Potential Targets' Analysis Reveals Dual PI3K/mTOR Pathway Inhibition as a Promising Therapeutic Strategy for Uterine Leiomyosarcomas-an ENITEC Group Initiative

Purpose: Uterine sarcomas are rare and heterogeneous tumors characterized by an aggressive clinical behavior. Their high rates of recurrence and mortality point to the urgent need for novel targeted therapies and alternative treatment strategies. However, no molecular prognostic or predictive biomarkers are available so far to guide choice and modality of treatment. Experimental Design: We investigated the expression of several druggable targets (phospho-S6(S240) ribosomal protein, PTEN, PDGFR-alpha, ERBB2, and EGFR) in a large cohort of human uterine sarcoma samples (288), including leiomyosarcomas, low-grade and high-grade endometrial stromal sarcomas, undifferentiated uterine sarcomas, and adenosarcomas, together with 15 smooth muscle tumors of uncertain malignant potential (STUMP), 52 benign uterine stromal tumors, and 41 normal uterine tissues. The potential therapeutic value of the most promising target, p-S6(S240), was tested in patient-derived xenograft (PDX) leiomyosarcoma models. Results: In uterine sarcomas and STUMPs, S6S240 phosphorylation (reflecting mTOR pathway activation) was associated with higher grade (P = 0.001) and recurrence (P = 0.019), as shown by logistic regression. In addition, p-S6(S240) correlated with shorter progression-free survival (P = 0.034). Treatment with a dual PI3K/mTOR inhibitor significantly reduced tumor growth in 4 of 5 leiomyosarcoma PDX models (with tumor shrinkage in 2 models). Remarkably, the 4 responding models showed basal p-S6(S240) expression, whereas the nonresponding model was scored as negative, suggesting a role for p-S6(S240) in response prediction to PI3K/mTOR inhibition. Conclusions: Dual PI3K/mTOR inhibition represents an effective therapeutic strategy in uterine leiomyosarcoma, and p-S6(S240) expression is a potential predictive biomarker for response to treatment. (C)2017 AACR.Peer reviewe

Breast Cancer Survival of BRCA1/BRCA2 Mutation Carriers in a Hospital-Based Cohort of Young Women

Background: The primary aim of the study was to investigate prognosis and long-term survival in young breast cancer patients with a BRCA1 or BRCA2 germline mutation compared with noncarriers. The secondary aim was to investigate whether differences in survival originate from associations with tumor characteristics, second cancers, and/or treatment response. Methods: We established a cohort of invasive breast cancer patients diagnosed younger than age 50 years in 10 Dutch hospitals between 1970 and 2003. BRCA1/2 testing of most prevalent mutations was mainly done using DNA isolate from formalin-fixed paraffin-embedded nontumor tissue. Survival estimates were derived using Cox regression and competing risk models. Results: In 6478 breast cancer patients, we identified 3.2% BRCA1 and 1.2% BRCA2 mutation carriers. BRCA1 mutation carriers had a worse overall survival independent of clinico-pathological/treatment characteristics, compared with noncarriers (adjusted hazard ratio [HR] = 1.20, 95% confidence interval [CI] = 0.97 to 1.47), though only statistically significant in the first five years of follow-up (adjusted HR = 1.40, 95% CI = 1.07 to 1.84). A large part of the worse survival was explained by incidence of ovarian cancers. Breast cancer-specific, disease-free, and metastasis-free survival results were less pronounced and mostly statistically nonsignificant but in the same direction with those of overall survival. Overall survival was worse, although not statistically significantly, within the ER-negative or ER-positive, grade 3, and small tumor subgroups. The worse survival was most pronounced in non-chemotherapy-treated patients (adjusted HR = 1.54, 95% CI = 1.08 to 2.19). Power for BRCA2 mutation carriers was limited; only after five years' follow-up overall survival was worse (adjusted HR = 1.47, 95% CI = 1.00 to 2.17). Conclusions:BRCA1/2 mutation carriers diagnosed with breast cancer before age 50 years are prone to a worse survival, which is partly explained by differences in tumor characteristics, treatment response, and second ovarian cancers

QPOLE:A Quick, Simple, and Cheap Alternative for POLE Sequencing in Endometrial Cancer by Multiplex Genotyping Quantitative Polymerase Chain Reaction

