Open-ST: High-resolution spatial transcriptomics in 3D.

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies.

OBJECTIVES: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS: A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors.

BACKGROUND: A high level of PD-L1 expression is the most relevant predictive parameter for response to immune checkpoint inhibitor (CPI) therapy in urinary bladder cancer. Existing data on the relationship between PD-L1 expression and the natural course of disease are controversial and sparse. METHODS: To expand our understanding of the relationship between PD-L1 expression and parameters of cancer aggressiveness, PD-L1 was analyzed on tissue microarrays containing 2710 urothelial bladder carcinomas including 512 patients with follow-up data who underwent radical cystectomy and follow-up therapies in the pre-immune checkpoint inhibitor therapy era. RESULTS: Tumor cell positivity in ≥10% of cells were seen in 513 (20%) and an immune cell positivity occurred in 872 (34%) of 2566 interpretable cancers. PD-L1 positivity in tumor cells increased from pTaG2 low grade (0.9% positive) to pTaG3 high grade (4.1%; p = 0.0255) and was even higher in muscle-invasive (pT2-4) carcinomas (29.3%; p < 0.0001). However, within pT2-4 carcinomas, PD-L1 positivity was linked to low pT stage (p = 0.0028), pN0 (p < 0.0001), L0 status (p = 0.0005), and a better prognosis within 512 patients with cystectomy who never received CPIs (p = 0.0073 for tumor cells and p = 0.0086 for inflammatory cells). PD-L1 staining in inflammatory cells was significantly linked to PD-L1 staining in tumor cells (p < 0.0001) and both were linked to a positive p53 immunostaining (p < 0.0001). CONCLUSION: It cannot be fully excluded that the strong statistical link between PD-L1 status and favorable histological tumor features as well as better prognosis could influence the outcome of studies evaluating CPIs in muscle-invasive urothelial carcinoma.

Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.

Cytokeratin 20 expression is linked to stage progression and to poor prognosis in advanced (pT4) urothelial carcinoma of the bladder.

Cytokeratin 20 (CK20) expression is limited to umbrella cells in the normal urothelium. Since CK20 is often upregulated in neoplastic urothelial cells including dysplasia and carcinoma in situ, immunohistochemical CK20 analysis is often used for the assessment of bladder biopsies. CK20 expression is a feature of luminal bladder cancer subtype, but its prognostic relevance is disputed. In this study, we investigated CK20 on >2700 urothelial bladder carcinomas in a tissue microarray format by immunohistochemistry. Cytoplasmic and membranous CK20 staining was seen in 1319 (51.8%) cancers. The fraction of CK20 positive and especially strongly positive cases increased from pTaG2 low grade (44.5% strongly positive) and pTaG2 high grade (57.7%) to pTaG3 high grade (62.3%; p = 0.0006) but was lower in muscle-invasive (pT2-4) carcinomas (51.1% in all pTa vs. 29.6% in pT2-4; p < 0.0001). Within pT2-4 carcinomas, CK20 positivity was linked to nodal metastasis and lymphatic vessel invasion (p < 0.0001 each) and to venous invasion (p = 0.0177). CK20 staining was unrelated to overall patient survival if all 605 pT2-4 carcinomas were jointly analyzed but subgroup analyses revealed a significant association of CK20 positivity with favorable prognosis in 129 pT4 carcinomas (p = 0.0005). CK20 positivity was strongly linked to the expression of GATA3 (p < 0.0001), another feature of luminal bladder cancer. The combined analysis of both parameters showed best prognosis for luminal A (CK20+/GATA3+, CK20+/GATA3-) and worst outcome for luminal B (CK20-/GATA3+) and basal/squamous (CK20-/GATA3-) in pT4 urothelial carcinomas (p = 0.0005). In summary, the results of our study demonstrate a complex role of CK20 expression in urothelial neoplasms including neoexpression in pTa tumors, a subsequent loss of CK20 expression in a subset of tumors progressing to muscle-invasion, and a stage dependent prognostic role in muscle-invasive cancers.

