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• W2002927706 abstract "Archival formalin-fixed, paraffin-embedded (FFPE) tissue specimens represent a readily available but largely untapped resource for gene expression profiling–based biomarker discovery. Several technologies have been proposed to cope with the bias from RNA cross-linking and degradation associated with archival specimens to generate data comparable with RNA from fresh-frozen materials. Direct comparison studies of these RNA expression platforms remain rare. We compared two commercially available platforms for RNA expression profiling of archival FFPE specimens from clinical studies of prostate and ovarian cancer: the Affymetrix Human Gene 1.0ST Array following whole-transcriptome amplification using the NuGen WT-Ovation FFPE System V2, and the NanoString nCounter without amplification. For each assay, we profiled 7 prostate and 11 ovarian cancer specimens, with a block age of 4 to 21 years. Both platforms produced gene expression profiles with high sensitivity and reproducibility through technical repeats from FFPE materials. Sensitivity and reproducibility remained high across block age within each cohort. A strong concordance was shown for the transcript expression values for genes detected by both platforms. We showed the biological validity of specific gene signatures generated by both platforms for both cohorts. Our study supports the feasibility of gene expression profiling and large-scale signature validation on archival prostate and ovarian tumor specimens using commercial platforms. These approaches have the potential to aid precision medicine with biomarker discovery and validation. Archival formalin-fixed, paraffin-embedded (FFPE) tissue specimens represent a readily available but largely untapped resource for gene expression profiling–based biomarker discovery. Several technologies have been proposed to cope with the bias from RNA cross-linking and degradation associated with archival specimens to generate data comparable with RNA from fresh-frozen materials. Direct comparison studies of these RNA expression platforms remain rare. We compared two commercially available platforms for RNA expression profiling of archival FFPE specimens from clinical studies of prostate and ovarian cancer: the Affymetrix Human Gene 1.0ST Array following whole-transcriptome amplification using the NuGen WT-Ovation FFPE System V2, and the NanoString nCounter without amplification. For each assay, we profiled 7 prostate and 11 ovarian cancer specimens, with a block age of 4 to 21 years. Both platforms produced gene expression profiles with high sensitivity and reproducibility through technical repeats from FFPE materials. Sensitivity and reproducibility remained high across block age within each cohort. A strong concordance was shown for the transcript expression values for genes detected by both platforms. We showed the biological validity of specific gene signatures generated by both platforms for both cohorts. Our study supports the feasibility of gene expression profiling and large-scale signature validation on archival prostate and ovarian tumor specimens using commercial platforms. These approaches have the potential to aid precision medicine with biomarker discovery and validation. With the advent of precision medicine, there is a growing interest in developing prognostic and predictive molecular signatures to help guide the care of patients with cancer.1Mirnezami R. Nicholson J. Darzi A. Preparing for precision medicine.N Engl J Med. 2012; 366: 489-491Crossref PubMed Scopus (498) Google Scholar Among approaches taken to realize the goal of identifying the ideal treatment for the patient at the correct time are mutation analysis,2Lynch T.J. Bell D.W. Sordella R. Gurubhagavatula S. Okimoto R.A. Brannigan B.W. Harris P.L. Haserlat S.M. Supko J.G. Haluska F.G. Louis D.N. Christiani D.C. Settleman J. Haber D.A. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib.N Engl J Med. 2004; 350: 2129-2139Crossref PubMed Scopus (9998) Google Scholar immunohistochemical staining,3Slamon D.J. Leyland-Jones B. Shak S. Fuchs H. Paton V. Bajamonde A. Fleming T. Eiermann W. Wolter J. Pegram M. Baselga J. Norton L. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2.N Engl J Med. 2001; 344: 783-792Crossref PubMed Scopus (9348) Google Scholar and mRNA expression analysis.4Paik S. Shak S. Tang G. Kim C. Baker J. Cronin M. Baehner F.L. Walker M.G. Watson D. Park T. Hiller W. Fisher E.R. Wickerham D.L. Bryant J. Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.N Engl J Med. 2004; 351: 2817-2826Crossref PubMed Scopus (4833) Google Scholar, 5Hoshida Y. Villanueva A. Kobayashi M. Peix J. Chiang D.Y. Camargo A. Gupta S. Moore J. Wrobel M.J. Lerner J. Reich M. Chan J.A. Glickman J.N. Ikeda K. Hashimoto M. Watanabe G. Daidone M.G. Roayaie S. Schwartz M. Thung S. Salvesen H.B. Gabriel S. Mazzaferro V. Bruix J. Friedman S.L. Kumada H. Llovet J.M. Golub T.R. Gene expression in fixed tissues and outcome in hepatocellular carcinoma.N Engl J Med. 2008; 359: 1995-2004Crossref PubMed Scopus (1034) Google Scholar, 6Cuzick J. Swanson G.P. Fisher G. Brothman A.R. Berney D.M. Reid J.E. Mesher D. Speights V. Stankiewicz E. Foster C.S. Møller H. Scardino P. Warren J.D. Park J. Younus A. Flake D.D. Wagner S. Gutin A. Lanchbury J.S. Stone S. Transatlantic Prostate GroupPrognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.Lancet Oncol. 2011; 12: 245-255Abstract Full Text Full Text PDF PubMed Scopus (536) Google Scholar Such studies require access to tissue specimens with detailed clinical annotation and often long-term follow-up evaluation. These clinical samples, including biopsy and surgical specimens, typically are formalin-fixed and embedded in paraffin to allow for morphologic assessment by a pathologist. Although this process preserves morphologic features of the tumors, it also makes RNA expression profiling more challenging relative to fresh-frozen tissue. RNA in formalin-fixed, paraffin-embedded (FFPE) samples is subject to degradation, fragmentation, and cross-linking, which typically limits library preparation and gene expression assessment.7von Ahlfen S. Missel A. Bendrat K. Schlumpberger M. Determinants of RNA quality from FFPE samples.PLoS One. 2007; 2: e1261Crossref PubMed Scopus (277) Google Scholar Importantly, traditional quality control measurements for RNA, such as RNA integrity number, are not necessarily predictive of the success of corresponding gene expression assays.8Waldron L. Simpson P. Parmigiani G. Huttenhower C. Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues.BMC Cancer. 2012; 12: 124Crossref PubMed Scopus (16) Google Scholar The large numbers of well-annotated FFPE tumor tissue samples currently archived remain a vast and underused resource in the genomic study of cancer. Notably, most large clinical and epidemiologic cohorts only collect FFPE samples. Given this wealth of archival material from patients with known outcomes and the continued FFPE processing of new clinical specimens, there is a need to develop and test reliable methods for profiling mRNA expression in FFPE materials. Several platforms have been developed in recent years to assess mRNA expression from FFPE tissue including whole-transcriptome amplification9Linton K. Hey Y. Dibben S. Miller C. Freemont A. Radford J. Pepper S. Methods comparison for high-resolution transcriptional analysis of archival material on Affymetrix Plus 2.0 and Exon 1.0 microarrays.Biotechniques. 2009; 47: 587-596Crossref PubMed Scopus (39) Google Scholar, 10Thomas M. Poignee-Heger M. Weisser M. Wessner S. Belousov A. An optimized workflow for improved gene expression profiling for formalin-fixed, paraffin-embedded tumor samples.J Clin Bioinforma. 2013; 3: 10Crossref PubMed Scopus (14) Google Scholar and direct assessment using multiplexed color-coded probes.11Reis P.P. Waldron L. Goswami R.S. Xu W. Xuan Y. Perez-Ordonez B. Gullane P. Irish J. Jurisica I. Kamel-Reid S. mRNA transcript quantification in archival samples using multiplexed, color-coded probes.BMC Biotechnol. 