Biology subjects

Noor, H.

Publications and source records attributed to Noor, H..

6 recordsLinked to original sources

bioRxiv2026

Multi-Omics Study of Ancestry in Adults with Intracranial Cancers Glioma (MOSAIC)

BackgroundMost genomic studies of adult-type diffuse gliomas have focused on predominantly European ancestry populations, limiting the generalizability of molecular classifications and precision medicine approaches. We assembled a multi-institutional glioma cohort of diverse patients to investigate how germline ancestry, molecular subtypes, and mutational processes shape tumor biology and clinical outcomes. MethodsWe analyzed 1,102 adults with WHO 2021-classified diffuse gliomas (IDH-mutant, 1p/19q-codeleted oligodendroglioma; IDH-mutant astrocytoma; IDH-wildtype glioma) from seven U.S. institutions. Whole-exome sequencing (WES) of FFPE tumors identified somatic alterations and COSMIC SBS v3.2 mutational signatures. Genetic ancestry was estimated from WES using 1000 Genomes reference populations. Overall survival was assessed using Kaplan-Meier and multivariable models. ResultsThe cohort included 66.9% European (EUR), 21.1% Admixed American/Hispanic (AMR), 10.3% Admixed African (AFR), and 1.6% Asian (AS) ancestry. Survival followed expected molecular hierarchy (median overall survival: oligodendroglioma 15.7 years, astrocytoma 10.6 years, IDH-wildtype glioma 1.9 years). Within oligodendroglioma, AMR patients showed improved survival versus EUR (HR 0.67, 95% CI 0.48-0.94; p=0.011), with similar trends across subtypes. Somatic profiling confirmed canonical subtype-defining alterations and revealed higher ATRX alterations in AFR and AMR IDH-wildtype tumors compared with EUR. ATRX alterations were associated with improved survival only in AFR (p=0.003). Mutational signature analysis identified subtype-specific signatures, including therapy-associated signatures. Chemotherapy-related signatures were more frequent in EUR and AMR than in AFR. ConclusionsThis ancestrally diverse glioma cohort confirms established molecular classifications and identifies ancestry-associated differences in survival, somatic alterations, and mutational processes, indicating the critical need for broad representation to inform precision neuro-oncology. Key PointsO_LIA multi-institutional glioma cohort validates subtype and survival patterns across ancestries. C_LIO_LITherapy-associated mutational signatures differ by ancestry, suggesting distinct treatment-related mutational processes. C_LIO_LIAdmixed American patients show improved survival, particularly in oligodendroglioma. C_LI Importance of the StudyMost genomic studies of adult-type diffuse glioma have focused on populations of predominantly European ancestry which limits the ability to examine variation in tumor biology and clinical outcomes across populations. In this study, we assembled one of the largest ancestrally diverse cohorts of molecularly characterized adult diffuse gliomas, integrating germline ancestry inference with tumor whole-exome sequencing and mutational signature analysis. We confirm that established molecular classifications and survival hierarchies remain robust across ancestry groups. However, we also identified ancestry-associated differences in survival within specific tumor subtypes, higher ATRX alteration frequencies in African American and admixed American patients with IDH-wildtype tumors, and variation in therapy-associated mutational signatures across ancestry groups. These findings highlight the importance of incorporating population differences into genomic studies of glioma and provide a resource for future multi-ancestry investigations of glioma risk, tumor evolution, and treatment response, ultimately supporting more inclusive precision neuro-oncology.

bioinformatics↗

bioRxiv2026

The metabolic profile of Extracellular Vesicles identifies and separates patients with Sarcoidosis and Anti-Synthetase Syndrome

Sarcoidosis is a multisystem disorder that primarily affects the lungs and is characterizedby granulomatous inflammation. However, much of the underlying disease mechanisms remain poorly understood. Extracellular vesicles (EVs) are small membrane-bound particles released by all cells and carry various cargos including metabolites. They are involved in intercellular communication that can be dysregulated in diseases.This study characterizes the metabolic cargo of EVs isolated from bronchoalveolar lavage fluid (BALF), using liquid chromatography-mass spectrometry (LC-MS)-based metabolomic analysis, in patients with sarcoidosis (n=37), compared to healthy controls (n=10). Additionally, the sarcoidosis signature was compared to another pulmonary disorder, anti-synthetase syndrome (ASyS, n=10). Arachidonic acid (AA) results were verified by ELISA. A total of 1202 metabolites were detected, with 111 annotated ones further analyzed. EVs from sarcoidosis patients showed distinct metabolomic profiles compared to both ASyS patients and healthy controls, with 38 annotated metabolites differentially expressed in any of the groups. In both annotated and non-annotated data, sarcoidosis patients clustered separately from ASyS patients and healthy individuals. Furthermore, sarcoidosis patients clustered in 3 subgroups, whereof one was similar to ASyS patients and one stood out as showing higher cell counts in BALF. Higher AA levels were found in sarcoidosis patient EVs by LC-MS, and AA results were verified by ELISA. Our data show that BALF EV metabolites are disease-dependent and support the notion thatsarcoidosis patients should be further subgrouped for better diagnosis and treatment.

immunology↗

bioRxiv2026

Dopamine release from Parkinson's patient-derived neurons is disrupted due to impaired synaptic vesicle loading

