Expansion of tumor-infiltrating lymphocytes from head and neck squamous cell carcinoma to assess the potential of adoptive cell therapy.

BACKGROUND: Adoptive transfer of in vitro expanded tumor-infiltrating lymphocytes (TILs) has been effective in regressing several types of malignant tumors. This study assessed the yield and factors influencing the successful expansion of tumor-infiltrating lymphocytes (TILs) from head and neck squamous cell carcinoma (HNSCC), along with their immune phenotypes. METHODS: TILs were expanded from 47 surgically resected HNSCC specimens and their metastasized lymph nodes. The cancer tissues were cut into small pieces (1-2 mm) and underwent initial expansion for 2 weeks. Tumor location, smoking history, stromal TIL percentage, human papillomavirus infection, and programmed death-ligand 1 score were examined for their impact on successful expansion of TILs. Expanded TILs were evaluated by flow cytometry using fluorescence-activated cell sorting. A second round of TIL expansion following the rapid expansion protocol was performed on a subset of samples with successful TIL expansion. RESULTS: TILs were successfully expanded from 36.2% samples. Failure was due to contamination (27.6%) or insufficient expansion (36.2%). Only the stromal TIL percentage was significantly associated with successful TIL expansion (p = 0.032). The stromal TIL percentage also displayed a correlation with the expanded TILs per fragment (r = 0.341, p = 0.048). On flow cytometry analysis using 13 samples with successful TIL expansion, CD4 + T cell dominancy was seen in 69.2% of cases. Effector memory T cells were the major phenotype of expanded CD4 + and CD8 + T cells in all cases. CONCLUSION: We could expand TILs from approximately one-third of HNSCC samples. TIL expansion could be applicable in HNSCC samples with diverse clinicopathological characteristics.

PD-L1 expression in resected lung adenocarcinoma: prevalence and prognostic significance in relation to the IASLC grading system.

AIMS: Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for adjuvant immunotherapy and has been linked to poor differentiation in lung adenocarcinoma. However, its prevalence and prognostic role in the context of the novel histologic grade has not been evaluated. METHODS: We analysed a cohort of 1233 patients with resected lung adenocarcinoma where PD-L1 immunohistochemistry (22C3 assay) was reflexively tested. Tumour PD-L1 expression was correlated with the new standardized International Association for the Study of Lung Cancer (IASLC) histologic grading system (G1, G2, and G3). Clinicopathologic features including patient outcome were analysed. RESULTS: PD-L1 was positive (≥1%) in 7.0%, 23.5%, and 63.0% of G1, G2, and G3 tumours, respectively. PD-L1 positivity was significantly associated with male sex, smoking, and less sublobar resection among patients with G2 tumours, but this association was less pronounced in those with G3 tumours. PD-L1 was an independent risk factor for recurrence (adjusted hazard ratio [HR] = 3.25, 95% confidence intervals [CI] = 1.93-5.48, P < 0.001) and death (adjusted HR = 2.69, 95% CI = 1.13-6.40, P = 0.026) in the G2 group, but not in the G3 group (adjusted HR for recurrence = 0.94, 95% CI = 0.64-1.40, P = 0.778). CONCLUSION: PD-L1 expression differs substantially across IASLC grades and identifies aggressive tumours within the G2 subgroup. This knowledge may be used for both prognostication and designing future studies on adjuvant immunotherapy.

Comprehensive Immunohistochemical Analysis of Mesonephric Marker Expression in Low-grade Endometrial Endometrioid Carcinoma.

