ライフサイエンスコーパス: drug response

1 re alignment (reflecting a core mechanism in drug response).

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

3 he importance of tissue lineage in mediating drug response.

4 ial genetic landscape in rMM associated with drug response.

5 ker and identified a brain region predicting drug response.

6 rtance of different data types in predicting drug response.

7 determinants of interindividual variation in drug response.

8 iomarkers for assessing cancer phenotype and drug response.

9 anding cellular processes, human disease and drug response.

10 cell surface markers, cell proliferation and drug response.

11 bone marrow microenvironment on survival and drug response.

12 ulations, disease cell populations and their drug response.

13 , further implicating cMET in the anticancer drug response.

14 pancy determines individual disease risk and drug response.

15 nting for such interindividual variations in drug response.

16 ls upon ATRA treatment as a key event in the drug response.

17 system-wide perturbation data in predicting drug response.

18 -invasive tracking of myobundle function and drug response.

19 anscriptomic, and phenotypic data related to drug response.

20 ntly, differences in disease progression and drug response.

21 es profound insights into leukemogenesis and drug response.

22 mutation and predicting future prognosis and drug response.

23 considered integral to tumor progression and drug response.

24 genomic features to regress or classify the drug response.

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

26 tivity and causes mitotic delay in cytotoxic drug response.

27 confirming the importance of this factor in drug response.

28 ciated with shorter overall survival but not drug response.

29 1 activation to apoptosis in the antitubulin drug response.

30 promoting cell survival and limiting overall drug response.

31 or the discovery of variants associated with drug response.

32 predisposes for solid tumors and influences drug response.

33 nd TNBC, with potential predictive value for drug response.

34 conducted a genome-wide association study of drug response.

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

36 ity in cancer cells and molecular markers of drug response.

37 ation, progression and ultimately to predict drug response.

38 henotypes such as disease susceptibility and drug response.

39 ns between molecular subtypes, pathways, and drug response.

40 eural plasticity are critical components for drug response.

41 medicine is patient-to-patient variation in drug response.

42 entify molecular markers of antihypertensive drug response.

43 n signaling that help explain differences in drug response.

44 umor organoids correlates with primary tumor drug response.

45 ylome analysis to understand determinants of drug response.

46 uces susceptibility to develop a cardiotoxic drug response.

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

48 tive marker for aggressiveness and effective drug response.

49 increasing placebo response, not decreasing drug response.

50 onfirming its predictive ability for in vivo drug response.

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

52 of specific oncogene expression patterns on drug response.

53 evelop machine learning models predictive of drug response.

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

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

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

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

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

59 es was exhibited by stratifying patients for drug responses.

60 effects of the microenvironment on cellular drug responses.

61 sease susceptibility, disease evolution, and drug responses.

62 thal disease with heterogeneous outcomes and drug responses.

63 and to study the neural basis of conditioned drug responses.

64 rly events of tumor invasion, metastasis and drug responses.

65 ration, angiogenesis, and altered anticancer drug responses.

66 rovide candidate mediators of individualized drug responses.

67 loci for cardiovascular disease and variable drug responses.

68 dividual genetic influences on GPCR-mediated drug responses.

69 ose and schedule, and rationalizing observed drug responses.

70 ologies can help to characterize and exploit drug responses.

71 s, while its presence is required for robust drug responses.

72 evidence for CRC proteomic subtype-specific drug responses.

73 to investigate colorectal cancer biology and drug responses.

74 genetic influences on human diseases and on drug responses.

75 in which the two proteins mediate sensitized drug responses.

76 uting to phenotypic features and influencing drug responses.

77 pe, which affects tumor growth, invasion and drug responses.

78 idate transcription factors in NAc regulates drug responses.

79 c information to predict and prevent adverse drug responses.

80 determine mechanistic similarity between two drug responses.

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

82 r characterized tumorigenesis and anticancer drug responses.

83 on plays critical roles in tumorigenesis and drug responses.

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

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

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

87 ed the effects of depleting endogenous i2 on drug response and attempted to unveil any additional bio

88 This resistance loop modulates drug response and could explain the unique sensitivity o

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

90 tic drugs like taxanes, has implications for drug response and drug resistance.

