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• W1977599269 abstract "This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in non–small-cell lung cancer. This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in non–small-cell lung cancer. There is unprecedented interest in the use of prognostic and predictive biomarkers to stratify patient populations. Clinical tests use both single (eg, HER2) and multiparametric (eg, gene signature–based tests, such as OncotypeDx) biomarkers. For a biomarker to be implemented as a clinically viable test, it must use readily available tissue and routine storage techniques.1Taube S.E. Clark G.M. Dancey J.E. McShane L.M. Sigman C.C. Gutman S.I. A perspective on challenges and issues in biomarker development and drug and biomarker codevelopment.J Natl Cancer Inst. 2009; 101: 1453-1463Crossref PubMed Scopus (84) Google Scholar, 2Tanney A. Kennedy R. Developing mRNA-based biomarkers from formalin-fixed paraffin-embedded tissue.Per Med. 2010; 7: 205-211Crossref Scopus (7) Google Scholar The use of formalin-fixed, paraffin-embedded (FFPE) tissue represents the most practical solution to this problem.2Tanney A. Kennedy R. Developing mRNA-based biomarkers from formalin-fixed paraffin-embedded tissue.Per Med. 2010; 7: 205-211Crossref Scopus (7) Google Scholar, 3Simon R.M. Paik S. Hayes D.F. Use of archived specimens in evaluation of prognostic and predictive biomarkers.J Natl Cancer Inst. 2009; 101: 1446-1452Crossref PubMed Scopus (807) Google Scholar, 4Bosman F.T. Yan P. Tejpar S. Fiocca R. Van Cutsem E. Kennedy R.D. Dietrich D. Roth A. Tissue biomarker development in a multicentre trial context: a feasibility study on the PETACC3 stage II and III colon cancer adjuvant treatment trial.Clin Cancer Res. 2009; 15: 5528-5533Crossref PubMed Scopus (26) Google Scholar However, it is unclear how representative a single sample is of the tumor as a whole. Because tumors frequently show evidence of histological diversity, it is feasible that different areas of individual tumors will exhibit distinct biomarker profiles.5Trautmann K. Steudel C. Grossmann D. Aust D. Ehninger G. Miehlke S. Thiede C. Expression profiling of gastric cancer samples by oligonucleotide microarray analysis reveals low degree of intra-tumor variability.World J Gastroenterol. 2005; 11: 5993-5996PubMed Google Scholar, 6Bachtiary B. Boutros P.C. Pintilie M. Shi W. Bastianutto C. Li J.H. Schwock J. Zhang W. Penn L.Z. Jurisica I. Fyles A. Liu F.F. Gene expression profiling in cervical cancer: an exploration of intratumor heterogeneity.Clin Cancer Res. 2006; 12: 5632-5640Crossref PubMed Scopus (122) Google Scholar, 7Nakamura T. Kuwai T. Kitadai Y. Sasaki T. Fan D. Coombes K.R. Kim S.J. Fidler I.J. Zonal heterogeneity for gene expression in human pancreatic carcinoma.Cancer Res. 2007; 67: 7597-7604Crossref PubMed Scopus (45) Google Scholar, 8Francis P. Fernebro J. Edén P. Laurell A. Rydholm A. Domanski H.A. Breslin T. Hegardt C. Borg A. Nilbert M. Intratumor versus intertumor heterogeneity in gene expression profiles of soft-tissue sarcomas.Genes Chromosomes Cancer. 2005; 43: 302-308Crossref PubMed Scopus (35) Google Scholar, 9Staub E. Groene J. Heinze M. Mennerich D. Roepcke S. Klaman I. Hinzmann B. Castanos-Velez E. Pilarsky C. Mann B. Brümmendorf T. Weber B. Buhr H.J. Rosenthal A. Genome-wide expression patterns of invasion front, inner tumor mass and surrounding normal epithelium of colorectal tumors.Mol Cancer. 2007; 6: 79Crossref PubMed Scopus (17) Google Scholar, 10Jochumsen K.M. Tan Q. Hølund B. Kruse T.A. Mogensen O. Gene expression in epithelial ovarian cancer: a study of intratumor heterogeneity.Int J Gynecol Cancer. 2007; 17: 979-985Crossref PubMed Scopus (16) Google Scholar, 11Lyng M.B. Laenkholm A.V. Pallisgaard N. Vach W. Knoop A. Bak M. Ditzel H.J. Intratumor genetic heterogeneity of breast carcinomas as determined by fine needle aspiration and TaqMan low density array.Cell Oncol. 