2011; 11: 46Crossref PubMed Scopus (215) Google Scholar As an initial step to developing prognostic and predictive mRNA signatures from archival tumor specimens, we performed head-to-head comparisons of gene expression profiles from prostate and ovarian cancer FFPE specimens from large-scale epidemiologic studies and clinical trials representative of a wide variety of fixation times, block ages, and block storage conditions using two platforms: the NuGen WT-Ovation FFPE System V2 + Affymetrix GeneChip Human Gene 1.0 ST Array [NuGen (NuGen, Inc., San Carlos, CA) + Affymetrix (Affymetrix, Santa Clara, CA)] and the NanoString nCounter Cancer panel [NanoString (NanoString Technologies, Seattle, WA)]. Archival FFPE radical prostatectomy specimens were collected from treating institutions for men diagnosed with prostate cancer who had enrolled in the prospective Physicians' Health Study.12Final report on the aspirin component of the ongoing Physicians' Health Study. Steering Committee of the Physicians' Health Study Research Group.N Engl J Med. 1989; 321: 129-135Crossref PubMed Scopus (2515) Google Scholar, 13Hennekens C.H. Buring J.E. Manson J.E. Stampfer M. Rosner B. Cook N.R. Belanger C. LaMotte F. Gaziano J.M. Ridker P.M. Willett W. Peto R. Lack of effect of long-term supplementation with beta carotene on the incidence of malignant neoplasms and cardiovascular disease.N Engl J Med. 1996; 334: 1145-1149Crossref PubMed Scopus (2138) Google Scholar Participants provided informed consent to collect biospecimens, and the study was approved by the Institutional Review Boards at the Harvard School of Public Health and Partners Health Care. Pathology was reviewed centrally to confirm the cancer diagnosis and to provide consistent histopathologic review including Gleason scores. Areas of high-density tumor were identified, and two to three 0.6-mm punches were taken from both tumor and adjacent nontumor prostatic tissue for RNA extraction. The cores were deparaffinized using 800 μL Citrisolv (Fisher Scientific, Pittsburgh, PA) at 60°C for 20 minutes followed by 1.2 mL Citrisolv:absolute alcohol (2:1) at room temperature for 10 minutes. Cores subsequently were washed with absolute alcohol, dried at 55°C, and incubated overnight at 45°C in 300 μL lysis buffer [10 mmol/L NaCl, 500 mmol/L Tris (pH 7.6), 20 mmol/L EDTA, 1% sodium dodecyl sulfate] containing 1 mg/mL proteinase K (Ambion, Austin, TX). RNA was extracted using the RecoverAll Total Nucleic Acid Isolation kit (Ambion). After tissue digestion, following the manufacturer's protocol, samples were incubated in 10× DNase (Ambion) and column-purified to elute RNA. The concentration was determined using the Nanodrop 1000 (Fisher Scientific) and RiboGreen RNA Assay Kit (Ambion). Samples and technical replicates were randomized and assigned study identifications to blind laboratory personnel. Ovarian cancer specimens were obtained from the Gynecologic Oncology Group tissue bank (Columbus, OH). All specimens were archival FFPE tissue samples obtained from women with advanced-stage epithelial ovarian cancer in clinical trial GOG218, a randomized phase III trial testing the impact of the addition of bevacizumab to standard chemotherapy for the up-front treatment of advanced-stage epithelial ovarian cancer.14Burger R.A. Brady M.F. Bookman M.A. Fleming G.F. Monk B.J. Huang H. Mannel R.S. Homesley H.D. Fowler J. Greer B.E. Boente M. Birrer M.J. Liang S.X. Gynecologic Oncology GroupIncorporation of bevacizumab in the primary treatment of ovarian cancer.N Engl J Med. 2011; 365: 2473-2483Crossref PubMed Scopus (1716) Google Scholar The specimens were obtained at the primary debulking surgery. The pathology was confirmed by the central Gynecologic Oncology Group review to ensure the correct histology and percentage of tumor. Ten-micron sections were made on positively charged slides. RNA was extracted from paraffin scrolls using the RNeasy FFPE kit (Qiagen, Valencia, CA) with modifications. In brief, 1 mL of xylene was added to a 25-mm paraffin scroll, vortexed vigorously for 10 seconds, and centrifuged at full speed for 2 minutes. Supernatant then was removed and 1 mL of ethanol (100%) was added to the pellet, mixed by vortexing, and centrifuged at full speed for 2 minutes. Supernatant was removed and the tube was incubated at room temperature for 10 minutes with the lid opened. RNA subsequently was extracted using reagents provided in the RNeasy FFPE extraction kit according to the manufacturer's instructions. Finally, purified