Biology subjects

Kleftogiannis, D.

Publications and source records attributed to Kleftogiannis, D..

9 recordsLinked to original sources

bioRxiv2025

High-dimensional Immune Profiling Following Autologous Hematopoietic Stem Cell Transplantation in Relapsing-Remitting Multiple Sclerosis

Background and ObjectivesAutologous hematopoietic stem cell transplantation (aHSCT) induces durable remission in treatment-refractory relapsing-remitting multiple sclerosis (RRMS). However, to what extent post-transplant immune recovery reflects restoration of pre-treatment state versus selective remodeling remains incompletely understood. We used high-dimensional whole-blood immune profiling to characterize lineage-specific remodeling in patients with RRMS following aHSCT and to compare trajectories with healthy reference profiles. MethodsPeripheral blood from 25 patients with RRMS treated with aHSCT in the RAM-MS trial was analyzed longitudinally at baseline, 100 days, and 6, 12, and 24 months post-transplant. Samples were compared with those from 9 age- and sex-matched healthy controls using a 41-marker mass cytometry panel. Unsupervised clustering and multi-level modeling were used to quantify lineage-level abundance changes and phenotypic remodeling across adaptive and innate compartments. ResultsImmune recovery after aHSCT was characterized by selective and sustained reshaping of specific T-and B-cell subsets rather than uniform restoration of pre-treatment composition. Naive CD4+ and CD8+ T cells gradually repopulated, whereas memory CD4+ T cells remained reduced at 24 months. Memory CD8+ T cells showed a transient early expansion before declining toward baseline levels. Naive B cells expanded early and remained elevated, while memory B cells were durably reduced. B-cell reconstitution involved expansion of activation-associated naive populations alongside contraction of selected memory subsets. Across T-cell compartments, CD161-expressing chemokine receptor-enriched subsets were selectively depleted. Several of these alterations persisted despite partial normalization of overall cell counts. These trajectories were consistent across patients, supporting a robust and reproducible effect of aHSCT on immune architecture. DiscussionaHSCT induces durable, lineage-specific reorganization of adaptive immunity along a shared trajectory across patients, characterized by contraction of inflammatory T-cell programs and hierarchical restructuring of B-cell maturation rather than simple immune restoration.

immunology↗

bioRxiv2023

Global and single-cell proteomics view of the co-evolution between neural progenitors and breast cancer cells in a co-culture model

Tumor neurogenesis, a process by which new nerves invade tumors, is a growing area of interest in cancer research. Nerve presence has been linked to aggressive features of various solid tumors, including breast and prostate cancer. A recent study suggested that the tumor microenvironment may influence cancer progression through recruitment of neural progenitor cells from the central nervous system. However, the presence of neural progenitors in human breast tumors has not been reported. Here, we investigate the presence of Doublecortin (DCX) and Neurofilament-Light (NFL) co-expressing (DCX+/NFL+) cells in patient breast cancer tissue using Imaging Mass Cytometry. To map the interaction between breast cancer cells and neural progenitor cells further, we created an in vitro model mimicking breast cancer innervation, and characterized using mass spectrometry-based proteomics on the two cell types as they co- evolved in co-culture. Our results indicate stromal presence of DCX+/NFL+ cells in breast tumor tissue from a cohort of 107 patient cases, and that neural interaction contribute to drive a more aggressive breast cancer phenotype in our co-culture models. Our results support that neural involvement plays an active role in breast cancer and warrants further studies on the interaction between nervous system and breast cancer progression.

cancer biology↗

bioRxiv2023

LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organisation of cellular ecosystems remains a challenging problem. To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging (MCI) technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organisation and deploys downstream analysis and visualisation utilities that can be used for data-driven patient risk stratification. Our case studies using MCI data from two well-annotated breast cancer cohorts re-confirmed that the spatial organisation of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients survival. Our work introduces an automated and versatile analysis tool for MCI data with many applications in future cancer research projects. Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR.

