Endometrial Carcinoma as the Presenting Malignancy in a Teenager With a Pathogenic TP53 Germline Mutation: A Case Report and Literature Review

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

Landscape of somatic mutated genes and inherited susceptibility genes in gynecological cancer

Traditionally, gynecological cancers have been classified based on histology. Since remarkable advancements in next-generation sequencing technology have enabled the exploration of somatic mutations in various cancer types, comprehensive sequencing efforts have revealed the genomic landscapes of some common forms of human cancer. The genomic features of various gynecological malignancies have been reported by several studies of large-scale genomic cohorts, including The Cancer Genome Atlas. Although recent comprehensive genomic profiling tests, which can detect hundreds of genetic mutations at a time from cancer tissues or blood samples, have been increasingly used as diagnostic clinical biomarkers and in therapeutic management decisions, germline pathogenic variants associated with hereditary cancers can also be detected using this test. Gynecological cancers are closely related to genetic factors, with approximately 5% of endometrial cancer cases and 20% of ovarian cancer cases being caused by germline pathogenic variants. Hereditary breast and ovarian cancer syndrome and Lynch syndrome are the two major cancer susceptibility syndromes among gynecological cancers. In addition, several other hereditary syndromes have been reported to be associated with gynecological cancers. In this review, we highlight the genes for somatic mutation and germline pathogenic variants commonly seen in gynecological cancers. We first describe the relationship between clinicopathological attributes and somatic mutated genes. Subsequently, we discuss the characteristics and clinical management of inherited cancer syndromes resulting from pathogenic germline variants in gynecological malignancies.