Wanjuan Yang
A5066108468- Computational Drug Discovery Methods
- Biosimilars and Bioanalytical Methods
- Health Systems, Economic Evaluations, Quality of Life
- Cancer Genomics and Diagnostics
- Advanced Breast Cancer Therapies
- Gene expression and cancer classification
- Mathematical Biology Tumor Growth
- Bioinformatics and Genomic Networks
- Cancer Cells and Metastasis
- Cell Image Analysis Techniques
- CAR-T cell therapy research
- Marine and fisheries research
- CRISPR and Genetic Engineering
- Fish Ecology and Management Studies
- Cancer Research and Treatments
- Statistical Methods in Clinical Trials
- RNA modifications and cancer
- Genetics, Bioinformatics, and Biomedical Research
- Lung Cancer Treatments and Mutations
- Diverse Aspects of Tourism Research
- Medical Research and Treatments
- RNA and protein synthesis mechanisms
- Protein Degradation and Inhibitors
- 3D Printing in Biomedical Research
- Fish Biology and Ecology Studies
Wellcome Sanger Institute
2012-2024
Hunan University of Arts and Science
2022
Wuhan Engineering Science & Technology Institute
2014
Howard Hughes Medical Institute
2012
Alterations in cancer genomes strongly influence clinical responses to treatment and many instances are potent biomarkers for response drugs. The Genomics of Drug Sensitivity Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource information on drug sensitivity cells molecular markers response. Data freely available without restriction. GDSC currently contains data almost 75 000 experiments, describing 138 anticancer drugs across 700 cell lines. To identify response,...
Abstract Combinations of anti-cancer drugs can overcome resistance and provide new treatments 1,2 . The number possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active the tissues molecular contexts in which they are most effective accelerate development combination treatments. Here we evaluate potency efficacy 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast,...
In vitro cancer cell cultures are facile experimental models used widely for research and drug development. Many lines available efforts ongoing to derive new representing the histopathological molecular diversity of tumours. Cell have been generated by multiple laboratories over decades consequently their annotation is incomplete inconsistent. Furthermore, relationships between many patient-matched derivative lost, accessing information datasets time-consuming difficult. Here, we describe...
CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data hundreds of highly annotated genes required for fitness prioritize novel oncology currently allows users investigate the effect 18 009 tested across 323 models. Through interactive interfaces, can by selecting specific gene, model or...
Abstract Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible is vast be context-specific. Systematic screens identify clinically relevant, actionable in defined patient subtypes. We present data 109 anticancer from AstraZeneca's oncology small molecule portfolio screened 755 pan-cancer cell lines. Combinations were a 7 × concentration matrix, with more than 4 million measurements sensitivity, producing an...
High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by summary statistic from best-fit dose-response curve, whilst neglecting uncertainty curve fit potential variability in raw readouts. Here, we model experimental variance using Gaussian Processes, subsequently, leverage estimates associated biomarkers with new Bayesian framework. Applied vitro...
Abstract The Genomic of Drug Sensitivity in Cancer (GDSC; www.cancerRxgene.org) resource facilitates development targeted cancer therapies through pre-clinical identification therapeutic biomarkers. GDSC is the largest public for information on drug sensitivity cells and links these data to extensive genomic identify molecular features that influence anticancer response. There compelling evidence alterations genomes strongly clinical responses therapies. are several examples where changes...
<p>Supplementary Table 9 shows the top 50 combinations (all cancer types) using different % response scoring thresholds</p>
<p>Combination activity of selumetinib plus venetoclax or AZD5991 in AML. <b>A</b> and <b>B,</b> Combo Emax versus HSA scores 19 AML cell lines exposed to combined with (a) (b) AZD5991. <b>C</b> <b>D,</b> NOMO1 growth inhibition excess the combination (c) (d) <b>E</b> <b>F,</b> Western blot analysis for apoptosis markers cells following time course treatment (300 nmol/L) (e) (f) (100 nmol/L). <b>G,</b>...
<p>Supplementary Table 1 lists cell lines used in this study</p>
<p>Supplementary Table 6 shows combination-cancer type pairs with selective activity</p>
<p>Supplementary Table 13 shows significant biomarkers and emergent biomarkers</p>
<p>Supplementary Table 15 shows biomarkers associated with top 5 enriched pathways for each drug combination category used in this study (CD = cell death, CS signaling)</p>
<p>Supplementary Table 14 shows the top 100 significant emergent biomarkers</p>
<p>Capivasertib (AZD5363) plus AZD5991 combination activity in endometrial cell lines. <b>A,</b> Screening results of combo Emax versus HSA lines treated with AZD5363 AZD5991. Cell high are red. <b>B,</b> Representative growth inhibition and excess matrix plots AN3CA cells. <b>C,</b> Matrix plot measuring apoptosis at indicated doses for 6 hours AN3-CA <b>D,</b> showing viability cells pretreated DMSO or QVD (caspase inhibitor) 16 prior...
<p>AZD2811 plus venetoclax combination in DLBCL. <b>A,</b> Combo Emax versus HSA 25 B-cell NHL cell lines including 11 DLBCL lines. Cell with high activity (combo > 0.5 and 0.1) are red. <b>B,</b> Growth inhibition excess matrices line WSUDLCL2. <b>C,</b> Western blot analysis for cleaved PARP WSUDLCL2 cells treated AZD2811 or alone combination. <b>D,</b> Matrix plots indicating (measured by growth inhibition) pretreated pan caspase...
<p>Supplementary Table 4 shows combination-cancer type pairs with less than 10 percent responder cell lines</p>
<p>Supplementary Table 17 shows large effect size and significant Bliss combo Emax biomarkers for selected top hits</p>
<p>Supplementary Table 1 lists cell lines used in this study</p>
<p>Supplementary Table 12 shows the number of top combinations:cancer type pairs hits in each combination drug category</p>