Qiagen is dedicated to developing companion diagnostics for Personalized and Precision Medicine by detecting clinically relevant genetic abnormalities for decision-making in diseases such as cancer. Qiagen currently has nine FDA approved companion diagnostics on the market, including EGFR for non-small cell lung cancer, KRAS for colorectal cancer, FGFR for urothelial cancer, PIK3CA for breast cancer, and BRAF for colorectal cancer. Qiagen is also working on collaborations with more than 25 companies to develop and commercialize new companion diagnostic tests for their drug candidates. Personally wrote multiple programs in Python and Perl for data validation and positioning of Nonsense Mediated Decay (NMD) demarcation zones within key cancer genes in the Qiagen Knowledge Base (KB) used for ACMG pathogenic classification of variants in the Qiagen Clinical Insights (QCI) application.

As Sr. Scientist in Personalized Medicine within Immuno-Oncology, provided leadership, data management, and bioinformatic analysis of biomarker and clinical data for two ongoing clinical trials involving ADU-S100, an agonist of the STING pathway. STING is known to be a central mediator of innate and adaptive immunity. When stimulated, STING induces the expression of type I interferon and other cytokines, resulting in priming of tumor-specific T cells, targeting tumors for destruction. Personally wrote in Perl and Python, multiple laboratory data validation and summarization programs to combine biomarker data from different laboratory platforms and clinical response data into a SQLite database of my own design. Personally built a Nanostring gene expression analysis pipeline in R to determine if the Tumor Inflammation Signature (TIS) could be used as a surrogate marker to predict responders for patient stratification. Personally built an RNA-seq analysis pipeline in R using kallisto and edgeR to create a new gene expression signature for response to ADU-S100 in different mouse models of cancer and used it to discover new biomarkers for ongoing clinical trials involving immunotherapy of cancer.

As a member of a Bioinformatics team of experienced PhD and MD scientists located around the world, supported the delivery of custom life science data curation and analysis solutions to various clients within academic environments, biotech industries, and pharmaceutical companies. Supported the GERMLINE Project by working on-site with a large pharmaceutical company located in South San Francisco. GERMLINE is an acronym for GEnetics Result and Metadata Live INtegrated Environment. The project is aimed at harmonizing data from various internal and external data sources and providing a common view of the data that will eventually become a portal for scientists to explore genetic and other forms of data for the discovery and/or validation of drug targets. Personally wrote programs in Perl and Python to harmonize data from disparate scientific domains into a common store database.

Defined the process flow and built NGS analysis pipelines for DNA-seq and RNA-seq experiments to discover and characterize Hepatitis B Virus (HBV) integration sites within paired normal liver and tumor samples. Determined the level of HBV surface antigen (sAg) gene expression and detected gene fusion products using STAR aligner for an RNA-seq experiment in liver cancer. Installed and ran a phylogenetic software pipeline for MUSCLE (MUltiple Sequence Comparison by Log-Expectation) and PhyML, critical components for performing genetic relatedness studies of Hepatitis B Virus (HBV) isolates . Designed the schema for a relational database used for combining genotype, phenotype, and biomarker data from specific groups of patients participating in clinical trials. This analysis database was designed as the repository for storing large volumes of genomic “big data” obtained through variant calling of Next Generation Sequence (NGS) data to be mined for genetic/genomic markers. Built an analysis pipeline using R and Python that pulled data directly from this database using SQL queries in order to perform genome wide association studies (GWAS), the goal being to collect genomic information from patients over the course of clinical trials and mine that data to develop companion diagnostics for personalized medicine. Using PLINK, determined the allele and genotype frequencies for SNPs from 200 chronic Hepatitis C Virus (HCV) patients with various levels of bilirubin toxicity following treatment with GS-9256, an NS3 Serine Protease Inhibitor. The results showed that risk for bilirubin elevation in these patients is unlikely to be influenced by common genetic variation, and is not associated with variations seen within previously suspected genes involved in membrane transport. See my list of publications for more details here:http://tinyurl.com/HengenPN-Publications

Developed Next Generation Sequencing (NGS) and Gene Expression Bioinformatics software GeneSifter Lab and Analysis Edition. GeneSifter is a cloud-based software data analysis tool used for interpreting the vast amounts of genetic data produced by hybridization array and Next Generation Sequencing technologies from all of the major platforms. Utilized Perl programming and bash scripting skills to build NGS analysis pipelines for GeneSifter. Detected novel genomic structural variations (SVs) using the program BreakDancer. Visualized the types and locations of identified structural variations by use of circos plots. Designed and built several SQLite databases to hold allele frequency data from variations discovered in the 1000 Genomes Project.

