Biology subjects

Lawlor, R.

Publications and source records attributed to Lawlor, R..

3 recordsLinked to original sources

bioRxiv2024

Clonal evolutionary analysis reveals patterns of malignant transformation in pancreatic cancer from Intraductal Papillary Mucinous IPMN Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are critical precursors to pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer due to late detection and rapid progression. Using multi-region whole-genome and transcriptome sequencing, we traced the evolution of PDAC from IPMN, constructing detailed phylogenetic trees to provide insights into subclonal architectures and progression pathways. Our analysis identified two distinct evolutionary trajectories: one driven by a single ancestral clone, and another involving multiple independent ancestral clones, potentially influencing the timing and nature of PDAC onset. We further explored the roles of mutational signatures and structural variants (SVs) in promoting clonal evolution. Complementing these genomic findings, our transcriptomic analysis revealed unique gene expression profiles and variations in the immune landscape, correlating with the different progression stages from IPMN to PDAC. These insights reveal the complex molecular dynamics of IPMN progression to PDAC, highlighting the need to refine early detection and treatment strategies.

cancer biology↗

bioRxiv2020

A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer

Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To-date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. Here, we complemented a classical new PC GWAS (1D) with spatial autocorrelation analysis (2D) and Hi-C maps (3D) to gain additional insight into the inherited basis of PC. In-silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. We replicated 17/40 previous PC-GWAS hits and identified novel variants with potential biological functions. The spatial autocorrelation approach prioritized low MAF variants not detected by GWAS. These were further expanded via 3D interactions to 54 target regions with high functional relevance. This multi-step strategy, combined with an in-depth in-silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.

genetics↗

bioRxiv2017

Revealing unidentified heterogeneity in different epithelial cancers using heterocellular subtype classification

Cancers are currently diagnosed, categorised, and treated based on their tissue of origin. However, how different cellular compartments of tissues (e.g., epithelial, immune and stem cells) are similar across cancer types is unknown. Here we used colorectal cancer subtypes and their signatures representing different colonic crypt cell types as surrogates to classify different epithelial cancers into five heterotypic cellular (heterocellular) subtypes. The stem-like and inflammatory heterocellular subtypes are ubiquitous across epithelial cancers so capture intrinsic, tissue-independent properties. Conversely, well-differentiated/specialized goblet-like/enterocyte heterocellular subtypes differ across cancer types due to their colorectum-specific genes. The transit-amplifying heterocellular subtype shows a dynamic range of cellular differentiation with shared common pathways (Wnt, FGFR) in certain cancer types. Importantly, this approach revealed previously unrecognised heterogeneity in pancreatic, breast, microsatellite-instability enriched and KRAS mutation-dependent cancers. Immune cell-type differences are common and useful for patient stratification for immunotherapy. This unique approach identifies cell type-dependent but tissue-independent heterogeneity in different cancers for precision medicine.

bioinformatics↗