Biology subjects

Cheng, W.-C.

Publications and source records attributed to Cheng, W.-C..

14 recordsLinked to original sources

bioRxiv2025

SHLD2 loss is a synthetic vulnerability to Polθ inhibition combined with radiotherapy

DNA polymerase theta (Pol{theta}) plays a crucial role in the repair of DNA double-strand breaks (DSBs) by microhomology-mediated end joining (MMEJ). We previously demonstrated that Pol{theta} inhibition (Pol{theta}i) is an effective and well-tolerated approach to sensitise tumours to radiotherapy (RT). Here, we profiled 54 cancer cell lines and found that Pol{theta}i induces significant radiosensitisation in most models, though with marked variability not explained by indicators of Pol{theta} activity. To pinpoint molecular determinants of radiosensitisation by Pol{theta}i, we performed a CRISPR knockout screen which revealed loss of the TP53BP1/Shieldin pathway component SHLD2 (FAM35A) as a vulnerability to Pol{theta}i combined with RT. We demonstrated that SHLD2 loss not only increases sensitivity to RT alone, but also enhances the radiosensitising effect of Pol{theta}i, both in vitro and in vivo. Importantly, we found that SHLD2 is deleted in a subset of human prostate cancers, often co-occurring with PTEN loss, an adverse prognostic factor. Furthermore, we show that SHLD2-deficient cancer cells are more reliant on Pol{theta} to prevent DSB accumulation and chromosomal instability. In summary, we discovered SHLD2 loss as a novel collateral vulnerability that can be exploited through combined treatment with Pol{theta}i and RT.

cancer biology↗

bioRxiv2024

iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome

Numerous biological processes and diseases are influenced by lipid composition. Advances in lipidomics are elucidating their roles, but analyzing and interpreting lipidomics data at the systems level remain challenging. To address this, we present iLipidome, a method for analyzing lipidomics data in the context of the lipid biosynthetic network, thus accounting for the interdependence of measured lipids. iLipidome enhances statistical power, enables reliable clustering and lipid enrichment analysis, and links lipidomic changes to their genetic origins. We applied iLipidome to investigate mechanisms driving changes in cellular lipidomes following supplementation of docosahexaenoic acid (DHA) and successfully identified the genetic causes of alterations. We further demonstrated how iLipidome can disclose enzyme-substrate specificity and pinpoint prospective glioblastoma therapeutic targets. Finally, iLipidome enabled us to explore underlying mechanisms of cardiovascular disease and could guide the discovery of early lipid biomarkers. Thus, iLipidome can assist researchers studying the essence of lipidomic data and advance the field of lipid biology.

systems biology↗

bioRxiv2023

Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. While proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1, L1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible detectable expression in corresponding normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore the potential of ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 M) ORF1p concentrations in patient plasma samples across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multi-analyte panel, and provides early therapeutic response monitoring in gastric and esophageal cancers. Together, these observations nominate ORF1p as a multi-cancer biomarker with potential utility for disease detection and monitoring. Statement of SignificanceLINE-1 ORF1p transposon protein is pervasively expressed in many cancers and a highly specific biomarker of multiple common, lethal carcinomas and their high-risk precursors in tissue and blood. Ultrasensitive ORF1p assays from as little as 25 L plasma are novel, rapid, cost-effective tools in cancer detection and monitoring.

cancer biology↗

bioRxiv2022

Antimicrobial Susceptibility Test by Surface-enhanced Raman Scattering of Bacterial Metabolites directly from Positive Blood Cultures

Effective management of sepsis requires timely administration of appropriate antibiotics; therefore, a reliable and rapid antimicrobial susceptibility test (AST) is crucial. To meet clinical needs, we developed a novel AST, referred to as SERS-AST, based on the surface-enhanced Raman Scattering (SERS) technology. In this study, we applied SERS-AST to eight most common pathogens causing bacteremia, including Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, E. faecium, Escherichia coli, Enterobacter cloacae, Klebsiella pneumoniae, and Acinetobacter baumannii. Seven different antibiotics were tested, including oxacillin, levofloxacin, vancomycin, ampicillin, cefotaxime, ceftazidime, levofloxacin, and imipenem. SERS-AST determines antibiotic susceptibility of bacteria directly from positive blood cultures based on variations in bacterial SERS signals derived from secreted purines and their derivatives. The whole process could be completed within 4 hours, and the agreement rates between SERS-AST and VITEK 2 results were 96% for Gram-positive bacteria and 97% for Gram-negative bacteria.

