ライフサイエンスコーパス: cancer gene

1 inting to ATXN7 as a previously unrecognized cancer gene.

2 aralogs suggests HELQ as a candidate ovarian cancer gene.

3 3.1% (31 of 1007) in CHEK2 or another breast cancer gene.

4 subpopulation (19%) with many overexpressed cancer genes.

5 alysis methods to specifically recover known cancer genes.

6 ncer, highlighting several potentially novel cancer genes.

7 ion in tumours, including the methylation of cancer genes.

8 downstream targets of commonly mutated human cancer genes.

9 was sequenced for known and candidate breast cancer genes.

10 ides a novel, systematic way to discover new cancer genes.

11 common alternative strategy in ranking known cancer genes.

12 ystem for the functional characterization of cancer genes.

13 d >1,000 previously undescribed MS indels in cancer genes.

14 ancer genes and pathways, and novel putative cancer genes.

15 e distribution of mutations in 119 canonical cancer genes.

16 ic retrotransposon insertions occur in known cancer genes.

17 ons from large cohorts of deeply resequenced cancer genes.

18 ctional interconnection and regulation among cancer genes.

19 latory elements and affect the expression of cancer genes.

20 enes re-found in new cancer types, and novel cancer genes.

21 strate they are regulatory elements of known cancer genes.

22 d on queries of specific anticancer drugs or cancer genes.

23 rtunity to study complex relationships among cancer genes.

24 ead suggested rapid epigenetic activation of cancer genes.

25 identified basal-like-specific, and general cancer genes.

26 essures, mutational processes, and disrupted cancer genes.

27 nes and 73 different combinations of mutated cancer genes.

28 metabolism resulting from mutated enzymes or cancer genes.

29 es, mapped to 72 genes, are selected as core cancer genes.

30 menting the sequencing-based census of human cancer genes.

31 ntifies variable length mutation clusters in cancer genes.

32 substitutions occur in yet-to-be-discovered cancer genes.

33 omosomal translocations and DNA deletions at cancer genes.

34 and we also identified candidate pancreatic cancer genes.

35 er coding mutations, including outside known cancer genes.

36 identified 45 recurrently mutated candidate cancer genes.

37 variants identified in HCC1954 overlap known cancer genes.

38 rallel testing of large numbers of inherited cancer genes.

39 iological effects of insertions on candidate cancer genes.

40 Breast cancer gene 1 (BRCA1) deficient cells not only are hyper

41 hase, which recruits Brc1 through its breast cancer gene 1 protein (BRCA1) C-terminal (BRCT) domains.

42 protein interaction (PPI) mediated by breast-cancer-gene 1 C-terminal (BRCT) is an attractive strateg

43 ried a pathogenic mutation in another breast cancer gene (29 in CHEK2, and 1 each in BRIP1 and NBN).

44 ents harbored germline uncertain variants in cancer genes (98%), pharmacogenetic variants (89%), and

45 point toward broader context dependencies of cancer gene action beyond tissue dependencies.

46 even MS indel driver hotspots: four in known cancer genes (ACVR2A, RNF43, JAK1, and MSH3) and three i

47 f 70 human tumor suppressor genes to uncover cancer genes affecting microtubule dynamic instability.

48 ne gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SY

49 h gliomagenesis, as well as a set of general cancer genes, also presented with splicing and expressio

50 r genomes provide a wealth of information on cancer gene alterations and have confirmed TP53 as the m

51 rmation gleaned from these studies on driver cancer gene alterations--mutations, copy number alterati

52 ons poses a formidable challenge to identify cancer genes among the large lists of mutations typicall

53 rs, we found driver mutations in at least 40 cancer genes and 73 different combinations of mutated ca

54 for the functional characterization of novel cancer genes and addresses many of the shortcomings of c

55 The analysis leads to the discovery of core cancer genes and also provides novel dynamic insights in

56 oson-based screen for gastrointestinal tract cancer genes and another based on the set of retroviral

57 y accounts for the dysregulation of prostate cancer genes and appears to disrupt multiple cancer gene

58 childhood cancer entity revealed a series of cancer genes and biologically relevant subtype diversity

59 containing significantly enriched consensus cancer genes and cancer-related functional pathways.

