The technical foundations of this shift are worth unpacking. Modern veterinary AI draws on the same deep learning architectures that revolutionized human healthcare: convolutional neural networks that parse medical images pixel by pixel, natural language processing models that mine unstructured clinical records, and machine learning classifiers that integrate multi-omics data for personalized treatment planning. What has changed is not the underlying mathematics but the surrounding ecosystem. Digital pathology scanners have made whole slide images routine, picture archiving systems have accumulated years of computed tomography and magnetic resonance studies, and cloud computing has placed training-grade hardware within reach of university labs. The editorial argues that these conditions have finally aligned, allowing veterinary researchers to move from proof-of-concept demonstrations toward validated, deployable systems that can assist clinicians in real time.

One of the most striking themes in the collection is the two-way traffic between veterinary and human oncology. In human breast cancer care, radiomics-based deep learning approaches have already proven effective at predicting outcomes and have been implemented across multiple health systems, including an internationally evaluated AI system for breast cancer screening published in Nature. Veterinary researchers are now adapting those methodologies to animal patients, and, remarkably, sending innovations back across the species barrier. Bertram and colleagues demonstrated a comparative approach to mitotic count detection in which a model trained on a canine breast cancer dataset showed excellent cross-collaborative performance, supported by a completely annotated whole slide image dataset of canine mammary tumors built specifically to aid human breast cancer research.

The traffic flows in the other direction too. Osteosarcoma, an aggressive bone cancer, is far more common in dogs than in people, which means canine histopathology archives hold a wealth of data that human medicine simply cannot match. Patkar and colleagues exploited this asymmetry by training a model through deep domain adversarial learning on canine histopathology slides to detect histologic subsets of osteosarcoma in human patients. Domain adversarial learning is a technique that deliberately teaches a network to ignore species-specific features, forcing it to focus on the morphological signatures of malignancy that are shared across species. The result is a species-agnostic classifier, and the editorial points to it as evidence that comparative artificial intelligence, executed with validation and a collective domain approach, has major implications for advancing human healthcare alongside veterinary care.

Within veterinary oncology itself, the collection documents rapid progress across nearly every diagnostic modality. In digital pathology, machine learning models have achieved automated diagnosis of seven canine skin tumor types directly from H&E-stained whole slide images, while computer vision systems originally developed to detect inflammatory pododermatitis of the paw are being repurposed for neoplastic skin conditions. On the imaging side, machine learning classifiers have been trained to categorize focal splenic lesions from their CT features and to predict the histologic type and grade of canine gliomas from MRI texture analysis, offering clinicians a non-invasive preview of what a pathologist would otherwise only see under the microscope. These are not speculative demos; they are published studies with quantified performance, and they sketch a future in which pre-predicting histology before surgery becomes routine.

Perhaps the most technically consequential work concerns reproducibility, a chronic weakness in diagnostic pathology. Mitotic count, the number of dividing cells counted per high-power field, is a cornerstone of tumor grading, yet it suffers from notorious interobserver variability. Deep learning algorithms have now been shown to outperform veterinary pathologists in identifying the mitotically most active tumor region, and computer-assisted mitotic counting using a deep learning-based algorithm has been shown to improve both interobserver reproducibility and accuracy. Automated nuclear morphometry, another deep learning approach, has been applied to prognostication in canine pulmonary carcinoma. By standardizing measurements that were previously subjective, these tools attack the reproducibility problem at its root, and the editorial suggests that such techniques may become the template for AI-assisted grading across tumor types.

Data itself is the other half of the equation, and the collection highlights both progress and friction. On the progress side, the release of curated datasets such as the pan-tumor CAnine cuTaneous Cancer Histology, or CATCH, dataset is helping to standardize research and accelerate development, giving labs everywhere a common benchmark. On the friction side, an evaluation of an open-source Named Entity Recognizer applied to veterinary oncology records, conducted by Pinard and colleagues, demonstrated the potential of natural language processing to streamline data curation while exposing real limitations. General-purpose NLP systems, trained mostly on human medical text, struggled with veterinary terminology without rigorous oversight, underscoring the need for domain-specific tools. Companion reviews on quality assurance in AI model development and external validation reinforce the message that a clever algorithm is worthless, or worse, without disciplined testing.

Then comes the uncomfortable question: who is watching the deployment? The editorial is candid that veterinary medicine currently has limited to no formal guidelines for the safe and adequate deployment of AI in hospital systems. In human healthcare, concerns about data privacy, algorithmic transparency, and inherent bias have already spurred rigorous regulatory frameworks, and the risks are not hypothetical. A widely cited modelling study found that AI systems can recognize a patient’s race from medical imaging itself, a capability with troubling implications for bias. In the veterinary sphere, Duggirala and colleagues have offered a regulatory perspective outlining current initiatives and future prospects for AI and machine learning deployment, while reviews of veterinary diagnostic imaging emphasize the need for clear guidelines and safe-deployment toolboxes. The editorial argues that these initiatives will form the foundation of tailored regulatory frameworks that safeguard patient welfare and promote responsible innovation.

What makes this moment genuinely viral, in the sense of an idea spreading fast, is the realization that veterinary oncology is not merely a consumer of human AI advances but a full partner in producing them. Dogs share our environments, develop spontaneously occurring cancers with striking molecular parallels to human disease, and age fast enough to generate longitudinal data at a pace clinical trials in people cannot match. A validated model trained on canine data can seed human applications, and vice versa, creating a translational loop that benefits patients on both ends of the leash. The editorial’s closing invitation is for clinicians, researchers, and regulatory bodies to engage with the collection and to keep collaborating, because the technology is arriving whether the guidelines are ready or not.

The collection, then, captures a pivotal moment. Computer-aided diagnostics, integrated multi-omics, specialized natural language processing tools, and cross-disciplinary comparative studies are converging on a field that has historically been data-poor and resource-constrained. The editorial’s message is measured rather than utopian: AI holds real promise for diagnostic precision and therapeutic outcomes, but only if the community invests simultaneously in curated datasets, rigorous validation, domain-specific language tools, and the ethical and regulatory scaffolding that human medicine has already begun to build. If it does, the future promised in the collection’s title may arrive in veterinary clinics first, and human oncology will be better for it.

Subject of Research: Artificial intelligence and informatics applications in veterinary oncology

Article Title: The future is here: an introduction to the Veterinary Oncology collection on Artificial Intelligence and Informatics

Article References: Pinard, C. J. (2025). The future is here: an introduction to the Veterinary Oncology collection on Artificial Intelligence and Informatics. Veterinary Oncology, 2(1), Article 10. https://doi.org/10.1186/s44356-025-00027-2

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00027-2

Keywords: artificial intelligence, veterinary oncology, deep learning, digital pathology, comparative oncology, mitotic count, natural language processing, radiomics, osteosarcoma, CATCH dataset, regulation, machine learning

A research team led by Floryne O. Buishand, Phoebe Y. K. Chan, Dong Xia and Lucy J. Davison at the Royal Veterinary College turned to single-cell RNA sequencing, a technique that reads the gene expression of individual cells rather than averaging signals across a bulk tumour sample. This matters because tumours are not uniform populations; they contain rare subpopulations that may underlie treatment resistance or drive metastasis. Bulk RNA sequencing of canine insulinomas had previously shown that early-stage primary tumours resemble normal pancreas, while late-stage tumours resemble metastatic lymph nodes, but such bulk approaches cannot resolve the internal diversity of a tumour. By profiling cells one by one, the team could detect distinct clusters within each sample and identify marker genes that would be diluted into invisibility in a bulk measurement.

The researchers applied this technology to four insulinoma cell lines spanning three species: canINS, a canine line established from a spontaneously occurring insulinoma; CM, a human insulinoma line; INS-1, a rat line derived from a radiation-induced tumour; and MIN6, a murine line from a transgenic mouse expressing the large T antigen under the rat insulin promoter. Each line carries its own history and its own limitations. CM has large chromosomal rearrangements involving the insulin gene that abolished insulin secretion from early passages. MIN6 and INS-1 both secrete insulin, but their artificial genetic backgrounds do not resemble spontaneous insulinoma. Canine insulinoma, sharing clinical and molecular features with the human malignant disease, has been proposed as a translational model, and earlier work showed that Notch pathway inhibition could target chemoresistant cancer stem cells in both canINS and CM cells.

Between 5,427 and 9,423 cells were analysed per cell line, with a median of 3,606 to 5,964 genes detected per cell and 43,042 to 68,959 mean reads per cell. Computational clustering in R and Seurat revealed five or six distinct cell clusters in each line. All four lines expressed neuroendocrine markers, including ENO2, MAP2 and NCAM1, confirming their neuroendocrine identity, but a striking split emerged in their differentiation status. INS-1 and MIN6 expressed the full panel of islet hormones and mature beta-cell markers tested, including insulin genes Ins1 and Ins2, glucagon, Mafa and Pdx1. In contrast, canINS and CM expressed only ACVR1C, and in the case of CM also MAFA, among the mature beta-cell markers, and neither expressed insulin. Instead, canINS and CM expressed genes normally activated during pancreatic development or in early endocrine progenitors, such as GATA4 and ISL1, which were the only lineage-specific pancreatic progenitor markers detected in canINS.

