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McNamara, D.

Publications and source records attributed to McNamara, D..

4 recordsLinked to original sources

Whole genome sequencing of pre-treatment and post-treatment locally advanced rectal cancer using long and short read technologies

We performed Illumina short-read and nanopore (ONT) whole genome sequencing (WGS) on samples obtained from a 37-year-old patient with locally advanced rectal cancer who did not respond to neoadjuvant chemoradiotherapy (nCRT). Pre-treatment and post-nCRT tumour biopsy, adjacent normal tissue, and matched pre-treatment blood samples were sequenced to identify somatic alterations. We used DNA methylation from ONT WGS to detect changes in the tumour epigenomes compared to adjacent normal tissue. We employed established pipelines for the identification of somatic alterations to assess the concordance of SNV, short indel, copy number and structural variation from the short-read and long-read tumour genome data. Overall, 69.4% and 30.1% SNVs were concordant between technologies in the pre- and post-treatment tumours, respectively. A multinomial logistic regression was performed to further understand discordant SNV calls, highlighting tumour purity, presence in a repetitive region, strand bias, and germline variant allele frequency as factors contributing to discordance. Indel concordance was poor (26.8% and 9.2% in the pre- and post-treatment tumour, respectively). Copy number alteration profiles were highly similar while minimal concordance was reported for structural variants (2.4% and 0.3% for pre- and post-treatment tumours, respectively). Finally, we used the ONT data to detect DNA modifications (5-methylcytosine and 5-hydroxymethylcytosine) between tumour and matched adjacent normal tissue. Based on genome-wide average CpG modification rates, global hypomethylation was observed in tumours compared to adjacent normal tissues. Biologically relevant epigenomic changes related to epithelial-mesenchymal transition were detected in the post-treatment tumour, as well as potential radiation-induced changes in the post-treatment adjacent normal tissue.

genomics↗

High-Resolution Spatial Proteomics Characterises Colorectal Cancer Consensus Molecular Subtypes

BackgroundIdentification of the consensus molecular subtypes (CMS) opened significant potential for understanding the tumor biology and intertumoral heterogeneity of colorectal cancer (CRC). However, molecular subtyping in CRC traditionally relies on bulk transcriptomics, therefore, lacks spatial and single-cell level aspect. MethodsWe constructed tissue microarrays using tumor cores from 222 CRC patients. Arrays were stained and imaged using 54 cell identity and cancer hallmark markers, delivering spatially resolved protein profiles of >2 million cells. RNA sequencing data and CMS classification were also available for these patients. After segmentation of cancer, stromal and immune cells, we investigated intratumoral heterogeneity within CMS subtypes using spatially resolved single-cell protein profiling (>2 million cells). We compared cell types, their spatial organization and their expression of cancer hallmark-related proteins in CMS 1-4 subtypes. ResultsWe revealed tissue atlases illustrating the cell types/states, spatial heterogeneity, cellular neighborhoods, cellular network, and single-cell protein profiles of CMS tumors. CMS1 tumors had more CD3+, CD8+, and PD1+ immune cells that were found in the epithelial layer frequently. CMS1 was also associated with higher levels of metabolic reprogramming markers such as upregulated glycolysis. CMS2 showed immune segregation, reactive stroma patterns and higher levels of apoptotic and proliferative signaling proteins. CMS3 exhibited clustered cancer cells with high RIP3 levels, suggesting a pro-inflammatory microenvironment. CMS4 displayed stromal-centric and immune-evasive tumors characterized by decreased HLA-1 levels and regulatory T-cell exclusion from epithelium. ConclusionWe present a spatial protein atlas of CRC at single-cell resolution and demonstrate novel aspects of CMS tumour structures.

cancer biology↗

An atlas of inter- and intra-tumor heterogeneityof apoptosis competency in colorectal cancertissue at single cell resolution

Cancer cells ability to inhibit apoptosis is key to malignant transformation and limits response to therapy. Here, we performed multiplexed immunofluorescence analysis on tissue microarrays with 373 cores from 168 patients, segmentation of 2.4 million individual cells and quantification of 20 cell lineage and apoptosis proteins. Ordinary differential equation-based modelling of apoptosis sensitivity at single cell resolution was conducted and an atlas of inter- and intra-tumor heterogeneity in apoptosis susceptibility generated. We identified an enrichment for BCL2 in immune, and BAK, SMAC and XIAP in cancer cells. ODE-based modelling at single cell resolution identified an enhanced sensitivity of cancer cells to mitochondrial permeabilization and executioner caspase activation compared to immune and stromal cells, with significant inter- and intra-tumor heterogeneity. However, we did not find increased spatial heterogeneity of apoptosis signaling in cancer cells, suggesting that such heterogeneity is an intrinsic, non-genomic property not increased by the process of malignant transformation.

cancer biology↗

A statistical modelling reveals bi-directional chromatin scanning by RAG in the human TCR-α locus

Rearrangements of germline variable (V), diversity (D), and joining (J) coding gene segments generate a diverse T cell receptor (TCR) repertoire. Two different molecular mechanisms govern a stochastic nature of gene choices by the recombination activating gene (RAG) protein complex. One is diffusion access in which a chromatin loop containing a coding gene bounces back and forth until the loop encounters a distant gene. The other is a linear chromatin scan, which extrudes chromatin loops to assemble a gene pair in a distance-dependent manner. However, the extent to which those two mechanisms underlie V(D)J recombination in human TCRs still remains unclear. Applying statistical modelling to TCR sequence data, we infer the stochastic process of gene choices during this recombination. The inferred usage of V and J gene segments vary from gene to gene. This pattern of gene usage is consistent across individuals, suggesting a fundamental bias in the gene choice. Our modelling shows the dependency of VJ pairing on their linear distance in the TCR- locus; proximal-proximal and distal-distal VJ pairs are more preferred than proximal-distal or distal-proximal gene pairs. These results support bi-directional RAG scanning into proximal and distal sides of the TCR- V gene locus.

genomics↗