The research team, led by Hedda Fjell Scheel of the Norwegian University of Life Sciences together with colleagues at Oslo University Hospital and the University of Oslo, took advantage of a publicly available dataset of whole transcriptome sequencing data from canine mammary tumors. After quality control and the removal of animals with incomplete survival records, the final cohort comprised 146 dogs aged between two and nineteen years, representing twenty different breeds. Thirty-eight of these dogs had a recorded time of death after surgery, ranging from one day to 709 days with a median of 205.5 days, while the remaining 108 were right censored, meaning they were still alive at their last follow-up. This substantial censoring proportion, roughly 74 percent of the cohort, posed a genuine statistical challenge that shaped every subsequent analytical decision.

The investigators built three competing Cox proportional hazards survival models. The first, a clinical model, used only variables measurable through standard veterinary workup. The second, a gene expression model, relied exclusively on standardized, pre-filtered transcriptomic features. The third combined both data types. Because the gene expression matrix contained 18,579 genes, vastly more predictors than the 38 survival events available, the team applied careful feature pre-filtering: they retained the 1,000 most variable genes by mean absolute deviation, removed highly correlated pairs above a threshold of 0.95, and selected genes significant in univariate Cox models at a false discovery rate below 0.1. This left forty candidate genes, none of which belonged to the PAM50 subtyping panel used in human breast cancer. Regularized elastic net regression with cross-validated tuning of the alpha and lambda parameters then selected the most informative features for the gene-based models.

Model performance was evaluated with Uno’s C-index, a censoring-robust concordance measure in which 0.5 represents random prediction and 1.0 perfect discrimination, and with time-dependent ROC-AUC at six months, one year, and two years after surgery. Across 100 iterations of Monte Carlo cross-validation with stratified train-test splits of 4:1, the results were strikingly clear. The clinical model achieved a median C-index of 0.646, marginally ahead of the gene expression model at 0.635 and the combined model at 0.621, though none of these differences reached statistical significance. More tellingly, at the clinically critical timepoints of six months and one year after surgery, the clinical model significantly outperformed both transcriptomic models, with a median ROC-AUC of 0.811 at six months compared with 0.727 for the gene expression model and 0.768 for the combined model.

What emerged as decisive were three humble, readily obtainable clinical variables. Stepwise regression favored a multivariate model containing age, lymphatic invasion assessed by tumor histology, and estrogen receptor status evaluated by immunohistochemistry. Lymphatic invasion exerted the largest effect on the hazard of death, confirming its previously reported prognostic power, while estrogen receptor positivity was associated with improved survival, mirroring the pattern well established in human breast cancer. Notably, histopathological grade and molecular subtype, both significant in univariate analyses, dropped out of the multivariate model, likely because they were strongly correlated with lymphatic invasion and estrogen receptor negativity and therefore added no independent information. The final clinical model explained 17.8 percent of the overall variation in survival.

The molecular subtyping analysis itself yielded biologically meaningful results. Using the single-sample MPAM50 classifier, every tumor in the cohort was assigned to either the luminal A or the basal-like subtype, with luminal A accounting for 58.2 percent of samples. Subtype was significantly associated with age, breed, lymphatic invasion, grade, and estrogen receptor status. Basal-like tumors were more frequently grade 2 or 3, more often showed lymphatic vessel invasion, and were overrepresented among estrogen receptor negative tumors, with 69.6 percent of the 23 receptor-negative tumors classified as basal-like. Dogs with luminal A tumors were on average one year younger, and Maltese dogs were more likely to harbor luminal A tumors. These findings indicate that the canine PAM50 subtypes capture tumor biology beyond what estrogen receptor status alone reveals, echoing their prognostic significance in human patients.