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

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

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

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

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

96 e into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug int

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

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

99 ering the DNA determinants of variability in drug response and tolerability.

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

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

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

103 FCA system may provide better predictions of drug responses and identification of a suitable treatmen

104 device offers a new approach for analysis of drug responses and toxicities in bone marrow as well as

105 protein-coupled receptors leads to variable drug responses and, thereby, compromises their therapeut

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

107 ntifying associations between a genotype and drug response, and established mechanisms of resistance.

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

109 udy of how human genetic information impacts drug response, and it aims to improve efficacy and reduc

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

111 tial for identifying patient-specific risks, drug responses, and novel genotypes.

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

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

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

115 DH proteins, which showed the same trends in drug response as the cognate cell lines.

116 r defense system as endogenous regulators of drug response as well as in oxidative stress.

117 entions in this model resulted in equivalent drug responses as observed in the corresponding patients

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

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

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

121 Here we focus on predicting drug response based on a cohort of genomic, epigenomic a

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

123 Variations in drug response between individuals have prevented us from

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

125 py doses along with differential patterns of drug response between T and SEZ in the same tumor.

126 ther, our results defined NmU as a candidate drug response biomarker for HER2-overexpressing cancers

127 ngs into ways to develop therapies, identify drug response biomarkers and stratify patients.

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

129 germline genetic variation will also affect drug response (both efficacy and toxicity), and here we

130 ata has resulted in biomarkers predictive of drug response, but the majority of response is not captu

131 Microbes affect drug responses, but mechanisms remain elusive.

132 BRAF (V600E)-mutated melanomas where initial drug response can be striking and yet relapse is commonp

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

134 To identify molecular markers of drug response, cell line drug sensitivity data are integ

135 oach will enhance efforts to exploit growing drug response compendia in order to advance personalized

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

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

138 lated between studies; however, the measured drug response data are highly discordant.

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

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

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

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 Here we use existing drug response data sets to demonstrate that multitask le

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

147 cer cell lines for which gene expression and drug response data was available.

148 esponse data and by an application to cancer drug response data.

149 ng diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling path

150 ed parental parasites differing ~500-fold in drug response, determined drug sensitivity and marker se

151 o nicotine impacts brain development and how drug responses differ from those seen in adults.

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

153 complex traits such as disease progression, drug response, etc.

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

155 type p53 still mediated arrest and inhibited drug response even in the context of heterozygous p53 po

156 , mRNA/microRNA expression, exon sequencing, drug response for clinically-relevant therapeutics and c

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

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

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

160 enes in immunogenetic phenotypes and adverse drug responses for other medications, and provide insigh

161 ms capable of inferring robust predictors of drug responses from genomic information are of great pra

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

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

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

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

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

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

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

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

170 c variants of modifier genes could influence drug response in cardiovascular disease in a variety of

171 ermine the integral physiologically relevant drug response in cell lines.

172 patients, the method may be used to predict drug response in GBM patients.

173 ections in the context of a large dataset of drug response in human cancer cell lines.

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

175 signature that will more accurately reflect drug response in human patients and in cerebrospinal flu

176 resolution may advance our understanding of drug response in inherently heterogeneous cell populatio

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

178 cribe an interdisciplinary platform to study drug response in multiple myeloma, an incurable cancer o

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

180 ion between mesenchymal status and metabolic drug response in other tumor indications.

181 ly implicating KRAS status as a biomarker of drug response in ovarian cancer.

182 nce of genetically determined differences in drug response in patients with schizophrenia related to

183 RXN1 have been associated with antipsychotic drug response in patients with schizophrenia.

184 prognostic markers in predicting antitubulin drug response in patients.

185 ing of the pathogenic basis and mechanism of drug response in SCD.

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

187 2D6-dependent interindividual differences in drug response in the context of personalized medicine.

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

189 The generation of genetic predictions of drug response in the preclinical setting and their incor

190 s for tumour suppression in normal cells and drug response in tumour cells.

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

192 Drug response in xenograft-derived organoids was validat

193 rrelation between drug lipophilicity and the drug response in yeast.

194 l diffusion, we use the model to predict the drug response in-vivo, beyond what would have been expec

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

196 ions between inferred protein activities and drug responses in breast cancer cell lines grouped sever

197 of considering sex-specific differences and drug responses in clinical trial design.