Altered p53/p16 expression is linked to urothelial carcinoma progression but largely unrelated to prognosis in muscle-invasive tumors.

BACKGROUND: Most inactivating p53 mutations result in a nuclear p53 accumulation - detectable by immunohistochemistry (IHC). p53 alterations leading to a complete lack of p53 protein and absence of immunostaining do also occur - not easily detectable by IHC. p16 is upregulated in p53 inactivated cells. We hypothesized that a positive p16 IHC may help to distinguish p53 inactivation in IHC negative cases. MATERIAL AND METHODS: We investigated p53 and p16 immunostaining on 2710 urothelial bladder carcinomas in a tissue microarray format to understand their impact in relation to clinicopathological parameters of disease progression and patient outcome. RESULTS: p16 immunostaining was absent in normal urothelium but occurred in 63.5% (30.4% strong) of cancers. p16 strongly positive cases increased from pTaG2 low-grade (9.6%) to pTaG3 high-grade tumors (46.5%, p < .0001) but decreased from pTaG3 to pT4 (33.3%; p = .0030). Among pT2-4 carcinomas, p16 positivity was linked to high-grade (p = .0005) but unrelated to overall survival. p53 staining was negative in 8.4%, very weak in 15.4%, weak in 55.3%, strong in 4.7%, and very strong in 16.2% cancers. p53 negative (potentially p53 null phenotype), strong, and very strong p53 positivity increased from pTaG2 low-grade to pTaG3 high-grade tumors (p < .0001) and from pTaG3 to pT2-4 cancers (p = .0007). p53 staining was largely unrelated to histopathological parameters or patient prognosis among pT2-4 carcinomas, except of p53 strong/very strong immunostaining. p16 expression predominated in tumors with very strong, strong, and negative p53 staining and the combination of p53 negative/p16 strongly positive cancers was linked to features of tumor aggressiveness. CONCLUSION: Aberrant p53 and p16 immunostaining increases during grade and stage progression although p53 negative and p16 positive immunostaining lack prognostic significance in pT2-4 carcinomas. Potential diagnostic features are that high level p16 expression is limited to neoplastic urothelium and p53 null phenotype to aggressive cancers (grade 3 and invasive).

The Immune Checkpoint Landscape in Tumor Cells of Pancreatic Ductal Adenocarcinoma.

Immune checkpoint therapy (ICT) has shown promising potential in the treatment of multiple solid tumors. However, the role of ICT in pancreatic ductal adenocarcinoma (PDAC) remains limited. Patterns of immune checkpoints (ICs) in PDAC represent the basis for establishing a potent ICT. The aim of this study is to create a profile of IC expression and its prognostic relevance in cancer cells of PDAC. Therefore, tumor cells from peripheral and central tissue microarray (TMA) spots from histologically confirmed PDAC of 68 patients after tumor resection were investigated in terms of expressions of TIM3, IDO, B7H4, LAG3, VISTA, and PD-L1 using immunohistochemistry. The presence of the respective ICs was compared to overall survival (OS). The presence of VISTA and PD-L1 significantly correlates with shorter OS (median OS: 22 months vs. 7 months and 22 months vs. 11 months, respectively, p < 0.05). For the presence of TIM3, IDO, B7H4, and LAG3, no difference in OS was observed (p > 0.05). The analysis of OS of combined subgroups for VISTA and PD-L1 (VISTA and PD-L1 neg., VISTA pos. and PD-L1 neg., VISTA neg. and PD-L1 pos., and VISTA and PD-L1 pos.) yielded overall statistical significance difference (p = 0.02). These results suggest that the presence of VISTA and PD-L1 is of prognostic relevance and potentially qualifies them as targets for ICT.

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer.

BACKGROUND: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. METHODS: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. RESULTS: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. CONCLUSION: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Loss of the SWI/SNF-ATPase subunit members SMARCF1 (ARID1A), SMARCA2 (BRM), SMARCA4 (BRG1) and SMARCB1 (INI1) in oesophageal adenocarcinoma.

