Critical Questions in Genomic Diagnostics for Veterinary Oncology: What We Need to Know

ABSTRACT

Genomic diagnostics are increasingly integrated into veterinary oncology practice, offering the possibility of refined approaches to tumour classification, risk stratification and therapeutic guidance through high‐throughput sequencing technologies. In human medicine, the clinical utility of genomic testing is underpinned by rigorous analytical and clinical validation, robust regulatory oversight and an expanding evidence base linking specific genomic alterations to disease phenotypes and therapeutic responses. In contrast, genomic testing in veterinary oncology remains underdeveloped, with limited standardisation, sparse species‐specific validation and frequent reliance on extrapolation from human data. This review delineates the current landscape of genomic testing in veterinary oncology, emphasising methodological considerations in assay design, validation requirements, and clinical interpretation. We highlight the need for analytical rigour, including the use of in silico and orthogonal validation strategies, and advocate for the establishment of performance benchmarks that account for assay sensitivity, specificity and reproducibility in relevant canine populations. In addition, we address the interpretive challenges posed by variants of uncertain significance and the limitations of inferring clinical actionability from human oncology frameworks. A critical, question‐driven approach is proposed to guide clinicians in evaluating the validity and applicability of genomic tests, focusing on test validation, intended clinical use and the functional relevance of identified alterations. Advancing genomic diagnostics in veterinary oncology will require coordinated efforts to improve transparency, expand validation cohorts and align clinical expectations with the current evidentiary base. These steps are essential to realising the full potential of precision medicine in veterinary oncology while maintaining scientific and clinical integrity.

1. Background

Genomic testing involves the comprehensive analysis of an individual's DNA sequence to detect genetic alterations that may influence their health. Increasingly, genomic analyses are being used to complement conventional diagnostics, offering detailed insights into diseases' genetic basis, which can be crucial for accurate diagnosis and treatment planning [1].

Genomic analyses play a critical role in human oncology, informing diagnosis, risk stratification and targeted therapeutic strategies, thus improving patient outcomes [2]. Diagnostic assessments can detect genetic alterations linked to specific malignancies, such as EGFR mutations or ALK rearrangements in non–small cell lung cancer [3]. Predictive evaluations, like BRCA1/2 mutation analysis, can determine an individual's hereditary cancer risk [4]. For therapeutic guidance, DNA sequencing techniques, including targeted panels, whole‐exome sequencing (WES) or whole‐genome sequencing (WGS), identify tumour alterations that can be targeted for personalised treatments. Another emerging innovation is the minimally invasive liquid biopsy, which detects circulating tumour DNA (ctDNA) or other tumour‐associated biomarkers released by neoplastic cells into biological fluids [5]. In veterinary medicine, current implementations include plasma/blood ctDNA assays, urine‐based BRAF mutation testing from exfoliated tumour cells, and serum/plasma nucleosome assays. Blood collection for ctDNA tests is routine and generally considered minimally invasive, similar to breast milk, semen, and saliva collection, in contrast to the collection of other biological fluids, such as cerebrospinal fluid, which is more invasive; and no validated veterinary liquid biopsy assays currently exist for these sample types.

One of the key advantages of genomic testing is its ability to enable precision medicine. Based on individuals' unique genetic profiles, personalised care can enhance diagnostic accuracy, improve risk assessment and increase treatment efficacy while minimising adverse effects [6]. Additional benefits include identifying hereditary cancer risks, which allows for preventive interventions, clarifying uncertain diagnoses, and predicting tumour behaviour to guide therapy. However, despite these advantages, genomic testing has several limitations, such as the potential for false‐positive and false‐negative results and privacy concerns related to the security of genetic data [7]. Furthermore, the lack of well‐defined genomic biomarkers in some cancers can limit the clinical utility of genomic testing in certain cases.

