Biology subjects

Brosnan-Cashman, J. A.

Publications and source records attributed to Brosnan-Cashman, J. A..

5 recordsLinked to original sources

bioRxiv2025

Loss of DAXX induces alternative lengthening of telomeres (ALT)-associated hallmarks in prostate cancer cells in a context-specific manner

BackgroundThe histone chaperone complex, consisting of the death domain-associated protein (DAXX) and the alpha-thalassemia/mental retardation X-linked protein (ATRX), plays a pivotal role in maintaining chromatin through the deposition of the histone variant H3.3. Mutations leading to loss of ATRX or DAXX function are linked to the non-telomerase, alternative lengthening of telomeres (ALT) phenotype in certain cancers. Engineered ATRX mutations have previously been found to induce features of ALT in prostate cancer cell lines, notably in LAPC-4, but not in CWR22Rv1. This study determined the impact of DAXX mutations on ALT-associated characteristics in CWR22Rv1 and LAPC-4. MethodologyMutations were induced in CWR22Rv1 and LAPC-4 cells by targeting exon 2 of DAXX using the CRISPR-Cas9 genome editing strategy. The resulting mutant clones were then evaluated for ALT-associated characteristics, including the presence of ALT-associated PML bodies (APBs), C-circles, telomere length heterogeneity, and a lack of telomerase activity. ResultsFour CWR22Rv1 DAXX mutant clones (DAXXMut1-4) and five LAPC-4 clones (DAXXMut1-5) were evaluated. In CWR22Rv1, DAXXMut1, DAXXMut2, and DAXXMut4 were true knockout clones with frameshift mutations in both copies, while CWR22Rv1 DAXXMut3 had a frameshift mutation in one copy and an in-frame mutation in the other. Protein expression was undetectable in all the CWR22Rv1 clones, including CWR22Rv1 DAXXMut3. In LAPC-4, DAXXMut1 was a true knockout, while DAXXMut2, DAXXMut3, DAXXMut4, and DAXXMut5 clones had at least one in-frame mutation. Among these LAPC-4 clones, only DAXXMut1 had undetectable protein by western blotting. ALT-associated characteristics such as APBs, C-circles, and telomere length heterogeneity were observed only in CWR22Rv1 DAXXMut4. All the clones maintained telomerase activity, regardless of whether ALT-associated hallmarks were observed. ImplicationsThe CWR22Rv1 and LAPC-4 DAXX mutant clone models provide useful tools for future studies on telomere maintenance mechanisms and DAXX-related biology, particularly in prostate cancer.

cancer biology↗

bioRxiv2025

Quantification of collagen and associated features from H&E-stained whole slide pathology images across cancer types using a physics-based deep learning model

BackgroundCollagen is the major component of the extracellular matrix (ECM). Collagen structural organization undergoes significant transformation during tumorigenesis. The visualization of collagen in histological tissue sections would aid in the study of tumor growth, encapsulation, and invasion. However, such visualization requires the use of special stains such as Picrosirius Red (PSR) or Massons Trichrome (MT), or more recently, second-harmonic generation imaging (SHG) in unstained tissue sections. However, PSR and MT both suffer from significant inter- (and intra-) lab stain variabilities, and SHG, while considered a ground truth by many, suffers from issues of system complexity/reliability, cost, and speed/throughput. These technical hurdles limit more widespread assessment of collagen in tissue samples. MethodsUsing high-contrast, high-throughput polarization imaging on PSR-stained slides to generate ground truth training polarization images, we developed a deep learning model (iQMAI) to infer the presence of collagen directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with high specificity. After iQMAI inference across WSIs, individual collagen fibers were extracted, and features describing overall collagen intensity and fiber morphology were computed. iQMAI pixel-and feature-wise outputs were compared to ground truth polarization imaging to assess model performance. The trained iQMAI model was deployed on H&E-stained WSI from the TCGA LUAD, LUSC, LIHC, and PAAD datasets for evaluation. iQMAI-derived collagen features were compared to tissue composition, gene expression, and overall survival. ResultsThe iQMAI model shows significant generalization across multiple indications. iQMAI collagen predictions were similar to polarization imaging measurements of the same sample, with a mean structural similarity index (SSIM) of 0.84 (95% CI 0.69-0.93), a mean patch-wise RMSE of 0.04 (95% CI 0.02-0.08), and a linear correlation (R2=0.93). Comparing features of the collagen fibers extracted from iQMAI vs. polarization images yielded similar linear correlations between computed fiber tortuosity, length, width, and relative angle. The relationship between collagen fiber density and fibroblast density was distinct in non-small cell lung cancer (LUAD and LUSC), hepatocellular carcinoma (LIHC), and pancreatic ductal adenocarcinoma (PAAD). In PAAD, fiber density and fiber width were both negatively associated with the LRRC-15 gene expression signature, and increased fiber width was associated with longer overall survival. ConclusionsiQMAI is a deep learning model that accurately predicts collagen from an H&E-stained WSI, allowing for spatially resolved quantification of collagen morphology and enabling investigation of the interplay between collagen and other TME components. We demonstrate an example of the utility of iQMAI-based collagen assessment in PAAD, where collagen features are associated with immunosuppressive cancer-associated fibroblasts and overall survival. Understanding the relationship between collagen, the tumor microenvironment composition, and disease progression may aid the development of effective immunotherapies in PAAD and other cancer types.