60 that 5 FA genes are in fact familial breast cancer genes and FA gene mutations are found frequently

61 diting tools can enable the in vivo study of cancer genes and faithfully recapitulate the mosaic natu

62 itutions and insertions and deletions of 360 cancer genes and genome-wide copy number aberrations in

63 ve primarily focused on a small set of known cancer genes and have thus provided a limited view of th

64 y detailing these complex interactions among cancer genes and how they differ between diseased and he

65 tructure can assist in the identification of cancer genes and in the understanding of the functional

66 ergenic regions highlights the repertoire of cancer genes and mutational processes operating, and pro

67 cision medicine requires an understanding of cancer genes and mutational processes, as well as an app

68 Aberrant expression of cancer genes and non-canonical RNA species is a hallmark

69 ogy to interrogate the function of essential cancer genes and pathways and has provided insights into

70 fferent cancer types, identifying both known cancer genes and pathways, and novel putative cancer gen

71 nfirms both known and under-appreciated lung cancer genes and pathways.

72 erstanding the complex pleiotropic effect of cancer genes and provides a possible link between genoty

73 anel genes as high- and moderate-risk breast cancer genes and provides estimates of breast cancer ris

74 r pathogenic mutations in multiple inherited cancer genes and review previously published examples to

75 We systematically catalog cancer genes and show that genes vary extensively in wha

76 as revealed a number of similarities between cancer genes and stem cell reprogramming genes, widespre

77 gy, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, cou

78 h-depth (median 600x) exonic coverage of 410 cancer genes and whole-genome copy number analysis.

79 ranscription factor regulates luminal breast cancer genes, and loss of TFAP2C induces epithelial-mese

80 n unsuspected post-transcriptional effect on cancer genes.APE1 plays an important role in the cellula

81 While some cancer genes are identified by analysis of recurrence, s

82 In these screens, candidate cancer genes are identified if their genomic location is

83 As new cancer genes are identified through large-scale sequenci

84 As more clinically relevant cancer genes are identified, comprehensive diagnostic ap

85 Although a few cancer genes are mutated in a high proportion of tumours

86 Many DNA-hypermethylated cancer genes are occupied by the Polycomb (PcG) represso

87 In some cases, mutated cancer genes are potent biomarkers for responses to targ

88 results support the hypothesis that multiple cancer genes are targeted by regional chromosome copy nu

89 A vast number of cancer genes are transcription factors that drive tumori

90 Remarkably, multiple cancer genes are under strong positive selection even in

91 Mutant epitopes encoded by cancer genes are virtually always located in the interio

92 ially expressed genes, which we term Class I cancer genes, are readily detected by most analytical to

93 uncover expressed mutations in several known cancer genes as well as a recurrent mutation in the moto

94 umour RNA-sequencing identifies co-regulated cancer genes associated with 2-oxoglutarate (2-OG) and s

95 thods,MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns.

96 Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutat

97 erwise observed in cancer-associated and non-cancer genes, both essential and non-essential.

98 applications in clinical analysis of breast cancer gene BRCA1.

99 randed DNA (dsDNA), a sequence of the breast cancer gene BRCA1.

100 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively.

101 The breast cancer gene, BRCA2, is essential for viability, yet pati

102 omatic point mutations with no impairment of cancer genes, but massive gene amplification and rearran

103 sed statistical tests to identify likely new cancer genes; but such approaches are challenging to val

104 ndicating a selective modulation of relevant cancer genes by IDH mutations.

105 copy number alterations for important kidney cancer genes by the consistency between databases, and c

106 jects enabled the identification of many new cancer gene candidates through computational approaches.