RNA was eluted from the RNeasy MinElute spin column (Qiagen) using 30 mL RNase-free water. We profiled seven paired tumor and adjacent normal prostate tissue samples from three patients with a Gleason score of 8, one patient with a Gleason score of 7, and three patients with a Gleason score of 6 disease on the NuGen + Affymetrix platform, and a subset of five pairs (two with a Gleason score of 6 and three with a Gleason score of 8) on NanoString. Block ages for prostate cancer specimens ranged from 11 to 21 years. We profiled two to three technical replicates for each sample with varying RNA input amounts. For ovarian cancer we selected five serous carcinoma and six clear cell carcinoma samples, and block ages for this cohort ranged from 4 to 7 years. All of these samples were profiled on both NuGen + Affymetrix and NanoString, with either two or three technical replicates. Complete information on all samples and technical replicates that were analyzed are presented in Supplemental Table S1. For array-based mRNA profiling, we first performed whole-transcriptome amplification using the WT-Ovation FFPE System V2 (NuGen). This approach initiates amplification at the 3′ end as well as randomly throughout the transcriptome, improving the performance in severely degraded FFPE samples. After isothermal amplification, 50 or 100 ng of total RNA was amplified to 4 to 7 μg of biotinylated cDNA complementary to the original mRNA. The amplification step has been optimized for RNA extracted from FFPE specimens15Hall J.S. Leong H.S. Armenoult L.S. Newton G.E. Valentine H.R. Irlam J.J. Moller-Levet C. Sikand K.A. Pepper S.D. Miller C.J. West C.M. Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples.Br J Cancer. 2011; 104: 971-981Crossref PubMed Scopus (31) Google Scholar, 16Kennedy R.D. Bylesjo M. Kerr P. Davison T. Black J.M. Kay E.W. et al.Development and independent validation of a prognostic assay for stage II colon cancer using formalin-fixed paraffin-embedded tissue.J Clin Oncol. 2011; 29: 4620-4626Crossref PubMed Scopus (154) Google Scholar and has shown comparable differential expression profiles to corresponding fresh-frozen tissues.17Abdueva D. Wing M. Schaub B. Triche T. Davicioni E. Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays.J Mol Diagn. 2010; 12: 409-417Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar After amplification, we hybridized 3.75 μg of amplified cDNA to the GeneChip Human Gene 1.0 ST Array (Affymetrix). The 1.0 ST Array profiles expression of >28,000 genes with an average of 26 probes per gene. We used the NanoString nCounter platform18Geiss G.K. Bumgarner R.E. Birditt B. Dahl T. Dowidar N. Dunaway D.L. Fell H.P. Ferree S. George R.D. Grogan T. James J.J. Maysuria M. Mitton J.D. Oliveri P. Osborn J.L. Peng T. Ratcliffe A.L. Webster P.J. Davidson E.H. Hood L. Dimitrov K. Direct multiplexed measurement of gene expression with color-coded probe pairs.Nat Biotechnol. 2008; 26: 317-325Crossref PubMed Scopus (1554) Google Scholar to capture and count 230 cancer-related human genes using a prebuilt kit supplied by the manufacturer. Following the manufacturer's instructions, we aliquoted 100 to 200 ng total RNA in 5 μL to initiate analysis. We normalized the Affymetrix data across samples and batches using Robust Multichip Average method.19Irizarry R.A. Hobbs B. Collin F. Beazer-Barclay Y.D. Antonellis K.J. Scherf U. Speed T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data.Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8451) Google Scholar, 20Irizarry R.A. Bolstad B.M. Collin F. Cope L.M. Hobbs B. Speed T.P. Summaries of Affymetrix GeneChip probe level data.Nucleic Acids Res. 2003; 31: e15Crossref PubMed Scopus (4014) Google Scholar For the NanoString platform the data were processed according to the manufacturer's recommendations. Briefly, background subtracted counts were multiplied by scaling factors proportional to the sum of counts for spiked-in positive control probes to account for individual assay efficiency variation, and to the geometric average of the housekeeping gene probes (CLTC, GAPDH, GUSB, HPRT1, PGK1, and TUBB) to account for variability in the mRNA content. Background signal was calculated as