PURPOSE: Detection of 11 pathogenic variants in the POLE gene in endometrial cancer (EC) is critically important to identify women with a good prognosis and reduce overtreatment. Currently, POLE status is determined by DNA sequencing, which can be expensive, relatively time-consuming, and unavailable in hospitals without specialized equipment and personnel. This may hamper the implementation of POLE-testing in clinical practice. To overcome this, we developed and validated a rapid, low-cost POLE hotspot test by a quantitative polymerase chain reaction (qPCR) assay, QPOLE. MATERIALS AND METHODS: Primer and fluorescence-labeled 5'-nuclease probe sequences of the 11 established pathogenic POLE mutations were designed. Three assays, QPOLE-frequent for the most common mutations and QPOLE-rare-1 and QPOLE-rare-2 for the rare variants, were developed and optimized using DNA extracted from formalin-fixed paraffin-embedded tumor tissues. The simplicity of the design enables POLE status assessment within 4-6 hours after DNA isolation. An interlaboratory external validation study was performed to determine the practical feasibility of this assay. RESULTS: Cutoffs for POLE wild-type, POLE-mutant, equivocal, and failed results were predefined on the basis of a subset of POLE mutants and POLE wild-types for the internal and external validation. For equivocal cases, additional DNA sequencing is recommended. Performance in 282 EC cases, of which 99 were POLE-mutated, demonstrated an overall accuracy of 98.6% (95% CI, 97.2 to 99.9), a sensitivity of 95.2% (95% CI, 90.7 to 99.8), and a specificity of 100%. After DNA sequencing of 8.8% equivocal cases, the final sensitivity and specificity were 96.0% (95% CI, 92.1 to 99.8) and 100%. External validation confirmed feasibility and accuracy. CONCLUSION: QPOLE is a qPCR assay that is a quick, simple, and reliable alternative for DNA sequencing. QPOLE detects all pathogenic variants in the exonuclease domain of the POLE gene. QPOLE will make low-cost POLE-testing available for all women with EC around the globe

Prognostic refinement of NSMP high-risk endometrial cancers using oestrogen receptor immunohistochemistry

Background: Risk-assessment of endometrial cancer (EC) is based on clinicopathological factors and molecular subgroup. It is unclear whether adding hormone receptor expression, L1CAM expression or CTNNB1 status yields prognostic refinement.Methods: Paraffin-embedded tumour samples of women with high-risk EC (HR-EC) from the PORTEC-3 trial (n = 424), and a Dutch prospective clinical cohort called MST (n = 256), were used. All cases were molecularly classified. Expression of L1CAM, ER and PR were analysed by whole-slide immunohistochemistry and CTNNB1 mutations were assessed with a next-generation sequencing. Kaplan–Meier method, log-rank tests and Cox’s proportional hazard models were used for survival analysis.Results: In total, 648 HR-EC were included. No independent prognostic value of ER, PR, L1CAM, and CTNNB1 was found, while age, stage, and adjuvant chemotherapy had an independent impact on risk of recurrence. Subgroup-analysis showed that only in NSMP HR-EC, ER-positivity was independently associated with a reduced risk of recurrence (HR 0.33, 95%CI 0.15–0.75).Conclusions: We confirmed the prognostic impact of the molecular classification, age, stage, and adjuvant CTRT in a large cohort of high-risk EC. ER-positivity is a strong favourable prognostic factor in NSMP HR-EC and identifies a homogeneous subgroup of NSMP tumours. Assessment of ER status in high-risk NSMP EC is feasible in clinical practice and could improve risk stratification and treatment.</p

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Background: Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. Methods: This combined analysis included diagnostic haematoxylin and eosin-stained slides and molecular and clinicopathological data from 2028 patients with intermediate-to-high-risk endometrial cancer from the PORTEC-1 (n=466), PORTEC-2 (n=375), and PORTEC-3 (n=393) randomised trials and the TransPORTEC pilot study (n=110), the Medisch Spectrum Twente cohort (n=242), a case series of patients with POLEmut endometrial cancer in the Leiden Endometrial Cancer Repository (n=47), and The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma cohort (n=395). PORTEC-3 was held out as an independent test set and a four-fold cross validation was performed. Performance was measured with the macro and class-wise area under the receiver operating characteristic curve (AUROC). Whole-slide images were segmented into tiles of 360 μm resized to 224 × 224 pixels. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. The top 20 tiles with the highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. We analysed 5-year recurrence-free survival and explored prognostic refinement by molecular class using the Kaplan-Meier method. Findings: im4MEC attained macro-average AUROCs of 0·874 (95% CI 0·856–0·893) on four-fold cross-validation and 0·876 on the independent test set. The class-wise AUROCs were 0·849 for POLEmut (n=51), 0·844 for MMRd (n=134), 0·883 for NSMP (n=120), and 0·928 for p53abn (n=88). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and the morphology of POLEmut and MMRd endometrial cancer overlapped. im4MEC highlighted a low tumour-to-stroma ratio as a potentially novel characteristic feature of the NSMP class. 5-year recurrence-free survival was significantly different between im4MEC predicted molecular classes in PORTEC-3 (log-rank p<0·0001). The ten patients with aggressive p53abn endometrial cancer that was predicted as MMRd showed inflammatory morphology and appeared to have a better prognosis than patients with correctly predicted p53abn endometrial cancer (p=0·30). The four patients with NSMP endometrial cancer that was predicted as p53abn showed higher nuclear atypia and appeared to have a worse prognosis than patients with correctly predicted NSMP (p=0·13). Patients with MMRd endometrial cancer predicted as POLEmut had an excellent prognosis, as do those with true POLEmut endometrial cancer. Interpretation: We present the first interpretable deep learning model, im4MEC, for haematoxylin and eosin-based prediction of molecular endometrial cancer classification. im4MEC robustly identified morpho-molecular correlates and could enable further prognostic refinement of patients with endometrial cancer. Funding: The Hanarth Foundation, the Promedica Foundation, and the Swiss Federal Institutes of Technology

