Biology subjects

Avet-Loiseau, H.

Publications and source records attributed to Avet-Loiseau, H..

9 recordsLinked to original sources

bioRxiv2026

Sequential acquisition of 1p31.1-p12 LOH and 1q Gain is a common double-hit event in relapsed/refractory myeloma

Therapy-driven genomic changes in multiple myeloma (MM) remain poorly defined. We analyzed whole-genome sequencing (WGS) data from relapsed/refractory MM (rrMM, N=386) and identified regional 1p31.1-p12 (hereafter 1pCEN, a region proximal to the centromere) loss-of-heterozygosity (LOH) as the only enriched aberration showing strong therapy-associated clonal selection (clonal timing rank fold-change = 3.7, P<2.2x10-16). This event showed enriched co-occurrence with 1qGain (OR = 2.3 (1.5-3.8), P=2x10-4) forming a recurrent "double-hit" in rrMM. To validate the clonal selection process, we examined three longitudinal cohorts (180 patients, 390 samples) and confirmed clonal expansion of 1pCEN and consistent prevalence of the 1pCEN+1q double-hit (20-24%). Survival analyses demonstrated significantly reduced progression-free survival in rrMM patients with this double-hit compared with those without. Comparison with a large newly diagnosed MM (ndMM) cohort confirmed previously-described 1p32 LOH is the prognostic locus at baseline, whereas 1pCEN is therapy-selected and largely independent of the 1p32 locus. Thus, 1pCEN+1q represents a recurrent double-hit event that clonally emerges in rrMM, conferring selective advantage under drug exposure and is distinct from the ndMM high-risk markers defined by current consensus guidelines. These findings nominate 1pCEN as a new genomic biomarker in rrMM and 1pCEN+1q may help patient stratification for therapeutic monitoring. Key PointsA therapy-driven common genomic double-hit (1p31.1-p12 LOH with 1q gain) clonally emerges in relapsed/refractory myeloma.

cancer biology↗

bioRxiv2024

Inflamed Natural Killer cells with adhesion defects are associated with a poor prognosis in Multiple Myeloma

The promising results obtained with immunotherapeutic approaches for multiple myeloma (MM) call for a better stratification of patients based on immune components. The most pressing being cytotoxic lymphocytes such as Natural Killer (NK) cells that are mandatory for MM surveillance and therapy. In this study, we performed a single cell RNA sequencing analysis of NK cells from 10 MM patients and 10 age/sex matched healthy donors (HD) that revealed important transcriptomic changes in NK cell landscape affecting both the bone marrow and peripheral blood compartment. The frequency of mature cytotoxic "CD56dim" NK cell subsets was reduced in MM patients at the advantage of late-stage NK cell subsets expressing NF{kappa}B and IFN-I inflammatory signatures. These NK cell subsets accumulating in MM patients were characterized by a low CD16 and CD226 expression and poor cytotoxic functions. MM CD16/CD226lo NK cells also had adhesion defects with reduced LFA-1 integrin activation and actin polymerization that may account for their limited effector functions in vitro. Finally, analysis of BM infiltrating NK cells in a retrospective cohort of 177 MM patients from the IFM 2009 trial demonstrated that a high frequency of NK cells and their low CD16 and CD226 expression were associated with a shorter overall survival. Thus, CD16/CD226lo NK cells with reduced effector functions accumulate along MM development and negatively impact patients clinical outcome. Given the growing interest in harnessing NK cells to treat myeloma, this improved knowledge around MM-associated NK cell dysfunction will stimulate the development of more efficient immunotherapeutic drugs against MM. Scientific category: Lymphoid Neoplasia; Immunobiology and Immunotherapy. KEY POINTSO_LIMM patients have increased CD16/CD226low NK cell subsets characterized by "inflammatory" signatures and reduced effector functions. C_LIO_LIThe frequency of CD16/CD226low NK cells correlate with MM patient clinical outcome C_LI

immunology↗

bioRxiv2022

Unsupervized identification of prognostic copy-numberalterations using segmentation and lasso regularization

