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We propose to better define genetic subtypes of canine lymphoma and characterising response to treatment of the different subtypes through a combination of more accurate phenotyping of lymphomas by integrating a) advanced image analysis b) genome sequencing, and c) total RNA sequencing (including micro-RNA sequencing).

Challenge

Diffuse large B����app�cell lymphoma (DLBCL) represents the most common form of lymphoma in dogs (48%) and humans (40%) with an aggressive clinical course. With chemotherapy, there is a cure rate of around 58% in humans, whereas in dogs the best remission responses are up to 36 months. Diagnosis and typing of lymphoma can be challenging for pathologists due to the overlap in appearance of neoplastic lymphocytes and normal lymphocytes, and complex classification of 20 subtypes. Simplistically it is subtyped into diffuse or nodular architecture, small or large nuclear size, and graded by mitotic rate. DLBCL can be further subtyped by appearance into centroblastic and immunoblastic based on nuclear features, and this has been found to strongly predict outcome in human medicine. Immunophenotype is established through immunohistochemical staining.

Solution

In humans, DLBCL has been shown to demonstrate genetic heterogeneity and miRNA profiles predicting disease prognosis. In dogs there are limited studies and relatively little genetic characterisation of lymphoma, meaning that it is difficult to find correlation with the human disease. Nevertheless, in both humans and dogs, there is a significant subset of lymphomas that are refractory to treatment.

Impact

Canine lymphoma can be more accurately diagnosed, treated and prognosticated through a synergistic molecular, genetics, and image based analysis.

Partners

In this project we are working closely with Dr Sophie LeCalvez of IDEXX laboratories who are also funding this project

Quote

With the generous funding from LIDO and IDEXX for this PhD studentship, we are hoping to further our understanding of this highly complex disease, through taking advantage of, and integrating advances in pathology imaging technology, deep learning and genomics.

Publications