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Snp 6.0 platform

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The SNP 6.0 platform is a high-throughput microarray-based genotyping solution designed for genome-wide association studies (GWAS) and copy number variation (CNV) analysis. The platform provides comprehensive coverage of single nucleotide polymorphisms (SNPs) and CNVs across the human genome.

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Lab products found in correlation

Chromosome analysis suite, by Thermo Fisher Scientific (2 mentions) Gistic, by Thermo Fisher Scientific (2 mentions) Human v3 microarray, by Thermo Fisher Scientific (1 mentions) Hiseq 2000 platform, by Illumina (1 mentions) Infinium 450k array, by Illumina (1 mentions) Ht 12 platform, by Illumina (1 mentions) Ta cloning kit, by Thermo Fisher Scientific (1 mentions)

17 protocols using snp 6.0 platform

1

Breast Cancer Genomic Data Processing

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The BRCA data were downloaded from the cBioPortal for Cancer Genomics (https://www.cbioportal.org)65 (link). It contained the METABRIC BRCA cohort assembled from 2509 primary breast cancer patients with 548 matched normals in the United Kingdom and Canada13 (link). The gene expression microarray data were generated using the Illumina Human v3 microarray for 1904 samples, while the CNVs data were measured on the Affymetrix SNP 6.0 platform for 2173 samples. In addition, 17,272 somatic mutations of 173 genes for 2369 samples were detected on the Illumina HiSeq 2000 platform. R scripts responsible for the work's implementation are provided in the Github repositories (https://github.com/hauldhut/drivergene) (See more detail in Supplementary File 1).
All omics data we pre-processed in the same way as in the reference paper13 (link). Specifically, we only matched the sample labels shared between the gene expression data and clinical data, and the CNVs data and clinical data, and obtained 1904 and 2173 matched patients, respectively.
Nguyen Q.H, & Le D.H. (2020). Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data. Scientific Reports, 10, 20521.
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2

Multi-dimensional Gastric Cancer Profiling

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Multi-dimensional data (level 3) for gastric cancer were derived from TCGA STAD cohort at Broad GDAC Firehose data run (version: 2016_01_28, http://gdac.broadinstitute.org/). A total of 272 patients for whom gene expression, miRNA expression, and copy number profiles were available were included in the analysis (Table S1). Gene expression profiles of 29 paired tumor and normal tissue samples were measured as a reads per kilobase per million mapped reads value, and miRNA expression profiles of 34 paired tumor and normal tissue samples were measured as a reads per million value. DNA copy number profiles for 272 patients were obtained from the Affymetrix SNP6.0 platform and processed using the circular binary segmentation method. DNA methylation profiles for 22 paired tumor and normal tissue samples were also included in the study, which were obtained from the Infinium HumanMethylation27 platform (Illumina, San Diego, CA, USA) and shown as a beta value. The Infinium HumanMethylation27 array covered 27,578 CpG sites in 14,495 human genes. The methylation value for a specific gene site was measured by calculating the mean value of all related probes. Detailed information regarding the data is shown in Table 1.
Ren W., Li W., Wang D., Hu S., Suo J, & Ying X. (2017). Combining multi-dimensional data to identify key genes and pathways in gastric cancer. PeerJ, 5, e3385.
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3

Comparative Genomic Hybridization Analysis

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Cell pellets containing 1×106 cells were sent to Origen Labs (Singapore) for comparative genomic hybridization (CGH) array hybridization using the Affymetrix SNP 6.0 platform. Data analysis was performed with Affymetrix Chromosome Analysis Suite.
Choong M.L., Yong J., Wang Y, & Lee M.A. (2014). Establishment and Characterization of a Singaporean Chinese Lung Adenocarcinoma Cell Line with Four Copies of the Epidermal Growth Factor Receptor Gene. BioResearch Open Access, 3(4), 176-182.
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4

