Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations. Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.
I study quality of care and patient outcomes in healthcare delivery systems, largely focusing on cardiovascular diseases. I use large national and regional data sets to answer fundamental questions about current healthcare delivery systems and how new approaches to designing these systems could save lives and reduce disease burden in large patient populations. My research includes the use of innovative data mining and machine learning tools.
As Director of the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), I collaborate with multidisciplinary investigators on predictive modeling and precision medicine initiatives. http://michamp.med.umich.edu/; http://midas.umich.edu/research/health/mchamp/
I have received funding from AHRQ, NIH, VA HSR&D and the BCBS Foundation of Michigan and participated on NIH and VA study sections targeting health services research.
I am Editor-in-Chief of Circulation: Cardiovascular Quality & Outcomes, a leading cardiovascular outcomes research journal published by the American Heart Association.
The Athey Lab in the Department of Computational Medicine and Bioinformatics (DCM&B) University of Michigan Medical School, is led by Dr. Brian Athey (see Atheylab.ccmb.med.umich.edu).
The lab is working on two complementary domains of research and development.
1. The Athey Lab’s recent research interests are in the creation and use of bioinformatics pipelines and machine learning methods to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response
2. The Athey Lab is also developing new high-throughput methods to analyze images of genes in the context of the cellular nucleus to better understand the machinery of bioinformatics in context. One main area of research is the application of high resolution fluorescence optical microscopy coupled with high-throughput analysis, 3D imaging and machine learning to explore the chromatin structure and nuclear architecture of cells. This research emphasizes the convergence between 3D structural predictions and 3D structural measurements with microscopy, to provide insight into the transcriptional architecture of the interphase nucleus.
This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response.
Collaborations: The lab works very closely with Assurex Health, Inc. (Mason, Ohio) on project 1. This work is governed by a Regents-approved Master Agreement between U-M and Assurex Health, Inc. Similarly, the lab collaborates closely with the tranSMART Foundation (tF), and this is also governed by a Master Agreement between U-M and tF.
The lab collaborates with the Brady Urological Institute at Johns Hopkins Medical School, lead by Dr. Ken Pienta, to build on their extensive 2D characterization of prostate tumors, by the introduction of simple chromatin dyes, advanced biomarkers, and 3D imaging systems.
The lab works closely with Dr. John Wiley of University of Michigan Health System, studying the effect of glucocorticoids on the neuroblastoma based cell line Sy5y before and after treatment with retinoic acid and BDNF, particularly in their terminally differentiated condition.
The lab also collaborates with Dr. Christoph Cremer from the Institute of Molecular Biology in Mainz, Germany, investigating super-resolution microscopy techniques.
Functional genomic data, such as RNA-seq, microarray, protein-protein interactions, are growing exponentially these days. The research in Guan Lab focuses on developing novel and high-accuracy algorithms that integrate these data for predicting gene functions and networks. We have the following ongoing projects in the lab:
1. Modeling dynamic networks: Biological networks may rewire during cell lineage differentiation, tissue development or a disease course. We are actively developing novel algorithms that capture such dynamics. The algorithm contributed by our group (The GuanLab Team) was one of the six best-performance methods (out of over 100 submissions) in the HPN-DREAM 2013 Competition. We achieved the best performance (along another team) to subchallenge 2A, as well as the best aggregate prediction to subchallenges 2A and 2B: network timecourse prediction.
2. Isoform-level analysis: We are exploring algorithms that allow us to go beyond the traditional gene-level analysis to the isoform level.