Spontaneously occurring cancers in dogs share similarities in aetiology, progression, and clinical outcomes with those seen in humans, likely due to shared genetic, environmental and immunological factors [8]. As in human medicine, genomic testing in veterinary oncology offers the possibility of early detection, risk stratification, prognosis and therapeutic decision‐making, although it may be limited [9]. Commercially available genomic tests for dogs are gaining popularity, providing insights into breed‐specific health risks, genetic predispositions and cancer biomarkers. These tests, often based on high‐throughput sequencing, analyse genetic markers associated with disease. Liquid biopsy, akin to its application in human medicine, shows promise in veterinary medicine by detecting genetic alterations from peripheral blood samples for disease management [10]. While associated diagnostic technologies have demonstrated encouraging results, challenges remain in assay sensitivity, validation across larger cohorts, and protocol standardisation for clinical application. Genomic testing may equip veterinary oncologists with critical data to inform prevention, diagnosis, and treatment strategies; however, careful consideration must be given to test validation, intended use and result interpretation to ensure optimal patient care.

2. Genomic Testing Process in Oncology

Genomic testing in human oncology follows a structured process to ensure accuracy and clinical relevance (Figure 1) [1]. It begins with acquiring patient samples, including tumour biopsies, blood‐derived ctDNA, or normal adjacent tissue. DNA is extracted, and genomic assays are conducted using next‐generation sequencing (NGS), employing targeted gene panels or WES/WGS. The sequenced data then undergo rigorous bioinformatic analysis, including variant calling, tumour mutation burden (TMB) assessment, and identification of actionable mutations with therapeutic relevance. Following bioinformatic analysis, a clinical report details key findings, which may be reviewed by a genomic tumour board in complex cases. Oncologists integrate clinical and genomic insights into treatment planning, incorporating targeted therapies and immunotherapies [11]. In addition, ctDNA profiling facilitates disease monitoring by detecting minimal residual disease (MRD) and early relapse indicators, aiding prognostic assessments and risk stratification [12]. These testing protocols thus offer a deeper understanding of individual cancer profiles.

FIGURE 1.

Overview of a cancer genomic diagnostics workflow, illustrating the key steps from sample collection to clinical decision‐making.

2.1. Evaluation of Genomic Tests

Before clinical deployment, human genomic tests undergo rigorous evaluation to ensure analytical performance, clinical validity and utility in patient management. Validation is critical in confirming that the assay reliably detects genetic variants with high accuracy, sensitivity, specificity and reproducibility (Table 1). The validation process includes analytical validation, which ensures the bioinformatic tools in the test accurately detect known mutations, and clinical validation, which correlates detected variants with disease outcomes [13]. Human genomic testing must also adhere to quality standards and regulatory compliance, including those established by Clinical Laboratory Improvement Amendments (CLIA) regulations [14]. However, CLIA oversight does not extend to veterinary laboratories, which operate outside this regulatory framework.

TABLE 1.

Glossary of commonly used terms in genomic diagnostic tests.