This differentiation landscape has direct practical value. Dedifferentiation of pancreatic beta cells in adherent monolayer culture, with loss of insulin-secretory capacity, is one of the main obstacles to building representative preclinical insulinoma models. Notably, canINS cells have previously regained insulin expression and secretion when grown in non-adherent conditions, suggesting that their epigenetic memory remains intact and that the absence of insulin expression in monolayer culture does not disqualify a cell line as an insulinoma model. The study’s single-cell maps now allow researchers to make informed choices: INS-1 and MIN6 for questions requiring mature, insulin-producing beta-cell features, and canINS and CM, with their spontaneous-tumour origins, for studies closer to the clinical disease.

The team also examined epithelial-mesenchymal status, a critical axis in cancer metastasis. When cultured in monolayers, pancreatic beta cells undergo epithelial-mesenchymal transition, becoming highly proliferative cells that can re-differentiate into insulin producers. All four lines expressed the epithelial or ductal markers ANXA4, CDH1 and SLC4A4, but vimentin, a mesenchymal marker, was detected in all lines except MIN6 and was significantly higher in canINS and CM than in INS-1. This means canINS and CM contain substantial populations of hybrid epithelial/mesenchymal cells, a state previously observed in prostate, lung and colorectal cancer lines. Hybrid E/M cells are thought to migrate collectively as clusters of circulating tumour cells, enhancing metastatic potential compared with individually migrating cells, making canINS and CM the preferred models for studying how insulinoma cells acquire metastatic behaviour.

Perhaps the most tantalising discovery came from the cross-species comparison. Because single-cell analysis exposes genes robustly expressed in subpopulations that bulk methods would miss, the researchers catalogued the top ten marker genes for each of the 23 clusters identified across the four lines. Nineteen of these clusters were characterised by genes consistently expressed across all cell lines. Filtering for unique cluster markers with plausible cancer-related functions that were conserved across species and expressed in all four lines yielded eight candidate genes: DEPTOR, BICC1, GHR, CCNB2, CENPA, LMO4, VANGL1 and L1CAM. Several of these have documented pro-tumour roles in other cancers. BICC1 drives pancreatic cancer stemness and chemoresistance, L1CAM promotes perineural invasion in pancreatic cancer, CENPA has been implicated in chromosomal instability of pancreatic neuroendocrine tumours, and LMO4 is overexpressed in late-stage pancreatic cancer.

Among these candidates, the growth hormone receptor gene GHR stands out for its translational potential. GHR expression has previously been demonstrated by immunohistochemistry in canine primary insulinomas and their metastases, and growth hormone and insulin-like growth factor 1 expression were increased in metastases compared with primary tumours. This has led to the hypothesis that targeting the GH/IGF-1 axis might inhibit insulinoma proliferation and prevent micrometastatic outgrowth after surgery. Pegvisomant, currently the only clinically available GHR antagonist, is FDA approved for acromegaly, and promising preclinical results have been obtained with pegvisomant and a variant called compound G against pancreatic cancer xenografts, particularly in combination with gemcitabine. Because GHR emerged as a cross-species conserved cluster marker in all four insulinoma lines, the study provides a direct rationale for testing GHR inhibition in these models.

The second major achievement of the study addresses a logistical bottleneck that has long hampered single-cell research on rare tumours. Ideally, fresh patient samples are dissociated into single-cell suspensions immediately upon retrieval to prevent ischaemia-related gene expression changes and RNA degradation, then loaded onto droplet-based platforms within minutes. For rare diseases, where samples arrive sporadically and often far from sequencing facilities, this workflow is difficult to sustain. Cryopreservation offers an obvious solution, but only if freezing does not distort the transcriptomic picture. To test this, the team froze canINS and CM cells in 90 percent fetal calf serum with 10 percent dimethyl sulfoxide, stored them at minus 80 degrees Celsius for four weeks, thawed them, and sequenced them alongside fresh counterparts.

The verdict was emphatically reassuring. Cryopreserved samples yielded five canINS clusters and seven CM clusters, and most clusters matched their fresh counterparts closely, sharing five to nine of the top ten marker genes. Merged datasets showed near-equal distribution of fresh and frozen cells across all clusters, and expression of neuroendocrine, epithelial-mesenchymal, islet hormone and progenitor markers was fully maintained. Differential expression analysis identified 1,395 genes changed in cryopreserved canINS, but only six exceeded a log2 fold change of one, with BTF3, MEI4, NUPR1, FOS and SLC25A6 upregulated; in CM, 1,484 genes were differentially expressed and 29 exceeded the threshold, with TNFRSF12A, CKS1B and PTTG1 upregulated. Only FOS upregulation had previously been linked to DMSO cryopreservation in other cell types. These modest perturbations, the authors note, will need consideration but do not compromise data quality. The message for the field is twofold: insulinoma researchers now have a validated, species-spanning toolkit of cell lines matched to specific experimental questions, and banks of cryopreserved patient samples can finally enter single-cell studies without sacrificing fidelity, reducing assay-based variability and opening the door to larger, multi-centre studies of one of veterinary and human medicine’s most stubborn tumours.

Subject of Research: Single-cell transcriptomic analysis of fresh and cryopreserved multispecies insulinoma cell lines

Article Title: Single-cell transcriptome conservation in a multispecies comparative analysis of fresh and cryopreserved insulinoma cell lines

Article References: Buishand, F. O., Chan, P. Y. K., Xia, D., & Davison, L. J. (2025). Single-cell transcriptome conservation in a multispecies comparative analysis of fresh and cryopreserved insulinoma cell lines. Veterinary Oncology, 2(1), Article 14. https://doi.org/10.1186/s44356-025-00025-4

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00025-4

Keywords: insulinoma, single-cell RNA sequencing, cryopreservation, canine cancer, pancreatic neuroendocrine tumour, beta cells, transcriptomics, comparative oncology, GHR, oncogenes, cell lines, veterinary oncology

The choice of dogs as a comparative model is not arbitrary. Unlike laboratory mice, whose tumors are typically chemically induced and often fail to capture the complexity of human disease, dogs develop cancers spontaneously, with intact immune systems and naturally evolving tumor microenvironments. Over the past decade, accumulating evidence has shown that canine cancers closely resemble human cancers in prevalence, growth patterns, histology and genetic foundations. Yet hepatocellular carcinoma in dogs differs from the human disease in one crucial respect: it is not typically associated with viral hepatitis, cirrhosis or alcohol consumption, the dominant risk factors in people. That distinction offers scientists a rare opportunity to study the intrinsic genetic drivers of liver cancer, stripped of the confounding influences of chronic inflammation and viral infection that dominate the human disease.

To build this cross-species picture, the researchers combined two powerful technologies. First, they performed whole exome sequencing on liver tumors and matched blood samples from ten dogs diagnosed with hepatocellular carcinoma, all confirmed by board-certified veterinary pathologists. The sequencing achieved an average depth of one hundred times coverage, with more than ninety-three percent of reads successfully mapped to the canine reference genome. Second, they applied single nucleus RNA sequencing to flash-frozen tumor biopsies from two of those dogs, profiling nearly twenty-four thousand individual nuclei to map the cellular composition of the canine tumor microenvironment. The exome data revealed a total of 1,895 non-synonymous single nucleotide variants across the cohort, averaging 189 mutations per sample, from which 645 mutations with high, moderate or low predicted impact remained after filtering.

The mutational landscape that emerged contained a familiar villain. The most recurrent missense mutation in the dog tumors was in the CTNNB1 gene, which was altered in five of the ten samples, or fifty percent of the cohort. CTNNB1 encodes beta-catenin, the central component of the Wnt/beta-catenin signaling pathway, which plays a key role in liver cancer development, tumor progression and immune evasion. The gene is also one of the most frequently mutated drivers in human hepatocellular carcinoma, present in roughly twenty-two percent of cases. More remarkably, when the researchers mapped the positions of the CTNNB1 mutations in both species, they found identical amino acid substitutions at the same protein locations: the D32Y, G34V and S37F changes appeared in five, one and one dog respectively, and in forty, fifty-four and thirty-nine human samples in the COSMIC database. A search of the Drug Gene Interaction Database identified sixty-six interactions for CTNNB1, including sixteen involving FDA-approved therapies, hinting at possible treatment avenues pending appropriate validation.

Beyond CTNNB1, the analysis identified ten putative driver genes mutated in at least twenty percent of the dog tumors, including frameshift variants in ARRDC3, found in three dogs but notably absent from human hepatocellular carcinoma data. The team also calculated tumor mutation burden, a metric that in humans helps predict response to immunotherapy. The median burden in the dog cohort was 4.01 mutations per megabase, with a range of 2.31 to 5.96, which sits comfortably within the estimated human hepatocellular carcinoma median range of 2.56 to 5 mutations per megabase. This similarity suggests that the biological determinants of immune visibility may be comparable across species, a finding with direct implications for designing canine immunotherapy trials that could, in turn, inform human studies.

