scholarly journals Neuroblastoma patient‐derived cultures are enriched for a mesenchymal gene signature and reflect individual drug response

2020 ◽  
Vol 111 (10) ◽  
pp. 3780-3792
Author(s):  
Esther Hee ◽  
Meng Kang Wong ◽  
Sheng Hui Tan ◽  
Zhang’E Choo ◽  
Chik Hong Kuick ◽  
...  
Keyword(s):  
Drug Response ◽  
Gene Signature ◽  
Individual Drug ◽  
Individual Drug Response ◽  
Neuroblastoma Patient

scholarly journals Inferences of Individual Drug Response-Related Long Non-coding RNAs Based on Integrating Multi-omics Data in Breast Cancer

2020 ◽  
Vol 20 ◽  
pp. 128-139
Author(s):  
Hao Cui ◽  
Hanqing Kong ◽  
Fuhui Peng ◽  
Chunjing Wang ◽  
Dandan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Response ◽  
Omics Data ◽  
Individual Drug ◽  
Non Coding Rnas ◽  
Individual Drug Response

scholarly journals Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

2014 ◽  
Vol 8 (1) ◽  
pp. 75 ◽  
Author(s):  
Silvia der Heyde ◽  
Christian Bender ◽  
Frauke Henjes ◽  
Johanna Sonntag ◽  
Ulrike Korf ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Individual Drug ◽  
Individual Drug Response

Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data

2021 ◽  
Vol 13 (603) ◽  
pp. eabf3637
Author(s):  
Maaike van der Lee ◽  
William G. Allard ◽  
Rolf H. A. M. Vossen ◽  
Renée F. Baak-Pablo ◽  
Roberta Menafra ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
Drug Response ◽  
Sequencing Data ◽  
Human Embryonic Kidney Cells ◽  
Continuous Scale ◽  
Long Read ◽  
Tamoxifen Metabolism ◽  
Individual Drug Response ◽  
A Prospective Study

Pharmacogenomics is a key component of personalized medicine that promises safer and more effective drug treatment by individualizing drug choice and dose based on genetic profiles. In clinical practice, genetic biomarkers are used to categorize patients into *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. However, this approach leaves a large part of variability in drug response unexplained. Here, we present a proof-of-concept approach that uses continuous-scale (instead of categorical) assignments to predict enzyme activity. We used full CYP2D6 gene sequences obtained with long-read amplicon-based sequencing and cytochrome P450 (CYP) 2D6–mediated tamoxifen metabolism data from a prospective study of 561 patients with breast cancer to train a neural network. The model explained 79% of interindividual variability in CYP2D6 activity compared to 54% with the conventional *-allele approach, assigned enzyme activities to known alleles with previously reported effects, and predicted the activity of previously uncharacterized combinations of variants. The results were replicated in an independent cohort of tamoxifen-treated patients (model R2 adjusted = 0.66 versus *-allele R2 adjusted = 0.35) and a cohort of patients treated with the CYP2D6 substrate venlafaxine (model R2 adjusted = 0.64 versus *-allele R2 adjusted = 0.55). Human embryonic kidney cells were used to confirm the effect of five genetic variants on metabolism of the CYP2D6 substrate bufuralol in vitro. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP2D6 enzyme activity and could potentially enable more accurate prediction of individual drug response.

scholarly journals CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response

2021 ◽  
Author(s):  
Zhongyang Liu ◽  
Jiale Liu ◽  
Xinyue Liu ◽  
Xun Wang ◽  
Qiaosheng Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Drug Response ◽  
Biomarker Discovery ◽  
Characteristic Curve ◽  
Resistance Mechanism ◽  
Predictive Biomarker ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Cancer Drug

Abstract To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document