<p>An integrated prediction model of recurrence in endometrial endometrioid cancers</p>

Abstract

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.

Identification of Modulated MicroRNAs Associated with Breast Cancer, Diet, and Physical Activity

“…As demonstrated in different studies, the availability of several cancer databases and of a huge amount of bioinformatic data on tumor molecular and clinical features has allowed for the accurate identification of useful biomarkers for the early diagnosis of malignant tumors or to predict the risk of cancer relapse [62][63][64][65][66].…”

The combined ratio of estrogen, progesterone, Ki‐67, and P53 to predict the recurrence of endometrial cancer

“…Endometrial cancer (EC) is one of the three most frequent malignant tumors in the female reproductive system with a gradually increasing morbidity and mortality in recent years, and the onset age is becoming younger. 1 Although, the overall survival (OS) of EC is relatively great, especially the early stage, the 5-year OS rate is above 80%, a large number of patients still develop recurrence, which is one of the major causes resulting in death. 2 The factors of predicting EC recurrence has always been the concentration of clinical research.…”

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

“…Rather, our aim was to assess the genetic background of patients with endometrial and ovarian cancer that we diagnose and treat, and compare them with the genetic background of patients with the same cancer types in TCGA. The motivation of this study stemmed from the need to validate a prediction model of clinical outcomes that integrated clinical, pathological, and diverse molecular data: gene and miRNA expression, gene copy number and somatic mutations [ 2 , 3 ]. The validation of these prediction models in TCGA datasets was not ideal, so we wanted to investigate possible reasons for this discordance.…”

“…Although TCGA and other publicly available datasets have made these validations much simpler, the limitations of doing so have yet to be fully addressed. For example, in our previous studies designed to predict clinical outcomes by integrating clinical, pathological and molecular features of patients with cancer, we found that the best prediction models, developed using our internal patient cohort (University of Iowa), performed 10–20 percentage points worse in TCGA datasets [ 2 , 3 ]. Based on the characteristics of our population (northern European origin), it was obvious that our patients’ backgrounds seemed to be more homogeneous than the population from which TCGA derived their datasets.…”