My research approach has 3 goals, learned from my PhD mentor Dr. Michael Green and Postdoctoral advisor Dr. Anne Novak.
#1 Identify genomic drivers of disease via bioinformatics analyses.
Students utilize public data to discover new genetic targets in lymphoma. These discoveries are incredibly exciting and freely sought with the wealth of publicly available genomics data.
#2 Validate functional mechanisms in cell line models.
Students test and manipulate human cell line models to make exciting new conclusions, possibly following up on their bioinformatics findings. Examining how genetic targets are manifesting effects inside the cell is key to understanding how to treat this heterogeneous disease.
#3 Translate results to a targeted therapy.
There is a massive need for pre-clinical validation of DLBCL therapies, especially for discovering synergy between compounds. Students can treat cell line models with combinations of therapies to uncover data that could translate directly to the clinic.
#1 Identify genomic drivers of disease via bioinformatics analyses.
Students utilize public data to discover new genetic targets in lymphoma. These discoveries are incredibly exciting and freely sought with the wealth of publicly available genomics data.
#2 Validate functional mechanisms in cell line models.
Students test and manipulate human cell line models to make exciting new conclusions, possibly following up on their bioinformatics findings. Examining how genetic targets are manifesting effects inside the cell is key to understanding how to treat this heterogeneous disease.
#3 Translate results to a targeted therapy.
There is a massive need for pre-clinical validation of DLBCL therapies, especially for discovering synergy between compounds. Students can treat cell line models with combinations of therapies to uncover data that could translate directly to the clinic.
The core engines of the lymphoma field may be shifting towards the microenvironment outside of the tumor, away from the genomics inside the cells, but attractive data never goes out of style. Excitingly, this means that classes like Evolution and Ecology are more important to bridge this new understanding.
And as much as I love a big batch of data to look into, testing drug combinations vs. live tumor cell lines is something me and my students never get tired of. Above is a never before seen synergistic combination that we discovered in our little Minnesota State lab.
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We took our latest venture into my BIOL 211 Genetics labs: priming immune cells like Macrophages and Natural Killer cells with epigenetic drugs to drive them into a tumor killing frenzy.
The results? It worked. |