Comprehensive genomic analysis of AML

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Copy number aberrations and chromosome deletions were based on available TCGA Acute Myeloid Leukemia data8 . Raw data was downloaded from TCGA Data Portal. Cancer genome datasets and bioinformatics tools for visualizing different parameters for analysis of genomic data are accessible through MSKCC cBioPortal (www.cbioportal.org). Copy number states (homozygous deletion, hemizygous deletion, gain, and amplification) were determined from Affymetrix SNP 6.0 platform by the copy number analysis algorithms GISTIC (PMID:21527027) and RAE (PMID: 18784837).
Human AML samples were obtained from the University of Chicago. SNP array based copy number analyses of 35 samples are from published results9 , and data analysis and expression level estimates were performed as described9 . Gene set enrichment analysis was performed using the GSEA method GSEA v2.1.0 (Gene set enrichment analysis—Broad Insititute) (PMID: 16199517). Multiple testing adjusted p-value (FDR.q.val) less than 0.05 were considered statistically significant.
Zhao Z., Chen C.C., Rillahan C.D., Shen R., Kitzing T., McNerney M.E., Diaz-Flores E., Zuber J., Shannon K., Le Beau M.M., Spector M.S., Kogan S.C, & Lowe S.W. (2015). Cooperative loss of RAS feedback regulation drives myeloid leukemognesis. Nature genetics, 47(5), 539-543.
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5

Integrative Molecular Profiling of Cancers

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Five molecular platforms, DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA were provided as input to iCluster. Data were pre-processed using the following procedures. Copy number alteration data was derived from CBS segmented data from the Affymetrix SNP6.0 platform, and further reduced to a set of non-redundant regions as described (Mo et al., 2013 (link)). For the methylation data (Illumina Infinium 450k arrays), the median absolute deviation was employed to select the top 1000 most variable CpG sites after beta-mixture quantile normalization. Methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes were removed. For mRNA and miRNA sequence data, lowly expressed genes were excluded based on median-normalized counts, and variance filtering led to 1266 mRNAs and 258 miRNAs for clustering. mRNA and miRNA expression features were log2 transformed, normalized and scaled before using as an input to iCluster.
Wheeler D.A, & Roberts L.R. (2017). Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell, 169(7), 1327-1341.e23.
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6

Comparative Copy Number Analysis in Glioma

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DNA Copy Number (41.6GB) Affy SNP data in the form of copy number by gene for 60 glioma cell lines was downloaded from the Cancer Cell Line Encyclopedia (CCLE) data portal at https://portals.broadinstitute.org/. Copy Number Segment data from the Affymetrix SNP 6.0 platform for 526 GBM tumor and matched normal tissue samples was downloaded from The Cancer Genome Atlas (TCGA) data portal at https://portal.gdc.cancer.gov. All downloaded data from CCLE and TCGA can be found in S1 Table. The DECIPHER Genome Browser compiles data on the general, healthy population from various sources into one online tool (Firth, H. V. et al. PMC, 2009). Raw copy number data of healthy individuals from studies included in DECIPHER was not downloaded or analyzed by our group as was done with the CCLE and TCGA data. The frequency of copy number variations across datasets is already summarized for us in the genome browser. We only had to query our CNV of interest using its chromosomal location (Ch. 2:213186816–213191560) for the Population: Copy-Number Variants Affy6 consolidated data set of the browser, as outlined in the methods section of the manuscript. This query was performed on April 20, 2017. Copy number data from all three databases was generated using the Affymetrix SNP 6.0 microarray system.
Jones D.C., Scanteianu A., DiStefano M., Bouhaddou M, & Birtwistle M.R. (2018). Analysis of copy number loss of the ErbB4 receptor tyrosine kinase in glioblastoma. PLoS ONE, 13(1), e0190664.
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7

