Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin‐eosin‐stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease‐free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.

Objectives: We carried out an integrated analysis based on multiple-dimensional types of data from cohorts of bladder cancer patients to identify multi-omics perspective (genomics and transcriptomics) on the tumor microenvironment on the bases of the programmed cell death 1 ligand (PD-L1) and CD8 T-cell infiltration in urothelial carcinoma.Methods: Multiple-dimensional types of data, including clinical, genomic and transcriptomic data of 408 bladder cancer patients were retrieved from the Cancer Genome Atlas database. Based on the median values of PD-L1 and CD8A, the tumor samples were grouped into four tumor microenvironment immune types (TMIT). The RNA sequencing profiles, somatic mutation and PD-L1 amplification data of bladder cancer were analyzed by different TMITs.Results: Our research demonstrated that 36.8% of the evaluated bladder cancer belonged to TMIT I (high PD-L1/high CD8A). TIMT subtypes were not significantly associated with overall survival or disease free survival in urothelial cancer. TMIT I facilitates CD8+ T-cell infiltration and activates T-effector and interferon gamma (IFN-γ) associated gene signature. The number of somatic mutations, cytolytic activity, IFN-γ mRNA expression and TIGIT mRNA expression in TMIT I was remarkably higher than those in other TMIT groups. Our results showed a high rate of C>T transversion and a high rate of transition/transversion (Ti/Tv) in TMIT I bladder tumors. The RB1 mutation was significantly associated with TMIT I bladder cancer and be significantly co-occurring with the TP53 mutation. However, FGFR3 mutation and TP53 mutation were mutually exclusive in TMIT II bladder tumors. More importantly, different amino acid changes by FGFR3/RB1 mutations were also found between TMIT I and TMIT II bladder cancer, such as amino acid changes in “Immunoglobulin I-set domain (260-356)”and “Protein tyrosine kinase (472-748)”. We also detected 9 genes as significantly cancer-associated genes in TMIT I bladder cancer, of which, RAD51C has been reported to play an important role in DNA damage responses. Further analysis concentrated on the potential molecular mechanism found that TMIT I was significantly associated with anti-tumor immune-related signaling pathway, and kataegis was present on chromosome 21 in TMIT I bladder tumors.Conclusions: The classification of bladder cancer into four TMITs on the bases of the PD-L1 expression and the CD8+ CTLs statuses is an appropriate approach for bladder tumor immunotherapy. TMIT I (high PD-L1/high CD8A) is significantly correlated with more somatic mutation burden, and facilitates CD8+ T-cell infiltration and activates T-effector and IFN-γ associated gene signature. Alteration landscape for somatic variants was different between TMIT I and TMIT II (low PD-L1/low CD8A).

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning‐based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56‐2.81, P < .0001) and the General cohort (HR = 5.32, 95% CI: 2.95‐9.59, P < .0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1‐, 3‐, and 5‐y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications.