Dataset Information

Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas.

ABSTRACT: Endometrial carcinoma (EC) molecular classification based on four molecular subclasses identified in The Cancer Genome Atlas (TCGA) has gained relevance in recent years due to its prognostic utility and potential to predict benefit from adjuvant treatment. While most ECs can be classified based on a single classifier (POLE exonuclease domain mutations - POLEmut, MMR deficiency - MMRd, p53 abnormal - p53abn), a small but clinically relevant group of tumours harbour more than one molecular classifying feature and are referred to as 'multiple-classifier' ECs. We aimed to describe the clinicopathological and molecular features of multiple-classifier ECs with abnormal p53 (p53abn). Within a cohort of 3518 molecularly profiled ECs, 107 (3%) tumours displayed p53abn in addition to another classifier(s), including 64 with MMRd (MMRd-p53abn), 31 with POLEmut (POLEmut-p53abn), and 12 with all three aberrations (MMRd-POLEmut-p53abn). MMRd-p53abn ECs and POLEmut-p53abn ECs were mostly grade 3 endometrioid ECs, early stage, and frequently showed morphological features characteristic of MMRd or POLEmut ECs. 18/28 (60%) MMRd-p53abn ECs and 7/15 (46.7%) POLEmut-p53abn ECs showed subclonal p53 overexpression, suggesting that TP53 mutation was a secondary event acquired during tumour progression. Hierarchical clustering of TCGA ECs by single nucleotide variant (SNV) type and somatic copy number alterations (SCNAs) revealed that MMRd-p53abn tumours mostly clustered with single-classifier MMRd tumours (20/23) rather than single-classifier p53abn tumours (3/23), while POLEmut-p53abn tumours mostly clustered with single-classifier POLEmut tumours (12/13) and seldom with single-classifier p53abn tumours (1/13) (both p???0.001, chi-squared test). Finally, the clinical outcome of patients with MMRd-p53abn and POLEmut-p53abn ECs [stage I 5-year recurrence-free survival (RFS) of 92.2% and 94.1%, respectively] was significantly different from single-classifier p53abn EC (stage I RFS 70.8%, p?=?0.024 and p?=?0.050, respectively). Our results support the classification of MMRd-p53abn EC as MMRd and POLEmut-p53abn EC as POLEmut. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

SUBMITTER: Leon-Castillo A

PROVIDER: S-EPMC7065184 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas.

León-Castillo Alicia A Gilvazquez Ester E Nout Remi R Smit Vincent Thbm VT McAlpine Jessica N JN McConechy Melissa M Kommoss Stefan S Brucker Sara Y SY Carlson Joseph W JW Epstein Elisabeth E Rau Tilman T TT Soslow Robert A RA Ganesan Raji R Matias-Guiu Xavier X Oliva Esther E Harrison Beth T BT Church David N DN Gilks C Blake CB Bosse Tjalling T

The Journal of pathology 20200112 3

Endometrial carcinoma (EC) molecular classification based on four molecular subclasses identified in The Cancer Genome Atlas (TCGA) has gained relevance in recent years due to its prognostic utility and potential to predict benefit from adjuvant treatment. While most ECs can be classified based on a single classifier (POLE exonuclease domain mutations - POLEmut, MMR deficiency - MMRd, p53 abnormal - p53abn), a small but clinically relevant group of tumours harbour more than one molecular classif ...[more]

PMID: 31829447

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Project description:BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

Dataset Information

Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas.

Publications

Clinicopathological and molecular characterisation of 'multiple-classifier' endometrial carcinomas.

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