a median value of the negative hybridization control probes. Normalized counts were log-transformed for downstream analysis. Raw and preprocessed data were submitted to the GEO database (http://www.ncbi.nlm.nih.gov/geo; accession number GSE54809). Data preprocessing and statistical analysis were performed using statistical software packages R version 3.1.1 (http://www.r-project.org) and Bioconductor version 2.14 (http://www.bioconductor.org).21Gentleman R.C. Carey V.J. Bates D.M. Bolstad B. Dettling M. Dudoit S. Ellis B. Gautier L. Ge Y. Gentry J. Hornik K. Hothorn T. Huber W. Iacus S. Irizarry R. Leisch F. Li C. Maechler M. Rossini A.J. Sawitzki G. Smith C. Smyth G. Tierney L. Yang J.Y. Zhang J. Bioconductor: open software development for computational biology and bioinformatics.Genome Biol. 2004; 5: R80Crossref PubMed Google Scholar Power analysis was performed using G*Power version 3.1.3 (http://www.gpower.hhu.de).22Faul F. Erdfelder E. Lang A.-G. Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences.Behav Res Methods. 2007; 39: 175-191Crossref PubMed Scopus (30513) Google Scholar We sought to evaluate several potential variables related to the performance of RNA expression assays. We assessed the sensitivity and reproducibility of the gene expression measurements across technical replicates of RNA extracted from the same tissue with respect to the following: i) RNA input levels, ii) FFPE block age, and iii) tumor grade and histology. We also used tumor grade and histology to assess the biological validity of the expression profiles. For each platform and each cohort we calculated the percentages of probes detectable above the background. In each platform, background probes comprise sequences not found in the human genome. We observed no significant association between the proportion of detectable probes and the block age, as quantified by the Pearson correlation coefficients (Table 1). The wider range in the percentage of probes present for the NuGen + Affymetrix data on the prostate cohort likely can be explained by differences in the instrument calibrations between the two batches in which the samples were assayed. One batch had higher intensities, which were corrected analytically by Robust Multichip Average method normalization (Supplemental Figure S1). With sample sizes of 24 (Ovarian and prostate NanoString studies, and Ovarian NuGen + Affymetrix) and 30 (Prostate NuGen + Affymetrix), we had a power of 0.86 and 0.93, respectively, to detect whether 30% or more of the variance in the percentage of the probes present was explained by the block age. Therefore, failure to find a significant correlation between the percentage of the probes present and block age was suggestive of the fact that block age indeed does not explain much of the variation in the percentage of the probes within each study.Table 1Ranges of the Percentages of the Probes with the Signal above the Background, Correlation of the Percentage of Present Probes with Block Age, and Ranges of Correlations between Technical Replicates for Each Cohort and Gene Expression PlatformPlatformPercent presentCorrelation with block ageCorrelations between technical replicatesProstate samples NuGen + Affymetrix0.17–0.580.020.91–0.98 NanoString0.40–0.79−0.110.88–0.97Ovarian samples NuGen + Affymetrix0.40–0.680.060.94–0.98 NanoString0.83–0.93−0.040.95–0.99 Open table in a new tab In the prostate samples with sufficient replicates at each RNA quantity we did not observe a statistically significant difference between the percentage of probes present at 50 versus 100 ng of input RNA for the NuGen + Affymetrix platform (analysis of variance power of 0.85 to detect whether 40% of variance in percentage of the present probes is explained by RNA quantity). A lower percentage of probes in the samples with smaller RNA input quantity was present in the NanoString data (Tukey honest significant difference test for 200 ng versus 100 ng, P = 0.04). For this analysis we had an analysis of variance power of 0.85 to detect whether 45% of variance in the percentage of probes present above the background is explained by RNA quantity. For ovarian samples only two replicates were of lower quantity, therefore