The single nucleus sequencing side of the study painted an equally detailed portrait of the tumor’s cellular ecosystem. After quality filtering and integration, the researchers identified seven major cell types in the dog tumors: fibroblasts, hepatocytes, macrophages, malignant cells, proliferating cells of myeloid origin, endothelial cells and T cells. Malignant cells were distinguished by the overexpression of genes such as CP, APOB, DST and PTGR1, which had previously been reported in malignant cells from human single-cell studies of the disease. To bolster the identification, the team scored each cell type against a published cancer stemness gene signature and found that malignant cells carried significantly higher stemness scores than normal hepatocytes, with a p-value below 2.2 times ten to the minus sixteenth. Both malignant cells and proliferating myeloid cells also showed elevated S and G2M cell cycle phase scores, confirming their proliferative character.

The most consequential comparison came when the researchers integrated the canine data with single nucleus RNA sequencing data from two human patients with non-viral hepatocellular carcinoma. Using the scVI computational framework to align the two species’ transcriptomes, they found that non-epithelial cells integrated far more effectively than epithelial ones. T cells, macrophages, fibroblasts and endothelial cells from dogs and humans formed shared clusters corresponding to their cell types, while hepatocytes remained largely species-specific, and dog malignant cells refused to merge with human epithelial cells at all. A Jaccard similarity analysis confirmed the pattern: human macrophages were most similar to dog macrophages, and conserved gene features included well-established macrophage markers such as MARCO and CD163, along with T cell markers including CD96, SKAP1, ITK and FYN. The authors interpret this conservation as reflecting shared evolutionary pressures on immune surveillance and stromal maintenance, functions that are critical across mammalian species.

Cell-to-cell communication analysis added another layer of insight. Using CellPhoneDB to infer ligand-receptor interactions, the team identified 222 significant pairs in the dog tumors and 227 in the human samples, spanning dozens of signaling pathways. In both species, fibroblasts, endothelial cells and malignant cells emerged as the strongest communicators. Crucially, several key interactions were conserved across species, including IGF1-IGF1R, ANGPT1-TEK, JAG1-NOTCH2 and THBS1-CD36 between malignant cells and fibroblasts, all linked to cancer stemness, angiogenesis and immune evasion, as well as PTPRC-MRC1 and APP-CD74 between macrophages and malignant cells. But the analysis also exposed species-specific crosstalk: dog tumors displayed unique interactions such as IL34-CSF1R and LGALS9-P4HB, both associated with macrophage regulation and tumor-driven immunosuppression, while human tumors showed Chemerin-CMKLR1 and GAS6 interactions with the TAM family receptors Tyro3, Axl and MerTK, known promoters of tumor progression.

The study is not without limitations, and the authors are candid about them. The canine cohort is small, and the single nucleus approach carries technical constraints: copy number variation inference, a common tool for distinguishing malignant from non-malignant cells in human single-cell studies, was limited in the dog data by lower resolution, high dropout rates and the absence of a high-quality normal liver reference genome. These constraints likely explain the clustering proximity between malignant and normal hepatocyte populations in the canine data and caution against over-interpreting some aspects of malignant cell biology. Nevertheless, the work represents the first integrated analysis of potential genetic drivers in canine hepatocellular carcinoma, and the raw sequencing data have been deposited in public repositories under accession numbers PRJNA1237364 and GSE292303, making the resource freely available to the research community.

The broader significance of the study lies in what it sets up. If dogs and humans share the same driver mutations, comparable tumor mutation burdens and conserved immune and stromal communication networks in liver cancer, then canine patients with hepatocellular carcinoma could serve as a genuine translational bridge, testing immunotherapies and targeted agents in a spontaneous disease model that laboratory mice cannot replicate. Conversely, the species-specific differences in malignant cell transcription and signaling interactions serve as a reminder that extrapolation between species demands care. As larger canine cohorts are sequenced and functional validation studies follow, the humble pet dog, sharing our homes and increasingly our cancers, may prove to be one of the most valuable research partners oncology has ever had, accelerating the discovery of therapeutic targets for both veterinary and human liver cancer patients.

Subject of Research: Comparative multi-omics analysis of the tumor microenvironment in canine and human hepatocellular carcinoma

Article Title: Comparative multi-omics evaluation of the tumor microenvironment in dog and human hepatocellular carcinoma

Article References: Nussbaum, Y. I., Bryan, J. N., Li, G., Rodney, A., Culp, W. T. N., Dickinson, P. J., Koutoulas, A., Megquier, K., An, H., Kramer, S. T., Ricemeyer, E. S., Mitchem, J. B., & Warren, W. C. (2025). Comparative multi-omics evaluation of the tumor microenvironment in dog and human hepatocellular carcinoma. Veterinary Oncology, 2(1), Article 25. https://doi.org/10.1186/s44356-025-00037-0

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00037-0

Keywords: hepatocellular carcinoma, comparative oncology, canine cancer, tumor microenvironment, CTNNB1, single nucleus RNA sequencing, whole exome sequencing, tumor mutation burden, cell-cell communication, macrophages, immunotherapy, liver cancer

The first of the highlighted studies tackles a long-standing obstacle in cancer biology: the difficulty of studying tumors in their native spatial context. Goldschmidt and colleagues applied state-of-the-art spatial transcriptomics to formalin-fixed paraffin-embedded biopsy samples from nine dogs diagnosed with COSCC. Spatial transcriptomics is a technique that measures gene expression while preserving the physical location of each cell within the tissue, allowing researchers to see not just which genes are active but where that activity occurs. This matters because tumors are not uniform masses; they contain distinct compartments, including normal epithelium, pre-malignant dysplastic epithelium, the tumor microenvironment, and regions of surface and deep tumor, each of which may behave differently and respond differently to therapy.

The spatial analysis revealed striking molecular patterns. Gene signatures associated with EGFR, the epidermal growth factor receptor, a well-known driver of cell proliferation in many human cancers, and with epithelial-to-mesenchymal transition, a process by which cancer cells acquire invasive, migratory properties, were increased and shared between dysplastic epithelium and tumor samples. This suggests that the molecular changes driving malignancy begin early, before full transformation. Within the cancer samples themselves, the researchers found enrichment of KRAS gene sets, along with the transcripts KRT17 and SPP1, markers often associated with aggressive tumor behavior. Perhaps most intriguing were the findings in the tumor microenvironment, defined in the study as peritumoral mesenchymal stromal tissue infiltrated with leukocytes. There, the team detected signals associated with an increased presence of tumor-associated macrophages, immune cells that tumors frequently co-opt to suppress anti-cancer immunity, and induction of IL10, an immunosuppressive signaling molecule. Enrichment of ICOS and CTLA4 was also noted, and these signals correlated with CD4-positive T cell transcripts in the microenvironment, hinting at a complex immune landscape that could be exploited therapeutically.

The second and third studies demonstrate how such molecular insights can be translated directly into new treatments. Katt and co-workers established several new COSCC cell lines using a combination of patient-derived xenografts and tissue-cultured cell lines created directly from COSCC biopsies. Patient-derived models are prized in oncology research because they preserve more of the biology of the original tumor than long-established laboratory lines. When the team investigated these new models, they uncovered evidence of RAS activation in every one of them. RAS is a signaling protein that, when switched on, drives cells to proliferate uncontrollably, and it sits upstream of MEK1/2, a pair of serine/threonine kinases that relay the growth signal deeper into the cell.

That molecular logic pointed straight at a drug. Trametinib is a small-molecule inhibitor of MEK1/2, already used in human medicine for cancers with activated RAS pathway signaling. The researchers screened trametinib against other cytotoxic and targeted agents using their novel cell lines and found, as predicted, that it exerted anti-tumor activity both in vitro and in a canine xenograft model. These preclinical results were compelling enough to justify testing the drug in pet dogs with COSCC. In a preliminary study of four dogs, objective responses were documented in two of the treated patients. A subsequent dose-escalating study of twenty dogs with COSCC confirmed that trametinib was well tolerated and showed a thirty percent objective response rate, with a further twenty-five percent of dogs exhibiting stable disease, meaning their tumors neither grew nor shrank significantly.

Crucially, the correlative analyses in that trial revealed a biomarker that could transform how dogs are selected for treatment. Dogs whose tumors carried BRAF p.V595E mutations responded significantly better to trametinib than dogs with BRAF wild-type tumors. This alignment with trametinib’s known mechanism of action in activated RAS signaling illustrates the essence of precision oncology: rather than treating every patient identically, clinicians can profile a tumor’s mutations and predict which patients are most likely to benefit. Together, the cell line work and the clinical trials illustrate a rational pathway for introducing new targeted therapies in COSCC, moving systematically from bench to patient-side, a journey that in human oncology often takes many years but here was compressed into a remarkably short arc.

