Prognostic biomarkers in endometrioid endometrial cancer

Endometrial cancer is a heterogenic group of malignancies with differences in pathogenesis and clinical behavior. Correct risk stratification of these patients is essential for successful allocation of treatment modalities. Currently, classification of endometrial cancer is based solely on clinicopathological parameters. The aim of this thesis was to study the genomic heterogeneity of endometrioid endometrial carcinoma (EEC) and to identify prognostic immunohistochemical biomarkers for disease stratification. This study is based on patient material derived from 640 patients with up to 30 years of follow-up. Gene expression profiling using two different microarray platforms revealed Apolipoprotein E (APOE) to be the most overexpressed gene in poorly differentiated EEC when compared to well-differentiated carcinoma. Immunohistochemical analysis of early stage EEC specimens suggested that progesterone receptor (PR) has an independent role in prognostication. Further studies suggested that l-asparaginase (ASRLG1) could serve as a novel prognostic biomarker in EEC. In an attempt to produce a clinically useful prognostic panel of biomarkers, immunohistochemical stainings of tissue microarrays combined with a machine learning-based method were employed. The results demonstrate that EEC patients can be stratified into three groups with significantly different clinical behavior using p53 and ASRGL1 stainings. In summary, the study highlights the importance of PR, p53 and the novel biomarker ASRGL1 in EEC prognostication. The present findings suggest that the panel of tissue biomarkers developed can be used for identification of patients who are at risk of aggressive disease course and an unfavorable outcome of EEC.Kohdun limakalvon syöpä eli kohdunrunkosyöpä on heterogeeninen tautiryhmä, jonka patogeneesissä ja kliinisessä käyttäytymisessä on eroja. Potilaan syövän ennusteen arvio on tärkeä, jotta oikeat hoitomuodot kohdentuvat niitä tarvitseville potilaille. Nykyisellään, ennusteen arvioinnissa käytettävät riskiluokitukset perustuvat sekä kliinisiin että patologis–anatomisiin muuttujiin eikä niissä käytetä merkkiaineita (”biomarkkereita”). Tämän tutkimuksen tavoitteena oli selvittää tavallisimman kohdunrunkosyövän, endometrioidin endometriumin syövän (EEC) heterogeenisyyttä sekä määrittää ennusteellinen, immunohistokemiallisiin värjäyksiin pohjautuva merkkiainepaneeli taudin luokittelemiseksi. Tutkimusaineisto muodostuu 640 potilaasta, joista on enimmillään 30 vuoden seurantaaika. Kahdella eri mikrosirualustalla toteutettu geeniekspressioanalyysi osoitti Apolipoproteiini E:n (APOE) olevan korkeimmin yliekspressoitunut geeni verrattaessa huonosti ja hyvin erilaistunutta tautia. Kohtuun rajoittuneen EEC:n immunohistokemiallinen analyysi viittaa progesteronireseptorin (PR) itsenäiseen ennusteelliseen rooliin. Myöhemmät tutkimukset viittasivat l-asparaginaasin (ASRGL1) mahdolliseen rooliin taudin käyttäytymistä ennustavana merkkiaineena EEC:ssä. Kliinisesti käyttökelpoisen, immunohistokemiallisiin värjäyksiin pohjautuvan ennusteellisen merkkiainepaneelin muodostamiseksi analysoitiin monikudosblokkeilla tehtyjä immunohistokemiallisia värjäyksiä kone-oppimiseen pohjautuvien analysointimenetelmien avulla. Analyysin tulokset osoittivat että p53- ja ASRGL1- värjäysten avulla voitiin EEC -potilaat luokitella kolmeen kliiniseltä taudinkuvaltaan ja ennusteeltaan eroavaan ryhmään. Tämä tutkimus korostaa PR, p53 ja ASRGL1 –merkkiaineiden merkitystä EEC:n riskinarvioinnissa. Kehitetyn merkkiainepaneelin avulla oli tässä tutkimuksessa mahdollista tunnistaa ne potilaat, joiden taudinkulku oli aggressiivinen ja ennuste epäsuotuisa.Siirretty Doriast

Endometriumkarsinooman molekulaarinen luokittelu ja kudosperäiset ennustetekijät

Teema : gynekologinen syöpä. English summaryPeer reviewe

Endometrial carcinoma: molecular subtypes, precursors and the role of pathology in early diagnosis

Endometrial carcinoma (EC) is classified into a wide range of morphological variants; this list has expanded over the past decade with the inclusion of mesonephric-like and de-differentiated carcinoma as EC variants in the fifth edition of the WHO Classification of Female Genital Tumours, and recognition that carcinosarcoma is a biphasic carcinoma rather than a sarcoma. Each EC variant has distinct molecular abnormalities, including TCGA-based molecular subtypes, allowing further subclassification and adding complexity. In contrast to this rapid progress in understanding EC, there are only two recognized EC precursor lesions: endometrial atypical hyperplasia/endometrioid intraepithelial neoplasia (EAH/EIN) and serous intraepithelial carcinoma, a situation that has not changed for many years. Diagnosis of EC precursors is a cornerstone of surgical pathology practice, with early diagnosis contributing to the relatively favorable prognosis of EC. In this review we relate the precursor lesions to each of the EC morphological variants and molecular subtypes, discuss how successful early diagnosis is for each variant/molecular subtype and how it might be improved, and identify knowledge gaps where there is insufficient understanding of EC histogenesis.</p