Comprehensive genomic analysis of AML

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Copy number aberrations and chromosome deletions were based on available TCGA Acute Myeloid Leukemia data8 . Raw data was downloaded from TCGA Data Portal. Cancer genome datasets and bioinformatics tools for visualizing different parameters for analysis of genomic data are accessible through MSKCC cBioPortal (www.cbioportal.org). Copy number states (homozygous deletion, hemizygous deletion, gain, and amplification) were determined from Affymetrix SNP 6.0 platform by the copy number analysis algorithms GISTIC (PMID:21527027) and RAE (PMID: 18784837).
Human AML samples were obtained from the University of Chicago. SNP array based copy number analyses of 35 samples are from published results9 , and data analysis and expression level estimates were performed as described9 . Gene set enrichment analysis was performed using the GSEA method GSEA v2.1.0 (Gene set enrichment analysis—Broad Insititute) (PMID: 16199517). Multiple testing adjusted p-value (FDR.q.val) less than 0.05 were considered statistically significant.
Zhao Z., Chen C.C., Rillahan C.D., Shen R., Kitzing T., McNerney M.E., Diaz-Flores E., Zuber J., Shannon K., Le Beau M.M., Spector M.S., Kogan S.C, & Lowe S.W. (2015). Cooperative loss of RAS feedback regulation drives myeloid leukemognesis. Nature genetics, 47(5), 539-543.
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8

eQTL Analysis of Breast Epithelium

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Expression quantitative trait locus (eQTL) analyses were performed for all genes in the 1 MB region spanning the associated SNPs using probe-level gene expression data for breast epithelium samples taken from normal tissue adjacent to the tumor of 135 breast cancer patients of European ancestry from the METABRIC study (21 (link)). These were assayed using the Illumina HT12 platform. We also analyzed eQTL data of 387 breast tumors from the Cancer Genome Atlas (TCGA) (303 ER-positive, 81 ER-negative, three unknown) assayed using the Agilent G4502A-07-3 array (35 (link)). Germline SNP genotypes were available for normal and tumor samples from the Affymetrix SNP 6.0 platform imputed into 1000 Genomes Project data (March 2012) for the three SNPs of interest: rs2059614 at 11q24.2, and rs148760487 and rs114860916 at 2q24.2 (see Results section). Association between genotype and expression was tested by linear regression with false discovery rate control.
Guo Q., Schmidt M.K., Kraft P., Canisius S., Chen C., Khan S., Tyrer J., Bolla M.K., Wang Q., Dennis J., Michailidou K., Lush M., Kar S., Beesley J., Dunning A.M., Shah M., Czene K., Darabi H., Eriksson M., Lambrechts D., Weltens C., Leunen K., Bojesen S.E., Nordestgaard B.G., Nielsen S.F., Flyger H., Chang-Claude J., Rudolph A., Seibold P., Flesch-Janys D., Blomqvist C., Aittomäki K., Fagerholm R., Muranen T.A., Couch F.J., Olson J.E., Vachon C., Andrulis I.L., Knight J.A., Glendon G., Mulligan A.M., Broeks A., Hogervorst F.B., Haiman C.A., Henderson B.E., Schumacher F., Le Marchand L., Hopper J.L., Tsimiklis H., Apicella C., Southey M.C., Cox A., Cross S.S., Reed M.W., Giles G.G., Milne R.L., McLean C., Winqvist R., Pylkäs K., Jukkola-Vuorinen A., Grip M., Hooning M.J., Hollestelle A., Martens J.W., van den Ouweland A.M., Marme F., Schneeweiss A., Yang R., Burwinkel B., Figueroa J., Chanock S.J., Lissowska J., Sawyer E.J., Tomlinson I., Kerin M.J., Miller N., Brenner H., Dieffenbach A.K., Arndt V., Holleczek B., Mannermaa A., Kataja V., Kosma V.M., Hartikainen J.M., Li J., Brand J.S., Humphreys K., Devilee P., Tollenaar R.A., Seynaeve C., Radice P., Peterlongo P., Bonanni B., Mariani P., Fasching P.A., Beckmann M.W., Hein A., Ekici A.B., Chenevix-Trench G., Balleine R., Phillips K.A., Benitez J., Zamora M.P., Arias Perez J.I., Menéndez P., Jakubowska A., Lubinski J., Jaworska-Bieniek K., Durda K., Hamann U., Kabisch M., Ulmer H.U., Rüdiger T., Margolin S., Kristensen V., Nord S., Evans D.G., Abraham J.E., Earl H.M., Hiller L., Dunn J.A., Bowden S., Berg C., Campa D., Diver W.R., Gapstur S.M., Gaudet M.M., Hankinson S.E., Hoover R.N., Hüsing A., Kaaks R., Machiela M.J., Willett W., Barrdahl M., Canzian F., Chin S.F., Caldas C., Hunter D.J., Lindstrom S., García-Closas M., Hall P., Easton D.F., Eccles D.M., Rahman N., Nevanlinna H, & Pharoah P.D. (2015). Identification of Novel Genetic Markers of Breast Cancer Survival. JNCI Journal of the National Cancer Institute, 107(5), djv081.
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9