2007; 29: 361-372PubMed Google Scholar, 12Kuwai T. Nakamura T. Kim S.J. Sasaki T. Kitadai Y. Langley R.R. Fan D. Hamilton S.R. Fidler I.J. Intratumoral heterogeneity for expression of tyrosine kinase growth factor receptors in human colon cancer surgical specimens and orthotopic tumors.Am J Pathol. 2008; 172: 358-366Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar, 13Jones A.M. Mitter R. Springall R. Graham T. Winter E. Gillett C. Hanby A.M. Tomlinson I.P. Sawyer E.J. Phyllodes Tumour ConsortiumA comprehensive genetic profile of phyllodes tumors of the breast detects important mutations, intra-tumoral genetic heterogeneity and new genetic changes on recurrence.J Pathol. 2008; 214: 533-544Crossref PubMed Scopus (80) Google Scholar Moreover, different regions may demonstrate variations in the expression of genes that respond to hypoxia, proximity to blood vessels, stromal tissue, or other microenvironments. Therefore, intratumoral heterogeneity represents a potential obstacle to successful biomarker discovery, validation, and application. Intratumor heterogeneity is well documented in several different cancers, including colorectal, breast, ovarian, prostate, and non–small-cell lung cancer (NSCLC)9Staub E. Groene J. Heinze M. Mennerich D. Roepcke S. Klaman I. Hinzmann B. Castanos-Velez E. Pilarsky C. Mann B. Brümmendorf T. Weber B. Buhr H.J. Rosenthal A. Genome-wide expression patterns of invasion front, inner tumor mass and surrounding normal epithelium of colorectal tumors.Mol Cancer. 2007; 6: 79Crossref PubMed Scopus (17) Google Scholar, 10Jochumsen K.M. Tan Q. Hølund B. Kruse T.A. Mogensen O. Gene expression in epithelial ovarian cancer: a study of intratumor heterogeneity.Int J Gynecol Cancer. 2007; 17: 979-985Crossref PubMed Scopus (16) Google Scholar, 11Lyng M.B. Laenkholm A.V. Pallisgaard N. Vach W. Knoop A. Bak M. Ditzel H.J. Intratumor genetic heterogeneity of breast carcinomas as determined by fine needle aspiration and TaqMan low density array.Cell Oncol. 2007; 29: 361-372PubMed Google Scholar, 12Kuwai T. Nakamura T. Kim S.J. Sasaki T. Kitadai Y. Langley R.R. Fan D. Hamilton S.R. Fidler I.J. Intratumoral heterogeneity for expression of tyrosine kinase growth factor receptors in human colon cancer surgical specimens and orthotopic tumors.Am J Pathol. 2008; 172: 358-366Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar, 13Jones A.M. Mitter R. Springall R. Graham T. Winter E. Gillett C. Hanby A.M. Tomlinson I.P. Sawyer E.J. Phyllodes Tumour ConsortiumA comprehensive genetic profile of phyllodes tumors of the breast detects important mutations, intra-tumoral genetic heterogeneity and new genetic changes on recurrence.J Pathol. 2008; 214: 533-544Crossref PubMed Scopus (80) Google Scholar, 14Lassmann S. Bauer M. Soong R. Schreglmann J. Tabiti K. Nährig J. Rüger R. Höfler H. Werner M. Quantification of CK20 gene and protein expression in colorectal cancer by RT-PCR and immunohistochemistry reveals inter- and intratumor heterogeneity.J Pathol. 2002; 198: 198-206Crossref PubMed Scopus (30) Google Scholar, 15Royce F.H. Van Winkle L.S. Yin J. Plopper C.G. Comparison of regional variability in lung-specific gene expression using a novel method for RNA isolation from lung subcompartments of rats and mice.Am J Pathol. 1996; 148: 1779-1786PubMed Google Scholar, 16Chhieng D.C. Frost A.R. Niwas S. Weiss H. Grizzle W.E. Beeken S. Intratumor heterogeneity of biomarker expression in breast carcinomas.Biotech Histochem. 