Identifying copy-number alteration with prognostic impact is typically done in a supervised approach, were candidate regions are user-selected (chomosome arms, oncogenes, etc). Yet CNA events may range from whole chromosome alterations to small focal amplifications or deletions, with no available approach to combine the potential prognostic impact of different aberration ranges. We propose and compare different statistical models to integrate the effects of multi-scale CNA events by exploiting the longitudinal structure of the genome, and assume that the survival distribution follows a Cox-proportional hazard model. These methods are adaptable to any cohorts screened for CNA by genome-wide assays such as CGH-array or whole-genome sequencing technologies, and with sufficient follow-up time. We show that combining a segmentation in the survival odds strategy with a lasso-regularization selection approach provides the best results in terms of recovering the true significant CNA regions as well as predicting survival outcomes. In particular, as shown on a 551 Multiple Myeloma patient cohort, this method allows to refine previously identified regions to exhibit potential novel driver genes.

bioinformatics↗

bioRxiv2021

Integrative multi-omics identifies high risk Multiple Myeloma segment associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Despite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval<1x10-6)], which uniquely presents a broad genomic loss (>9% of entire genome, t.test pval<1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Statement of SignificanceUsing multi-omics unsupervised clustering we discovered a new high-risk multiple myeloma patient segment. We linked its diverse genetic markers (previously known, and new including genomic loss) to transcriptional dysregulation (cell cycle, DNA repair and DNA damage) and identified master regulators that control these key biological pathways.

cancer biology↗

bioRxiv2020

The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability

BackgroundCancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. ResultsHere, we combined the notions of epipolymorphism and epiallele switching to analyze DNA methylation heterogeneity in MM patients. We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High entropy change is associated with poor outcomes and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. ConclusionWe propose that disordered methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.

cancer biology↗

bioRxiv2019

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

The 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.

genomics↗

bioRxiv2019

DNA hydroxymethylation reveals transcription regulation networks and prognostic signatures in multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy that remains challenging to cure despite a substantially improving median survival. During the last decade, DNA copy number variation and gene expression studies have described the pathology and its heterogeneity among patients. Epigenetic modifications play important roles in MM, but they are rarely associated with clinical aspects of the disease. In this epigenomics study, we produced quantifications of genomic 5-methylcytosine (5mC) and of 5-hydroxymethylcytosine (5hmC) as well as genome-wide maps of hydroxymethylation to analyse myeloma cells taken from a cohort of 40 newly diagnosed and homogeneously treated patients. We found 5hmC to be globally depleted in MM compared to normal plasma cells, as well as being reduced in advanced clinical stages of the disease. From the hydroxymethylome data, we observed that remaining 5hmC is organised in large peak clusters and is associated with well-known disease-related genes. Based on their signal correlation, these 5hmC peak clusters can be gathered in 2 regulation networks involving core transcription factors such as IRF4, MYC, PRDM1 and TCF3. By performing paired hydroxymethylomes at diagnosis and at relapse, we found the disease progression to be heterogeneous and patient-specific. We found that the location of 5hmC at tumor suppressor TP53INP1 is associated with better outcome while global high level of 5hmC tends to be associated with better overall survival. Together, our study suggests that 5hmC provides new biological insights of the disease severity and progression, and can be used for retrospective studies.\n\nKey PointsO_LI5hmC is globally depleted in MM and even more in advanced stages of the disease.\nC_LIO_LI5hmC is locally detected at transcriptionally active regions of MM where its presence can be associated with survival.\nC_LI

cancer biology↗

bioRxiv2019

Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier, PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.\n\nKey pointsO_LIMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.\nC_LIO_LIIdentification of PHF19 as the expression based biomarker most strongly associated with rapid progression in MM patients.\nC_LI

cancer biology↗

bioRxiv2018

Genomic landscape and chronological reconstruction of driver events in multiple myeloma

Multiple myeloma (MM) has a heterogeneous genome, evolving through both pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM genomes serially collected from 30 patients together with public datasets, we establish a hierarchy of driver lesions. Point mutations, structural variants and copy number aberrations define at least 7 genomic subgroups of MM, each with distinct sets of co-operating driver mutations. Complex structural events are major drivers of MM, including chromothripsis, chromoplexy and a replication-based mechanism of templated insertions: these typically occur early. Hyperdiploidy also occurs early, with individual chromosomes often gained in more than one chronological epoch of MM evolution, showing a preferred order of acquisition. Positively selected point mutations frequently occur in later phases of disease development, as do structural variants involving MYC. Thus, initiating driver events of MM, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of subsequent evolution.

cancer biology↗