Biology subjects

Nassiri, S.

Publications and source records attributed to Nassiri, S..

12 recordsLinked to original sources

bioRxiv2026

TumorArchetypeR: A modular framework to derive signature-based tumor subtypes

MotivationThe tumor microenvironment (TME) dictates cancer progression and therapeutic response, yet translating TME subtypes into robust clinical biomarkers remains a significant challenge. Existing classification models typically rely on static gene signatures and cohort-dependent normalization, making them ill-suited for application to the small, unbalanced datasets common in early-phase clinical trials. To better guide drug development, methods are required that offer the flexibility to target specific biological contexts and bridge the gap between the discovery of tumor archetypes and their robust translation to individual patient samples. ResultsWe developed TumorArchetypeR, a modular R package that unifies unsupervised subtype discovery with the generation of rank-based, single-sample classifiers. By leveraging a systematic parameter grid search, the framework identifies stable, data-driven subtypes rather than relying on arbitrary defaults. Crucially, to ensure clinical translatability, the package includes a module to train a robust classifier using binary gene-pair rules, enabling prediction without cohort-level preprocessing. Applying TumorArchetypeR to colorectal cancer, we resolved the heterogeneity of fibrotic tumors, distinguishing an immunosuppressive "Immune-enriched/Fibrotic" state from an immune-excluded "Fibrotic/Myeloid" phenotype. Furthermore, we identified a distinct "Th/B-cell enriched" archetype associated with superior survival, a group largely obscured by existing pan-cancer models. With our rank-based classifier demonstrating robust performance on previously unseen samples, these findings highlight TumorArchetypeR as a scalable, end-to-end solution for refining patient stratification and optimizing precision oncology strategies. The TumorArchetypeR package and documentation are openly available on GitHub at https://github.com/lutgem/TumorArchetypeR.

cancer biology↗

bioRxiv2026

Performance of the 10X Genomics Flex Single-Cell Sequencing Assay and its Application to Overcome Challenges in Clinical Trial Samples

BackgroundSingle-cell RNA sequencing (scRNA-seq) has become essential for understanding disease biology, yet its application in clinical trials is often limited by logistical challenges associated with the handling of biospecimens. Newly developed protocols aim to address these limitations by enabling profiling of fixed tissue. These new solutions need to be benchmarked against well-established protocols to assess their performance and suitability for clinical research. ResultsWe systematically compared different scRNA-seq protocols in a set of samples commonly analysed in clinical trials. The 10X Genomics GEM-X Flex Gene Expression assay (GEM-X Flex), in combination with the "chop-fix" preprocessing protocol, demonstrated superior performance to the standard GEM-X Universal Gene Expression solution when applied to both primary tumor tissue fragments and FFPE blocks. Moreover, the quality of the data obtained from GEM-X Flex applied to FFPE blocks outperformed that of single-nuclei RNA sequencing (snRNA-seq) from frozen biopsies, more robustly capturing the biological signals associated with the mechanism of action of a drug evaluated in an internal clinical trial. ConclusionsGEM-X Flex generates reliable, comprehensive transcriptomic data from both fixed tissue and clinical biopsies. By overcoming some of the limitations of fresh and frozen tissue analysis, this protocol offers a robust solution for the broad implementation of scRNA-seq in clinical trials.

