Hormone receptor loss in endometrial carcinoma curettage predicts lymph node metastasis and poor outcome in prospective multicentre trial

Trovik, Jone; Wik, Elisabeth; Werner, Henrica M.J.; Krakstad, Camilla; Helland, Harald; Vandenput, Ingrid; Njølstad, Tormund S.; Stefansson, Ingunn M.; Marcickiewicz, Janusz; Tingulstad, Solveig; Staff, Anne Cathrine; Amant, Frédéric; Akslen, Lars A.; Salvesen, Helga B.

doi:10.1016/j.ejca.2013.06.016

Cited by 136 publications

(155 citation statements)

References 37 publications

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“…Some univariable factors, FGFR2 mutation and hormone receptor status, lost significance in multivariable analysis. This may be due to the fact that FGFR2 mutations were equally frequent in MSI and NSMP endometrial carcinomas, and that hormone receptor loss was mainly found in p53-mutant and L1CAM-positive endometrial carcinomas but was also frequently observed in POLE-mutant endometrial carcinomas (10,(26)(27)(28). MSI, p53, and L1CAM proved stronger independent prognosticators in this analysis.…”

Section: Discussionmentioning

confidence: 73%

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Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts

Stelloo

Nout

Osse

et al. 2016

Clinical Cancer Research

632

733

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Purpose: Recommendations for adjuvant treatment for women with early-stage endometrial carcinoma are based on clinicopathologic features. Comprehensive genomic characterization defined four subgroups: p53-mutant, microsatellite instability (MSI), POLE-mutant, and no specific molecular profile (NSMP). We aimed to confirm the prognostic capacity of these subgroups in large randomized trial populations, investigate potential other prognostic classifiers, and integrate these into an integrated molecular risk assessment guiding adjuvant therapy.Experimental Design: Analysis of MSI, hotspot mutations in 14 genes including POLE, protein expression of p53, ARID1a, b-catenin, L1CAM, PTEN, ER, and PR was undertaken on 947 available early-stage endometrioid endometrial carcinomas from the PORTEC-1 and -2 trials, mostly high-intermediate risk (n ¼ 614). Prognostic value was determined using univariable and multivariable Cox proportional hazard models. AUCs of different risk stratification models were compared.Results: Molecular analyses were feasible in >96% of the patients and confirmed the four molecular subgroups: p53-mutant (9%), MSI (26%), POLE-mutant (6%), and NSMP (59%). Integration of prognostic molecular alterations with established clinicopathologic factors resulted in a stronger model with improved risk prognostication. Approximately 15% of highintermediate risk patients had unfavorable features (substantial lymphovascular space invasion, p53-mutant, and/or >10% L1CAM), 50% favorable features (POLE-mutant, NSMP being microsatellite stable, and CTNNB1 wild-type), and 35% intermediate features (MSI or CTNNB1-mutant).Conclusions: Integrating clinicopathologic and molecular factors improves the risk assessment of patients with early-stage endometrial carcinoma. Assessment of this integrated risk profile is feasible in daily practice, and holds promise to reduce both overtreatment and undertreatment.

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Section: Discussionmentioning

confidence: 73%

“…Several molecular alterations, such as hormone receptor expression, CTNNB1 and FGFR2 mutations, have been previously reported as having prognostic potential in single biomarker studies (8,9,13,19,22,26,27). Some univariable factors, FGFR2 mutation and hormone receptor status, lost significance in multivariable analysis.…”

Section: Discussionmentioning

confidence: 98%

Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts

Stelloo

Nout

Osse

et al. 2016

Clinical Cancer Research

632

733

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“…Other approaches have been conducted to try to estimate LN metastatic risk 57 . For example, double negative hormone receptor status in EC curettage has recently been suggested to independently predict LN metastasis 57 .…”

Section: Groups Risk Based On Biomarkers To Predict Ln Metastatic Promentioning

confidence: 99%

“…For example, double negative hormone receptor status in EC curettage has recently been suggested to independently predict LN metastasis 57 . In Trovik et al study, 26% of patients with combined loss of estrogen-and progesterone receptors expression had LN metastasis.…”

Section: Groups Risk Based On Biomarkers To Predict Ln Metastatic Promentioning

confidence: 99%

Staging for endometrial cancer: The controversy around lymphadenectomy – Can this be resolved?

Koskas¹,

Rouzier²,

Amant³

2015

Best Practice & Research Clinical Obstetrics & Gynaecol

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Endometrial cancer remains the most common malignancy of the female genital tract. Lymph node metastasis is one of the most important prognostic factors and stratification into pelvic lymph node invasion (stage IIIC1) and para-aortic lymph node invasion (stage IIIC2) improved the predictive value of the 2009 FIGO classification.Radiological examination such as magnetic resonance imaging and positron emission tomography-computed tomography do not have good enough sensitivity to avoid lymphadenectomy for the assessment of lymph node invasion. Prediction scores are becoming more and more valuable to exclude lymph node metastasis in low risk groups and biomarkers could help to identify patients with high risk lymph node metastatic probability.The therapeutic role of lymph node dissection remains debated. Several endpoints can be considered to evaluate the opportunity of lymphadenectomy in endometrial cancer.Firstly, we compare survival according to the realization, the extent and the numbers of nodes removed during lymphadenectomy. Secondly, we assess the opportunity of lymphadenectomy in order to tailor adjuvant treatment modalities. Thirdly, we analyze the surgical complication rate after pelvic lymphadenectomy.

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“…Most of the current molecular signatures under consideration perform the first step but not the second; that is, they contain a set of key biomarkers, but do not apply a systematic method for predicting the outcome on an independent testing cohort. For example, absence of expression of the estrogen receptor (ER) and progesterone receptor (PR) genes, the so-called double-negative situation, correlates with increased risk of lymph node metastasis [7]. However, this correlation does not appear to translate into a prognostic test that can predict lymph node metastasis on a patientby-patient basis.…”

mentioning

confidence: 99%

Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer

Ahsen

Boren

Singh

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Background: Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. Results: A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Conclusion: Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

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Hormone receptor loss in endometrial carcinoma curettage predicts lymph node metastasis and poor outcome in prospective multicentre trial

Cited by 136 publications

References 37 publications

Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts

Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts

Staging for endometrial cancer: The controversy around lymphadenectomy – Can this be resolved?

Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer

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