pathology↗

bioRxiv2023

Cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

cancer biology↗

bioRxiv2023

Chromogenic detection of telomere lengths in situ aids the identification of precancerous lesions in the prostate

Telomeres are terminal chromosomal elements that are essential for the maintenance of genomic integrity. The measurement of telomere content provides useful diagnostic and prognostic information, and fluorescent methods have been developed for this purpose. However, fluorescent-based tissue assays are cumbersome for investigators to undertake, both in research and clinical settings. Here, a robust chromogenic in situ hybridization (CISH) approach was developed to visualize and quantify telomere content at single cell resolution in human prostate tissues, both frozen and formalin-fixed, paraffin-embedded (FFPE). This new assay ("Telo-CISH") produces permanently stained slides that are viewable with a standard light microscope, thus avoiding the need for specialized equipment and storage. The assay is compatible with standard immunohistochemistry, thereby allowing simultaneous assessment of histomorphology, identification of specific cell types, and assessment of telomere status. In addition, Telo-CISH eliminates the problem of autofluorescent interference that frequently occurs with fluorescent-based methods. Using this new assay, we demonstrate successful application of Telo-CISH to help identify precancerous lesions in the prostate by the presence of markedly short telomeres specifically in the luminal epithelial cells. In summary, with fewer restrictions on the types of tissues that can be tested, and increased histologic information provided, the advantages presented by this novel chromogenic assay should extend the applicability of tissue-based telomere length assessment in research and clinical settings.

pathology↗

bioRxiv2020

Pervasive promoter hypermethylation of silenced TERT alleles in human cancers

In cancers, maintenance of telomeres often occurs through activation of the catalytic subunit of telomerase, encoded by TERT. Yet, most cancers show only modest levels of telomerase gene expression, even in the context of activating hotspot promoter mutations (C228T and C250T). The role of epigenetic mechanisms, including DNA methylation, in regulating telomerase gene expression in cancer cells is not fully understood. Here, we have carried out the most comprehensive characterization to date of TERT promoter methylation using ultra-deep bisulfite sequencing spanning the CpG island surrounding the core TERT promoter in 96 different human cell lines. In general, we observed that immortalized and cancer cell lines were hypermethylated in a region upstream of the recurrent C228T and C250T TERT promoter mutations, while non-malignant primary cells were comparatively hypomethylated in this region. However, at the allele-level, we generally observe hypermethylation of promoter sequences in cancer cells is associated with repressed expression, and the remaining unmethylated alleles marked with open chromatin are largely responsible for the observed TERT expression in cancer cells. Our findings suggest that hypermethylation of the TERT promoter alleles signals transcriptional repression of those alleles, leading to the attenuation of TERT activation in cancer cells. SIGNIFICANCEHypermethylation of the TERT promoter alleles to attenuate TERT activation in cancer cells may account for the modest activation of TERT expression in most cancers.

cancer biology↗