Integration of Clinical and Molecular Features into Prediction Models for Outcomes in Endometrial Cancer.

Endometrial cancer recurrence carries a poor prognosis. The rising incidence of endometrial cancer calls for improvements in treatment of advanced and recurrent diseases. Efforts have been made to molecularly characterize endometrial cancer with the goal of improving therapies. The study presented here describes the utilization of molecular features of endometrial cancer tumors that are likely to recur, along with clinical characteristics utilized together to predict recurrence. This work further studies recurrent endometrial cancers to group them into "clusters" based on the tumor's molecular makeups with the ultimate aim to focus therapy on the molecular pathways potentially leading to recurrence.

Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA.

In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4⁻6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.

A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables.

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

Placenta-Specific Protein 1 Expression in Human Papillomavirus 16/18-Positive Cervical Cancers Is Associated With Tumor Histology.

OBJECTIVE: Expression of the trophoblast-specific gene placenta-specific protein 1 (PLAC1) has been detected in a wide variety of cancers. However, to date, PLAC1 expression has not been shown in cervical cancer. We have carried out a preliminary study that shows for the first time that PLAC1 is expressed in cervical cancers. METHODS: A total of 16 primary cervical tumors were obtained from patients shown to be human papillomavirus (HPV) 16/18 positive. Total cellular RNA, genomic DNA, and total protein were purified from each tumor. These materials were then used to determine PLAC1 expression, TP53 mutation status, and p53 expression. RESULTS: The PLAC1 expression was demonstrated in all 16 primary cervical tumors. The highest levels of expression were found in the more aggressive squamous and adenosquamous histologic types compared with adenocarcinomas. Moreover, the proportion of total PLAC1 message coming from the P1 promoter, also termed the distal or cancer promoter, was significantly greater in the more aggressive squamous and adenosquamous histologic types compared with adenocarcinomas. Finally, in spite of all 16 tumors being HPV-16/18 positive, 3 of 8 squamous cell cancers and 2 of 5 adenocarcinomas expressed wild-type p53 protein. Consistent with the recently shown suppression of the PLAC1P1 promoter by wild-type p53, these p53 positive tumors displayed among the lowest P1-specific PLAC1 expression levels. CONCLUSIONS: The PLAC1 expression has been demonstrated for the first time in cervical cancers. This preliminary study has further revealed a complex relationship between PLAC1 expression, cervical cancer histologic type, p53, and HPV type that requires further investigation.

An integrated prediction model of recurrence in endometrial endometrioid cancers.

Objectives: Endometrial cancer incidence and mortality are rising in the US. Disease recurrence has been shown to have a significant impact on mortality. However, to date, there are no accurate and validated prediction models that would discriminate which individual patients are likely to recur. Reliably predicting recurrence would be of benefit for treatment decisions following surgery. We present an integrated model constructed with comprehensive clinical, pathological and molecular features designed to discriminate risk of recurrence for patients with endometrioid endometrial adenocarcinoma. Subjects and methods: A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation. Results: Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence. Conclusion: By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.

The miR-503 cluster is coordinately under-expressed in endometrial endometrioid adenocarcinoma and targets many oncogenes, cell cycle genes, DNA repair genes and chemotherapy response genes.

BACKGROUND: The miR-503 miRNA cluster, located at Xq23.1, is composed of six miRNAs; miR-424, miR-503, miR-542, miR-450a-1, miR-450a-2 and miR-450b. Numerous studies have focused on the relationship of one or two members of the cluster and various human cancers. Here, we suggest that the entire cluster as a single coordinately expressed polycistron transcribed from a single promoter in endometrial endometrioid adenocarcinoma (EEA). SUBJECTS AND METHODS: A tissue panel composed of twenty histologically confirmed endometrial endometrioid adenocarcinomas (EEA) and four benign endometrium was assembled under informed consent. Expression of each member of the miR-503 cluster was determined by quantitative PCR and differences in expression between EEA and benign tissues were assessed via the standard ΔΔCt method. In addition, the role of promoter methylation status in miRNA expression was examined in Ishikawa H cells following exposure to the cytidine analog Decitabine. RESULTS: Expression of each member of the miR-503 cluster is significantly downregulated in EEA in our tumor sample. Both in our tumor sample and in The Cancer Genome Atlas (TCGA) there is evidence of highly correlated expression further supporting the idea that the miR-503 cluster is a polycistron. Looking at each member of the miR-503 cluster we were able to identify 55 unique experimentally validated target genes which include a substantial number of genes involved in carcinogenesis, DNA damage response, cell cycle regulation and chemotherapeutic response. We also found preliminary evidence that regulation of the miR-503 cluster is governed by methylation of the promoter in EEA. CONCLUSION: The totality of the data presented here strongly suggest that the miR-503 cluster as a whole merits further investigation as an important potential therapeutic target in EEA.