2004; 79: 25-36Crossref PubMed Scopus (13) Google Scholar, 17Woloszynska-Read A. Mhawech-Fauceglia P. Yu J. Odunsi K. Karpf A.R. Intertumor and intratumor NY-ESO-1 expression heterogeneity is associated with promoter-specific and global DNA methylation status in ovarian cancer.Clin Cancer Res. 2008; 14: 3283-3290Crossref PubMed Scopus (76) Google Scholar, 18Thelen P. Burfeind P. Grzmil M. Voigt S. Ringert R.H. Hemmerlein B. cDNA microarray analysis with amplified RNA after isolation of intact cellular RNA from neoplastic and non-neoplastic prostate tissue separated by laser microdissections.Int J Oncol. 2004; 24: 1085-1092PubMed Google Scholar, 19Gruber M.P. Coldren C.D. Woolum M.D. Cosgrove G.P. Zeng C. Barón A.E. Moore M.D. Cool C.D. Worthen G.S. Brown K.K. Geraci M.W. Evaluating variance of gene expression in the human lung.Am J Respir Cell Mol Biol. 2006; 35: 65-71Crossref PubMed Scopus (32) Google Scholar, 20Blackhall F.H. Pintilie M. Wigle D.A. Jurisica I. Liu N. Radulovich N. Johnston M.R. Keshavjee S. Tsao M.S. Stability and heterogeneity of expression profiles in lung cancer specimens harvested following surgical resection.Neoplasia. 2004; 6: 761-767Abstract Full Text PDF PubMed Scopus (45) Google Scholar, 21Hawthorn L. Stein L. Panzarella J. Loewen G.M. Baumann H. Characterization of cell-type specific profiles in tissues and isolated cells from squamous cell carcinomas of the lung.Lung Cancer. 2006; 53: 129-142Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 22Inamura K. Shimoji T. Ninomiya H. Hiramatsu M. Okui M. Satoh Y. Okumura S. Nakagawa K. Noda T. Fukayama M. Ishikawa Y. A metastatic signature in entire lung adenocarcinomas irrespective of morphological heterogeneity.Hum Pathol. 2007; 38: 702-709Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar, 23Baker G.L. Shultz M.A. Fanucchi M.V. Morin D.M. Buckpitt A.R. Plopper C.G. Assessing gene expression in lung subcompartments utilizing in situ RNA preservation.Toxicol Sci. 2004; 77: 135-141Crossref PubMed Scopus (24) Google Scholar, 24Aslanian H.R. Burgart L.J. Harrington J.J. Mahoney D.W. Zinsmeister A.R. Thibodeau S.N. Ahlquist D.A. Altered DNA mismatch repair expression in synchronous and metachronous colorectal cancers.Clin Gastroenterol Hepatol. 2008; 6: 1385-1388Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar, 25Barry W.T. Kernagis D.N. Dressman H.K. Griffis R.J. Hunter J.D. Olson J.A. Marks J.R. Ginsburg G.S. Marcom P.K. Nevins J.R. Geradts J. Datto M.B. Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome.J Clin Oncol. 2010; 28: 2198-2206Crossref PubMed Scopus (94) Google Scholar; however, this research has primarily focused on the technical challenges surrounding the use of small biopsy specimens for biomarker discovery. Other studies24Aslanian H.R. Burgart L.J. Harrington J.J. Mahoney D.W. Zinsmeister A.R. Thibodeau S.N. Ahlquist D.A. Altered DNA mismatch repair expression in synchronous and metachronous colorectal cancers.Clin Gastroenterol Hepatol. 2008; 6: 1385-1388Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar, 26Sakurada A. Lara-Guerra H. Liu N. Shepherd F.A. Tsao M.S. Tissue heterogeneity of EGFR mutation in lung adenocarcinoma.J Thorac Oncol. 2008; 3: 527-529Crossref PubMed Scopus (53) Google Scholar, 27Ushiki A. Koizumi T. Kobayashi N. Kanda S. Yasuo M. Yamamoto H. Kubo K. Aoyagi D. Nakayama J. Genetic heterogeneity of EGFR mutation in pleomorphic carcinoma of the lung: response to gefitinib and clinical outcome.Jpn J Clin Oncol. 2009; 39: 267-270Crossref PubMed Scopus (55) Google Scholar, 28Gow C.H. Chang Y.L. Hsu Y.C. Tsai M.F. Wu C.T. Yu C.J. Yang C.H. Lee Y.C. Yang P.C. Shih J.Y. Comparison of epidermal growth factor receptor mutations between primary and corresponding metastatic tumors in tyrosine kinase inhibitor-naïve non-small-cell lung cancer.Ann Oncol. 2009; 20: 696-702Crossref PubMed Scopus (185) Google Scholar, 29Gomez-Roca C. Raynaud C.M. Penault-Llorca F. Mercier O. Commo F. Morat L. Sabatier L. Dartevelle P. Taranchon E. Besse B. Validire P. Italiano A. Soria J.C. Differential expression of biomarkers in primary non-small cell lung cancer and metastatic sites.J Thorac Oncol. 