Biology subjects

Boeva, V.

Publications and source records attributed to Boeva, V..

27 records · Page 2Linked to original sources

bioRxiv2023

The super-enhancer landscape reflects molecular subgroups of adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare cancer of the adrenal gland with generally very unfavourable outcome. Two molecular subgroups, C1A and C1B, have been previously identified with a significant association with patient survival. In this work, we study chromatin state organization characterized by histone modifications using ChIP-sequencing in adult ACC. We describe the super-enhancer landscape of ACC, characterized by H3K27ac, and identify super-enhancer regulated genes that play a significant role in tumorigenesis. We show that the super-enhancer landscape reflects differences between the molecular sub-groups C1A and C1B and identify networks of master transcription factors mirroring these differences. Additionally, we study the effects of molecules THZ1 and JQ1 previously reported to affect super-enhancer-driven gene expression in ACC cell lines. Our results reveal that the landscape of histone modifications in ACC is linked to its molecular subgroups and thus provide the groundwork for future analysis of epigenetic reprogramming in ACC.

cancer biology↗

bioRxiv2022

SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival Models

Multi-omics data, which include genomic, transcriptomic, epigenetic, and proteomic data, are gaining increasing importance for determining the clinical outcomes of cancer patients. Several recent studies have evaluated various multi-modal integration strategies for cancer survival prediction, highlighting the need for standardizing model performance results. Addressing this issue, we introduce SurvBoard, a benchmark framework that standardizes key experimental design choices. SurvBoard enables comparisons between single-cancer and pan-cancer data models and assesses the benefits of using patient data with missing modalities. We also address common pitfalls in preprocessing and validating multi-omics cancer survival models. We apply SurvBoard to several exemplary use cases, further confirming that statistical models tend to outperform deep learning methods, especially for metrics measuring survival function calibration. Moreover, most models exhibit better performance when trained in a pan-cancer context and can benefit from leveraging samples for which data of some omics modalities are missing. We provide a web service for model evaluation and to make our benchmark results easily accessible and viewable: https://www.survboard.science/. All code is available on GitHub: https://github.com/BoevaLab/survboard/. All benchmark outputs are available on Zenodo: https://zenodo.org/records/11066227. O_TEXTBOXKey MessagesO_LIWe introduce SurvBoard, a comprehensive benchmarking framework for the standardized evaluation of multi-omics cancer survival models. SurvBoard provides an easily accessible platform for the reproducible comparison of models trained on single-cancer and pan-cancer datasets. The platform addresses issues such as the impact of missing modalities and variability in experimental setups. SurvBoard integrates data from four major cancer programs-TCGA, ICGC, TARGET, and METABRIC-to ensure a comprehensive evaluation across diverse types of cancer and research centers. C_LIO_LISurvBoard results confirm that statistical models generally outperform deep learning models in survival function calibration. We also find that pan-cancer training enhances model performance and that models benefit from incorporating data with missing modalities. C_LIO_LISurvBoard includes a web service that allows researchers to submit models for benchmarking and evaluation. A leaderboard is accessible via https://survboard.science/ to promote transparency and the continuous assessment of models performance. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2022

CanSig: Discovering de novo shared transcriptional programs in single cancer cells

Single-cell RNA sequencing (scRNA-seq) facilitates the discovery of gene signatures that define cell states across patients, which could be used in patient stratification and drug discovery. However, the lack of standardization in computational methodologies to analyse these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a gene signature correlation metric to score according to rediscovery, cross-dataset reproducibility, and clinical relevance. We applied CanSig to ten methods and to ten scRNA-seq datasets from four human cancer types--glioblastoma, breast cancer, lung adenocarcinoma, and cutaneous squamous cell carcinoma-- representing 116 patients and 105,000 malignant cells. Our results identify BBKNN as a leading method. We showed that the signatures identified with these methods correlate with clinically relevant outcomes, including patient survival and lymph node metastasis. Thus, CanSig establishes a standardized framework for reproducible cancer transcriptomics analysis.

bioinformatics↗

bioRxiv2022

scROSHI - robust supervised hierarchical identification of single cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene sets and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.

bioinformatics↗

bioRxiv2022

Clinically driven knowledge distillation for sparsifying high-dimensional multi-omics survival models

