Translational oncotargets for immunotherapy: From pet dogs to humans

Exploring the Tumor-Associated Risk of Mesenchymal Stem Cell Therapy in Veterinary Medicine

Mesenchymal stem cell (MSC) therapy has been actively applied in veterinary regenerative medicine to treat various canine and feline diseases. With increasing emphasis on safe cell-based therapies, evaluations of their tumorigenic potential are in great demand. However, a direct confirmation of whether tumors originate from stem cells or host cells is not easily achievable. Additionally, previous studies evaluating injections of high doses of MSCs into nude mice did not demonstrate tumor formation. Recent research focused on optimizing MSC-based therapies for veterinary patients, such as MSC-derived extracellular vesicles in treating different diseases. This progress also signifies a broader shift towards personalized veterinary medicine, where treatments can be tailored to individual pets based on their unique genetic profiles. These findings related to different treatments using MSCs emphasize their future potential for veterinary clinical applications. In summary, because of lower tumor-associated risk of MSCs as compared to embryonic and induced pluripotent stem cells, MSCs are considered a suitable source for treating various canine and feline diseases.

CanISO: a database of genomic and transcriptomic variations in domestic dog (Canis lupus familiaris)

BACKGROUND

The domestic dog, Canis lupus familiaris, is a companion animal for humans as well as an animal model in cancer research due to similar spontaneous occurrence of cancers as humans. Despite the social and biological importance of dogs, the catalogue of genomic variations and transcripts for dogs is relatively incomplete.

RESULTS

We developed CanISO, a new database to hold a large collection of transcriptome profiles and genomic variations for domestic dogs. CanISO provides 87,692 novel transcript isoforms and 60,992 known isoforms from whole transcriptome sequencing of canine tumors (N = 157) and their matched normal tissues (N = 64). CanISO also provides genomic variation information for 210,444 unique germline single nucleotide polymorphisms (SNPs) from the whole exome sequencing of 183 dogs, with a query system that searches gene- and transcript-level information as well as covered SNPs. Transcriptome profiles can be compared with corresponding human transcript isoforms at a tissue level, or between sample groups to identify tumor-specific gene expression and alternative splicing patterns.

CONCLUSIONS

CanISO is expected to increase understanding of the dog genome and transcriptome, as well as its functional associations with humans, such as shared/distinct mechanisms of cancer. CanISO is publicly available at https://www.kobic.re.kr/caniso/ .

Preclinical studies performed in appropriate models could help identify optimal timing of combined chemotherapy and immunotherapy

Immune checkpoint inhibitors (ICI) have been revolutionary in the field of cancer therapy. However, their success is limited to specific indications and cancer types. Recently, the combination treatment of ICI and chemotherapy has gained more attention to overcome this limitation. Unfortunately, many clinical trials testing these combinations have provided limited success. This can partly be attributed to an inadequate choice of preclinical models and the lack of scientific rationale to select the most effective immune-oncological combination. In this review, we have analyzed the existing preclinical evidence on this topic, which is only limitedly available. Furthermore, this preclinical data indicates that besides the selection of a specific drug and dose, also the sequence or order of the combination treatment influences the study outcome. Therefore, we conclude that the success of clinical combination trials could be enhanced by improving the preclinical set up, in order to identify the optimal treatment combination and schedule to enhance the anti-tumor immunity.

A Phase 2, Single-Arm, Open-Label Clinical Trial on Adjuvant Peptide-Based Vaccination in Dogs with Aggressive Hemangiosarcoma Undergoing Surgery and Chemotherapy

To test the antitumor effect and safety of peptide-based anticancer vaccination in dogs with hemangiosarcoma undergoing the standard of care (SOC; surgery and doxorubicin), canine hemangiosarcoma cells were infected with Salmonella typhi Ty21a to release immunogenic endoplasmic reticulum stress-related peptides into the extracellular milieu via CX43 hemichannels opening. The infected tumor cell secretome constituted the vaccine. Following the SOC, dogs with biologically aggressive hemangiosarcoma were vaccinated a total of five times, once every 3 weeks, and were followed up with serial imaging. A retrospective population of dogs undergoing the SOC alone served as controls. The primary endpoints were the time to progression (TTP) and overall survival (OS), and the secondary endpoints were toxicity and immune responses. A total of 28 dogs were vaccinated along with the SOC, and 32 received only the SOC. A tumor-specific humoral response along with a vaccine-specific T-cell response was observed. Toxicity did not occur. The TTP and OS were significantly longer in vaccinated versus unvaccinated dogs (TTP: 195 vs. 160 days, respectively; p = 0.001; OS: 276 vs. 175 days, respectively; p = 0.002). One-year survival rates were 35.7% and 6.3% for vaccinated and unvaccinated dogs, respectively. In dogs with hemangiosarcoma undergoing the SOC, the addition of a peptide-based vaccine increased the TTP and OS, while maintaining a safe profile. Moreover, vaccinated dogs developed a tumor-specific response, supporting the feasibility of future phase three studies.

Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57-0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.