Biology subjects

Ng, C. K. Y.

Publications and source records attributed to Ng, C. K. Y..

5 recordsLinked to original sources

bioRxiv2026

Comprehensive characterization of genomic, transcriptomic and epigenomic artifacts introduced in formalin-fixed, paraffin-embedded tissues.

Genomic, transcriptomic and epigenomic characterization has accelerated the discovery of clinically-relevant alterations in cancer, predominantly using fresh frozen (FF) specimens. However, clinical molecular pathology laboratories prefer formalin-fixed paraffin-embedded (FFPE) methods, known to introduce artifacts at the nucleic acid level, over fresh frozen methods. Extending the multi-platform analysis to FFPE specimens for comprehensive clinical molecular diagnosis requires a thorough understanding of the consequence of formalin-fixation. We present a detailed multi-platform characterization of FFPE preservation using paired FF specimens as the 'gold standard'. DNA and RNA were obtained from 38 patients across 6 cancer types using a FFPE optimized co-isolation. The impact of FFPE on exome sequencing was dependent on filtering, where a minimum coverage or supporting read filter can mitigate FFPE-specific false positives. Copy number alterations, MSI assessment, mutational signatures, and DNA methylation were comparable between FFPE and FF. FFPE biases in RNA expression can be overcome when using biology-relevant genes and we describe a novel consequence of FFPE on miRNA species diversity. Collectively, this data provides a broad view of FFPE artifact and offers best practices for overcome these biases.

bioinformatics↗

bioRxiv2026

High regeneration-associated stress defines a distinct HCC subgroup with therapeutically exploitable vulnerabilities

Background: To date, no precision oncology approach has been established for HCC. Despite the diverse underlying causes, HCC development exhibits a uniform pathophysiology characterised by chronic hyper-proliferation, resulting from hepatocyte apoptosis and compensatory liver regeneration. This chronic hyper-proliferative pressure, termed regeneration stress, drives genomic instability during HCC onset, yet its therapeutic potential remains poorly explored. This study aimed to identify targetable vulnerabilities tied to regeneration stress and establish clinically applicable markers for treatment stratification. Methods: Weighted gene co-expression network analysis (WGCNA) was applied on external bulk RNA-seq datasets to define a LIVer REgeneration Stress Signature (LIVRESS). The signature was functionally validated using HCC patient-derived organoids (HCC-Org), and vulnerabilities were mapped using mid-throughput drug screening, single-molecule and single-cell assays, and multi-omic integration. Results: High LIVRESS scores, characterised by enrichment in replication, mitotic and DNA damage repair pathways, identified a subset of HCC patients with aggressive disease and poorer survival across aetiologies. HCC-Org with high LIVRESS scores displayed exquisite sensitivity to multiple inhibitors of the checkpoint kinase ATR. Although HCC-Org models exhibited a baseline reduction in replication fork speed, sensitivity to ATR inhibitor (ATRi) was decoupled from replication fork dynamics and rather linked to intrinsic mitotic instability. ATR inhibition triggers mitotic failure and apoptosis in LIVRESSHigh HCC-Org. This killing effect was significantly potentiated by combining ATRi with PARPi or WEE1i. Multi-omic integration identified KPNA2 as a surrogate biomarker of ATRi sensitivity. Conclusion: Our findings demonstrate that a subset of HCC-Org, characterised by high liver regeneration-associated stress, is vulnerable to ATRi-based therapies. By focusing on a comprehensive regenerative stress model, we establish a framework to stratify HCC patients and implement biomarker-driven, ATR-based therapies for HCC patients with advanced disease. Impact and implications: Regeneration stress is a key factor that drives genomic instability in HCC, providing a basis for the LIVRESS to identify patients dependent on ATR-mediated checkpoints. These findings reveal a conceptual shift for researchers and trialists: ATRi efficacy is decoupled from replication fork dynamics and instead leverages mitotic fragility. Practically, the LIVRESS and its IHC surrogate marker (KPNA2) offer a scalable roadmap for physicians to improve patient stratification in ATRi-based precision oncology trials. While requiring prospective validation, these results pave the way toward biomarker-driven therapies for advanced HCC.

cancer biology↗

bioRxiv2026

What we miss shapes what we protect: sea turtles as a case study of grey literature and language bias in an evidence synthesis

Evidence-based approaches are increasingly advocated and applied in conservation to provide reliable recommendations for management and policymaking. Although substantial efforts have been undertaken to generate ecological knowledge across languages and publication types, many syntheses and meta-analyses fail to include relevant literature sources; drawing into question their reliability. To assess the extent of biases resulting from ignoring grey literature and non-English publications, we reviewed and synthesised movement and migratory connectivity data for green turtles across Southeast Asia from multiple literature types and eight languages. We identified substantial losses in connectivity information, with at least half of all locations losing more than 88% of their connections, and shifts in demographic representation and sampling techniques when grey literature was excluded from the evidence base. The exclusion of non-English literature had a comparatively smaller impact. The regional connectivity network generated through this study highlights the need for holistic reviews to develop evidence bases to underpin transboundary management of migratory species. Ignoring grey literature and, potentially, regional languages in the development may lead to misallocation of limited management resources. Holistic reviews can further support the establishment of Important Areas for migratory species and inform how we meet and report on global biodiversity commitments.

ecology↗

bioRxiv2021

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments. Author summaryMulti-omics approaches to cancer subtyping can provide more insights into molecular changes in tumors compared to single-omics approaches. However, most multi-omics clustering methods do not take into account that gene products interact, for example, as parts of protein complexes or signaling networks. Here we present bnClustOmics, a Bayesian network mixture model for unsupervised clustering of multi-omics data, which can represent dependencies among molecular changes of various omics types explicitly. Unlike other approaches that use data from public interaction databases as ground truth, bnClustOmics learns the dependencies between genes from the analyzed multi-omics dataset. At the same time, our approach can also account for prior knowledge from public interaction databases and use it to guide network learning without losing the ability to learn new dependencies. We applied bnClustOmics to a multi-omics HCC dataset and identified three subtypes similar to those identified in other HCC studies. The cluster-specific networks learned by bnClustOmics revealed additional insights into the molecular characterization of the discovered subgroups and highlighted the changes in signaling networks leading to distinct HCC phenotypes.

systems biology↗

bioRxiv2018

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we whole-exome sequenced (WES) three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA where 82% of the somatic variants were detected reliably, only 36%-78% were found consistently in technical replicate pairs. Overall 34%-80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable.

genomics↗