Biology subjects

Awadalla, P.

Publications and source records attributed to Awadalla, P..

8 recordsLinked to original sources

bioRxiv2026

Enrichment of methylated cell-free placental DNA

Abstract. Introduction: Preterm birth drives adverse perinatal maternal and infant health outcomes through heterogeneous symptoms, severity, and etiologies. Delivery prior to reaching 37 weeks of gestation may result from medically indicated intervention for pregnancy complications or spontaneously in the absence of prior symptoms. Placental tissue collected following preterm birth exhibits differential DNA methylation compared to full-term placentas and may indicate pregnancy health during gestation. Placental DNA currently has limited utility for assessing health of ongoing pregnancy, as sampling placental tissue during gestation increases the risk of infection and miscarriage. Risks associated with placental sampling during pregnancy limit the use of DNA methylation in clinical preterm birth prediction. Assessing preterm birth risk during gestation requires non-invasive methods for characterizing placental DNA methylation. Results: We quantified genome-wide DNA methylation patterns of hypermethylated cell-free DNA in pregnant (n = 99) and non-pregnant (n = 93) plasma using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). In each sample, we assessed DNA methylation status in 300-bp genomic windows, examining both sequencing read counts and calculated absolute molar DNA amount. Known hypermethylated placental regions, including RASSF1, STAT5A, and ERG promoters showed significantly increased odds of detection in pregnant samples, suggesting enrichment of cell-free placental DNA. Of the 536,444 300-bp windows examined, 173,071 (32%) showed significant enrichment in pregnant plasma. Linear modeling identified 107,505 differentially methylated regions (DMRs) associated with pregnancies later diagnosed with intrauterine growth restriction (IUGR) (n = 22). Alu elements showed increased representation in these DMRs than expected, while other repetitive elements exhibited underrepresentation. Discussion: These results demonstrate cfMeDIP-seq's ability to enrich for cell-free placental DNA and characterize cell-free DNA methylation signatures of pregnancies complicated by IUGR. Enrichment of cell-free placental DNA enables non-invasive profiling of placental DNA methylation from maternal plasma. Detectable epigenetic signatures in maternal plasma may identify pregnancies at elevated risk for preterm birth before clinical symptoms appear. Our findings further highlight the potential of cell-free placental DNA for monitoring pregnancy health.

genomics↗

bioRxiv2026

A Pan-Cancer Single-Cell Compendium of Intratumoural Heterogeneity.

Intratumoural heterogeneity remains one of the most formidable challenges in oncology, driving treatment resistance, disease progression, and poor patient outcomes. To uncover the cellular programs that define tumour composition and diversity, we interrogated over 3.6 million single cells derived from more than 1,000 primary and metastatic tumours spanning 14 diverse cancer types. Through the identification and quantification of coordinated gene expression programs, we delineated heterogeneous cancer cell states, exhibiting unique and clinically significant associations with patient survival. Integration with high-resolution spatial transcriptomics identified prognostic cancer cell states marked by aggressive transcriptional programs, co-localizing with invasive histological features and significantly enriched in metastatic settings. Functional drug perturbation screening uncovered drug vulnerabilities specific to distinct cancer cell states, supporting both direct targeting strategies and therapeutic reprogramming toward less aggressive cellular phenotypes. Collectively, this study offers novel insights into the relationship between intratumoural transcriptional heterogeneity, spatial organization, clinical outcomes, and therapeutic vulnerabilities.

cancer biology↗

bioRxiv2022

Pan-cancer classification of single cells in the tumour microenvironment

Single-cell RNA sequencing reveals valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >250,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancer types and employed a novel hierarchical approach, outperforming current classification methods. We extensively confirmed scATOMICs accuracy on 198 tumour biopsies and 54 blood samples encompassing >420,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMICs practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyze multicellular cancer TMEs.

bioinformatics↗

bioRxiv2021

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks -- substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient. TumE is publicly available for use at https://github.com/tomouellette/TumE.