microbiology↗

bioRxiv2022

Peptide-based drug predictions for cancer therapy using deep learning

BackgroundTherapeutic drugs used in cancer treatment have ineffectiveness and resistance to drug action problems. Anticancer peptides (ACPs) are selective and toxic to cancer cells and quickly produced. Thus, ACPs can be a satisfactory substitute for therapeutic drugs. We developed AI4ACP, a user-friendly web-server ACP predictor that can predict the anticancer property of query peptides, thus promoting the discovery of peptides with anticancer activity. ResultOur results revealed that the performance of our ACP predictor trained using the new ACP collection was superior to that of the available high-performance ACP predictors. ConclusionsAI4ACP is a user-friendly web-server ACP predictor that can be used to determine whether a query sequence is an ACP. This tool can be beneficial for drug development for cancer treatment. AI4ACP is freely accessible at https://axp.iis.sinica.edu.tw/AI4ACP/

cancer biology↗

bioRxiv2021

Developing an Antiviral Peptides Predictor with Generative Adversarial Network Data Augmentation

MotivationNew antiviral drugs are urgently needed because of emerging viral pathogens increasing severity and drug resistance. Antiviral peptides (AVPs) have multiple antiviral properties and are appealing candidates for antiviral drug development. We developed a sequence-based binary classifier to identify whether an unknown short peptide has AVP activity. We collected AVP sequence data from six existing databases. We used a generative adversarial network to augment the number of AVPs in the positive training dataset and allow our deep convolutional neural network model to train on more data. ResultsOur classifier achieved outstanding performance on the testing dataset compared with other state-of-the-art classifiers. We deployed our trained classifier on a user-friendly web server. Availability and implementationAI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/ Contactcylin@iis.sinica.edu.tw Supplementary informationSupplementary data is also available.

microbiology↗

bioRxiv2020

AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning

MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. ResultsOur results show that our prediction model is superior to the existing AMP predictors. AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contactcylin@iis.sinica.edu.tw

microbiology↗

bioRxiv2020

Persistent broken chromosome inheritance drives genome instability

Persistent DNA damage arising from unrepaired broken chromosomes or telomere loss can promote DNA damage checkpoint adaptation, and cell cycle progression, thereby increasing cell survival but also genome instability. However, the nature and extent of such instability is unclear. We show, using Schizosaccharomyces pombe, that inherited broken chromosomes, arising from failed homologous recombination repair, are subject to cycles of segregation, DNA replication and extensive end-processing, termed here SERPent cycles, by daughter cells, over multiple generations. Following Chk1 loss these post-adaptive cycles continue until extensive processing through inverted repeats promotes annealing, fold-back inversion and a spectrum of chromosomal rearrangements, typically isochromosomes, or chromosome loss, in the resultant population. These findings explain how persistent DNA damage drives widespread genome instability, with implications for punctuated evolution, genetic disease and tumorigenesis. One Sentence SummaryReplication and processing of inherited broken chromosomes drives chromosomal instability.

molecular biology↗

bioRxiv2020

Hypoxia-induced SETX links replication stress with the unfolded protein response

The levels of hypoxia associated with resistance to radiotherapy significantly impact cancer patient prognosis. These levels of hypoxia initiate a unique transcriptional response with the rapid activation of numerous transcription factors in a background of global repression of transcription. Here, we show that the biological response to radiobiological hypoxia includes the induction of the DNA/RNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. SETX plays a key role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we show that the mechanism of SETX induction is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to radiobiological hypoxia, which includes both a replication stress dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways.

cancer biology↗

bioRxiv2018

Low-Density Lipoprotein Receptor-Mediated Lipidome-Transcriptome Reprogramming Impulses to Cisplatin Insensitivity

Platinum-based therapy remains the cornerstone for cancer patient management; however, its efficacy varies. Theis study demonstrated the differential expressions of low-density lipoprotein receptor (LDLR) in subtypes of epithelial ovarian carcinoma (EOC) determines cisplatin sensitivity. Its sensitive in serous EOCs (low LDLR), where insensitive in endometrioid and clear cell EOCs (high LDLR). Meanwhile, knocked-down or overexpressed LDLR in EOC could reversed the chemosensitivity pattern both in vitro and in vivo. Mechanistic dissection with transcriptome vs. lipidome trans-omics analyses elucidated the LDLR[->]LPC (Lyso-PhosphotidylCholine)[->]FAM83B (phospholipase-related)[->]FGFRs (cisplatin sensitivity and phospholipase-related) regulatory axis in cisplatin insensitivity. Implementing LPC-liposome encapsulated cisplatin could facilitate DNA-adduct formation via lipid droplets (LDs) delivery. Furthermore, Bioinformatics analyses found that the LDL/R[->]LD homeostasis alteration is critical for therapeutic prognosis. Lastly, using LPC-liposome-cisplatin improved cisplatin sensitivities in gastric cancer, renal cell carcinoma, hepatocellular carcinoma, cholangiocarcinoma, and pancreatic adenocarcinoma cells. In conclusion, this report discovered a LDL/R-reprogrammed transcriptome-lipidome network, by which impulses platinum insensitivity and disease outcome. The drug specific lipidome for liposome manufacture might be an efficienct pharmaceutics strategy for chemoagents.\n\nSignificanceLDLR reprograms cellular lipidome and transcriptome profiles to determines chemotherapy therapeutic efficacy. The LDLR-reduced LPC abundance disturbs phospholipids homeostasis of Lands cycle in LD, by which attenuates intracellular platinum transportation for DNA-adduct formation. Targeting LDLR-LD-lipidome with LPC-liposome-platinum could boost therapeutic efficacy for insensitivity.