Perhaps the most intriguing result came when the researchers used the best-performing clinical model to assign each dog a risk score and split the cohort at the median into high-risk and low-risk groups of 73 dogs each. Survival differed significantly between the groups, with a log-rank p-value of 0.0036. Differential gene expression analysis then uncovered 433 genes significantly differentially expressed between the groups, 279 upregulated in high-risk tumors and 154 in low-risk tumors. Gene set enrichment analysis using the Hallmark collection from the Molecular Signatures Database revealed twenty-four significantly enriched pathways. High-risk tumors were enriched for signatures of proliferation, including E2F targets and the G2M checkpoint, and for metabolic programs such as oxidative phosphorylation and glycolysis, while low-risk tumors showed enrichment of immune-related pathways, including TNF alpha signaling, IL6-JAK-STAT signaling, inflammatory response, and interferon alpha response.

That immune activity marks the low-risk group is a finding with real translational resonance. In human breast cancer, both innate and adaptive immune components are associated with recurrence-free survival, and immune gene expression carries prognostic weight particularly in hormone receptor negative tumors. The canine data suggest that the immune system plays an equally central role in anti-tumor response across species, reinforcing the value of dogs as a comparative model for human breast cancer research. The two diseases share spontaneous onset, histological subtypes, underlying genetic alterations, and gene expression changes, yet molecular markers that transformed human breast cancer management, from PAM50 subtyping to gene expression signatures like MammaPrint, have never been incorporated into routine canine staging. This study represents one of the most systematic attempts to close that gap.

Why, then, did gene expression fail to improve prediction? The authors offer several candid explanations. Adding thousands of transcriptomic features to a small, heavily censored dataset may introduce more noise than signal, a phenomenon documented in large-scale benchmark studies showing that multi-omics survival models are widely sensitive to noise. Linear Cox models may also miss non-linear interactions between genomic and clinical features. Moreover, with only 38 events, statistical power is inherently limited, and a larger number of events might reveal differences that this cohort cannot detect. None of the models exceeded a time-independent C-index of 0.65, which, while comfortably above random, falls short of what would be needed for clinical implementation, though comparable multi-omics studies in humans rarely exceed 0.7 either.

The practical message for veterinary oncology is nonetheless concrete. Estrogen receptor status measured by immunohistochemistry and lymphatic invasion assessed by routine histology could be valuable additions to current TNM staging, potentially refining survival predictions and informing decisions such as whether ovariohysterectomy at the time of tumor removal might benefit a particular patient, since dogs with high serum estradiol and receptor-positive tumors appear more likely to benefit from that intervention. The authors also emphasize the limitations of using overall survival rather than cancer-specific survival in an elderly cohort, and they call for future studies to record cause of death, complete TNM parameters, and richer genomic data. Alternative modeling approaches such as boosting methods and random survival forests may yet extract more from transcriptomic data. For now, the study stands as a sobering and instructive reminder that in the era of big data, the humble microscope slide and the immunohistochemistry stain still hold their ground.

Subject of Research: Machine learning-based identification of prognostic factors for survival in canine mammary gland tumors

Article Title: Unraveling prognostic factors in canine mammary gland tumors using machine learning

Article References: Scheel, H. F., Zobolas, J., Lien, T. G., Lingaas, F., & Bergholtz, H. (2025). Unraveling prognostic factors in canine mammary gland tumors using machine learning. Veterinary Oncology, 2(1), Article 16. https://doi.org/10.1186/s44356-025-00030-7

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00030-7

Keywords: canine mammary tumors, machine learning, Cox regression, gene expression, estrogen receptor, lymphatic invasion, survival prediction, TNM staging, PAM50 subtyping, gene set enrichment analysis, veterinary oncology, precision medicine

The core of the argument rests on three epidemiological realities. First, roughly half of all canine mammary tumors are benign, meaning that any staging protocol applied indiscriminately before a diagnosis of malignancy is confirmed will expose a large share of patients to unnecessary testing. Second, a clinically relevant proportion of malignant mammary tumors are low grade and highly unlikely to metastasize, so even among dogs with cancer, many derive little benefit from systemic imaging. Third, and perhaps most strikingly, distant metastasis at the time of initial presentation appears to be genuinely rare. In data drawn from the PennVet Shelter Canine Mammary Tumor Program, only 13 of 249 dogs screened for enrollment had detectable distant metastasis, and the median tumor size in that subgroup was 6.7 centimeters, suggesting that metastasis at staging is largely a phenomenon of advanced local disease.