Immunohistochemical markers shown to be useful in identifying/confirming mesonephric/mesonephric-like differentiation (MLD markers) include thyroid transcription factor (TTF1), GATA-binding protein 3 (GATA3), and cluster of differentiation 10 (CD10). Only a few studies have examined the expression levels of MLD markers in endometrial endometrioid carcinomas (EECs). This study aimed to analyze the frequency and pattern of MLD marker expression in low-grade EECs. We performed immunostaining for the detection of TTF1, GATA3, and CD10 expression in 50 low-grade EEC tissue samples and evaluated their staining proportion and intensity. Nine tumors (18.0%) expressed at least one MLD marker in varying proportions and intensities, and 2 of these tumors were positive for 2 MLD markers (TTF1/GATA3 and GATA3/CD10, respectively). Three (6.0%) tumors showed moderate-to-strong nuclear TTF1 immunoreactivity in ≤5% of the tumor cells. Five tumors (10.0%) had at least moderate nuclear GATA3 staining, and three of them displayed a staining proportion of ≥15%. Three tumors (6.0%) were focal (mean proportion, 15%) but strongly positive for CD10. Our findings indicate that a subset of EEC can express one or more MLD markers with varying staining proportions and intensities. Given that a diagnosis of uterine mesonephric-like adenocarcinoma should be established based on a combination of characteristic histologic features, unique immunophenotypes, and confirmed molecular findings, pathologists should not exclude EEC based only on the presence of focal immunoreactivity for MLD markers. Awareness of the atypical expression patterns of MLD markers in EEC helps pathologists avoid misdiagnosing EEC as a uterine mesonephric-like adenocarcinoma.

PIK3CA mutation subtype delineates distinct immune profiles in gastric carcinoma.

PIK3CA mutations in cancer regulate tumour immunogenicity. Given that PIK3CA mutation subtypes influence therapeutic responses to AKT inhibitor and that H1047R mutation confers selective growth advantages after immunotherapy, we hypothesised that immune phenotypes may depend on PIK3CA mutation subtypes. We investigated 133 gastric cancers (GCs) harbouring PIK3CA mutation [21 E542K (15.8%), 36 E545X (27.1%), 26 H1047X (19.5%), and 46 others (34.6%)]. Four patients (3.0%) had a combination of mutations (E542K + E545K in 3 patients and E545K + H1047R in 1 patient). Epstein-Barr virus (EBV) and microsatellite instability (MSI) status, PD-L1 (programmed death-ligand 1) combined positive score (CPS), and stromal tumour-infiltrating lymphocytes (TILs) were assessed. Concurrent genomic alterations, GeoMx digital spatial profiling (DSP), and OPAL multiplex immunohistochemistry (mIHC) were analysed, and correlation between the two assays was investigated. Of the 133 PIK3CA-mutant (PIK3CAm ) GCs, MSI-high GC was significantly frequent in the H1047X mutation subtype (p = 0.005), while EBV positivity did not affect the mutation subtypes. There was no significant survival difference between the E542K, E545X, and H1047X subgroups. However, in the subgroup analysis for EBV-positive GC, H1047Xm GC showed a trend towards shorter survival than E542K and E545Xm GC (p = 0.090 and 0.062). With DSP analysis, H1047Xm GC showed elevated VISTA (p = 0.0003), granzyme B (p < 0.0001), CD4 (p = 0.0001), and CD45 (p < 0.0001) expression compared with the E542Km or E545Xm GC subgroups, and only VISTA expression remained significant (p < 0.0001) using OPAL mIHC. DSP and OPAL analyses showed a moderate correlation of CD4 (ρ = 0.42, p = 0.004) and CD8 (ρ = 0.62, p < 0.001) expression levels in a comparison of six antibodies. Immune-related protein expression levels were evident when classified by the three PIK3CA hotspot mutations, and H1047Xm GC showed the highest immune-related protein expression compared with E542Km or E545Xm GC. Our results demonstrated distinct immune profiles in GC with PIK3CA hotspot mutations using GeoMx DSP and OPAL mIHC, and there was a correlation between the two multiplex platforms. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Sudden Death Associated With Possible Flare-Ups of Multiple Sclerosis After COVID-19 Vaccination and Infection: A Case Report and Literature Review.

