Dataset Information

HER2 Status in High-Risk Endometrial Cancers (PORTEC-3): Relationship with Histotype, Molecular Classification, and Clinical Outcomes.

ABSTRACT: HER2 status has not been investigated in the context of the molecular endometrial cancer (EC) classification. Here, we aimed to determine the clinicopathological features and prognostic significance of the HER2 status in the molecularly classified PORTEC-3 trial population of patients with high-risk EC (HREC). HER2 testing was performed on tumor tissues of 407 molecularly classified HREC. HER2 status was determined by HER2 immunohistochemistry (IHC; all cases) and subsequent HER2 dual in situ hybridization for cases with any (in) complete moderate to strong membranous HER2 IHC expression. The ?² test and Spearman's Rho correlation coefficient were used to compare clinicopathological and molecular features. The Kaplan-Meier method, log-rank test, and Cox proportional hazards models were used for survival analysis. We identified 24 (5.9%) HER2-positive EC of various histological subtypes including serous (n = 9, 37.5%), endometrioid (n = 6, 25.0%), and clear cell (n = 5, 20.8%). HER2 positivity was highly associated with the p53-abnormal subgroup (p53abn, 23/24 cases; p < 0.0001). The correlation between p53abn and the HER2 status (? = 0.438; p < 0.0001) was significantly stronger (p < 0.0001) than between serous histology and the HER2 status (? = 0.154; p = 0.002). HER2 status did not have independent prognostic value for survival after correction for the molecular classification. Our study strongly suggests that molecular subclass-directed HER2 testing is superior to histotype-directed testing. This insight will be relevant for future trials targeting HER2.

SUBMITTER: Vermij L

PROVIDER: S-EPMC7795222 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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HER2 Status in High-Risk Endometrial Cancers (PORTEC-3): Relationship with Histotype, Molecular Classification, and Clinical Outcomes.

Vermij Lisa L Horeweg Nanda N Leon-Castillo Alicia A Rutten Tessa A TA Mileshkin Linda R LR Mackay Helen J HJ Leary Alexandra A Powell Melanie E ME Singh Naveena N Crosbie Emma J EJ Smit Vincent T H B M VTHBM Creutzberg Carien L CL Bosse Tjalling T

Cancers 20201225 1

HER2 status has not been investigated in the context of the molecular endometrial cancer (EC) classification. Here, we aimed to determine the clinicopathological features and prognostic significance of the HER2 status in the molecularly classified PORTEC-3 trial population of patients with high-risk EC (HREC). HER2 testing was performed on tumor tissues of 407 molecularly classified HREC. HER2 status was determined by HER2 immunohistochemistry (IHC; all cases) and subsequent HER2 dual in situ hy ...[more]

PMID: 33375706

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Project description:PurposeThe molecular classification of endometrial cancer (EC) has proven to have prognostic value and is predictive of response to adjuvant chemotherapy. Here, we investigate its predictive value for response to external beam radiotherapy (EBRT) and vaginal brachytherapy (VBT) in early-stage endometrioid EC (EEC).MethodsData of the randomized PORTEC-1 trial (n = 714) comparing pelvic EBRT with no adjuvant therapy in early-stage intermediate-risk EC and the PORTEC-2 trial (n = 427) comparing VBT with EBRT in early-stage high-intermediate-risk EC were used. Locoregional (including vaginal and pelvic) recurrence-free survival was compared between treatment groups across the four molecular classes using Kaplan-Meier's methodology and log-rank tests.ResultsA total of 880 molecularly classified ECs, 484 from PORTEC-1 and 396 from PORTEC-2, were included. The majority were FIGO-2009 stage I EEC (97.2%). The median follow-up was 11.3 years. No locoregional recurrences were observed in EC with a pathogenic mutation of DNA polymerase-ε (POLEmut EC). In mismatch repair-deficient (MMRd) EC, locoregional recurrence-free survival was similar after EBRT (94.2%), VBT (94.2%), and no adjuvant therapy (90.3%; P = .74). In EC with a p53 abnormality (p53abn EC), EBRT (96.9%) had a substantial benefit over VBT (64.3%) and no adjuvant therapy (72.2%; P = .048). In EC with no specific molecular profile (NSMP EC), both EBRT (98.3%) and VBT (96.2%) yielded better locoregional control than no adjuvant therapy (87.7%; P < .0001).ConclusionThe molecular classification of EC predicts response to radiotherapy in stage I EEC and may guide adjuvant treatment decisions. Omitting radiotherapy seems to be safe in POLEmut EC. The benefit of radiotherapy seems to be limited in MMRd EC. EBRT yields a significantly better locoregional recurrence-free survival than VBT or no adjuvant therapy in p53abn EC. VBT is the treatment of choice for NSMP EC as it is as effective as EBRT and significantly better than no adjuvant therapy for locoregional tumor control.