2009; 4: 1212-1220Crossref PubMed Scopus (88) Google Scholar have described variations in epidermal growth factor receptor (EGFR) mutation status within single tumors and compared gene expression between primary tumors and metastatic sites. The ability to associate biomarker measurements with clinical outcome is dependent on the effect size, the number of samples used, and the level of experimental error within the system. In addition, biomarker discovery studies using clinical samples have an unavoidable level of noise because of interpatient variation. Intrapatient variation due to intratumor heterogeneity and the inherent degradation associated with FFPE material further exacerbate this problem. These issues manifest as additional, but measurable, uncertainty in the accuracy of biomarker measurements within clinical samples. Recently, a method to quantify intratumoral heterogeneity in a single biomarker and its subsequent effect on powering biomarker development studies was described.30Pintilie M. Iakovlev V. Fyles A. Hedley D. Milosevic M. Hill R.P. Heterogeneity and power in clinical biomarker studies.J Clin Oncol. 2009; 27: 1517-1521Crossref PubMed Scopus (26) Google Scholar The authors concluded that, to determine whether an association exists between a biomarker and outcome, a pilot study must be performed in which multiple measurements per patient are taken. In addition to measuring the effect size, this study design enables an estimation of the degree of heterogeneity of the biomarker within and between individuals and can be used to design an adequately powered study. We applied this method to NSCLC microarray data to investigate heterogeneity within NSCLC tumors and the implications for microarray-based single and multiparametric biomarker discovery. Pintilie et al30Pintilie M. Iakovlev V. Fyles A. Hedley D. Milosevic M. Hill R.P. Heterogeneity and power in clinical biomarker studies.J Clin Oncol. 2009; 27: 1517-1521Crossref PubMed Scopus (26) Google Scholar describe measurement of heterogeneity in a known single biomarker. Herein, we applied their single biomarker method to estimate heterogeneity levels across the transcriptome for estimation of sufficient sample size before a microarray-based discovery study. We demonstrate applicability of their method for use on this popular platform for biomarker discovery. Furthermore, we extend the method to investigate the effects of gene-level heterogeneity on multiparametric biomarker discovery. NSCLC biomarker research is focused around single predictive markers [eg, EGFR and excision repair cross-complementing 1 (ERCC1)] and prognostic gene signatures, several of which are undergoing large-scale clinical validation.31Potti A. Mukherjee S. Petersen R. Dressman H.K. Bild A. Koontz J. Kratzke R. Watson M.A. Kelley M. Ginsburg G.S. West M. Harpole DH J.R. Nevins J.R. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.N Engl J Med. 2006; 355: 570-580Crossref PubMed Scopus (527) Google Scholar, 32Dziadziuszko R. Hirsch F.R. Advances in genomic and proteomic studies of non-small-cell lung cancer: clinical and translational research perspective.Clin Lung Cancer. 2008; 9: 78-84Abstract Full Text PDF PubMed Scopus (18) Google Scholar In early-stage NSCLC, theoretically up to 20 surgical FFPE blocks could be made available from each patient, each one potentially representing a different area of the tumor and, therefore, exhibiting substantial heterogeneity. Intratumor heterogeneity manifests itself by introducing errors in measurement and represents a considerable hurdle to the successful development, validation, and application of biomarkers using this tissue.33Gibbs A.R. Attanoos R.L. ACP Best Practice No 161: examination of lung specimens.J Clin Pathol. 