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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.
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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.
Subject(s)
Imaging, Three-Dimensional , Transcriptome , Animals , Mice , Humans , Transcriptome/genetics , Imaging, Three-Dimensional/methods , Software , Gene Expression Profiling/methods , Lymph Nodes/pathology , Lymph Nodes/metabolism , Head and Neck Neoplasms/genetics , Head and Neck Neoplasms/pathology , Brain/metabolism , Mice, Inbred C57BL , Lymphatic Metastasis , FemaleABSTRACT
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.
Subject(s)
B7-H1 Antigen , Carcinoma, Transitional Cell , Immune Checkpoint Inhibitors , Neoplasm Invasiveness , Urinary Bladder Neoplasms , Humans , Urinary Bladder Neoplasms/pathology , Urinary Bladder Neoplasms/drug therapy , Urinary Bladder Neoplasms/metabolism , B7-H1 Antigen/analysis , B7-H1 Antigen/biosynthesis , Male , Female , Prognosis , Carcinoma, Transitional Cell/pathology , Carcinoma, Transitional Cell/drug therapy , Carcinoma, Transitional Cell/metabolism , Aged , Middle Aged , Immune Checkpoint Inhibitors/therapeutic use , Aged, 80 and over , Retrospective StudiesABSTRACT
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.
Subject(s)
Algorithms , Artificial Intelligence , Colorectal Neoplasms , Lymphatic Metastasis , Aged , Female , Humans , Male , Middle Aged , Colorectal Neoplasms/pathology , Colorectal Neoplasms/diagnosis , Lymph Nodes/pathology , Lymphatic Metastasis/pathology , Lymphatic Metastasis/diagnosis , Reproducibility of ResultsABSTRACT
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.
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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.
Subject(s)
Urinary Bladder Neoplasms , Humans , Urinary Bladder Neoplasms/immunology , Urinary Bladder Neoplasms/pathology , Urinary Bladder Neoplasms/mortality , Prognosis , Lymphocytes, Tumor-Infiltrating/immunology , Lymphocytes, Tumor-Infiltrating/pathology , CD8-Positive T-Lymphocytes/immunology , Immunohistochemistry , Male , Female , Tissue Array Analysis , Urothelium/immunology , Urothelium/pathology , Carcinoma, Transitional Cell/immunology , Carcinoma, Transitional Cell/pathology , Carcinoma, Transitional Cell/mortality , Aged , Tumor Microenvironment/immunology , Biomarkers, Tumor/analysis , Middle AgedABSTRACT
The prognostic role of tumor cells in pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head with direct microscopic infiltration (DMI) or in close proximity (≤1â 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 ≤1â 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 ≤1â 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 ≤1â 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 ≤1â 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 (1â mm) and intensified preoperative prediction of R1 resection as a basis for neoadjuvant therapy.
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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.
Subject(s)
Carcinoma, Squamous Cell , Head and Neck Neoplasms , Mouth Neoplasms , Humans , Squamous Cell Carcinoma of Head and Neck , Carcinoma, Squamous Cell/metabolism , B7-H1 Antigen , Lymphocytes, Tumor-Infiltrating/metabolism , Biomarkers, Tumor/metabolism , Prognosis , Tumor MicroenvironmentABSTRACT
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.
Subject(s)
Carcinoma, Transitional Cell , Urinary Bladder Neoplasms , Humans , Urinary Bladder Neoplasms/pathology , Carcinoma, Transitional Cell/pathology , Urinary Bladder/pathology , Uroplakin Ia/metabolism , Uroplakin Ib/metabolism , Biomarkers, Tumor/metabolismABSTRACT
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.
Subject(s)
Adenocarcinoma of Lung , Lung Neoplasms , Humans , Thyroid Nuclear Factor 1/genetics , Neoplasm Grading , Neoplasm Staging , Neoplasm Recurrence, Local/pathology , Adenocarcinoma of Lung/pathology , Lung Neoplasms/pathology , PrognosisABSTRACT
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).