107 differentially expressed genes are Class II cancer gene candidates.

108 We found that 93 protein-coding cancer genes carried probable driver mutations.

109 efficiently orchestrate the gain and loss of cancer gene cassettes that engage many oncogenic pathway

110 Mutations in consensus cancer genes (category III) were found in an additional

111 r (TNBC), functional validation of candidate cancer genes (CCG) remains unsolved.

112 identify 1,232 recurrently mutated candidate cancer genes (CCGs) from 70 SB-driven melanomas.

113 lusion of BRCA1, BRCA2, and syndromic breast cancer genes (CDH1, PTEN, and TP53), observed pathogenic

114 ty in known cancer-implicated genes from the Cancer Gene Census (CGC).

115 ong the mutated genes were almost 200 COSMIC Cancer Gene Census genes, many of which were recurrently

116 commonly in moderate-risk breast and ovarian cancer genes (CHEK2, ATM, and PALB2) and Lynch syndrome

117 It is recognized that some mutated cancer genes contribute to the development of many cance

118 cancer genes and appears to disrupt multiple cancer genes coordinately.

119 needed to more reliably interpret NGS-based cancer gene copy number data in the context of clinical

120 te meta-analyses, we developed the Candidate Cancer Gene Database.

121 troviral insertions in the Retroviral Tagged Cancer Gene Database.

122 An enhanced understanding of cancer gene dependencies may help to unravel vulnerabili

123 ss of wild-type SF3B1 as a novel, non-driver cancer gene dependency.

124 er Genes is a manually curated repository of cancer genes derived from the scientific literature.

125 gate long-range interactions at three breast cancer gene deserts mapping to 2q35, 8q24.21, and 9q31.2

126 n will significantly enhance the accuracy of cancer gene discovery in forward genetic screens and pro

127 variants for constitutive or tissue-specific cancer gene discovery screening.

128 transposon insertional mutagenesis to enable cancer gene discovery starting with human primary cells.

129 proved methods of clinical MSI diagnosis and cancer gene discovery.

130 g in mice has emerged as a powerful tool for cancer gene discovery.

131 nts in the 3' untranslated region (3'UTR) of cancer genes disrupting microRNA (miRNA) regulation have

132 The three deleted in liver cancer genes (DLC1-3) encode Rho-specific GTPase-activat

133 patients and recovered somatic mutations in cancer genes EGFR, PIK3CA, and TP53 We further showed th

134 two unreliable assumptions of translational cancer gene expression analysis: that "small" departures

135 method, we conducted a meta-analysis of lung cancer gene expression based on publicly available data.

136 synthetic and 48 real datasets, including 35 cancer gene expression benchmark datasets and 13 cancer

137 sed exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR

138 y large and diverse public databases of lung cancer gene expression constitute a rich source of candi

139 We apply BicMix to breast cancer gene expression data and to gene expression data

140 We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas

141 Analysis of breast cancer gene expression data indicates that HOTAIR is co-

142 Interrogation of human cancer gene expression data revealed that high TNC expre

143 et prediction algorithms and metastatic lung cancer gene expression data reveals the TGF-beta co-rece

144 We have analyzed lung cancer gene expression data, immunostained lung tumors f

145 g to learn the hierarchical structure within cancer gene expression data.

146 ly and clinically meaningful abstractions of cancer gene expression data.

147 analysis on 11 independent human colorectal cancer gene expression datasets and applied expression d

148 xperiments using multiple independent breast cancer gene expression datasets and PPI networks.

149 lic data repositories a collection of breast cancer gene expression datasets with over 7000 patients.

150 ough integrative analysis of clinical breast cancer gene expression datasets, cell line models of bre

151 n apply this technique to several well-known cancer gene expression datasets, showing that COMMUNAL p

152 In prostate cancer, prostate cancer gene expression marker 1 (PCGEM1) is an androgen-

153 Cancer gene expression profiles are not normally-distrib

154 nd performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets contai

155 investigate their role in defining prostate cancer gene expression profiles.

