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An NCI-designated Comprehensive Cancer Center, affiliated with the University of Pittsburgh School of Medicine
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Using a combination of multi-omics data integration, machine learning, and computer vision-assisted pathology image recognition, Dr. Bao’s work bridges methodological advances and biomedical applications with a direct impact on accelerating the knowledge discovery to new clinical trials that could benefit patients. Her lab focuses on the data-driven discovery of resistance mechanisms to cancer immunotherapy, with major contributions to the identification of WNT/ß-catenin activation as the first tumor-intrinsic mechanism that drives immune exclusion, commensal microbiome as the modulator of anti-PD1 efficacy, and systemic discovery of oncogenic pathways that contribute to the absence of immune infiltration across human solid tumors. Those findings are of particular importance because they provide the scientific rationale to new trials that combine therapeutic targets, such as IDH1 inhibitors, with anti-PD1. Dr. Bao is Co-Leader of Bioinformatics/Biostatistics in the Melanoma and Skin Cancer SPORE and Head and Neck Cancer SPORE programs. She also serves as the UPMC Hillman Cancer Center Informatics Committee, providing critical advice on data accessibility, analysis, integration, and infrastructure for translational research across the Cancer Center. Dr. Bao is a member of The American Association of Immunologists, Society for Immunotherapy of Cancer, and Medical Image Computing and Computer Assisted Intervention.
Dr. Camacho’s main research interests focus on modeling the physical interactions responsible for molecular recognition, and in the development of new technologies for structural prediction, their substrates, and supramolecular assemblies. Any progress in these fundamental problems is bound to bring about a better understanding of how proteins work cooperatively in a cell, promoting breakthroughs in every aspect of the biological sciences. Dr. Camacho has multiple patents in cancer and cancer-related targets.
I direct the Genomics Analysis Core, a Health Science shared resource and co-direct the Cancer Bioinformatics Services (CBS) for UPMC Hillman Cancer Center. The GAC and CBS’s aims are to 1) provide genomics data analysis, 2) support team science projects such as consortia projects with computational infrastructure for analysis, storage and sharing of large genomics datasets, 3) assist with University of Pittsburgh initiatives for genomics education. GAC and CBS are an interdisciplinary collaboration between my team, the Department of Biomedical Informatics faculty with bioinformatics expertise, UPMC Hillman Cancer Center, the Institute for Personalized Medicine, the Pittsburgh Supercomputing Center (PSC) and the University of Pittsburgh’s Center for Research Computing (CRC). My team and I have experience working with all genomic platforms and applications RNA Seq, Whole Exome Seq (WXS) and Whole Genome Seq (WGS), single cell seq and digital spatial profiling (DSP). We support both cancer and non-cancer studies and from cell culture, model organisms and human datasets such as The Cancer Genome Atlas Project (TCGA).
My team contributes to team science projects by providing expertise in data analysis, metadata annotation, FAIR principles of data sharing and high performance computing. Examples of such projects include the Breast Cancer Research Foundation’s multi-institution AURORA metastatic breast cancer project in which CBS and PSC collaborate in hosting the data coordination center (DCC). My group also plays a key role in the Breast Cancer Research Foundation’s Data Hub for all 250 BCRF sites.
In the area of genomics education, I teach bioinformatics lectures for DBMI’s Intro to Biomedical Informatics course. My team and I also work closely with the CRC’s genomics education initiative funded by a Pitt seed grant. We have taught hands-on workshops in RNA Seq, metagenomics, single cell genomics and next flow (nf-core) pipelines. The courses are archived and are available through the CRC course website.
Dr. Cooper is Distinguished Professor, UPMC Endowed Chair, and Vice Chair of Research in the Department of Biomedical Informatics, with a secondary appointment in the Intelligent Systems Program. His research focuses on the development and application of methods for probabilistic modeling, machine learning, Bayesian statistics, and artificial intelligence to help advance biomedical research and clinical care. He has published over 200 peer-reviewed papers on these and related topics.
His current projects include causal discovery from observational and experimental biomedical and clinical data, personalized cancer outcome prediction, clinical alerting based on machine learning from an electronic medical record (EMR) archive, and infectious disease outbreak detection and characterization.