Utilized Perl programming skills to write specific software for probe pair selection to be used in the NanoString nCounter® Digital Analysis System. Perl programs were designed to interact specifically with a mySQL database housed on a Linux server. Data was pulled from tables of the database, manipulated within the Perl programs, and then data was returned to the database for further analysis and data-mining. Applications included creating assays for gene expression studies, genotyping of copy number variation (CNVs) within human and other species, and assays for the direct detection of pathogenic species of microorganisms within complex mixtures of nucleic acids. Accomplishment: Conceived and spearheaded the development of a novel human molecular karyotyping assay utilizing the NanoString nCounter® platform.

Identified molecular markers of risk for therapy-related myelodysplastic syndrome (t-MDS) after autologous bone marrow transplantation with hematopoietic stem cells (HSC). t-MDS and acute myologenous leukemia (AML) are serious bone marrow complications following cancer therapy. We used Affymetrix Gene Chip hybridization arrays to identify genes misregulated within peripheral blood stem cells (PBSC) and bone marrow stem cells in patients undergoing bone marrow transplant (BMT). Gene Set Enrichment Analysis (GSEA) revealed many genes within cell signaling pathways altered in their expression levels and these genes were further analyzed for their potential as therapeutic targets. Follow-up studies with mathematical models of nuclear transcription factor binding sites should elucidate the mechanisms of gene regulation in the development of leukemia and secondary bone marrow cancers.

As part of a team, worked in collaboration with wet bench scientists to identify molecular markers of prostate cancer progression and metastasis to bone. The androgen receptor (AR) belongs to a family of ligand-activated nuclear receptors that mediate gene expression through transcriptional activation. The role of the AR in prostate cancer initiation and progression is well established; however, there is little knowledge of how AR target genes play a role in prostate cancer initiation and progression, typically resulting in secondary metastasis to the bone marrow. We combined the laboratory method of ChIP-on-chip and a bioinformatic approach utilizing a mathematical model of AR DNA binding sites to discover and characterize novel AR target genes in prostate cancer cells. The goal was to investigate the possible role of these genes in formation of bone marrow cancer.

For this company, personally designed the genetics object model for ABI's instrumentation and data analysis software. This object-oriented domain model, based on fundamental principles of Mendelian characterization, was designed to be independent of instrumentation. The design allows for genetic data to be gathered from phenotype, genotype, and gene expression information sources and then combined for the purpose of association studies, mathematical modeling, and data-mining. The genetics object model was specifically designed to aid the discovery of new predictors of genetic processes (biomarkers) and for applications in molecular medicine.

Conducted a genetic association study of 200 patients undergoing addiction treatment at the Ernest Gallo Clinic and Research Center at the University of California, San Francisco. Wrote various Perl programs to interpret raw trace data coming off of an Applied Biosystems ABI3700 capillary electrophoresis instrument.

As part of a team used information theory techniques to create mathematical models of DNA binding sites. By comparing the models to natural binding sites, made predictions about the properties of DNA-protein interactions concerning transcriptional regulation (gene expression) and other molecular recognition. Showed the feasibility of using such a method for scanning DNA sequences to predict sites bound by the Factor for Inversion Stimulation (Fis), a pleiotropic protein that enhances site-specific recombination, controls DNA replication, and regulates transcription of a number of genes in Escherichia coli and Salmonella typhimurium.

Authored over 45 articles published as a `Methods and Reagents' monthly series within Trends in Biochemical Sciences from 1993 through 1997. The introductory article was published in the November 1993 issue of TiBS. All manuscripts are freely available on the web from Tom Schneider's archive site at http://schneider.ncifcrf.gov/methods/TIBS/ OR as PDF files from Science Direct at the website below:http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleListID=-918114694&_sort=r&_st=13&view=c&md5=9ff27469914c4b429016ddbd66dd2f89&searchtype=aIn addition, authored several laboratory technique articles for the online magazine BiteSizeBio. The articles can be found at http://bitesizebio.com/profile/paul-hengen/