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An NCI-designated Comprehensive Cancer Center, affiliated with the University of Pittsburgh School of Medicine
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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.
My laboratory investigates the intercellular communications between stroma, tumor cells and immune cells within the tumor microenvironment. I am particularly focused on gaining a better understanding of how factors, secreted by stromal and tumor cells, modulate the immunosuppressive activities of tumor-associated myeloid cells, driving resistance to immunotherapies. Using mouse models of ovarian cancer and clinical samples, my long-term goal is to identify novel therapeutical approaches to enhance anti-tumor immunity.
Dr. Cauley's primary research interest is the epidemiology of osteoporosis, osteoporosis treatment and the consequences of osteoporosis in both men and women. She also has a major interest in breast cancer and served on the American Society of Clinical Oncology Writing Group about the use of bisphosphonates in women with breast cancer. Her other research has focused on women's health and aging, falls, the interaction between endogenous and exogenous hormones, risk factors, inflammation, and disease outcomes. She examines the physiological changes that occur across the menopausal transition.
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.
The work of our group (jointly directed by Patrick Moore and Yuan Chang) has focused on human tumor viruses since the early 1990s when we identified Kaposi's sarcoma associated herpesvirus (KSHV/HHV8) and showed that this virus was causally associated with Kaposi's sarcoma, the most common AIDS-related cancer in the United States and the most common malignancy in parts of Africa. We sequenced the KSHV genome, developed serologic assays, determined its prevalence in human populations, and characterized many of its critical viral oncoproteins. We have continued to study virus-host cell interactions in the context of dysregulation of pro-proliferative and anti-apoptotic pathways. We recently identified the seventh human tumor virus, Merkel cell polyomavirus (MCV), from a Merkel cell carcinoma (MCC). We characterized the transcriptional products of MCV and described the early region viral T antigen oncoproteins. Our work has established that MCV causes ~80% of MCC: we determined that the virus is clonally integrated in MCC tumor cells; that human tumor-associated Large T (LT) antigens contain signature truncation mutations; that T antigen proteins are expressed in MCC tumor cells by novel antibodies we developed; and we are the first laboratory to show rodent cell transformation by MCV sT antigen but not the LT antigen. We have identified several novel cellular interactors for MCV T antigens that open new avenues of investigating critical oncogenic signaling pathways. We have focused on many aspects of cancer etiology as modeled through oncogenic tumor viruses.
Dr. Chen’s research concentrates on developing machine learning methods, especially deep learning models (DLMs) (e.g. Deep Neural Networks, Boltzmann Machine, and topic modeling), to study cancer cell signaling systems, disease mechanisms and cancer pharmacogenomics. Dr. Chen uses the concise representations learned from the DLM with the causal inference to guide the identification of molecular signatures/biomarkers and predicts the clinical outcomes including drug sensitivity and patient survival. Based on Dr. Chen’s strong research background in bioinformatics, biomedical informatics, biology and machine learning, she successfully develops comprehensive AI models that precisely represent the state of signaling systems in cancer cells and use such information to improve the tumor-specific precision medicine (precision oncology).
Due to genomic and epigenetic instability of cancer cells, inter-patient and intra-patient heterogeneity in tumors creates formidable challenges in identifying optimal treatments. To address the challenges, I aim to establish comprehensive high-throughput multi-omics single-cell analysis including genome, epigenome, transcriptome, proteome, functional, and morphological methods. With large amounts of data collected from high-throughput single-cell multi-omics analysis, machine learning techniques can predict patient prognosis and suggest treatments for precision medicine. The integrated approach will change how we understand and treat cancer and ultimately improve outcomes for patients.
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Yu-Chiao (Chris) Chiu, PhD, is an Assistant Professor of Medicine in the Division of Hematology/Oncology at the University of Pittsburgh. Dr. Chiu’s research interests include bioinformatics, machine learning, cancer genomics, and pharmacogenomics. The goal of his laboratory is to systematically model genomics and pharmacogenomics to better understand cancer biology and improve cancer therapy. His laboratory has been funded by NIH/NCI (K99/R00 Pathway to Independence Award), NIH/OD (R03 and Administrative Supplement), UPMC Hillman Cancer Center Developmental Pilot Program, Pittsburgh Liver Research Center Pilot and Feasibility Grant, Leukemia Research Foundation, and Fund for Innovation in Cancer Informatics. To date, Dr. Chiu has published more than 100 journal articles and conference articles/abstracts. His research is well-recognized by the broad cancer and bioinformatics communities, including recent publications in Science Advances (highlighted by @NCIgenomics as the #1 favorite paper of 2021), BMC Medical Genomics (selected as Springer Nature Research Highlights in Genetics of 2019), and Bioinformatics Advances. Dr. Chiu’s membership in the Cancer Therapeutics Program of the UPMC Hillman Cancer Center expands the impact of his research by teaming up with clinical, translational, and basic cancer scientists – to bridge cutting-edge computational algorithms to unmet needs in precision oncology.
