Inactivation of TP53 correlates with disease progression and low miR-34a expression in previously treated chronic lymphocytic leukemia patients.

In chronic lymphocytic leukemia (CLL) patients, disruptions of the TP53 tumor suppressor pathway by 17p13 deletion (del17p), somatic TP53 mutations, or downregulation of microRNA-34a have been associated with a poor prognosis. So far, the impact of the various TP53 defects has not been evaluated in a large cohort of previously treated and relapsed CLL patients. Here, we present the results of TP53 gene sequencing and fluorescence in situ hybridization for del17p in a phase 3 clinical trial (REACH [Rituximab in the Study of Relapsed Chronic Lymphocytic Leukemia]). Of the 457 patients, 52 had TP53 mutations and 37 had del17p. In 24 (46%) of the TP53 mutated patients, no del17p was found and in 9 of the del17p patients, no TP53 mutation was identified. Based on a predicted proportion of TP53 disruption, a complete disruption of TP53 function, either by a combination of point mutations and/or del17p, was associated with a high risk for disease progression. Progression-free survival of patients with a heterozygous TP53 mutation was not significantly different from patients with a completely intact TP53 locus. In addition, only a complete loss of TP53 function correlated with low microRNA-34a expression levels. This trial was registered at www.clinicaltrials.gov as #NCT00090051.

An integrative genomics approach to infer causal associations between gene expression and disease.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

Mapping the genetic architecture of gene expression in human liver.

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Exon and junction microarrays detect widespread mouse strain- and sex-bias expression differences.

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.

Computational identification of transcriptional regulatory elements in DNA sequence.

Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges.

Cis-regulatory variations: a study of SNPs around genes showing cis-linkage in segregating mouse populations.

BACKGROUND: Changes in gene expression are known to be responsible for phenotypic variation and susceptibility to diseases. Identification and annotation of the genomic sequence variants that cause gene expression changes is therefore likely to lead to a better understanding of the cause of disease at the molecular level. In this study we investigate the pattern of single nucleotide polymorphisms (SNPs) in genes for which the mRNA levels show cis-genetic linkage (gene expression quantitative trait loci mapping in cis, or cis-eQTLs) in segregating mouse populations. Such genes are expected to have polymorphisms near their physical location (cis-variations) that affect their mRNA levels by altering one or more of the cis-regulatory elements. This led us to characterize the SNPs in promoter (5 Kb upstream) and non-coding gene regions (introns and 5 Kb downstream) (cis-SNPs) and the effects they may have on putative transcription factor binding sites. RESULTS: We demonstrate that the cis-eQTL genes (CEGs) have a significantly higher frequency of cis-SNPs compared to non-CEGs (when both sets are taken from the non-IBD regions, i.e. regions not identical by descent). Most CEGs having cis-SNPs do not contain these SNPs in the phylogenetically conserved regions. In those CEGs that contain cis-SNPs in the phylogenetically conserved regions, enrichment of cis-SNPs occurs both within and outside of the conserved sequences. A higher fraction of CEGs are also seen to harbor cis-SNP that affect predicted transcription factor binding sites, a likely consequence of the higher cis-SNPs density in these genes. CONCLUSION: This present study provides the first genome-wide investigation of the putative cis-regulatory variations in a large set of genes whose levels of expression give rise to cis-linkage in segregating mammalian populations. Our results provide insights into the challenges that exist in identifying polymorphisms regulating gene expression using bioinformatic sequence analysis approaches. The data provided herein should benefit future investigations in this area.

Coevolution of protein and RNA structures within a highly conserved ribosomal domain.

The X-ray crystal structure of a ribosomal L11-rRNA complex with chloroplast-like mutations in both protein and rRNA is presented. The global structure is almost identical to that of the wild-type (bacterial) complex, with only a small movement of the protein alpha helix away from the surface of the RNA required to accommodate the altered protein residue. In contrast, the specific hydrogen bonding pattern of the mutated residues is substantially different, and now includes a direct interaction between the protein side chain and an RNA base edge and a water-mediated contact. Comparison of the two structures allows the observations of sequence variation and relative affinities of wild-type and mutant complexes to be clearly rationalized, but reinforces the concept that there is no single simple code for protein-RNA recognition.

