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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).

Introduction/Background*Detection of pathogenic somatic variants in the exonuclease domain (EDM) of the POLE-gene is of prognostic importance because of the excellent clinical outcomes of POLE-mutated endometrial cancers (EC). It is hypothesised that patients with POLE-mutated EC benefit of treatment de-escalation. Since 8–10% of EC carry a pathogenic POLE-mutation, it is of strong clinical importance to accurately determine the presence of these mutations. In current practice POLE-status can only be determined by DNA-sequencing methods, e.g. Sanger or Next-Generation-Sequencing (NGS). These techniques require a molecular biologist for correct interpretation, are relatively time consuming, not widely available and/or expensive. Due to the long turnaround time, it can be challenging to fit POLE-testing in tight timelines of clinical practice. To overcome this we have developed, and are in the process of validating, a rapid, simple, reliable and low-cost quantitative polymerase-chain-reaction (qPCR) assay for pathogenic POLE-mutations.MethodologyPrimer and fluorescence-labelled 5’-nuclease probe-sequences of the five most frequently occurring pathogenic variations within exons 9, 13 and 14 of the POLE EDM have been developed and optimized using DNA extracted from formalin-fixed paraffin-embedded tumour tissues. The simplicity of the design enables POLE-status assessment within 4 hours. Ongoing calibration studies are evaluating the minimal amount and quality of DNA required.Result(s)*Cut offs for failed, POLE-negative and -positive results were predefined based on 50 POLE-wildtypes and 6 POLE-mutated cases. In the range of uncertainty in between, NGS-testing is recommended. In our first testing set of 115 cases (40 POLE-mutated, 75 POLE-wildtype), two samples failed, (one POLE-mutant, one wildtype) possibly due to low DNA quality. Four samples fell in the range of uncertainty (three POLE-mutant, one wildtype). Almost all POLE-mutants (33/36) were positive with no false-positives. Sensitivity and specificity were 92% (95%CI 83%–100%) and 100%. Post-hoc adjusment of the lower cut-off yielded a sensitivity of 97% (95%CI 92%–100%) and specifity of 100%.Conclusion*With this qPCR assay we developed a faster and simple alternative for targeted NGS-sequencing of the five most common pathogenic POLE-mutations. The assay–s simplicity in design and methods will make universal low-cost POLE-testing available for all EC patients.

Introduction/Background*Microcystic, elongated fragmented (MELF) pattern of myometrial invasion is a distinct histologic feature occasionally seen in low-grade endometrial carcinomas (EC). The prognostic relevance of the presence of MELF is uncertain due to conflicting data and has not appropriately been studied in the context of the novel molecular EC classification. We aimed to determine the relation of MELF pattern of invasion with clinicopathological and molecular characteristics, and define its prognostic relevance in early stage (high)intermediate risk EC.MethodologySingle haematoxylin and eosin (H&E) stained whole tumour slides of 929 of the 1141 early stage (high)intermediate risk EC of patients included in the post-operative radiation therapy in endometrial carcinoma (PORTEC)-1/-2 trials were available for review for the presence of MELF. Histological type, stage and grade, presence and extent of lymph-vascular-space-invasion (LVSI), molecular subclass, L1-cell-adhesion-molecule (L1CAM) overexpression, and β-catenin exon-3 (CTNNB1) and KRAS mutational status were compared between MELF-positive and negative cases. Differences in patient and tumour characteristics were analysed with chi-square or Fisher’s exact test for categorical and Mann-Whitney U test for continuous variables. Time-to-event analyses were done using the Kaplan-Meier method, log-rank tests and Cox’ proportional hazards models.Result(s)*MELF pattern of invasion was identified in 129 (16%) cases, and was associated with grade 1-2 and deep myometrial invasion (table 1). MELF positive tumours were significantly more often found in the no-specific-molecular-profile (NSMP) subclass (n=95, 84.8%). Of these NSMP MELF positive tumours 91.1% were CTNNB1-wildtype (n=82) and 26.5% KRAS-mutated (n=22). Uncorrected survival analysis showed a significantly favourable impact of MELF on risk of recurrence (p=0.031). After correction for stage, grade, LVSI, molecular EC class, L1CAM and CTNNB1, MELF pattern of invasion did not significantly impact clinical outcome (HR 0.63 95%CI 0.28 – 1.41, p=0.26), table 2.Abstract 577 Table 1Clinicopathological features of MELF positive and negative casesAbstract 577 Table 2Multivariable analysis of risk factors for endometrial cancer recurrenceConclusion*MELF-pattern of invasion was identified in 16% of early stage (high)intermediate risk EC, and had no independent prognostic impact. However, our results show that MELF pattern of invasion is more frequently found in NSMP KRAS-mutated EC without CTNNB1 mutations. These distinct molecular features could contribute to further refinement of the NSMP-subgroup of EC pointing to potential novel treatment targets.