I am a radiation oncologist whose clinical interest is in advancing care for patients with central nervous system tumors. My laboratory research is specifically on targeting a glucose transporter as a radiosensitizer for brain tumors.
My long-term goal is to develop a program of research focused on preventing tobacco-related cancer mortalities. I have a diverse background in computer science, social network analysis, online social media, and cancer prevention. The focus of my research has been leveraging innovative technologies to study tobacco control. My recent projects include exploring the presence of tobacco companies on social media and analyzing their behavior and strategies in marketing; studying the diffusion of anti-vaccination topics online; interventions for electronic cigarette use by adolescents; modeling new tobacco trends to inform regulatory agencies.
The Cillo Lab focuses on understanding how immune cells make cell fate decisions, how intercellular communication influences these cell fate decisions, and the ways in which cell-cell interactions shape community dynamics in the tumor microenvironment. To address these questions, we are utilizing high-dimensional systems based approaches including spectral flow cytomtery, single-cell RNAseq and spatial transcriptomics in the context of patients with solid tumors such as head and neck cancer, sarcoma, and melanoma among others. Defining the contributions of individual immune populations and their behavior in aggregate will allow us to understand how current immunotherapies promote antitumor immunity and why current immunotherapies fail in some patients. Additionally, it will enable us to identify new therapeutic targets to promote antitumor immunity in a larger proportion of patients with solid tumors.
The Clark lab focuses on determining the molecular and cellular regulators of metastatic dormancy and recurrence within the liver. We utilize a novel all-human ex vivo 3D liver microphysiological system to model metastasis. The system has not only enabled the recreation of dormant-emergent metastatic cancer progression as observed in vivo but also the identification of mechanisms, candidate biomarkers, and new therapeutic opportunities to target the various stages of metastasis. Our current research centers on i) investigating how dysregulated gut homeostasis drives emergence from metastatic dormancy in the liver, and ii) examining how the bi-directional crosstalk mediated by extracellular vesicles regulates metastatic breast cancer dormancy in the liver.
In her research, Dr. Conklin is applying a translational perspective to the investigation of subjective, physiological, and behavioral reactivity to drug-related cues in adult smokers, and on identifying the types of cues and other environmental contexts that have the greatest impact on smoking maintenance and cessation. She has served as the Principal Investigator for five federally funded grants, including a current project that examines tDCS brain stimulation + an Approach / Avoidance Task to reduce the impact of personalized smoking cues on smoking behavior and relapse. The long-term goal of this research is not only to understand underlying mechanisms of drug addiction, but to develop novel behavioral techniques to enhance the efficacy of drug dependence treatments.
Dr. Conley’s research interests are in the field of molecular genetics. She has a fully equipped molecular genomics laboratory located within the School of Nursing, and her lab is involved with several research projects. Her current research focuses on genomic and epigenomic studies of patient outcomes after traumatic brain injury, stroke, and therapeutic interventions for cancer, as well as genomic studies of age-related macular degeneration.
I study the structure and function of macromolecular complexes, such as virus capsids, using cryo-electron microscopy (cryoEM) to detail protein folds and interfaces. The resolution achieved by cryoEM depends on the sample, and we routinely aim for 3-5 Ångstroms but in some cases can reach below 2 Å resolution. Systems currently being studied include herpesviruses and dsDNA bacteriophages such as HK97, lambda, D3, T5 and others. These tailed phages have important structural similarities with each other and with animal viruses such as herpesvirus, indicating a long evolutionary connection between them. The dynamic aspects of the virus lifecycle – assembly, DNA packaging, infection, and DNA delivery – are better suited to cryoEM study than crystallography, and the non-symmetric but functionally important regions such as the capsid portal vertex and the phage tail-tip can now be resolved due to recent advances in cryoEM technology. In recent work we imaged the phage HK97 portal vertex where the dodecameric portal ring occupies a 5-fold symmetric vertex of the icosahedral capsid. The portal has several vital functions, including nucleating capsid assembly, packaging and releasing the viral dsDNA, and binding the phage tail assembly. However, the nature of the symmetry mismatch with the capsid has been a long-standing puzzle, but one we could solve by cryoEM visualization to reveal that the capsid protein's scaffold domain interfaces with the portal in a quasi-symmetric 12-10 arrangement. This has a number of implications for capsid assembly and maturation that extend to the herpesviruses. With Fred Homa (Dept. MMG) we have visualized a series of herpesvirus capsid mutants that build a picture of the structural steps involved in DNA packaging and we are applying the phage analysis methods to similarly detail the symmetry-breaking portal vertex. Characterizing the structural and functional repertoire of a virus throughout its lifecycle reveals protein-protein and protein-DNA interactions that could be targeted by highly specific anti-virals. In addition to these viral studies, I am involved with numerous groups to enable their structural investigations of SARS2-Covid 19 (Ambrose/Watkins, Cheng/Shi), GPCRs (Cheng/Xie), encapsulins (NIH), lipid nanoparticles and exosomes (Song, CMU, Duquesne), and Merkel Cell Polyomavirus (Chang/Moore).
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.
