The Dietlein lab pairs statistical innovations with scalable experimental data to unlock the power of biobank-scale clinical and genomics datasets for a systematic exploration of the genomic architecture of cancer and its translation into therapies and diagnostics. Our research is at the intersection of the computational, experimental, and clinical disciplines
Tumor cells depend on a few “driver” mutations, and this “Achilles heel” can be exploited as the target of cancer therapies. Our lab has developed bioinformatics tools for interpreting mutations in coding and noncoding regions of the cancer genome by combining innovations in statistics with genome biology. Furthermore, our lab is committed to sharing results as user-friendly resources and software that can be run in the hands of non-computational scientists. We collaborate with scientists from other disciplines to apply bioinformatics methods to clinically focused questions.
Dietlein et al. Science 2022
Dietlein et al. Nature Genetics 2020
Conway, Dietlein et al. Nature Genetics 2020
Reardon, …, Dietlein, Van Allen. Nature Cancer 2021
MutPanning tool on genepattern.org
Small molecules target enzymes essential in tumors but not in normal cells. After identifying vulnerabilities in tumor genomes, mechanistic insights are required to translate them into effective therapies. Although many driver lesions can be targeted directly, others require more complex strategies, such as exploiting synthetic lethality. Our lab develops treatments for various genomic lesions and pathways. A specific focus is the advancement of biologically informed combination therapies by developing bioinformatics tools because most tumors develop therapeutic resistance to single agents in a short period of time.
Dietlein et al. Cell 2015
Dietlein et al. Cancer Discovery 2014
Malchers*, Dietlein* et al. Cancer Discovery 2014
Sos*, Dietlein* et al. PNAS 2012
In addition to targeted therapies, molecular targets can be used to enhance the detection of tumor lesions. For example, many prostate cancer patients experience recurrence of their disease after surgery or radiotherapy, typically diagnosed by rising PSA levels. Determining the exact location of the recurrent lesion through PET/CT imaging is critical for initiating the correct salvage therapy. In collaboration with clinicians, our lab develops computational approaches for extracting clinically relevant features to tailor tumor diagnostics.
Dietlein et al. JNM 2021
Dietlein et al. JNM 2020a
Dietlein et al. JNM 2020b
Dietlein et al. JNM 2017
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