Characterisation of tumour-immune phenotypes and PD-L1 positivity in squamous bladder cancer.

AIMS: Immune checkpoint inhibitor (ICI) therapy has become a viable treatment strategy in bladder cancer. However, treatment responses vary, and improved biomarkers are needed. Crucially, the characteristics of immune cells remain understudied especially in squamous differentiated bladder cancer (sq-BLCA). Here, we quantitatively analysed the tumour-immune phenotypes of sq-BLCA and correlated them with PD-L1 expression and FGFR3 mutation status. METHODS: Tissue microarrays (TMA) of n = 68 non-schistosomiasis associated pure squamous cell carcinoma (SCC) and n = 46 mixed urothelial carcinoma with squamous differentiation (MIX) were subjected to immunohistochemistry for CD3, CD4, CD8, CD56, CD68, CD79A, CD163, Ki67, perforin and chloroacetate esterase staining. Quantitative image evaluation was performed via digital image analysis. RESULTS: Immune infiltration was generally higher in stroma than in tumour regions. B-cells (CD79A) were almost exclusively found in stromal areas (sTILs), T-lymphocytes and macrophages were also present in tumour cell areas (iTILs), while natural killer cells (CD56) were nearly missing in any area. Tumour-immune phenotype distribution differed depending on the immune cell subset, however, hot tumour-immune phenotypes (high density of immune cells in tumour areas) were frequently found for CD8 + T-cells (33%), especially perforin + lymphocytes (52.2%), and CD68 + macrophages (37.6%). Perforin + CD8 lymphocytes predicted improved overall survival in sq-BLCA while high PD-L1 expression (CPS ≥ 10) was significantly associated with higher CD3 + , CD8 + and CD163 + immune cell density and high Ki67 (density) of tumour cells. Furthermore, PD-L1 expression was positively associated with CD3 + /CD4 + , CD3 + /CD8 + and CD68 + /CD163 + hot tumour-immune phenotypes. FGFR3 mutation status was inversely associated with CD8 + , perforin + and CD79A + lymphocyte density. CONCLUSIONS: Computer-based image analysis is an efficient tool to analyse immune topographies in squamous bladder cancer. Hot tumour-immune phenotypes with strong PD-L1 expression might pose a promising subgroup for clinically successful ICI therapy in squamous bladder cancer and warrant further investigation.

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

BACKGROUND: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available. OBJECTIVE: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer. DESIGN, SETTING, AND PARTICIPANTS: We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist. RESULTS AND LIMITATIONS: In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants. CONCLUSIONS: Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings. PATIENT SUMMARY: In this report, a computer-based artificial intelligence (AI) system was applied to histological slides to predict genetic alterations of the FGFR3 gene in bladder cancer. We found that the AI system was able to find the alteration with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.

Correlation of Hsp70 Serum Levels with Gross Tumor Volume and Composition of Lymphocyte Subpopulations in Patients with Squamous Cell and Adeno Non-Small Cell Lung Cancer.

Heat-shock protein 70 (Hsp70) is frequently found on the plasma membrane of a large number of malignant tumors including non-small cell lung cancer (NSCLC) and gets released into the blood circulation in lipid vesicles. On the one hand, a membrane (m)Hsp70-positive phenotype correlates with a high aggressiveness of the tumor; on the other hand, mHsp70 serves as a target for natural killer (NK) cells that had been pre-stimulated with Hsp70-peptide TKD plus low-dose interleukin-2 (TKD/IL-2). Following activation, NK cells show an up-regulated expression of activatory C-type lectin receptors, such as CD94/NKG2C, NKG2D, and natural cytotoxicity receptors (NCRs; NKp44, NKp46, and NKp30) and thereby gain the capacity to kill mHsp70-positive tumor cells. With respect to these results, the efficacy of ex vivo TKD/IL-2 stimulated, autologous NK cells is currently tested in a proof-of-concept phase II clinical trial in patients with squamous cell NSCLC after radiochemotherapy (RCT) at the TUM. Inclusion criteria are histological proven, non-resectable NSCLC in stage IIIA/IIIB, clinical responses to RCT and a mHsp70-positive tumor phenotype. The mHsp70 status is determined in the serum of patients using the lipHsp70 ELISA test, which enables the quantification of liposomal and free Hsp70. Squamous cell and adeno NSCLC patients had significantly higher serum Hsp70 levels than healthy controls. A significant correlation of serum Hsp70 levels with the gross tumor volume was shown for adeno and squamous cell NSCLC. However, significantly elevated ratios of activated CD69(+)/CD94(+) NK cells that are associated with low serum Hsp70 levels were observed only in patients with squamous cell lung cancer. These data might provide a first hint that squamous cell NSCLC is more immunogenic than adeno NSCLC.