Biology subjects

Cheng, W.-C.

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

3 recordsLinked to original sources

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↗