cancer biology↗

bioRxiv2018

AICAR induces apoptosis and inhibits migration of prostate cancer cells through an AMPK/mTOR-dependent pathway

AICAR (5-aminoimidazole-4-carbox-amide-1-{beta}-D-ribofuranoside), an AMP-activated protein kinase (AMPK) agonist, has demonstrated antitumor activities for several types of cancers. However, the activity of AICAR on the cell growth and metastasis of prostate cancer has not been extensively studied. Herein we examine the effects of AICAR on the cell growth and metastasis of prostate cancer cells, 22RV1 cells. Cell growth was performed by MTT assay and soft agar assay. Cell apoptosis was examined by Annexin V/PI staining and PARP cleavage Western blot. Cell migration was evaluated by wound-healing assay. The expression of EMT-related protein and the activity of the AMPK/ mTOR-dependent pathway were analyzed by Western blot. In addition, we also tested the effect of AICAR on the chemosensitivity to docetaxel using MTT assay. Our results indicated that AICAR inhibits cell growth, induces apoptosis, attenuates TGF-{beta}-induced cell migration and EMT-related protein expression, and enhances the chemosensitivity to docetaxel through regulating the AMPK/mTOR-dependent pathway. Collectively, these findings support AICAR as a potential therapeutic agent for the treatment of prostate cancer.

cancer biology↗

bioRxiv2018

Hypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine-resistance in breast cancer

Tumor hypoxia is associated with poor patient outcomes in estrogen receptor- (ER) positive breast cancer. Hypoxia is known to affect tumor growth by reprogramming metabolism and regulating amino acid (AA) uptake. Here we show that the glutamine transporter, SNAT2, is the AA transporter most frequently induced by hypoxia in breast cancer and it is regulated by HIF1 both in-vitro and in-vivo in xenografts. SNAT2 induction in MCF7 cells was also regulated by ER but it became predominantly a HIF-1-dependent gene under hypoxia. Relevant to this, binding sites for both HIF-1 and ER overlap in SNAT2s cis-regulatory elements. In addition, the downregulation of SNAT2 by the ER antagonist fulvestrant was reverted in hypoxia.\n\nOverexpression of SNAT2 in-vitro to recapitulate the levels induced by hypoxia caused enhanced growth, particularly after ER inhibition, in hypoxia, or when glutamine levels were low. SNAT2 upregulation in-vivo caused complete resistance to anti-estrogen and, partially, anti-VEGF therapies. Finally, high SNAT2 expression levels correlate with HIF-1 and worse outcome in patients given anti-estrogen therapy. Our findings show a switch in regulation of SNAT2 between ER and HIF-1, leading to endocrine resistance in hypoxia. Development of drugs targeting SNAT2 may be of value for a subset of hormone-resistant breast cancer.

cancer biology↗

bioRxiv2017

Nanopore Sequencing Reveals High-Resolution Structural Variation in the Cancer Genome

Acquired genomic structural variants (SVs) are major hallmarks of the cancer genome. Their complexity has been challenging to reconstruct from short-read sequencing data. Here, we exploit the long-read sequencing capability of the nanopore platform using our customized pipeline, Picky, to reveal SVs of diverse architecture in a breast cancer model. From modest sequencing coverage, we identified the full spectrum of SVs with superior specificity and sensitivity relative to short-read analyses and uncovered repetitive DNA as the major source of variation. Examination of the genome-wide breakpoints at nucleotide-resolution uncovered micro-insertions as the common structural features associated with SVs. Breakpoint density across the genome is associated with propensity for inter-chromosomal connectivity and transcriptional regulation. Furthermore, an over-representation of reciprocal translocations from chromosomal double-crossovers was observed through phased SVs. The comprehensive characterization of SVs using the robust long-read sequencing approach in cancer cohorts will facilitate strategies to monitor genome stability during tumor evolution and improve therapeutic intervention.

genomics↗

bioRxiv2017

sigQC: A procedural approach for standardising the evaluation of gene signatures

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

bioinformatics↗