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

3 to obtain a deeper characterization of known cancer genes.

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

5 ntifies variable length mutation clusters in cancer genes.

6 omosomal translocations and DNA deletions at cancer genes.

7 and we also identified candidate pancreatic cancer genes.

8 er coding mutations, including outside known cancer genes.

9 identified 45 recurrently mutated candidate cancer genes.

10 variants identified in HCC1954 overlap known cancer genes.

11 rallel testing of large numbers of inherited cancer genes.

12 tations were absent in several major bladder cancer genes.

13 alysis methods to specifically recover known cancer genes.

14 ncer, highlighting several potentially novel cancer genes.

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

16 genomic regions containing well-known breast-cancer genes.

17 downstream targets of commonly mutated human cancer genes.

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

19 common alternative strategy in ranking known cancer genes.

20 ystem for the functional characterization of cancer genes.

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

22 ic retrotransposon insertions occur in known cancer genes.

23 ons from large cohorts of deeply resequenced cancer genes.

24 ctional interconnection and regulation among cancer genes.

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

26 luential genes are enriched in essential and cancer genes.

27 temozolomide and sequenced approximately 300 cancer genes.

28 ation, and targeted sequence analysis of 563 cancer genes.

29 ally, we extend this analysis to uncover pan-cancer genes.

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

31 e distribution of mutations in 119 canonical cancer genes.

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

33 iological effects of insertions on candidate cancer genes.

34 was sequenced for known and candidate breast cancer genes.

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

36 latory elements and affect the expression of cancer genes.

37 strate they are regulatory elements of known cancer genes.

38 ted selective cytotoxic effect toward breast cancer gene 1 ( BRCA1)-deficient cells compared to isoge

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

40 g in recurrent mutations across more than 30 cancer genes(1-7).

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

42 ever, the serial genetic evolution of mutant cancer genes(7,8) and the allelic context in which they

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

44 t leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve

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

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

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

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

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

50 rough the introduction of any combination of cancer gene alterations to normal organoids.

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

52 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more t

53 loid leukemia (AML) tumor and identify known cancer genes and additional driver candidates.

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

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

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

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

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

61 sues were found to harbor mutations in known cancer genes and hotspots.

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

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

64 s in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.

65 yses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-codi

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 cancer types, and patients, affecting known cancer genes and novel candidates.

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

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

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

73 is revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored lan

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

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

76 associated with increased expression of pro-cancer genes and reduced expression of cancer suppressor

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

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

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

80 oviding a scalable platform to test putative cancer genes and to create mutation-directed, bespoke ly

81 is poorly understood in both common and rare cancer genes and tumour types.

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

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

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

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

86 Somatic alterations in cancer genes are being detected in normal and premaligna

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

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

89 that many previously identified OSCC-related cancer genes are non-essential and could have limited th

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

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

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

93 oss 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial chan

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

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

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

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

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

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

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

101 A minority of cancers have breast cancer gene (BRCA) mutations that confer sensitivity to

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

103 applications in clinical analysis of breast cancer gene BRCA1.

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

105 of tumors deficient in the hereditary breast cancer genes BRCA1 and BRCA2 (BRCA).

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

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

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

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

110 enumeration of copy numbers of eight breast cancer genes by multicolor fluorescence in situ hybridiz

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

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

113 differentially expressed genes are Class II cancer gene candidates.

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

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

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

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

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

119 ciencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating obse

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

121 OSMIC's deep and broad variant coverage, the Cancer Gene Census (CGC) describes a curated catalogue o

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

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

124 ~700 cancer-related sequences in the COSMIC Cancer Gene Census, 178 sequences are predicted to have

125 ull repertoire of 715 proteins in the COSMIC Cancer Gene Census.

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

127 ntly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA dam

128 Lentivirus-based in vivo delivery of cancer genes conditioned by FLP/FRT-mediated recombinati

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

130 howcase the portal with known and overlooked cancer genes, demonstrating the utility of the portal vi

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

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

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

134 overall mutated allele fractions of myeloid cancer genes did not increase on EPAG.

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

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

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

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

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

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

141 on protein-protein interaction network with cancer gene dysregulation profile show that the reported

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

143 Luminal breast cancer gene expression and proliferation are driven by e

144 Detecting cancer gene expression and transcriptome changes with mR

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

146 nt and non-redundant gene markers from three cancer gene expression benchmark data sets.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

161 The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-

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

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

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

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

166 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis fo

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

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

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

170 tributing to dysregulated local and regional cancer gene expression.

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

172 We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set

173 It is well known that in cancer gene families some members are more frequently mu

174 out of 4) downregulated and hypermethylated 'cancer' genes following acute and chronic RE respectivel

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

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

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

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

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

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

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

182 re variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene

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

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

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

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

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

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

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

190 of all exons and selected introns of 468 key cancer genes in formalin-fixed, paraffin-embedded tumors

191 ibe a machine learning algorithm to identify cancer genes in individual patients considering all type

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

208 Integration of cancer genes into networks offers opportunities to revea

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

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

211 Discovering dual role cancer genes is difficult because of their elusive conte

212 RNA splicing by spliceosome mutations or in cancer genes is increasingly recognized as a hallmark of

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

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

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

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

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

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

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

220 ing and tested against a custom panel of 347 cancer genes.Measurements and Main Results: Sequencing d

221 ively Parallel Sequencing for Familial Colon Cancer Genes, Medullary Thyroid Carcinoma (MTC) Surveill

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

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

224 Finally, diffusing pan-cancer gene mutation scores based on different Graphlet

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

226 Cancer gene mutations (e.g., KRAS, TP53) and microsatell

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 of G659Vfs*41 and its association with other cancer gene mutations, and found that the mutation occur

231 The Network of Cancer Genes (NCG) is a manually curated repository of 2

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

233 Regarding cancer gene networks construction and gene function pred

234 signaling, gene co-expression, homology and cancer-gene networks.

235 Cancer genes not detected by mutation recurrence also te

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

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

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

239 Whole exome sequencing, a cancer gene panel, or both were carried out.

240 ysis and Tumor Subtyping in High-Risk Breast-Cancer Gene Pedigrees, Study of Shared Genomic Segment A

241 Within skeletal muscle tissue, these 'cancer' genes predominant functions were associated with

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

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

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

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

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

247 We created a breast cancer gene regulatory network comprising transcription

248 BCL genetics, identification of non-mutated cancer genes remains challenging.

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

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

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

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

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

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

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

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

257 NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin

258 -regulation of canonical aggressive prostate cancer genes, such as MMP7, ETV1, NTS, and SCHLAP1, we a

259 rly-onset cancers, the larger gHFI burden in cancer genes suggests a greater contribution of germline

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

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

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

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

264 ese approaches have defined the landscape of cancer genes that are operative in Wilms tumour, many of

265 lands frequently carry 'driver' mutations in cancer genes, the burden of which increases with age and

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

267 This cancer gene therapy strategy was in vitro challenged dem

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

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

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

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

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

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

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

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

276 utations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous

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

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

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

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

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

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

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

290 r exploration include targeting Wilms tumour cancer genes with a non-redundant role in nephrogenesis

291 vers were enriched for functionally relevant cancer genes with amplification-driven dependencies, whi

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 d frequently mutated pathways and additional cancer genes with infrequent mutations.

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

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

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

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

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