156 lated in cancers, but how they influence the cancer gene expression program during cancer initiation

157 Cancer gene expression signatures extracted from individ

158 cell lines that recapitulate human prostate cancer gene expression, which metastasize in immune-comp

159 e transcription while also facilitating anti-cancer gene expression.

160 tributing to dysregulated local and regional cancer gene expression.

161 ions driving most of the variation in breast cancer gene expression.

162 er, SEs are found near oncogenes and dictate cancer gene expression.

163 In treated animals that developed cancer, gene expression was analyzed.

164 ith poor outcome in two independent prostate cancer gene-expression datasets.

165 Bioinformatic analysis of human prostate cancer gene-expression sets revealed increased c-Myc tra

166 find approximately 51% of the eligible known cancer genes form detectable mutation hotspots.

167 Many cancer genes form mutation hotspots that disrupt their f

168 performed next-generation sequencing of 341 cancer genes from 117 patient-derived PDTCs and ATCs and

169 NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 18

170 ntroduced a more robust procedure to extract cancer genes from published cancer mutational screenings

171 d interpretation for obtaining insights into cancer gene function and genetic tumor evolution.

172 ncluding information on somatic mutations in cancer genes, gene amplification and deletion, tissue ty

173 Hence, the 5' UTRs of select cancer genes harbour a targetable requirement for the eI

174 subtypes, where recurrent mutations of known cancer genes have been identified.

175 In addition to well-known cancer genes (i.e., TP53, PIK3CA, PPP2R1A, KRAS, FBXW7),

176 ntary approaches increase the specificity of cancer gene identification.

177 nactivation and highlights a new approach to cancer gene identification.

178 iles were remarkably concordant with mutated cancer genes identified in a large series of human poorl

179 alysis pipeline, Identification of Metabolic Cancer Genes (iMetCG), to infer the functional impact on

180 background mutational load (13-60% recall of cancer genes impacted by somatic single-nucleotide varia

181 ve NOTCH signaling regulator, SPEN, as a new cancer gene in ACC with mutations in 5 cases.

182 ns, and mutations in LYST, a potential novel cancer gene in chordoma.

183 was LYST (10%), which may represent a novel cancer gene in chordoma.Chordoma is a rare often incurab

184 twork biology approaches to uncover Class II cancer genes in coordinating functionality in cancer net

185 eously resequenced 33 clinically informative cancer genes in eight cell line and 45 clinical cancer s

186 Targeted resequencing of cancer genes in large cohorts of patients is important t

187 nsposons as insertional mutagens to identify cancer genes in mice has generated a wealth of informati

188 is a powerful tool for identifying putative cancer genes in mice.

189 genetic screening technique used to identify cancer genes in mouse model systems.

190 The study of cancer genes in mouse models has traditionally relied on

191 Several such mutations are detected in known cancer genes in multiple cancer types.

192 We performed ultradeep sequencing of 74 cancer genes in small (0.8 to 4.7 square millimeters) bi

193 These metabolic genes were similar to known cancer genes in terms of their network connectivity, iso

194 Our analysis implicates candidate cancer genes in the deregulation of apoptosis and transl

195 bles functional characterization of putative cancer genes in the lung and other tissues using autocht

196 nomic analysis can identify nearly all known cancer genes in these tumour types.

197 tabase encompassing perturbations of over 90 cancer genes, in combination with a large breast cancer

198 ver mutations were identified in several new cancer genes including AKT2, ARID1B, CASP8, CDKN1B, MAP3

199 quencing of the coding sequence of 275 known cancer genes including GNAQ was performed in both specim

200 enic lesions involving Cdkn2a loss and other cancer genes including Nras, Kras and Bcor.

