Biology subjects

Hardy, C.

Publications and source records attributed to Hardy, C..

4 recordsLinked to original sources

bioRxiv2021

The longitudinal dynamics and natural history of clonal haematopoiesis

Human cells acquire somatic mutations throughout life, some of which can drive clonal expansion. Such expansions are frequent in the haematopoietic system of healthy individuals and have been termed clonal haematopoiesis (CH). While CH predisposes to myeloid neoplasia and other diseases, we have limited understanding of how and when CH develops, what factors govern its behaviour, how it interacts with ageing and how these variables relate to malignant progression. Here, we track 697 CH clones from 385 individuals aged 55 or older over a median of 13 years. We find that 92.4% of clones expanded at a stable exponential rate over the study period, with different mutations driving substantially different growth rates, ranging from 5% (DNMT3A, TP53) to over 50%/yr (SRSF2-P95H). Growth rates of clones with the same mutation differed by approximately +/-5%/yr, proportionately impacting "slow" drivers more substantially. By combining our time-series data with phylogenetic analysis of 1,731 whole genome-sequenced haematopoietic colonies from 7 older individuals, we reveal distinct patterns of lifelong clonal behaviour. DNMT3A-mutant clones preferentially expanded early in life and displayed slower growth in old age, in the context of an increasingly competitive oligoclonal landscape. By contrast, splicing gene mutations only drove expansion later in life, while growth of TET2-mutant clones showed minimal age-dependency. Finally, we show that mutations driving faster clonal growth carry a higher risk of malignant progression. Our findings characterise the lifelong natural history of CH and give fundamental insights into the interactions between somatic mutation, ageing and clonal selection.

cancer biology↗

bioRxiv2021

Immunohistochemical assays for bladder cancer molecular subtyping: Optimizing parsimony and performance using Lund taxonomy

Transcriptomic and proteomic profiling reliably classifies bladder cancers into luminal and basal molecular subtypes. Based on their prognostic and predictive associations, these subtypes may improve clinical management of bladder cancers. However, the complexity of published subtyping algorithms has limited their translation into practice. Here we optimize and validate compact subtyping algorithms based on the Lund taxonomy. We reanalyzed immunohistochemistry (IHC) expression data of muscle-invasive bladder cancer samples from Lund 2017 (n=193) and 2012 (n=76) cohorts. We characterized and quantified IHC expression patterns, and determined the simplest, most accurate decision tree models to identify subtypes. We tested the utility of a previously published algorithm using routine antibody assays commonly available in surgical pathology laboratories (GATA3, KRT5 and p16) to identify basal/luminal subtypes and to distinguish between luminal subtypes, Urothelial-Like (Uro) and Genomically Unstable (GU). We determined the dominant decision tree classifiers using four-fold cross-validation with separate uniformly distributed train (75%) and validation (25%) sets. Using the three-antibody algorithm resulted in 86-95% accuracy across training and validation sets for identifying basal/luminal subtypes, and 67-86% accuracy for basal/Uro/GU subtypes. Although antibody assays for KRT14 and RB1 are not routinely used in pathology practice, these features achieved the simplest and most accurate models to identify basal/luminal and Uro/GU/basal subtypes, achieving 93-96% and 85-86% accuracies, respectively. When translated to a more complex model using eight antibody assays, accuracy was comparable to simplified models, with 86% (train) and 82% (validation). We conclude that a simple immunohistochemical classifier can accurately identify luminal (Uro, GU) and basal subtypes and pave the way for clinical implementation.

pathology↗

bioRxiv2021

Sexual dimorphism in outcomes of non-muscle invasive bladder cancer: a role of CD163+ M2 macrophages, B cells and PD-L1 immune checkpoint.

Non-muscle invasive bladder cancer (NMIBC) is significantly more common in men than women. However, female patients with NMIBC often present with more aggressive disease and do not respond as well to immunotherapy treatments. We hypothesized that sexual dimorphism in the tumor immune microenvironment (TIME) may contribute to the inferior clinical outcomes observed in female patients. To test this hypothesis, we interrogated the expression patterns of genes associated with specific immune cell types and immune regulatory pathways using tumor whole transcriptome profiles from male (n=357) and female (n=103) patients with NMIBC. High-grade tumors from female patients exhibited significantly increased expression of CD40, CTLA4, PDCD1, LAG3 and ICOS immune checkpoint genes. Based on the significant differences in expression profiles of these genes and the cell types that most commonly express these in the TIME, we evaluated the density and spatial distribution of CD8+Ki67+ (activated cytotoxic T cells), FoxP3+ (regulatory T cells), CD103+ (tissue resident T cells), CD163+ (M2-like tumor associated macrophages), CD79a+ (B cells), PD-L1+ (Programmed-Death Ligand-1) and PD-1+ cells using multiplexed immunofluorescence in an independent cohort of 332 patient tumors on a tissue microarray (n=259 males and n=73 females). Tumors from female patients showed significantly higher infiltration of CD163+ macrophages and PD-L1+ cells compared to tumors from male patients. Notably, increased infiltration of CD163+ macrophages and CD79a+ B cells independently associated with decreased recurrence free survival. Not only do these results have the potential to inform the rational utilization of immunomodulatory therapies based on the TIME of both male and female patients with NMIBC, these novel findings highlight the necessity of considering sexual dimorphism in the design of future immunotherapy trials.

immunology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