair Compound Library was screened on the JJN3 model in a high-throughput manner using the CellTox™ Green cytotoxicity assay. Validation of selected compounds was performed using CellTiter® Glo viability assay or clonogenic assays. Combination indices (CI) were calculated using CompuSyn software. Results Data on CN, OS and structural variants were available for 730 newly diagnosed MM patients and of these, 69 (9.5%) were identified to have a CN loss of TRIM33. These patients have poorer OS compared to those without TRIM33 loss (52.3 months vs 72.6 months; p<0.0001). Moreover, they exhibit a significantly higher median number of structural variants (deletions, duplications, inversions, and translocations; 38 vs 26; p<0.0001), indicative of increased chromosomal instability. Our data in MM cell lines has shown that TRIM33 is rapidly recruited to chromatin within 5 minutes of induced DNA damage. TRIM33 knockdown led to an increase in 53BP1 foci formation and endogenous γH2AX (P<0.001) indicating unrepaired DNA double-strand breaks (DSBs) typical of a DDR defect. In response to these DSBs both ATM and ATR kinases were activated as demonstrated by increased pKAP1 Ser824 and pCHK1 Ser345 respectively (p<0.001). Additionally, we observed a reduction in RAD51 (p<0.05) indicative of a potential defect in the DSB repair pathway homologous recombination (HR). To identify therapeutic vulnerabilities relating to TRIM33 loss, we performed a high-throughput screen to assess sensitivity to 160 unique DNA damaging compounds. TRIM33 knockdown cells exhibited increased sensitivity to 27 compounds across a range of drug classes. Additional studies confirmed that compared to control cells, TRIM33 knockdown sensitized cells to the PARP inhibitor Olaparib and ATR inhibitors BAY-1895344 and VE-821. Further investigation with VE-821 demonstrated that whilst treatment induced PARP cleavage and DSBs in both control and knockdown cells within 48 hours, knockdown cells exhibited significantly more pCHK1 Ser345 inhibition (p<0.01). Furthermore, combining VE-821 with bortezomib yielded synergistic effects in TRIM33 knockdown cells across a range of doses (CI range 0.57-0.9) while no synergy was observed in control cells (CI>1 for all combinations). Conclusion We have identified a subset of MM patients with TRIM33 loss who display high-risk disease characterized by chromosomal abnormalities and defective DDR. Alongside this we have identified PARP and ATR inhibitors as therapeutic vulnerabilities in cell line models of TRIM33 loss. Moreover, we demonstrate that ATR inhibition increases the efficacy of bortezomib in TRIM33 knockdown cells. Further investigation into these compounds could lead to novel therapies for patients with TRIM33 loss. Disclosures No relevant conflicts of interest to declare.

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Redefining Mutational Profiling Using RNA-Seq: Insight into the Functional Mutational Landscape of Multiple Myeloma

Blood ◽

10.1182/blood.v126.23.837.837 ◽

2015 ◽

Vol 126 (23) ◽

pp. 837-837 ◽

Cited By ~ 2

Author(s):

David Mosen-Ansorena ◽

Niccolo Bolli ◽

Mehmet K Samur ◽

Florence Magrangeas ◽

Stephane Minvielle ◽

...

Keyword(s):

