Correlation of histopathologic characteristics to protein expression and function in malignant melanoma

BACKGROUND: Metastatic melanoma is still one of the most prevalent skin cancers, which upon progression has neither a prognostic marker nor a specific and lasting treatment. Proteomic analysis is a versatile approach with high throughput data and results that can be used for characterizing tissue samples. However, such analysis is hampered by the complexity of the disease, heterogeneity of patients, tumors, and samples themselves. With the long term aim of quest for better diagnostics biomarkers, as well as predictive and prognostic markers, we focused on relating high resolution proteomics data to careful histopathological evaluation of the tumor samples and patient survival information. PATIENTS AND METHODS: Regional lymph node metastases obtained from ten patients with metastatic melanoma (stage III) were analyzed by histopathology and proteomics using mass spectrometry. Out of the ten patients, six had clinical follow-up data. The protein deep mining mass spectrometry data was related to the histopathology tumor tissue sections adjacent to the area used for deep-mining. Clinical follow-up data provided information on disease progression which could be linked to protein expression aiming to identify tissue-based specific protein markers for metastatic melanoma and prognostic factors for prediction of progression of stage III disease. RESULTS: In this feasibility study, several proteins were identified that positively correlated to tumor tissue content including IF6, ARF4, MUC18, UBC12, CSPG4, PCNA, PMEL and MAGD2. The study also identified MYC, HNF4A and TGFB1 as top upstream regulators correlating to tumor tissue content. Other proteins were inversely correlated to tumor tissue content, the most significant being; TENX, EHD2, ZA2G, AOC3, FETUA and THRB. A number of proteins were significantly related to clinical outcome, among these, HEXB, PKM and GPNMB stood out, as hallmarks of processes involved in progression from stage III to stage IV disease and poor survival. CONCLUSION: In this feasibility study, promising results show the feasibility of relating proteomics to histopathology and clinical outcome, and insight thus can be gained into the molecular processes driving the disease. The combined analysis of histological features including the sample cellular composition with protein expression of each metastasis enabled the identification of novel, differentially expressed proteins. Further studies are necessary to determine whether these putative biomarkers can be utilized in diagnostics and prognostic prediction of metastatic melanoma

The Human Phenotype Ontology in 2024: phenotypes around the world

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

Obesity promotes the expansion of metastasis-initiating cells in breast cancer

Obesity is a strong predictor of poor prognosis in breast cancer, especially in postmenopausal women. In particular, tumors in obese patients tend to seed more distant metastases, although the biology behind this observation remains poorly understood.Methods: To elucidate the effects of the obese microenvironment on metastatic spread, we ovariectomized C57BL/6 J female mice and fed them either a regular diet (RD) or a high-fat diet (HFD) to generate a postmenopausal diet-induced obesity model. We then studied tumor progression to metastasis of Py230 and EO771 grafts. We analyzed and phenotyped the RD and HFD tumors and the surrounding adipose tissue by flow cytometry, qPCR, immunohistochemistry (IHC) and western blot. The influence of the microenvironment on tumor cells was assessed by performing cross-transplantation of RD and HFD tumor cells into other RD and HFD mice. The results were analyzed using the unpaired Student t test when comparing two variables, otherwise we used one-way or two-way analysis of variance. The relationship between two variables was calculated using correlation coefficients.Results: Our results show that tumors in obese mice grow faster, are also less vascularized, more hypoxic, of higher grade and enriched in CD11b+Ly6G+ neutrophils. Collectively, this favors induction of the epithelial-to-mesenchymal transition and progression to claudin-low breast cancer, a subtype of triple-negative breast cancer that is enriched in cancer stem cells. Interestingly, transplanting HFD- derived tumor cells in RD mice transfers enhanced tumor growth and lung metastasis formation.Conclusions: These data indicate that a pro-metastatic effect of obesity is acquired by the tumor cells in the primary tumor independently of the microenvironment of the secondary site