Abstract B1-54: Genomic findings in the UCEC study: Identifying diagnostically relevant changes in early-stage endometrial cancer
Authors: Arvai Kevin J. , Billouin-Frazier Shere S. , Wang Yongbao , Jones Dan 
Published: May 2016

Background: Endometrial cancer (EC) encompasses the common endometroid histologic subtype, with variable clinical outcomes, and the less common papillary serous/clear cell carcinoma (PSC), with uniformly adverse prognosis. The primary unmet diagnostic need in EC is to identify cases of low-stage endometriod histology that have risk of recurrence and would benefit from adjuvant chemotherapy. The multicenter Uterine Corpus Endometrial Carcinoma (UCEC) study (Nature 2013;497:67–73) recently employed a multiplatform investigation to address this need and identified four distinct molecular clusters of EC. These included a group of POLE-mutated cases with an extremely high mutation rate and favorable prognosis and a group of mostly but not exclusively PSC cases with TP53 mutations, frequent genomic copy-number (CN) changes and poor prognosis. However, most of the cases presented with more variable outcomes (155/232, 66.8%) including endometriod EC with unmutated TP53 and few CN changes or microsatellite instability (MSI). We examined here in more detail how mutation pattern, MSI status, total number of CN alterations, and mutation load interact in the UCEC data set to predict outcome and recurrence in the clinically relevant stage I/II endometriod cases.

Methods and Findings: We performed data analysis for the full 232 UCEC tumor set and, separately, for the 155 cases in the endometriod subset and of those, the 127 cases in the stage I/II set. Prognostic information provided in the UCEC data set included recurrence and outcome; the overall recurrence rate was 19% (45/232), with 23 deaths (10%) reported. In the entire data set, recurrence was more common in the CN-high group (22/60; 37%) and did not occur in the POLE-mutated group. A model filtered for the 5 most significantly mutated (chi-squared) genes and MSI status could predict the four outcome clusters reported in the UCEC study with 96% accuracy. However, Kaplan-Meier analysis showed no significant outcome prediction power for binary MSI status and CN class when analysis was restricted to the stage I/II endometriod subgroup (p=0.41). The number of mutations per case was significantly lower in low-stage cases (p&lt;0.01) but did not correlate with recurrence. Commonly mutated oncogenes in EC, including PTEN and PIK3CA, were not significantly differentially mutated by clinical stage or recurrence status in endometriod cases. In contrast, several other genes not previously well-studied in EC were differentially mutated in the stage I/II endometroid subgroup. These included the estrogen receptor-α gene (ESR1), in which mutations were differentially associated with recurrence (p &lt; 0.01, Fisher's exact). Higher CN alteration scores on microarray analysis were also significantly associated with recurrence in that subgroup (p &lt; 0.01, Student's t-test). Genomic complexity in low-stage endometriod cases was not associated with TP53 mutation (124/127 unmutated) or TP53 loss (126/127 with no deletion), implicating other genome maintenance alterations.

Conclusions: We were able to accurately replicate the previously identified prognostic groups from the UCEC data using the mutation status of just a small number of genes and MSI status. However, the outcome prediction power of this model in the stage I/II endometriod subset is limited. In contrast, genomic complexity and mutation status of a small set of genes, including ESR1, show promise as recurrence risk predictors in low-stage endometriod tumors. ESR1 mutation was previously identified to be a marker of aggressive disease in metastatic breast cancer (Nature Genetics 2013;45;1446–1451). Independent studies with adequate statistical power and an extended outcome analysis of the UCEC study are needed to expand on these findings.

Note: The results shown here are in whole based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov.

Citation Format: Kevin J. Arvai, Shere S. Billouin-Frazier, Yongbao Wang, Dan Jones. Genomic findings in the UCEC study: Identifying diagnostically relevant changes in early-stage endometrial cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-54.