Concept	Definition	Example in veterinary genomic diagnostics in veterinary oncology
Validation of a genomic test	The process of assessing a genomic test's performance characteristics to ensure accuracy, reliability and reproducibility for its intended clinical application	A new genomic test for canine osteosarcoma is validated by assessing its sensitivity, specificity and reproducibility before clinical use
Analytical validation	Evaluates the technical performance of the test, including accuracy, precision, specificity, sensitivity and reproducibility. Uses positive and negative controls with a well‐balanced sample size	A test for feline leukaemia virus undergoes analytical validation to confirm that it correctly identifies infected and noninfected cats
Clinical validation	Determines whether the test accurately detects or predicts clinical conditions	A genomic test for canine lymphoma is validated by demonstrating its ability to predict treatment response
Orthogonal testing methods	Use of independent methodologies to confirm test results, ensuring accuracy and reliability	A droplet digital PCR (ddPCR) method is used to verify results from a next‐generation sequencing (NGS)‐based canine cancer test
Limit of detection (LoD)	The lowest quantity of a target genetic sequence that a test can reliably detect	A genomic test for feline lymphoma has an LoD of 1% variant allele frequency, meaning it can detect mutations even when they are present at very low levels in a sample
Cut‐off thresholds	Defined values distinguishing between positive and negative test results	A test for canine bladder cancer has a predefined cut‐off value to determine the presence of a clinically significant mutation
Reference materials and controls	Use of well‐characterised positive and negative control samples to validate test performance	A laboratory validates its assay by testing against known DNA samples from canine patients with and without cancer
Assessment of a genomic test	Analysis of the clinical utility of a test's results in a specific context	Assessing a new genomic test for canine lymphoma to determine its effectiveness in predicting response to treatment
Specificity	The ability of a test to correctly identify those without the disease (true negative rate). Formula: specificity = (true negatives)/(true negatives + false positives)	A genomic test for feline leukaemia virus (FeLV) has a specificity of 90%, meaning that if the test result is negative, it correctly identifies 90% of healthy cats. However, 10% of healthy cats may receive a false‐positive result
Sensitivity	The ability of a test to correctly identify those with the disease (true positive rate). Formula: sensitivity = (true positives)/(true positives + false negatives)	A genomic test for canine osteosarcoma (OSA) has a sensitivity of 70%, meaning that 70% of affected dogs test positive. However, 30% of affected dogs may receive a false‐negative result
Positive predictive value (PPV)	The probability that a positive test result correctly indicates the presence of a disease. Formula: PPV = true positives/(true positives + false positives)	If a genomic test detects a mutation associated with mast cell tumours and has a PPV of 85%, this means that 85% of dogs with a positive result actually have the tumour, while 15% might be false positives
Negative predictive value (NPV)	The probability that a negative test result correctly indicates the absence of a disease. Formula: NPV = true negatives/(true negatives + false negatives)	If a genomic test indicates that a dog does not carry a pathognomonic mutation for a certain cancer and the NPV is 95%, there is a 95% probability that the dog truly does not have that cancer, while 5% may be false negatives
Accuracy	The overall ability of a test to correctly classify individuals as having or not having a disease. Formula: accuracy = (true positives + true negatives)/(true positives + true negatives + false positives + false negatives)	A genomic test correctly identifying 95% of malignant tumours and 90% of benign cases results in an overall accuracy of 92%
Applicability of a genomic test	Use of a validated test in a clinical setting to manage a patient's disease based on their genomic information	Using a genomic test to determine if a dog with lymphoma has a mutation indicating sensitivity to a targeted drug
Clinical utility	The ability of a test to improve patient outcomes by guiding diagnosis, prognosis or treatment selection	A validated test shows that identifying a specific mutation in canine hemangiosarcoma helps select the most effective treatment
Driver mutation	A genetic mutation that directly contributes to the development of cancer and/or drives tumour progression	BRAF mutations in canine transitional cell carcinoma are driver mutations essential for tumour growth
Passenger mutation	A genetic mutation that does not contribute to cancer progression but occurs alongside driver mutations	Passenger mutations, such as synonymous variants in oncogenes, are often detected in canine cancers but have no known functional role
Predictive value of genomic mutations	Some genomic tests suggest potential driver mutations, but in‐depth functional studies are required to confirm their role in cancer progression	A test may identify a TP53 mutation in a dog's tumour, but further research is needed to determine whether it drives malignancy
Diagnosis	Identification of a disease based on genetic and molecular biomarkers	Using NGS to confirm a dog's sarcoma subtype based on its genetic profile
Prognosis	Prediction of disease progression and potential outcomes based on genomic and clinical data	Predicting survival time in cats with intestinal adenocarcinoma based on specific mutational patterns
Therapy	Selection of treatment options, including targeted therapies, based on a patient's genomic profile	Recommending a PARP inhibitor for a dog with BRCA1 mutations in mammary carcinoma

The validation process involves selecting reference samples with known mutations, assessing performance metrics and verifying reproducibility across different conditions. Orthogonal testing methods, such as Sanger sequencing, digital PCR (dPCR), and fluorescence in situ hybridisation (FISH), provide independent confirmation, reducing false positives and enhancing reliability [15]. The robustness of a genomic test depends on the number and diversity of validation samples. Analytical validation typically requires thousands of samples [16], while clinical validation studies may involve hundreds to thousands, ensuring applicability across diverse patient populations [17].

