An Integrative Approach for Interpretation of Clinical NGS Genomic Variant Data.

Antibody (Ab) discovery research has accelerated as monoclonal Ab (mAb)-based biologic strategies have proved efficacious in the treatment of many human diseases, ranging from cancer to autoimmunity. Initial steps in the discovery of therapeutic mAb require epitope characterization and preclinical studies in vitro and in animal models often using limited quantities of Ab. To facilitate this research, our Shared Resource Laboratory (SRL) offers microscale Ab conjugation. Ab submitted for conjugation may or may not be commercially produced, but have not been characterized for use in immunofluorescence applications. Purified mAb and even polyclonal Ab (pAb) can be efficiently conjugated, although the advantages of direct conjugation are more obvious for mAb. To improve consistency of results in microscale (<100ug) conjugation reactions, we chose to utilize several different varieties of commercial kits. Kits tested were limited to covalent fluorophore labeling. Established quality control (QC) processes to validate fluorophore labeling either rely solely on spectrophotometry or utilize flow cytometry of cells expected to express the target antigen. This methodology is not compatible with microscale reactions using uncharacterized Ab. We developed a novel method for cell-free QC of our conjugates that reflects conjugation quality, but is independent of the biological properties of the Ab itself. QC is critical, as amine reactive chemistry relies on the absence of even trace quantities of competing amine moieties such as those found in the Good buffers (HEPES, MOPS, TES, etc.) or irrelevant proteins. Herein, we present data used to validate our method of assessing the extent of labeling and the removal of free dye by using flow cytometric analysis of polystyrene Ab capture beads to verify product quality. This microscale custom conjugation and QC allows for the rapid development and validation of high quality reagents, specific to the needs of our colleagues and clientele. Next generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis. Most analysis pipelines do not connect genomic variants to disease and protein specific information during the initial filtering and selection of relevant variants. Robust bioinformatics pipelines were implemented for trimming, genome alignment, SNP, INDEL, or structural variation detection of whole genome or exon-capture sequencing data from Illumina. Quality control metrics were analyzed at each step of the pipeline to ensure data integrity for clinical applications. We further annotate the variants with statistics regarding the diseased population and variant impact. Custom algorithms were developed to analyze the variant data by filtering variants based upon criteria such as quality of variant, inheritance pattern (e.g. dominant, recessive, X-linked), and impact of variant. The resulting variants and their associated genes are linked to Integrated Genome Browser (IGV) in a genome context, and to the PIR iProXpress system for rich protein and disease information. This poster will present detailed analysis of whole exome sequencing performed on patients with facio-skeletal anomalies. We will compare and contrast data analysis methods and report on potential clinically relevant leads discovered by implementing our new clinical variant pipeline. Our variant analysis of these patients and their unaffected family members resulted in more than 500,000 variants. By applying our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for further filtering of disease relevant variants that impact protein coding genes. Taken together, the integrative approach allows better selection of disease relevant genomic variants by using both genomic and disease/protein centric information. This type of clustering approach can help clinicians better understand the association of variants to the disease phenotype, enabling application to personalized medicine approaches.