Renal cell carcinoma pathology in 2021: 'new need for renal cancer immune profiling'

Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma

This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.

Oncolytic adenovirus-mediated expression of CCL5 and IL12 facilitates CA9-targeting CAR-T therapy against renal cell carcinoma

Chimeric antigen receptor T-cell (CAR-T) is particularly prominent in hematological but not in solid tumors, mainly based on the complex tumor immune microenvironment. Oncolytic virus (OVs) is an emerging adjuvant therapy method. OVs may prime tumor lesions to induce anti-tumor immune response, thereby enhancing CAR-T cells functionality and possibly increasing response rates. Here, we combined CAR-T cells targeting carbonic anhydrase 9 (CA9) and an oncolytic adenovirus (OAV) carrying chemokine (C-C motif) ligand 5 (CCL5), cytokine interleukin-12 (IL12) to explore the anti-tumor effects of this combination strategy. The data showed that Ad5-ZD55-hCCL5-hIL12 could infect and replicate in renal cancer cell lines and induced a moderate inhibition of xenografted tumor in nude mice. IL12 mediated by Ad5-ZD55-hCCL5-hIL12 promoted the phosphorylation of Stat4 in CAR-T cells, induced CAR-T cells to secrete more IFN-γ. We also found that Ad5-ZD55-hCCL5-hIL-12 combined with CA9-CAR-T cells significantly increased the infiltration of CAR-T cells in tumor mass, prolonged the survival of the mice and restrained tumor growth in immunodeficient mice. Ad5-ZD55-mCCL5-mIL-12 could also increase CD45⁺CD3⁺T cell infiltration and prolong mice survival in immunocompetent mice. These results provided feasibility for the combination of oncolytic adenovirus and CAR-T cells, which demonstrated the sufficient potential and prospects of CAR-T for the treatment of solid tumors.

Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel-sized WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogeneous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers and validated it by predicting the prognosis of 3,950 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks.

Oncolytic adenovirus-mediated expression of decorin facilitates CAIX-targeting CAR-T therapy against renal cell carcinoma

Although chimeric antigen receptor T cell (CAR-T) therapy has been successful for hematological malignancies, it is less effective for solid tumors. The primary reason is that the immune microenvironment restricts CAR-T cells from infiltrating and proliferating in tumors. Oncolytic virotherapy has emerged as a novel immunogenic therapy to augment antitumor immune response. Here we combined an oncolytic adenovirus carrying decorin with a CAR-T targeting carbonic anhydrase IX (CAIX) to perform the antitumor activity for renal cancer cells. We found that OAV-Decorin combined with CAIX-CAR-T exhibited significantly reduced tumor burden, altered the composition of extracellular matrix (ECM) by inhibiting the distribution of collagen fibers, decreased the expression of TGF-β in tumor cells, enhanced IFN-γ secretion, and obtained higher numbers of CAR-T cells. The combination treatment modality showed prolonged mice survival. The intratumoral injection of OAV-Decorin into tumor-bearing immunocompetent mice activated the inflammatory immune status and resulted in tumor regression. These data supported further investigation of the combination of OAV-Decorin and CAIX-CAR-T cells in solid tumors.