Subject(s)
Carcinoma, Transitional Cell , Urinary Bladder Neoplasms , Humans , Carcinoma, Transitional Cell/genetics , Urinary Bladder Neoplasms/genetics , Urinary Bladder Neoplasms/pathology , Tumor Suppressor Protein p53/genetics , Biomarkers, Tumor/genetics , Prognosis , Muscles/pathology , Cyclin-Dependent Kinase Inhibitor p16/geneticsABSTRACT
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.
Subject(s)
Artificial Intelligence , Colorectal Neoplasms , Humans , Algorithms , Biopsy , Medical Oncology , Radiopharmaceuticals , Colorectal Neoplasms/diagnosisABSTRACT
BACKGROUND: The enzyme indoleamine 2,3-dioxygenase 1 (IDO1) plays a crucial role in regulating the immune system's response to tumors, but its exact role in cancer, especially in high-grade serous ovarian cancer (HGSOC), remains controversial. We aimed to investigate the prognostic impact of IDO1 expression and its correlation with tumor-infiltrating lymphocytes (TILs) in HGSOC. METHODS: Immunohistochemical (IHC) staining and bioimage analysis using the QuPath software were employed to assess IDO1 protein expression in a well-characterized cohort of 507 patients with primary HGSOC. Statistical evaluation was performed using SPSS, and in silico validation considering IDO1 mRNA expression in bulk and single-cell gene expression datasets was conducted. Additionally, IDO1 expression in interferon-gamma (IFNG) stimulated HGSOC cell lines was analyzed. RESULTS: Our findings revealed that IDO1 protein and mRNA expression serve as positive prognostic markers for overall survival (OS) and progression-free survival (PFS) in HGSOC. High IDO1 expression was associated with a significant improvement in OS by 21 months (p < 0.001) and PFS by 6 months (p = 0.016). Notably, elevated IDO1 expression correlated with an increased number of CD3+ (p < 0.001), CD4+ (p < 0.001), and CD8+ TILs (p < 0.001). Furthermore, high IDO1 mRNA expression and protein level were found to be associated with enhanced responsiveness to pro-inflammatory cytokines, particularly IFNG. CONCLUSIONS: Our study provides evidence that IDO1 expression serves as a positive prognostic marker in HGSOC and is associated with an increased number of CD3+, CD4+ and CD8+ TILs. Understanding the intricate relationship between IDO1, TILs, and the tumor microenvironment may hold the key to improving outcomes in HGSOC.
Subject(s)
Ovarian Neoplasms , Humans , Female , Ovarian Neoplasms/pathology , Lymphocytes, Tumor-Infiltrating , Prognosis , Carcinoma, Ovarian Epithelial/pathology , RNA, Messenger , Tumor Microenvironment/geneticsABSTRACT
The worldwide approval of the combination maintenance therapy of olaparib and bevacizumab in advanced high-grade serous ovarian cancer requires complex molecular diagnostic assays that are sufficiently robust for the routine detection of driver mutations in homologous recombination repair (HRR) genes and genomic instability (GI), employing formalin-fixed (FFPE) paraffin-embedded tumor samples without matched normal tissue. We therefore established a DNA-based hybrid capture NGS assay and an associated bioinformatic pipeline that fulfils our institution's specific needs. The assay´s target regions cover the full exonic territory of relevant cancer-related genes and HRR genes and more than 20,000 evenly distributed single nucleotide polymorphism (SNP) loci to allow for the detection of genome-wide allele specific copy number alterations (CNA). To determine GI status, we implemented an %CNA score that is robust across a broad range of tumor cell content (25-85%) often found in routine FFPE samples. The assay was established using high-grade serous ovarian cancer samples for which BRCA1 and BRCA2 mutation status as well as Myriad MyChoice homologous repair deficiency (HRD) status was known. The NOGGO (Northeastern German Society for Gynecologic Oncology) GIS (GI-Score) v1 assay was clinically validated on more than 400 samples of the ENGOT PAOLA-1 clinical trial as part of the European Network for Gynaecological Oncological Trial groups (ENGOT) HRD European Initiative. The "NOGGO GIS v1 assay" performed using highly robust hazard ratios for progression-free survival (PFS) and overall survival (OS), as well a significantly lower dropout rate than the Myriad MyChoice clinical trial assay supporting the clinical utility of the assay. We also provide proof of a modular and scalable routine diagnostic method, that can be flexibly adapted and adjusted to meet future clinical needs, emerging biomarkers, and further tumor entities.