BACKGROUND: The SWI/SNF complex is an important chromatin remodeler, commonly dysregulated in cancer, with an estimated mutation frequency of 20%. ARID1A is the most frequently mutated subunit gene. Almost nothing is known about the other familiar members of the SWI/SNF complexes, SMARCA2 (BRM), SMARCA4 (BRG1) and SMARCB1 (INI1), in oesophageal adenocarcinoma (EAC). METHODS: We analysed a large cohort of 685 patients with EAC. We used four different antibodies to detect a loss-of-protein of ARID1A BRM, BRG1 and INI1 by immunohistochemistry and correlated these findings with molecular and clinical data. RESULTS: Loss of ARID1A, BRG1, BRM and INI1 was observed in 10.4, 3.4, 9.9 and 2% of EAC. We found a co-existing protein loss of ARID1A and BRM in 9.9% and of ARID1A and BRG1 in 2.2%. Patients with loss of ARID1A and TP53 wildtype EACs showed a shortened overall survival compared with AIRDA1A-positive tumours [median overall survival was 60.1 months (95%CI 1.2-139.9 months)] in patients with ARIDA-1A expression and 26.2 months (95%CI 3.7-19.1 months) in cases of ARIDA-1A loss (p = 0.044). Tumours with loss or expression of ARID1A and TP53 loss were not associated with a difference in survival. Only one tumour revealed high microsatellite instability (MSI-H) with concomitant ARID1A loss. All other ARID1A loss-EACs were microsatellite-stable (MSS). No predictive relevance was seen for SWI/SNF-complex alterations and simultaneous amplification of different genes (PIK3CA, KRAS, c-MYC, MET, GATA6, ERBB2). CONCLUSION: Our work describes, for the first time, loss of one of the SWI/SNF ATPase subunit proteins in a large number of adenocarcinomas of the oesophagus. Several papers discuss possible therapeutic interventions for tumours showing a loss of function of the SWI/SNF complex, such as PARP inhibitors or PI3K and AKT inhibitors. Future studies will be needed to show whether SWI/SNF complex-deficient EACs may benefit from personalized therapy.

Stepwise Analysis of Resection Margin Impact on Survival and Distant Metastasis in Pancreatic Head Ductal Adenocarcinoma.

The prognostic role of tumor cells in pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head with direct microscopic infiltration (DMI) or in close proximity (≤1 mm) to the resection margin (RM) remains unclear. This single-center, retrospective study included specimens from 75 patients who underwent oncological resection of pancreatic head PDAC between February 2013 and July 2020. Two pathologists independently re-measured the distance between tumors and the multiple RMs. The impact of RM involvement for DMI, tumor cells within ≤1 mm, in general, and for individual RMs on overall survival (OS) and development of distant pulmonary (PM) and hepatic (HM) metastasis was analyzed. DMI of RMs was significantly associated with a shorter OS (median 5 vs 19 months, P = .02). The presence of tumor cells within ≤1 mm of RMs yielded a negative impact on OS with a trend toward significance (median 9 vs 21 months, P = .09). DMI and tumor cells within ≤1 mm of the pancreatic transection margin (PRM), individually, had a significant negative impact on OS (median 4 vs 19 months and 6 vs 19 months, P < .05), but not for any other individual RM. RM involvement of ≤1 mm of only the vascular circumferential resection margin (VCRM) resulted in a shorter time to HM development (P = 0.05). DMI of the posterior circumferential resection margin (PCRM) and VCRM, individually, showed shorter time to PM (P < .05). Potential clinical considerations include extended intraoperative evaluation of the PRM (1 mm) and intensified preoperative prediction of R1 resection as a basis for neoadjuvant therapy.

Spatial heterogeneity of tumor cells and the tissue microenvironment in oral squamous cell carcinoma.

