The K9 lymphoma assay allows a genetic subgrouping of canine lymphomas with improved risk classification

Abstract

We present here the K9 lymphoma assay, a novel 31-gene targeted next-generation sequencing panel designed for genomic profiling of canine lymphoid neoplasms. Addressing the growing demand for advanced diagnostics in veterinary oncology, this assay enables sensitive identification of known and actionable mutations specific to canine lymphomas, while evaluating its prognostic potential to facilitate diagnosis and prognosis. Our analysis, spanning several B- and T-cell lymphoma histotypes, unveiled distinct mutational landscapes distinguishing tumors derived from immature versus mature lymphocytes. Clustering analysis revealed a shared genetic origin between diffuse large B-cell lymphoma and marginal zone lymphoma, aligning with findings in human lymphomas, with TRAF3 emerging as the most frequently mutated gene across B-cell lymphoma subtypes. Significantly, TP53 mutations demonstrated universal adverse prognostic implications across B-cell lymphomas. Additionally, SETD2 mutations contributed to shorter time-to-progression, underscoring the role of epigenetic dysregulation in B-cell tumors. In T-cell lymphomas, SATB1 and FBXW7 were frequently mutated, warranting further investigation in larger cohorts. Our findings advocate for tailored therapeutic approaches based on the genetic profile, impacting treatment decisions and outcomes in canine lymphoma management. This study provides pivotal insights bridging veterinary and human oncology, paving the way for comprehensive genomic diagnostics and therapeutic strategies in comparative oncology.

Figure 1

Oncoplot of mutated genes in B-cell lymphoma. Genes of the K9 lymphoma assay harboring putative somatic short variants (SNVs and indels) identified in B-cell lymphomas are depicted. Genes are represented in descending order according to the frequency of mutation. Mutation frequency is calculated on dogs harboring at least one protein-coding mutation in a gene of the panel. Different mutation types are identified with different colors. Clinico-pathological data including diagnosis, aggressive or indolent histotype, sex, breed, stage and substage are shown at the bottom.

Figure 2

Lollipop plots of the top-four mutated genes in B-cell lymphomas. Localization of putative somatic short variants (SNVs and indels) at protein level for TRAF3 (a), POT1 (b), FBXW7 (c) and MYC (d). The height of each bar represents the frequency at which each mutation occurred. Proteins’ functional domains were retrieved from UniProt database (https://www.uniprot.org/). For each protein, the longest isoform was considered.

Figure 3

Hierarchical clustering. Hierarchical clustering was used to investigate variations in mutation profiles among present histotypes. Two resulting clusters emerge clearly from the above figure, distinguishing lymphomas originating from the BM from those originating from lymph nodes. The relevant genes for this clustering are MYC, POT1, TRAF3 and TBL1XR1.

Figure 4

Oncoplot of mutated genes in TCL. Genes of the K9 lymphoma assay harboring putative somatic short variants (SNVs and indels) identified in TCLs are depicted. Genes are represented in descending order according to the frequency of mutation. Mutation frequency is calculated on dogs harboring at least one protein-coding mutation in one panel gene. Different mutation types are identified with different colors. Clinico-pathological data including diagnosis, aggressive or indolent histotype, sex, breed, stage and substage are shown at the bottom.

Figure 5

Regression tree model (all B-cell lymphomas). All relevant features were processed through a regression tree model (displayed above). The prognostic relevance of TP53 mutations and treatment are immediately visible. Furthermore, the interplay between TRAF3 status and indolent diagnosis, along with the correlation between clinical stages and chemo-immunotherapy, show the benefit of this approach in uncovering masked factors strongly affecting the prognosis.

Figure 6

Regression tree model (DLBCL). To provide a deeper understanding of DLBCL malignancies, we built the regression tree model shown above. It revealed that treatment choice emerges as the most relevant prognostic factor. Additionally, the other features of the tree primarily pertain to gene status, displaying the importance and complexity of genetic variations.