Introduction/Background*The molecular endometrial cancer (EC) classification has proven prognostic value and can direct adjuvant treatment decisions. Despite this, a relatively large group of EC is still molecularly unclassified (NSMP-EC). In this study we aimed to identify biomarkers among high-risk NSMP-EC patients with prognostic and/or predictive relevance.MethodologyParaffin-embedded tumour material (n=423) from the PORTEC-3 HREC trial were available for analysis. All patients with NSMP-EC were selected, hence those without pathogenic POLE mutations, mismatch repair deficiency and p53-abnormal immunohistochemistry (IHC). Protein expression of L1CAM, ER and PR (ongoing) were analysed by IHC using a 10% threshold for positivity. Tumour DNA was analysed for pathogenic somatic mutations using a next generation sequencing (NGS) cancer hotspot panel. Time to recurrence was analysed using the Kaplan-Meier method, log-rank tests and Cox’s proportional hazard models.Result(s)*In total, 126 NSMP-EC were identified in PORTEC-3, the majority were hormone receptor positive (ER n=106/121, 87.6%, PR will be presented at ESGO2021). L1CAM overexpression was observed in 11.2% (n=14/125) and mutations in CTNNB1-exon-3 were identified in 34.3% (n=36/105). Clustering showed that ER-positive NSMP-EC were predominantly endometrioid EC (n=99, 93.4%), low grade (n=88, 83.0%) and L1CAM-negative (n=102, 97.1%) (figure 1). PIK3CA and KRAS mutations were present in 27.3% (n=24) and 19.3% (n=17), respectively. ER-negative NSMP-EC were frequently non-endometrioid (n=11, 73.3%), L1CAM-positive (n=11, 73.3%) and rarely harboured PTEN and CTNNB1 mutations (n=1, 7.1% and 0%, respectively). ER-positivity was associated with lower risk of recurrence (HR 0.32 [95%CI 0.14-0.70]; figure 2A), while L1CAM-overexpression and CTNNB1-exon-3 mutations were not (HR 2.25 [95%CI 0.93-5.43] and HR 1.20 [95%CI 0.57-2.54], respectively). Multivariable analysis confirmed independent favourable prognostic impact of ER-positivity and LVSI. Figure 2B shows impact of LVSI on time to recurrence among patients with ER-positive NSMP-EC.Abstract 397 Figure 1Overview of clinicopathological and molecular characteristics of NSMP-EC from PORTEC-3Abstract 397 Figure 2Time to recurrence within NSMP-ECConclusion*The vast majority of NSMP-HREC are ER-positive (87.6%) and are likely sensitive to hormonal therapy. Other treatment targets might be found in this subgroup too as 27.3% had PIK3CA and 19.3% had KRAS mutations. NSMP-EC with loss of ER-expression were often of non-endometrioid histology and had a high risk of recurrence. Future studies should investigate whether this subgroup would benefit from other systemic therapies.

Introduction/Background*Tertiary lymphoid structures (TLS) are ectopic lymphoid tissues that form in and around cancers. TLS consist of a germinal centre (GC) with proliferating B-cells and follicular dendritic cells (FDCs), as well as a peripheral T-cell zone. Local and systemic B- and T-cell responses are thought to be initiated and maintained at the TLS. Here, we analysed whether TLS were associated with anti-tumour immunity and a reduced risk of recurrence in endometrial cancer (EC).MethodologyTLS were quantified by an expert pathologist (TB) on H&E-stained tumour slides of the cancer genome atlas uterine cancer cohort (TCGA UCEC), and by immuno-histochemistry (IHC) on tumour slides from the PORTEC-3 trial biobank. Time to recurrence analysis were performed according to Kaplan-Meier’s method, using log-rank tests and Cox’ proportional hazards models, including prespecified multivariable analysis with clinicopathological and molecular risk factors.Result(s)*Differential gene expression analysis of TLS-positive and TLS-negative cases from TCGA UCEC identified, among others, L1-cell adhesion molecule (L1CAM) (figure 1A). IHC of PORTEC-3 cases revealed expression of L1CAM in TLS GCs, where it co-localised with CD21 on FDCs. TLS were observed in the myometrial wall distal and proximal to the tumour (figure 1B). Tumour tissues of 377 PORTEC-3 participants could be included for analysis of L1CAM-defined TLS. L1CAM-defined TLS were identified independent of L1CAM expression in tumour cells and across all molecular subgroups, though enriched in mismatch repair deficient (MMRd) and polymerase-epsilon mutant (POLEmut) EC (figure 2). TLS were significantly more frequent in MMRd EC with secondary TP53 mutations (p=0.008). Intra-tumoural CD8+-cell densities were significantly higher in TLS-positive cases. Five-year risk of recurrence was 7.2% (95%CI 0.9-13.1) in EC patients with TLS (n=70), and 27.8% (95%CI 22.6-32.7, p-value <0.0001) in EC patients without TLS (n=307). This favourable prognostic impact was independent of clinicopathological and molecular factors (adjusted HR 0.32 95%CI 0.14-0.74, p=0.0077).Abstract 482 Figure 1Identification of L1CAM as a marker of mature TLS in endometrial cancerAbstract 482 Figure 2Relation between TLS, CD8+ cel density and molecular classifiers in high risk endometrical cancerConclusion*L1CAM identifies tertiary lymphoid structures with germinal centres. Our data suggest a pivotal role of TLS in the risk of recurrence of EC. L1CAM IHC is simple, available across many study cohorts and could be readily implemented as biomarker of TLS in future trials and clinical care.

Introduction/Background*The TransPORTEC Consortium was established in 2013 by the PIs and translational science representatives of the PORTEC-3 trial groups from the Netherlands, United Kingdom, Australia, Canada and France. Purpose of the collaboration is to improve treatment of endometrial cancer (EC) patients. Here, we evaluate our experience with international collaboration to identify challenges and strengths.MethodologyResult(s)*: Since its establishment, TransPORTEC had a strong scientific team of chief investigators, translational leads and core members from participating groups. Twice-yearly TransPORTEC-meetings were organised to build and maintain friendships, share results and discuss new proposals. Over time, the TransPORTEC-biobank has expanded with PORTEC-trial tumour tissues and other cohorts, and is now the world’s largest set of molecularly classified ECs. The research focus has expanded to include molecular cancer immunology and digital pathology. The group’s output include 10 scientific papers and numerous posters and presentations on (inter)national meetings. Their analysis of PORTEC-3 showing differences in chemotherapy effect by molecular group led to initiating an international program with 4 clinical trials on Refining Adjuvant treatment IN endometrial cancer Based On molecular features (RAINBO) to compare personalised to standard treatment in terms of efficacy, toxicity, quality of life and cost-utility (figure 1). Tumour material of all participants will be collected for translational research. To achieve this, the consortium evolved: new talented members were attracted and trial-specific and expertise teams were installed (figure 2). Despite this impacting on group equilibrium, the collaboration is continuously productive. Keys to success were frequent meetings, sharing of draft protocols and experiences with contacting (inter)national research organisations and potential funders. The first of the RAINBO trials is expected to open by the end of 2021 and the program will probably fuel translational research for years to come.Abstract 595 Figure 1Abstract 595 Figure 2Conclusion*International research collaborations are dynamic and demanding. Challenges include: balancing between a stable organisational structure and flexibility to adapt to opportunities; providing all members with a satisfying share; and acquisition of funding for academic-sponsored international trials. Strengths are the profound interaction and trust between members with different expertise and backgrounds and shared ambitions and successes, resulting in unique and innovative academic research projects with leverage.