genomics↗

bioRxiv2025

Distributed Biomarker Discovery for Immuno-Oncology

BackgroundIdentifying robust predictive biomarkers for immuno-oncology (IO) therapy response remains challenging due to the complexity of tumor host interactions and the limited availability of public datasets. While achieving clinical relevance requires biomarkers that generalize across diverse datasets, privacy concerns surrounding sensitive clinical and molecular data further restrict data sharing, impeding progress in biomarker discovery. Materials and MethodsWe developed a scalable, cloud-based distributed pipeline, implemented as an in-silico simulation of federated learning, to enable privacy-preserving integration of clinical and molecular data across institutions. This approach supports the development of genomic prediction algorithms without sharing sensitive patient data. Using curated gene expression profiles, we evaluated the association of tumor microenvironment and IO gene expression signatures with clinical outcomes across pan-cancer, cancer-specific, and treatment-specific settings, and developed a multivariable distributed model to predict IO response. ResultsWe implemented an integrated meta-analysis pipeline using harmonized data across 18 datasets comprising 967 patients and seven cancer types treated with PD-1/PD-L1, CTLA-4, or combination immunotherapies. This approach identified biomarkers specific to lung cancer and CTLA-4 therapy previously unreported in pan-cancer and pan-IO analyses, highlighting the value of context-specific associations. Similar gene expression signatures were associated with progression-free survival and response in both pan-cancer and melanoma datasets, with notable overlap between dual checkpoint blockade and PD-1/PD-L1 therapies. Distributed multivariable XGBoost models outperformed the average of locally trained models, indicating improved generalizability. ConclusionWe present a new distributed pipeline for biomarker discovery in immuno-oncology that preserves patient privacy through federated analysis and secure data handling. This study highlights the importance of larger, diverse datasets to refine cancer- and treatment-specific biomarkers, paving the way for more precise and personalized IO therapies.

bioinformatics↗

bioRxiv2025

Targeting CXCR4-expressing TAMs in muscle-invasive bladder cancer to enhance tumor control after immunotherapy

Bladder cancer (BC) is a prevalent malignancy with poor prognosis in advanced stages. While immune checkpoint blockade has revolutionized immunotherapy, its efficacy remains limited for most advanced BC patients. The detailed characterization of BCs tumor microenvironment (TME) is a prerequisite to understand these mechanisms of resistance and to develop new therapeutic strategies. In this study, we used a genetically engineered BC mouse model resistant to anti-PD1 treatment, and BC patient samples, to investigate the evolution of tumor-associated macrophages (TAMs) during BC progression. We identified a subset of pro-tumor TAMs expressing CXCR4, predominantly found in advanced stages of BC-bearing mice and in half of muscle-invasive BC patients from the studied cohort. Interestingly, CXCR4+ TAM-rich regions were associated with CD8 T cell-excluded areas in both mice and patients. Administration of a small molecule CXCR4 inhibitor significantly reduced the number of pro-tumor TAMs within the tumor and markedly prolonged mouse survival. Incorporating this inhibitor into a tri-immunotherapy regimen further enhanced survival, highlighting the potential of targeting multiple pathways to strongly enhance anti-tumor effects and offering new hope for improving immunotherapy in advanced BC.

immunology↗

bioRxiv2024

Imbalance and Composition Correction Ensemble Learning Framework (ICCELF): A novel framework for automated scRNA-seq cell type annotation

Single-cell RNA sequencing (scRNA-seq) has gained broad utility and success in revealing novel biological insight in preclinical and clinical investigations. Cell type annotation remains a key analysis task with great influence on downstream interpretation of scRNA-seq data. Traditional machine learning approaches proposed for automated cell type annotation often overlook the inherent imbalance of cell type proportions within biological samples, and the compositional nature of sequencing-based gene expression quantification. In this study, we highlight the importance of accounting for cell type imbalance and compositionality of sequencing count data, and introduce the Imbalance and Composition Corrected Ensemble Learning Framework (ICCELF) as a novel approach to automated cell type annotation. We show via comprehensive evaluation on both simulated and real-world scRNA-seq data that by effectively addressing class imbalance and data compositionality. ICCELF offers a robust and efficient solution that facilitates accurate and reliable cell type annotation, paving the way for enhanced biological discoveries.

bioinformatics↗

bioRxiv2023

Mcadet: a feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, technical biases, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to address technical biases, identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and rare cell types, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for unique molecular identifiers (UMIs) scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing rare cell populations. By addressing the challenges of feature selection in scRNA-seq analysis, Mcadet provides a valuable tool for improving the reliability and accuracy of downstream analyses in single-cell transcriptomics.