Recently, various methods have been proposed to integrate different heterogeneous high-dimensional genomic data sources to predict cancer survival, often in addition to widely available and highly predictive clinical data. Although clinical applications of survival models have high sparsity requirements, most state-of-the-art models do not naturally exhibit this sparsity, as they are based on random forests or deep learning. We propose to use[l] 1-penalized linear student models within a knowledge distillation framework to sparsify underlying multi-omics black-box teachers. We show that by excluding clinical variables from our[l] 1 penalty, we can effectively guide the knowledge distillation, reaching virtually identical discriminative performance to the teachers while using on average 140 features or less across the 17 cancer datasets from The Cancer Genome Atlas (TCGA) considered in our study.

bioinformatics↗

bioRxiv2021

Supervised Hierarchical Autoencoders for Multi-Omics Integration in Cancer Survival Models

With decreasing costs of high-throughput sequencing, more and more datasets providing omics profiles of cancer patients become available. Thus, novel survival analysis approaches integrating these differently sized and heterogeneous molecular and clinical groups of variables start being developed. Due to the difficulty of the task of multi-omics data integration, the Cox Proportional-Hazards (PH) model using clinical data has remained one of the best-performing techniques, barely outperformed by models using molecular data modalities. There is therefore a need for methods that can successfully perform multi-omics integration in survival analysis and outperform the clinical Cox PH model. Moreover, while certain deep learning methods have been shown to provide state-of-the-art accuracy of cancer survival prediction, most of them show no benefit or even decay in performance when integrating a larger number of modalities, further motivating a need to investigate how modality-specific representations should be integrated when using neural networks for multi-omics integration. We benchmarked multiple integration techniques for a neural network architecture, revealing that hierarchical autoencoder-based integration of modality-specific representations outperformed other methods such as max-pooling and was comparable with state-of-the-art statistical approaches for multi-omics integration. Further, we showed that the hierarchical autoencoder-based integration of modality-specific representations achieved increased performance through a soft modality selection mechanism, focusing on the most informative modalities for each cancer. We thus framed multiomics integration as a partial group-wise feature selection problem, highlighting that only those models performed well that could adequately weight important modalities in the presence of the high noise imposed by less important modalities.

bioinformatics↗

bioRxiv2021

Interplay between intrinsic reprogramming potential and microenvironment controls neuroblastoma cell plasticity and identity

Two cell identities, noradrenergic and mesenchymal, have been characterized in neuroblastoma cell lines according to their epigenetic landscapes relying on specific circuitries of transcription factors. Yet, their relationship and relative contribution in patient tumors remain poorly defined. Here, we demonstrate that the knock-out of GATA3, but not of PHOX2A or PHOX2B, in noradrenergic cells induces a mesenchymal phenotype. Our results document spontaneous plasticity in several models between both identities and show that plasticity relies on epigenetic reprogramming. We demonstrate that an in vivo microenvironment provides a powerful pressure towards a noradrenergic identity for these models. Consistently, tumor cells with a mesenchymal identity are not detected in a series of PDX models. Further study of the intra-tumor noradrenergic heterogeneity reveals two distinct cell populations exhibiting features of chromaffin-like or sympathoblast-like cells. This work emphasizes that both external cues of the environment and intrinsic factors control plasticity and cell identity in neuroblastoma.

cancer biology↗

bioRxiv2019

CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci

Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions can play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore, not suitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, that reconstructs structural ensembles based on Hi-C data and identifies significant many-body interactions. For a diverse set of highly-active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase-accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions.

bioinformatics↗

bioRxiv2019

Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single cell resolution

EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation. However, it remains elusive how genetically homogeneous EwS cells can drive heterogeneity of transcriptional programs. Here, we combined independent component analysis of single cell RNA-sequencing data from diverse cell types and model systems with time-resolved mapping of EWSR1-FLI1 binding sites and of open chromatin regions to characterize dynamic cellular processes associated with EWSR1-FLI1 activity. We thus defined an exquisitely specific and direct, super-enhancer-driven EWSR1-FLI1 program. In EwS tumors, cell proliferation was associated with a well-defined range of EWSR1-FLI1 activity; moreover, cells with a high EWSR1-FLI1 activity presented a strong oxidative phosphorylation metabolism. In contrast, a subpopulation of cells from below and above optimal EWSR1-FLI1 activity was characterized by increased hypoxia. Overall, our study reveals sources of intratumoral heterogeneity within Ewing tumors.

cancer biology↗