2.2. Assessment and Clinical Applicability

Implementing genomic testing in clinical decision‐making requires systematically evaluating its analytical validity, clinical utility, and predictive value (Table 1). Key performance metrics, as derived from analytical validation studies, include sensitivity and specificity, which determine the ability to detect true‐positive and true‐negative cases while minimising errors. Positive predictive value (PPV) and negative predictive value (NPV) assess the likelihood of clinically actionable results, while overall accuracy reflects the test's ability to distinguish between pathogenic, protective (risk‐reducing), and non‐pathogenic variants.

Beyond validation, genomic tests intended for variant interpretation should distinguish driver mutations, which contribute directly to oncogenesis and may inform therapeutic or prognostic decisions, from passenger mutations, which lack known biological impact. However, for certain applications such as liquid biopsy‐based detection or disease monitoring, the presence of specific mutations or SNPs—irrespective of their driver status—can still provide clinically meaningful information (Table 1) [18]. Functional validation, including experimental assays and computational modelling, ensures biological and therapeutic relevance [19]. However, genomic testing often yields probabilistic rather than definitive results, particularly for variants of uncertain significance (VUS). Consequently, genomic findings must be integrated with histopathological, proteomic, and clinical data to enhance interpretation and applicability.

2.3. Challenges and Considerations in Genomic Testing for Canine Oncology

In canine diagnostics, while approaches to genomic testing share similarities with those used in human oncology, the landscape remains far less structured, with significant gaps in regulatory oversight and clinical validation. In contrast, despite a complex and fragmented regulatory landscape, laboratories performing human clinical tests operate under established quality standards such as CLIA certification. No comparable standards exist for veterinary genomic testing for dogs, and many canine assays are adapted or repurposed from human applications without full evaluation of their analytical or clinical performance in the target species. This lack of species‐specific standardisation, regulatory oversight, and rigorous validation presents major challenges. Bridging these gaps in genomic testing for canine oncology requires a comprehensive approach incorporating rigorous validation, standardisation and assessments of clinical applicability.

Given that conducting large‐scale experimental and clinical validation studies in dogs is relatively more challenging than those in human oncology, alternative strategies must be employed to ensure the reliability of these tests in dogs. One such strategy is in silico validation, which involves using rigorously curated or simulated genomic datasets containing known ‘truth set’ variants. The genomic test under evaluation is applied to these datasets, and the results are compared against existing gold‐standard methods to assess detection accuracy. It is critical to acknowledge that published variant catalogues may include unvalidated or pipeline‐dependent calls, and thus, transparent documentation of variant discovery methods and filtering criteria is essential. This in silico evaluation should be complemented by orthogonal validation, where detected variants are cross‐verified using alternative bioinformatic methods or sequencing platforms to confirm results and evaluate the test's robustness. Finally, it is essential to define clear performance metrics, including sensitivity, specificity, precision, and accuracy. These metrics should be benchmarked using a dataset with sufficient statistical power (i.e., adequate and balanced samples) to ensure reliable evaluation. The sensitivity and specificity of canine genomic tests must be carefully scrutinised, as these values are often not clearly defined or rigorously validated in dog populations. To address this, minimum acceptable thresholds for clinical implementation should be established through consensus guidelines developed by the veterinary oncology and genomic research communities.