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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.
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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).
Subject(s)
Bile Duct Neoplasms , Biliary Tract Neoplasms , Cholangiocarcinoma , Humans , DNA Methylation , Algorithms , Cholangiocarcinoma/diagnosis , Cholangiocarcinoma/genetics , Bile Duct Neoplasms/diagnosis , Bile Duct Neoplasms/genetics , Bile Ducts, IntrahepaticABSTRACT
OBJECTIVES: Skip-N2 metastasis (N0N2), thus N2 metastasis in the absence of N1 metastasis, occurs in â¼20-30% of non-small-cell lung cancer patients. N0N2 patients have a better prognosis than continuous-N2 metastasis (N1N2) patients following surgery. However, this effect remains controversial. Therefore, we conducted a multicentre study to compare the long-term survival and disease-free interval (DFI) of N1N2- and N0N2 patients. METHODS: One- and 3-year survival rates were measured. Kaplan-Meier curves and a Cox proportional hazards model assessed survival and were used to identify prognostic factors for overall survival. In addition, we performed propensity score matching (PSM) to rule out confounding factors. All patients received adjuvant chemoradiation therapy according to European guidelines. RESULTS: Between January 2010 and December 2020, 218 stage IIIA/B N2 patients were included in our analysis. The Cox regression analysis revealed that N1N2 significantly influenced the overall survival rate. Before PSM, N1N2 patients showed significantly more metastatic lymph nodes (P < 0.001) and significantly larger tumours (P = 0.05). After PSM, baseline characteristics did not differ between groups. Before and after PSM, N0N2 patients showed significantly better 1- (P = 0.01; P = 0.009) and 3-year (P < 0.001) survival rates than N1N2 patients. Furthermore, N0N2 patients showed significantly longer DFI than N1N2 patients before and after PSM (P < 000.1). CONCLUSIONS: Prior and after PSM analysis, N0N2 patients were confirmed to have better survival and DFI than N1N2 patients. Our results demonstrate that stage IIIA/B N2 patients are heterogeneous and would benefit from a more precise subdivision and differential treatment.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/surgery , Neoplasm Staging , Retrospective Studies , Lymphatic Metastasis/pathology , Prognosis , Lymph Nodes/pathology , Survival Rate , Disease-Free SurvivalABSTRACT
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.
ABSTRACT
BACKGROUND: Oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction are among the most common malignant epithelial tumours. Most patients receive neoadjuvant therapy before complete tumour resection. Histological assessment after resection includes identification of residual tumour tissue and areas of regressive tumour, data which are used to calculate a clinically relevant regression score. We developed an artificial intelligence (AI) algorithm for tumour tissue detection and tumour regression grading in surgical specimens from patients with oesophageal adenocarcinoma or adenocarcinoma of the oesophagogastric junction. METHODS: We used one training cohort and four independent test cohorts to develop, train, and validate a deep learning tool. The material consisted of histological slides from surgically resected specimens from patients with oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction from three pathology institutes (two in Germany, one in Austria) and oesophageal cancer cohort of The Cancer Genome Atlas (TCGA). All slides were from neoadjuvantly treated patients except for those from the TCGA cohort, who were neoadjuvant-therapy naive. Data from training cohort and test cohort cases were extensively manually annotated for 11 tissue classes. A convolutional neural network was trained on the data using a supervised principle. First, the tool was formally validated using manually annotated test datasets. Next, tumour regression grading was assessed in a retrospective cohort of post-neoadjuvant therapy surgical specimens. The grading of the algorithm was compared with that of a group of 12 board-certified pathologists from one department. To further validate the tool, three pathologists processed whole resection cases with and without AI assistance. FINDINGS: Of the four test cohorts, one included 22 manually annotated histological slides (n=20 patients), one included 62 sides (n=15), one included 214 slides (n=69), and the final one included 22 manually annotated histological slides (n=22). In the independent test cohorts the AI tool had high patch-level accuracy for identifying both tumour and regression tissue. When we validated the concordance of the AI tool against analyses by a group of pathologists (n=12), agreement was 63·6% (quadratic kappa 0·749; p<0·0001) at case level. The AI-based regression grading triggered true reclassification of resected tumour slides in seven cases (including six cases who had small tumour regions that were initially missed by pathologists). Use of the AI tool by three pathologists increased interobserver agreement and substantially reduced diagnostic time per case compared with working without AI assistance. INTERPRETATION: Use of our AI tool in the diagnostics of oesophageal adenocarcinoma resection specimens by pathologists increased diagnostic accuracy, interobserver concordance, and significantly reduced assessment time. Prospective validation of the tool is required. FUNDING: North Rhine-Westphalia state, Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation.