PURPOSE: This study describes the morphologic and phenotypic spatial heterogeneity of tumor cells and the tissue microenvironment (TME), focusing on immune infiltration in OSCCs. STUDY DESIGN: Patients with OSCCs and planned surgical tumor resection were eligible for the study. Two biopsies each from the tumor center and the tumor rim were obtained. Immunohistochemical characterization of tumor and immune cells was performed using a panel of immunohistochemical markers. RESULTS: Thirty-six biopsies were obtained from the 9 patients. All patients showed an individual marker expression profile with ITH. Within the same biopsy, the CPS and TPS scores showed relevant variations in PD-L1 expression. Comparisons between the tumor center and rim revealed significant differences in the up/downregulation of p53. Marker expression of patients with recurrences clustered similarly, with the higher expression of FoxP3, IDO, CD4, CD68, and CD163 at the tumor rim. CONCLUSION: OSCCs were found to exhibit relevant ITH involving both tumor cells and TME, suggesting that biomarker analysis of multiple tumor regions may be helpful for clinical decision making and tumor characterization. The analysis of multiple spots within a biopsy is recommended for a reliable determination of PD-L1 expression and other biomarkers, impacting current clinical assessments.

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading.

OBJECTIVES: Thyroid transcription factor 1 (TTF-1) is a well-established independent prognostic factor in lung adenocarcinoma (LUAD), irrespective of stage. This study aims to determine if TTF-1's prognostic impact is solely based on histomorphological differentiation (tumor grading) or if it independently relates to a biologically more aggressive phenotype. We analyzed a large bi-centric LUAD cohort to accurately assess TTF-1's prognostic value in relation to tumor grade. PATIENTS AND METHODS: We studied 447 patients with resected LUAD from major German lung cancer centers (Berlin and Cologne), correlating TTF-1 status and grading with clinical, pathologic, and molecular data, alongside patient outcomes. TTF-1's impact was evaluated through univariate and multivariate Cox regression. Causal graph analysis was used to identify and account for potential confounders, improving the statistical estimation of TTF-1's predictive power for clinical outcomes. RESULTS: Univariate analysis revealed TTF-1 positivity associated with significantly longer disease-free survival (DFS) (median log HR -0.83; p = 0.018). Higher tumor grade showed a non-significant association with shorter DFS (median log HR 0.30; p = 0,62 for G1 to G2 and 0.68; p = 0,34 for G2 to G3). In multivariate analysis, TTF-1 positivity resulted in a significantly longer DFS (median log HR -0.65; p = 0.05) independent of all other parameters, including grading. Adjusting for potential confounders as indicated by the causal graph confirmed the superiority of TTF-1 over tumor grading in prognostics power. CONCLUSIONS: TTF-1 status predicts relapse and survival in LUAD independently of tumor grading. The prognostic power of tumor grading is limited to TTF-1-positive patients, and the effect size of TTF-1 surpasses that of tumor grading. We recommend including TTF1 status as a prognostic factor in the diagnostic guidelines of LUAD.

Loss of Upk1a and Upk1b expression is linked to stage progression in urothelial carcinoma of the bladder.

BACKGROUND: Uroplakin-1a (Upk1a) and uroplakin-1b (Upk1b) have recently been identified as diagnostic markers for the distinction of urothelial carcinomas from other solid tumor entities. Both proteins play an important role in the stabilization and strengthening of epithelial cells that line the bladder. METHODS: To evaluate the prognostic role of uroplakin expression in urothelial carcinomas, more than 2700 urothelial neoplasms were analyzed in a tissue microarray format by immunohistochemistry. To further assess the diagnostic role of uroplakin immunohistochemistry, results were compared with preexisting GATA3 data. RESULT: The fraction of Upk1a/Upk1b positive cases decreased slightly from pTaG2 low-grade (88% positive for Upk1a/87% positive for Upk1b) and pTaG2 high-grade (92%/89%) to pTaG3 (83%/88%; p > 0.05) and was lower in muscle-invasive (pT2-4) carcinomas (42%/64%; p < 0.0001/p < 0.0001 for pTa vs. pT2-4). Within pT2-4 carcinomas, high expression of Upk1a and Upk1b was linked to nodal metastasis and lymphatic vessel infiltration (p < 0.05) but unrelated to patient outcome. There were significant associations between Upk1a, Upk1b and GATA3 immunostaining (p < 0.0001 each), but 11% of GATA3 negative cancers were Upk1a/b positive and 8% of Upk1a/b negative cancers were GATA3 positive. Absence of GATA3/Upk1a/b staining was significantly linked to poor patient survival in the subgroup of 126 pT4 carcinomas (p = 0.0004) but not in pT2 and pT3 cancers. CONCLUSIONS: In summary, the results of our study demonstrate that Upk1a and/or Upk1b immunohistochemistry can complement GATA3 for the distinction of urothelial carcinomas. Furthermore, a progressive loss of Upk1a/b expression during stage progression and a prognostic role of the combination GATA3/Upk1a/Upk1b in pT4 carcinomas is evident.

