The TiHoCL panel for canine lymphoma: a feasibility study integrating functional genomics and network biology approaches for comparative oncology targeted NGS panel design

Abstract

Targeted next-generation sequencing (NGS) enables the identification of genomic variants in cancer patients with high sensitivity at relatively low costs, and has thus opened the era to personalized human oncology. Veterinary medicine tends to adopt new technologies at a slower pace compared to human medicine due to lower funding, nonetheless it embraces technological advancements over time. Hence, it is reasonable to assume that targeted NGS will be incorporated into routine veterinary practice in the foreseeable future. Many animal diseases have well-researched human counterparts and hence, insights gained from the latter might, in principle, be harnessed to elucidate the former. Here, we present the TiHoCL targeted NGS panel as a proof of concept, exemplifying how functional genomics and network approaches can be effectively used to leverage the wealth of information available for human diseases in the development of targeted sequencing panels for veterinary medicine. Specifically, the TiHoCL targeted NGS panel is a molecular tool for characterizing and stratifying canine lymphoma (CL) patients designed based on human non-Hodgkin lymphoma (NHL) research outputs. While various single nucleotide polymorphisms (SNPs) have been associated with high risk of developing NHL, poor prognosis and resistance to treatment in NHL patients, little is known about the genetics of CL. Thus, the ~100 SNPs featured in the TiHoCL targeted NGS panel were selected using functional genomics and network approaches following a literature and database search that shielded ~500 SNPs associated with, in nearly all cases, human hematologic malignancies. The TiHoCL targeted NGS panel underwent technical validation and preliminary functional assessment by sequencing DNA samples isolated from blood of 29 lymphoma dogs using an Ion Torrent^™ PGM System achieving good sequencing run metrics. Our design framework holds new possibilities for the design of similar molecular tools applied to other diseases for which limited knowledge is available and will improve drug target discovery and patient care.

Keywords: canine lymphoma; comparative genomics; comparative oncology; genomic variants; network biology; patient stratification; personalized oncology; targeted next-generation sequencing.

Figure 1

The TiHoCL targeted sequencing panel relies on SNPs associated with NHL to detect variants in CL. (A) Venn diagram showing the sources of the 592 human lymphoma-associated SNPs; 89 SNPs were retrieved from the OMIM, 173 from the GWAS Catalog, 228 from PubMed, and 219 through manual literature curation. (B) Venn diagram displaying how the SNPs from (A) were selected based on the different filtering strategies. The brightness of the filling color in (A,B) corresponds to the number of SNPs. (C) Number of interaction partners for the DEGs. (D) Distance between selected 98 potential lymphoma-associated loci and the nearest known SNPs in the canine genome, which was also a filtering criteria for the SNPs. (E) Circos plot showing the nearest gene for each target. (F) Distribution of the 93 targets across the dog genome.

Figure 2

TiHoCL targeted sequencing panel achieved good run metrics. (A) Distribution of read lengths obtained after mapping quality (MAPQ) filtering with the panel on 29 dog samples. (B) Target coverage per sample. The Median coverage was greater than 500X for all samples. (C) Coverage of all samples per target. For seven targets the median coverage was smaller than 500X, two of them being outliers with a median coverage smaller than 100X.

Figure 3

TiHoCL targeted sequencing panel enabled detection of natural genetic variation in the study cohort and of variants associated with CL relapse and survival. (A) Heatmap of polymorphic “pooled” variants across 29 dog samples. Variants are indicated with respect to the reference genome. REF: reference allele; ALT: alternative allele. No association was observed between WHO clinical stage classification, sex, neuter status, treatment protocol, and age at diagnosis and PFS or OS using univariate Cox regression modeling. Heatmap columns were clustered hierarchically using complete linkage, based on their euclidean distances. Heatmap rows were sorted according to sample number. (B) Graphical workflow of the approach applied to identify genetic variants associated with PFS and OS. The numbers in boxes indicate the number of genetic variants selected in the corresponding step for PFS (violet) and OS (green). (C) Forest plot of cox regression hazard ratios for analysis of PFS and OS. A significant p-value and a hazard ratio smaller than 1 indicate a strong relationship between the variant and a decreased risk of death (OS)/relapse (PFS); a significant p-value and a hazard ratio greater than 1 indicate a strong relationship between the variant and an increased risk of death (OS)/relapse (PFS). Non-significant variants in the models are displayed in gray. The variant chr30:7335189G > C, which is placed on the same target and features the same pattern as chr30:7335099GGT > TGG, was removed prior to the analysis, but would also have a positive hazard ratio for PFS. Similarly, the variant chr2:64656755G > A, which is placed on the same target and features the same pattern as chr2:64656696G > A, was removed prior to the analysis, but would also have a negative hazard ratio for OS. (D,E) Survival based on samples separated by the median risk scores derived from the final cox models for PFS (D) and (E) OS.