Ankita Srivastava Research
1020 Locust Street
Suite 314C
Philadelphia, PA 19107
We pursue translational research in end-stage liver disease, liver transplantation, and prostate cancer. A key goal is to uncover disease mechanisms and improve diagnosis, prognosis, and clinical decision-making by translating high-resolution molecular and histopathological data into actionable patient-specific insights. Our interdisciplinary approach integrates spatial transcriptomics, histopathology, AI-based image analysis, and machine learning towards advancing precision medicine. We are pursuing collaborative projects on:
- Integrated pathomics and transcriptomics towards developing therapy for alcohol- associated liver disease
- Enhancing liver transplantation by providing data-driven prognostic tools for increased utilization of donor livers
- Deciphering morpho-molecular mechanisms driving tumor aggressiveness in prostate cancer
Research Projects
Integrating Pathomics & Spatial Transcriptomics for Translational Research in Chronic & End-stage Liver Disease
How does the liver tissue microenvironment evolve during the progression of chronic to end-stage liver disease? Which high-resolution morpho-molecular features can enhance prognostic accuracy and inform therapeutic strategies?
We are addressing the limited prognostic tools and therapeutic options in chronic liver disease and acute liver failure by integrating AI-driven pathology and spatial transcriptomics. Our goal is to identify molecular pathways and metabolic dysfunctions that can guide therapeutic interventions. Current efforts include analysis of ductular reaction morphology in Alcoholic Hepatitis (AH) and analysis of spatial transcriptomics data collected from AH cases to characterize the metabolic reprogramming that leads to liver failure. We aim to extend these studies to NASH and cryptogenic cirrhosis cases
Collaborators:
- Rajanikanth Vadigepalli, PhD - Internal Medicine, University of New Mexico Health Sciences Center
- Ramon Bataller, MD, PhD - Hospital Clinic of Barcelona, Spain
- Adam Ertel, PhD - Pathology and Genomic Medicine, Thomas Jefferson University
- Steven N. Schwartz, MD – Pathology and Genomic Medicine, Thomas Jefferson University
Funding: National Institutes of Alcohol Abuse and Alcoholism
Morpho-molecular Analysis for Enhancing Organ Utilization in Liver Transplantation
How do accepted and rejected donor livers differ in tissue-scale morphology and spatial molecular patterns? Can a standardized morpho-molecular classification system improve donor liver assessment and reduce variability across transplant centers?
Many donated livers are deemed unsuitable for transplantation, even as there is a large unmet demand for liver transplants. We aim to improve transplant utilization of marginal donor livers by providing better diagnostic tools available at the site of organ collection or during subsequent machine perfusion to improve donor liver function. Our recent study identified a molecular signature distinguishing a subset of rejected livers potentially suitable for transplantation. Building on these findings, we are developing histopathological clinical decision support tools with turnaround times aligned with transplantation workflows. We are also interested in assessing the functional improvement of donor liver during machine perfusion towards prognostic biomarkers for organ suitability for transplantation.
Collaborators:
- Rajanikanth Vadigepalli, PhD - Internal Medicine, University of New Mexico Health Sciences Center
- Adam Ertel, PhD - Pathology and Genomic Medicine, Thomas Jefferson University
- Ashesh Shah, MD – Liver Transplant Center, Jefferson Health
- Steven N. Schwartz, MD – Pathology and Genomic Medicine, Thomas Jefferson University
Funding: Transplant Foundation, Gift of Life
Morpho-molecular mechanisms driving tumor aggressiveness in prostate cancer
What tumor microenvironment mechanisms drive aggressive tumours in prostate cancer? Which high-resolution morpho-molecular features not accessible to conventional histopathology are useful in improving prognosis?
We are developing an AI-based analysis platform to extract the tissue- and cellular-scale morphological features of the aggressive prostate tumor microenvironment. In parallel, we are building a spatial transcriptomics landscape to link these morphological features with molecular pathways that reflect underlying cell biology mechanisms. Integrating morpho-molecular data with clinical outcomes, we aim to identify prognostic markers for prostate cancer. So far, we have found morphologically distinct features of aggressive tumors in African American patients compared to grade-matched tumors in white patients. Ongoing studies include tissue microarray analysis to compare the AI-derived features from whole slide images. We are also conducting multiplex imaging and building associated custom AI tools to unravel the tumor environment in aggressive prostate cancer.
Collaborators:
- Rajanikanth Vadigepalli, PhD - Internal Medicine, University of New Mexico Health Sciences Center
- Charalambos C. Solomides, MD - Pathology and Genomic Medicine, Thomas Jefferson University
- Adam Ertel, PhD - Pathology and Genomic Medicine, Thomas Jefferson University
- Adam Dicker, MD, PhD - Radiation Oncology
- William Kevin Kelly, MD - Medical Oncology
Funding: Philadelphia Prostate Cancer Biome Project; Prostate Cancer Foundation