We present an autopsy case of a 19-year-old man with a history of epilepsy whose unwitnessed sudden death occurred unexpectedly in the night. About 4 years before death, he was diagnosed with unilateral optic neuritis (ON). Demyelinating disease was suspected, but he was lost to follow up after the recovery. Six months before death, he received a second dose of mRNA coronavirus disease 2019 (COVID-19) vaccine. Three months before death, he experienced epileptic seizures for the first time. Seventeen days before death, he was infected with COVID-19, which showed self-limited course under home isolation. Several days before death, he complained of seizures again at night. Autopsy revealed multifocal gray-tan discoloration in the cerebrum. Histologically, the lesions consisted of active and inactive demyelinated plaques in the perivenous area of the white matter. Perivascular lymphocytic infiltration and microglial cell proliferation were observed in both white matter and cortex. The other major organs including heart and lung were unremarkable. Based on the antemortem history and postmortem findings, the cause of death was determined to be multiple sclerosis with suspected exacerbation. The direct or indirect involvement of cortex and deep gray matter by exacerbated multiple sclerosis may explain the occurrence of seizures. Considering the absence of other structural abnormalities except the inflammatory demyelination of the cerebrum, fatal arrhythmia or laryngospasm in the terminal epileptic seizure may explain his sudden unexpected death in the benign circumstances. In this case, the onset of seizure was preceded by COVID-19 vaccination, and the exacerbation of seizure was preceded by COVID-19 infection, respectively. Literature reporting first manifestation or relapse of multiple sclerosis temporally associated with COVID-19 vaccination or infection are reviewed.

Myocarditis-induced Sudden Death after BNT162b2 mRNA COVID-19 Vaccination in Korea: Case Report Focusing on Histopathological Findings.

We present autopsy findings of a 22-year-old man who developed chest pain 5 days after the first dose of the BNT162b2 mRNA vaccine and died 7 hours later. Histological examination of the heart revealed isolated atrial myocarditis, with neutrophil and histiocyte predominance. Immunohistochemical C4d staining revealed scattered single-cell necrosis of myocytes which was not accompanied by inflammatory infiltrates. Extensive contraction band necrosis was observed in the atria and ventricles. There was no evidence of microthrombosis or infection in the heart and other organs. The primary cause of death was determined to be myocarditis, causally-associated with the BNT162b2 vaccine.

Regional Lymph Node Metastasis of Scalp Angiosarcoma: A Detailed Clinical Observation Study of 40 Cases.

BACKGROUND: The incidence of lymph node metastasis (LNM) of angiosarcomas is reported to be less than 15%, and elective neck management has not been indicated. This study evaluated the incidence and pattern of regional LNM in patients with scalp angiosarcomas using the clinical data of its full course to understand time-event sequences of scalp angiosarcomas. METHODS: This retrospective study included all consecutive cases of pathology-confirmed angiosarcomas and analyzed 40 cases of scalp angiosarcomas. The survival plots were estimated using the Kaplan-Meier method, and the results are presented mainly in a descriptive manner. RESULTS: The overall survival rate for the patients was 35.8% at 2 years. In contrast to previous reports, regional LNM was observed in more than half of the patients (52.5%) with scalp angiosarcoma. Meanwhile, a direct spread to distant organs occurred in only 27.5% of the patients. Regional LNM could predict clinical manifestation of systemic disease within 3 to 6 months. No differences in survival rates between patients with and without LNM were observed in this series. Occurrence of LNM seemed to be correlated with a high mitotic rate of primary tumors, but not with tumor grade or tumor dimension. The first-echelon lymph nodes from scalp angiosarcoma were peri-parotid, post-auricular, and level 2 lymph nodes. CONCLUSIONS: For a localized scalp angiosarcoma, it seems reasonable for initial curative surgery to include prophylactic evaluation of regional lymph nodes for pathologic nodal staging, prognosis estimation, and the decision for systemic treatments.

Real-time Histological Evaluation of Gastric Cancer Tissue by Using a Confocal Laser Endomicroscopic System.

BACKGROUND/AIM: The need for instant histological evaluation of fresh tissue, especially in cancer treatment, remains paramount. The conventional frozen section technique has inherent limitations, prompting the exploration of alternative methods. A recently developed confocal laser endomicroscopic system provides real-time imaging of the tissue without the need for glass slide preparation. Herein, we evaluated its applicability in the histologic evaluation of gastric cancer tissues. MATERIALS AND METHODS: A confocal laser endomicroscopic system (CLES) with a Lissajous pattern laser scanning, was developed. Fourteen fresh gastric cancer tissues and the same number of normal gastric tissues were obtained from advanced gastric cancer patients. Fluorescein sodium was used for staining. Five pathologists interpreted 100 endomicroscopic images and decided their histologic location and the presence of cancer. Following the review of matched hematoxylin and eosin (H&E) slides, their performance was evaluated with another 100 images. RESULTS: CLES images mirrored gastric tissue histology. Pathologists were able to detect the histologic location of the images with 65.7% accuracy and differentiate cancer tissue from normal with 74.7% accuracy. The sensitivity and specificity of cancer detection were 71.9% and 76.1%. Following the review of matched H&E images, the accuracy of identifying the histologic location was increased to 92.8% (p<0.0001), and that of detecting cancer tissue was also increased to 90.9% (p<0.001). The sensitivity and specificity of cancer detection were enhanced to 89.1% and 93.2% (p<0.0001). CONCLUSION: High-quality histological images were immediately acquired by the CLES. The operator training enabled the accurate detection of cancer and histologic location raising its potential applicability as a real-time tissue imaging modality.

Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy.

There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS: In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION: PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.

Artificial Intelligence in the Pathology of Gastric Cancer.

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Extraskeletal Mesenchymal Chondrosarcoma of the Uterus.

Mesenchymal chondrosarcoma is an uncommon malignant mesenchymal tumor with an aggressive behavior. Diagnoses of mesenchymal chondrosarcoma are established based on histomorphological, immunohistochemical, and molecular findings. Only one case of extraskeletal mesenchymal chondrosarcoma (EMC) of the uterus has been reported. This article presents the second case of primary uterine EMC, occurring in a 33-year-old woman. We describe the histological and immunophenotypical features of EMC. Our observations will help pathologists and clinicians perform accurate histological diagnoses of uterine EMC and plan appropriate treatment strategies for this rare tumor.

Down-regulation of TRPV6 Is Associated With Adverse Prognosis in Hepatocellular Carcinoma Treated With Curative Resection.

BACKGROUND/AIM: Transient receptor potential vanilloid 6 (TRPV6), an endothelial Ca2+-selective entry channel, is expressed in various cancer types, and a selective TRPV6 inhibitor is currently being investigated in a clinical trial. However, TRPV6 expression in hepatocellular carcinoma (HCC) has not been reported. MATERIALS AND METHODS: We evaluated TRPV6 expression in 219 cases of HCC and analyzed its association with clinicopathological parameters and prognostic significance. TRPV6 mRNA expression was compared between HCC and non-tumor liver tissues using various public datasets, and its prognostic effect was examined in The Cancer Genome Atlas (TCGA) cohort. RESULTS: Low TRPV6 expression was found in 37.4% of patients, which was significantly associated with adverse histologic features, and patients with low TRPV6 expression had shorter recurrence-free and disease-free survival. TRPV6 mRNA expression was consistently lower in HCC compared to non-tumor liver samples in public datasets, at the whole tissue level as well as single-cell level. Patients with low TRPV6 expression in the TCGA cohort had shorter progression-free survival. CONCLUSION: TRPV6 expression is down-regulated in HCCs and associated with a poor prognosis. TRPV6 may be a prognostic biomarker in HCC.

Gastric Cancer: Mechanisms, Biomarkers, and Therapeutic Approaches.

Gastric cancer (GC) remains one of the most common deadly malignancies worldwide. Recently, several targeted therapeutics for treating unresectable or metastatic GC have been developed. Comprehensive characterization of the molecular profile and of the tumor immune microenvironment of GC has allowed researchers to explore promising biomarkers for GC treatment and has enabled a new paradigm in precision-targeted immunotherapy. In this article, we review established and promising new biomarkers relevant in GC, with a focus on their clinical implications, diagnostic methods, and the efficacy of targeted agents.

Core needle biopsy and ultrasonography are superior to fine needle aspiration in the management of follicular variant papillary thyroid carcinomas.

PURPOSE: Follicular variant papillary thyroid carcinoma (FVPTC) is a problematic entity. FVPTCs are often misdiagnosed by the standard fine needle aspiration (FNA); in addition, FVPTCs represent a mixed group of tumors with two biologically distinct subtypes: The indolent encapsulated FVPTC and the aggressive infiltrative FVPTC. Recent changes in guidelines suggests that FVPTC management may be improved if subtypes can be determined preoperatively. Preoperative assays, FNA, core needle biopsy (CNB), and ultrasonography (US) were compared for their ability to identify and subtype FVPTCs to determine the most appropriate test to manage FVPTCs. METHODS: The preoperative assays and clinicopathologic variables of 255 resected FVPTCs cases at Samsung Medical Center between 2012 and 2016 were retrospectively evaluated. RESULTS: CNB had the overall best ability to manage FVPTCs with the highest rate of diagnosis indicating surgery, lowest rate of inconclusive results, high sensitivity (88.9%), specificity (87.7%), negative predictive value (97.0%), diagnostic odds ratio (DOR; 56.9), and excellent predictive ability (AUC 0.906) for differentiating FVPTC subtypes. US had a moderate DOR (12.8), good predictive ability (AUC 0.802), high sensitivity (75.0%) and specificity (81.0%). CNB and US both had significantly higher accuracy for discriminating FVPTC subtypes than FNA (AUC 0.908 and 0.877 > 0.671; p < 0.05). The excellent performance of CNB could be attributed to distinct histologic differences between FVPTC subtypes. CONCLUSION: CNB and US had superior performance to FNA in the identification and subtyping of FVPTC. In institutions with skilled and experienced operators, CNB is the preferred method for evaluating possible FVPTC lesions.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response.

BACKGROUND: Manual evaluation of programmed death ligand 1 (PD-L1) tumour proportion score (TPS) by pathologists is associated with interobserver bias. OBJECTIVE: This study explored the role of artificial intelligence (AI)-powered TPS analyser in minimisation of interobserver variation and enhancement of therapeutic response prediction. METHODS: A prototype model of an AI-powered TPS analyser was developed with a total of 802 non-small cell lung cancer (NSCLC) whole-slide images. Three independent board-certified pathologists labelled PD-L1 TPS in an external cohort of 479 NSCLC slides. For cases of disagreement between each pathologist and the AI model, the pathologists were asked to revise the TPS grade (<1%, 1%-49% and ≥50%) with AI assistance. The concordance rates among the pathologists with or without AI assistance and the effect of the AI-assisted revision on clinical outcome upon immune checkpoint inhibitor (ICI) treatment were evaluated. RESULTS: Without AI assistance, pathologists concordantly classified TPS in 81.4% of the cases. They revised their initial interpretation by using the AI model for the disagreement cases between the pathologist and the AI model (N = 91, 93 and 107 for each pathologist). The overall concordance rate among the pathologists was increased to 90.2% after the AI assistance (P < 0.001). A reduction in hazard ratio for overall survival and progression-free survival upon ICI treatment was identified in the TPS subgroups after the AI-assisted TPS revision. CONCLUSION: The AI-powered TPS analyser assistance improves the pathologists' consensus of reading and prediction of the therapeutic response, raising a possibility of standardised approach for the accurate interpretation.

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer.

PURPOSE: Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS: We have developed an artificial intelligence (AI)-powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non-small-cell lung cancer (NSCLC). RESULTS: Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION: The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

Serous Carcinoma of the Endometrium with Mesonephric-Like Differentiation Initially Misdiagnosed as Uterine Mesonephric-Like Adenocarcinoma: A Case Report with Emphasis on the Immunostaining and the Identification of Splice Site TP53 Mutation.

We present herein a rare case of uterine serous carcinoma with mesonephric-like differentiation (SC-MLD) initially misdiagnosed as mesonephric-like adenocarcinoma (MLA). A 51-year-old woman underwent total hysterectomy for a uterine tumor. Histologically, the tumor exhibited various architectures, including papillary, glandular, tubular, cribriform, and cystic. On the basis of this architectural diversity accompanied by intraluminal eosinophilic secretions and intermediate-grade nuclear atypia, the initial diagnosis was MLA. However, the tumor was diffusely and strongly positive for the expression of p16 and negative for the expression of GATA-binding protein 3 (GATA3). Furthermore, we identified a pathogenic tumor protein 53 (TP53) mutation affecting an acceptor splice site in intron 9, despite a wild-type p53 immunostaining pattern. The observations of diffuse and strong p16 expression, lack of GATA3 expression, pathogenic TP53 mutation, and wild-type Kirsten rat sarcoma viral oncogene homolog indicate that this tumor was not an MLA but an SC-MLD. Both uterine SC and MLA can exhibit various histological growth patterns. Our comprehensive clinicopathological and molecular analyses can serve to improve the understanding of this rare condition and help pathologists in making an accurate diagnosis.