bioinformatics↗

bioRxiv2021

Using image-based haplotype alignments to map global adaptation of SARS-CoV-2

Quantifying evolutionary change among viral genomes is an important clinical device to track critical adaptations geographically and temporally. We built image-based haplotype-guided evolutionary inference (ImHapE) to quantify adaptations in expanding populations of non-recombining SARS-CoV-2 genomes. By combining classic population genetic summaries with image-based deep learning methods, we show that different rates of positive selection are driving evolutionary fitness and dispersal of SARS-CoV-2 globally. A 1.35-fold increase in evolutionary fitness is observed within the UK, associated with expansion of both the B.1.177 and B.1.1.7 SARS-CoV-2 lineages.

evolutionary biology↗

bioRxiv2020

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

A small population of self-renewing, hematopoietic stem cells continuously reconstitutes our immune system. As we age, these cells, or their pluripotent descendants, accumulate somatic mutations; some of these mutations provide selection advantages and increase in frequency in the peripheral blood cell population. This process of positive selection, deemed age-related clonal hematopoiesis (ARCH), is associated with increased risk for cardiac disease and blood malignancies, like acute myeloid leukemia (AML). However, it remains unclear why some people with ARCH do not progress to AML, even when their blood cells harbor well-known AML driver mutations. Here, we examine whether negative selection can play a role in determining AML progression by modelling the complex interplay of positive and negative selective processes. Using a novel approach combining deep learning and population genetic models, we detect pervasive negative selection in targeted sequence data from the blood of 92 pre-AML individuals and 385 healthy controls. We find that the relative proportion of passenger to driver mutations is critical in determining if the selective advantage conferred to a cell by a known driver mutation is able to overwhelm negative selection acting on passenger mutations and allow clones harbouring disease-predisposing mutations to rise to dominance. We find that a subset of non-driver genes is enriched for mildly damaging mutations in healthy individuals fitting purifying models of evolution suggesting that mutations in these genes might confer a protective role against disease-predisposing clonal expansions. Through exploring non drivercentric models of evolution, we show how different classes of evolution act to shape hematopoietic dynamics and subsequent health outcome which may better inform disease prediction and unveil novel therapeutic targets. We anticipate that our results and modelling techniques can be broadly applied to identify both driver mutations and those mildly damaging passenger mutations, as well as help understand the early evolution of cancer in other cells and tissues.

cancer biology↗

bioRxiv2017

Motif Disruption Domains Lead To Cancer Gene Expression Rewiring

Somatic mutations accumulate in non-coding regions of the genome during tumorigenesis, but their functional characterization presents a challenge. Somatic non-coding mutations rarely overlap among patients, which necessitates large sample sizes to detect associations. We analysed somatic mutations called from whole-genome sequencing (WGS) and RNA sequencing (RNAseq) from 3000 tumors across the Pan-Cancer Analysis of Whole Genomes to identify and functionally characterize mutation accumulation and its impact on gene dysregulation in cancer. We identified 1.5 million motif disruption domains (MDDs) across 40 cancer types, which we characterized as pan-cancer targets for recurrent mutation accumulation. These MDDs deregulate gene expression in cancer-specific and pan-cancer patterns by disrupting transcription factor binding sites in regulatory and insulator elements. Disruption is most recurrent across individuals at MDDs in conserved open chromatin, revealing potential drivers. This accumulation of somatic variants targeting regulatory and structural elements in MDDs generates gene expression dysregulation during tumorigenesis.

cancer biology↗

bioRxiv2017

Genomic and Environmental Contributions to Chronic Diseases in Urban Populations

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the GxE interactions in humans. We used a system biology approach to comprehensively assess the interactions between 1.6 million environmental exposure data, health, and expression phenotypes, together with whole genome genetic variation, for [~]1000 individuals from a founder-population in Quebec. We reveal a substantial impact of the urbanization gradient on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. In detail, air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits when controlling for genetic ancestry. Finally, we capture 34 clinically associated expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects chronic disease development, and that genetic variation, including rare variants, can modulate individuals response to environmental challenges.\n\nHighlightsO_LIFine scale environmental effects overpower those of ancestry on gene expression\nC_LIO_LIAir pollution (geographic and temporal) is associated with transcriptional response\nC_LIO_LIGene-by-environment interactions with air pollution include asthma associated loci\nC_LIO_LIInflammatory pathways and cardio-respiratory clinical traits are among those affected\nC_LI

genomics↗