ライフサイエンスコーパス: drug response

1 determining the sensitivity of cells to the drug response.

2 n and phenotypic variability, cell fate, and drug response.

3 ning droplets with potential implications in drug response.

4 ls, suggesting a role for Notch signaling in drug response.

5 is a crucial component in predicting patient drug response.

6 determine tumor growth, patient survival and drug response.

7 vax CQR by a cross of parasites differing in drug response.

8 f toxic metabolites which can interfere with drug response.

9 ylome analysis to understand determinants of drug response.

10 nting for such interindividual variations in drug response.

11 anscriptomic, and phenotypic data related to drug response.

12 es profound insights into leukemogenesis and drug response.

13 explaining up to 83% of the variance in the drug response.

14 A receptors could lead to altered or adverse drug response.

15 entify molecular markers of antihypertensive drug response.

16 n signaling that help explain differences in drug response.

17 umor organoids correlates with primary tumor drug response.

18 inappropriately tailored in vitro assays of drug response.

19 uces susceptibility to develop a cardiotoxic drug response.

20 ods were compared for prediction accuracy of drug response.

21 c variation plays a role in differential CAE drug response.

22 tive marker for aggressiveness and effective drug response.

23 increasing placebo response, not decreasing drug response.

24 onfirming its predictive ability for in vivo drug response.

25 single gene is inadequate as a predictor of drug response.

26 of specific oncogene expression patterns on drug response.

27 evelop machine learning models predictive of drug response.

28 ell-type specific suppression of an adaptive drug response.

29 AD-a1% as sensitive parameters in predicting drug response.

30 istration, and a massive parallel testing of drug response.

31 or studying tumor progression, invasion, and drug response.

32 alcoholic fatty liver disease (NAFLD) alters drug response.

33 he importance of tissue lineage in mediating drug response.

34 ial genetic landscape in rMM associated with drug response.

35 ker and identified a brain region predicting drug response.

36 rtance of different data types in predicting drug response.

37 , learn biological mechanisms underlying the drug response.

38 ferent types of omics profiles for assessing drug response.

39 ene expression is a good predictor of cancer drug response.

40 d approach for translating in vitro to human drug response.

41 gens, based on their early positive/negative drug response.

42 investigated how SHOC2 impact leukemic cells drug response.

43 te understanding of the mechanisms governing drug response.

44 significantly govern cancer progression and drug response.

45 expression and subsequent drug variation in drug response.

46 tant role in the differences found in the 3D drug response.

47 highly heterogeneous with poor prognosis and drug response.

48 titative platform for measuring whole animal drug response.

49 evidence for CRC proteomic subtype-specific drug responses.

50 idate transcription factors in NAc regulates drug responses.

51 Ps reducing, and BP-like sequences allowing, drug responses.

52 r characterized tumorigenesis and anticancer drug responses.

53 on plays critical roles in tumorigenesis and drug responses.

54 in a given cancer driver gene elicit similar drug responses.

55 en surveying a large database of anti-cancer drug responses.

56 FGFR1 and HER2 or PDGFRalpha led to enhanced drug responses.

57 es was exhibited by stratifying patients for drug responses.

58 effects of the microenvironment on cellular drug responses.

59 mmune microenvironments, transcriptomes, and drug responses.

60 s from complex bulk sequence data to predict drug responses.

61 lectively influences receptor signalling and drug responses.

62 versely, UM-SCC-47 exhibited a more constant drug response across culture conditions.

63 genes most associated with the variation of drug response across different individuals, based on gen

64 These findings suggest that variable drug responses among cells are not merely experimental a

65 discovering a brain region predicting active drug response and demonstrating the adverse effect of ac

66 t stratification of patients), prediction of drug response and drug synergy for individual tumors (tr

67 credential changes in CTCs as biomarkers of drug response and facilitating future studies to underst

68 ment provides insight into the complexity of drug response and grounds for a more objective rationale

69 spho-flow (PF) cytometric profiling to study drug response and identify predictive biomarkers in acut

70 reveals complex gene networks which control drug response and illustrates how such data can add subs

71 of gene-environment interactions relevant to drug response and immunity, and we highlight how such im

72 While large databases of drug response and molecular profiles of preclinical in-v

73 Thus, we applied this approach to evaluate drug response and observed that relationships between co

74 metinib illustrated a time-course of initial drug response and persistence, followed by the developme

75 combination potentiates PIK3CA-mutant tumor drug response and reduces the metastatic lesion size.