2000; 53: 507-512Crossref PubMed Scopus (17) Google Scholar To address this concern, multiple FFPE blocks were taken from individual patients with NSCLC and profiled on an NSCLC-specific DNA microarray platform. Variation in gene expression was assessed at the intrapatient and interpatient (FFPE block) level and between samples with different histological features. For each patient, both whole FFPE sections and macrodissected tumor sections were analyzed. The impact of these variations on powering both single and multiparametric clinical biomarker discovery studies is discussed. To evaluate the power of a single biomarker study (in which the identity of the biomarker is unknown), the distribution of variation of all probe sets on the array was considered. Each probe set on the microarray is likely to be subject to different levels of variation in expression because of tumor heterogeneity. The use of microarrays allows heterogeneity to be observed for every probe set on the array. A prediction for power of a single biomarker can be calculated based on an average, minimum, or maximum heterogeneity observed, depending on the stringency required. To extend the scope of this analysis for application to multiparametric tests, we investigated the effect of combining individual probe sets to mimic gene signatures. Simulated signatures of varying lengths and content were sampled from this data set to represent the natural distribution of variation likely to be observed in signatures generated from NSCLC data. We experimented with multiple simulated signatures using repeated sampling over a range of signature lengths to investigate the relationship between signature length and effect of combined heterogeneity of signature content. Three sampling plans were applied to randomly select probe sets for inclusion in the simulated signatures. In random sampling, all probe sets were included, because potentially any feature could be selected for membership in a signature. Median-based sampling focused on probe sets with heterogeneity close to the median because this represents most of the data. Finally, sliding sampling focused on probe sets with low heterogeneity because feature selection algorithms used in signature generation may naturally favor less variable probe sets. In this study, we examined tumor heterogeneity in NSCLC and its implications for developing a single biomarker or a multiparametric gene signature using FFPE tissue. We show that failing to account for intratumor heterogeneity will result in underpowered studies, which may lead to the failure of a biomarker to make it into a clinical setting. To allow the effects of intratumor heterogeneity to be studied (rather than the effect of other factors, such as center and block age), FFPE blocks from 10 patients were chosen from a single sample cohort (Queens University, Belfast, UK). Six adenocarcinoma and four squamous samples were chosen (Table 1). In addition, block selection aimed to constrain block age, stage, and percentage tumor content (>50%). Samples were restaged according to the American Joint Committee on Cancer 7 guidelines.34Goldstraw P. Crowley J. Chansky K. Giroux D.J. Groome P.A. Rami-Porta R. Postmus P.E. Rusch V. Sobin L. International Association for the Study of Lung Cancer International Staging Committee; Participating InstitutionsThe IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumors.J Thorac Oncol. 