CEA (CEACAM5) expression is common in muscle-invasive urothelial carcinoma of the bladder but unrelated to the disease course.

Objectives: Carcinoembryonic antigen (CEA) is a cell surface glycoprotein that represents a promising therapeutic target. Serum measurement of shedded CEA can be utilized for monitoring of cancer patients. Material and Methods: To evaluate the potential clinical significance of CEA expression in urothelial bladder neoplasms, CEA was analysed by immunohistochemistry in more than 2500 urothelial bladder carcinomas in a tissue microarray format. Results: CEA staining was largely absent in normal urothelial cells but was observed in 30.4% of urothelial bladder carcinomas including 406 (16.7%) with weak, 140 (5.8%) with moderate, and 192 (7.9%) with strong staining. CEA positivity occurred in 10.9% of 411 pTaG2 low-grade, 32.0% of 178 pTaG2 high-grade, and 43.0% of 93 pTaG3 tumours (p < 0.0001). In 1335 pT2-4 carcinomas, CEA positivity (34.1%) was lower than in pTaG3 tumours. Within pT2-4 carcinomas, CEA staining was unrelated to pT, pN, grade, L-status, V-status, overall survival, recurrence free survival, and cancer specific survival (p > 0.25). Conclusion: CEA increases markedly with grade progression in pTa tumours, and expression occurs in a significant fraction of pT2-4 urothelial bladder carcinomas. The high rate of CEA positivity in pT2-4 carcinomas offers the opportunity of using CEA serum measurement for monitoring the clinical course of these cancers. Moreover, CEA positive urothelial carcinomas are candidates for a treatment by targeted anti-CEA drugs.

Prognostic Impact and Spatial Interplay of Immune Cells in Urothelial Cancer.

BACKGROUND AND OBJECTIVE: Quantity and the spatial relationship of specific immune cell types can provide prognostic information in bladder cancer. The objective of the study was to characterize the spatial interplay and prognostic role of different immune cell subpopulations in bladder cancer. METHODS: A total of 2463 urothelial bladder carcinomas were immunostained with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry in a tissue microarray format and analyzed using a framework of neuronal networks for an image analysis. Spatial immune parameters were compared with histopathological parameters and overall survival data. KEY FINDINGS AND LIMITATIONS: The identification of > 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. Thirty-nine immune parameters showed prognostic significance in univariate analyses, of which 16 were independent from pT, pN, and histological grade in muscle-invasive bladder cancer. Among all these parameters, the strongest association with prolonged overall survival was identified for intraepithelial CD8+ cytotoxic T cells (time-dependent area under receiver operating characteristic curve [AUC]: 0.70), while stromal CD8+ T cells were less relevant (AUC: 0.65). A favorable prognosis of inflamed cancers with high levels of "exhaustion markers" suggests that TIM3, PD-L1, PD-1, and CTLA-4 on immune cells do not hinder antitumoral immune response in tumors rich of tumor infiltrating immune cells. CONCLUSIONS AND CLINICAL IMPLICATIONS: The density of intraepithelial CD8+ T cells was the strongest prognostic feature in muscle-invasive bladder cancer. Given that tumor cell killing by CD8+ cytotoxic T lymphocytes through direct cell-to-cell-contacts represents the "terminal end route" of antitumor immunity, the quantity of "tumor cell adjacent CD8+ T cells" may constitute a surrogate for the efficiency of cancer recognition by the immune system that can be measured straightaway in routine pathology as the CD8 labeling index. PATIENT SUMMARY: Quantification of intraepithelial CD8+ T cells, the strongest prognosticfeature identified in muscle-invasive bladder cancer, can easily be assessed by brightfield immunohistochemistry and is therefore "ready to use" for routine pathology.