76 onents are therefore essential to understand drug response and resistance mechanisms.

77 ferent omics to identify novel biomarkers of drug response and suggests VASP as a potential determina

78 promise for the development of biomarkers of drug response and the design of new therapeutic options,

79 sentations to predict disease comorbidity or drug response and to suggest drug repositioning, altoget

80 nts (SNVs) that contribute to differences in drug response and understanding their underlying mechani

81 ERAD disruption could influence therapeutic drug response and/or toxicity, warranting serious consid

82 mation on dietary and environmental factors, drug responses and adverse drug events.

83 upstream transcriptional regulators to alter drug responses and aspects of the addicted phenotype.

84 umeration of metastasis, on-chip anti-cancer drug responses and biological molecular analysis.

85 izing enzymes (DXME) play important roles in drug responses and carcinogenesis.

86 unique tool for interrogating cardiomyocyte drug responses and discovering the genes that modulate t

87 nd ventricular tissues with chamber-specific drug responses and gene expression.

88 lyses of cellular response to radiation with drug responses and genome-wide molecular data.

89 disease progression and management including drug responses and management outcomes.

90 GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 ap

91 These studies show that PDX drug responses and sequence results are reproducible acr

92 The PDXNet Consortium shows that PDX drug responses and sequencing results are reproducible a

93 tionships between genes and their mutations, drug responses and treatments in the context of a certai

94 lore whether there is an interaction between drug-response and placebo-response in terms of effect si

95 tion areas: a metabolomics study of pathogen drug response, and an Escherichia coli metabolic model.

96 TGFbeta in breast cancer pathophysiology and drug response, and highlight this signaling axis as a co

97 osis, shape tumour evolution, metastasis and drug response, and may advance precision oncology.

98 has a critical role in receptor degradation, drug response, and resistance mechanism.

99 inical characteristics, disease progression, drug response, and risk of complications.

100 may spur novel therapies, assure consistent drug responses, and encourage the shift from population-

101 is and autophagy and that potentially affect drug responses, and suggest that these effects underlie

102 for each agent demonstrates that changes to drug response are due to inherent changes in the system

103 Although patient-specific drug responses are common, for many patients, combinatio

104 orm better than existing approaches when the drug responses are correlated.

105 in supporting cancer growth, metastasis, and drug responses are less understood.

106 We further applied these devices to study drug responses, as well as the contractile development o

107 which factors, if any, specifically moderate drug-response, as they may be different from those moder

108 ancer surgical specimens in the histoculture drug response assay (HDRA) based on three-dimensional cu

109 they increase the sensitivity of short-term drug response assays to cell-to-cell heterogeneities and

110 Viability, proliferation, migration, and drug response assays were carried out to assess the role

111 We have studied drug-response associated (DRA) gene expressions by apply

112 tation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogeneti

113 These advantages enable REP to estimate drug response at any stage of a given treatment from som

114 Here we focus on predicting drug response based on integration of the heterogeneousl

115 expression model (GEM) for cancer patients' drug responses based on gene expression and drug activit

116 Variations in drug response between individuals have prevented us from

117 TCA) was evaluated as a means of normalizing drug response between patients to develop broadly applic

118 biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs

119 models identified 24 clinically established drug-response biomarkers, and provided evidence for six

120 n zebrafish larvae for phenotypic testing of drug response bring this tiny vertebrate to the forefron

121 Microbes affect drug responses, but mechanisms remain elusive.

122 They may also affect drug response by within-pathway or cross-pathway means.

123 Notably, in predicting drug response, CAAs substantially outperform mutations a

124 Genomic analysis of drug response can provide unique insights into therapies

125 Accurate prediction of cancer drug response (CDR) is challenging due to the uncertaint

126 Prediction of clinical drug response (CDR) of cancer patients, based on their c

127 and accurate quantitative measures of acute drug response comparable to gold standard assays, but wi

128 ber aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05).

129 k were used to search for genomic markers of drug response, connecting shared perturbations to differ

130 Identifying robust biomarkers of drug response constitutes a key challenge in precision m

131 ation, differential gene expression network, drug response correlation with gene expression, and prot

132 nt evidence and discusses how differences in drug response could inform selection of optimal type 2 d

133 tifying actionable mutations and the related drug responses currently remain formidable challenges.

134 onsider prediction of a single metric of the drug response curve such as AUC or IC(50).

135 llected for pre-clinical models, and patient drug response data are often lacking, there is an urgent

136 These imputed drug response data are then associated with somatic gene

137 Using drug response data collected in the Cancer Cell Line Enc

138 For this purpose, we represent drug response data for a large cohort of cell lines as a

139 s access and explore molecular profiling and drug response data for the NCI-60.

140 rt data from observational clinical cohorts, drug response data from cell culture models and drug res

141 g response data from cell culture models and drug response data from mouse xenograft tumour models.

142 Obtaining accurate drug response data in large cohorts of cancer patients i

143 the genomic profiling seems consistent, the drug response data is not.

144 rough a systematic analysis of proteomic and drug response data of 14 HGS-OvCa PDXs demonstrate that

145 In contrast, gene expression and drug response data provided valuable information about p

146 multiscale clusters with gene expression and drug response data to illuminate the functional and clin

147 Finally, integrating radioresponse with drug response data, we found that drug classes impacting

148 Then, a NLME model was fit to the drug response data, with the estimated random effects us

149 We tested CNet using drug-response data, multidimensional cancer genomics dat

150 As very large drug response datasets have been collected for pre-clini

151 portant in this process were associated with drug response, demonstrating the potential clinical valu

152 as a means to separate desirable and adverse drug responses downstream of G protein-coupled receptor

153 targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway ac

154 vibrational spectroscopy to investigate the drug response following incubation of S. aureus with oxa

155 This yields an imputed drug response for every drug in each patient.

156 drug screen could enable accurate testing of drug response for individualized cancer treatment.

157 is by identifying predictors of differential drug response for people based on their characteristics

158 ions and the signalling networks that govern drug response for the implementation of any consistently

159 PDX studies, the PDXNet tested temozolomide drug response for three prevalidated PDX models (sensiti

160 ty prediction, several studies have measured drug responses for cytotoxic and targeted therapies acro

161 re of the data and integrates past values of drug responses for subsequent predictions.

162 able models of protein kinase inhibition and drug response ([Formula: see text]) profiles in cell lin

163 We show that statistical modeling of single drug response from drug combination data can help determ

164 learning has been utilized to predict cancer drug response from multi-omics data generated from sensi

165 ition of targetable proteins associated with drug responses further identified corresponding synergis

166 The connection between genetic variation and drug response has long been explored to facilitate the o

167 Genomics-based predictors of drug response have the potential to improve outcomes ass

168 can mimic human pathophysiology and predict drug response, having profound implications for drug dis

169 t contribute to cancer phenotypes, including drug responses, here we have expanded the characterizati

170 c response when comparing individual ex vivo drug response in 86 patients with refractory hematologic

171 imaging approach that can directly visualize drug response in an inducible RAF-driven, autochthonous

172 and their regulators determine variations in drug response in asthma treatment are lacking.

173 er variant were associated with differential drug response in CAE.

174 stematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alter

175 these patterns can be used as biomarkers of drug response in human cells.

176 atistical models relating gene expression to drug response in large panels of cancer cell lines and a

177 iscover novel genetic markers that influence drug response in order to develop personalized treatment

178 tabolic imaging (OMI) is highly sensitive to drug response in organoids, and OMI in tumor organoids c

179 bout 73% of our top predicted genes modulate drug response in selected cancer cell lines.

180 to recapitulate CPVT2 disease phenotype and drug response in the culture dish, to provide novel insi

181 omputational method that allows us to impute drug response in very large clinical cancer genomics dat

182 identifying novel treatments or predicting a drug response in, for example, cancer patients, from the

183 organoids can be used to accurately predict drug responses in a personalized treatment setting.

184 and the effect of the BM microenvironment on drug responses in AML, we conducted a comprehensive eval

185 ata in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy b

186 triatal p11 might mediate motor function and drug responses in parkinsonian mice.

187 ducible, and low-cost platform for assessing drug responses in patient tumors ex vivo, thereby enabli

188 We assessed drug responses in PDTCs on a high-throughput platform an

189 entially explaining some of the diversity of drug responses in RA patients.

190 patient-derived organoids (PDOs) can predict drug responses in the clinic, but the ability of PDOs to

191 etabolic changes in the microbiota can alter drug responses in the host has been largely unexplored.

192 nsider also the specific factors influencing drug-response in order to fully understand the differenc

193 G-LASSO identified genes associated with the drug response, including known targets and pathways invo

194 nes along with their clinical phenotypes and drug response information.

195 oaded and cultured with functional cells for drug response investigation and organ tissues that are i

196 We conclude that a conditioned drug response is a powerful tool to entrain, drive, and

197 Therefore, functional assays where drug response is directly evaluated in tumor cells are a

198 containing both patient gene expression and drug response is needed to support future work of machin

199 racterization of tumors with pharmacological drug response is the next step toward clinical applicati

200 ave different impact on placebo-response and drug-response, it is important to consider also the spec

201 utics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molec

202 We derive alternative small molecule drug-response metrics that are insensitive to division n

203 le pathways, they show that the multivariate drug response models derived from cell line data were ap

204 collective model merging the two individual drug response models was designed to investigate the pot

205 man lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for c

206 hown that experimental conditions modify the drug response observed in functional assays.

207 ext-dependent role for ARF in modulating the drug response of bladder cancer.

208 of mitochondrial dynamics in the biology and drug response of cancer cells.

209 ception that most mutations do not influence drug response of cancer, and points to an updated approa

210 d the molecular and functional features, and drug response of cardiomyocytes (PSC-CMs) and endothelia

211 t algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients.

212 nated with checkpoint kinase Chk2, regulates drug response of glioblastoma cells.

213 (SNPs) in a particular genomic region and a drug response of interest.

214 stigates cellular morphology, viability, and drug response of organoids derived from frozen tissues.

215 and frozen tissue and can be used to detect drug response of organoids grown from frozen tissues.

216 is of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emergin

217 s traditional Random Forest when the average drug responses of cancer types are different.

218 In this configuration, detailed drug responses of dozens of cells can be followed for in

219 The dependence of anti-mitotic drug response on Bcl-xL and Mcl-1 that we derived from t

220 this representation, we train predictors of drug response on pre-clinical data and apply these predi

221 a label-free method for evaluating cellular drug responses only by high-throughput bright-field imag

222 utility as candidate diagnostics to predict drug response or to design tactics to circumvent resista

223 r factors had a significant effect on either drug-response or placebo-response.

224 E models to achieve more stable estimates of drug response parameters.

225 d with PIK3CA mutations have a heterogeneous drug-response pattern.

226 lation of data resources to decipher complex drug response patterns, thus potentially improving cance

227 ce, offering new insights into the basis for drug response, persistence, and resistance.

228 and provide new insights into the basis for drug response, persistence, and resistance.Significance:

229 -regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer

230 indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or

231 ug-regulostat interactions in predetermining drug response phenotypes in cancer cells.

232 been used to discover the alleles that drive drug response phenotypes in the most lethal human malari

233 and explore the possibility of manipulating drug response phenotypes, we developed a network-based p

234 how genomic variations impact variations in drug response phenotypes.

235 ate the power of transfer learning for three drug response prediction applications including drug rep

236 oves the prediction performance in all three drug response prediction applications with all three pre

237 overcome these challenges, we here formulate drug response prediction as a link prediction problem.

238 athway activity across passages and confirms drug response prediction biomarkers in PDX.See related c

239 vel REcursive Prediction (REP) framework for drug response prediction by taking advantage of time-cou

240 to estimate mean and confidence interval of drug response prediction errors based on ensemble approa

241 Previous transfer learning studies for drug response prediction focused on building models to p

242 Drug response prediction is a well-studied problem in wh

243 We built a drug response prediction model (called PDXGEM) in a rand

244 ed to improve the performance of anti-cancer drug response prediction models.

