Biology subjects

Tan, I.

Publications and source records attributed to Tan, I..

5 recordsLinked to original sources

bioRxiv2025

A Computational Framework for Analysis of cfDNA Fragmentation Profiles

Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized to derive an NDR score, which was subsequently used as a proxy for inferring gene expression. To reduce transcript-to-transcript variability and enhance the clarity of these expression-associated signals, we implement a method for GC-bias correction of cfDNA samples. A computational framework (NDRDiff) was then developed to enable comparative analyses of NDR score profiles across different sample groups. The GC-bias correction preserved the overall trend of the NDR signal while improving the separation of gene expression levels, as demonstrated by comparisons of healthy donor cfDNA samples with matched blood RNA-seq data. Validation on a simulated dataset showed that NDRDiff achieved an area under the precision-recall curve (AUPRC) of 0.916, outperforming a standard t-test (AUPRC of 0.777). When applied to a comparison of healthy donor cfDNA and metastatic colorectal cancer (mCRC) cfDNA, NDRDiff identified 531 differential NDR score (DNS) genes that facilitated clear separation between the two groups. These DNS genes were found to correlate with tumor fraction estimates (down-regulated DNS genes: Pearson R = 0.89, p < 0.05; up-regulated DNS genes: Pearson R = -0.88, p < 0.05) and included CLDN4, BIN2, and IRAG2, which exhibit strong associations with colorectal cancer or blood cell expression signatures. Gene set enrichment analysis further revealed enrichment of colon and other gastrointestinal tissue signatures. Collectively, these findings underscore the potential of NDR-based cfDNA analysis as a minimally invasive tool for monitoring tumor-related molecular features in cancer.

cancer biology↗

bioRxiv2023

Quantification of circulating tumor DNA using deep learning

Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naive methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naive gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.

bioinformatics↗

bioRxiv2019

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Analysis of circulating cell-free DNA (cfDNA) data has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with freely-available bioinformatics pipelines that provide ultra-sensitive detection of single nucleotide variants (SNVs), and reliable identification of copy number variations (CNVs) directly from plasma DNA. In a cohort of 35 BC patients, our approach detected actionable driver and clonal SNVs at low (~0.5%) frequency levels in cfDNA that were concordant (83.3%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.

genomics↗

bioRxiv2017

A Low-Cost Multiplex Biomarker Assay Stratifies Colorectal Cancer Patient Samples into Clinically-Relevant Subtypes

Previously, we classified colorectal cancers (CRCs) into five CRCA subtypes with different prognoses and potential treatment responses, using a 786-gene signature. We merged our subtypes and those described by five other groups into four consensus molecular subtypes (CMS) that are similar to CRCA subtypes. Here we demonstrate the analytical development and application of a custom NanoString platform-based biomarker assay to stratify CRC into subtypes. To reduce costs, we switched from the standard protocol to a custom modified protocol (NanoCRCA) with a high Pearson correlation coefficient (>0.88) between protocols. Technical replicates were highly correlated (>0.96). The assay included a reduced robust 38-gene panel from the 786-gene signature that was selected using an in-laboratory developed computational pipeline of class prediction methods. We applied our NanoCRCA assay to untreated CRCs including fresh-frozen and formalin-fixed paraffin-embedded (FFPE) samples (n=81) with matched microarray or RNA-Seq profiles. We further compared the assay results with CMS classification, different platforms (microarrays/RNA-Seq) and gene-set classifiers (38 and 786 genes). NanoCRCA classified fresh-frozen samples (n=39; not including those showing a mixture of subtypes) into all five CRCA subtypes with overall high concordance across platforms (89.7%) and with CMS subtypes (84.6%), independent of tumour cellularity. This analytical validation of the assay shows the association of subtypes with their known molecular, mutational and clinical characteristics. Overall, our modified NanoCRCA assay with further clinical assessment may facilitate prospective validation of CRC subtypes in clinical trials and beyond.\n\nNovelty and ImpactWe previously identified five gene expression-based CRC subtypes with prognostic and potential predictive differences using a 786-gene signature and microarray platform. Subtype-driven clinical trials require a validated assay suitable for routine clinical use. This study demonstrates, for the first time, how molecular CRCA subtype can be detected using NanoString Technology-based biomarker assay (NanoCRCA) suitable for clinical validation. NanoCRCA is suitable for analysing FFPE samples, and this assay may facilitate patient stratification within clinical trials.

cancer biology↗

bioRxiv2017

Dynamic expression of tRNA-derived small RNAs define cellular states

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have recently emerged as important regulators of protein translation and shown to have diverse biological functions. However, the underlying cellular and molecular mechanisms of tsRNA function in the context of dynamic cell-state transitions remain unclear. Here we report the identification of a set of tsRNAs upregulated in differentiating mouse embryonic stem cells (mESCs). Mechanistic analyses revealed primary functions of tsRNAs in regulating polysome assembly and translation. Notably, interactome studies with differentially-enriched tsRNAs revealed a switch in associations with effector RNPs and target mRNAs in different cell-states. We also demonstrate that a specific pool of tsRNAs can interact with Igf2bp1, an RNA-binding protein, to influence the expression of the pluripotency-promoting factor-c-Myc, thereby providing evidence for tsRNAs in modulating stem cell-states in mESCs. Finally, tsRNA expression analyses in distinct, heterologous cell and tissue models of stem/transformed versus differentiated/normal states reveal that tsRNA-mediated regulation of protein translation may represent a global biological phenomenon associated with cell-state transitions.\n\nOne Sentence SummaryIdentification and functional characterization of tRNA-derived small RNAs (tsRNAs) in cell state switches.

molecular biology↗