Mutational Landscape of T-cell Acute Lymphoblastic Leukemia

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Diagnostic DNA from all STIL-TAL1 cases was analysed for mutations in known T-ALL mutational hotspots in NOTCH1 (exons 26, 27 and 34), FBXW7 (exons 9 and 10), PTEN (exon 7) and IL7R (exon 6) using previously published methods [13 (link)–16 (link)]. All diagnostic samples were analysed by SNP-array to identify genomic losses and gains using the Affymetrix SNP 6.0 platform. Genotyping and generation of QC data were performed in Genotyping ConsoleTM v4.1.4 software (Affymetrix). CNAG version 3.3.0.1 beta was used to normalise output to a self-reference (patient remission DNA) or via a batch pairwise analysis using sex-matched control samples. The STIL-TAL1 patient-specific gene fusion was sequenced for the three cases that underwent single-cell genotyping analyses using previously published methods [17 (link)]. The TA Cloning Kit® (Invitrogen by Life TechnologiesTM) was used for cloning experiments according to the manufacturer’s instructions.
Furness C.L., Mansur M.B., Weston V.J., Ermini L., van Delft F.W., Jenkinson S., Gale R., Harrison C.J., Pombo-de-Oliveira M.S., Sanchez-Martin M., Ferrando A.A., Kearns P., Titley I., Ford A.M., Potter N.E, & Greaves M. (2018). The subclonal complexity of STIL-TAL1+ T-cell acute lymphoblastic leukaemia. Leukemia, 32(9), 1984-1993.
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10

Genome-wide CRISPR Fitness Screens Across 250 Cell Lines

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Publicly available CRISPR-Cas9, BROAD DepMap 18Q3, drop-out screens across 250 cell lines was utilized to assess the loss of fitness (LOF) impact of knocking-out 17,328 genes [22 (link), 27 ]. Raw sequence counts of each sgRNA were downloaded and corrected by library size in each sample. Non-targeting plasmid control sample was used and sgRNAs with lower than 30 counts were discarded. Log2 sgRNA fold changes were estimated between samples and the plasmid control. Gene level estimates of the fold changes were calculated by averaging all mapping sgRNA fold changes. Single nucleotide polymorphism (SNP) array hybridization using the Affymetrix SNP6.0 platform was performed according to Affymetrix protocols. Segment copy-number variants were obtained using PICNIC [36 (link)] as previously described [29 (link)]. RNA-seq experiments for CRISPR-Cas9 profiled cell lines were assembled from multiple data-sets [37 ]. To minimize technical bias, all samples were processed with the same pipeline, iRAP [38 ], to obtain raw counts. Genes with Reads Per Kilobase per Million (RPKM) with zero counts were termed as non-expressed in the particular sample. Non-expressed genes were defined as those with a RPKM lower than 1.
Gonçalves E., Behan F.M., Louzada S., Arnol D., Stronach E.A., Yang F., Yusa K., Stegle O., Iorio F, & Garnett M.J. (2019). Structural rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biology, 20, 27.
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