42 research outputs found
Early Relapse Risk in Patients with Newly Diagnosed Multiple Myeloma Characterized by Next-generation Sequencing
Expression, maturation and turnover of DrrS, an unusually stable, DosR regulated small RNA in Mycobacterium tuberculosis
Mycobacterium tuberculosis depends on the ability to adjust to stresses encountered in a range of host environments, adjustments that require significant changes in gene expression. Small RNAs (sRNAs) play an important role as post-transcriptional regulators of prokaryotic gene expression, where they are associated with stress responses and, in the case of pathogens, adaptation to the host environment. In spite of this, the understanding of M. tuberculosis RNA biology remains limited. Here we have used a DosR-associated sRNA as an example to investigate multiple aspects of mycobacterial RNA biology that are likely to apply to other M. tuberculosis sRNAs and mRNAs. We have found that accumulation of this particular sRNA is slow but robust as cells enter stationary phase. Using reporter gene assays, we find that the sRNA core promoter is activated by DosR, and we have renamed the sRNA DrrS for DosR Regulated sRNA. Moreover, we show that DrrS is transcribed as a longer precursor, DrrS+, which is rapidly processed to the mature and highly stable DrrS. We characterise, for the first time in mycobacteria, an RNA structural determinant involved
in this extraordinary stability and we show how the addition of a few nucleotides can lead to acute destabilisation. Finally, we show how this RNA element can enhance expression of a heterologous gene. Thus, the element, as well as its destabilising derivatives may be employed to post-transcriptionally regulate gene expression in mycobacteria in combination with different promoter variants. Moreover, our findings will facilitate further investigations into the severely understudied topic of mycobacterial RNA biology and into the role that regulatory RNA plays in M. tuberculosis pathogenesis
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Meeting report: Advances in minimal residual disease testing in multiple myeloma 2018
This report summarizes the 5th annual symposium on minimal residual disease (MRD) testing in multiple myeloma gathered experts from academia, industry and the FDA in New York City on 14 September 2018. Three recommendations were made: (a) MRD testing should be performed in patients who achieve a very good partial response (VGPR) in addition to complete response (CR); (b) MRD negativity at one tumor cell in 1 000 000 bone marrow cells (10−6) should be considered as a response category alongside the current definition of MRD negativity at 10−5; and (c) clinical trials reports should specify the sensitivity of MRD testing as well as the applied threshold for MRD negativity (ie, 10−5 or 10−6), and if possible report outcome data for both MRD thresholds. Overall, as discussed at the meeting, there is already solid evidence that bone marrow based MRD assessment is one of the strongest prognostic factors in multiple myeloma, and deeper responses correlate with increasingly favorable outcomes. Indeed, achieving MRD negativity appears to be more important than what treatment was used to get there. Going forward, there is a need for clinical trials focusing on MRD‐directed therapy to determine the role of MRD testing in everyday clinical decision‐making. Standardization of MRD testing and harmonization across centers will make it easier to learn from common experiences. Ongoing efforts are anticipated to establish MRD status as a regulatory endpoint for accelerated drug approval in multiple myeloma in the near future
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Reconstructing the evolutionary history of multiple myeloma
Multiple myeloma is the second most common lymphoproliferative disorder, 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
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Moving From Cancer Burden to Cancer Genomics for Smoldering Myeloma A Review
This review summarizes current clinical challenges and discusses available models for risk stratification in the context of smoldering multiple myeloma.
Importance All patients who develop multiple myeloma have a preceding asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance or smoldering multiple myeloma (SMM). During the past decade, significant progress has been made in the classification and risk stratification of SMM. Observations This review summarizes current clinical challenges and discusses available models for risk stratification in the context of SMM. Owing to several novel, more effective, and less toxic drugs, these aspects are becoming increasingly important to identify patients eligible for early treatment. However, all proposed criteria were built around indirect markers of disease burden and therefore are generally able to identify a fraction of patients with SMM in whom transformation to multiple myeloma and 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 more effective treatment and better precision regarding the optimal timing of treatment initiation. Conclusions and Relevance Owing to modern technologies, in the near future, prognostic models derived from genomic signatures independent of the disease burden will allow better identification of the optimal timing to treat a plasma cell clonal disorder at the very early stages, when the chances of eradication are higher
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Positive selection as the unifying force for clonal evolution in multiple myeloma.
