Biology subjects

Thennavan, A.

Publications and source records attributed to Thennavan, A..

5 recordsLinked to original sources

bioRxiv2026

Trp53 heterozygosity defines novel states impacting tumor plasticity and progression in Kras-initiated non-small cell lung cancer

KRAS and TP53 are frequently mutated in non-small cell lung cancer (NSCLC), the leading cause of cancer-related mortality worldwide. The most common event at the TP53 locus is mutation/loss of one allele and retention of a wild-type allele, which confers better survival outcomes than complete TP53 loss. To study the contribution of TP53 heterozygosity, we generated a murine lung tumor model with a KrasG12D mutation and intact (K) or single-allele loss (KP+/-) of Trp53 and compared it with publicly available complete loss (KP) data. K and KP+/- mice lived longer than KP mice, paralleling clinical survival data. Single-cell transcriptomic analysis of tumor-burdened lungs indicated that Trp53 heterozygosity expanded transcriptional plasticity that was not present in K and KP tumors. Genomic instability was also higher in KP+/- mice relative to the KP genotype. Trp53 gene dosage, thus, regulates phenotypic plasticity and evolutionary trajectories of tumor states across K, KP+/-, and KP tumors. Murine KP+/- tumor signatures and copy-number profiles were present in human NSCLC datasets. Two of these signatures correlated with better and worse patient survival outcomes, respectively. TP53 heterozygosity in KRAS-mutant lung cancers presents a distinct genomic landscape that should be taken into account in the development of novel therapeutic strategies.

cancer biology↗

bioRxiv2024

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

genomics↗

bioRxiv2023

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

genomics↗

bioRxiv2023

Conserved Chamber-Specific Polyploidy Maintains Heart Function in Drosophila

Developmentally programmed polyploidy (whole-genome-duplication) of cardiomyocytes is common across evolution. Functions of such polyploidy are essentially unknown. Here, we reveal roles for precise polyploidy levels in cardiac tissue. We highlight a conserved asymmetry in polyploidy level between cardiac chambers in Drosophila larvae and humans. In Drosophila, differential Insulin Receptor (InR) sensitivity leads the heart chamber to reach a higher ploidy/cell size relative to the aorta chamber. Cardiac ploidy-reduced animals exhibit reduced heart chamber size, stroke volume, cardiac output, and acceleration of circulating hemocytes. These Drosophila phenotypes mimic systemic human heart failure. Using human donor hearts, we reveal asymmetry in nuclear volume (ploidy) and insulin signaling between the left ventricle and atrium. Our results identify productive and likely conserved roles for polyploidy in cardiac chambers and suggest precise ploidy levels sculpt many developing tissues. These findings of productive cardiomyocyte polyploidy impact efforts to block developmental polyploidy to improve heart injury recovery.

developmental biology↗

bioRxiv2019

SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

bioinformatics↗