ライフサイエンスコーパス: driver mutation

1 whereas ATCs were BRAF-like irrespective of driver mutation.

2 signatures that are affected by a recurrent driver mutation.

3 utations, of which 1 clone had an additional driver mutation.

4 monstrating that L265P is a gain-of-function driver mutation.

5 athways is defined by lineage rather than by driver mutation.

6 rms (or novel isoforms), without an apparent driver mutation.

7 suggesting that Barrett's initiates without driver mutations.

8 altered by the presence or absence of other driver mutations.

9 represents one of the most common oncogenic driver mutations.

10 protein-coding cancer genes carried probable driver mutations.

11 distinct GBM subtypes governed by identical driver mutations.

12 e enriched for NRAS mutations and additional driver mutations.

13 ukaemia (CLL) cases, yet have relatively few driver mutations.

14 sting they might precede selection of cancer driver mutations.

15 enograft models (PDXs) with a diverse set of driver mutations.

16 nd reported in angiosarcomas alongside other driver mutations.

17 ly at discrete genomic regions and generates driver mutations.

18 effects and transcriptional dysregulation of driver mutations.

19 s of subclonal variants, including subclonal driver mutations.

20 ive with all other known lung adenocarcinoma driver mutations.

21 as failed to reveal any additional recurrent driver mutations.

22 lay important roles in tumours without known driver mutations.

23 liferative neoplasms, regardless of founding driver mutations.

24 copy number variations producing the tumors' driver mutations.

25 in the genesis of lung cancers lacking known driver mutations.

26 sition of del(5q) preceded diverse recurrent driver mutations.

27 gastric tumors and these do not appear to be driver mutations.

28 del to potentiate the functional analysis of driver mutations.

29 that one-third of cohorts lack identifiable driver mutations.

30 d non-small cell lung cancer that have these driver mutations.

31 ytic leukemia and are unlikely to operate as driver mutations.

32 hese methods are appropriate for identifying driver mutations.

33 nally, we provide a ranked list of candidate driver mutations.

34 heterogeneous and contain many passenger and driver mutations.

35 improved by the identification of actionable driver mutations.

36 ew biologically and therapeutically relevant driver mutations.

37 g elevated mutation rates and do not contain driver mutations.

38 modifications, including repair of oncogenic driver mutations.

39 hen subjects carry different combinations of driver mutations.

40 ciated with a specific HLA allele or somatic driver mutations.

41 ll line from a tumor with none of the common driver mutations.

42 Both small deletions and inversions generate driver mutations.

43 the potential to generate rare significant "driver" mutations.

44 y critical base pairs and potential disease 'driver' mutations.

45 We report a higher overall prevalence of driver mutations (13.7%), which occurred mostly (93%) in

46 in treating cancers that are caused by such driver mutations, a large body of methods have been deve

47 ical successes in treating cancers caused by driver mutations, a variety of methodologies that attemp

48 ependent on the continued presence of single-driver mutations-a phenomenon dubbed "oncogene addiction

49 We identified 5234 driver mutations across 76 genes or genomic regions, wit

50 Putative driver mutations affecting WNT (wingless-related integra

51 o class-defining lesions, other co-occurring driver mutations also had a substantial effect on overal

52 branching process that starts with a single driver mutation and proceeds as each new driver mutation

53 ng and array-based cytogenetics identified a driver mutation and/or structural variant in 91% (63/69)

54 oid carcinomas are known to harbor oncogenic driver mutations and advances in sequencing technology n

55 r cells are characterized by key founder and driver mutations and are enriched for cytogenetic altera

56 While cancer driver mutations and copy-number alterations have been s

57 algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in

58 ed by all predictors that attempt to predict driver mutations and discuss how this could impact high-

59 upported by data, facilitates the search for driver mutations and for therapeutic targets.

60 s of cancer has led to therapies that target driver mutations and has helped match patients with more

61 and epigenomes has defined large numbers of driver mutations and molecular subgroups, leading to the

62 The driver mutations and mutational processes operative in b

63 ved performances when distinguishing between driver mutations and other germ line variants (both dise

64 2 vs 17.6 years), with triple negativity for driver mutations and presence of HMR mutations represent

65 arsimony-based approach to prioritize cancer driver mutations and provides dramatic improvements over

66 malignancies, challenging identification of driver mutations and targeted therapies.

67 Dysregulation of genes with reported driver mutations and the NF-kappaB pathway were found to

68 ons remain regarding the factors that induce driver mutations and the processes that shape mutation s

69 to gain precision on the exact prevalence of driver mutations and the proportions of affected genes.

70 The discovery of oncogenic driver mutations and the subsequent developments in targ

71 Identifying driver mutations and their functional consequences is cr

72 pharmacological successes in treating these driver mutations and their resulting tumors, a variety o

73 tissue independently of histological type or driver mutation, and detection of acute treatment respon

74 exy, that do not engage a single discernable driver mutation, and whose clinical relevance is unclear

75 ions between inherited factors and phenotype driver mutations, and effects related to the order in wh

76 We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour he

77 inantly homogenous, independent of oncogenic driver mutations, and similar in benign and malignant ce

78 dissemination potential, the selection of co-driver mutations, and the appearance of naturally occurr

79 s remain unknown in patients with targetable driver mutations, and use of PD-L1 expression to guide t

80 inical decision-making because the different driver mutations are associated with distinct clinical f

81 to melanoma genetics, systematic surveys for driver mutations are challenged by an abundance of passe

82 The numbers of tumor driver mutations are differentiated (p < 0.05) over the

83 Pathogenic driver mutations are largely unknown.

84 reast cancer progression and that additional driver mutations are often acquired, posing both challen

85 Driver mutations are required for the cancer phenotype,

86 rimary tumor in untreated patients, and PDAC driver mutations are shared by all subclones.

87 rrent research suggests that a small set of "driver" mutations are responsible for tumorigenesis whil

88 ndings that defy the orthodoxy of oncogenic "driver mutations" are now accumulating: the ubiquitous p

89 d found evidence of branched evolution, with driver mutations arising before and after subclonal dive

90 provides a simple formula for the number of driver mutations as a function of the total number of mu

91 We identified driver mutations as predominantly clonal (e.g., MYD88, t

92 ific genomic landscape, that is, type of MPN driver mutations, association with other mutations, and

93 icient for clonal expansions, and additional driver mutations at the TMD stage do not necessarily pre

94 g cells, that seeding metastasis may require driver mutations beyond those required for primary tumou

95 iants that modify the phenotype of a primary driver mutation, broad-based genetic testing should be e

96 Drug development strategies target driver mutations, but inter- and intratumoral heterogene

97 mary tumor facilitates the identification of driver mutations by application of phylogeny-based tests

98 erogeneity complicates the identification of driver mutations by their recurrence across samples, as

99 dology when identifying potential activating driver mutations by utilizing a graph theoretic approach

100 methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure

101 Last, the temporal order of occurrence of driver mutations can be inferred from phylogenetic analy

102 important role in cancer development.Cancer driver mutations can occur within noncoding genomic sequ

103 ts, with 4.5% harboring presumptive leukemia driver mutations (CH-PD).

104 The acquisition of clonal hematopoiesis-driver mutations (CHDMs) occurs with normal aging and th

105 Sample analyses were designed to detect driver mutations, chromosome copy number aberrations, an

106 " "NSCLC," "synthetic lethality," "oncogenic driver mutations," "clinical trials," and "phase 3 clini

107 onsiderable between-patient heterogeneity in driver mutations complicates evidence-based personalizat

108 A subset, known as driver mutations, confer clonal selective advantage on c

109 subset of these somatic alterations, termed driver mutations, confer selective growth advantage and

110 east cancers to advance understanding of the driver mutations conferring clonal advantage and the mut

111 However, there was evidence for 'driver' mutations contributing to the development of the

112 findings demonstrate that vaccination to key driver mutations cooperates with checkpoint blockade and

113 The regionally separated driver mutations, coupled with the relentless and hetero

114 These observations indicate that driver mutations define distinct disease entities within

115 activity is increased by serum or oncogenic driver mutations depend on the 8q24 super-enhancer regio

116 ion, sometimes five, ten, or more, and these driver mutations do not necessarily assort randomly.

117 rovide us with new insights in understanding driver mutation dysregulation in tumor genome and develo

118 tion, predominantly involving subclones with driver mutations (e.g., SF3B1 and TP53) that expanded ov

119 cers may have acquired several somatic "mini-driver" mutations, each with weaker effects than classic

120 of clones harbouring del(8p) with additional driver mutations (EP300, MLL2 and EIF2A), with one patie

121 +) TILs that targeted the KRAS(G12D) hotspot driver mutation found in many human cancers.

122 umors, providing a signal for distinguishing driver mutations from a larger number of random passenge

123 eneration sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to fac

124 etected due to lack of power to discriminate driver mutations from the background mutational load (13

125 Distinguishing such "driver" mutations from innocuous "passenger" events is c

126 he somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a

127 iver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mu

128 Driver mutations generally target cellular signaling and

129 derived from the BCR-ABL fusion (BAp), a key driver mutation, generated a small population of mice th

130 distribution of sizes of subclones carrying driver mutations had a heavy right tail at the time of t

131 Driver mutations had equivalent prognostic significance,

132 The fact that the MLL-FP is the main driver mutation has allowed for a wide range of differen

133 The identification of somatic driver mutations has resulted in new drugs that target t

134 methods proposed for the detection of cancer driver mutations have been based on the estimation of ba

135 More than a hundred driver mutations have been characterized that confer the

136 Although both founding and subclonal driver mutations have been shown to have prognostic sign

137 e increasingly being used to assess putative driver mutations identified by large-scale sequencing of

138 eria, where a gene is identified as having a driver mutation if it is altered in significantly more s

139 ential areas of treatment, such as targeting driver mutations, immunotherapy, stem cell modulation, a

140 of these data is to distinguish functional "driver mutations" important for cancer development from

141 Amplification of MYCN is a driver mutation in a subset of human neuroendocrine tumo

142 15 of 39 tumors (38%) harbored at least one driver mutation in angiogenesis signaling genes.

143 onine protein kinase (BRAF V600E) is the key driver mutation in hairy cell leukemia (HCL), suggesting

144 BRAF-V600E is the key driver mutation in HCL and distinguishes it from other B

145 rate that JAK2V617I is likely to be the sole driver mutation in JAK2V617I-positive individuals with t

146 This work thus identifies a new driver mutation in LCH that is potentially actionable in

147 We identified at least one potential driver mutation in nearly all AML samples and found that

148 erapy can specifically target the BRAF(V600) driver mutation in the tumor cells and potentially sensi

149 Combining driver mutations in 111 cancer genes with cytogenetic an

150 are permissive for accumulation of multiple driver mutations in a single cell.

151 ere, we discuss subsets defined by so-called driver mutations in ALK, HER2 (also known as ERBB2), BRA

152 We identified nonoverlapping somatic driver mutations in all 26 cases through candidate gene

153 Among the 100 tumours, we found driver mutations in at least 40 cancer genes and 73 diff

154 Driver mutations in both NRAS and BRAF have important im

155 ors, resulting in characteristic patterns of driver mutations in BRAF, NRAS, and other genes.

156 istal regulatory elements harboring putative driver mutations in breast cancer.

157 adhesion kinase pathways as targets of rare driver mutations in breast, colorectal cancer, and gliob

158 oblems, including determination of potential driver mutations in cancer and other diseases, elucidati

159 Many driver mutations in cancer are specific in that they occ

160 It is poorly understood how driver mutations in cancer genes work together to promot

161 s that are not cancer causing and pathogenic driver mutations in cancer genes.

162 Efforts to identify driver mutations in cancer have largely focused on genes

163 on the "selective advantage" relation among driver mutations in cancer progression and investigate i

164 tiregion whole-exome sequencing suggest that driver mutations in cancer-relevant genes including EGFR

165 Longitudinal analyses identified early driver mutations in chromatin regulator genes (CREBBP, E

166 The spatial and temporal homogeneity of main driver mutations in DIPG implies they will be captured b

167 mutated bMMRD cancers acquired early somatic driver mutations in DNA polymerase varepsilon or delta.

168 cal approach to identify candidate noncoding driver mutations in DNase I hypersensitive sites in brea

169 Driver mutations in EGFR, MET, BRAF, and TP53 were almos

170 ne domain of the Neu (c-ErbB-2) gene are the driver mutations in ENU-induced malignant schwannomas, t

171 Cancer genome characterization has revealed driver mutations in genes that govern ubiquitylation; ho

172 tion of a response to agents that target key driver mutations in human cancer.

173 Identification of driver mutations in human diseases is often limited by c

174 Identification of driver mutations in human diseases is often limited by c

175 s have led to the discovery of nearly 90% of driver mutations in JMML, all of which thus far converge

176 of alleles on penetrance and expressivity of driver mutations in key developmental and homeostatic pa

177 ions can lead to the identification of novel driver mutations in known tumor suppressors and oncogene

178 GA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in

179 quencing approach was used to detect somatic driver mutations in matched tumor DNA (tDNA) and plasma

180 pecies comparative oncogenomics, identifying driver mutations in mouse cancer models and validating t

181 SERs identified by SPARROW reveal known driver mutations in multiple human cancers, along with k

182 In cutaneous melanoma, driver mutations in NRAS and BRAF promote CDK4/6 activat

183 KRAS is one of the most common oncogenic driver mutations in NSCLC, with prior attempts at direct

184 white background for the presence of somatic driver mutations in NSCLC.

185 er, 85% of the BCCs also harbored additional driver mutations in other cancer-related genes.

186 tected in subsets of patients, and subclonal driver mutations in other genes were found to be associa

187 We identified frequent driver mutations in plasma ctDNA and tDNA in EGFR, KRAS,

188 ALL samples from 39 DS patients, we uncover driver mutations in RAS, (KRAS and NRAS) recurring to a

189 We show that recurrent PC-driver mutations in speckle-type POZ protein (SPOP) stab

190 Establishing a significant presence of driver mutations in such data sets is of biological inte

191 linked to a hyperactivated RAS pathway, with driver mutations in the KRAS, NRAS, NF1, PTPN11, or CBL

192 these tumors were hypermutated and harbored driver mutations in the RB (retinoblastoma) and Akt-mTOR

193 We uncovered new driver mutations in the replication-repair-associated DN

194 ers, and it did not overlap with other known driver mutations in these tumors.

195 mor were undetected at recurrence, including driver mutations in TP53, ATRX, SMARCA4, and BRAF; this

196 Somatic driver mutations in tumor DNA (tDNA) and pre- and post-o

197 The recent discovery of major driver mutations in uveal melanoma provide a rational ba

198 Supporting its classification as a "driver" mutation in the cells in which it is expressed,

199 Known "driver" mutations in genes for melanoma, including CDKN2

200 assenger', cancer mutations from causal, or 'driver', mutations in these data sets.

201 The MPN-restricted driver mutations, including those in JAK2, calreticulin

202 With increasing mutation burden, numbers of driver mutations increase, but not linearly.

203 survival deteriorated steadily as numbers of driver mutations increased.

204 show that the expected number of accumulated driver mutations increases exponentially in time if the

205 The KRAS oncogenic driver mutation is noted in 15% to 25% of patients with

206 utations." A common approach for identifying driver mutations is to find genes that are mutated at si

207 with data including annotation of prevalent driver mutations (KRAS and EGFR) and tumor suppressor mu

208 pite the identification of several oncogenic driver mutations leading to constitutive JAK-STAT activa

209 gle driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of cl

210 Half of the driver mutations located on the branches of tumor phylog

211 that tumor cells with different progression driver mutations may coevolve rather than compete during

212 number variants (SCNVs) comprise 92% of all driver mutations (mean of 11.8 pathogenic SCNVs versus 1

213 ignificant differences in mutational burden, driver mutations, mutational processes, and copy number

214 providing a quantitative measure of the cell driver mutations needed for invading the bone tissue.

215 Most distant metastases acquired driver mutations not seen in the primary tumor, drawing

216 human melanocytes, specifically by melanoma driver mutations NRASQ61K and BRAFV600E, causes expressi

217 Thus, subsequent to a driver mutation, NTHi-induced inflammation promotes prol

218 nd a racial group as an "experimental unit", driver mutation numbers demonstrate a significant (r = 0

219 Cancer genomics demonstrates that these few driver mutations occur alongside thousands of random pas

220 Potential driver mutations occurred in classical tumor suppressor

221 contrast, the majority of truncal and clonal driver mutations occurred in tumor-suppressor genes, inc

222 r Cell, Woll and colleagues demonstrate that driver mutations occurring in MDS definitively occur in

223 for PDAC induced by oncogenic Kras, the key driver mutation of PDAC.

224 studies establish NAB2-STAT6 as the defining driver mutation of SFT and provide an example of how neo

225 ata implicated CRAF R391W as the alternative driver mutation of this melanoma.

226 We studied the impact of driver mutations of JAK2, CALR, (calreticulin gene) or M

227 ecular analysis frequently detected hallmark driver mutations of myeloid neoplasms (such as JAK2V617F

228 Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, reve

229 genomic landscape, identifies new recurrent driver mutations of the disease, and suggests clinical i

230 active drug target because they are probable driver mutations of this disease.

231 al analysis of gene expression relative to a driver mutation on patient samples could provide us with

232 owever, it remains unclear which are the key driver mutations or dependencies in a given cancer and h

233 rogeneity of TNBC and lack of high frequency driver mutations other than TP53 have hindered the devel

234 d to reliably prioritize biologically active driver mutations over inactive passengers in high-throug

235 2% of normal skin cells at a density of ~140 driver mutations per square centimeter.

236 despite the predominance of single oncogenic driver mutations, perhaps due to second metabolic or gen

237 Cancers often show major changes in driver mutation presence or frequency after treatment, o

238 ons were subclonal, confounding estimates of driver mutation prevalence.

239 Finally, the spectrum of driver mutations provided unequivocal genomic evidence f

240 Defining the hierarchy of driver mutations provides insights into the process of t

241 number data contextualized the landscape of driver mutations, providing oncogenic insights in BRAF-

242 However, how driver mutations regulate the transcriptome to affect ce

243 ic lesions, but the cellular consequences of driver mutations remain unclear, especially during the e

244 s disease, with multiple different oncogenic driver mutations representing possible therapeutic targe

245 ve two to six, indicating that the number of driver mutations required during oncogenesis is relative

246 nonconsequential mutations) and identifying "driver" mutations responsible for tumorigenesis and/or m

247 eory suggests that there are only a few key "driver" mutations responsible for tumorigenesis.

248 Two frequent cancer-driver mutation sequences (EGFR-L861Q, NRAS-Q61K) were t

249 ng drugs that treat cancers that carry these driver mutations, several methods that rely on mutationa

250 t demonstration that computationally derived driver mutation signatures can be overall superior to si

251 erogeneity presents a problem for predicting driver mutations solely from their frequency of occurren

252 cy, leukemic progression requires "third-hit driver" mutations/somatic copy-number alterations found

253 tions, cancers typically carry more than one driver mutation, sometimes five, ten, or more, and these

254 r of the six tumor pairs showed KRAS hotspot driver mutations specifically in the mucinous tumor.

255 proach is confounded by the observation that driver mutations target multiple cellular signaling and

256 Under the hypothesis that driver mutations tend to cluster in key regions of the p

257 Based on the hypothesis that driver mutations tend to cluster in key regions of the p

258 ages of the CBL activation cycle to identify driver mutations that affect CBL stability, binding, and

259 sting has been the recent discovery of major driver mutations that allow predictive testing of respon

260 at an increasing rate to identify actionable driver mutations that can inform therapeutic interventio

261 leukemia (AML) results from the activity of driver mutations that deregulate proliferation and survi

262 We identified novel driver mutations that developed during adenoma and cance

263 Epithelial stem cells are targets for driver mutations that give rise to SCCs, but it is unkno

264 It can detect genes harboring rare driver mutations that may be missed by existing methods.

265 ParsSNP identified many known and likely driver mutations that other methods did not detect, incl

266 earch suggests that there is a small set of "driver" mutations that are primarily responsible for tum

267 ancer genomes, which are composed of causal "driver" mutations that promote tumor progression along w

268 tive pressure for the ROCK1 gene to acquire 'driver' mutations that result in kinase activation.

269 These treatments include drugs that target driver mutations, those that target presumed important m

270 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted.

271 of genetic "predestination," in which early driver mutations, typically affecting genes involved in

272 atched control samples to identify potential driver mutations underlying MPM.

273 The identification of oncogenic driver mutations underlying sensitivity to epidermal gro

274 es have been developed to identify potential driver mutations using methods such as machine learning

275 ated therapies targeting fitness-increasing (driver) mutations usually decrease the tumour burden but

276 Furthermore, presence of a subclonal driver mutation was an independent risk factor for rapid

277 The frequency of driver mutations was not significantly different from th

278 s well as detecting the known bladder cancer driver mutations, we report the identification of recurr

279 Driver mutations were confidently assigned to most patie

280 Known aldosterone driver mutations were identified in 8 of 23 (35%) APCCs,

281 ur targeted sequencing approach, endometrial driver mutations were identified in all seven women who

282 Driver mutations were identified in several new cancer g

283 In addition, relatively high allele fraction driver mutations were identified in the lavage fluid of

284 ticipants with detected driver and potential driver mutations were significantly older (mean age muta

285 The different types of driver mutations were similarly distributed between the

286 s contribute to tumor progression (known as "driver" mutations) whereas the majority of these mutatio

287 e hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rat

288 ncer genomics is the identification of novel driver mutations which often target genes that regulate

289 Identifying key "driver" mutations which are responsible for tumorigenesi

290 ends upon distinguishing disease-associated 'driver' mutations, which have causative roles in maligna

291 mics to meticulously match subgroup-specific driver mutations with cellular compartments to model and

292 n objective function to guide the search for driver mutations within a pathway.