201 riven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and M

202 ing somatic SNVs affected a preponderance of cancer genes, including FGFR2, MEN1, HOOK3, EZH2, MLF1,

203 ith the cancer phenotype than other hallmark cancer genes, including hexokinase 2 and pyruvate kinase

204 These studies have revealed scores of new cancer genes, including many in processes not previously

205 Further, mutations were identified in known cancer genes, including PIK3CA, ATM, CDKN2A, SF3B1, SUFU

206 rrangements affecting the most common breast cancer genes, including PIK3CA, TP53, PTEN, BRCA2 and MY

207 , we identified recurrent mutations of known cancer genes, including TP53, CYLD, CDKN2A, BAP1 and PBR

208 OncoPPi is a focused PPI resource that links cancer genes into a signalling network for discovery of

209 Integration of cancer genes into networks offers opportunities to revea

210 mutation in BRCA1 or BRCA2 or another breast cancer gene is not known.

211 The Network of Cancer Genes is a manually curated repository of cancer

212 As we find that the number of targeted cancer genes is a naive proxy for a cancer miR family, w

213 We propose a hypothesis called the cancer gene island model, whereby gene islands encompass

214 ith mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer

215 As genomics advances reveal the cancer gene landscape, a daunting task is to understand

216 tinct markers and remains orphan of specific cancer gene lesions.

217 PI hubs reveal new regulatory mechanisms for cancer genes like MYC, STK11, RASSF1 and CDK4.

218 We have identified 119 new metabolic cancer genes likely to be involved in rewiring cancer ce

219 entifying targetable alterations in multiple cancer genes, little is known about how physicians will

220 Drugs targeted to mutant cancer genes may act not only on tumor cells but also, d

221 ased frequency of nonsynonymous SSNVs in Pan-Cancer genes (mean 1.4 vs. 0.26, P = 0.002), and increas

222 Analysis of ovarian cancer gene microarray data showed that higher expressio

223 e independent of mutations in key regulatory cancer genes, microsatellite instability, and other gene

224 For cancer genes mutated in rearranged regions, this informa

225 ng technology for clinical diagnosis such as cancer gene mutation detection, infectious disease detec

226 To emphasize depth of knowledge on known cancer genes, mutation information is curated manually f

227 e adequate to identify the majority of known cancer gene mutations in localized lung adenocarcinomas.

228 40% to 50% more individuals with hereditary cancer gene mutations than does testing for BRCA1/2 alon

229 76% of all mutations and 20 out of 21 known cancer gene mutations were identified in all regions of

230 Because first hit cancer gene mutations would specifically mark cancer-ind

231 Rb1 pathway loss rapidly triggers additional cancer gene mutations, accounting for rapid tumour onset

232 In addition to collecting cancer genes, NCG also provides information on the exper

233 We identified 88 candidate cancer genes near common sites of proviral insertion.

234 Cancer genes not detected by mutation recurrence also te

235 Additional mutated or amplified cancer genes of potential clinical importance included P

236 has led to the identification of hundreds of cancer genes on the basis of the presence of mutations i

237 ome with recurrent mutations identified in a cancer gene panel that used next-generation sequencing i

238 tic basis of kidney cancer and of the kidney cancer gene pathways and, most importantly, to provide t

239 Negative genetic interactions among cancer genes point toward broader context dependencies o

240 nterconnectivity between known and candidate cancer gene products, providing unbiased evidence for an

241 SASEs in selected cancer gene promoters were associated with over-expressi

242 s,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel can

243 calcium-sensing receptor (CaSR) and ovarian cancer gene receptor 1 (OGR1) are two GPCRs that sense e

244 cancer will require an understanding of how cancer genes regulate tumor biology.

245 We created a breast cancer gene regulatory network comprising transcription

246 We assessed genetic changes in a conserved cancer gene, Retinoblastoma (Rb), in association with hi

247 h-grade primary uRCC, incorporating targeted cancer gene sequencing, RNA sequencing, single-nucleotid

248 ractice has been to test candidate inherited cancer genes sequentially until a pathogenic mutation is

249 sive analysis of seven independently curated cancer gene sets as well as six disease or trait associa

250 ntified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer

251 We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNA

252 promoters of several genes, including known cancer genes such as MYC, BCL2, RBM5 and WWOX.

