Precision Medicine Program for the Liver


Bhat Lab has the following main goals:

  • To improve long-term outcomes after liver transplantation

  • Using the liver transplant model as a way to understand cancer and liver disease progression

  • Use advanced biostatistical machine learning tools to inform improved clinical practice in transplant

Liver Transplant Word Art

While an increasing number of Canadians have benefited from life-saving liver transplantation with excellent short-term survival, there has been no significant improvement in longer-term outcomes and survival beyond one year. Our precision medicine program uses a combination of laboratory, bioinformatics, and data science (artificial intelligence and biostatistical) tools to examine metabolic and malignant complications (specifically Recurrent Hepatocellular carcinoma, Non-alcoholic Steatohepatitis, and Post-Transplant Diabetes), which are the most common reasons for compromised long-term survival after liver transplantation.

We employ a bench-to-bedside paradigm that connects clinical outcomes with basic research using tools from systems biology, including bioinformatic analysis of ‘omic datasets (e.g., methylation, whole exome sequencing, transcriptomic, intestinal microbiome), network and integrative analysis. Validation of findings is performed using patient samples/clinical cohorts, as well as in silico, in vitro and in vivo approaches.

Our work will contribute to a better understanding of the key pathways that underpin these clinical conditions, providing insights that inform more personalized preventive and therapeutic strategies post-transplant.

We have assembled a dynamic multidisciplinary research team conducting projects at the intersection of basic science, informatics, and clinical outcomes to inform a precision medicine approach to liver disease and transplantation. Based at the UHN Ajmera Transplant Program, this is a unique translational research program dedicated to helping transplant recipients live better for longer.


Looking to join our fantastic team?

We are always open to applications from excellent students, including graduate students and postdoctoral fellows!
This includes Lab-based, with either Wet lab or Computational biology/Machine learning skillsets as well as Clinical research fellows


LANCET DIGITAL HEALTH PAPER – Lancet Digit Health. 2021 Apr 9;S2589-7500(21)00040-6.

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data

Osvald Nitski, Amirhossein Azhie, Fakhar Ali Qazi-Arisar, Xueqi Wang, Shihao Ma, Leslie Lilly, Kymberly D Watt, Josh Levitsky, Sumeet K Asrani, Douglas S Lee, Barry B Rubin, Mamatha Bhat*, Bo Wang*

PMID: 33858815


Background: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models.

Methods: In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC).

Findings: In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0·0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 (99% CI 0·795-0·854) for 1-year predictions and 0·733 (0·729-0·769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 (0·795-0·842) for 1-year predictions and 0·722 (0·705-0·764) for 5-year predictions. AUROCs ranged from 0·695 (0·680-0·713) for prediction of death from infection within 5 years to 0·859 (0·847-0·871) for prediction of death by graft failure within 1 year.

Interpretation: Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features.

FASEB J. 2021 May;35(5):e21570.

The hippo pathway: A master regulator of liver metabolism, regeneration, and disease

Anh Thu Nguyen-Lefebvre, Nazia Selzner, Jeffrey L Wrana, Mamatha Bhat

PMID: 33831275


The liver is the only visceral organ in the body with a tremendous capacity to regenerate in response to insults that induce inflammation, cell death, and injury. Liver regeneration is a complicated process involving a well-orchestrated activation of non-parenchymal cells in the injured area and proliferation of undamaged hepatocytes. Furthermore, the liver has a Hepatostat, defined as adjustment of its volume to that required for homeostasis. Understanding the mechanisms that control different steps of liver regeneration is critical to informing therapies for liver repair, to help patients with liver disease. The Hippo signaling pathway is well known for playing an essential role in the control and regulation of liver size, regeneration, stem cell self-renewal, and liver cancer. Thus, the Hippo pathway regulates dynamic cell fates in liver, and in absence of its downstream effectors YAP and TAZ, liver regeneration is severely impaired, and the proliferative expansion of liver cells blocked. We will mainly review upstream mechanisms activating the Hippo signaling pathway following partial hepatectomy in mouse model and patients, its roles during different steps of liver regeneration, metabolism, and cancer. We will also discuss how targeting the Hippo signaling cascade might improve liver regeneration and suppress liver tumorigenesis.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY PAPER.Clin Gastroenterol Hepatol. 2021 Jan 16;S1542-3565(21)00071-9.

Predicting Long-Term Survival After Liver Transplantation in Patients With NASH Cirrhosis

Ravikiran S Karnam, Nicholas Mitsakakis, Giovanna Saracino, Leslie Lilly, Sumeet K Asrani, Mamatha Bhat

PMID: 33465480


Nonalcoholic steatohepatitis (NASH) cirrhosis is the second most common indication for liver transplantation (LT) in the United States.1 Patients are increasingly older at presentation, with higher rates of metabolic syndrome, obesity, hyperlipidemia, diabetes mellitus, and renal failure.2 They are also at higher risk of cardiovascular events and mortality while on the waiting list1 and in the post-transplant period.3,4 We sought to identify predictors of long-term benefit based on 5-year survival post-LT in NASH cirrhosis, thereby delineating those patients that derive a clear benefit from LT versus those in whom LT may be futile.


Carsten Deppermann, Moritz Peiseler, Joel Zindel, Lori Zbytnuik, Woo‐Yong Lee, Elisa Pasini, Cristina Baciu, John Matelski, Yun Lee, Deepali Kumar, Atul Humar, Bas Surewaard, Paul Kubes*, Mamatha Bhat*


Kupffer cells are the resident intravascular phagocyte population of the liver and critical to the capture and killing of bacteria. Calcineurin/Nuclear Factor of Activated T cells (NFAT) inhibitors (CNIs) such as tacrolimus are used to prevent rejection in solid organ transplant recipients. While their effect on lymphocytes has been studied extensively, there is limited experimental data about if and how CNIs shape innate immunity, and whether this contributes to the higher rates of infection observed in patients taking CNIs. Here, we investigated the impact of tacrolimus treatment on innate immunity and more specifically on the capability of Kupffer cells to fight infections. Retrospective analysis of data of more than 2,700 liver transplant recipients showed that taking calcineurin inhibitors such as tacrolimus significantly increased the likelihood of Staphylococcus aureus infection. Using a mouse model of acute methicillin‐resistant Staphylococcus aureus (MRSA) bacteremia, most bacteria were sequestered in liver and we found that bacteria were more likely to disseminate and kill the host in tacrolimus‐treated mice. Using imaging we unveiled the mechanism underlying this observation: the reduced capability of Kupffer cells to capture, phagocytose and destroy bacteria in tacrolimus‐treated animals. Further, in a gene expression analysis of infected Kupffer cells, the TREM‐1 pathway was the one with the most significant downregulation after tacrolimus treatment. TREM‐1 inhibition likewise inhibited Kupffer cell bacteria capture. TREM‐1 levels on neutrophils as well as the overall neutrophil response after infection were unaffected by tacrolimus treatment. Our results indicate that tacrolimus treatment has a significant impact directly on Kupffer cells and on TREM‐1, thereby compromising their capacity to fend off infections.

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Learn about the Precision Medicine Program to enhance life after liver transplant and the Research we do.