How do different organs and tissues arise? What are the genetic and epigenetic mechanisms that drive this development? To address these questions, we design statistical methods and algorithms and apply them to large-scale, genome-wide data. Ultimately, our goal is to generate, test, and confirm hypotheses that are relevant to human health. Current projects include methods for biology at single cell resolution, disease-specific variant prioritization through non-coding regulator loci, and embryonic development of the heart and eye.
The laboratory studies the molecular basis of breast cancer development and resistance to therapy, with the goal to improve precision medicine and outcomes for breast cancer patients. The laboratory employs a systems biology approach, utilizing a combination of single cell and bulk sequencing, computational methods, and biological models to identify and validate new drivers and therapeutic targets. Hypotheses are tested in vitro and in vivo and then moved to clinical trials. The majority of studies incorporate analysis of human specimens, in collaboration with a large network of clinicians and nurses. This includes computational analysis and modeling of large biomedical and genomic datasets including electronic health record data. A major goal is new model development including patient-derived organoids and patient-derived xenografts. A major focus of the laboratory is identifying mechanisms of resistance to endocrine therapy, and new approaches to blocking breast cancer metastasis through precision medicine. This includes the study of estrogen receptor (ESR1) mutations and fusions and synergism with growth factor pathways. Methods include liquid biopsies and use of a rapid autopsy program,. A special focus is on the understanding of invasive lobular cancer (ILC), the second most common but understudied histological subtype of breast cancer. The laboratory has a very strong training environment, with attention to diversity and inclusion and each individuals’ successful career development. One of the top priorities is to maintain a healthy lab environment, ensuring high productivity and rigor.
The main research interest in Dr. Gang Li's lab is to understand the molecular mechanisms underlying the contribution of disease-associated, non-coding functional SNPs to aging-related diseases by focusing on Alzheimer's disease and atherosclerosis. Dr. Li's lab has developed multiple techniques such as Reel-seq, SNP-seq, FREP/SDCP-MS and AIDP-Wb to identify the causal SNPs as well as the SNP-bound regulatory proteins based on genome wide association studies (GWAS). The lab's goal is to use human genetics (GWAS) as a guide to identify new drug targets and, ultimately, to apply these findings to develop allele-specific precision drugs for aging-related human diseases as well as other diseases.
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The main interest of Dr. Oesterreich's laboratory is to further our understanding of hormone action in women's cancers (including both breast and ovarian cancers), with the ultimate goal to use this knowledge for improved diagnosis and endocrine treatment. These studies include many aspects of translational breast cancer research utilizing basic biochemistry, molecular and cell biology, and cell lines, mouse models and clinical samples. Over the last few years, Dr. Oesterreich has developed a strong research interest in in situ and invasive lobular disease, the second most common yet understudied histological subtype of breast cancer. In her role as Director of Education at the Women's Cancer Research Center, Dr. Oesterreich is also very interested in providing outstanding training opportunities to the next generation of women's cancer researchers.
Our group's primary focus is on developing integrative machine learning approaches for extracting therapeutic and biological insights from highly heterogeneous omic datasets, clinical and drug response data, with the purpose of advancing precision medicine. Our projects span across the following areas:
Our projects are executed through multi-disciplinary collaborations, recognizing that precision medicine requires expertise from various domains. By leveraging machine learning and integrating diverse datasets, our aim is to contribute to the advancement of precision medicine, ultimately leading to more targeted and effective treatments for patients.
Jonathan Silverstein, MD, MS, FACS, FACMI, serves as Chief Research Informatics Officer and Professor of Biomedical Informatics at University of Pittsburgh School of Medicine. He is internationally known for his expertise, and federally funded research, in the application of advanced computing architectures to biomedicine and on the design, implementation and evaluation of high-performance collaboration and visualization environments for anatomic education and surgery.
Dr. George Tseng is Professor and Vice Chair for Research in the Departments of Biostatistics, School of Public Health, University of Pittsburgh. He also has secondary appointments in Human Genetics, and Computational and Systems Biology. He received BS (1997) and MS (1999) in Mathematics from the National Taiwan University under Dr. Hung Chen, and ScD (2003) in Biostatistics from the Harvard School of Public Health under Dr. Wing Hung Wong's lab. He joined Pitt since 2003 and leads a research group in Bioinformatics and Statistical Learning. His research interests focus on statistical modeling and applications for -omics and bioinformatic problems to improve precision medicine and human health. His research group has published 90+ methodological/major papers and 115+ collaborative papers (as of Mar 2023), in addition to co-invention of 5 patents. He has received multiple awards, including ASA Fellow, Statistician of the Year (ASA Pittsburgh Chapter), and Provost's Award for Excellence in PhD Mentoring (University of Pittsburgh). Collaboration with biological and clinical labs plays an important role where most of his projects and methodological ideas come from.
Dr. Tseng has actively served in the statistical community, including President of ASA Pittsburgh Chapter in 2014-2017 (President-Elect, President and Past-President), Chair of ASA Section on Statistics in Genomics and Genetics (SSGG) in 2023-2025 (Chair-Elect, Chair and Past-Chair), and Board of Directors of International Chinese Statistical Association (ICSA) in 2024-2026.
The Cancer Genome Project Initiatives have generated a daunting amount of genomic and deep sequencing data for tens of thousands of human tumors. An overarching challenge of this post-genomic era is to identify and recognize the cancer drivers and targets from these big genomic data, especially those that can be therapeutically targeted to improve the clinical outcome. The mission of our lab is to apply a multiple disciplinary approach inclusive of integrative bioinformatics, cancer genetics, molecular cancer biology, and translational studies to identify driving genetic aberrations and appropriate cancer targets on the basis of deep sequencing and genomic profiling datasets. Our research projects are composed of both computational and laboratory components. Our dry lab researches focus on developing innovative and integrative computational technologies to discover causal genetic and epigenetic alternations, viable therapeutic targets, and predictive biomarkers in cancer. In particular, we have innovated a concept signature (ConSig) analysis that employs molecular fingerprints for high-throughput interpretation of the biological function of candidate targets in cancer (Nature biotech 2009). In addition, we have formulated a 'fusion breakpoint principle' that describes the intragenic copy number aberrations characteristic of recurrent gene fusions, thus enabling genome-wide detection of copy number breakpoints generating gene fusions. Based on these principles we further developed a powerful bioinformatics tool called 'Fusion Zoom' that identifies recurrent pathological gene fusions via integrative analyses of RNA sequencing, copy number, and gene concept datasets (Nature Commun 2014). Further, we have discovered the crucial application of ConSig analysis in revealing the primary oncogenes targeted by genomic amplifications, and developed a new integrative genomic analysis called 'ConSig-Amp' to detect viable cancer targets. Moreover we also developed an integrated computational-experimental approach called HEPA-PARSE for the genome-wide detection of clinically important tumor specific antigen (TSA) targets (Cancer Research 2012). Our wet lab researches focus on experimentally characterizing individual genetic and epigenetic aberrations in breast cancer such as recurrent gene fusions, genomic amplifications, and epimutations, as well as qualifying viable cancer targets and predictive biomarkers for the development of precision therapeutics in breast cancer. Our current disease focus is clinically intractable breast cancers, such as luminal B or basal-like tumors. In particular, by applying the FusionZoom analysis to the RNAseq and copy number data from The Cancer Genome Atlas, we have discovered a novel recurrent gene fusion involving the estrogen receptor gene in a subset of breast cancers. This fusion called ESR1-CCDC170 is preferentially present in 6-8% of luminal B tumors -- a more aggressive subtype of estrogen receptor positive breast cancer. To date, this is the first and most frequent gene fusion yet reported in this tumor entity (Nature Commun. 2014). We are now assessing the druggability of this fusion with the goal of developing effective targeted therapy against this genomic target. We expect that our new discoveries will yield novel insights into the recurring genetic abnormalities leading to breast cancer initiation, progression, and therapeutic resistance, and establish viable targets for effective intervention.
