Pittsburgh Liver Research Center/Director’s Innovative Award
University of Pittsburgh
The University of Pittsburgh & UPMC Pittsburgh Liver Research Center (PLRC) is pleased to announce the Director’s Award in New Direction and Innovation (DANDI).
In June of 2021, the PLRC/University of Pittsburgh awarded a grant aimed at developing a biobank of human iPSC’s carrying genetic variants of liver disease, carrying different mutations related to metabolic or cholestatic diseases.
We are delighted to announce PLRC member and junior investigator Dr. Rodrigo Florentino has been selected as recipient of the PLRC–DANDI! The award period is March 15, 2021 – March 14, 2022. The awardee will work closely with PLRC members and lab mentor, Dr. Alex Soto and Dr. Lans Taylor.
“Generation of a biobank of human induced pluripotent stem cells carrying liver disease related genetic variants”
Generation of a biobank of human induced pluripotent stem cells carrying liver disease related genetic variants
My long-term research goal is to uncover the role of genetic variants and mutations in the development of endstage liver disease, specifically single nucleotide polymorphisms identified in fatty livers, cirrhosis, and inborn errors of metabolism, in order to design therapeutic strategies to prevent and treat disease development. To achieve this goal, I plan to bring the promise and potential of induced pluripotent stem cells and the ability to differentiate these into hepatic cell-like cells as a model to understand the mechanisms involved when genetic variants are present. My training and experience in cell/molecular biology, genetic engineering and synthetic biology have allowed me to identify the key obstacles that must be overcome to realize this goal. The success of human genome sequencing in large populations of patients correlating liver disease and specific genetic variants is strong evidence for continued research in this area. The research proposed in this award application will explore these areas of inquiry and advance an important area of stem cell-based genetic engineering for the understanding of liver disease.
We already have more than one hundred primary liver cells genotyped for the most relevant point mutations described in the literature, such as PNPLA3, MBOAT7, GCKR, HSD17B13 for metabolic liver diseases and ATP8B1, ABCB11, TJP2 and others for the different subtypes of Progressive familial intrahepatic cholestasis. The opportunity to apply for the Director’s Award in New Direction and Innovation – DANDI, will give me the opportunity to create a human iPS cell biobank that carries different mutations related to metabolic or cholestatic diseases. This will open new prospects and collaborations. Furthermore, the DANDI will give me confidence to move forward on my own projects. The management of the award funds will also be a good opportunity for me to showcase my administrative skills. Additionally, this proposal will be beneficial for the Pittsburgh Liver Research Center (PLRC) members since the creation of this iPS cell biobank will accelerate research and collaborations and grant applications for many of the PLRC members that wish to use the human iPS cells. Given my recent success in using human cells to genetically edit and generate iPS cells and my strong motivation in the field, I will continue the search for funding to develop further the initial proposed work after obtaining this grant.
In March of 2021, the PLRC/University of Pittsburgh awarded a grant aimed at developing and evaluating artificial intelligence models for the non-invasive detection of beta-catenin in HCC patients. These models will utilize MRI and CT images and be able to non-invasively detect and identify beta-catenin mutations to optimize treatment planning and provide prognostic assessment.
We are delighted to announce PLRC member and junior investigator Dr. Dooman Arefan, in collaboration with PLRC member Dr. Alessandro Furlan, has been selected as recipient of the PLRC–DANDI! The award period is March 15, 2021 – March 14, 2022. The awardee will work closely with PLRC members and lab mentor, Dr. Shandong Wu, and Dr. Paul Monga to help assess the development of AI in clinical use of beta-catenin detection/identification. PLRC members Dr. Alessandro Furlan and Dr. Aatur Singhi will lend their clinical expertise as clinical co-mentors.
“A radio-genomics study for predicting beta-catenin mutation in hepatocellular carcinoma patients using multi-modal imaging data and artificial intelligence”
Clinical Co-Mentor – Dr. Alessandro Furlan and Research Co-Mentor – Dr. Shandong Wu
A radio-genomics study for predicting beta-catenin mutation in hepatocellular carcinoma patients using multi-modal imaging data and artificial intelligence
Abstract
Hepatocellular carcinoma (HCC) is a major health-care issue causing about 700,000 deaths each year worldwide and being associated with high cost of care. Current clinical guidelines allow a non-invasive diagnosis of HCC without the need for a confirmatory biopsy when the lesion shows typical imaging features on liver magnetic resonance imaging (MRI) or computed tomography (CT). MRI and CT are typical diagnostic imaging for HCC management; they can provide a volumetric characterization on HCC tumor’s heterogeneity and may carry more information to inform prognosis and treatment of HCC patients. CTNNB1, one of the primary oncogenes involved in HCC development, encodes beta-catenin, a protein that serves multiple important cellular functions. Beta-catenin mutation may affect prognosis of HCC and offer a target for established and novel therapeutic agents. The unmet clinical need is a non-invasive assessment of the beta-catenin status of HCC in order to facilitate the decision-making process and inform prognosis in the context of a “precision medicine” approach. Artificial intelligence (AI) has been revolutionizing medical imaging analysis where radiomics and machine/deep learning are representative computational modeling techniques. AI provides game-changing ways to quantitatively interpret radiological images in-depth (especially volumetric images), going beyond the qualitative assessment provided by the interpreting radiologists. The goal of this study is to develop and evaluate AI models (i.e., a radiomic model and a deep learning model) for the non-invasive detection of beta-catenin status in HCC patients, using both liver MRI and CT images. The AI models will be able to detect/identify HCC patients with beta-catenin mutation in a non-invasive way and using standard-of-care clinical imaging data, which has great potential to 1) avoid unnecessary invasive procedures, 2) optimize treatment planning and provide prognostic assessment, 3) improve efficiency of clinical procedures and reduce costs, and 4) improve overall patient experience.
Relevance
We propose to develop and evaluate artificial intelligence (AI) models (i.e., a radiomic model and a deep learning model) for the non-invasive detection of beta-catenin status in hepatocellular carcinoma (HCC) patients, using both liver magnetic resonance imaging (MRI) and computed tomography (CT) images. The AI models will be able to detect/identify HCC patients with beta-catenin mutation in a non-invasive way to avoid unnecessary invasive procedures and optimize treatment planning and provide prognostic assessment.