Digital and Genomic Analysis Can Detect the Aggressiveness of Uterine Tumors and Predict Metastasis

Spanish researchers have designed a method based on digital and genomic analysis of tumors and healthy tissue from patients to detect the aggressiveness of uterine tumors and predict possible metastasis. The work has been published in the journal Frontiers in Cell and Developmental Biology.

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They have developed this methodology to analyze the elements that form part of the tumor invasion front, the interface between tumor tissue and healthy tissue

The Translational Research Group on Pediatric Solid Tumors of the Incliva Health Research Institute, together with other groups of the Low Prevalence Tumor Research Program of Ciber Cancer (Ciberonc) have developed this methodology to analyze the elements that are part of the tumor invasion front, the interface between tumor tissue and healthy tissue. With this analysis, they can deduce the aggressiveness of adenocarcinomas, tumors that originate in the endometrium, and leiomyosarcomas, tumors that originate in the myometrium.

Although uterine cancer is the most frequent cancer of the female reproductive system, it has a low prevalence in society. This explains an inferior knowledge with respect to other types of more frequent tumors. Ciberonc’s Diagnostic and Precision Therapy Group is trying to increase this knowledge.

With this analysis, they can deduce the aggressiveness of adenocarcinoma and leiomyosarcoma uterine tumors

The starting point was a previous study in which the same tools were used to evaluate the differences between these two types of tumors before metastasis. There, small differences in their antimicrobial response were already observed. In the current study, which has been carried out over two years, they have compared different aspects of the composition of tumor elements in the zone of invasion into healthy tissue in biopsy samples of adenocarcinoma and leiomyosarcoma before and after metastasis to the lung.

First, by digital image analysis, the scaffolding patterns of one type of collagen fibers, the reticular fibers, have been studied. The organization of the fibers at the tumor invasion front in other tumors, such as breast cancer, already makes it possible to determine whether the tumor behaves more or less aggressively. Secondly, information has been obtained on the types of immune cells that infiltrate the invasion zone, since immune cells are of essential value in the fight against tumor aggressiveness and have allowed the emergence of novel therapies such as immunotherapy.

The epigenomic and transcriptomic changes in the two types of tumors have been studied in detail

The researchers observed a clear association between the arrangement of reticulin fibers, the composition of immune cells, and genomic changes.

Finally, changes at two genomic levels have been studied in detail. On the one hand, the epigenomic level determines how gene activation is facilitated, and on the other, the transcriptomic level shows which genes are active. Comparing these two types of aggressive uterine tumors, the researchers observed a clear association between the arrangement of reticulin fibers, the composition of immune cells, and genomic changes. These factors would indicate that one tumor is more aggressive than the other.

Together with oversight and validation by expert pathologists, the development of these integrative methodologies could be incorporated into routine pathologic evaluation to increase accuracy at the diagnostic, prognostic, and therapeutic levels. The study, in addition to addressing future clinical alternatives for the oncologic treatment of aggressive uterine tumors, lays the groundwork for close multidisciplinary and cross-disciplinary collaboration in the investigation of other more prevalent tumor types.

They will seek to optimize artificial intelligence systems, biosensors, and synthetic 3D models.
In order to develop a more integrative strategy, researchers will attempt to optimize artificial intelligence systems, biosensors, and synthetic 3D models to identify potential therapeutic targets and implement personalized therapeutic regimens. The field of precision oncology will benefit greatly from the emergence of such tools. They make it possible to determine which of the elements that make up the tumor are key to predicting metastasis, which types of novel therapies can increase the effectiveness of current treatments, and which are more specific and personalized to improve the quality of life of patients.

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(Featured image by jarmoluk via Pixabay)

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First published in iSanidad, a third-party contributor translated and adapted the article from the original. In case of discrepancy, the original will prevail.

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