bioinformatics↗

bioRxiv2022

Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning

Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterisation of cellular heterogeneity in cancer and other diseases. However, computational identification of cell populations from CyTOF, and utilisation of single cell data for biomarker discoveries faces several technical limitations, and although some computational approaches are available, high-dimensional analyses of single cell data remains quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely: a) automated annotation of cell populations guided by a reference dataset, and b) systematic utilisation of single cell data for more effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good tradeoff between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients, reported salient interactions between signalling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects. Datasets and codes are publicly available at: https://github.com/dkleftogi/singleCellClassification

cancer biology↗

bioRxiv2022

Development of an antibody panel for imaging mass cytometry to investigate cancer-associated fibroblast heterogeneity and spatial distribution in archival tissues

To decipher the interactions between various components of the tumor microenvironment (TME) and tumor cells in a preserved spatial context, a multiparametric approach is essential. In this pursuit, imaging mass cytometry (IMC) emerges as a valuable tool, capable of concurrently analyzing up to 40 parameters at subcellular resolution. In this study, a set of antibodies was selected to spatially resolve multiple cell types and TME elements, including a comprehensive panel targeted at dissecting the heterogeneity of cancer-associated fibroblasts (CAF), a pivotal TME component. This antibody panel was standardized and optimized using formalin-fixed paraffin-embedded tissue (FFPE) samples from different organs/lesions known to express the markers of interest. The final composition of the antibody panel was determined based on the performance of conjugated antibodies in both immunohistochemistry (IHC) and IMC. Tissue images were segmented employing the Steinbock framework. Unsupervised clustering of single-cell data was carried out using a bioinformatics pipeline developed in R program. This paper provides a detailed description of the staining procedure and analysis workflow. Subsequently, the panel underwent validation on clinical FFPE samples from head and neck squamous cell carcinoma (HNSCC). The panel and bioinformatics pipeline established here proved to be robust in characterizing different TME components of HNSCC while maintaining a high degree of spatial detail. The platform we describe shows promise for understanding the clinical implications of TMA heterogeneity in large patient cohorts with FFPE tissues available in diagnostic biobanks worldwide.

cancer biology↗

bioRxiv2022

Neurogenesis and angiogenesis are associated features of aggressive breast cancer

The tumor microenvironment (TME) is important for cancer growth and progression. While angiogenesis is an established hallmark of cancer, the role of nerve fibers is less studied. Here, we investigated neurogenesis and angiogenesis in breast cancer and found them to be closely associated. Single-cell based spatial mapping by imaging mass cytometry (IMC) indicated close proximity between neural and vascular structures. Subsequent validation by tissue-based markers of neurogenesis and angiogenesis, supported by proteomics and transcriptomics data of tissues and cell lines, supported a link between these processes. A consolidated neuro-angiogenic signature score was linked to high-grade breast cancer and reduced patient survival, also within the low-grade luminal tumor subgroup. Our findings support that neurogenesis and angiogenesis are related in aggressive breast cancer and might possibly improve tumor stratification and clinical management.

cancer biology↗

bioRxiv2019

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Analysis of circulating cell-free DNA (cfDNA) data has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with freely-available bioinformatics pipelines that provide ultra-sensitive detection of single nucleotide variants (SNVs), and reliable identification of copy number variations (CNVs) directly from plasma DNA. In a cohort of 35 BC patients, our approach detected actionable driver and clonal SNVs at low (~0.5%) frequency levels in cfDNA that were concordant (83.3%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

genomics↗

bioRxiv2018

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).\n\nMethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.\n\nResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.\n\nConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

genomics↗

bioRxiv2017

Motif signatures of transcribed enhancers

In mammalian cells, transcribed enhancers (TrEn) play important roles in the initiation of gene expression and maintenance of gene expression levels in spatiotemporal manner. One of the most challenging questions in biology today is how the genomic characteristics of enhancers relate to enhancer activities. This is particularly critical, as several recent studies have linked enhancer sequence motifs to specific functional roles. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers genomic code in a more systematic way. To address this problem, we developed a novel computational method, TELS, aimed at identifying predictive cell type/tissue specific motif signatures. We used TELS to compile a comprehensive catalog of motif signatures for all known TrEn identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that distinct cell type/tissue specific motif signatures characterize TrEn. These signatures allow discriminating successfully a) TrEn from random controls, proxy of non-enhancer activity, and b) cell type/tissue specific TrEn from enhancers expressed and transcribed in different cell types/tissues. TELS codes and datasets are publicly available at http://www.cbrc.kaust.edu.sa/TELS.

bioinformatics↗