Comparative Approach to the Temporo-Spatial Organization of the Tumor Microenvironment

Translating energy balance research from the bench to the clinic to the community: Parallel animal-human studies in cancer

Advances in energy balance and cancer research to date have largely occurred in siloed work in rodents or patients. However, substantial benefit can be derived from parallel studies in which animal models inform the design of clinical and population studies or in which clinical observations become the basis for animal studies. The conference Translating Energy Balance from Bench to Communities: Application of Parallel Animal-Human Studies in Cancer, held in July 2021, convened investigators from basic, translational/clinical, and population science research to share knowledge, examples of successful parallel studies, and strong research to move the field of energy balance and cancer toward practice changes. This review summarizes key topics discussed to advance research on the role of energy balance, including physical activity, body composition, and dietary intake, on cancer development, cancer outcomes, and healthy survivorship.

The ferroptosis signature predicts the prognosis and immune microenvironment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a cancer with a high metastatic rate and poor prognosis. Growing studies suggest that ferroptosis take part in the development of tumours. At the same time, the connection between ferroptosis-related genes (FRGs) and the prognosis of NPC remains unclear. In this study, we explored the dysregulated FRGs between normal control and tumour samples of NPC. Firstly, 14 of 36 differentially expressed FRGs were identified in NPC tissues compared to normal tissues, among which ABCC1, GLS2, CS and HMGCR were associated with poor prognosis for patients. The four ferroptosis genes were used for consensus cluster analysis and two risk-related FRGs (ABCC1 and GLS2) were used in a risk model. The ROC curve revealed the good predictive performance of this risk signature. Multivariate analysis revealed that risk score and intratumoral TILs were independent risk factors linked to prognosis. Additionally, our results suggested that the risk signature was attached to the immune microenvironment. Moreover, the NPC patients with high risk were sensitive to chemotherapeutic drugs including axitinib, docetaxel, embelin, epothilone.B, parthenolide, thapsigargin, tipifarnib, vinorelbine. Finally, the expression of ABCC1 and GLS2 was validated in NPC tissues using immunohistochemistry. Together, these results revealed ferroptosis may be a potential biomarker in NPC and representing a promising future direction in prognosis and therapeutic strategy for the treatment of NPC.

Increased risk of cancer in dogs and humans: a consequence of recent extension of lifespan beyond evolutionarily-determined limitations?

Cancer is among the most common causes of death for dogs (and cats) and humans in the developed world, even though it is uncommon in wildlife and other domestic animals. We provide a rationale for this observation based on recent advances in our understanding of the evolutionary basis of cancer. Over the course of evolutionary time, species have acquired and fine-tuned adaptive cancer protective mechanisms that are intrinsically related to their energy demands, reproductive strategies, and expected lifespan. These cancer protective mechanisms are general across species and/or specific to each species and their niche, and they do not seem to be limited in diversity. The evolutionarily acquired cancer-free longevity that defines a species' life history can explain why the relative cancer risk, rate, and incidence are largely similar across most species in the animal kingdom despite differences in body size and life expectancy. The molecular, cellular, and metabolic events that promote malignant transformation and cancerous growth can overcome these adaptive, species-specific protective mechanisms in a small proportion of individuals, while independently, some individuals in the population might achieve exceptional longevity. In dogs and humans, recent dramatic alterations in healthcare and social structures have allowed increasing numbers of individuals in both species to far exceed their species-adapted longevities (by 2-4 times) without allowing the time necessary for compensatory natural selection. In other words, the cancer protective mechanisms that restrain risk at comparable levels to other species for their adapted lifespan are incapable of providing cancer protection over this recent, drastic and widespread increase in longevity.

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Osteosarcoma has a guarded prognosis. A major hurdle in developing more effective osteosarcoma therapies is the lack of disease-specific biomarkers to predict risk, prognosis, or therapeutic response. Exosomes are secreted extracellular microvesicles emerging as powerful diagnostic tools. However, their clinical application is precluded by challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal messenger RNAs (mRNAs). The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts (SKA2, NEU1, PAF1, PSMG2, and NOB1) was validated in dogs with spontaneous osteosarcoma by real-time quantitative reverse transcription PCR (qRT-PCR), while a machine learning model assigned dogs into healthy or disease groups. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups ("healthy", "osteosarcoma", "other bone tumor", or "non-neoplastic disease"). Pre-treatment samples from osteosarcoma cases were used as the training set, and a validation set from post-treatment samples was used for testing, classifying as "osteosarcoma detected" or "osteosarcoma-NOT detected". Dogs in a validation set whose post-treatment samples were classified as "osteosarcoma-NOT detected" had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof of concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response.

Pro-angiogenic activity and vasculogenic mimicry in the tumor microenvironment by leptin in cancer

The acquired ability to induce the formation of a functional vasculature is a hallmark of cancer. Blood vessels in tumors are formed through various mechanisms, among the most important in cancer biology, angiogenesis, and vasculogenic mimicry have been described. Leptin is one of the main adipokines secreted by adipocytes in normal breast tissue and the tumor microenvironment. Here, we provide information on the relationship between leptin and the development of angiogenesis and vasculogenic mimicry in different types of cancer. Here, we report that leptin activates different pathways such as JAK-STAT3, MAPK/ERK, PKC, JNK, p38, and PI3K-Akt to induce the expression of various angiogenic factors and vasculogenic mimicry. In vivo models, leptin induces blood vessel formation through the PI3K-Akt-mTOR pathway. Interestingly, the relationship between leptin and vasculogenic mimicry was more significant in breast cancer. The information obtained suggests that leptin could be playing an essential role in tumor survival and metastasis through the induction of vascular mechanisms such as angiogenesis and vasculogenic mimicry; thus, leptin-induced pathways could be suggested as a promising therapeutic target.

Modulating tumor infiltrating myeloid cells to enhance bispecific antibody-driven T cell infiltration and anti-tumor response

BACKGROUND

Tumor microenvironment (TME) is a dynamic cellular milieu to promote tumor angiogenesis, growth, proliferation, and metastasis, while derailing the host anti-tumor response. TME impedes bispecific antibody (BsAb) or chimeric antigen receptor (CAR)-driven T cells infiltration, survival, and cytotoxic efficacy. Modulating tumor infiltrating myeloid cells (TIMs) could potentially improve the efficacy of BsAb.

METHODS

We evaluated the effects of TIM modulation on BsAb-driven T cell infiltration into tumors, their persistence, and in vivo anti-tumor response. Anti-GD2 BsAb and anti-HER2 BsAb built on IgG-[L]-scFv platform were tested against human cancer xenografts in BALB-Rag2-/-IL-2R-γc-KO (BRG) mice. Depleting antibodies specific for polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC), monocytic MDSC (M-MDSC), and tumor associated macrophage (TAM) were used to study the role of each TIM component. Dexamethasone, an established anti-inflammatory agent, was tested for its effect on TIMs.

RESULTS

BsAb-driven T cells recruited myeloid cells into human tumor xenografts. Each TIM targeting therapy depleted cells of interest in blood and in tumors. Depletion of PMN-MDSCs, M-MDSCs, and particularly TAMs was associated with enhanced T cell infiltration into tumors, significantly improving tumor control and survival in multiple cancer xenograft models. Dexamethasone premedication depleted monocytes in circulation and TAMs in tumors, enhanced BsAb-driven T cell infiltration, and anti-tumor response with survival benefit.

CONCLUSION

Reducing TIMs markedly enhanced anti-tumor effects of BsAb-based T cell immunotherapy by improving intratumoral T cell infiltration and persistence. TAM depletion was more effective than PMN- or M-MDSCs depletion at boosting the anti-tumor response of T cell engaging BsAb.

Translational oncotargets for immunotherapy: From pet dogs to humans

Preclinical studies in rodent models have been a pivotal role in human clinical research, but many of them fail in the translational process. Spontaneous tumors in pet dogs have the potential to bridge the gap between preclinical models and human clinical trials. Their natural occurrence in an immunocompetent system overcome the limitations of preclinical rodent models. Due to its reasonable cellular, molecular, and genetic homology to humans, the pet dog represents a valuable model to accelerate the translation of preclinical studies to clinical trials in humans, actually with benefits for both species. Moreover, their unique genetic features of breeding and breed-related mutations have contributed to assess and optimize therapeutics in individuals with different genetic backgrounds. This review aims to outline four main immunotherapy approaches - cancer vaccines, adaptive T-cell transfer, antibodies, and cytokines -, under research in veterinary medicine and how they can serve the clinical application crosstalk with humans.

Machine learning application identifies novel gene signatures from transcriptomic data of spontaneous canine hemangiosarcoma

Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domestic dogs, and share pathological and clinical features with human angiosarcomas. Typical pathognomonic features of this tumor are irregular vascular channels that are filled with blood and are lined by a mixture of malignant and nonmalignant endothelial cells. The current gold standard is the histological diagnosis of angiosarcoma; however, microscopic evaluation may be complicated, particularly when tumor cells are undetectable due to the presence of excessive amounts of nontumor cells or when tissue specimens have insufficient tumor content. In this study, we implemented machine learning applications from next-generation transcriptomic data of canine hemangiosarcoma tumor samples (n = 76) and nonmalignant tissues (n = 10) to evaluate their training performance for diagnostic utility. The 10-fold cross-validation test and multiple feature selection methods were applied. We found that extra trees and random forest learning models were the best classifiers for hemangiosarcoma in our testing datasets. We also identified novel gene signatures using the mutual information and Monte Carlo feature selection method. The extra trees model revealed high classification accuracy for hemangiosarcoma in validation sets. We demonstrate that high-throughput sequencing data of canine hemangiosarcoma are trainable for machine learning applications. Furthermore, our approach enables us to identify novel gene signatures as reliable determinants of hemangiosarcoma, providing significant insights into the development of potential applications for this vascular malignancy.