biostudies-arrayexpress00690Jonathan PollackHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-GEOD-15361Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.biostudies-arrayexpressNucleic Acid Extraction - not providedLabeling - not providedHybridization - not providedMIAME ScoreOrganizationAssays and DataProcessed DataMAGE-TAB FilesArray DesignsFeature Extraction - VALUE is Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm)Assay Data Transformation - ID_REF = ID_REF<br>CH1I_MEAN = Mean feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEAN = Mean feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEDIAN = The median feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_MEDIAN = The median feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH1D_MEAN = The mean feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2D_MEAN = .The mean feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1I_MEDIAN = Median feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEDIAN = Median feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEAN = The mean feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Background<br>CH2B_MEAN = The mean feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Background<br>CH1D_MEDIAN = The median feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH2D_MEDIAN = The median feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH1_PER_SAT = The percentage of feature pixels at wavelength 532 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH2_PER_SAT = The percentage of feature pixels at wavelength 635 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH1I_SD = The standard deviation of the feature intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2I_SD = The standard deviation of the feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1B_SD = The standard deviation of the feature background intensity at wavelength 532 nm.; Type: float; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_SD = The standard deviation of the feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>PERGTBCH1I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH1I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>SUM_MEAN = The sum of the arithmetic mean intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>SUM_MEDIAN = The sum of the median intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>RAT1_MEAN = Ratio of the arithmetic mean intensities of each spot for each wavelength, with the median background subtracted. Channel 1/Channel 2 ratio, (CH1I_MEAN - CH1B_MEDIAN)/(CH2I_MEAN - CH2B_MEDIAN) or Green/Red ratio.; Type: float; Scale: linear_scale<br>RAT2_MEAN = The ratio of the arithmetic mean intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_MEDIAN = The ratio of the median intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEAN = The geometric mean of the pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEDIAN = The median of pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_SD = The geometric standard deviation of the pixel intensity ratios.; Type: float; Scale: linear_scale<br>TOT_SPIX = The total number of feature pixels.; Type: integer; Scale: linear_scale<br>TOT_BPIX = The total number of background pixels.; Type: integer; Scale: linear_scale<br>REGR = The regression ratio of every pixel in a 2-feature-diameter circle around the center of the feature.; Type: float; Scale: linear_scale<br>CORR = The correlation between channel1 (Cy3) & Channel 2 (Cy5) pixels within the spot, and is a useful quality control parameter. Generally, high values imply better fit & good spot quality.; Type: float; Scale: linear_scale<br>DIAMETER = The diameter in um of the feature-indicator.; Type: integer; Scale: linear_scale<br>X_COORD = X-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>Y_COORD = Y-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>TOP = Box top: int(((centerX - radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>BOT = Box bottom: int(((centerX + radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>LEFT = Box left: int(((centerY - radius) - yoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>RIGHT = Box right: int(((centerY + radius) - yoffset) / pixelSize); Type: integer; Scale: linear_scale<br>FLAG = The type of flag associated with a feature: -100 = user-flagged null spot; -50 = software-flagged null spot; 0 = spot valid.; Type: integer; Scale: linear_scale<br>CH2IN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity (CH2I_MEAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH2BN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) background (CH2B_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH2DN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>RAT2N_MEAN = Type: float; Scale: linear_scale<br>CH2IN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity (CH2I_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale<br>CH2DN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEDIAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale<br>RAT1N_MEAN = Ratio of the means of Channel 1 (usually 532 nm) intensity to normalized Channel 2 (usually 635 nm) intensity with median background subtracted (CH1D_MEAN/CH2DN_MEAN). Channel 1/Channel 2 ratio normalized or Green/Red ratio normalized.; Type: float; Scale: linear_scale<br>RAT2N_MEDIAN = Channel 2/Channel 1 ratio normalized, RAT2_MEDIAN/Normalization factor or Red/Green median ratio normalized.; Type: float; Scale: linear_scale<br>LOG_RAT2N_MEAN = Log (base 2) of the ratio of the mean of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEAN)].; Type: float; Scale: log_base_2<br>VALUE = Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEDIAN)].; Type: float; Scale: log_base_2Image Adquisition - GenePixAssay Data Transformation - ID_REF = ID_REF<br>CH1I_MEAN = Mean feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEAN = Mean feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEDIAN = The median feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_MEDIAN = The median feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH1D_MEAN = The mean feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2D_MEAN = .The mean feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1I_MEDIAN = Median feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEDIAN = Median feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEAN = The mean feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Background<br>CH2B_MEAN = The mean feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Background<br>CH1D_MEDIAN = The median feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH2D_MEDIAN = The median feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH1_PER_SAT = The percentage of feature pixels at wavelength 532 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH2_PER_SAT = The percentage of feature pixels at wavelength 635 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH1I_SD = The standard deviation of the feature intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2I_SD = The standard deviation of the feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1B_SD = The standard deviation of the feature background intensity at wavelength 532 nm.; Type: float; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_SD = The standard deviation of the feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>PERGTBCH1I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH1I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>SUM_MEAN = The sum of the arithmetic mean intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>SUM_MEDIAN = The sum of the median intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>RAT1_MEAN = Ratio of the arithmetic mean intensities of each spot for each wavelength, with the median background subtracted. Channel 1/Channel 2 ratio, (CH1I_MEAN - CH1B_MEDIAN)/(CH2I_MEAN - CH2B_MEDIAN) or Green/Red ratio.; Type: float; Scale: linear_scale<br>RAT2_MEAN = The ratio of the arithmetic mean intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_MEDIAN = The ratio of the median intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEAN = The geometric mean of the pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEDIAN = The median of pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_SD = The geometric standard deviation of the pixel intensity ratios.; Type: float; Scale: linear_scale<br>TOT_SPIX = The total number of feature pixels.; Type: integer; Scale: linear_scale<br>TOT_BPIX = The total number of background pixels.; Type: integer; Scale: linear_scale<br>REGR = The regression ratio of every pixel in a 2-feature-diameter circle around the center of the feature.; Type: float; Scale: linear_scale<br>CORR = The correlation between channel1 (Cy3) & Channel 2 (Cy5) pixels within the spot, and is a useful quality control parameter. Generally, high values imply better fit & good spot quality.; Type: float; Scale: linear_scale<br>DIAMETER = The diameter in um of the feature-indicator.; Type: integer; Scale: linear_scale<br>X_COORD = X-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>Y_COORD = Y-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>TOP = Box top: int(((centerX - radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>BOT = Box bottom: int(((centerX + radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>LEFT = Box left: int(((centerY - radius) - yoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>RIGHT = Box right: int(((centerY + radius) - yoffset) / pixelSize); Type: integer; Scale: linear_scale<br>FLAG = The type of flag associated with a feature: -100 = user-flagged null spot; -50 = software-flagged null spot; 0 = spot valid.; Type: integer; Scale: linear_scale<br>CH2IN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity (CH2I_MEAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH2BN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) background (CH2B_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH2DN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>RAT2N_MEAN = Type: float; Scale: linear_scale<br>CH2IN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity (CH2I_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale<br>CH2DN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEDIAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale<br>RAT1N_MEAN = Ratio of the means of Channel 1 (usually 532 nm) intensity to normalized Channel 2 (usually 635 nm) intensity with median background subtracted (CH1D_MEAN/CH2DN_MEAN). Channel 1/Channel 2 ratio normalized or Green/Red ratio normalized.; Type: float; Scale: linear_scale<br>RAT2N_MEDIAN = Channel 2/Channel 1 ratio normalized, RAT2_MEDIAN/Normalization factor or Red/Green median ratio normalized.; Type: float; Scale: linear_scale<br>LOG_RAT2N_MEAN = Log (base 2) of the ratio of the mean of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEAN)].; Type: float; Scale: log_base_2<br>VALUE = same as UNF_VALUE but with flagged values removed<br>UNF_VALUE = Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEDIAN)].; Type: float; Scale: log_base_2UnknownTranscriptomicsGenomicsProteomics<h4>Background</h4>Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes-luminal A, luminal B, ERBB2-associated, basal-like and normal-like-with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes.<h4>Methods</h4>Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression.<h4>Findings</h4>Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem/progenitor-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 multi-copy deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes.<h4>Conclusions</h4>Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, cancer stem cell biology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes.transcription profiling by arrayHomo sapiensMolecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery.Yun ChoiMelanie BocanegraKao J, Salari K, Bocanegra M, Choi YL, Girard L, Gandhi J, Kwei KA, Hernandez-Boussard T, Wang P, Gazdar AF, Minna JD, Pollack JRJonathan PollackJessica Kao69falseMolecular profiling of breast cancer cell lines defines relevant tumor models (gene expression)Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.E-GEOD-15361GSE1536119582160EFO_000276819582160