Biology subjects

Tan, I. B. H.

Publications and source records attributed to Tan, I. B. H..

4 recordsLinked to original sources

bioRxiv2026

Accessible spatial host-microbe profiling in tumour tissues

The ability of current spatial transcriptomics platforms to sensitively and specifically detect tissue-resident microbes alongside the whole host transcriptome remains limited. Here we present HOst MicrobE Spatial-seq (HOMES-seq), a Visium-based workflow for joint spatial profiling of microbial species and the host transcriptome in formalin-fixed paraffin-embedded (FFPE) colorectal tumours. HOMES-seq incorporates an analytical framework to distinguish contaminant-derived signals from bona fide tissue-resident microbes while improving detection sensitivity and reducing sequencing costs relative to existing approaches.

microbiology↗

bioRxiv2026

Systematic benchmarking of multi-modal approaches for tumor-naive ctDNA detection and quantification

Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.

bioinformatics↗

bioRxiv2025

Polygenic Risk Scores Across Genomic Platforms for Reliable Breast Cancer Risk Stratification

PurposeWe evaluated differences in a 313-variant breast cancer polygenic risk score (PRS313) across genomic platforms and their impact on risk stratification. MethodsWe compared PRS313 derived from genotyping arrays (Global Screening Array [GSA], OncoArray-500K [OncoArray], Global Diversity Array [GDA], custom Axiom_PrecipV1 array [ThermoFisher]) and low-coverage genome sequencing (lc-WGS) in 2 cell lines and 92 individuals. Probes were designed for all variants on ThermoFisher (success rate: 259/313). Sanger sequencing was performed to profile indels. Concordance of high-risk classification (PRSscore>0.6) across platforms was assessed using Kappa statistics. ResultsPRS313-lc-WGS was identical in the 4 cell line repeats. In saliva samples, indel concordance with Sanger sequencing varied widely (Kappa: 0.007-1.000). PRS313-ThermoFisher was predictable from other platforms using linear models, despite systematic differences. Greater agreement was observed between arrays with high imputation overlap (e.g., GDA[~]GSA slope=0.986). Pre-calibration agreement in high-risk classification was moderate (Fleiss Kappa=0.552) and improved post-calibration (Kappa=0.650). Arrays with similar designs showed higher pre-calibration agreement (Kappa=0.745). Calibration narrowed high-risk proportions from 4-45% to 15-21% -28% were high-risk by any platform, while 8% were high-risk across all five. ConclusionPlatform-specific biases affect PRS interpretation. Calibration enhances consistency in identifying high-risk individuals. STATEMENT OF SIGNIFICANCEThis study compares the performance of a validated 313-variant breast cancer polygenic risk score across platforms, revealing systematic biases in risk stratification and raising concerns about including inconsistent indels in the model.

genetics↗

bioRxiv2024

Modelling oxaliplatin resistance in colorectal cancer reveals a SERPINE1-based gene signature (RESIST-M) and therapeutic strategies for pro-metastatic CMS4 subtype

Drug resistance and distant metastases are major contributors to mortality in colorectal cancer (CRC). Here we investigate mechanisms underlying acquired resistance to oxaliplatin, a first-line, standard-of-care CRC treatment. We generated oxaliplatin-resistant CRC tumor cells with clinically relevant dosing regimen, which displayed enhanced metastatic potential. Transcriptomic and phenotypic analyses revealed a critical function for cholesterol biogenesis in modulating TGF-{beta} signaling activity, which in turn regulates SERPINE1 expression, a gene we identified as a key player in promoting drug resistance and metastasis. Additionally, we uncovered a SERPINE1-associated nine-gene expression signature, RESIST-M, that can predict overall and relapse-free survival (RFS) in clinical cohorts and is able to stratify patients into CMS4/iCMS3-fibrotic CRC-subtypes, underscoring its clinical utility. Using mouse tumor models, we provide further evidence that targeting SERPINE1 and cholesterol biogenesis can be viable approaches to re-sensitize the resistant pro-metastatic CRC cells to oxaliplatin. This study not only elucidates the molecular underpinnings of drug resistance and metastasis in primary CRC, but also offers prognostic and therapeutic strategies to guide clinical management of the disease. SignificanceThis study reveals critical resources and insights on oxaliplatin resistance and metastasis in CRC via a novel TGF-{beta} cholesterol axis. We generated improved oxaliplatin-resistant models that enabled identification of a prognostic SERPINE1-based gene signature to predict oxaliplatin resistance-induced metastasis in CRC. This gene signature derived from our models showed that the models can mimic CMS-4/iCMS-fibrotic-like metastatic CRC patients. We validated therapeutic candidates targeting CMS-4/iCMS-fibrotic-like metastatic CRC cells which can reverse drug resistance and metastasis.

cancer biology↗