However, even with rigorous validation, the mere ability to detect genetic variants is insufficient—clinical relevance is key. The clinical utility of genomic testing should not be solely determined by the number of genes sequenced but by the depth of knowledge surrounding those genes. While WES may identify thousands of variants, targeted panels focusing on well‐characterised oncogenes and tumour suppressor genes may offer greater diagnostic, prognostic and therapeutic significance. Ultimately, the utility of genomic testing is defined not by its comprehensiveness but by its ability to generate clinically actionable insights that guide the effective management of the specific disease under investigation (Figure 2).

FIGURE 2.

Illustrative case example demonstrating the integration of genomic testing in canine cancer management (findings and potential sensitivity to targeted therapies are hypothetical examples to illustrate the decision‐making framework).

Beyond identifying oncogenic drivers, it is also crucial to determine their functional significance in canine cancers. Though certain oncogenic drivers are conserved between humans and dogs, their functional relevance in canine oncology must be empirically validated before they can be considered for targeted therapies. Cross‐species genomic studies, including functional assays, are essential to confirming whether human‐derived therapeutic targets exhibit similar biological behaviour in dogs. Moreover, both preclinical (in vitro) and clinical validation of the putative oncogenic drivers' impact on drug sensitivity may be necessary for each target, each drug and even each specific tumour type. The same mutation can produce divergent responses to treatment depending on the tissue of origin and the accompanying mutational landscape. For instance, BRAF V600E mutations are commonly observed in human melanoma, where targeted therapy with vemurafenib can lead to significant tumour reduction or elimination [20]. However, while the BRAF V600E mutation is also present in thyroid cancers, particularly papillary thyroid carcinoma, its responsiveness to vemurafenib is notably lower [21]. Similarly, canine urothelial carcinoma cells carrying the orthologous BRAF V595E mutation are insensitive to vemurafenib in vitro [22]. Such assessments are crucial for translating insights from human oncology into effective veterinary applications. Although not all the above steps can be implemented fully, ensuring the highest standards for variant detection and performance metrics remains imperative.

To ensure the responsible and effective application of genomic testing in canine oncology, clinicians must critically assess any test's validity and clinical utility. This evaluation should be grounded in the test's intended use, the strength of supporting evidence, and its relevance to specific cancer types. Ultimately, selecting the right genomic assay involves understanding its technical performance and clinical applicability, guiding practitioners towards tests that are accurate but also actionable and meaningful in real‐world settings (Figure 3).

FIGURE 3.

Key considerations for evaluating and selecting genomic diagnostic tests in canine oncology.

3. Conclusions

Genomic testing is revolutionising oncology by enhancing diagnostic precision and prognostic prediction and enabling targeted therapeutic strategies. It provides the benefits of precision medicine, including improved accuracy and therapeutic efficacy. Nevertheless, significant challenges remain, particularly regarding validation, accessibility, cost, ethical concerns, and the interpretation of test results.

The potential for genomic testing in veterinary oncology, especially for canine cancers that share similarities with human diseases, is immense for early detection and personalised therapy. However, canine genomic testing faces a less structured landscape, with gaps in regulation and validation compared to human diagnostics. In addition, these tests are prohibitively priced and not accessible to all. Rigorous validation, standardisation, and clinical applicability assessments are essential to ensure its effectiveness. Strategies such as in silico and orthogonal validation and clear performance metrics are crucial. This will ensure that the tests meet scientific and clinical standards and demonstrate functional relevance in a veterinary context. Clinicians must be equipped to critically evaluate genomic tests' validity and clinical applicability, avoiding the uncritical acceptance of claims without sufficient evidence. In parallel, diagnostic companies must adopt transparency in their validation methodologies, presenting evidence‐based data that underscores their tests' reliability and clinical utility.

Ultimately, the goal is to integrate genomic advances responsibly and ensure they bring tangible benefits to animal healthcare while avoiding potential pitfalls. This will require sustained collaboration between clinicians and diagnostic companies to ensure that genomic testing in veterinary oncology is both scientifically robust and clinically relevant, thereby providing the highest standards in diagnostic and therapeutic care.

Funding

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The authors have nothing to report.