Subject(s)
Adenocarcinoma , Esophageal Neoplasms , Humans , Artificial Intelligence , Retrospective Studies , Esophageal Neoplasms/diagnosis , Esophageal Neoplasms/pathology , Esophageal Neoplasms/surgery , Algorithms , Adenocarcinoma/diagnosis , Adenocarcinoma/pathology , Adenocarcinoma/surgeryABSTRACT
BACKGROUND: Molecular bladder cancer (BC) subtypes define distinct biological entities and were shown to predict treatment response in neoadjuvant and adjuvant settings. The extent of intratumoral heterogeneity (ITH) might affect subtyping of individual patients. OBJECTIVE: To comprehensively assess the ITH of molecular subtypes in a cohort of muscle-invasive BC. DESIGN, SETTING, AND PARTICIPANTS: A total of 251 patients undergoing radical cystectomy were screened. Three cores of the tumor center (TC) and three cores of the invasive tumor front (TF) of each patient were assembled in a tissue microarray. Molecular subtypes were determined employing 12 pre-evaluated immunohistochemical markers (FGFR3, CCND1, RB1, CDKN2A, KRT5, KRT14, FOXA1, GATA3, TUBB2B, EPCAM, CDH1, and vimentin). A total of 18 072 spots were evaluated, of which 15 002 spots were assessed based on intensity, distribution, or combination. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Allocation to one of five different molecular subtypes-urothelial like, genomically unstable, small-cell/neuroendocrine like, basal/squamous cell carcinoma like, and mesenchymal like-was conducted for each patient for the complete tumor, individual cores, TF, and TC separately. The primary objective was to assess the ITH between the TF and TC (n = 208 patients). The secondary objective was the evaluation of multiregion ITH (n = 191 patients). An analysis of the composition of ITH cases, association with clinicopathological parameters, and prognosis was conducted. RESULTS AND LIMITATIONS: ITH between the TF and TC was seen in 12.5% (n = 26/208), and ITH defined by at least two different subtypes of any location was seen in 24.6% (n = 47/191). ITH was more frequent in locally confined (pT2) versus advanced (pT ≥3) BC stages (38.7% vs 21.9%, p = 0.046), and pT4 BC presented with significantly more basal subtypes than pT2 BC (26.2% vs 11.5%, p = 0.049). In our cohort, there was no association of subtype ITH with prognosis or accumulation of specific molecular subtypes in ITH cases. The key limitations were missing transcriptomic and mutational genetic validation as well as investigation of ITH beyond subtypes. CONCLUSIONS: Several molecular subtypes can be found in nearly every fourth case of muscle-invasive BC, when using immunohistochemistry. ITH must be given due consideration for subtype-guided strategies in BC. Genomic validation of these results is needed. PATIENT SUMMARY: Different molecular subtypes can be found in many cases of muscle-invasive bladder cancer. This might have implications for individualized, subtype-based therapeutic approaches.