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Human cancer cell lines are an important resource for research and drug development. However, the available annotations of cell lines are sparse, incomplete, and distributed in multiple repositories. Re-analyzing publicly available raw RNA-Seq data, we determined the human leukocyte antigen (HLA) type and abundance, identified expressed viruses and calculated gene expression of 1,082 cancer cell lines. Using the determined HLA types, public databases of cell line mutations, and existing HLA binding prediction algorithms, we predicted antigenic mutations in each cell line. We integrated the results into a comprehensive knowledgebase. Using the Django web framework, we provide an interactive user interface with advanced search capabilities to find and explore cell lines and an application programming interface to extract cell line information. The portal is available at http://celllines.tron-mainz.de. [ABSTRACT FROM AUTHOR]

Background: Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. Results: We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. Conclusions: CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies. [ABSTRACT FROM AUTHOR]

Background: We report on a 6-year-old Turkish boy with profound sensorineural deafness, balance disorder, severe disorder of oral motor function, and mild developmental delay. Further findings included scaphocephaly, plagiocephaly, long palpebral fissures, high narrow palate, low-set posteriorly rotated ears, torticollis, hypoplastic genitalia and faulty foot posture. Parents were consanguineous. Methods and results: Computed tomography and magnetic resonance imaging showed bilateral single widened cochlear turn, narrowing of the internal auditory canal, and bilateral truncation of the vestibulo-cochlear nerve. Microarray analysis and next generation sequencing showed a homozygous deletion of chromosome 5q31.1 spanning 115.3 kb and including three genes: NEUROG1 (encoding neurogenin 1), DCNP1 (dendritic cell nuclear protein 1, C5ORF20) and TIFAB (TIFA-related protein). The inability to chew and swallow, deafness and balance disorder represented congenital palsies of cranial nerves V (trigeminal nerve) and VIII (vestibulo-cochlear nerve) and thus a congenital cranial dysinnervation disorder. Conclusions: Based on reported phenotypes of neurog1 null mutant mice and other vertebrates, we strongly propose NEUROG1 as the causative gene in this boy. The human NEUROG1 resides within the DFNB60 locus for non-syndromic autosomal recessive deafness on chromosome 5q22-q31, but linkage data have excluded it from being causative in the DFNB60 patients. Given its large size (35 Mb, >100 genes), the 5q22-q31 area could harbor more than one deafness gene. We propose NEUROG1 as a new gene for syndromic autosomal recessive hearing loss and congenital cranial dysinnervation disorder including cranial nerves V and VIII. [ABSTRACT FROM AUTHOR]

We present a method, seq2HLA, for obtaining an individual's human leukocyte antigen (HLA) class I and II type and expression using standard next generation sequencing RNA-Seq data. RNA-Seq reads are mapped against a reference database of HLA alleles, and HLA type, confidence score and locus-specific expression level are determined. We successfully applied seq2HLA to 50 individuals included in the HapMap project, yielding 100% specificity and 94% sensitivity at a P-value of 0.1 for two-digit HLA types. We determined HLA type and expression for previously un-typed Illumina Body Map tissues and a cohort of Korean patients with lung cancer. Because the algorithm uses standard RNA-Seq reads and requires no change to laboratory protocols, it can be used for both existing datasets and future studies, thus adding a new dimension for HLA typing and biomarker studies. [ABSTRACT FROM AUTHOR]

Background: DNA copy number variations occur within populations and aberrations can cause disease. We sought to develop an improved lab-automatable, cost-efficient, accurate platform to profile DNA copy number. Results: We developed a sequencing-based assay of nuclear, mitochondrial, and telomeric DNA copy number that draws on the unbiased nature of next-generation sequencing and incorporates techniques developed for RNA expression profiling. To demonstrate this platform, we assayed UMC-11 cells using 5 million 33 nt reads and found tremendous copy number variation, including regions of single and homogeneous deletions and amplifications to 29 copies; 5 times more mitochondria and 4 times less telomeric sequence than a pool of non-diseased, blood-derived DNA; and that UMC-11 was derived from a male individual. Conclusion: The described assay outputs absolute copy number, outputs an error estimate (p-value), and is more accurate than array-based platforms at high copy number. The platform enables profiling of mitochondrial levels and telomeric length. The assay is lab-automatable and has a genomic resolution and cost that are tunable based on the number of sequence reads. [ABSTRACT FROM AUTHOR]

Background: Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions. Results: Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes which are highly expressed in all tissues with lower variance than many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well. Conclusion: We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns. [ABSTRACT FROM AUTHOR]