A Randomized Phase II Trial of Sipuleucel-T with Concurrent versus Sequential Abiraterone Acetate plus Prednisone in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: This phase II open-label study evaluated the effect of concurrent or sequential administration of abiraterone acetate plus prednisone (AA + P) on sipuleucel-T manufacture and immune responses in metastatic castration-resistant prostate cancer (mCRPC) patients. EXPERIMENTAL DESIGN: mCRPC patients received sipuleucel-T followed by AA + P 1 day (concurrent) or 10 weeks (sequential) after the first sipuleucel-T infusion. AA + P treatment continued for 26 weeks. The primary endpoint was cumulative antigen presenting cell (APC) activation, and secondary endpoints included cumulative APC number and total nucleated cell counts. Additional endpoints included in vivo peripheral immune responses to sipuleucel-T (T-cell responses, T-cell proliferation, humoral responses, and antigen spread) as well as safety. RESULTS: Sixty-nine mCRPC patients were enrolled, with 35 and 34 patients randomized to the concurrent and sequential arms, respectively. Ex vivo APC activation was significantly greater at the second and third infusions compared with baseline in both arms (P < 0.05), indicative of an immunologic prime-boost effect. In both arms, sipuleucel-T product parameter profiles and peripheral immune responses were consistent with previously conducted sipuleucel-T phase III trials. Antigen spread was similarly observed in both arms and consistent with the other immunologic endpoints. CONCLUSIONS: These data suggest that sipuleucel-T can be successfully manufactured during concurrent administration of AA + P without blunting immunologic effects or altering immune parameters that correlate with sipuleucel-T's clinical benefit. Combination of these agents was well tolerated, with no new safety signals emerging.

Humoral Immune Response against Nontargeted Tumor Antigens after Treatment with Sipuleucel-T and Its Association with Improved Clinical Outcome.

PURPOSE: Antitumor activity of cancer immunotherapies may elicit immune responses to nontargeted (secondary) tumor antigens, or antigen spread. We evaluated humoral antigen spread after treatment with sipuleucel-T, an immunotherapy for asymptomatic or minimally symptomatic metastatic castration-resistant prostate cancer (mCRPC), designed to target prostatic acid phosphatase (PAP; primary antigen). EXPERIMENTAL DESIGN: Serum samples from patients with mCRPC enrolled in the placebo-controlled phase III IMPACT study (evaluable n = 142) were used to assess humoral antigen spread after treatment with sipuleucel-T. Immunoglobulin G (IgG) responses to self-antigens (including tumor antigens) were surveyed using protein microarrays and confirmed using Luminex xMAP. IgG responses were subsequently validated in ProACT (n = 33), an independent phase II study of sipuleucel-T. Association of IgG responses with overall survival (OS) was assessed using multivariate Cox models adjusted for baseline prostate-specific antigen (PSA) and lactate dehydrogenase levels. RESULTS: In patients from IMPACT and ProACT, levels of IgG against multiple secondary antigens, including PSA, KLK2/hK2, K-Ras, E-Ras, LGALS8/PCTA-1/galectin-8, and LGALS3/galectin-3, were elevated after treatment with sipuleucel-T (P < 0.01), but not control. IgG responses (≥ 2-fold elevation posttreatment) occurred in ≥ 25% of patients, appeared by 2 weeks after sipuleucel-T treatment, and persisted for up to 6 months. IgG responses to PSA and LGALS3 were associated with improved OS in sipuleucel-T-treated patients from IMPACT (P ≤ 0.05). CONCLUSIONS: Sipuleucel-T induced humoral antigen spread in patients with mCRPC. IgG responses were associated with improved OS in IMPACT. The methods and results reported may identify pharmacodynamic biomarkers of clinical outcome after sipuleucel-T treatment, and help in clinical assessments of other cancer immunotherapies. See related commentary by Hellstrom and Hellstrom, p. 3581.

Applications of systems biology in cancer immunotherapy: from target discovery to biomarkers of clinical outcome.

Immunotherapies are coming to the forefront as a treatment paradigm in cancer with multiple US FDA approvals in recent years and a better understanding of their therapeutic mode of action. The control of tumor growth by the immune system is orchestrated by a complex array of cellular interactions and molecular pathways, both in the immune cells as well as the tumor. Although research over the past three decades has elucidated many aspects of tumor immunosurveillance, given the inherent complexity of the immune cell phenotypes and function, high-throughput molecular profiling ('omics') approaches have now become essential to support the discovery and development of new therapies. Technologies, such as DNA and protein microarrays, deep sequencing, mass spectrometry, as well as the computational methods for their analyses, are advancing the contributions of systems biology towards the development and mechanistic understanding of cancer immunotherapies. In this review, the authors illustrate this through some recently reported studies.

Predictive biomarkers of sensitivity to the phosphatidylinositol 3' kinase inhibitor GDC-0941 in breast cancer preclinical models.