Despite these numbers, recent recommendations have shifted toward more elaborate pre-surgical workups, including thoracic computed tomography, abdominal ultrasound, and sentinel lymph node mapping. The authors contend that this trend is driven partly by the availability of more sensitive imaging technology and partly by a fear of missing occult metastatic lesions, a fear that has not been critically examined against actual detection rates. The consequences are not trivial. Advanced imaging adds substantial cost, requires additional anesthesia or heavy sedation, and delays definitive surgical treatment. In some cases, the accumulated expense of staging may prevent owners from pursuing the surgery that would actually cure their dog, a paradox the authors describe as directly contrary to the goal of cancer care.

To build their case, the researchers looked closely at how human breast cancer, long the conceptual model for canine mammary tumor management, handles the same question. Human breast cancer staging begins with mammography and biopsy, and only after malignancy is confirmed do clinicians proceed to sentinel lymph node biopsy, molecular typing, and, selectively, systemic imaging. Notably, guidelines from the National Comprehensive Cancer Network have evolved over the past decade in the opposite direction from veterinary practice: women with early-stage, low-risk disease based on receptor profile and sentinel lymph node status now forgo systemic imaging altogether. The rationale is that metastasis is rare in early-stage asymptomatic patients, and false-positive imaging findings automatically trigger cascades of additional diagnostics, cost, and anxiety. The canine situation, the authors argue, shares these same mathematical and psychological dynamics, yet veterinary recommendations have moved toward more testing rather than less.

Sentinel lymph node biopsy receives particularly detailed scrutiny. In human breast cancer, the technique replaced radical axillary dissection because it accurately identifies the first lymph node to receive drainage from the tumor with far less morbidity. In dogs, the anatomy is simpler: the axillary lymphocenter typically contains only one or two nodes, and the predictable drainage pattern sends the two cranial mammary glands to the axillary node and the two caudal glands to the superficial inguinal node, with the third gland draining in either direction. Reviews of lymphographic studies show that this normal pattern persists as the predominant route in most dogs with tumors, with deviations being rare and possibly linked to high tumor grade, which promotes lymphangiogenesis, inflammation, and vascular remodeling within the tumor microenvironment.

Yet the authors identify practical obstacles that make routine sentinel node biopsy in dogs problematic. Dogs commonly present with multiple independent mammary tumors, with studies reporting that 60 to 70 percent have tumors in different glands, each with its own histology and biological behavior. The largest tumor is often, but not always, the most aggressive, so selecting a single tumor for mapping risks sampling the wrong lesion, while injecting multiple tumors yields multiple biopsied nodes and larger, more painful surgeries with higher complication rates. Furthermore, because sentinel node techniques require peritumoral or subareolar contrast injection before complete resection, dogs without a confirmed diagnosis of malignancy are routinely subjected to the procedure even when their tumors turn out to be benign or low risk. The authors also note that in dogs, unlike in women, lymph node status has weak implications for chemotherapy decisions, since dogs with high-grade tumors are likely to receive systemic therapy regardless of nodal findings, making the procedure’s main human indication largely moot.

Thoracic imaging comes under similar examination. Three-view thoracic radiographs remain a reasonably sensitive, widely available, and cost-effective method that can be performed under light sedation. Computed tomography detects more small pulmonary nodules, but the clinical significance of that sensitivity is questionable in this population. In human early-stage breast cancer, the false-positive rate for chest CT, about 10 percent, actually exceeded the overall yield of metastasis detection, roughly 2 percent, a cautionary statistic for anyone considering withholding surgery based on small CT findings. Classic necropsy work by Fidler and Brodey established that pulmonary metastasis was present in 81 percent of dogs with metastatic disease, compared with only 5 to 16 percent for abdominal organs, meaning the lungs are the primary metastatic site and abdominal spread is unlikely when thoracic imaging is negative. The authors’ own prospective trial data found that every dog later confirmed at necropsy to have pulmonary metastasis had preceding three-view radiographs documenting it.

