Identification of candidate SNPs for drug induced toxicity from differentially expressed genes in associated tissues.

The growing collection of publicly available high-throughput data provides an invaluable resource for generating preliminary in silico data in support of novel hypotheses. In this study we used a cross-dataset meta-analysis strategy to identify novel candidate genes and genetic variations relevant to paclitaxel/carboplatin-induced myelosuppression and neuropathy. We identified genes affected by drug exposure and present in tissues associated with toxicity. From ten top-ranked genes 42 non-synonymous single nucleotide polymorphisms (SNPs) were identified in silico and genotyped in 94 cancer patients treated with carboplatin/paclitaxel. We observed variations in 11 SNPs, of which seven were present in a sufficient frequency for statistical evaluation. Of these seven SNPs, three were present in ABCA1 and ATM, and showed significant or borderline significant association with either myelosuppression or neuropathy. The strikingly high number of associations between genotype and clinically observed toxicity provides support for our data-driven computations strategy to identify biomarkers for drug toxicity.

Ontology-based meta-analysis of global collections of high-throughput public data.

BACKGROUND: The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. METHODOLOGY/RESULTS: We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. CONCLUSIONS: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Demonstration of an in vivo generated sub-picomolar affinity fully human monoclonal antibody to interleukin-8.

The high specificity and affinity of monoclonal antibodies make them attractive as therapeutic agents. In general, the affinities of antibodies reported to be high affinity are in the high picomolar to low nanomolar range and have been affinity matured in vitro. It has been proposed that there is an in vivo affinity ceiling at 100 pM and that B cells producing antibodies with affinities for antigen above the estimated ceiling would have no selective advantage in antigen-induced affinity maturation during normal immune responses. Using a transgenic mouse producing fully human antibodies, we have routinely generated antibodies with sub-nanomolar affinities, have frequently rescued antibodies with less than 10 pM affinity, and now describe the existence of an in vivo generated anti-hIL-8 antibody with a sub-picomolar equilibrium dissociation constant. This confirms the prediction that antibodies with affinities beyond the proposed affinity ceiling can be generated in vivo. We also describe the technical challenges of determining such high affinities. To further understand the importance of affinity for therapy, we have constructed a mathematical model to predict the relationship between the affinity of an antibody and its in vivo potency using IL-8 as a model antigen.

eBLOCKs: enumerating conserved protein blocks to achieve maximal sensitivity and specificity.

Classifying proteins into families and superfamilies allows identification of functionally important conserved domains. The motifs and scoring matrices derived from such conserved regions provide computational tools that recognize similar patterns in novel sequences, and thus enable the prediction of protein function for genomes. The eBLOCKs database enumerates a cascade of protein blocks with varied conservation levels for each functional domain. A biologically important region is most stringently conserved among a smaller family of highly similar proteins. The same region is often found in a larger group of more remotely related proteins with a reduced stringency. Through enumeration, highly specific signatures can be generated from blocks with more columns and fewer family members, while highly sensitive signatures can be derived from blocks with fewer columns and more members as in a superfamily. By applying PSI-BLAST and a modified K-means clustering algorithm, eBLOCKs automatically groups protein sequences according to different levels of similarity. Multiple sequence alignments are made and trimmed into a series of ungapped blocks. Motifs and position-specific scoring matrices were derived from eBLOCKs and made available for sequence search and annotation. The eBLOCKs database provides a tool for high-throughput genome annotation with maximal specificity and sensitivity. The eBLOCKs database is freely available on the World Wide Web at http://motif.stanford.edu/eblocks/ to all users for online usage. Academic and not-for-profit institutions wishing copies of the program may contact Douglas L. Brutlag (brutlag@stanford.edu). Commercial firms wishing copies of the program for internal installation may contact Jacqueline Tay at the Stanford Office of Technology Licensing (jacqueline.tay@stanford.edu; http://otl.stanford.edu/).