The fourth study extends the comparative approach to feline patients and to immunotherapy-adjacent territory. Signal transducer and activator of transcription 3, or STAT3, is a transcription factor that is hyperactivated in many cancers, including FOSCC, where it drives tumor cell proliferation, immunosuppression, and drug resistance. A prior phase 0 trial in humans with head and neck squamous cell carcinoma had shown that a STAT3 decoy, an engineered molecule that binds and sequesters the transcription factor’s activation machinery, could downregulate STAT3 target genes. Building on that human data, Grandis and colleagues evaluated a systemically administered linear STAT3 decoy in multiple preclinical murine models of head and neck squamous cell carcinoma and then undertook a phase 1 clinical trial of twenty cats diagnosed with FOSCC.

The feline trial produced results that are encouraging for one of the most aggressive malignancies in companion animal medicine. A thirty-five percent disease control rate was documented, comprising two partial responses and five cases of stable disease. Importantly, the favorable responses were accompanied by measurable biological changes: shifts in peripheral blood immune parameters and increased intratumoral expression of PDCD1, the gene encoding the PD-1 immune checkpoint protein. That last finding is particularly significant because PD-1 expression within tumors is a marker of T cell exhaustion, the state in which anti-tumor immune cells become dysfunctional. Its increase in responding patients suggests the STAT3 decoy may be relieving one layer of immunosuppression and potentially sensitizing tumors to checkpoint blockade, a class of drugs that releases the brakes on anti-tumor immunity. Beyond its clinical promise for cats, the study exemplifies the power of a comparative approach in cancer research, in which findings in one species inform and accelerate progress in others, with the aim of benefiting multiple species simultaneously.

Weaving these threads together, Atherton’s editorial argues that veterinary medicine is now entering the age of immune oncology, heralded in part by the roll-out of checkpoint blockade. The identification of CTLA4 upregulation within COSCC by the spatial transcriptomics study, alongside the prognostic implications of increased PDCD1 expression in FOSCC patients treated with the STAT3 decoy, provides impetus to investigate these molecules as mediators of T cell exhaustion. That, in turn, may set the scene for future trials of anti-CTLA-4 or anti-PD-1 therapy in oral squamous cell carcinoma patients of both species. Meanwhile, the early but encouraging clinical data from dogs treated with trametinib offers cautious optimism that this targeted therapy could be added to the currently limited arsenal of useful anti-cancer drugs for COSCC.

Ongoing studies are expected to determine the optimal way to incorporate trametinib into treatment regimens. The editorial notes several plausible roles: the drug could be applied in a neo-adjuvant setting, given before definitive surgery or radiation with the aim of improving the tolerability and outcomes of conventional treatment, or it could serve as an additional palliative option for more advanced COSCC cases where curative intent is no longer feasible. What makes this moment notable is not any single result but the demonstration that the full precision-oncology workflow, from spatially resolved molecular profiling through patient-derived models to biomarker-guided clinical trials, now functions in veterinary patients. For the dogs and cats facing these cancers, and for the humans whose head and neck cancers share many of the same molecular vulnerabilities, the comparative road ahead looks considerably brighter than it did only a few years ago.

Subject of Research: Recent advances in the diagnosis and targeted therapy of canine and feline oral squamous cell carcinoma

Article Title: An editorial spotlight on recent progress in veterinary oncology

Article References: Atherton, M. J. (2025). An editorial spotlight on recent progress in veterinary oncology. Veterinary Oncology, 2(1), Article 30. https://doi.org/10.1186/s44356-025-00047-y

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00047-y

Keywords: veterinary oncology, oral squamous cell carcinoma, spatial transcriptomics, trametinib, MEK inhibitor, STAT3 decoy, tumor microenvironment, checkpoint blockade, BRAF mutation, comparative oncology, precision medicine, immunotherapy

The study emerged from a confluence of two research currents. Cancer remains the leading cause of death in dogs, yet veterinary oncology has lagged behind its human counterpart, particularly in immunotherapy. Immune checkpoint inhibitors, which revolutionized human cancer care, have only just begun entering canine clinical trials. In parallel, however, other immunotherapeutic approaches have advanced in dogs over the past decade. Among them is a vaccine designed to stimulate immunity against EGFR and HER2, two cell-surface proteins frequently exploited by tumors. Earlier work showed that this vaccine could provoke anti-tumor activity in both T cells and B cells and reduce metastatic burden in canine patients. Meanwhile, in human oncology, a growing body of evidence has tied gut microbiome composition to immunotherapy outcomes, spurring clinical trials of fecal microbiota transplants as adjuncts to checkpoint blockade. What remained entirely unexplored was whether the same microbiome-survival relationship exists in dogs receiving anti-cancer immunization.

To find out, a collaboration spanning the National Cancer Institute, Oregon State University, Yale University, and the Bridge Animal Referral Center in Edmonds, Washington, enrolled 51 canine cancer patients in a clinical trial of the EGFR/HER2 peptide vaccine. The cohort included 28 females and 34 purebred dogs, with a mean age at diagnosis of 9.5 years. The most common diagnosis was osteosarcoma, a malignant bone tumor affecting 22 dogs, followed by hemangiosarcoma, an aggressive cancer of blood-forming vessels, in 8 dogs; the remaining 21 animals carried a mix of other tumor types. On the day each dog received its first vaccine dose, the team collected rectal swabs to capture the baseline gut microbiome—the microbial community as it existed before any vaccine-driven immune changes could take hold. All animals also continued to receive standard veterinary care, and the patients were scheduled to receive two vaccine injections three weeks apart.

The laboratory work relied on 16S rRNA gene sequencing, a standard technique that reads a conserved bacterial gene to identify which microbes are present and in what proportions. DNA extracted from the swabs was amplified and barcoded at the National Cancer Institute’s Microbiome and Genetics Core Facility and sequenced on an Illumina MiSeq platform, yielding an average of roughly 32,000 reads per sample and a total of 899 amplicon sequence variants—the fine-grained units used to distinguish bacterial populations. Bioinformatic pipelines built on DADA2 and QIIME2 processed the reads, assigned taxonomy against the SILVA reference database, and predicted functional potential using PICRUSt2. The team then applied survival analysis, including Kaplan-Meier curves and Cox proportional hazards regression, to ask a single pointed question: did the abundance of any bacterial taxon at baseline correlate with how long each dog lived?

The first results were, in a sense, reassuringly unremarkable. Neither alpha diversity—the richness and evenness of microbes within a sample—nor beta diversity, which measures compositional differences between samples, varied significantly by cancer type, sex, or breed. No specific taxa or predicted functional pathways distinguished one diagnostic group from another. Within the limits of the sample size, the canine gut microbiome did not appear to encode a signature of tumor type. But that null result carried an important implication: it suggested the study population carried no inherent microbiome bias that could confound the search for survival-related microbes. The microbiome differences the team eventually found would therefore be less likely to reflect pre-existing differences between cancer groups.

Clinical covariates told a familiar story. Dogs with hemangiosarcoma and dogs older than 11.9 years at diagnosis had significantly poorer survival, consistent with prior veterinary reports, while breed made no difference. Neither recent antibiotic use, chemotherapy, nor surgery predicted outcome. Armed with these confounders, the researchers ran Cox regression separately within the osteosarcoma and other-cancer subgroups, deliberately excluding hemangiosarcoma because its grim prognosis and small sample size could distort the analysis. Under a relaxed false discovery rate threshold, they identified 57 survival-associated taxa among osteosarcoma patients and 46 among the other-cancer group. Crucially, 15 taxa appeared in both groups, and 11 of those—73 percent—pointed in the same direction in each, with matching hazard ratios. That convergence across biologically distinct tumors hinted at shared microbial signatures tied to survival rather than to any single malignancy.

When the team pooled all 51 dogs, including the hemangiosarcoma cases, the signal strengthened rather than dissolving. All 11 taxa remained significantly associated with survival under the stricter criterion of a false discovery rate below 0.1. Seven taxa, when abundant, were linked to an elevated risk of death, while four were associated with prolonged survival. Kaplan-Meier curves built on uniform low-versus-high abundance thresholds made the separation visible across the entire cohort. The associations also withstood the study’s most stringent test: after statistically adjusting for hemangiosarcoma diagnosis and advanced age—the two strongest predictors of poor outcome—every one of the 11 microbes retained its significant relationship with survival, confirming the robustness of the microbial signal in the face of powerful clinical confounders.

Which microbes made the list? While the full taxonomic details reside in the study’s supplementary tables, the published discussion highlights several recognizable genera. Some, such as Enterococcus and Ruminiclostridium, showed associations that diverge from certain human studies, a discrepancy the authors attribute partly to differences in treatment modality and partly to the resolution limits of 16S sequencing, which often cannot identify bacteria at the species or strain level—precisely the classifications most likely to share functional traits. Others, including Flavonifractor, members of the Lachnospiraceae family, and Shigella, have been reported in human cancer immunotherapy research with directions of effect that echo the canine findings. The partial overlap, and partial divergence, underscores both the promise and the early stage of cross-species microbiome oncology: canine and human gut communities are related but not identical, and therapies differ fundamentally between the species.

The authors are careful about what the study can and cannot claim. A baseline sample taken before vaccination cannot, by itself, prove whether the 11 taxa are general prognostic markers of cancer survival or biomarkers specific to the anti-EGFR/HER2 immunotherapy response—though their independence from cancer type, age, and poor-prognosis factors tips the balance toward the latter interpretation. The cohort of 51 dogs was too small to resolve cancer-type-specific biomarkers, and diet data, a major microbiome determinant, were unavailable. Generalizing the results will require larger cohorts and independent validation, which the collaborative group says it is actively pursuing. Still, the mechanistic stakes are high. Gut microbes have been shown in mouse models and human patients to either enhance or blunt immunotherapy effects, and the systemic immune influence of the gut may reach even anatomically distant tumors like osteosarcomas of the bone.

The road ahead, the researchers argue, runs from correlation toward causation. Computational approaches designed to infer causal relationships from observational data—Mendelian randomization, transkingdom network analysis, and mediation analysis—could help identify microbes that do not merely flag better survival but actively drive it. The ultimate test would be interventional: randomized clinical trials of fecal microbiota transplants or anti-cancer probiotics in dogs undergoing immunotherapy. If those experiments succeed, the implications could flow in both directions across the species line. Dogs develop spontaneously occurring cancers in a way few laboratory models replicate, making veterinary oncology a genuine translational bridge. Eleven bacterial taxa, measured from a simple rectal swab on the day of a first vaccine dose, may prove to be the first microbiome-based prognostic tools in canine cancer—and a comparative oncology resource that could ultimately sharpen immunotherapy for human patients as well.

Subject of Research: Gut microbiome biomarkers of survival in dogs with cancer receiving anti-EGFR/HER2 immunotherapy

Article Title: Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival

Article References: Rodrigues, R. R., Karumuru, V., Nuss, S., Elliott, M., Shriver, I., Chao, C.-M., Berriatua, R. C., Doyle, H. A., Tripp, C., Mamula, M. J., Dzutsev, A., Morgun, A., & Shulzhenko, N. (2025). Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival. Veterinary Oncology, 2(1), Article 36. https://doi.org/10.1186/s44356-025-00048-x

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00048-x

Keywords: canine cancer, gut microbiome, immunotherapy, EGFR/HER2 vaccine, 16S rRNA sequencing, survival biomarkers, veterinary oncology, Cox regression, osteosarcoma, hemangiosarcoma, comparative oncology, fecal microbiota transplant

Canine diffuse large B-cell lymphoma is far more than a veterinary curiosity. It accounts for roughly 50 to 60 percent of all lymphoid cancers in dogs and mirrors human diffuse large B-cell lymphoma, which makes up about 40 percent of non-Hodgkin lymphomas in people. Because pet dogs develop the disease spontaneously, live in the same environments as their owners, and possess intact immune systems, they offer a clinically relevant model that laboratory mice cannot match. Drugs such as venetoclax, ibrutinib, and acalabrutinib have already been explored through this comparative framework, and the typical time to relapse in dogs, six to eight months, is far shorter than in humans, allowing therapeutic studies to move at an accelerated pace.

In the new study, the researchers analyzed lymph node samples from 15 dogs with multicentric DLBCL and two healthy control animals treated at the Governador Laudo Natel Veterinary Hospital at UNESP in Jaboticabal. Tissue fragments were snap-frozen and stored at minus 80 degrees Celsius before total RNA was extracted and converted into sequencing libraries using the Illumina TruSeq protocol. The team sequenced the libraries on an Illumina HiScanSQ platform with paired-end chemistry, then aligned the resulting reads against the canine reference genome CanFam6 using the STAR aligner. Differential expression analysis was carried out with the EdgeR package, modeling read counts with a negative binomial distribution and controlling the false discovery rate through the Benjamini-Hochberg procedure.

The results were striking in their asymmetry. The analysis identified 410 differentially expressed genes, of which 408 were downregulated in the tumor samples compared with normal lymph nodes, while only two genes, CPLX3 and a poorly characterized locus, were upregulated. Most of the silenced genes are involved in immune function, the very machinery that healthy tissue uses to police malignant cells. The researchers then cross-referenced their findings with four previous RNA-sequencing studies of canine B-cell lymphoma, comparing the overlapping gene lists to identify candidates of highest biological relevance. From this convergence, five genes emerged as the focus of the study: STOM, TBC1D8, HMOX1, ABCB1, and CTLA4.

Each of these five genes tells a distinct mechanistic story. STOM encodes stomatin, an integral membrane protein that organizes ion channels and helps structure the immunological synapses through which immune cells communicate; its disruption has been linked to gastric MALT lymphoma, colorectal cancer, and altered glucose transport via GLUT1. TBC1D8 encodes a GTPase-activating protein that regulates Rab-mediated vesicular trafficking, a process essential for antigen presentation, and in human colorectal and ovarian cancers it has been tied to invasiveness and poor prognosis through metabolic reprogramming toward aerobic glycolysis, the Warburg effect. HMOX1, or heme oxygenase 1, plays a paradoxical role in tumor biology, acting cytoprotectively at moderate levels while promoting lipid peroxidation and ferroptotic cell death when overexpressed.

The remaining two genes connect the signature directly to therapy. ABCB1 encodes P-glycoprotein, the ATP-dependent efflux pump responsible for multidrug resistance, which exports chemotherapeutic agents from tumor cells and undermines treatment efficacy; inhibiting it has been shown to restore drug sensitivity in lymphoma models. CTLA4 is one of the most famous targets in modern immunology, an immune checkpoint receptor that dampens T-cell activation by competing with CD28 for binding to CD80 and CD86. Its dysregulation enables tumors to escape immune surveillance, and checkpoint inhibitors targeting CTLA4, often combined with anti-PD-1 therapy, have transformed human cancer treatment. The enrichment analysis and protein-protein interaction networks built with the STRING database and Enrichr tool further connected these genes to the nuclear receptor meta-pathway, anion transport regulation, and xenobiotic transport across the blood-brain barrier, hinting at how drug metabolism and tumor stress responses converge.

When the team correlated expression of the five-gene signature with clinical outcomes, the picture was suggestive but not definitive. All 15 lymphoma patients had been treated with the CHOP chemotherapy protocol, and their survival times ranged from just 2 weeks to 160 weeks, with a mean of 48.8 weeks. Kaplan-Meier analysis showed a trend toward reduced overall survival in dogs with lower expression of the individual genes and of the composite signature, but none of these associations reached statistical significance, likely reflecting the small cohort size. The authors note that a priori power analysis was performed, and that the study was designed as a vanguard investigation capable of detecting very large effect sizes, but they acknowledge that larger, multicentric validation studies will be needed before these markers enter clinical practice.

Beyond the gene expression data, the study offers one of its most vivid insights into the tumor microenvironment itself. Using three independent computational deconvolution methods, CIBERSORT, quanTIseq, and EPIC, the researchers estimated the proportions of immune cell types within each sample. Tumor tissue was overwhelmingly dominated by neoplastic B cells, as expected in a B-cell malignancy, but the effector arm of the immune system was conspicuously depleted: cytotoxic CD8-positive T cells and macrophages were markedly reduced compared with healthy lymph nodes, which displayed a diverse repertoire of macrophages, monocytes, CD4-positive T cells, and neutrophils. This immunosuppressive architecture suggests active evasion of immune detection by the tumor. Intriguingly, an age-stratified analysis found that younger dogs tended to show higher CD4-positive T-cell infiltration, a trend the authors interpret as possibly reflecting preserved antitumor immunity before age-related immunosenescence takes hold.

The Brazilian setting of the study adds an important dimension to the global picture. Transcriptomic data from South American canine populations had been largely absent from the literature, despite evidence that environmental factors shape cancer biology. Previous case-control studies in São Paulo and the metropolitan region of Recife found that dogs living near high-traffic roads or exposed to solvents and paints faced substantially elevated risks of developing lymphoma, with one study reporting a nearly fivefold increase in risk, positioning pet dogs as sentinels for environmental carcinogens that may also affect humans. The authors also highlight a methodological point with consequences for the entire field: because earlier studies aligned their reads against the older CanFam3.1 or even CanFam2 reference genomes, while this study used the more complete and accurately annotated CanFam6, discrepancies in the numbers and identities of differentially expressed genes across studies are partly artifacts of genome assembly rather than true biological differences.

The study is not without limitations, and the authors are candid about them. The cohort comprised only 15 tumor samples and two controls, drawn from multiple breeds, and breed predispositions were not incorporated into the power analysis. Yet the consistency of the normal-sample expression profiles across all comparable published studies supports the validity of the small control group, and the magnitude of the transcriptional differences observed lent the analysis statistical footing despite its scale. What the work ultimately delivers is a framework: five interconnected genes governing membrane dynamics, vesicular transport, oxidative stress, immune regulation, and drug resistance, set against a tumor microenvironment that has lost its cytotoxic defenders. If validated in larger cohorts, these signatures could guide individualized treatment decisions in veterinary oncology, from checkpoint inhibitor combinations to strategies for reversing multidrug resistance, while reinforcing the dog’s standing as one of the most valuable spontaneous models for advancing human lymphoma research.

Subject of Research: Transcriptomic profiling and immune microenvironment analysis of canine diffuse large B-cell lymphoma

Article Title: Transcriptomic profiling reveals prognostic gene signatures and immune landscape alterations in canine diffuse large B-cell lymphoma

Article References: Anai, L. A., Cavalca, A. M. B., Xavier, P. L. P., Semolin, L. M. S., Jark, P. C., Senhorello, I. L. S., Fukumasu, H., Demarchi Munhoz, T., Figueiredo, M. L., Fernandes, C. C., Costa, M. T., Kobayashi, P. E., Santana, Á. E., & Fonseca-Alves, C. E. (2026). Transcriptomic profiling reveals prognostic gene signatures and immune landscape alterations in canine diffuse large B-cell lymphoma. Veterinary Oncology, 3(1), Article 1. https://doi.org/10.1186/s44356-025-00051-2

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00051-2

Keywords: canine lymphoma, diffuse large B-cell lymphoma, RNA sequencing, transcriptomics, tumor microenvironment, CTLA4, ABCB1, HMOX1, STOM, TBC1D8, immune evasion, comparative oncology

The study, published in the journal Veterinary Oncology, was led by Ingrid Marie Moberg and colleagues at the Norwegian University of Life Sciences, Oslo University Hospital, and the University of Oslo. The team analyzed RNA sequencing data from 179 canine mammary tumors, drawn from a cohort of naturally occurring tumors removed from companion dogs during routine surgery. Their goal was twofold: to define molecular subgroups of canine mammary tumors without reference to histology, and then to ask whether tumors from the same dog share those molecular identities. The answer to the second question, in most cases, was no.

To classify the tumors, the researchers first reduced their high-dimensional gene expression data using principal component analysis, then applied unsupervised consensus clustering, an algorithm that groups samples based purely on expression similarity and stability across repeated resampling. This revealed five robust transcriptomic clusters. Crucially, the clusters did not map cleanly onto histological diagnosis. Each cluster contained a mixture of benign and malignant tumors, confirming that the microscopic appearance of these tumors tells only part of the story of their underlying biology.

Gene set enrichment analysis then revealed that the five clusters bear a remarkable resemblance to molecular subtypes long recognized in human breast cancer. Three of the clusters showed enrichment of hormone-related gene programs, including estrogen response, and were found to harbor the transcription factor GATA3, a classic marker of luminal breast cancer in humans. One cluster combined cell-cycle proliferation with immune and interferon signaling, evoking the aggressive basal-like or triple-negative phenotype, while a fifth cluster was defined by epithelial-mesenchymal transition, angiogenesis, and inflammatory modules, reminiscent of the claudin-low subtype. The parallel with human breast cancer taxonomy is not merely cosmetic; it strengthens the case for dogs as a comparative model in which the biology of mammary cancer can be studied in a spontaneously arising disease.

Beyond clustering, the team constructed a gene co-expression network using the hCoCena framework, identifying ten modules of genes that are expressed together and that encode distinct biological processes. One module captured hormone signaling, two captured immune and interferon responses, three reflected metabolism and proliferation through glycolysis, PI3K-AKT-mTOR signaling, oxidative phosphorylation, and MYC targets, and others traced epithelial differentiation through WNT signaling, epithelial-mesenchymal transition, cell-cycle regulation through E2F targets and the G2M checkpoint, and tumor microenvironment features such as angiogenesis. Transcription factor enrichment within the modules pointed to GATA3 as a regulator of the hormonal program and E2F1 as a driver of the proliferative module, providing candidate master switches behind the observed phenotypes.

The heart of the study, however, lies in its analysis of synchronous tumors. The researchers focused on 45 dogs that each carried exactly two tumors, yielding 90 paired samples classified as benign-benign, malignant-benign, or malignant-malignant. When they compared cluster assignments within each pair, concordance was low. Only about 45 percent of tumor pairs landed in the same transcriptomic cluster overall, with malignant-malignant pairs showing the highest agreement at 56 percent, benign-benign pairs at 44 percent, and mixed malignant-benign pairs at just 36 percent. In other words, the majority of dogs carried two tumors with fundamentally different molecular identities.

To quantify this divergence at the level of individual genes, the team calculated intraclass correlation coefficients for every gene across the paired samples. Genes were scored as low, moderate, or high in correlation between a dog’s two tumors. The results were unambiguous: between roughly 76 and 90 percent of genes showed low correlation across all diagnostic categories, and fewer than one percent of genes were highly correlated within any category. This pattern of widespread discordance held regardless of whether both tumors were benign, both malignant, or one of each, indicating that molecular independence between synchronous tumors is the norm rather than the exception.

A small set of exceptions proved informative. The analysis identified a handful of genes, including OMD and EN1, whose expression is strongly correlated within certain categories of synchronous tumor pairs and which have been reported as prognostic markers in human cancers. The authors suggest that these genes may point to shared disease processes or protective mechanisms against malignant transformation, and that they deserve further investigation, potentially at the DNA level, to uncover any genetic factors underlying their coordinated behavior. Meanwhile, examination of module-level variation showed that programs linked to cell-cycle activity, hormone signaling, and immune responses fluctuated most between paired tumors, while modules tied to epithelial differentiation and metabolism remained comparatively stable within individuals.

The clinical implications of this work reach in two directions at once. For veterinary medicine, the findings suggest that each tumor in a multi-tumor patient should be evaluated as a biologically independent lesion rather than assumed to be representative of its neighbors. Because molecular subtypes in human breast cancer drive dramatically different treatment decisions, from endocrine therapy for hormone receptor-positive disease to chemotherapy and PARP inhibitors for triple-negative tumors, a biology-driven approach could eventually refine the limited therapeutic options currently available for canine patients. The authors note, for instance, that identifying hormone-positive subtypes could inform whether ovariohysterectomy offers real benefit at the time of tumor removal, a decision veterinarians currently make without molecular guidance.

For comparative oncology, the study reinforces the value of the canine model in a way that human cohorts cannot easily replicate. Synchronous bilateral breast cancer occurs in only around one percent of human patients, whereas synchronous mammary tumors affect the majority of affected dogs. Studying these paired tumors within the same genetic background eliminates many host-specific confounders, offering a natural experiment in tumor evolution and inter-individual heterogeneity. The authors acknowledge limitations inherent to bulk RNA sequencing, which averages expression across all cells in a tissue and may reflect tissue composition as much as tumor biology, and they caution that a single RNA sample may not represent an entire tumor. Even so, their conclusion stands firm: histopathology alone does not capture the molecular reality of canine mammary tumors, and expression-based profiling offers a more faithful map of the biological terrain that clinicians and researchers alike will need to navigate.

Subject of Research: Transcriptomic heterogeneity of synchronous canine mammary tumors and their molecular resemblance to human breast cancer subtypes

Article Title: High molecular heterogeneity in synchronous canine mammary tumors detected by transcriptomic analysis

Article References: Moberg, I. M., Murphy, S. L., Hansen, N., Borge, K. S., Gunnes, G., Sørlie, T., Lingaas, F., Bergholtz, H., & Solbakken, M. H. (2026). High molecular heterogeneity in synchronous canine mammary tumors detected by transcriptomic analysis. Veterinary Oncology, 3(1), Article 11. https://doi.org/10.1186/s44356-026-00066-3

Image Credits: AI Generated

DOI: 10.1186/s44356-026-00066-3

Keywords: canine mammary tumors, transcriptomics, RNA sequencing, tumor heterogeneity, synchronous tumors, breast cancer subtypes, gene co-expression network, consensus clustering, comparative oncology, veterinary pathology, High, molecular

Hemangiosarcoma is the most common splenic malignancy in dogs, arising from endothelial cells that line blood vessels. It is notorious for its genetic heterogeneity, high metastatic rate and grim prognosis. The standard of care—surgical removal of the spleen followed by chemotherapy with agents such as doxorubicin or carboplatin—produces only modest improvements in survival, because many tumors resist conventional protocols. Earlier genomic studies, including work by Gorden and colleagues in 2014 and by Wang and colleagues in 2020, had already hinted that canine hemangiosarcoma comprises multiple molecular subtypes with striking similarities to human angiosarcoma. What remained missing was a practical, cost-effective way to detect those subtypes in the formalin-fixed, paraffin-embedded tissue samples that veterinary pathologists routinely produce.

The São Paulo team, led by Heidge Fukumasu and first author Sonia Prince Fiebig, addressed that gap using the Oncomapa qPCR panel, a targeted reverse-transcription quantitative PCR assay designed for clinical molecular profiling. The retrospective study drew on diagnostic tests performed between January 2021 and July 2023 on samples fixed in buffered formalin for at least 48 hours. For each spleen, pathologists sampled at least seven distinct regions histologically—a deliberate strategy to reduce the risk of misclassification in a tumor notorious for focal variation. Two senior pathologists confirmed every diagnosis, evaluating cellular pleomorphism, necrosis, capsular invasion and mitotic counts.

Technically, the workflow was built for robustness on degraded material. RNA extracted from the FFPE specimens was reverse-transcribed and quantified in technical duplicates for 45 cancer genes plus three housekeeping controls: GAPDH, RPL13A and RPS5. RefFinder analysis, which integrates GeNorm, NormFinder, BestKeeper and the ΔΔCT method, identified GAPDH as the most stable reference gene, and all targets were normalized to the geometric mean of the three controls using the 2^-ΔΔCT method. Genes were flagged as highly or lowly expressed when their log2 fold-change exceeded ±2 relative to the cohort average—equivalent to at least fourfold deviation. Missing cycle-threshold values, a common nuisance with fragmented FFPE RNA, were imputed with Multiple Imputation by Chained Equations, and the data were Z-score normalized before clustering. Notably, DHFR was undetectable in 23 of 27 samples, a finding with direct clinical relevance because DHFR expression is associated with methotrexate resistance.

With the expression matrix in hand, the team deployed an unsupervised machine-learning pipeline. Principal component analysis showed that the first two components explained 21.5 and 15.7 percent of the variance respectively, with the first three components accounting for roughly half of the total variation. K-means clustering, validated by the elbow method on within-cluster sum of squares and by an average silhouette score of 0.17, pointed to three clusters. Ward’s hierarchical clustering with Ward.D2 linkage independently reproduced the same three-group structure, with a cophenetic correlation coefficient of 0.68 confirming a good fit between the dendrogram and the underlying distance matrix. Kruskal-Wallis tests with false discovery rate correction then isolated the genes driving the separation.

The three signatures that emerged are strikingly coherent. Cluster 1, comprising eight tumors, was marked by elevated expression of MET, MAP2K1/2, FLT3, SRC, AKT2, TP53, PDGFRA, SETD2 and NR3C1—a profile indicating activation of the MAPK and PI3K/AKT cascades, engagement of specific tyrosine kinase receptors including MET, FLT3 and PDGFRA, and stress and immune regulation mediated by NR3C1 and TP53. This aligns with prior evidence that PIK3CA pathway alterations occur in roughly 46 percent of canine hemangiosarcomas, and with in vitro work showing that the PI3K inhibitor alpelisib is active against PIK3CA-mutated canine HSA cell lines. Significantly, this group also showed low expression of the targets of conventional chemotherapy, suggesting a built-in resistance to standard protocols.

Cluster 2, the largest group with ten tumors, overexpressed BCL2, NOTCH1, RET and KIT. This signature points to evasion of apoptosis and enhanced cell survival, and it highlights some of the most druggable targets in the entire study. The authors propose that RET and KIT signaling could be attacked with veterinary-approved tyrosine kinase inhibitors such as toceranib and masitinib, while elevated BCL2—an anti-death protein exploited by many cancers—makes the tumors rational candidates for venetoclax, a BH3 mimetic that has already shown activity against canine hematological cancers in recent studies. Cluster 3, with nine tumors, displayed a hyperproliferative phenotype: overexpression of MYC, TOP2A, RRM2, TYMS and BRCA1 signaled runaway cell-cycle activity, DNA synthesis and DNA repair. Consistent with the molecular data, these tumors carried significantly more mitotic figures than the other groups—24.43 versus roughly 11 per high-power field, a statistically significant difference. Their gene signature also suggests vulnerabilities to existing cytotoxics: doxorubicin targets TOP2A, hydroxyurea and gemcitabine hit RRM2, and 5-fluorouracil or capecitabine exploit TYMS dependence.

The authors are careful to frame these therapeutic links as hypotheses rather than prescriptions. Because the study was retrospective, the dogs were not treated according to cluster assignment, and standardized survival data were not available, the work cannot yet demonstrate that cluster-guided therapy improves outcomes. Bulk RNA from FFPE blocks also averages the signal across heterogeneous cell populations, meaning intratumoral diversity—well documented in solid tumors—could blur the boundaries between subtypes. The cohort of 27 dogs was small and not validated against an independent external dataset, and protein-level confirmation through immunohistochemistry was not possible with the available archived material. The team argues that the natural next step is prospective validation in larger, independently annotated cohorts, following the established trajectory of translational biomarker development that transformed human oncology after landmark expression-profiling studies in breast cancer and diffuse large B-cell lymphoma.

Even with those caveats, the study carries real weight for the emerging field of veterinary precision oncology. It demonstrates that clinically actionable molecular stratification is feasible using routine FFPE tissue and a comparatively inexpensive qPCR panel, bypassing the cost and infrastructure demands of next-generation sequencing. Expression profiling captures functional pathway activity that DNA mutation panels often miss, and it can in principle track therapy resistance dynamically. Because off-label use of human drugs is already common in veterinary medicine, and because agents such as venetoclax and sorafenib are increasingly deployed against canine tumors, the distance between a molecular signature and a treatment trial is shorter than ever. Given the documented molecular parallels between canine hemangiosarcoma and human angiosarcoma, a better molecular map of the canine disease may ultimately inform the human one as well—turning one of veterinary medicine’s most feared diagnoses into a test bed for personalized cancer therapy.

Subject of Research: RNA expression profiling reveals three molecular subtypes of canine splenic hemangiosarcoma for precision oncology

Article Title: Molecular profiling by RNA expression reveals distinct subtypes of canine splenic hemangiosarcoma

Article References: Fiebig, S. P., Ferrero, A. T., Nunes, A. T., Xavier, P. L. P., Qazi, T. J., Strefezzi, R. F., Sueiro, F. A., Rodrigues, L., & Fukumasu, H. (2026). Molecular profiling by RNA expression reveals distinct subtypes of canine splenic hemangiosarcoma. Veterinary Oncology, 3(1), Article 15. https://doi.org/10.1186/s44356-026-00064-5

Image Credits: AI Generated

DOI: 10.1186/s44356-026-00064-5

Keywords: canine hemangiosarcoma, gene expression profiling, molecular subtypes, precision veterinary oncology, qPCR, FFPE, PI3K/AKT pathway, tyrosine kinase inhibitors, comparative oncology, targeted therapy, dog cancer, transcriptomics

Meningioma, a tumor arising from the meninges—the protective membranes enveloping the brain and spinal cord—is the most frequently diagnosed intracranial neoplasm in dogs, accounting for roughly half of all canine primary brain tumors. In humans, meningiomas represent approximately 40 percent of primary brain tumors. Despite this shared prominence, diagnostic criteria for canine meningioma have remained poorly codified, and until now no study had subjected a large cohort of canine cases to standardized review by a panel of multiple pathologists working alongside physician neuropathologists.

The CBTC board, which brought together veterinary pathologists and human neuropathologists from institutions across the United States and Europe, undertook a comprehensive evaluation of 190 cases of canine meningioma. Tumors were collected from the diagnostic archives of ten veterinary schools, including Auburn, Colorado State, Cornell, Mississippi State, Ohio State, Texas A&M, UC Davis, Illinois, Penn, and Virginia Tech. Samples came exclusively from treatment-naïve patients, prioritized when collected within the previous five years and accompanied by clinical follow-up data such as tumor location, therapy, progression, and cause of death. Histology slides were cut at four micrometers, stained with hematoxylin and eosin, and digitally scanned at 40X resolution, allowing every reviewer to examine identical digital images.

To enable direct comparison with human medicine, the board applied the 2016 World Health Organization classification and grading guidelines for human meningioma to each canine tumor. Reviewers assessed histologic subtype, mitotic count within a fixed region of 2.37 square millimeters, invasion into surrounding nervous tissue, geographic coagulative necrosis, and the atypical features used in human grading: sheeting architecture, small-cell formation, hypercellularity, and macronucleoli. The process began with a virtual meeting in October 2020 to familiarize the physician neuropathologists with canine histology, followed by additional consensus sessions to resolve difficult diagnostic calls before a final independent assessment of all cases by October 2021.

The results demonstrated that canine meningiomas mirror the morphologic diversity of the human disease. The overwhelming majority of tumors—140 of 190, or roughly 74 percent—were classified as meningothelial, with transitional, microcystic, psammomatous, rhabdoid, fibrous, metaplastic, clear cell, and chordoid subtypes making up the remainder. Most cases, 109 tumors or 57 percent, were assigned grade 2 under the human WHO scheme, while 79 were grade 1 and only two reached grade 3. Roughly 60 percent of tumors with assessable adjacent brain or spinal cord tissue showed invasion into the nervous system, and features such as necrosis, sheeting, and hypercellularity were commonly observed, closely paralleling the histologic picture of atypical human meningioma.

Yet the clinical correlation told a more complicated story. Among the subset of patients with detailed progression and cause-of-death information, and again in a larger cohort restricted to live/dead status, there was no statistically significant difference in outcome between grade 1 and grade 2 canine tumors. Relative risk ratios for death at one, two, and three years after diagnosis—0.75, 0.93, and 1.02 respectively—were all statistically insignificant. This challenges the assumption that the human WHO grading framework, which strongly predicts recurrence and progression in people, can be transplanted wholesale to veterinary patients. Several features that signal higher grade in humans, notably macronucleoli and small-cell formation, appeared frequently even in grade 1 canine tumors, and medical pathologists on the board repeatedly observed that macronucleoli occur more often in canine tumors regardless of grade.

One feature did emerge as potentially meaningful: necrosis. Although many progressing tumors lacked necrosis, every case in the outcome-linked subset that did exhibit necrosis went on to show clinical progression, compared with 57 percent of cases lacking it. The authors suggest that necrosis, when present, may be a significant indicator of more aggressive behavior and a critical feature for future prognostic studies. Brain tumors were also significantly more likely than spinal tumors to show necrosis. Notably, the study echoes a shifting paradigm in human medicine, where tumors showing brain invasion without other atypical features—so-called brain-invasive, otherwise benign tumors—now benefit from additional molecular testing, suggesting that molecular characterization could likewise improve prognostication in dogs.

The review also shed light on the reproducibility of diagnosis across specialists. Interobserver agreement, measured by intraclass correlation coefficients, was generally low for most histologic features, with the highest agreement—0.44 for both—achieved for necrosis and CNS invasion, the same features that command the highest concordance in human meningioma review. Concordance exceeded 70 percent for most histologic subtypes, especially those with distinctive appearances such as clear cell and microcystic tumors, while mitotic counting proved the least reliable. Interestingly, veterinary pathologists agreed with one another significantly more often than physician neuropathologists on histologic subtype, hypercellularity, and macronucleoli, suggesting that species-specific experience is critical for diagnostic consistency.

Clinically, the cohort reflected established patterns of canine disease. The median patient age was ten years, and mixed-breed dogs, Golden Retrievers, Labrador Retrievers, Boxers, and West Highland Terriers were the most commonly represented breeds. Most tumors arose over the brain, particularly the olfactory and frontal lobes, with a smaller proportion in the cervical spinal cord. Surgery remained the mainstay of treatment, with a median survival of 437 days for dogs treated surgically, while dogs receiving surgery combined with radiation or other adjuvant therapies survived longer on average, at 28 to 55 months, consistent with previously reported ranges.

The CBTC board concluded that while canine and human meningioma share important histologic features, a revised grading framework specific to dogs is needed, distinct from the human WHO scheme. The consortium recommends that future studies include review of all slides by at least three pathologists, systematic evaluation of location, subtype, mitotic count, invasion, necrosis, and other atypical features, and enrollment limited to cases with complete clinical follow-up. The authors also call for the kind of large-scale, shared cancer databases in veterinary medicine that exist in human oncology, and note that molecular characterization, transcriptional profiling, and immune landscape studies of related canine tumor collections are already underway. If successful, these efforts could position the canine patient as a genuinely translational model—accelerating biomarker discovery and therapeutic development for both dogs and the humans who love them.

Subject of Research: Comparative pathology of canine and human meningioma and the validation of dogs as a naturally occurring model for human meningioma research

Article Title: A comparative evaluation of canine meningioma supporting the canine patient as a naturally occurring animal model for human meningioma: a report from the NCI Comparative Brain Tumor Consortium (CBTC) meningioma pathology board

Article References: Church, M. E., Rissi, D. R., Koehler, J. W., Miller, A. D., Beck, J. A., Belluco, S., Bitar, M., Chkheidze, R., Corps, K. N., Matiasek, K., Phillips, J. J., Rajan, S., Stemmer-Rachamimov, A., Yip, S., Shih, J. H., Mazcko, C., & LeBlanc, A. (2026). A comparative evaluation of canine meningioma supporting the canine patient as a naturally occurring animal model for human meningioma: a report from the NCI Comparative Brain Tumor Consortium (CBTC) meningioma pathology board. Veterinary Oncology, 3(1), Article 16. https://doi.org/10.1186/s44356-026-00068-1

Image Credits: AI Generated

DOI: 10.1186/s44356-026-00068-1

Keywords: meningioma, canine, comparative oncology, brain tumor, WHO grading, pathology, NCI Comparative Brain Tumor Consortium, veterinary oncology, CNS invasion, tumor necrosis, animal model, One Health

Hemangiosarcoma stands out as one of the most aggressive neoplasms in canine patients, predominantly affecting older dogs, particularly golden retrievers. The insidious nature of the disease allows it to remain undetected until it reaches an advanced stage, at which point the tumors may rupture, resulting in acute medical emergencies. Alarmingly, the prognosis for affected dogs is grim, with only 10% of diagnosed canines expected to survive beyond the first year and none living past two years. This stark reality compels veterinary researchers to explore better diagnostic and therapeutic interventions.

The sheer prevalence of hemangiosarcoma in the canine population offers a unique advantage for oncological research. With more than 50,000 cases diagnosed annually in veterinary settings across the United States, researchers have a substantial pool of data to analyze, which bears significant potential for translating findings to human oncology. This is especially crucial for understanding human angiosarcoma, which, while similarly lethal, affects far fewer individuals—approximately 1,000 Americans each year, making clinical data collection for this rare cancer challenging.

Jon Kim, D.V.M., Ph.D., the lead research scientist and an assistant professor at the University of Florida, articulates the intrinsic value of studying canine cancer biology. By analyzing tumors in dogs, researchers glean invaluable insights that can apply to human cancer research paradigms. Kim’s assertion posits that the canine model acts as a natural laboratory where complex tumor interactions can be examined and understood in a manner that would be difficult to replicate in human research due to the rarity of corresponding cancers.

The research team’s findings, recently published in notable journals, reveal a critical mechanism by which hemangiosarcoma proliferates and propagates. Researchers discovered that this type of cancer does not merely construct its blood supply independently; rather, it co-opts surrounding healthy cells, manipulating them to facilitate its own nutrient and oxygen acquisition. This cancer’s ability to hijack normal cellular processes may provide insights into potential therapeutic targets and diagnostic markers.

Intriguingly, the study has highlighted a pivotal genetic mutation in the PIK3CA gene, known to be prevalent in various human cancers. This mutation has been shown to contribute to the aberrant signaling pathways that impinge upon the immune system, leading to a confused immune response. Such immune evasion is a hallmark of many cancers, allowing tumor cells to grow unchecked by the body’s defenses. Understanding this mutation’s role provides a roadmap for developing targeted treatments that might correct or inhibit these pathological processes in both canine and human subjects.

While there has been substantial knowledge regarding the implications of PIK3CA mutations, the intricate dynamics of how these alterations specifically govern cancer growth and treatment response remain somewhat nebulous. Kim and his colleagues aim to bridge these gaps through their research, shedding light on the molecular underpinnings that dictate the disease’s behavior and therapeutic responses. This research could foster innovative treatment strategies that harness the insights gained from canine pathology to combat similar challenges in human oncology.

The scarcity of angiosarcoma cases in humans has impeded the scientific community’s ability to conduct robust studies that could lead to effective treatments or understand the fundamental biology of the disease, which often inhibits the development of meaningful clinical trials. In stark contrast, the canine counterpart offers a treasure trove of clinical data and biological insights that can inform research efforts targeting both species. Researchers believe that the comparative approach might pave the way for groundbreaking advancements in treating these fatal cancers.

Through laboratory-based investigations, the research team found that hemangiosarcoma cells possess an uncanny ability to stimulate the production of blood cells, potentially fostering a microenvironment that promotes the development of ‘cancer-friendly’ immune cells. These cells, in turn, may facilitate the tumor’s growth while simultaneously misleading the immune system. The connection between the mutant PIK3CA gene and immune disruption opens new avenues for therapeutic interventions designed to counteract this phenomenon, leading to better patient outcomes in both dogs and humans.

Kim expresses optimism regarding the potential of this research to unravel the complexities associated with clinical and translational aspects of canine hemangiosarcoma and its relationship to human cancers. The ultimate aim is to leverage the wealth of knowledge gleaned from studying these tumors in dogs to develop innovative cancer treatments that may improve prognosis and quality of life for affected individuals across species.

The implications of such findings are profound, emphasizing the importance of canine cancer models in scientific research. By elucidating the mechanisms that underlie tumor genesis and immune system interactions, this research serves as a crucial stepping stone toward developing effective therapies that may enhance survival rates and therapeutic success for both canine and human patients battling aggressive vascular malignancies.

In conclusion, this research conducted by the University of Florida represents a significant leap in understanding the interplay between genetics and the immune system within the context of canine hemangiosarcoma. With the potential to impact treatment strategies not only for dogs but also for the relatively scarce human counterparts of this condition, the study underscores the vital role domestic animals play in advancing medical research and therapeutic innovation.

Subject of Research: Canine hemangiosarcoma and its genetic link to immune system signaling
Article Title: PIK3CA mutation fortifies molecular determinants for immune signaling in vascular cancers
News Publication Date: 21-Dec-2024
Web References: Nature
References: Available upon request
Image Credits: Not applicable

Keywords: canine cancer research, hemangiosarcoma, PIK3CA mutation, immune system interaction, veterinary oncology, translational medicine, angiosarcoma treatment, cancer biology, comparative oncology, innovative therapies.