245 herefore could become an attractive tool for drug response prediction studies.

246 ures in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney tr

247 open-source R implementation for integrating drug response predictions from various genomic character

248 g techniques hold immense promise for better drug response predictions, but most have not reached cli

249 t need for methods that efficiently transfer drug response predictors from pre-clinical models to the

250 In particular, employing drug response predictors generated on data derived from

251 By integrating genomic, transcriptomic, and drug response profiles from the Genomics of Drug Sensiti

252 In vitro drug response profiling revealed that the combination of

253 Crucial to the identification of drug response related genetic features is the ability to

254 Drug response remained stable over time.

255 cs data to identify biomarkers predictive of drug response, representing a major step forward in prec

256 non-coding RNAs) in cancer pathogenesis and drug response/resistance, and discuss the therapeutic po

257 The use of large-scale genomic and drug response screening of cancer cell lines depends cru

258 ible mechanisms regulating directionality of drug response sensitivity.

259 mpound target genes and large collections of drug response signatures demonstrates its advantages in

260 The results showed UM-SCC-1 drug response significantly changed in the different cul

261 ding pocket resulted in a 9-fold decrease in drug response, suggesting that the bulkier tryptophan re

262 with the AFM/MEA platform for diseased CMs' drug response testing and DMD characterization.

263 of multiple mutations are more predictive of drug response than single gene predictors.

264 tissue yielded organoids with more accurate drug response than the flash frozen tissues, and thus bu

265 onsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffec

266 We observe drug responses that depend on inter-tissue interaction,

267 ed as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating ch

268 dynamic strategies that cancers use to evade drug response, the promise of upfront combination and in

269 Based on drug responses, the disease could be organized into phen

270 roving our understanding of heterogeneity in drug response, thereby facilitating the discovery of mor

271 prove useful for predicting patient-specific drug responses through in vitro patient-specific drug tr

272 mber alterations (CNAs), gene expression and drug response to BCa patient profiles in The Cancer Geno

273 ension cells, show a substantially different drug response to cells grown in monolayer, which increas

274 for patient stratification and prediction of drug responses to tamoxifen and chemotherapy.

275 s reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level.

276 icle, we consider the individual modeling of drug responses to tumor and normal cells and utilize the

277 tected in many cancer types, correlated with drug response, tumor prognosis, or patient survival.

278 MAR measurement predicted drug response using samples as small as 25 mul of periph

279 Our goal is to predict drug response values at every stage of a long-term treat

280 se-time varying genetic characterization and drug response values, we have used the HMS-LINCS databas

281 provide a complementary way of understanding drug response variation among individuals.

282 nformation, would be almost 3 days longer if drug response was predicted ignoring the difference in p

283 nants underlying variability in anti-mitotic drug response, we constructed a single-cell dynamical Bc

284 ing Pavlovian procedures to suppress operant drug response, we determined the anti-relapse action of

285 ing Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently

286 nd the genes most informative for predicting drug response were enriched in well-known cancer signali

287 Ex vivo drug responses were associated with outcome.

288 Drug responses were measured using growth assays.

289 bset of NSCLCs, also regulates cyclin D1 and drug response when SMARCA4 is absent.

290 A new drug discovery method based on drug response, which can complement the structure-based

291 potential actionable mutations predictive of drug response, which provide rich resources of molecular

292 terrogating mechanisms of mutation-dependent drug response, which will have a significant impact on c

293 robust and clinically relevant differential drug response with all noninsulin treatments after metfo

294 lysis through integration of PDX model tumor-drug response with genetic data.

295 levels as a predictive biomarker of in vivo drug response with high specificity (92%) and strong pos

296 zed to quantitatively describe time-resolved drug responses within a patient context.

297 affordable and scalable model to investigate drug responses within a physiologically relevant 3D plat

298 plying this FLIM assay as early predictor of drug response would meet one of the important goals in c

299 nderstanding between genetic alterations and drug responses would facilitate precision treatment for

300 Therefore, biomarkers for depression and drug-response would help tailor treatment strategies.