What’s New in Veterinary Oncology Diagnostics?

Last updated:
April 3, 2023
A veterinarian trying to collect fine needle aspirate from canine patient for diagnosis

Accurate and timely diagnosis is essential for choosing the most effective drugs for cancer patients. The importance of accurate diagnosis has led to a flurry of new technologies – but have these tools yielded tangible benefits, or is there still a lot left to be done? Let's review contemporary diagnostic tools in veterinary oncology.

What is new, and what can the cells used in these tools tell us?

Different methods have been developed for cancer diagnosis. Increasingly, DNA, RNA, and proteins from cancer cells and liquid biopsies are part of the diagnostic  toolbox for selecting the most effective cancer therapeutics. Each method used to interrogate these biomarkers has its benefits and challenges. 

Many new genomic approaches for diagnostic and treatment support are emerging. In particular, DNA and RNA sequencing are powerful methods to produce tremendous amounts of data to help make patient care decisions. Some notable sequencing technologies include whole genome, whole transcriptome, exome, and targeted gene sequencing. However, none of these methods provide the full picture and may miss important epigenetic or protein changes. Moreover, sequencing methods are not cost-effective or practical for many clinics. Another method, spatial genomics,1 provides a readout of important DNA, RNA, or protein alterations in the tissue itself so that the relationship between the alteration and the diseased tissue is evaluated together. Complicating2 the wide adoption of spatial genomics is the need for costly instrumentation and skilled professionals who can interpret both the tissue architecture and molecular readout.

Other long-standing methods that are used to assist with treatment decisions include immunohistochemistry (IHC), immunofluorescence (IF), flow cytometry (FC), in-situ hybridization (ISH/FISH/CISH), and polymerase chain reaction (PCR). In particular, approaches like IHC and IF are important tools when expert pathologists are available, but that is not always feasible. Similarly, there is some evidence that FISH has a lower rate of equivocal results than IHC (<5%) as the method requires more expertise to perform. There are many useful PCR-based testing methods in cases with equivocal cytology and histopathology. However, PCR is not currently an accepted method that can be used for phenotyping independent of FC or cytological methods.

An emerging source of clinical importance are liquid biopsies.3 Liquid biopsies are a non-invasive and economical sample type that promises to help detect minimal residual disease and inform treatment decisions. The important elements within liquid biopsy samples are circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free tumor RNA, and proteins which can be related to risk of progressive disease and can inform treatment decisions. However, the amount of these components in a liquid biopsy sample makes analysis challenging and requires specialized techniques. In addition, there are many unknown factors that could be better revealed with Artificial Intelligence (AI). AI is acutely sensitive to patterns that are not obvious by standard analytical methods.

What can the Cells tell us themselves with Artificial Intelligence (AI)

Advances in computing power, the rapid development of neural networks, and remarkable progress in data science have resulted in new research and clinical applications of artificial intelligence (AI) systems. AI has the potential to greatly improve the accuracy and efficiency of cancer diagnosis and treatment in veterinary oncology. By quickly analyzing large amounts of data and identifying patterns that may not be apparent to the human eye/brain, AI can assist veterinarians in providing the best possible care for pets with cancer. Radiation oncology has already seen notable improvements because of AI.

Analyzing the results of chemosensitivity assays using live cancer cells with AI is intended to provide an accurate predictor of drug response to help oncologists choose the best possible treatments. Recent studies have suggested that combining chemosensitivity with other 'omics' data, such as DNA, RNA, and proteomics, or using patient-derived tumor micro-organospheres can improve the clinical value of these tests.

The current challenges with in vitro chemosensitivity assays encompass a lack of pharmacokinetic and pharmacodynamic information with regard to the tumor microenvironment. There is also a lack of validated studies and a need for randomized controlled trials for establishing the value of drug response predictions generated by chemosensitivity data and AI.

AI-augmented chemosensitivity assays are currently a good option in situations such as resistant lymphoma phenotypes, one-shot treatment, rescue chemotherapy, and when there is a need to avoid empirical chemotherapy or costly and toxic protocols.

Top oncologists believe that the ideal test for chemosensitivity assays should be cost-effective, have a quick turnaround time, and be able to provide effective recommendations in addition to what is already included in the standard of care. Using AI in combination with chemosensitivity assays is a promising way to achieve these goals.


ImpriMed is the only precision medicine company that uses artificial intelligence (AI) and comprehensive analyses of a dog's live cancer cells to make personalized drug response predictions. ImpriMed offers a personalized prediction profile (PPP) that provides both diagnostic testing and AI-based drug response predictions. Oncologists use the PPP for naïve patients when choosing between CHOP therapy and alternative treatment options and to quickly find an effective treatment option for progressive disease or when CHOP therapy fails.

ImpriMed’s tests have been used on over 3,700 canine and feline patients over the past 5 years, with over 10,000 individual samples analyzed. The ImpriMed clinical outcome predictions help guide treatment decisions for naïve or relapsed patients, identify potential CHOP failure patients, and predict the best drugs for patients with comorbidities. ImpriMed has two published studies 4,5 regarding the effectiveness of its predictive AI model. The other available market options in this domain offer targeted exome panel tests that focus on specific genes of interest, with currently no published data on efficacy or toxicity. There are also concerns about conflict of interest as some of these companies sell chemotherapy drugs.

ImpriMed is currently offering canine lymphoma and leukemia drug prediction profiles. The profiles have the lowest cost and a turnaround of one week as compared to other companies, which have higher costs and a minimum turnaround of 2 weeks. ImpriMed also offers a 50% discount on serial assays or repeat samples in the same patient. 

ImpriMed offers the following diagnostic services as well for canine lymphoma and leukemia:

  1. Clonality testing by PCR for Antigen Receptor Rearrangements (PARR) assay
  2. Immunophenotyping by flow cytometry: ImpriMed correlated data with Colorado State University (CSU) and had a 100% correlation 

Oncologists who have worked with ImpriMed have a very favorable opinion of the company because of the transparent way it works and excellent customer service. ImpriMed is constantly gathering insights from top veterinary oncologists to live up to the hope it offers for higher rates of cancer remissions. 

To learn more about ImpriMed's Personalized Prediction Profile, please go to this page.

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  1. Zhao T, Chiang ZD, Morriss JW, et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature. 2022;601(7891):85-91. doi:10.1038/s41586-021-04217-4
  2. Technology Development Driving Genomics and Life Sciences. Cell.com. Published October 13, 2021. Accessed Jan 25, 2023. https://www.cell.com/cell-genomics/pdf/S2666-979X(21)00009-4.pdf
  3. Chibuk J, Flory A, Kruglyak KM, et al. Horizons in Veterinary Precision Oncology: Fundamentals of Cancer Genomics and Applications of Liquid Biopsy for the Detection, Characterization, and Management of Cancer in Dogs. Front Vet Sci. 2021;8:664718. Published 2021 Mar 23. doi:10.3389/fvets.2021.664718
  4. Koo J, Choi K, Lee P, Polley A, Pudupakam RS, Tsang J, Fernandez E, Han EJ, Park S, Swartzfager D, Qi NSX, Jung M, Ocnean M, Kim HU, Lim S. Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model. Veterinary Sciences. 2021; 8(12):301. https://doi.org/10.3390/vetsci8120301
  5. Bohannan, Z, Pudupakam, RS, Koo, J, et al. Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model. Vet Comp Oncol. 2021; 19: 160– 171. https://doi.org/10.1111/vco.12656