Gut microbiota Parabacteroides distasonis enchances the efficacy of immunotherapy for bladder cancer by activating anti-tumor immune responses.

OBJECTIVE: Bladder cancer(BCa) was a disease that seriously affects patients' quality of life and prognosis. To address this issue, many researches suggested that the gut microbiota modulated tumor response to treatment; however, this had not been well-characterized in bladder cancer. In this study, our objective was to determine whether the diversity and composition of the gut microbiota or the density of specific bacterial genera influence the prognosis of patients with bladder cancer. METHODS: We collected fecal samples from a total of 50 bladder cancer patients and 22 matched non-cancer individuals for 16S rDNA sequencing to investigate the distribution of Parabacteroides in these two groups. Further we conducted follow-up with cancer patients to access the impact of different genera of microorganisms on patients survival. We conducted a Fecal Microbiota Transplantation (FMT) and mono-colonization experiment with Parabacteroides distasonis to explore its potential enhancement of the efficacy of anti-PD-1 immunotherapy in MB49 tumor-bearing mice. Immunohistochemistry, transcriptomics and molecular experiment analyses were employed to uncover the underlying mechanisms. RESULTS: The 16S rDNA showed that abundance of the genus Parabacteroides was elevated in the non-cancer control group compared to bladder cancer group. The results of tumor growth curves showed that a combination therapy of P. distasonis and ICIs treatment significantly delayed tumor growth and increased the intratumoral densities of both CD4+T and CD8+T cells. The results of transcriptome analysis demonstrated that the pathways associated with antitumoral immune response were remarkably upregulated in the P. distasonis gavage group. CONCLUSION: P. distasonis delivery combined with α-PD-1 mAb could be a new strategy to enhance the effect of anti-PD-1 immunotherapy. This effect might be achieved by activating immune and antitumor related pathways.

An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study.

Background: The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. Methods: In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. Findings: In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64-73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953-0.998) to 0.992 (0.982-1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908-1.000]) surpassed that of two junior pathologists (0.862 [0.746-0.939], P = 0.023; 0.879 [0.767-0.950], P = 0.041) by 10-12% and showed no difference to that of two senior pathologists (both 0.983 [0.908-1.000], both P > 0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P = 0.041) to the level of senior pathologists (both P > 0.99), and substantially reduced the four pathologists' slide reviewing time (-31%, P < 0.0001; -34%, P < 0.0001; -29%, P < 0.0001; and -27%, P = 0.00031). Interpretation: ProCaLNMD demonstrated high diagnostic capabilities for identifying LNM in prostate cancer, reducing the likelihood of missed diagnoses by pathologists and decreasing the slide reviewing time, highlighting its potential for clinical application. Funding: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.