198 idate, monitor disease progression and track drug responses in clinical trials.

199 response of xenografts and measure antitumor drug responses in human tumor-derived organoids.

200 iable ex vivo models that predict anticancer drug responses in human tumors accurately.

201 mesolimbic dopamine release and conditioned drug responses in laboratory animals-could inhibit mesol

202 cellular adhesion properties as well as GPCR drug responses in LCLs, which are suspension cells.

203 nsor method for characterizing GPCR-mediated drug responses in LCLs.

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

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

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

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

208 ve imaging can be used to gain insights into drug responses in situ.

209 Observing drug responses in the tumor microenvironment in vivo can

210 e methods for discovering genetic factors in drug response, including genome-wide association studies

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

212 Interindividual heterogeneity in drug response is a central feature of all drug therapies

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

214 The dichotomous behaviour of MITF in drug response is corroborated in vemurafenib-resistant b

215 G protein-coupled receptors (GPCRs) affects drug response is essential for precision medicine.

216 nderstanding of the mechanism underlying the drug response is limited.

217 lecular mechanisms of disease progression or drug response is often challenging and limited to a few

218 ount for genetically mediated differences in drug responses is an exciting opportunity to improve pat

219 al considerable interindividual variation in drug response, leading to a failure to appreciate clinic

220 Due to the concern of complex drug responses, many beta-lactams are typically ruled ou

221 Further, blood NfL levels may qualify as drug response markers in SCI.

222 g together with a molecular understanding of drug responses may help define optimal cocktails to over

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

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

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

226 sease-specific hiPSC-CMs may predict adverse drug responses more accurately than the standard human e

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

228 correct preclinical models need to reproduce drug response observed in patients.

229 crepancies between in vitro drug studies and drug responses observed in patients.

230 similarity network (DSN) data to predict the drug response of a given cell line using a weighted mode

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

232 We apply the method to predict the drug response of breast cancer cell lines.

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

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

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

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

237 s the therapy parameters based on the unique drug response of individual patient.

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

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

240 Furthermore, the molecular profile and drug response of these cell lines correlate with distinc

241 In vivo drug response of xenotransplanted RAS mutant organoids c

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

243 tude numerical distinctions (>10-fold) among drug responses of genetically contrasting cancers were c

244 undertook a detailed kinetic analysis of the drug responses of K13 wild-type and mutant isolates of P

245 mutations and motivates forecast of clinical drug response on a patient-by-patient basis.

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

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

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

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

250 DCB can help in understanding drug response patterns and prioritizing drug/cancer cell

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

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

253 (ARMD, n = 17), and genes known to influence drug response (PGx, n = 14).

254 While most efforts are directed at inferring drug response phenotype based on genotype, there is very

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

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

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

258 There is great variation in drug-response phenotypes, and a "one size fits all" para

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

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

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

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

263 nstrate that small changes in the parasite's drug response profile can dramatically alter the sensiti

264 vered distinct mutation, gene expression and drug response profiles in TCF3-HLF-positive and treatmen

265 Drug response profiling of matched patient-derived xenog

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

267 Drug response remained stable over time.

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

269 ible mechanisms regulating directionality of drug response sensitivity.

270 new targets for therapeutic intervention or drug response stratification.

271 expression datasets from viral infection and drug response studies in humans.

272 , it could be used to analyze cell lysate in drug response studies or pricks of blood from small anim

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

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

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

276 t large stage-dependent differences in their drug response that correlate with hemoglobin digestion t

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

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

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

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

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

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

283 Patient stratification according to drug responses, together with the discovery of novel ant

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

285 We track drug response using fluorescent proteins as transcriptio

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

287 many genomic alterations into biomarkers of drug response, using Boolean set operations coupled with

288 egrated network model to fill in the missing drug response values in the CGP dataset.

289 ated as a population average effect, despite drug responses varying among individuals according to a

290 e microenvironment contributes critically to drug response via regulation of vascular permeability an

291 The roles of individual cyclophilins in drug response was evaluated by small interfering RNA kno

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

293 terplay with p73/DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represen

294 Ex vivo drug responses were associated with outcome.

295 association of rs12467557and rs10490162 with drug response, whereby individuals homozygous for the A

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

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

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

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

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