2007; 2: 706-714Crossref PubMed Scopus (2895) Google Scholar Between three and five blocks were available for each patient. Two pathologists (P.K. and J.A.J.) independently confirmed histological characteristics (Table 1). No information was available regarding the position of each FFPE block within the original tumor.Table 1Sample CharacteristicsSample nameSexAge at surgery (years)Histological featuresNo. of blocksBlock age (years)AJCC 7 stageTumor (individual block %)SQ1Male63SQ37.8IA60, 65, 80SQ2Female70SQ38.0IIA50, 80, 90SQ3Male81SQ56.9IIB60, 90, 95, 100, 100SQ5Male75SQ48.7IA50, 55, 85, 100AD1Male59AD46.2IB75, 80, 95, 100AD2Female61AD47.4IIIA50, 50, 60, 95AD3Male69AD37.8IA95, 95, 95AD4Male68AD47.9IV50, 70, 85, 90AD5Male71AD38.3IV80, 90, 95AD6Male73AD311.3IIIB80, 95, 100AD, adenocarcinoma; AJCC, American Joint Committee on Cancer; SQ, squamous. Open table in a new tab AD, adenocarcinoma; AJCC, American Joint Committee on Cancer; SQ, squamous. Block cutting and all subsequent processing steps were randomized to prevent confounding batch effects. Three adjacent sections were cut from each FFPE block. Initially, a section (10 μm thick) was taken for RNA extraction, followed by a section (4 μm thick) for H&E staining and a further section (10 μm thick). All sections were prepared in RNase-free conditions and floated onto glass slides. The pathologist (J.A.J.) then marked the tumor portion of the H&E slide to allow macrodissection of the adjacent section (10 μm thick) to be performed. Macrodissection of tissue of interest was performed using a sterile disposable scalpel blade, and tissue was subsequently stored in 100% ethanol before RNA extraction. RNA extraction of whole FFPE sections was performed by scraping all available tissue from the slide into 100% ethanol. RNA was then extracted using the Roche (Basel, Switzerland) High Pure RNA Paraffin Kit, in accordance with manufacturers' instructions. Amplified cDNA targets were prepared using the Nugen (San Carlos, CA) WT-Ovation FFPE System V2 in combination with the Nugen FL-Ovation cDNA Biotin Module V2, and analyses were performed in accordance with manufacturers' instructions. Hybridization, washing, staining, and scanning of fragmented, labeled cDNA was performed according to standard Affymetrix (Santa Clara, CA) protocols. Between 3.0 and 3.5 μg of fragmented, labeled cDNA was hybridized to the Lung Cancer DSA microarray (Almac Diagnostics, Craigavon, UK), which contains 60,416 probe sets expressed in both lung tumor and normal tissues.35Tanney A. Oliver G.R. Farztdinov V. Kennedy R.D. Mulligan J.M. Fulton C.E. Farragher S.M. Field J.K. Johnston P.G. Harkin D.P. Proutski V. Mulligan K.A. Generation of a non-small cell lung cancer transcriptome microarray.BMC Med Genomics. 2008; 1: 20Crossref PubMed Google Scholar A single microarray lot number was used, and microarrays were scanned on a single Affymetrix 7G scanner. Four samples were excluded, because <3 μg of cDNA was obtained. Microarray quality control was performed to assess the quality and integrity of the data generated. Sixty microarrays were selected for data analysis. Array data were preprocessed using Robust Multi-Array Average36Irizarry 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 (8454) Google Scholar background correction, normalization, and summarization implemented in Partek software, version 6.4 (copyright 2008; Partek Inc., St Louis, MO). Affymetrix control probes were filtered out before subsequent analysis. Unsupervised principal component analysis (PCA) was performed using the correlation matrix for all 60,354 noncontrol probe sets using Partek software, version 6.4. The independent variables (random effects: patient and block; fixed effects: histological characteristics and macrodissection/whole) were fit to a five-way mixed-model analysis of variance using the method of moments to test the null hypotheses that there is no difference in the mean gene expression in different blocks.37Eisenhart C. The assumptions underlying the analysis of variance.Biometrics. 