Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma

Mareschal, Sylvain; Ruminy, Philippe; Alcantara, Marion; Villenet, Céline; Figeac, Martin; Dubois, Sydney; Bertrand, Philìppe; Bouzelfen, Abdelilah; Viailly, Pierre-Julien; Penther, Dominique; Tilly, Hervé; Bastard, Christian; Jardin, Fabrice

doi:10.1093/bioinformatics/btx309

Cited by 3 publications

(2 citation statements)

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“…Copy Number Variations (CNVs) were identified performing Comparative Genomic Hybridization (CGH) on 190 patients after whole-genome amplification, using Agilent SurePrint G3 4 × 180 K microarrays. Briefly, arrays were scanned with Agilent Feature Extraction and processed with cghRA [29]. Recurring CNVs were identified running GISTIC [30] version 2.0.22 (Supplementary Methods).…”

Section: Copy Number Variationsmentioning

confidence: 99%

Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles

et al. 2019

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BackgroundGene expression profiling (GEP), next-generation sequencing (NGS) and copy number variation (CNV) analysis have led to an increasingly detailed characterization of the genomic profiles of DLBCL. The aim of this study was to perform a fully integrated analysis of mutational, genomic, and expression profiles to refine DLBCL subtypes. A comparison of our model with two recently published integrative DLBCL classifiers was carried out, in order to best reflect the current state of genomic subtypes.Methods223 patients with de novo DLBCL from the prospective, multicenter and randomized LNH-03B LYSA clinical trials were included. GEP data was obtained using Affymetrix GeneChip arrays, mutational profiles were established by Lymphopanel NGS targeting 34 key genes, CNV analysis was obtained by array CGH, and FISH and IHC were performed. Unsupervised independent component analysis (ICA) was applied to GEP data and integrated analysis of multi-level molecular data associated with each component (gene signature) was performed.FindingsICA identified 38 components reflecting transcriptomic variability across our DLBCL cohort. Many of the components were closely related to well-known DLBCL features such as cell-of-origin, stromal and MYC signatures. A component linked to gain of 19q13 locus, among other genomic alterations, was significantly correlated with poor OS and PFS. Through this integrated analysis, a high degree of heterogeneity was highlighted among previously described DLBCL subtypes.InterpretationThe results of this integrated analysis enable a global and multi-level view of DLBCL, as well as improve our understanding of DLBCL subgroups.

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Section: Copy Number Variationsmentioning

confidence: 99%

Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles

et al. 2019

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“…CGH was previously performed for these samples after whole-genome amplification against a Promega normal DNA pool using Agilent SurePrint G3 K microarrays. Briefly, arrays were scanned with Agilent Feature Extraction and processed with cghRA pipeline as previously described [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

et al. 2021

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Background Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. Results Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. Conclusion We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

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Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML

et al. 2021

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Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing–based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)–defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA–related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss ≥200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing–based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.

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Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 3 publications

References 56 publications

Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles

Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML

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