Multiple Myeloma ◽

Board Of Directors ◽

Expression Profiles ◽

Rna Seq ◽

Driver Genes ◽

Advisory Committees ◽

Mutational Landscape ◽

Recurrent Mutations ◽

The Impact ◽

Insight Into

Abstract Whole genome and exome sequencing (WGS, WES) have enabled the identification of mutational signatures in Multiple Myeloma (MM) and other cancer types. In studies that assess the impact of coding mutations on protein structure and function, only reads mapping to the exome are pertinent. Thus, WES is typically preferred over WGS, as it provides deeper coverage given the same amount of total reads. However, exome enrichment - a necessary step in WES, limits the ability to call mutations, as coverage is restricted to the capture regions and affected by their GC content. Furthermore, without transcriptional information, it is not possible to determine which coding mutations found by WGS or WES are expressed and, therefore, more likely to be relevant. As an alternative, RNA-seq data directly targets the transcriptome, providing deep coverage, not requiring an enrichment step and intrinsically omitting non-expressed mutations. Moreover, when RNA-seq data is already available for evaluation of gene expression profiles, one can further leverage the data to explore expressed mutational profiles. However, limitations in pipelines to analyze RNA-seq data have restricted their applicability so far. Using paired WES and RNA-seq data from MM patient samples, we have observed that the majority of recurrent mutations in MM occur within genes with very low or no detectable expression (only 27% of mutated genes express). Here, we have further analyzed a large RNA-seq sample set to describe a comprehensive transcriptional mutational landscape in MM and identify potential mutational driver genes. Specifically, we performed RNA-seq on CD138+ MM cells from 292 newly-diagnosed patients and 16 normal bone marrow plasma cell (NBM) samples. The unstranded 50bp paired-end reads were mapped to the human genome using MapSplice followed by a workflow for variant analysis based on GATK. Output was filtered for germline variants and technical artifacts, then evaluated computationally for functional impact, and finally further filtered at the gene level. Using this workflow we were able to identify most reported recurrently mutated genes in MM, including but not limited to TP53 (14%), NRAS (14%), KRAS (11%), ACTG1 (4%), CCND1 (4%), TRAF3 (3%), FAM46C (3%), CYLD (3%) and DIS3 (2%). Importantly, we were also able to identify novel putative mutational driver genes of lower frequency, including several genes involved in the NF-κB pathway (BCR, TAOK2, NFKBIA, PIM1) and genes coding for proteins forming the mTORC2 complex (SIN1, RICTOR, MTOR). We observe that the average mutational frequency, which is a convolution of clonality and relative allelic expression, is slightly below 0.5. Yet, we find diverse mutational frequencies across samples for each given gene. For instance, FAM46C shows a pattern representative of highly subclonal mutations, whereas CCND1 presents mostly bi-allelic and clonal mutations, and others such as TRAF3 show a wide spectrum of mutational frequencies. Further developments will be needed to deconvolve these frequencies. We also applied the workflow to 10 of the samples for which we reported mutations at the DNA level, and observe CCND1, TP53 and KRAS to be recurrently mutated using either WES or RNA-seq. Nevertheless, some mutations are not shared, including 3 WES-exclusive BRAF mutations and one seen in CCND1 through RNA-seq only. In conclusion, we report the first computational analysis to identify mutational driver genes using RNA-seq data, providing additional insight into the mutational landscape of MM. Our findings demonstrate that RNA-seq of unpaired tumor samples can suffice to characterize the most salient characteristics of cancer mutational landscapes. Disclosures Campbell: 14M genomics: Other: Co-founder and consultant. Munshi:celgene: Membership on an entity's Board of Directors or advisory committees; onyx: Membership on an entity's Board of Directors or advisory committees; millenium: Membership on an entity's Board of Directors or advisory committees; novartis: Membership on an entity's Board of Directors or advisory committees.

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

Nature Communications ◽

10.1038/s41467-021-22140-0 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Bénedith Oben ◽

Guy Froyen ◽

Kylee H. Maclachlan ◽

Daniel Leongamornlert ◽

Federico Abascal ◽

...

Keyword(s):

Multiple Myeloma ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genome Sequence ◽

Monoclonal Gammopathy ◽

Whole Genome Sequence ◽

Whole Genome ◽

Driver Genes ◽

Smoldering Myeloma ◽

Undetermined Significance

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

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PRODIGY: personalized prioritization of driver genes

Bioinformatics ◽

10.1093/bioinformatics/btz815 ◽

2019 ◽

Author(s):

Gal Dinstag ◽

Ron Shamir

Keyword(s):

Drug Targets ◽

Somatic Mutations ◽

Supplementary Information ◽

Gene Identification ◽

Centrality Measures ◽

Patient Specific ◽

Driver Gene ◽

Tree Model ◽

Driver Genes ◽

The Impact

Abstract Motivation Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge. Methods We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. Results In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further towards personalized medicine and treatment. Availability The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. Supplementary information Supplementary data are available at Bioinformatics online.