PURPOSE: The class I phosphatidylinositol 3' kinase (PI3K) plays a major role in proliferation and survival in a wide variety of human cancers. A key factor in successful development of drugs targeting this pathway is likely to be the identification of responsive patient populations with predictive diagnostic biomarkers. This study sought to identify candidate biomarkers of response to the selective PI3K inhibitor GDC-0941. EXPERIMENTAL DESIGN: We used a large panel of breast cancer cell lines and in vivo xenograft models to identify candidate predictive biomarkers for a selective inhibitor of class I PI3K that is currently in clinical development. The approach involved pharmacogenomic profiling as well as analysis of gene expression data sets from cells profiled at baseline or after GDC-0941 treatment. RESULTS: We found that models harboring mutations in PIK3CA, amplification of human epidermal growth factor receptor 2, or dual alterations in two pathway components were exquisitely sensitive to the antitumor effects of GDC-0941. We found that several models that do not harbor these alterations also showed sensitivity, suggesting a need for additional diagnostic markers. Gene expression studies identified a collection of genes whose expression was associated with in vitro sensitivity to GDC-0941, and expression of a subset of these genes was found to be intimately linked to signaling through the pathway. CONCLUSION: Pathway focused biomarkers and the gene expression signature described in this study may have utility in the identification of patients likely to benefit from therapy with a selective PI3K inhibitor.

Identification of genes and networks driving cardiovascular and metabolic phenotypes in a mouse F2 intercross.

To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.

Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks.

We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue. Although coexpression networks of highly correlated genes were largely conserved between sexes, some exhibited striking sex dependence. We observed strong body fat and lipid correlations with sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks related to genetic and metabolic traits.

Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle.

Two homeodomain proteins, Yox1 and Yhp1, act as repressors at early cell cycle boxes (ECBs) to restrict their activity to the M/G1 phase of the cell cycle in budding yeast. These proteins bind to Mcm1 and to a typical homeodomain binding site. The expression of Yox1 is periodic and directly correlated with its binding to, and repression of, ECB activity. The absence of Yox1 and Yhp1 or the constitutive expression of Yox1 leads to the loss of cell-cycle regulation of ECB activity. Therefore, the cell-cycle-regulated expression of these repressors defines the interval of ECB-dependent transcription. Twenty-eight genes, including MCM2-7, CDC6, SWI4, CLN3, and a number of genes required during late M phase have been identified that are coordinately regulated by this pathway.

Novel transcription regulatory elements in Caenorhabditis elegans muscle genes.

We report the identification of three new transcription regulatory elements that are associated with muscle gene expression in the nematode Caenorhabditis elegans. Starting from a subset of well-characterized nematode muscle genes, we identified conserved DNA motifs in the promoter regions using computational DNA pattern-recognition algorithms. These were considered to be putative muscle transcription regulatory motifs. Using the green-fluorescent protein (GFP) as a reporter, experiments were done to determine the biological activity of these motifs in driving muscle gene expression. Prediction accuracy of muscle expression based on the presence of these three motifs was encouraging; nine of 10 previously uncharacterized genes that were predicted to have muscle expression were shown to be expressed either specifically or selectively in the muscle tissues, whereas only one of the nine that scored low for these motifs expressed in muscle. Knockouts of putative regulatory elements in the promoter of the mlc-2 and unc-89 genes show that they significantly contribute to muscle expression and act in a synergistic manner. We find that these DNA motifs are also present in the muscle promoters of C. briggsae, indicating that they are functionally conserved in the nematodes.

Identification of a novel cis-regulatory element involved in the heat shock response in Caenorhabditis elegans using microarray gene expression and computational methods.

We report here the identification of a previously unknown transcription regulatory element for heat shock (HS) genes in Caenorhabditis elegans. We monitored the expression pattern of 11,917 genes from C. elegans to determine the genes that were up-regulated on HS. Twenty eight genes were observed to be consistently up-regulated in several different repetitions of the experiments. We analyzed the upstream regions of these genes using computational DNA pattern recognition methods. Two potential cis-regulatory motifs were identified in this way. One of these motifs (TTCTAGAA) was the DNA binding motif for the heat shock factor (HSF), whereas the other (GGGTGTC) was previously unreported in the literature. We determined the significance of these motifs for the HS genes using different statistical tests and parameters. Comparative sequence analysis of orthologous HS genes from C. elegans and Caenorhabditis briggsae indicated that the identified DNA regulatory motifs are conserved across related species. The role of the identified DNA sites in regulation of HS genes was tested by in vitro mutagenesis of a green fluorescent protein (GFP) reporter transgene driven by the C. elegans hsp-16-2 promoter. DNA sites corresponding to both motifs are shown to play a significant role in up-regulation of the hsp-16-2 gene on HS. This is one of the rare instances in which a novel regulatory element, identified using computational methods, is shown to be biologically active. The contributions of individual sites toward induction of transcription on HS are nonadditive, which indicates interaction and cross-talk between the sites, possibly through the transcription factors (TFs) binding to these sites.

A comprehensive transcript index of the human genome generated using microarrays and computational approaches.

BACKGROUND: Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. RESULTS: The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. CONCLUSIONS: These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized.