Simultaneous p53 and p16 Immunostaining for Molecular Subclassification of Head and Neck Squamous Cell Carcinomas.

PURPOSE: Our aim was to assess the ability of simultaneous immunohistochemical staining (IHC) for p16 and p53 to accurately subclassify head and neck squamous cell carcinomas (HNSCC) as HPV-associated (HPV-A) versus HPV-independent (HPV-I) and compare p53 IHC staining patterns to TP53 mutation status, p16 IHC positivity and HPV status. METHODS: We stained 31 HNSCCs for p53 and p16, and performed next-generation sequencing (FoundationOne©CDx) on all cases and HPV in-situ hybridization (ISH) when sufficient tissue was available (n = 23). p53 IHC staining patterns were assessed as wildtype (wt) or abnormal (abn) patterns i.e. overexpression, null or cytoplasmic staining. RESULTS: In a majority of cases (28/31) interpretation of p16 and p53 IHC was straightforward; 10 were considered HPV-A (p16+/p53wt) and 18 cases were HPV-I (p16-/p53abn). In the remaining three tumours the unusual immunophenotype was resolved by molecular testing, specifically (i) subclonal p16 staining and wild type p53 staining in a tumour positive for HPV and with no TP53 mutation (HPV-A), (ii) negative p16 and wild type p53 staining with a TP53 mutation and negative for HPV (HPV-I), and (iii) equivocally increased p16 staining with mutant pattern p53 expression, negative HPV ISH and with a TP53 mutation (HPV-I). CONCLUSION: Performing p16 and p53 IHC staining simultaneously allows classification of most HNSCC as HPV-A (p16 +, p53 wild type (especially basal sparing or null-like HPV associated staining patterns, which were completely specific for HPV-A SCC) or HPV-I (p16 -, p53 mutant pattern expression), with the potential for limiting additional molecular HPV or mutational testing to selected cases only.

Asparaginase-like protein 1 as a prognostic tissue biomarker in clinicopathologically and molecularly characterized endometrial cancer.

OBJECTIVE: Prognostic stratification of endometrial cancer involves the assessment of stage, uterine risk factors, and molecular classification. This process can be further refined through annotation of prognostic biomarkers, notably L1 cell adhesion molecule (L1CAM) and hormonal receptors. Loss of asparaginase-like protein 1 (ASRGL1) has been shown to correlate with poor outcome in endometrial cancer. Our objective was to assess prognostication of endometrial cancer by ASRGL1 in conjunction with other available methodologies. STUDY DESIGN: This was a retrospective study of patients who underwent primary treatment at a single tertiary center. Tumors were molecularly classified by the Proactive Molecular Risk Classifier for Endometrial Cancer. Expression of ASRGL1, L1CAM, estrogen receptor, and progesterone receptor was determined by immunohistochemistry. ASRGL1 expression intensity was scored into four classes. RESULTS: In a cohort of 775 patients, monitored for a median time of 81 months, ASRGL1 expression intensity was related to improved disease-specific survival in a dose-dependent manner (P < 0.001). Low expression levels were associated with stage II-IV disease and presence of uterine factors, i.e. high grade, lymphovascular space invasion, and deep myometrial invasion (P < 0.001 for all). Among the molecular subgroups, low expression was most prevalent in p53 abnormal carcinomas (P < 0.001). Low ASRGL1 was associated with positive L1CAM expression and negative estrogen and progesterone receptor expression (P < 0.001 for all). After adjustment for stage and uterine factors, strong ASRGL1 staining intensity was associated with a lower risk for cancer-related deaths (hazard ratio 0.56, 95 % confidence interval 0.32-0.97; P = 0.038). ASRGL1 was not associated with the outcome when adjusted for stage, molecular subgroups, L1CAM, and hormonal receptors. When analyzed separately within the different molecular subgroups, ASRGL1 showed an association with disease-specific survival specifically in "no specific molecular profile" subtype carcinomas (P < 0.001). However, this association became nonsignificant upon controlling for confounders. CONCLUSIONS: Low ASRGL1 expression intensity correlates with poor survival in endometrial cancer. ASRGL1 contributes to more accurate prognostication when controlled for stage and uterine factors. However, when adjusted for stage and other biomarkers, including molecular subgroups, ASRGL1 does not improve prognostic stratification.

Is it safe to operate selected low-risk endometrial cancer patients in secondary hospitals?

INTRODUCTION: The aim of this study was to assess the accuracy of a preoperative screening algorithm in identifying low-risk endometrial cancer (EC) patients to ensure optimal care. METHODS: A total of 277 patients with primary EC confirmed through biopsy underwent magnetic resonance imaging (MRI). Patients with risk factors for advanced high-risk EC, such as non-endometrioid histology, high-grade differentiation status, deep myometrial invasion, or spread beyond the uterine corpus, were systematically excluded. The remaining preoperatively screened patients with stage IA low-grade endometrioid EC (EEC) (n = 93) underwent surgery in a tertiary hospital. The accuracy of the preoperative diagnosis was evaluated by comparing the findings with the postoperative histopathological results. Disease-free survival (DFS) and overall survival (OS) were analyzed using 8-year follow-up data. RESULTS: Postoperative histopathological analysis revealed that all patients had grade 1-2 EEC localized to the corpus uteri. Only three patients had deep myometrial invasion (stage IB), but they remained disease-free after 6-9 years of follow-up. The median follow-up time for all patients was 8.7 years. The DFS was 7.6 years, and the OS was 8.6 years. Two patients with stage IA grade 1 EEC experienced relapse and, despite treatment, died of EC. No other EC-related deaths occurred. CONCLUSIONS: The screening algorithm accurately identified low-risk EC patients without compromising survival. Therefore, the algorithm appears to be feasible for selecting patients for surgery in secondary hospitals.

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

Endometrial Carcinosarcomas are Almost Exclusively of p53abn Molecular Subtype After Exclusion of Mimics.

SUMMARY: Our aim was to assess the molecular subtype(s) and perform a detailed morphologic review of tumors diagnosed as carcinosarcoma in a population-based cohort. Forty-one carcinosarcomas were identified from a cohort of 973 endometrial carcinomas diagnosed in 2016. We assessed immunostaining and sequencing data and undertook expert pathology reviews of these cases as well as all subsequently diagnosed (post-2016) carcinosarcomas of no specific molecular profile (NSMP) molecular subtype (n=3) from our institutions. In the 2016 cohort, 37 of the 41 carcinosarcomas (91.2%) were p53abn, 2 (4.9%) were NSMP, and 1 each (2.4%) were POLE mut and mismatch repair deficiency molecular subtypes, respectively. Of the 4 non-p53abn tumors on review, both NSMP tumors were corded and hyalinized (CHEC) pattern endometrioid carcinoma, the mismatch repair deficiency tumor was a grade 1 endometrioid carcinoma with reactive stromal proliferation, and the POLE mut tumor was grade 3 endometrioid carcinoma with spindle cell growth, that is, none were confirmed to be carcinosarcoma on review. We found 11 additional cases among the 37 p53abn tumors that were not confirmed to be carcinosarcoma on the review (3 undifferentiated or dedifferentiated carcinomas, 5 carcinomas with CHEC features, 2 carcinomas showing prominent reactive spindle cell stroma, and 1 adenosarcoma). In the review of institutional cases reported as NSMP carcinosarcoma after 2016, 3 were identified (1 adenosarcoma and 2 mesonephric-like adenocarcinoma on review). In this series, all confirmed endometrial carcinosarcomas were p53abn. The finding of any other molecular subtype in a carcinosarcoma warrants pathology review to exclude mimics.

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.