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

BACKGROUND: Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. METHODS: This combined analysis included diagnostic haematoxylin and eosin-stained slides and molecular and clinicopathological data from 2028 patients with intermediate-to-high-risk endometrial cancer from the PORTEC-1 (n=466), PORTEC-2 (n=375), and PORTEC-3 (n=393) randomised trials and the TransPORTEC pilot study (n=110), the Medisch Spectrum Twente cohort (n=242), a case series of patients with POLEmut endometrial cancer in the Leiden Endometrial Cancer Repository (n=47), and The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma cohort (n=395). PORTEC-3 was held out as an independent test set and a four-fold cross validation was performed. Performance was measured with the macro and class-wise area under the receiver operating characteristic curve (AUROC). Whole-slide images were segmented into tiles of 360 μm resized to 224 × 224 pixels. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. The top 20 tiles with the highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. We analysed 5-year recurrence-free survival and explored prognostic refinement by molecular class using the Kaplan-Meier method. FINDINGS: im4MEC attained macro-average AUROCs of 0·874 (95% CI 0·856-0·893) on four-fold cross-validation and 0·876 on the independent test set. The class-wise AUROCs were 0·849 for POLEmut (n=51), 0·844 for MMRd (n=134), 0·883 for NSMP (n=120), and 0·928 for p53abn (n=88). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and the morphology of POLEmut and MMRd endometrial cancer overlapped. im4MEC highlighted a low tumour-to-stroma ratio as a potentially novel characteristic feature of the NSMP class. 5-year recurrence-free survival was significantly different between im4MEC predicted molecular classes in PORTEC-3 (log-rank p<0·0001). The ten patients with aggressive p53abn endometrial cancer that was predicted as MMRd showed inflammatory morphology and appeared to have a better prognosis than patients with correctly predicted p53abn endometrial cancer (p=0·30). The four patients with NSMP endometrial cancer that was predicted as p53abn showed higher nuclear atypia and appeared to have a worse prognosis than patients with correctly predicted NSMP (p=0·13). Patients with MMRd endometrial cancer predicted as POLEmut had an excellent prognosis, as do those with true POLEmut endometrial cancer. INTERPRETATION: We present the first interpretable deep learning model, im4MEC, for haematoxylin and eosin-based prediction of molecular endometrial cancer classification. im4MEC robustly identified morpho-molecular correlates and could enable further prognostic refinement of patients with endometrial cancer. FUNDING: The Hanarth Foundation, the Promedica Foundation, and the Swiss Federal Institutes of Technology

Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group

Patients with advanced triple‐negative breast cancer (TNBC) benefit from treatment with atezolizumab, provided that the tumor contains ≥1% of PD‐L1/SP142‐positive immune cells. Numbers of tumor‐infiltrating lymphocytes (TILs) vary strongly according to the anatomic localization of TNBC metastases. We investigated inter‐pathologist agreement in the assessment of PD‐L1/SP142 immunohistochemistry and TILs. Ten pathologists evaluated PD‐L1/SP142 expression in a proficiency test comprising 28 primary TNBCs, as well as PD‐L1/SP142 expression and levels of TILs in 49 distant TNBC metastases with various localizations. Interobserver agreement for PD‐ L1 status (positive versus negative) was high in the proficiency test: the corresponding scores as percentages showed good agreement with the consensus diagnosis. In TNBC metastases, there was substantial variability in PD‐L1 status at the individual patient level. For one in five patients, the chance of treatment was essentially random, with half of the pathologists designating them as positive and half negative. Assessment of PD‐L1/SP142 and TILs as percentages in TNBC metastases showed poor and moderate agreement, respectively. Additional training for metastatic TNBC is required to enhance interobserver agreement. Such training, focusing on metastatic specimens, seems worthwhile, since the same pathologists obtained high percentages of concordance (ranging from 93% to 100%) on the PD‐L1 status of primary TNBCs