Introduction/BackgroundThe TCGA molecular classification of endometrial cancer (EC) has proven prognostic impact for patients with intermediate-risk EC. The randomised PORTEC-3 trial investigated the benefit of combined adjuvant chemotherapy and radiotherapy (CTRT) compared with RT alone for women with high-risk EC (HREC). We evaluated the prognostic significance of the molecular classification in HREC using tissues from consenting PORTEC-3 trial participants.Methodology423 paraffin-embedded tissue samples (64% of 660 participants) were collected. Through targeted DNA-sequencing for pathogenic POLE-exonuclease domain mutations (EDM) and immunohistochemistry for p53 and mismatch repair (MMR) proteins, the HREC were classified as POLE-ultramutated (POLEmut), p53 mutant staining (p53abn), MMR-deficient (MMRd) or no specific molecular profile (NSMP). The Kaplan-Meier method, log-rank test and Cox’s proportional hazard model were used for analysis.ResultsMolecular analysis was successful in 410 HREC (97% of the 423), identifying 4 molecular subgroups: p53abn (n=92, 22%), POLEmut (n=52, 13%), MMRd (n=137, 33%) and NSMP (n=129, 32%). Five-year recurrence-free survival (RFS) for patients with POLEmut, p53abn, MMRd and NSMP EC was 98%, 50%, 74% and 76%, respectively (p<0.0001), and overall survival was 98%, 55%, 81% and 88% (p<0.0001). Multivariable analysis showed that p53abn was the strongest prognostic factor for decreased survival, while pathogenic POLE EDM was the strongest favourable factor (table 1). Patients with p53abn HREC had significant benefit of combined adjuvant chemotherapy and radiotherapy (5-year RFS with CTRT 61% versus 37% for RT, log-rank p=0.015).Abstract – Table 1Multivariable analysis of molecular subgroups and clinicopathological features (n=410)ConclusionThe molecular classification provides better risk stratification than histopathology alone. Patients with POLEmut HREC have excellent clinical outcome, suggesting these should be classified as low-risk, independent of other pathologic variables. P53abn EC is the strongest predictor of poor clinical outcome, and these patients had significant benefit from added chemotherapy. Molecular characteristics should be incorporated in clinical diagnostics and decision making and future trials should address molecular subgroup-based treatments.DisclosureAL reports receiving advisory board fees from AstraZeneca, Tesaro, Clovis, MSD, Grisdstone, Seattle Genetics, Gamamabs, and Biocad, and travel support paid to her institution from Roche and AstraZeneca. HN reports is the founder of SME Vicinivax and has collaborated with Aduro, TRON & Merck.

The main challenge in CT screening for lung cancer is the high prevalence of pulmonary nodules and the relatively low incidence of lung cancer. Management protocols use thresholds for nodule size and growth rate to determine which nodules require additional diagnostic procedures, but these should be based on individuals' probabilities of developing lung cancer. In this prespecified analysis, using data from the NELSON CT screening trial, we aimed to quantify how nodule diameter, volume, and volume doubling time affect the probability of developing lung cancer within 2 years of a CT scan, and to propose and evaluate thresholds for management protocols. Eligible participants in the NELSON trial were those aged 50–75 years, who have smoked 15 cigarettes or more per day for more than 25 years, or ten cigarettes or more for more than 30 years and were still smoking, or had stopped smoking less than 10 years ago. Participants were randomly assigned to low-dose CT screening at increasing intervals, or no screening. We included all participants assigned to the screening group who had attended at least one round of screening, and whose results were available from the national cancer registry database. We calculated lung cancer probabilities, stratified by nodule diameter, volume, and volume doubling time and did logistic regression analysis using diameter, volume, volume doubling time, and multinodularity as potential predictor variables. We assessed management strategies based on nodule threshold characteristics for specificity and sensitivity, and compared them to the American College of Chest Physicians (ACCP) guidelines. The NELSON trial is registered at www.trialregister.nl, number ISRCTN63545820. Volume, volume doubling time, and volumetry-based diameter of 9681 non-calcified nodules detected by CT screening in 7155 participants in the screening group of NELSON were used to quantify lung cancer probability. Lung cancer probability was low in participants with a nodule volume of 100 mm3 or smaller (0·6% [95% CI 0·4–0·8]) or maximum transverse diameter smaller than 5 mm (0·4% [0·2–0·7]), and not significantly different from participants without nodules (0·4% [0·3–0·6], p=0·17 and p=1·00, respectively). Lung cancer probability was intermediate (requiring follow-up CT) if nodules had a volume of 100–300 mm3 (2·4% [95% CI 1·7–3·5]) or a diameter 5–10 mm (1·3% [1·0–1·8]). Volume doubling time further stratified the probabilities: 0·8% (95% CI 0·4–1·7) for volume doubling times 600 days or more, 4·0% (1·8–8·3) for volume doubling times 400–600 days, and 9·9% (6·9–14·1) for volume doubling times of 400 days or fewer. Lung cancer probability was high for participants with nodule volumes 300 mm3 or bigger (16·9% [95% CI 14·1–20·0]) or diameters 10 mm or bigger (15·2% [12·7–18·1]). The simulated ACCP management protocol yielded a sensitivity and specificity of 90·9% (95% CI 81·2–96·1), and 87·2% (86·4–87·9), respectively. A diameter-based protocol with volumetry-based nodule diameter yielded a higher sensitivity (92·4% [95% CI 83·1–97·1]), and a higher specificity (90·0% [89·3–90·7). A volume-based protocol (with thresholds based on lung cancer probability) yielded the same sensitivity as the ACCP protocol (90·9% [95% CI 81·2–96·1]), and a higher specificity (94·9% [94·4–95·4]). Small nodules (those with a volume <100 mm3 or diameter <5 mm) are not predictive for lung cancer. Immediate diagnostic evaluation is necessary for large nodules (≥300 mm3 or ≥10 mm). Volume doubling time assessment is advocated only for intermediate-sized nodules (with a volume ranging between 100–300 mm3 or diameter of 5–10 mm). Nodule management protocols based on these thresholds performed better than the simulated ACCP nodule protocol. Zorgonderzoek Nederland Medische Wetenschappen and Koningin Wilhelmina Fonds.

