Biology subjects

Leung, S. Y.

Publications and source records attributed to Leung, S. Y..

3 recordsLinked to original sources

bioRxiv2024

Divergent lineage trajectories and genetic landscapes in human gastric intestinal metaplasia organoids associated with early neoplastic progression

ObjectiveGastric intestinal metaplasia (IM) is a pre-cancerous stage spanning a morphological spectrum that is poorly represented by human cell line models. We aim to establish and characterize human IM cell models to better understand IM progression along the cancer spectrum. DesignA large human gastric IM organoid (IMO) cohort (n=28) was established, along with normal gastric organoids (n=42) for comparison, and comprehensive multi-omics profiling and functional characterization were performed. ResultsSingle-cell transcriptomes revealed IMO cells spanning a spectrum from hybrid gastric/intestinal to advanced intestinal differentiation, and unveiled lineage trajectories that connected different cycling and quiescent stem and progenitors, highlighting their differences in gastric to IM transition. The hybrid IMO cells showed impaired differentiation potential, high lineage plasticity beyond gastric or intestinal fates, and reactivation of a fetal gene program. Cell populations in gastric IM and cancer tissues were found to be highly similar to those derived from IMOs and exhibited fetal signature. Genomically, IMOs showed an elevated mutation burden, frequent chromosome 20 gain, and epigenetic de-regulation of many intestinal and gastric genes. Functionally, IMOs downregulated FGFR2 and became independent of FGF10 for survival. Several IMOs exhibited a cell-matrix adhesion independent (CMi) subpopulation that displayed chromosome 20 gain but lacked key cancer driver mutations, which could represent the earliest neoplastic precursor of IM-induced gastric cancer. ConclusionsOverall, our IM organoid biobank captured the heterogeneous nature of IM, revealing mechanistic insights on IM pathogenesis and its neoplastic progression, offering an ideal platform for studying early gastric neoplastic transformation and chemoprevention.

cancer biology↗

bioRxiv2024

The somatic mutation landscape of normal gastric epithelium

The landscapes of somatic mutation in normal cells inform on the processes of mutation and selection operative throughout life, permitting insight into normal ageing and the earliest stages of cancer development. Here, by whole-genome sequencing of 238 microdissections from 30 individuals, including 18 with gastric cancer, we elucidate the developmental trajectories of normal and malignant gastric epithelium. We find that gastric glands are units of monoclonal cell populations which accrue [~]28 somatic single nucleotide variants per year, predominantly attributable to endogenous mutational processes. In individuals with gastric cancer, glands often show elevated mutation burdens due to acceleration of mutational processes linked to proliferation and oxidative damage. These hypermutant glands were primarily detected in the gastric antrum and were mostly associated with chronic inflammation and intestinal metaplasia, known cancer risk factors. Unusually for normal cells, gastric epithelial cells often carry recurrent trisomies of specific chromosomes, which are highly enriched in a subset of individuals. Surveying approximately 8,000 gastric glands by targeted sequencing, we found somatic driver mutations in a distinctive repertoire of known cancer genes, including ARID1A, CTNNB1, KDM6A and ARID1B. Their prevalence increases with age to occupy approximately 5% of the gastric epithelial lining by age 60 years. Our findings provide insights into the intrinsic and extrinsic influences on somatic evolution in the gastric epithelium, in healthy, precancerous and malignant states.

genomics↗

bioRxiv2023

XClone: detection of allele-specific subclonal copy number variations from single-cell transcriptomic data

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

bioinformatics↗