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Revealing the Impact of Recurrent and Rare Structural Variations in Multiple Myeloma
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
Integrative analysis of the genomic and transcriptomic landscape of double-refractory multiple myeloma
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The Genomic Complexity of Multiple Myeloma Precursor Disease Can be Predicted Using Copy Number Signatures on Targeted Sequencing and SNP Array Data
Introduction
Current clinical models for predicting the progression from myeloma precursor disease (smoldering multiple myeloma (SMM) and monoclonal gammopathy of undetermined significance (MGUS)) to multiple myeloma (MM) are based on tumor burden, and not designed to capture heterogeneity in tumor biology. With the advent of whole genome sequencing (WGS), complex genomic change including the catastrophic event of chromothripsis has been detected in a significant fraction of MM patients. Chromothripsis is associated with other features of aggressive biology (i.e. biallelic TP53 deletion and increased APOBEC activity), and in newly diagnosed MM (NDMM), patients harboring chromothripsis have a shorter progression free survival (PFS) (Rustad BioRxiv 2019). Chromothripsis has also been demonstrated in SMM which later progressed to MM (Maura Nat Comm 2019) and our preliminary results indicate that the absence of chromothripsis is associated with stable precursor disease (Oben ASH 2020).
We have demonstrated that chromothripsis can be accurately predicted in NDMM using copy-number variation (CNV) signatures on both WGS and whole exome sequencing (Maclachlan ASH 2020). As with WGS, CNV signature analysis in less comprehensive assays (e.g. targeted sequencing panels and single nucleotide polymorphism (SNP) arrays) demonstrated that chromothripsis-associated CNV signatures are associated with shorter PFS. The aim of this study was to define the landscape of CNV signatures in myeloma precursor disease, and to compare the results with CNV signatures in NDMM.
Methods
CNV signature analysis uses 6 fundamental features: i) breakpoint count per 10 Mb, ii) absolute CN of segments, iii) difference in CN between adjacent segments, iv) breakpoint count per chromosome arm, v) lengths of oscillating CN segments, and vi) the size of segments (Macintyre Nat Gen 2018). The number of subcategories for each feature (which may differ between cancer and assay types) was established using a mixed effect model (mclust R package). For both targeted sequencing (myTYPE panel; (n=19, 4 MGUS, 15 SMM) and SNP array (n=78, 16 MGUS, 62 SMM), de novo CNV signature extraction was performed by hierarchical dirichlet process, running the analysis together with NDMM samples for reliable signature detection.
Results
Our analysis identified 4 and 6 CNV signatures from myTYPE and SNP array data respectively, with the extracted signatures being analogous to those from WGS, which are highly predictive of chromothripsis (Maclachlan ASH 2020).
Compared with NDMM (myTYPE; n=113; SNP array; n=217), precursor samples contained significantly fewer breakpoints / chromosome arm (myTYPE; p= 0.0003, SNP; p <0.0001), fewer breakpoints / 10 Mb (both; p <0.0001), shorter lengths of oscillating CN (myTYPE; p= 0.013, SNP; p= 0.018), fewer jumps between CN states (myTYPE; p= 0.0043, SNP; p < 0.0001), lower absolute CN (myTYPE; p= 0.0059, SNP; p < 0.0001) and fewer small segments of CN change (myTYPE; p= 0.0007, SNP; p= 0.0008). Chromothripsis-associated CNV signatures were significantly enriched in NDMM compared to precursor disease (p<0.0001), with only 8.2% of precursors having a significant contribution from these signatures (NDMM; 38.7%). Overall, every CNV feature consistent with chromothripsis was measured at a significantly lower level in precursors than NDMM.
As 5 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