Striatal dopamine release defects are an early pathological feature observed in diverse models of Parkinsons disease. However, the underlying molecular mechanisms responsible, and potential links to disease aetiology in humans, have been elusive. Here, we tested the hypothesis that dopamine release deficits are a characteristic feature of disease-relevant human neurons, using human Parkinsons patient iPSC-derived dopamine neurons carrying the SNCA-triplication mutation. We reveal deficits in dopamine release from SNCA-triplication patient-derived neurons, and identify that this is due to reduced dopamine content arising from a lower capacity to store dopamine through reduced expression and function of vesicular monoamine transporter 2 (VMAT2) compared to healthy controls. In turn, by imaging VMAT substrate FFN206, and reporters for synaptic vesicular dynamics, SynaptopHluorin and CypHer5E, we reveal corresponding deficits in the size of either VMAT-containing, presynaptic releasing or recycling vesicle pools. Consistent with diminished synaptic vesicle loading and recycling, the cytosolic turnover of dopamine indicated by the ratio of concentrations of dopamine metabolite DOPAC to dopamine was elevated. By contrast, glutamate release events and VGLUT2 levels in neurons in the same preparations were not disturbed, demonstrating that vesicular dysfunction is limited to vesicles for dopamine. These findings therefore reveal dopamine loading into vesicles as a locus of dysfunction in human Parkinsons-derived neurons. These disturbances will not only drive deficits in dopamine release but could potentially also be detrimental to dopamine neuron viability through an increased burden of oxidative stress associated with elevated cytosolic dopamine, thus contributing to both symptoms and aetiology of Parkinsons pathology and offering a strategic target for improved therapies.

neuroscience↗

bioRxiv2025

Response to anti-angiogenic therapy is affected by AIMP protein family activity in glioblastoma and lower-grade gliomas

BackgroundGlioblastoma (GBM) is a highly vascularized, heterogeneous tumor, yet anti-angiogenic therapies have yielded limited survival benefits. The lack of validated predictive biomarkers for treatment response stratification remains a major challenge. Aminoacyl tRNA synthetase complex-interacting multicomplex proteins (AIMPs) 1/2/3 have been implicated in CNS diseases, but their roles in gliomas remain unexplored. We investigated their association with angiogenesis and their significance as predictive biomarkers for anti-angiogenic treatment response. MethodsIn this multi-cohort retrospective study we analyzed glioma samples from TCGA, CGGA, Rembrandt, Gravendeel, BELOB and REGOMA trials, and four single-cell transcriptomic datasets. Multi-omic analyses incorporated transcriptomic, epigenetic, and proteomic data. Kaplan-Meier and Cox proportional hazards models were used to assess the prognostic value of AIMPs in heterogeneous and homogeneous treatment-groups. Using single-cell transcriptomics, we explored spatial and cell-type-specific AIMP2 expression in GBM. ResultsAIMP1/2/3 expressions correlated significantly with angiogenesis across TCGA cancers. In gliomas, AIMPs were upregulated in tumor vs. normal tissues, higher- vs. lower-grade gliomas, and recurrent vs. primary tumors (p<0.05). Upon retrospective analysis of two clinical trials assessing different anti-angiogenic drugs, we found that high-AIMP2 subgroups had improved response to therapies in GBM (REGOMA: HR 4.75 [1.96-11.5], p<0.001; BELOB: HR 2.3 [1.17-4.49], p=0.015). AIMP2-cg04317940 methylation emerged as a clinically applicable stratification marker. Single-cell analysis revealed homogeneous AIMP2 expression in tumor tissues, particularly in AC-like cells, suggesting a mechanistic link to tumor angiogenesis. ConclusionsThese findings provide novel insights into the role of AIMPs in angiogenesis, offering improved patient stratification and therapeutic outcomes in recurrent GBM.

bioinformatics↗

bioRxiv2024

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data. Moonlight2R is available on GitHub (https://github.com/ELELAB/Moonlight2R) and BioCondcutor (https://bioconduc-tor.org/packages/release/bioc/html/Moonlight2R.html). The associated case studies presented here are available on GitHub (https://github.com/ELELAB/Moon-light2_GMA_case_studies) and OSF (https://osf.io/j4n8q/). Author summaryCancer is a complex disease and a main cause of mortality worldwide. This heterogeneous disease arises due to accumulation of changes which occur in driver genes that drive cancer progression when they are altered. These driver genes are commonly divided into oncogenes, which promote cancer, and tumor suppressors, which prevent it. A major goal of cancer research is identifying these driver genes, crucial for increasing our current understanding of cancer biology and for developing novel treatment approaches. A large number of cancer driver genes have already been identified. However, the underlying mechanisms for the alterations in these genes is challenging to predict given their context-dependent behavior and the complexity of cancer. Such explanations are the focus of this study with the aim of providing evidence of why certain genes do not function normally in cancer. Within this context, we present new functionalities to our previously developed cancer driver predictive framework, Moonlight. These new functionalities integrate multiple data types to predict oncogenes and tumor suppressors in a systems-biology-oriented manner that is freely available as a R package for the community.

bioinformatics↗

bioRxiv2023

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

bioinformatics↗