Morphological, clinical and molecular characteristics in ARID1a­deficient microsatellite­stable oesophageal adenocarcinoma.

Publications describe the relevance of the AT-rich interactive domain-containing protein 1A (ARID1a) mutation in gastric adenocarcinoma, which occurs predominantly in the microsatellite instable (MSI)- and Epstein-Barr virus (EBV)-associated subtypes. It is unclear whether potential therapeutic, prognostic or morphologic descriptions are not epiphenomena of MSI (or EBV). Since personalised therapeutics are largely lacking for oesophageal adenocarcinoma (EAC), clinical trials investigating the efficacy of these therapeutics specifically in this subgroup are useful. To the best of our knowledge, this was the first study analysing the relevant tumour subset of microsatellite-stable (MSS) EAC with loss of function of ARID1a. A total of 875 patients with EAC and data from The Cancer Genome Atlas (TCGA) were analysed. Statistical analyses associating previously known molecular characteristics of the present tumour cohort, overall survival, morphological growth patterns and tumour heterogeneity issues were considered. Subsequently, 10% of EAC were ARID1a-deficient, the majority of which were MSS (7.5%). There was no characteristic growth pattern. Approximately 60% of tumours were PD-L1 positive to varying degrees. TP53 mutations occurred together with ARID1a defective EAC in the present cohort and in the TCGA collective. The extent of 7.5% MSS-EAC with ARID1a loss was unaffected by neoadjuvant therapy. ARID1a loss was often detected to be homogeneous (92%). ARID1a loss is not an epiphenomenon of MSI in EAC. The high homogeneity of ARID1a loss tumour clones could be considered an argument for the effectiveness of potential therapeutics. Since the majority of genomic ARID1a alterations result in protein loss, immunohistochemistry is a useful screening technique, especially in the absence of morphological characteristics.

cCPE Fusion Proteins as Molecular Probes to Detect Claudins and Tight Junction Dysregulation in Gastrointestinal Cell Lines, Tissue Explants and Patient-Derived Organoids.

Claudins regulate paracellular permeability, contribute to epithelial polarization and are dysregulated during inflammation and carcinogenesis. Variants of the claudin-binding domain of Clostridium perfringens enterotoxin (cCPE) are highly sensitive protein ligands for generic detection of a broad spectrum of claudins. Here, we investigated the preferential binding of YFP- or GST-cCPE fusion proteins to non-junctional claudin molecules. Plate reader assays, flow cytometry and microscopy were used to assess the binding of YFP- or GST-cCPE to non-junctional claudins in multiple in vitro and ex vivo models of human and rat gastrointestinal epithelia and to monitor formation of a tight junction barrier. Furthermore, YFP-cCPE was used to probe expression, polar localization and dysregulation of claudins in patient-derived organoids generated from gastric dysplasia and gastric cancer. Live-cell imaging and immunocytochemistry revealed cell polarity and presence of tight junctions in glandular organoids (originating from intestinal-type gastric cancer and gastric dysplasia) and, in contrast, a disrupted diffusion barrier for granular organoids (originating from discohesive tumor areas). In sum, we report the use of cCPE fusion proteins as molecular probes to specifically and efficiently detect claudin expression, localization and tight junction dysregulation in cell lines, tissue explants and patient-derived organoids of the gastrointestinal tract.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours.

BACKGROUND: Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. METHODS: We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier. FINDINGS: On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. INTERPRETATION: We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. FUNDING: This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).