bioinformatics↗

bioRxiv2022

NFAT5 induction by the tumor microenvironment enforces CD8 T cell exhaustion

Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. We discovered that NFAT5, an NFAT family member lacking an AP-1 docking site, is highly expressed in exhausted T cells from murine and human tumors and is a central player in tumor-induced exhaustion. While NFAT5 overexpression reduced tumor control, NFAT5 deletion improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that expressed less TOX and PD-1 and produced more cytokines particularly among precursor exhausted cells. Conversely, NFAT5 had no effect on chronic infection-induced T cell exhaustion. Mechanistically we found that TCR triggering induced NFAT5 expression and that hyperosmolarity stimulated transcriptional activity of NFAT5. We propose that NFAT5 takes over NFAT1/2 to promote exhaustion specifically in tumor-infiltrating CD8+ T cells.

immunology↗

bioRxiv2019

Microglia control small vessel calcification via TREM2

Microglia participate in CNS development and homeostasis and are often implicated in modulating disease processes in the CNS. However, less is known about the role of microglia in the biology of the neurovascular unit (NVU). In particular, data are scant on whether microglia are involved in CNS vascular pathology. In this study, we use a mouse model of primary familial brain calcification (PFBC) - Pdgfbret/ret to investigate the role of microglia in calcification of the NVU. We report that microglia enclosing vessel-calcifications, coined calcification-associated microglia (CAM), display a distinct activation signature. Pharmacological ablation of microglia with the CSF1R inhibitor - PLX5622 leads to aggravated vessel calcification. Additionally, depletion of microglia in wild-type and Pdgfbret/ret mice causes the development of bone protein (osteocalcin, osteopontin) containing axonal spheroids in the white matter. Mechanistically, we show that microglia require functional TREM2 for controlling vessel-associated calcification. In conclusion, our results demonstrate that microglial activity in the setting of pathological vascular calcification is beneficial. In addition, we identify a new, previously unrecognized function of microglia in halting the expansion of ectopic calcification.

neuroscience↗

bioRxiv2019

The human CD8 T stem cell-like memory phenotype appears in the acute phase in Yellow Fever virus vaccination

Long-term memory is a fundamental feature of cytotoxic CD8 T cell immunity. Yet when do memory cells arise, especially in humans, is poorly documented, the pathways of effector / memory cell differentiation being largely debated. Based on a cross-sectional study, we previously reported that the live-attenuated Yellow Fever virus vaccine YF-17D induces a stem cell-like memory (SCM) CD8 T cell population persisting for at least 25 years. Here we present longitudinal data revealing that an activated SCM phenotype is distinctly detectable early on following YF-17D vaccination, i.e. at the same time as activated effector cells. In the long-run, the cells that express the transcription factor T cell factor 1 (TCF1) preferentially persist, consistent with the role of TCF1 in memory establishment. By performing t-distributed Stochastic Neighbour Embedding of flow cytometry data on standard differentiation and activation markers, we obtained a time-lapse representation of the dynamics of the CD8 T cell response: SCM cells appear early and remain closely related to the baseline Naive cells, while effector cells burst out of baseline and gradually contract after the peak of the response. Altogether, we observe heterogeneity in differentiation phenotypes in both the acute phase and the decade-long-term phases, including cells with memory phenotypes very early in the response. As opposed to models where memory cells develop from effector cells, our data support differentiation models where long-term memory cells are established by the early decision to retain proximity to the Naive state in a memory-dedicated pool of cells.

immunology↗

bioRxiv2019

Differential stromal reprogramming in benign and malignant naturally occurring canine mammary tumours identifies disease-promoting stromal components