253 Well established and emerging cancer genes such as MYC, IDH1/2 and KEAP1 regulate tumo

254 Here, using full-exome or cancer genes-targeted sequencing of 42 ALL samples from

255 ry tumor, drawing from a wider repertoire of cancer genes than early drivers.

256 s, we identify ZBTB7A as a context-dependent cancer gene that can act as an oncogene in some contexts

257 Thus, Phf6 is a "lineage-specific" cancer gene that plays opposing roles in developmentally

258 ce indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating o

259 ntiation but also function pathologically as cancer genes that contribute to tumorigenesis.

260 powerful tool to facilitate the discovery of cancer genes that drive tumorigenesis in mouse models.

261 We identified 322 DLBCL cancer genes that were recurrently mutated in primary DL

262 rapy offers a promising approach for suicide cancer gene therapy in cells with high constitutive ARE

263 ase matrices for the delivery of viruses for cancer gene therapy in preclinical models.

264 The objective of a systemically administered cancer gene therapy is to achieve gene expression that i

265 broblasts to secrete WNT16B, enabling potent cancer gene therapy with few side effects.

266 cal utility of GALVs as viral vectors and in cancer gene therapy, full genome sequences have not been

267 ovirus (Ad) vector's numerous advantages for cancer gene therapy, such as high ability of endosomal e

268 noviruses (Ads) are an attractive option for cancer gene therapy, the intravenous administration of n

269 se as viral vectors for gene transfer and in cancer gene therapy.

270 of considerable practical utility to T-cell cancer gene therapy.

271 of polymer-adenovirus combination in bladder cancer gene therapy.

272 ntratracheal strategy for administering lung cancer gene therapy.

273 nd report the discovery of large sets of new cancer genes through a pancreatic insertional mutagenesi

274 Discovery of cancer genes through interrogation of genomic dosage is

275 and massively parallel DNA sequencing of 619 cancer genes to compare the gene mutations and copy numb

276 rom the elucidation of the hereditary kidney cancer gene, TRC8, which functions partly to degrade key

277 The breast cancer gene trinucleotide-repeat-containing 9 (TNRC9; TO

278 and transmembrane signaling domains, whereas cancer genes undergoing amplification or deletion tend t

279 sequenced for 23 known and candidate breast cancer genes using BROCA, a targeted multiplexed gene pa

280 cessive genetic screening or high-throughput cancer gene validation in mice.

281 ysis by massively parallel sequencing of 504 cancer genes was performed at Dana-Farber Cancer Institu

282 ngle nucleotide variants (SNVs) affecting 15 cancer genes was performed to identify mutations support

283 ity of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exo

284 of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate

285 Somatic mutations in established thyroid cancer genes were detected in 14 of 22 (64%) tumors and

286 Analysis of integration sites showed that cancer genes were preferentially targeted, raising conce

287 n previously undetermined interactions among cancer genes were revealed by assessing gene pairs that

288 FR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker ini

289 ion sequencing using the MSK-IMPACT panel of cancer genes, which we modified to include all SB candid

290 C-SC super-enhancer landscape and downstream cancer genes while ETS2-overactivation in epidermal-SCs

291 Identification cooperating cancer genes will result in the development of combinato

292 Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defi

293 We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, c

294 fies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier app

295 drivers along with three to six cooperating cancer genes with SB-driven expression changes.

296 Burden testing identifies 13 cancer genes with significant enrichment of rare truncat

297 eloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer ar

298 c and polygenic variation in known and novel cancer genes, with implications for risk management and

299 is poorly understood how driver mutations in cancer genes work together to promote tumor development.

300 ic copy-number alterations (SCNAs) affecting cancer genes, yet the extent to which recurrent SCNAs ex