A Novel Approach to Preoperative Risk Stratification in Endometrial Cancer: The Added Value of Immunohistochemical Markers

Abstract

Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER ( p = 0.024), PR (0.048), and higher levels of L1CAM ( p = 0.001) were observed; in cervical involvement a higher expression of L1CAM ( p = 0.001), lower PR ( p = 0.014); in tumors with positive LVSI, higher L1CAM ( p = 0.014); in cases with positive LN, lower expression of ER/PR ( p < 0.001), higher L1CAM ( p = 0.002) and frequent mutation of p53 ( p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% ( p = 0.001), PR <88% ( p = 0.008), and L1CAM ≥4% ( p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8–96.5%), 63% (52.1–72.8%), 83% (70.5–90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5–63.4%), 65% (55.6–74.0%), and 77% (67.3–84.2%). Mutation of p53 revealed PPV 94% (67.4–99.1%) and NPV 61% (56.1–66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.

Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature

“…et al, 2018;Song et al, 2018;Teng et al, 2018;Liu et al, 2019;Pan et al, 2019;Wang et al, 2019). Others, such as CPNE8, MAGED1, RNF144A, SOX14, ACOT13 (cg15486740), EID1 (cg00481239), RPS4X (cg08859156), and TTRAP (cg15486740), have been identified in various tumors other than GC (Kamio et al, 2010;Zeng et al, 2012;Zhou et al, 2013;Kuang et al, 2017;Stanisavljevic et al, 2017;Liu X. et al, 2018;Tosic et al, 2018;Nagasawa et al, 2019;Yang et al, 2019). The remaining biomarkers, including hsa-mir-653, hsa-mir-6808, LOC91450, ZNF22, C1orf144 (cg14791193), GOLGA3 (cg26856948), HLA-DPB1 (cg20100408), LRFN4 (cg22740006), MDH2 (cg22813794), MIR365-2 (cg24361571), NUFIP2 (cg25161386), PREP (cg12485556), STYXL1 (cg22813794), TMEM117 (cg07020967), ZC3H10 (ZC3H10), IL1RAPL1 (cg20350671), PC (cg22740006), SHC4 (cg00481239), and ATXN10 (cg22395807), have not been previously reported.…”

“…Particularly, high expressions of MAP7D2, ACOT13, EID1, RPS4X, and TTRAP have been associated with poor prognosis in gastric, lung, and pancreatic cancers as well as hepatic carcinoma, respectively, while a high TTRAP expression reportedly inhibits the growth of osteosarcoma (Kamio et al, 2010;Zhou et al, 2013;Kuang et al, 2017;Liu K.T. et al, 2018;Liu X. et al, 2018). Notably, the relationship between methylation levels and corresponding gene expression profiles is unknown, necessitating further research.…”

Fertility-sparing treatment in early endometrial cancer: current state and future strategies

“…While the Gynecologic Oncology Branch of the Chinese Medical Association requires progesterone receptor status in patients undergoing fertility-sparing treatment, the European Society for Medical Oncology (ESMO) does not currently recommend routine testing because patients who test negative for progesterone status still respond to treatment, although their chance of a response is significantly lower [ 65 ]. The inclusion of immunohistochemical markers [ 95 ] and serum levels of cancer antigen CA125 [ 96 ] to predict response to treatment will require further studies. Risk classification of tumors in a fertility-sparing setting using molecular markers for p53 and MMR abnormalities has been trialed with promising results [ 97 ].…”

Ultrasound‐based risk model for preoperative prediction of lymph‐node metastases in women with endometrial cancer: model‐development study

“…Molecular markers offer the advantage of objective results, as they are based on the presence or the absence of a protein or mutation. They have been used in endometrial cancer to improve sub classification [109][110][111][112][113][114][115] , to detect prognostic subgroups [116][117][118][119][120][121][122] , in risk prediction models [123][124][125][126] and in individualized therapy 127 .…”