The importance of cancer-associated stroma (CAS) for initiation and progression of cancer is well accepted. However, as stromal changes in benign forms of naturally occurring tumours are poorly understood, it remains unclear how CAS from benign and malignant tumours compare. Spontaneous canine mammary tumours are viewed as excellent models of human mammary carcinomas (mCA). We have recently reported highly conserved stromal reprogramming between canine and human mCA based on transcriptome analysis of laser-capture-microdissected FFPE specimen. To identify stromal changes between benign and malignant mammary tumours, we have analysed CAS and matched normal stroma from 13 canine mammary adenomas and compared them to 15 canine mCA. Our analyses revealed distinct stromal reprogramming even in small benign tumours. While similarities in stromal reprogramming exist, the CAS signature clearly distinguished adenomas from mCA, suggesting that it may reliably discriminate between benign and malignant tumours. We identified strongly discriminatory genes and found strong differential enrichment in several hallmark signalling pathways between benign and malignant CAS. The distinction between CAS from adenoma and mCA was further substantiated by differential abundance in cellular composition. Finally, to determine key players in CAS reprograming between adenomas and mCA, a network-based gene screening method identified modules of co-expressing genes with distinct expression profile in benign and malignant CAS, and revealed several hub genes as potential molecular drivers in CAS. Given the relevance of canine CAS as a model for the human disease, our approach identifies potential stromal drivers of tumour malignancy with implications for human mCA.\n\nSummary statementRNAsequencing-based analysis of stromal reprogramming between benign and malignant naturally occurring canine mammary tumours identifies potential molecular drivers in cancer-associated stroma that support tumour growth and malignancy.

cancer biology↗

bioRxiv2019

Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations

PurposeCurrent sequencing strategies can genetically solve 55-60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy number variations (CNVs), which we believe are a major contributor to unsolved IRD cases.\n\nMethodsFive hundred IRD patients were analyzed with targeted next generation sequencing (NGS). The NGS data was used to detect CNVs with ExomeDepth and gCNV and the results were compared to CNV detection with a SNP-Array. Likely causal CNV predictions were validated by quantitative (q)PCR.\n\nResultsLikely disease-causing single nucleotide variants (SNVs) and small indels were found in 55.8% of subjects. PVs in USH2A (11.6%), RPGR (4%) and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene.\n\nConclusionsCNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.

genomics↗

bioRxiv2019

Pericytes regulate vascular immune homeostasis in the CNS

Brain endothelium possesses several organ-specific features collectively known as the blood-brain barrier (BBB). In addition, trafficking of immune cells in the healthy central nervous system (CNS) is tightly regulated by CNS vasculature. In CNS autoimmune diseases such as multiple sclerosis (MS), these homeostatic mechanisms are overcome by autoreactive lymphocyte entry into the CNS causing inflammatory demyelinating immunopathology. Previous studies have shown that pericytes regulate the development of organ-specific characteristics of brain vasculature such as the BBB and astrocytic end-feet. Whether pericytes are involved in the control of leukocyte trafficking remains elusive. Using adult, pericyte-deficient mice (Pdgfbret/ret), we show here that brain vasculature devoid of pericytes shows increased expression of VCAM-1 and ICAM-1, which is accompanied by increased leukocyte infiltration of dendritic cells, monocytes and T cells into the brain, but not spinal cord parenchyma. Regional differences enabling leukocyte trafficking into the brain as opposed to the spinal cord inversely correlate with the pericyte coverage of blood vessels. Upon induction of experimental autoimmune encephalitomyelitis (EAE), pericyte-deficient mice succumb to severe neurological impairment. Treatment with first line MS therapy - fingolimod significantly reverses EAE, indicating that the observed phenotype is due to the massive influx of immune cells into the brain. Furthermore, pericyte-deficiency in mice that express myelin oligodendrocyte glycoprotein peptide (MOG35-55) specific T cell receptor (Pdgfbret/ret; 2D2Tg) leads to the development of spontaneous neurological symptoms paralleled by massive influx of leukocytes into the brain, suggesting altered brain vascular immune quiescence as a prime cause of exaggerated neuroinflammation. Thus, we show that pericytes indirectly restrict immune cell transmigration into the CNS under homeostatic conditions and during autoimmune-driven neuroinflammation by inducing immune quiescence of brain endothelial cells.

neuroscience↗