Relevance of Molecular Profiling in Patients With Low-Grade Endometrial Cancer

Molecular Profiling; Endometrial CancerPerfil molecular; Càncer d'endometriPerfil molecular; Cáncer de endometrioImportance Patients with low-grade (ie, grade 1-2) endometrial cancer (EC) are characterized by their favorable prognosis compared with patients with high-grade (ie, grade 3) EC. With the implementation of molecular profiling, the prognostic relevance of tumor grading might lose attention. As most patients present with low-grade EC and have an excellent outcome, the value of molecular profiling for these patients is unclear. Objective To determine the association of molecular profiling with outcomes among patients with low-grade EC. Design, Setting, and Participants This retrospective cohort study included a multicenter international European cohort of patients diagnosed with EC between 1994 and 2018, with a median follow-up of 5.9 years. Molecular subgroups were determined by next-generation sequencing using single-molecule molecular inversion probes and by immunohistochemistry. Subsequently, tumors were classified as polymerase epsilon (POLE)-altered, microsatellite instable (MSI), tumor protein p53 (TP53)-altered, or no specific molecular profile (NSMP). Patients diagnosed with any histological subtypes and FIGO (International Federation of Gynecology and Obstetrics) stages of EC were included, but patients with early-stage EC (FIGO I-II) were only included if they had known lymph node status. Data were analyzed February 20 to June 16, 2022. Exposures Molecular testing of the 4 molecular subgroups. Main Outcomes and Measures The main outcome was disease-specific survival (DSS) within the molecular subgroups. Results A total of 393 patients with EC were included, with a median (range) age of 64.0 (31.0-86.0) years and median (range) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 29.1 (18.0-58.3). Most patients presented with early-stage (290 patients [73.8%]) and low-grade (209 patients [53.2%]) disease. Of all patients, 33 (8.4%) had POLE-altered EC, 78 (19.8%) had MSI EC, 72 (18.3%) had TP53-altered EC, and 210 (53.4%) had NSMP EC. Across all molecular subgroups, patients with low-grade EC had superior 5-year DSS compared with those with high-grade EC, varying between 90% to 100% vs 41% to 90% (P < .001). Multivariable analysis in the entire cohort including age, tumor grade, FIGO stage, lymphovascular space invasion, and the molecular subgroups as covariates found that only high-grade (hazard ratio [HR], 4.29; 95% CI, 2.15-8.53; P < .001), TP53-altered (HR, 1.76; 95% CI, 1.04-2.95; P = .03), and FIGO stage III or IV (HR, 4.26; 95% CI, 2.50-7.26; P < .001) disease were independently associated with reduced DSS. Conclusions and Relevance This cohort study found that patients with low-grade EC had an excellent prognosis independent of molecular subgroup. These findings do not support routine molecular profiling in patients with low-grade EC, and they demonstrate the importance of primary diagnostic tumor grading and selective profiling in low-grade EC to increase cost-effectiveness

Molecular subtype diagnosis of endometrial carcinoma: comparison of the next-generation sequencing panel and Proactive Molecular Risk Classifier for Endometrial Cancer classifier

The Cancer Genome Atlas -based molecular classification of endometrial carcinoma (EC) has the potential to better identify those patients whose disease is likely to behave differently than predicted when using traditional risk stratification; however, the optimal approach to molecular subtype assignment in routine practice remains undetermined. The aim of this study was to compare the results of two different widely available approaches to diagnosis the EC molecular subtype. EC specimens from 60 patients were molecularly subclassified using two different methods, by using the FoundationOne CDx next-generation sequencing (NGS) panel and using the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) classifier and performing immunostaining for mismatch repair proteins and p53. POLE mutation status was derived from FoundationOne results in both settings. Molecular classification based on ProMisE was successful for all 60 tumors. Microsatellite instability status could be determined based on the NGS panel results in 53 of 60 tumors, so ProMisE and NGS molecular subtype assignment could be directly compared for these 53 tumors. Molecular subtype diagnosis based on NGS and ProMisE was in agreement for 52 of 53 tumors. One tumor was microsatellite stable but showed loss of MLH1 and PMS2 expression. Molecular subtype diagnosis of EC based on the NGS panel of formalin-fixed paraffin-embedded ECs and based primarily on immunostaining (ProMisE) yields identical results in 98.1% (52/53, kappa Z 0.97) of cases. Although results obtained using these two approaches are comparable, each has advantages and disadvantages that will influence the choice of the method to be used in clinical practice

Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma

Objective. In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information. Methods. A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability. Results. A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (295%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P <0.001) of dying of EEC compared to the low-risk group. Conclusions. P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities. (C) 2018 Elsevier Inc. All rights reserved.Peer reviewe

Combined expression of HOXA11 and CD10 identifies endometriosis versus normal tissue and tumors

The gold standard for diagnosing endometriosis is by laparoscopic visual demonstration of ectopic endometrial lesions outside the uterus, preferably verified by biopsy and microscopical examination. Molecular markers to facilitate the microscopical diagnosis of endometriosis and for distinguishing endometriosis from other benign and malignant lesions are lacking. Our aim was to test and validate an immunohistochemical antibody panel for improved diagnostic accuracy of endometriosis. Both CD10 and HOXA11 have been implicated in regulation of endometrial homeostasis. Here we have analyzed the expression pattern of these two proteins using immuno-histochemistry on human tissues in a tissue microarray format. CD10 and HOXA11 expression in endometriosis lesions were compared to expression patterns in a range of normal tissues and in primary-and metastatic lesions of endometrial-, cervical-and ovarian cancer. HOXA11 and CD10 were expressed in 98% and 91% of endo-metriosis lesions and the combined double-positive expression profile of both HOXA11 and CD10 was highly sensitive for ectopic endometrial tissue (90%). The specificity and sensitivity for this double-positive signature in endometriosis was significantly different from all investigated tissues, cancers and metastases except normal, eutopic endometrial-and cervical mucosa. The combination of HOXA11 and CD10 expression profiles provides a useful tool to identify ectopic endometrial tissue and for distinguishing endometriosis from various types of gynecological malignancies and metastases

STING pathway expression in low-grade serous carcinoma of the ovary: an unexpected therapeutic opportunity?

Ovarian carcinoma histotypes are distinct diseases with variable clinical outcomes and response to treatment. There is a need for new subtype-specific treatment modalities, especially for women with widespread and chemo-resistant disease. Stimulator of interferon genes (STING) is a part of the cGAS-STING pathway that mediates innate immune defence against infectious DNA-containing pathogens and also detects tumour-derived DNA and generates intrinsic antitumour immunity. The STING signalling pathway is suppressed by several mechanisms in a variety of malignant diseases and, in some cancers that may be a requirement for cellular transformation. The aim of this study was to use immunohistochemistry to evaluate STING protein expression across normal tissue, paratubal and ovarian cysts, and ovarian tumour histotypes including ovarian carcinomas. Herein, we show that the fallopian tube ciliated cells express STING protein, whereas the secretory cells are negative. STING expression differs among ovarian cancer histotypes; low-grade serous ovarian carcinomas and serous borderline tumours have uniform high STING expression, while high-grade serous and endometrioid carcinomas have heterogeneous expression, and clear cell and mucinous carcinomas show low expression. As low-grade serous carcinomas are known to be genomically stable and typically lack a prominent host immune response, the consistently high STING expression is unexpected. High STING expression may reflect pathway activation or histogenesis and the mechanisms may be different in different ovarian carcinoma histotypes. Further studies are needed to determine whether the STING signalling pathway is active and whether these tumours would be candidates for therapeutic interventions that trigger innate immunity activation

Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist’s classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen’s kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen’s kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen’s kappa of 0.43 but was comparable for the binary classification with a substantial Cohen’s kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.publishedVersio

The amount of preoperative endometrial tissue surface in relation to final endometrial cancer classification

Objective: To evaluate whether the amount of preoperative endometrial tissue surface is related to the degree of concordance with final low- and high-grade endometrial cancer (EC). In addition, to determine whether discordance is influenced by sampling method and impacts outcome. Methods: A retrospective cohort study within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC). Surface of preoperative endometrial tissue samples was digitally calculated using ImageJ. Tumor samples were classified into low-grade (grade 1-2 endometrioid EC (EEC)) and high-grade (grade 3 EEC + non-endometroid EC). Results: The study cohort included 573 tumor samples. Overall concordance between pre- and postoperative diagnosis was 60.0%, and 88.8% when classified into low- and high-grade EC. Upgrading (preoperative low-grade, postoperative high-grade EC) was found in 7.8% and downgrading (preoperative high-grade, postoperative low-grade EC) in 26.7%. The median endometrial tissue surface was significantly lower in concordant diagnoses when compared to discordant diagnoses, respectively 18.7 mm2 and 23.5 mm2 (P = 0.022). Sampling method did not influence the concordance in tumor classification. Patients with preoperative high-grade and postoperative low-grade showed significant lower DSS compared to patients with concordant low-grade EC (P = 0.039). Conclusion: The amount of preoperative endometrial tissue surface was inversely related to the degree of concordance with final tumor low- and high-grade. Obtaining higher amount of preoperative endometrial tissue surface does not increase the concordance between pre- and postoperative low- and high-grade diagnosis in EC. Awareness of clinically relevant down- and upgrading is crucial to reduce subsequent over- or undertreatment with impact on outcome