Low-dose CT screening is recommended for individuals at high risk of developing lung cancer. However, CT screening does not detect all lung cancers: some might be missed at screening, and others can develop in the interval between screens. The NELSON trial is a randomised trial to assess the effect of screening with increasing screening intervals on lung cancer mortality. In this prespecified analysis, we aimed to assess screening test performance, and the epidemiological, radiological, and clinical characteristics of interval cancers in NELSON trial participants assigned to the screening group. Eligible participants in the NELSON trial were those aged 50–75 years, who had smoked 15 or more cigarettes per day for more than 25 years or ten or more cigarettes for more than 30 years, and were still smoking or had quit less than 10 years ago. We included all participants assigned to the screening group who had attended at least one round of screening. Screening test results were based on volumetry using a two-step approach. Initially, screening test results were classified as negative, indeterminate, or positive based on nodule presence and volume. Subsequently, participants with an initial indeterminate result underwent follow-up screening to classify their final screening test result as negative or positive, based on nodule volume doubling time. We obtained information about all lung cancer diagnoses made during the first three rounds of screening, plus an additional 2 years of follow-up from the national cancer registry. We determined epidemiological, radiological, participant, and tumour characteristics by reassessing medical files, screening CTs, and clinical CTs. The NELSON trial is registered at www.trialregister.nl, number ISRCTN63545820. 15 822 participants were enrolled in the NELSON trial, of whom 7915 were assigned to low-dose CT screening with increasing interval between screens, and 7907 to no screening. We included 7155 participants in our study, with median follow-up of 8·16 years (IQR 7·56–8·56). 187 (3%) of 7155 screened participants were diagnosed with 196 screen-detected lung cancers, and another 34 (<1%; 19 [56%] in the first year after screening, and 15 [44%] in the second year after screening) were diagnosed with 35 interval cancers. For the three screening rounds combined, with a 2-year follow-up, sensitivity was 84·6% (95% CI 79·6–89·2), specificity was 98·6% (95% CI 98·5–98·8), positive predictive value was 40·4% (95% CI 35·9–44·7), and negative predictive value was 99·8% (95% CI 99·8–99·9). Retrospective assessment of the last screening CT and clinical CT in 34 patients with interval cancer showed that interval cancers were not visible in 12 (35%) cases. In the remaining cases, cancers were visible when retrospectively assessed, but were not diagnosed because of radiological detection and interpretation errors (17 [50%]), misclassification by the protocol (two [6%]), participant non-compliance (two [6%]), and non-adherence to protocol (one [3%]). Compared with screen-detected cancers, interval cancers were diagnosed at more advanced stages (29 [83%] of 35 interval cancers vs 44 [22%] of 196 screen-detected cancers diagnosed in stage III or IV; p<0·0001), were more often small-cell carcinomas (seven [20%] vs eight [4%]; p=0·003) and less often adenocarcinomas (nine [26%] vs 102 [52%]; p=0·005). Lung cancer screening in the NELSON trial yielded high specificity and sensitivity, with only a small number of interval cancers. The results of this study could be used to improve screening algorithms, and reduce the number of missed cancers. Zorgonderzoek Nederland Medische Wetenschappen and Koningin Wilhelmina Fonds.

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal ( n  = 353) and two external ( n  = 160 and n  = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC. A multimodal deep learning prognostic model based on histopathology outperforms current gold standards for identifying patients with endometrial cancer with different outcomes, in multiple external validation cohorts.