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Characterization of Rare Germline Variants in Familial Multiple Myeloma

Blood ◽

10.1182/blood-2020-142253 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 45-46

Author(s):

Calogerina Catalano ◽

Joanna Blocka ◽

Stefanie Huhn ◽

Nagarajan Paramasivam ◽

Matthias Schlesner ◽

...

Keyword(s):

Multiple Myeloma ◽

Medicinal Product ◽

Board Of Directors ◽

Research Funding ◽

High Impact ◽

Investigational Medicinal Product ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Advisory Committees ◽

The Impact

Introduction: The risk of developing Multiple Myeloma (MM) is 2-4 fold higher in first-degree relatives of patients with MM compared to the general population, suggesting genetic predisposition to this cancer. Indeed, recent genome-wide association studies have identified common risk alleles that predispose for MM. Yet, the impact of these variants on MM risk is too low to explain familial aggregation of MM. High-impact alleles have been identified for other cancers such as ovarian and breast cancer (BRCA1,-2) and melanoma (CDKN2A) but the search for such alleles in MM is still in its infancy. In order to identify high-impact alleles in MM we have performed whole genome/exon sequencing (WGS/WES) in members of MM high risk families. Methods: We included 21 families with multiple cases of MM/MGUS. Whole genome/exome sequencing was performed on a total of 46 affected and 20 unaffected family members. Filtering and prioritization of the variants were performed in accordance with the criteria of our in-house familial cancer variant prioritization pipeline version 2 (FCVPPv2). Loss-of-function variants were further screened using MutPred-LOF, Translate tool and IntOGen/c-BioPortal in order to discriminate pathogenic and neutral variants, to translate a nucleotide sequence to a protein sequence and to visualize the domain affected by the variant and the portion of the protein lost after the newly formed stop codon. Variants were analyzed for predicted effects on splicing by using Human Splicing Finder. Results: We found a total of 148 potentially pathogenic variants, 109 non-synonymous and 39 LOF, in 18 out of 21 MM families. Among our genes, many affect protein metabolism, immune system, and other have known links to carcinogenesis. Additionally, some of them are known to interact with key signaling pathways in MM, including PI3K/Akt/mTOR, Ras/Raf/MEK/MAPK, JAK/STAT, NF-κB, Wnt/β-catenin, and RANK/RANKL/OPG, showing congruency with previously reported literature. Interestingly, we also found different missense variants in the same two genes in two unrelated families. Conclusions: We have identified potentially pathogenic gene variants in 85% of MM/MGUS families. Our results can offer a useful reference to gene finding efforts by others in order to improve screening, early diagnosis and personalized therapy of individuals at risk of developing MM. Disclosures Durie: Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Goldschmidt:Merck Sharp and Dohme (MSD): Research Funding; Molecular Partners: Research Funding; Incyte: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Johns Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product:; Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; University Hospital Heidelberg, Internal Medicine V and National Center for Tumor Diseases (NCT), Heidelberg, Germany: Current Employment; GlaxoSmithKline (GSK): Honoraria; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Novartis: Honoraria, Research Funding; Mundipharma GmbH: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Whole genome sequencing provides evidence of two biologically and clinically distinct entities of asymptomatic monoclonal gammopathies: progressive versus stable myeloma precursor condition

10.1101/2020.11.06.372011 ◽

2020 ◽

Author(s):

Bénedith Oben ◽

Guy Froyen ◽

Kylee H. Maclachlan ◽

Daniel Leongamornlert ◽

Federico Abascal ◽

...

Keyword(s):

Multiple Myeloma ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Monoclonal Gammopathy ◽

Whole Genome Sequence ◽

Whole Genome ◽

Driver Genes ◽

Smoldering Myeloma ◽

First Time ◽

Undetermined Significance

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

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