Biology subjects

Roncador, M.

Publications and source records attributed to Roncador, M..

5 recordsLinked to original sources

bioRxiv2024

DemoTape: Computational demultiplexing of targeted single-cell sequencing data

BackgroundSingle-cell sequencing can provide novel insights into the understanding and treatment of diseases. In cancer, for example, intratumor heterogeneity is a major cause of treatment resistance and relapse. Although technological progress has substantially increased the throughput of sequenced cells, single-cell sequencing remains cost and labor-intensive. Multiplexing, i.e., the pooling and subsequent joint preparation and sequencing of samples, followed by a demultiplexing step, is a common practice to reduce expenses and confounding batch effects, especially in single-cell RNA sequencing. ResultsHere, we introduce demoTape, a computational demultiplexing method for targeted single-cell DNA sequencing (scDNA-seq) data based on a distance metric between individual cells at single-nucleotide polymorphisms loci. To validate demoTape, we sequence three B-cell lymphoma patients separately and multiplexed on the Tapestri platform. We find similar genotypes, clones, and evolutionary histories in all three samples when comparing the individual with the demultiplexed samples. Using the three individually sequenced samples, we simulate multiplexed ground truth data and show that demoTape outperforms state-of-the-art demultiplexing methods designed for RNA sequencing data. Additionally, we demonstrate through downsampling that the inferred clonal composition remained largely stable for samples with fewer cells despite the inevitable loss in resolution of low-frequency clones. ConclusionsMultiplexing and subsequent genotype-based demultiplexing of scDNA-seq will reduce costs and workload, eventually allowing the sequencing of more samples. This will open new possibilities and accelerate the investigation of biological questions where cellular heterogeneity on the genomic level plays a crucial role.

bioinformatics↗

bioRxiv2023

Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies

Myeloid malignancies exhibit considerable heterogeneity with overlapping clinical and genetic features among different subtypes. Current classification schemes, predominantly based on clinical features, fall short of capturing the complex genomic landscapes of these malignancies. Here, we present a data-driven approach that integrates mutational features and clinical covariates within networks of their probabilistic relationships, enabling the discovery of de novo cancer subgroups. In a cohort of 1323 patients across acute myeloid leukemia, myelodysplastic syndromes, chronic myelomonocytic leukemia and myeloproliferative neoplasms, we identified novel subgroups that outperform established risk classifications in prognostic accuracy. Our findings suggest that mutational patterns are often shared across different types of myeloid malignancies, with distinct subtypes potentially representing evolutionary stages en route to leukemia. Within the novel subgroups, our integrative method discerns unique patterns combining genomic and clinical features to provide a comprehensive view of the multifaceted genomic and clinical landscape of myeloid malignancies. This in turn may guide the development of targeted therapeutic strategies and offers a pathway to enhanced patient stratification.

cancer biology↗

bioRxiv2020

Integrated genomics and comprehensive validation reveal novel drivers of genomic evolution in esophageal adenocarcinoma

Identification of genes driving genomic evolution can provide novel targets for cancer treatment and prevention. Here we show identification of a genomic instability gene signature, using an integrated genomics approach. Elevated expression of this signature correlated with poor survival in esophageal adenocarcinoma (EAC) as well as three other human cancers. Knockout and overexpression screens confirmed the relevance of this signature to genomic instability. Indepth evaluation of TTK (a kinase), TPX2 (spindle assembly factor) and RAD54B (recombination protein) further confirmed their role in genomic instability and tumor growth. Mutational signatures identified by whole genome sequencing and functional studies demonstrated that DNA damage and homologous recombination were common mechanisms of genomic instability induced by these genes. Consistently, a TTK inhibitor impaired EAC cell growth in vivo, and increased chemotherapy-induced cytotoxicity while inhibiting genomic instability in surviving cells. Thus inhibitors of TTK and other genes identified in this study have potential to inhibit/delay genomic evolution and tumor growth. Such inhibitors also have potential to increase chemotherapy-induced cytotoxicity while reducing its harmful genomic impact in EAC and possibly other cancers.

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↗

bioRxiv2018

DROSHA associates to DNA damage sites and is required for DNA repair

The DNA damage response (DDR) is the signaling cascade through which a cell recognizes DNA lesions, and promotes their resolution via the repair pathways of Non-Homologous End Joining (NHEJ), or Homologous Recombination (HR). We recently demonstrated that DROSHA boosts DDR signaling by processing damage-induced long non-coding RNAs into smaller DNA damage response RNAs (DDRNAs). However, the location at which DROSHA exerts its DDR functions, relative to sites of DNA damage, remains unknown.\n\nTo investigate DROSHAs localization during DDR activation, we used the DiVA cellular system, which allows the controlled induction of several DNA double strand breaks (DSBs) in the human genome. Indeed, by genome wide chromatin immunoprecipitation followed by next generation sequencing, we demonstrate that DROSHA associates with DSBs. In support of this, DSB-recruitment of DROSHA is detectable at the single-cell level by Proximity Ligation Assay between DROSHA and known DDR markers, and by DNA damage in situ ligation followed by Proximity Ligation Assay (DI-PLA), which demonstrates proximity of DROSHA to DNA ends. DROSHA recruitment occurs at both genic and inter-genic DSBs, suggesting that its recruitment is independent from ongoing transcription preceding damage generation. DROSHAs recruitment to DNA lesions occurs throughout the cell cycle, and with a preference for NHEJ-prone DSBs. Consistently, inhibition of the HR pathway increases DROSHA recruitment, and DROSHA knock down strongly impairs NHEJ efficiency in a GFP-reporter cellular system for monitoring NHEJ DNA repair. Overall, these results demonstrate that DROSHA acts locally at sites of DNA damage to promote NHEJ DNA repair.

cell biology↗