Abdominal ultrasound fares similarly in the analysis. Because the lungs dominate the metastatic pattern, the risk of metastasis confined to abdominal organs in the face of negative thoracic radiographs is low. Ultrasound also cannot reliably distinguish benign from malignant hepatic or splenic nodules, generating false positives that invite further invasive diagnostics. A large prospective screening study in healthy middle-aged and older dogs found that physical examination plus cytology of superficial masses detected the great majority of cancers, undercutting the argument that abdominal imaging is needed to catch unrelated occult tumors. Follow-up data from the authors’ earlier cohort showed that non-mammary tumors detected during monitoring emerged a median of 500 to 600 days after the original surgery, indicating they were new developments rather than lesions that staging had missed.

Fine needle aspiration of the tumors themselves is likewise judged unreliable as a screening tool. Reported sensitivity for distinguishing benign from malignant mammary lesions ranges from 25 to 95.2 percent across studies, and even modern cytological grading systems show poor concordance with histology for low-grade tumors, precisely the category where misclassification matters most for staging decisions. Because cytologists evaluate cellular atypia without the architectural context available to surgical pathologists, and because even pathologists disagree about the benign-malignant boundary in low-grade lesions across institutions and countries, the authors decline to recommend routine cytology as a gatekeeper for staging. They do concede it retains value for identifying non-mammary masses such as lipomas or mast cell tumors.

The alternative framework the authors propose is elegantly simple: let tumor size and clinical risk factors decide. For tumors under 1 centimeter, or under 3 centimeters in WHO T1 category, no pre-surgical staging is recommended; post-operative thoracic radiographs are applied by grade, skipped for grade 1, optional for grade 2, and recommended for grade 3. Tumors larger than 3 centimeters warrant pre-surgical three-view thoracic radiographs, as do any tumors with cytologically positive local nodes or obvious malignant features such as rapid growth, ulceration, or deep fixation. Routine fine needle aspiration of palpably normal regional nodes is deemed unnecessary for small tumors, since inguinal nodes are typically removed anyway with the fifth gland, but firm or enlarged nodes should always be aspirated before surgery to adjust the operative plan. Patent blue dye is endorsed for identifying axillary nodes during removal of larger cranial tumors. Abdominal ultrasound is reserved for dogs with positive inguinal nodes, to assess iliac chains, and thoracic CT is judged not indicated in most cases. The authors acknowledge that a small number of small tumors with metastatic nodes could slip through the pre-surgical net, but argue that post-operative grade assessment catches grade 2 and 3 cases. Their closing message extends beyond mammary tumors: staging recommendations across veterinary oncology should be judged by whether results change prognosis or treatment, because diagnostic intensity that offers minimal clinical benefit while imposing financial and emotional costs may ultimately harm the very patients it aims to protect.

Subject of Research: Evidence-based revision of pre- and post-surgical staging practices for canine mammary tumors

Article Title: Considerations for revised staging practices in canine mammary tumors

Article References: Sorenmo, K. U., Cofone, M. A., Donnelly, L., Duda, L., & Stefanovski, D. (2026). Considerations for revised staging practices in canine mammary tumors. Veterinary Oncology, 3(1), Article 9. https://doi.org/10.1186/s44356-026-00061-8

Image Credits: AI Generated

DOI: 10.1186/s44356-026-00061-8

Keywords: canine mammary tumors, cancer staging, veterinary oncology, sentinel lymph node biopsy, thoracic radiographs, thoracic CT, abdominal ultrasound, fine needle aspiration, tumor grade, metastasis, risk-based staging, dog health

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