Managing data from large-scale projects (such as The Cancer Genome Atlas (TCGA)) for further analysis is an important and time consuming step for research projects. Several efforts, such as the ...Firehose project, make TCGA pre-processed data publicly available via web services and data portals, but this information must be managed, downloaded and prepared for subsequent steps. We have developed an open source and extensible R based data client for pre-processed data from the Firehouse, and demonstrate its use with sample case studies. Results show that our RTCGAToolbox can facilitate data management for researchers interested in working with TCGA data. The RTCGAToolbox can also be integrated with other analysis pipelines for further data processing.
The RTCGAToolbox is open-source and licensed under the GNU General Public License Version 2.0. All documentation and source code for RTCGAToolbox is freely available at http://mksamur.github.io/RTCGAToolbox/ for Linux and Mac OS X operating systems.
BCMA targeting chimeric antigen receptor (CAR) T cell therapy has shown deep and durable responses in multiple myeloma. However, relapse following therapy is frequently observed, and mechanisms of ...resistance remain ill-defined. Here, we perform single cell genomic characterization of longitudinal samples from a patient who relapsed after initial CAR T cell treatment with lack of response to retreatment. We report selection, following initial CAR T cell infusion, of a clone with biallelic loss of BCMA acquired by deletion of one allele and a mutation that creates an early stop codon on the second allele. This loss leads to lack of CAR T cell proliferation following the second infusion and is reflected by lack of soluble BCMA in patient serum. Our analysis suggests the need for careful detection of BCMA gene alterations in multiple myeloma cells from relapse following CAR T cell therapy.
Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and ...develop a model that identifies patients with long survival.
We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.
We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent
mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.
This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.
Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile ...and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 1.44-2.96; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 1.51-5.05; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.
Amplification of 1q21 occurs in approximately 30% of de novo and 70% of relapsed multiple myeloma (MM) and is correlated with disease progression and drug resistance. Here, we provide evidence that ...the 1q21 amplification-driven overexpression of ILF2 in MM promotes tolerance of genomic instability and drives resistance to DNA-damaging agents. Mechanistically, elevated ILF2 expression exerts resistance to genotoxic agents by modulating YB-1 nuclear localization and interaction with the splicing factor U2AF65, which promotes mRNA processing and the stabilization of transcripts involved in homologous recombination in response to DNA damage. The intimate link between 1q21-amplified ILF2 and the regulation of RNA splicing of DNA repair genes may be exploited to optimize the use of DNA-damaging agents in patients with high-risk MM.
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•ILF2 is a 1q21 amplification-specific cancer-relevant gene•ILF2 promotes multiple myeloma cell resistance to DNA-damaging agents•ILF2 interacts with RNA-binding proteins involved in the DNA damage response•ILF2/YB-1 interaction modulates DNA damage-induced splicing regulation
Marchesini et al. show that in multiple myeloma the overexpression of ILF2, resulting from chromosome 1q21 amplification, drives resistance to DNA-damaging agents partly by modulating the interaction between YB-1 and the splicing factor U2AF65 to promote the processing and stabilization of transcripts involved in homologous recombination.
The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM ...genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.
We analyzed whole genomes of unique paired samples from smoldering multiple myeloma (SMM) patients progressing to multiple myeloma (MM). We report that the genomic landscape, including mutational ...profile and structural rearrangements at the smoldering stage is very similar to MM. Paired sample analysis shows two different patterns of progression: a "static progression model", where the subclonal architecture is retained as the disease progressed to MM suggesting that progression solely reflects the time needed to accumulate a sufficient disease burden; and a "spontaneous evolution model", where a change in the subclonal composition is observed. We also observe that activation-induced cytidine deaminase plays a major role in shaping the mutational landscape of early subclinical phases, while progression is driven by APOBEC cytidine deaminases. These results provide a unique insight into myelomagenesis with potential implications for the definition of smoldering disease and timing of treatment initiation.
Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual ...laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.
canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.
At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.
The canEvolve web portal is available at http://www.canevolve.org/.
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