Background: Spontaneous tumors in dog have been demonstrated to share many features with their human counterparts, including relevant molecular targets, histological appearance, genetics, biological behavior and response to conventional treatments. Mammary tumors in dog therefore provide an attractive alternative to more classical mouse models, such as transgenics or xenografts, where the tumour is artificially induced. To assess the extent to which dog tumors represent clinically significant human phenotypes, we performed the first genome-wide comparative analysis of transcriptional changes occurring in mammary tumors of the two species, with particular focus on the molecular pathways involved. Results: We analyzed human and dog gene expression data derived from both tumor and normal mammary samples. By analyzing the expression levels of about ten thousand dog/human orthologous genes we observed a significant overlap of genes deregulated in the mammary tumor samples, as compared to their normal counterparts. Pathway analysis of gene expression data revealed a great degree of similarity in the perturbation of many cancer-related pathways, including the 'PI3K/AKT', 'KRAS', 'PTEN', 'WNT-beta catenin' and 'MAPK cascade'. Moreover, we show that the transcriptional relationships between different gene signatures observed in human breast cancer are largely maintained in the canine model, suggesting a close interspecies similarity in the network of cancer signalling circuitries. Conclusion: Our data confirm and further strengthen the value of the canine mammary cancer model and open up new perspectives for the evaluation of novel cancer therapeutics and the development of prognostic and diagnostic biomarkers to be used in clinical studies. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon expression using male and female mice from three different inbred strains. Results: 71 liver RNA samples from three mouse strains - DBA/2J, C57BL/6J and C3H/HeJ - were profiled using a custom-designed microarray monitoring exon and exon-junction expression of 1,020 genes representing 9,406 exons. Gene expression was calculated via two different methods, using the 3'-most exon probe ("3' gene expression profiling") and using all probes associated with the gene ("whole-transcript gene expression profiling"), while exon expression was determined using exon probes and flanking junction probes that spanned across the neighboring exons ("exon expression profiling"). Widespread strain and sex influences were detected using a two-way Analysis of Variance (ANOVA) regardless of the profiling method used. However, over 90% of the genes identified in 3' gene expression profiling or whole transcript profiling were identified in exon profiling, along with 75% and 38% more genes, respectively, showing evidence of differential isoform expression. Overall, 55% and 32% of genes, respectively, exhibited strain- and sex-bias differential gene or exon expression. Conclusion: Exon expression profiling identifies significantly more variation than both 3' gene expression profiling and whole-transcript gene expression profiling. A large percentage of genes that are not differentially expressed at the gene level demonstrate exon expression variation suggesting an influence of strain and sex on alternative splicing and a need to profile expression changes at sub-gene resolution. [ABSTRACT FROM AUTHOR]

Background: Peptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. By allowing the parallel analysis of thousands of peptides in a single run they are suitable for high-throughput settings. Since data characteristics of peptide microarrays differ from DNA oligonucleotide microarrays, computational methods need to be tailored to these specifications to allow a robust and automated data analysis. While follow-up experiments can ensure the specificity of results, sensitivity cannot be recovered in later steps. Providing sensitivity is thus a primary goal of data analysis procedures. To this end we created rapmad (Robust Alignment of Peptide MicroArray Data), a novel computational tool implemented in R., Results: We evaluated rapmad in antibody reactivity experiments for several thousand peptide spots and compared it to two existing algorithms for the analysis of peptide microarrays. rapmad displays competitive and superior behavior to existing software solutions. Particularly, it shows substantially improved sensitivity for low intensity settings without sacrificing specificity. It thereby contributes to increasing the effectiveness of high throughput screening experiments., Conclusions: rapmad allows the robust and sensitive, automated analysis of high-throughput peptide array data. The rapmad R-package as well as the data sets are available from http://www.tron-mz.de/compmed.

Background: mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications., Methods: We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples., Results: When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-7)). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling., Conclusion: We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.

Background: Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions., Results: Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes which are highly expressed in all tissues with lower variance than many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well., Conclusion: We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns.

Background: Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon expression using male and female mice from three different inbred strains., Results: 71 liver RNA samples from three mouse strains - DBA/2J, C57BL/6J and C3H/HeJ - were profiled using a custom-designed microarray monitoring exon and exon-junction expression of 1,020 genes representing 9,406 exons. Gene expression was calculated via two different methods, using the 3'-most exon probe ("3' gene expression profiling") and using all probes associated with the gene ("whole-transcript gene expression profiling"), while exon expression was determined using exon probes and flanking junction probes that spanned across the neighboring exons ("exon expression profiling"). Widespread strain and sex influences were detected using a two-way Analysis of Variance (ANOVA) regardless of the profiling method used. However, over 90% of the genes identified in 3' gene expression profiling or whole transcript profiling were identified in exon profiling, along with 75% and 38% more genes, respectively, showing evidence of differential isoform expression. Overall, 55% and 32% of genes, respectively, exhibited strain- and sex-bias differential gene or exon expression., Conclusion: Exon expression profiling identifies significantly more variation than both 3' gene expression profiling and whole-transcript gene expression profiling. A large percentage of genes that are not differentially expressed at the gene level demonstrate exon expression variation suggesting an influence of strain and sex on alternative splicing and a need to profile expression changes at sub-gene resolution.