the formal testing was not performed. The Pearson correlation coefficients between all pairs of technical replicates within each platform ranged from 0.88 to 0.99 (Table 1 and Figure 1, A–D). The consistency of gene expression levels, as measured by the magnitude of the correlation, between technical replicates did not change with block age. We had a power of 0.85 to detect the following percentages of the concordance explained by the block age: 32% and 35% for prostate NuGen + Affymetrix and NanoString, respectively, and 41% for ovarian samples on both platforms. The consistency of gene expression levels depended on the input RNA amounts for the NanoString platform (Supplemental Figure S2). This can be explained by higher input amounts corresponding to a higher percentage of detectable probes. We had a power of 0.85 to detect 45% of the concordance explained by RNA input amounts. We also found lower correlations between replicates among the ovarian samples that presented with more extensive tissue necrosis. The presence of necrosis was not measured in the prostate samples although it usually is quite rare. Because correlation coefficients could be driven by outlying values, we also assessed concordance for each pair of the replicates by considering the fraction of genes that are either below or above certain expression intensities for both members of the pair (Figure 1, E–H). Lower concordance was observed at lower, less reliably measured, expression intensities, as expected. Only a small fraction (<2%) of genes with medium to high expression intensities in one replicate had low expression in the other replicate. As expected, the gene-wise correlations (Figure 1, I–L) between the replicate pairs tended to be lower for genes with lower average intensities than for genes with higher average intensities, suggesting that gene expression quantification from FFPE tissues still was reliable when genes were stratified by expression level. By using NetAffx annotations (Affymetrix), we mapped all 236 genes surveyed on the NanoString Cancer panel assay to a total of 256 transcript clusters on the Affymetrix GeneChip Human Gene 1.0 ST array. For these genes we calculated correlations between the samples assayed on both platforms. For this analysis, we averaged transcript expression values for technical replicates within each platform. Most of the genes had high positive correlations, with lower correlations predominantly found for the genes with lower expression levels (Figure 2, A and B). Of the 256 correlations, 77 (30%) in prostate and 33 (13%) in ovarian were less than 0.3. Among these genes with lower correlation, 18 mapping pairs, corresponding to 15 unique gene symbols, were common in both diseases: BCR, CASP10, CEBPA, CSF3, CYP1A1, FLT3, GATA1, HRAS, LMO2, MLH1, MLL, MPL, TFE3, WEE1, and WNT10B. We performed a permutation analysis and obtained distributions of the correlations between pairs of genes measured on two platforms under the null scenario of spurious correlations between the unrelated measurements, which can be high for relatively small sample sizes. Null scenario correlations were computed for 100 permutations of the sample labels for one of the platforms. From this analysis we show that for both prostate and ovarian cohorts the observed correlations between the platforms were higher than expected by chance (Figure 2, C and D). To provide evidence that both platforms provide biologically useful information we considered established gene signatures that distinguish prostate cancer versus noncancerous tissue and high versus low Gleason grade in prostate cancer, and clear cell versus serous adenocarcinoma for the ovarian cancer cohort. The prostate tumor versus normal tissue signature was obtained from Oncomine (Life Technologies, Carlsbad, CA), and was defined by genes that were significantly up- or down-regulated in tumor versus normal comparison in four prostate cancer studies,23Grasso C.S. Wu Y.M. Robinson D.R. Cao X. Dhanasekaran S.M. Khan A.P. Quist M.J. Jing X. Lonigro R.J. Brenner J.C. Asangani I.A. Ateeq B. Chun S.Y. Siddiqui J. Sam L. Anstett M. Mehra R. Prensner J.R. Palanisamy N. Ryslik G.A. Vandin F. Raphael B.J. Kunju L.P. Rhodes D.R. Pienta K.J. Chinnaiyan A.M. Tomlins S.A. The mutational landscape of lethal castration-resistant prostate cancer.Nature. 