Adam Ertel, PhD
Research Assistant Professor
Contact Information
233 South 10th Street
BLSB 1009
Philadelphia, PA 19107
215-503-7452
215-503-9142 fax
Research Assistant Professor
Medical School
PhD, Biomedical Engineering/Bioinformatics, Drexel University, Philadelphia, PA - 2008
BS, Electrical Engineering, University of Connecticut , CT
Most Recent Peer-Reviewed Publications
- Master Transcription Factor Reprogramming Unleashes Selective Translation Promoting Castration Resistance and Immune Evasion in Lethal Prostate Cancer
- Erratum: Endogenous Cyclin D1 Promotes the Rate of Onset and Magnitude of Mitogenic Signaling via Akt1 Ser473 Phosphorylation (Cell Reports (2020) 32(11), (S2211124720311402), (10.1016/j.celrep.2020.108151))
- Harnessing transcriptionally driven chromosomal instability adaptation to target therapy-refractory lethal prostate cancer
- SMARCB1-Retained and SMARCB1-Deficient SNUC are Genetically Distinct: A Pilot Study Using RNA Sequencing
- Altered genome-wide hippocampal gene expression profiles following early life lead exposure and their potential for reversal by environmental enrichment
Expertise & Research Interests
Bioinformatics and pathway-based approaches to gene expression profiling.
Gene interactions and molecular profiles in cancer.
SNP genotype and copy number analysis for genome-wide association studies.
My research focuses on patterns of gene expression, gene regulation, and gene product interactions that can be inferred from large collections of mRNA expression data. This approach is useful for identifying normal interaction and regulatory connections between genes as well as the disruption of these connections in complex diseases such as cancer. Bioinformatics approaches allow these connections to be easily extended into the context of biological pathways in order to understand global changes with respect to disease states or treatment. I’ve collaborated extensively with other Principal Investigators in the KCC to establish automated analysis pipelines for genes, gene signatures, and interaction profiles that provide informative readouts of pathway function and dysfunction associated with disease states.
Future plans include the automation of a publically accessible web-based tool that provides a user-friendly readout of genes, gene signatures, and interaction profiles across multiple phenotypes, disease states, and therapeutic interventions. As this tool evolves, it will be useful for designing and implementing classification algorithms to assist disease diagnosis and guided therapy.