nCounter® Human Organ Transplant Panel
Are you looking to develop signatures pre- and post-organ transplant to help determine risk of rejection? Then the nCounter Human Organ Transplant Panel is the research tool for you.
Helping Your Research
The Human Organ Transplant Panel was created through a collaboration between NanoString and the Banff Foundation for Allograft Pathology, a global consortium of researchers from multiple prestigious institutes, including researchers from the University of Alberta, Erasmus Medical Center Rotterdam, Imperial College London, Massachusetts General Hospital, University of Oxford, and the Paris Transplant Group. The consortium aims to improve organ transplant outcomes through advanced molecular characterization of the in-situ response in the allograft and to make available a transformational new approach for research that can be used to accelerate the identification of new biomarkers of rejection, uncover the mechanisms behind tissue damage, and monitor toxicities brought on by immunosuppressive drugs and infections.
Banff 2019 Meeting Report
Read the Banff 2019 Meeting Report to learn more about how the content for the Human Organ Transplant panel was created and how the Banff Molecular Diagnostics Working Group (MDWG) plans to maximize data collected using the panel through the formation of a consortium and access to a shared database.
How it Works
You can comprehensively profile 770 genes across 37 pathways to identify biomarkers for rejection, uncover the mechanisms of tissue damage, and study toxicities brought on by immunosuppressive drugs.
Study the immune response to transplanted tissue
Discover biomarkers for organ rejection and tissue damage for kidney, heart, liver, and lung
Evaluate immunosuppressive drug pathways
Understand mechanisms behind drug-induced toxicity
Identify BK Polyomavirus, Cytomegalovirus, and Epstein-Barr virus
Quantify the relative abundance of 14 different immune cells
Panel Selection Tool
Find the gene expression panel for your research with easy to use panel proFind Your Panel
Solid organ and hematopoietic transplant recipients are at increased risk for developing complications from opportunistic viral infections and may even inherit a viral infection from the donor. Knowing if a viral infection is present can be essential to understanding both the immune response and the potential impact of immunosuppressive treatments. Included in the Human Organ Transplant panel are probes specific for the detection of BK Polyomavirus, Cytomegalovirus (CMV) and Epstein-Barr virus (EBV).
Probes included in the Human Organ Transplant Panel have been confirmed to also have high homology to non-human primates providing a valuable tool for translational comparative studies using both human and non-human samples.
Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer.
Purpose: Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment.
Spatial profiling reveals association between WNT pathway activation and T-cell exclusion in acquired resistance of synovial sarcoma to NY-ESO-1 transgenic T-cell therapy.
Background: Genetically engineered T-cell immunotherapies for adoptive cell transfer (ACT) have emerged as a promising form of cancer treatment, but many of these patients develop recurrent disease. Furthermore, delineating mechanisms of resistance may be challenging since the analysis of bulk tumor profiling can be complicated by spatial heterogeneity.
A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.
To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model.