Project description:BackgroundClassification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.MethodsGenomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.ResultsReplication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for 'copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined 'high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.ConclusionsMolecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.

Project description:Our aim was to investigate whether molecular classification can be used to refine prognosis in grade 3 endometrial endometrioid carcinomas (EECs). Grade 3 EECs were classified into 4 subgroups: p53 abnormal, based on mutant-like immunostaining (p53abn); MMR deficient, based on loss of mismatch repair protein expression (MMRd); presence of POLE exonuclease domain hotspot mutation (POLE); no specific molecular profile (NSMP), in which none of these aberrations were present. Overall survival (OS) and recurrence-free survival (RFS) rates were compared using the Kaplan-Meier method (Log-rank test) and univariable and multivariable Cox proportional hazard models. In total, 381 patients were included. The median age was 66 years (range, 33 to 96?y). Federation Internationale de Gynecologie et d'Obstetrique stages (2009) were as follows: IA, 171 (44.9%); IB, 120 (31.5%); II, 24 (6.3%); III, 50 (13.1%); IV, 11 (2.9%). There were 49 (12.9%) POLE, 79 (20.7%) p53abn, 115 (30.2%) NSMP, and 138 (36.2%) MMRd tumors. Median follow-up of patients was 6.1 years (range, 0.2 to 17.0?y). Compared to patients with NSMP, patients with POLE mutant grade 3 EEC (OS: hazard ratio [HR], 0.36 [95% confidence interval, 0.18-0.70]; P=0.003; RFS: HR, 0.17 [0.05-0.54]; P=0.003) had a significantly better prognosis; patients with p53abn tumors had a significantly worse RFS (HR, 1.73 [1.09-2.74]; P=0.021); patients with MMRd tumors showed a trend toward better RFS. Estimated 5-year OS rates were as follows: POLE 89%, MMRd 75%, NSMP 69%, p53abn 55% (Log rank P=0.001). Five-year RFS rates were as follows: POLE 96%, MMRd 77%, NSMP 64%, p53abn 47% (P=0.000001), respectively. In a multivariable Cox model that included age and Federation Internationale de Gynecologie et d'Obstetrique stage, POLE and MMRd status remained independent prognostic factors for better RFS; p53 status was an independent prognostic factor for worse RFS. Molecular classification of grade 3 EECs reveals that these tumors are a mixture of molecular subtypes of endometrial carcinoma, rather than a homogeneous group. The addition of molecular markers identifies prognostic subgroups, with potential therapeutic implications.

Project description:Standard molecular classification of endometrial cancers (EC) is now endorsed by the WHO and identifies p53-abnormal (p53abn) EC as the subgroup with the poorest prognosis and the most likely to benefit from adjuvant chemo(radio)therapy. P53abn EC are POLE wildtype, mismatch repair proficient and show abnormal immunohistochemical (IHC) staining for p53. Correct interpretation of routinely performed p53 IHC has therefore become of paramount importance. We aimed to comprehensively investigate abnormal p53 IHC patterns and their relation to clinicopathological and molecular features. Tumor material of 411 molecularly classified high-risk EC from consenting patients from the PORTEC-3 clinical trial were collected. p53 IHC was successful in 408 EC and was considered abnormal when the tumor showed a mutant expression pattern (including subclonal): overexpression, null or cytoplasmic. The presence of pathogenic mutations was determined by next generation sequencing (NGS). Abnormal p53 expression was observed in 131/408 (32%) tumors. The most common abnormal p53 IHC pattern was overexpression (n = 89, 68%), followed by null (n = 12, 9%) and cytoplasmic (n = 3, 2%). Subclonal abnormal p53 staining was observed in 27 cases (21%), which was frequently but not exclusively, associated with POLE mutations and/or MMRd (n = 22/27; p < 0.001). Agreement between p53 IHC and TP53 NGS was observed in 90.7%, resulting in a sensitivity and specificity of 83.6% and 94.3%, respectively. Excluding POLEmut and MMRd EC, as per the WHO-endorsed algorithm, increased the accuracy to 94.5% with sensitivity and specificity of 95.0% and 94.1%, respectively. Our data shows that awareness of the abnormal p53 IHC patterns are prerequisites for correct EC molecular classification. Subclonal abnormal p53 expression is a strong indicator for POLEmut and/or MMRd EC. No significant differences in clinical outcomes were observed among the abnormal p53 IHC patterns. Our data support use of the WHO-endorsed algorithm and combining the different abnormal p53 IHC patterns into one diagnostic entity (p53abn EC).

Project description:BACKGROUND:Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers. METHODS:To determine effects of oncogenic mutations on protein profiles, we used the energy distance, which compares the Euclidean distances of protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers. RESULTS:We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings. CONCLUSIONS:This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs.

Project description:BackgroundMultiplatform molecular subtyping has been put into clinical practice as an alternative for The Cancer Genome Atlas (TCGA)-based classification for endometrial cancer (EC), which proved a tool for predicting prognosis and guiding treatment. The traditional methods for the molecular classification of EC only based on pathological indicators are not accurate. The present study aimed to classify EC on a molecular level and explored the possibility of a one-time solution to guide clinical treatment and prognosis determination by utilizing data from a next-generation sequencing (NGS) panel. The ultimate aim was to utilize multiplatform testing to overcome disadvantages of long detection periods and limitations in the information regarding genetic variation.MethodsAn NGS-panel was produced using FFPE samples isolated from 86 patients pathologically diagnosed with EC, and molecular subtyping was performed according to the recommended criteria. In addition, 45 matched samples from 86 patients were randomly selected for immunohistochemical (IHC) staining of P53, MLH1, MSH2, PMS2, and MSH6. Another 41 samples were not analyzed due to incomplete IHC staining results. SPSS (V26.0; IBM Corp., Armonk, NY, USA) was used for receiver operating characteristic (ROC) curve analysis.ResultsThe molecular typing ratio of the 86 cases of endometrial carcinoma was calculated to be 16.28% for POLE type, 17.44% for MSI-H type, 47.67% for CN-L type, 12.79% for CN-H type, 5.81% for unclassified case. A comparison between IHC ProMisE-based subtyping and NGS-based subtyping of the 45 cases revealed that 3 cases were classified as MSI-H by IHC but as MSS by NGS. Among these cases, 1 case was deficient in MLH1 expression and PMS2 protein expression but had wild-type P53 protein, and the P53 sequencing data of this sample showed a missense mutation. Good overall consistency between the 2 determination methods was shown. Receiver operating characteristic (ROC) analysis showed that NGS had particularly high specificity and sensitivity for detecting the MSI and CN subtypes [area under the curve (AUC) =0.893>0.5, P=0.000029<0.01].ConclusionsThe present study suggested that NGS-based subtyping could serve as an effective approach for the molecular typing of EC. Both NGS and IHC bear their own unique advantages and challenges in clinical practice.

Dataset Information

HER2 Status in High-Risk Endometrial Cancers (PORTEC-3): Relationship with Histotype, Molecular Classification, and Clinical Outcomes.

Publications

HER2 Status in High-Risk Endometrial Cancers (PORTEC-3): Relationship with Histotype, Molecular Classification, and Clinical Outcomes.

Similar Datasets

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