1947; 3: 1-21Crossref PubMed Scopus (271) Google Scholar, 38Tamhane A.C. Dunlop D.D. Statistics and Data Analysis from Elementary to Intermediate.in: Prentice Hall, New Jersey2000: 473-474Google Scholar The F statistic was calculated for each probe set using equation 1. The resultant F statistics were averaged and evaluated for their contributions (relative to experimental error) to the sources of variation, as seen in the dependent variables (normalized gene expression values). An analysis of variance was performed using Partek software, version 6.4. Yijklm=μ+patient Number(Histolog⁡y)im+Sample Typei+Histolog⁡ym +Sample Name × Block Number × Histolog⁡y(Patient Number)ijkm + Errorijklm(1) where Yijklm = the gene expression for that probe set on i-th Patient Number, j-th Sample Name k-th Block Number, l-th Sample Type, m-th Histology; μ = the common effect for the whole experiment; errorijklm = the random error present in the observation on the i-th Patient Number, j-th Sample Name, k-th Block Number, l-th Sample Type, m-th Histology. The errors are assumed to be normally and independently distributed with mean 0 and SD for all measurements. The impact of gene expression variation, within patient block and between patients, on powering clinical biomarker discovery studies using either whole or macrodissected FFPE material from either squamous carcinoma or adenocarcinoma was determined following methods outlined by Pintilie et al30Pintilie M. Iakovlev V. Fyles A. Hedley D. Milosevic M. Hill R.P. Heterogeneity and power in clinical biomarker studies.J Clin Oncol. 2009; 27: 1517-1521Crossref PubMed Scopus (26) Google Scholar: The error in biomarker measurement, or heterogeneity score, ℓk, due to within- and between-patient variability is defined as equation 2: ℓk=σ^w2kσ^w2k+σ^B2,(2) where k is the number of replicates measured within patient and σ^w2 and σ^B2 are the within- and between-patient variances, respectively. Within the context of this work, the variability in the marker throughout the tissue sample and the technical variation in each measurement are treated as a combined error in biomarker measurement or measurement error, for short. This must not be confused with the more typical definition of the term measurement error in a quantitative bioassay, which represents the difference between the measured value of the quantity and its true value. The modified power equation, including heterogeneity score as an error term, is shown in equation 3: Ζ1−β=nd σ^B1−ℓk log⁡(HR)−Ζ1−α / 2,(3) where HR is the hazard ratio; nd is the number of events; α and β are types I and II error, respectively; Z1-β and Z1-α/2 are the quantiles of the standard normal distribution for 1-β and 1-α/2, respectively; and ℓk and σ^B2 are previously defined. Within- and between-group variances and subsequent error in biomarker measurement were calculated per probe set from the microarray data. The median between-group variance and the median error were then used for calculation of number of events using equation 3. Power analysis was performed using Matlab 2009b (Mathworks, Natick, MA). Gene signatures of different size (4, 8, 10, 50, 100, 500, and 1000 probe sets) were simulated using three different sampling plans to select component probe sets. For each signature size (n), and each sampling plan, a population of 100,000 signatures was generated by repeat sampling of n probe sets from the NSCLC expression data. All selected probe sets were combined to provide a single-signature expression value per patient sample, using a weighted summation strategy. Combinations of positive and negative weights were applied to represent the different relationships of probe sets in a signature. Probe sets to f" @default.
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• W1977599269 title "Implications for Powering Biomarker Discovery Studies" @default.
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