Francesco Piva

Tool Comparison for Detecting Tumour Cells in Endometrial Cancer via Single‐Cell Copy Number Variations Analysis

ABSTRACT Copy number variations (CNVs) are considered a hallmark of cancer and their inference from high‐resolution single‐cell transcriptome (scRNA‐seq) analyses may offer great opportunities for the study of tumor heterogeneity. We compared the results of four major tools (SCEVAN, CopyKAT, InferCNV and sciCNV) that use inferred CNVs to predict endometrial cancer (EC) cells, in order to assess their reliability and offer useful suggestions to researchers to improve the accuracy of their predictions. In this study, we identified EC cells from publicly available scRNA‐seq data using well‐established EC biomarkers reported in the literature. SCEVAN and CopyKAT tools have moderate sensitivity, but significantly overestimate the true number of true EC tumour cells. However, a comparative analysis of the different tumour subclones revealed that a lower number of false positives can be obtained by selecting only those that contain a high percentage of epithelial cells. In contrast, InferCNV and sciCNV do not directly predict tumour cells, but rather infer CNVs and compute CNV scores. However, the score distribution curves of the CNV scores did not clearly distinguish between malignant and non‐malignant cell populations, and therefore we were unable to evaluate the performance of either software. We highlight the lack of agreement between the tools and also towards the expected results. Our findings suggest exercising caution in the automated use of these tools. Until more accurate algorithms become available, we recommend filtering predictions ensuring that the necessary but not sufficient condition that the predicted tumour cells are at least epithelial is met.

SCENE: Signature Collection for Endometrial Cancer Prognosis

ABSTRACTEndometrial cancer (EC) is the most common malignancy of the female reproductive tract; its prognosis is difficult to predict. Despite the technique of single‐cell transcriptomic analysis (scRNA‐seq) returning single‐cell level expression data and promising to improve the accuracy of prognosis prediction, a tool that correlates transcriptomic signatures with survival is missing. To this aim, we have created SCENE, a database that collects information to correlate EC transcriptomic signatures with patient prognosis. We performed a review of the literature present in PubMed to collect transcriptomic signatures annotated with their characteristics, differential expression between healthy and sick patients, between patients with more and less favourable prognosis, and cellular pathways in which the genes are involved, as well as references to the original studies. The analysis of about 200 studies has allowed us to obtain 700 mRNA signatures, 60 microRNA (miRNA), and 150 long non‐coding RNA (lncRNA), involved in 60 molecular pathways. Each signature is annotated with its specific prognostic outcome that it influences, such as overall survival (OS), progression‐free survival (PFS), relapse‐free survival (RFS), and disease‐specific survival (DSS). The SCENE resource collects and annotates information that is widespread in the literature to facilitate the interpretation of transcriptomic data obtained with any technique in EC. In the case of scRNA‐seq data, SCENE may reveal cells predisposed to develop therapy resistance and metastasis.

Copy number variations in endometrial cancer: from biological significance to clinical utility

The molecular basis of endometrial cancer, which is the most common malignancy of the female reproductive organs, relies not only on onset of mutations but also on copy number variations, the latter consisting of gene gains or losses. In this review, we introduce copy number variations and discuss their involvement in endometrial cancer to determine the perspectives of clinical applicability. We performed a literature analysis on PubMed of publications over the past 30 years and annotated clinical information, including histological and molecular subtypes, adopted molecular techniques for identification of copy number variations, their locations, and the genes involved. We highlight correlations between the presence of some specific copy number variations and myometrial invasion, lymph node metastasis, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, high grade, drug response, and cancer progression. In particular, type I endometrial cancer cells have few copy number variations and are mainly located in 8q and 1q, while type II, high grade, and advanced FIGO stage endometrial cancer cells are aneuploid and have a greater number of copy number variations. As expected, the higher the number of copy number variations the worse the prognosis, especially if they amplify CCNE1, ERBB2, KRAS, MYC, and PIK3CA oncogenes. Great variability in copy number and location among patients with the same endometrial cancer histological or molecular subtype emerged, making them interesting candidates to be explored for the improvement of patient stratification. Copy number variations have a role in endometrial cancer progression, and therefore their detection may be useful for more accurate prediction of prognosis. Unfortunately, only a few studies have been carried out on the role of copy number variations according to the molecular classification of endometrial cancer, and even fewer have explored the correlation with drugs. For these reasons, further studies, also using single cell RNA sequencing, are needed before reaching a clinical application.