2012; 487: 239-243Crossref PubMed Scopus (1783) Google Scholar, 24Lapointe J. Li C. Higgins J.P. van de Rijn M. Bair E. Montgomery K. Ferrari M. Egevad L. Rayford W. Bergerheim U. Ekman P. DeMarzo A.M. Tibshirani R. Botstein D. Brown P.O. Brooks J.D. Pollack J.R. Gene expression profiling identifies clinically relevant subtypes of prostate cancer.Proc Natl Acad Sci U S A. 2004; 101: 811-816Crossref PubMed Scopus (1055) Google Scholar, 25Taylor B.S. Schultz N. Hieronymus H. Gopalan A. Xiao Y. Carver B.S. Arora V.K. Kaushik P. Cerami E. Reva B. Antipin Y. Mitsiades N. Landers T. Dolgalev I. Major J.E. Wilson M. Socci N.D. Lash A.E. Heguy A. Eastham J.A. Scher H.I. Reuter V.E. Scardino P.T. Sander C. Sawyers C.L. Gerald W.L. Integrative genomic profiling of human prostate cancer.Cancer Cell. 2010; 18: 11-22Abstract Full Text Full Text PDF PubMed Scopus (2725) Google Scholar, 26Tomlins S.A. Mehra R. Rhodes D.R. Cao X. Wang L. Dhanasekaran S.M. Kalyana-Sundaram S. Wei J.T. Rubin M.A. Pienta K.J. Shah R.B. Chinnaiyan A.M. Integrative molecular concept modeling of prostate cancer progression.Nat Genet. 2007; 39: 41-51Crossref PubMed Scopus (734) Google Scholar and had a median rank of the differential expression of less than 150 across these four studies. An mRNA signature related to Gleason grade was taken from Penney et al.27Penney K.L. Sinnott J.A. Fall K. Pawitan Y. Hoshida Y. Kraft P. Stark J.R. Fiorentino M. Perner S. Finn S. Calza S. Flavin R. Freedman M.L. Setlur S. Sesso H.D. Andersson S.O. Martin N. Kantoff P.W. Johansson J.E. Adami H.O. Rubin M.A. Loda M. Golub T.R. Andren O. Stampfer M.J. Mucci L.A. mRNA expression signature of Gleason grade predicts lethal prostate cancer.J Clin Oncol. 2011; 29: 2391-2396Crossref PubMed Scopus (120) Google Scholar For ovarian cancer, the signature was retrieved as the union of three gene signatures describing differences between serous and clear cell carcinomas from GeneSigDb.28Culhane A.C. Schroder M.S. Sultana R. Picard S.C. Martinelli E.N. Kelly C. Haibe-Kains B. Kapushesky M. St Pierre A.A. Flahive W. Picard K.C. Gusenleitner D. Papenhausen G. O'Connor N. Correll M. Quackenbush J. GeneSigDB: a manually curated database and resource for analysis of gene expression signatures.Nucleic Acids Res. 2012; 40: D1060-D1066Crossref PubMed Scopus (90) Google Scholar, 29Schwartz D.R. Kardia S.L. Shedden K.A. Kuick R. Michailidis G. Taylor J.M. Misek D.E. Wu R. Zhai Y. Darrah D.M. Reed H. Ellenson L.H. Giordano T.J. Fearon E.R. Hanash S.M. Cho K.R. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas.Cancer Res. 2002; 62: 4722-4729PubMed Google Scholar, 30Zorn K.K. Bonome T. Gangi L. Chandramouli G.V. Awtrey C.S. Gardner G.J. Barrett J.C. Boyd J. Birrer M.J. Gene expression profiles of serous, endometrioid, and clear cell subtypes of ovarian and endometrial cancer.Clin Cancer Res. 2005; 11: 6422-6430Crossref PubMed Scopus (320) Google Scholar, 31Stany M.P. Vathipadiekal V. Ozbun L. Stone R.L. Mok S.C. Xue H. Kagami T. Wang Y. McAlpine J.N. Bowtell D. Gout P.W. Miller D.M. Gilks C.B. Huntsman D.G. Ellard S.L. Wang Y.Z. Vivas-Mejia P. Lopez-Berestein G. Sood A.K. Birrer M.J. Identification of novel therapeutic targets in microdissected clear cell ovarian cancers.PLoS One. 2011; 6: e21121Crossref PubMed Scopus (64) Google Scholar Gene signatures used for validation are presented in Supplemental Table S2. We performed principal components analysis using genes from each signature that were represented on Affymetrix and NanoString platforms (Figure 3). For each comparison we observed meaningful separation of the classes defined by the signatures on the first two principal components. In addition, for the Gleason signature genes, we calculated the log-fold changes in the gene expression values between high and low Gleason grade tumors. We co" @default.
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• W2002927706 title "Comparing Platforms for Messenger RNA Expression Profiling of Archival Formalin-Fixed, Paraffin-Embedded Tissues" @default.
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