Renal cell carcinoma (RCC) is the seventh most common cancer in men and the ninth most common cancer in women worldwide. There is plenty of evidence about the role of the immune system in surveillance against tumors. Thanks to a better understanding of immunosurveillance mechanisms, immunotherapy has been introduced as a promising cancer treatment in recent years. Renal cell carcinoma (RCC) has long been thought chemoresistant but highly immunogenic. Considering that up to 30% of the patients present metastatic disease at diagnosis, and around 20–30% of patients undergoing surgery will suffer recurrence, we need to identify novel therapeutic targets. The introduction of immune checkpoint inhibitors (ICIs) in the clinical management of RCC has revolutionized the therapeutic approach against this tumor. Several clinical trials have shown that therapy with ICIs in combination or ICIs and the tyrosine kinase inhibitor has a very good response rate. In this review article we summarize the mechanisms of immunity modulation and immune checkpoints in RCC and discuss the potential therapeutic strategies in renal cancer treatment.

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Globally, clear-cell renal cell carcinoma (ccRCC) represents the most prevalent type of kidney cancer. Surgery plays a key role in the treatment of this cancer, although one third of patients are diagnosed with metastatic ccRCC and about 25% of patients will develop a recurrence after nephrectomy with curative intent. Molecular-target-based agents, such as tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), are recommended for advanced cancers. In addition to cancer cells, the tumor microenvironment (TME) includes non-malignant cell types embedded in an altered extracellular matrix (ECM). The evidence confirms that interactions among cancer cells and TME elements exist and are thought to play crucial roles in the development of cancer, making them promising therapeutic targets. In the TME, an unfavorable pH, waste product accumulation, and competition for nutrients between cancer and immune cells may be regarded as further possible mechanisms of immune escape. To enhance immunotherapies and reduce resistance, it is crucial first to understand how the immune cells work and interact with cancer and other cancer-associated cells in such a complex tumor microenvironment.

Mucin1 (MUC1), a glycoprotein associated with an aggressive cancer phenotype and chemoresistance, is aberrantly overexpressed in a subset of clear cell renal cell carcinoma (ccRCC). Recent studies suggest that MUC1 plays a role in modulating cancer cell metabolism, but its role in regulating immunoflogosis in the tumor microenvironment remains poorly understood. In a previous study, we showed that pentraxin-3 (PTX3) can affect the immunoflogosis in the ccRCC microenvironment by activating the classical pathway of the complement system (C1q) and releasing proangiogenic factors (C3a, C5a). In this scenario, we evaluated the PTX3 expression and analyzed the potential role of complement system activation on tumor site and immune microenvironment modulation, stratifying samples in tumors with high (MUC1H) versus tumors with low MUC1 expression (MUC1L). We found that PTX3 tissue expression was significantly higher in MUC1H ccRCC. In addition, C1q deposition and the expressions of CD59, C3aR, and C5aR were extensively present in MUC1H ccRCC tissue samples and colocalized with PTX3. Finally, MUC1 expression was associated with an increased number of infiltrating mast cells, M2-macrophage, and IDO1+ cells, and a reduced number of CD8+ T cells. Taken together, our results suggest that expression of MUC1 can modulate the immunoflogosis in the ccRCC microenvironment by activating the classical pathway of the complement system and regulating the immune infiltrate, promoting an immune-silent microenvironment.

Background Cutaneous melanoma is one of the most aggressive forms of skin cancer with a high mortality rate. 1 A prognosis improvement in cutaneous melanoma patients is crucial to better plan personalized treatments. Currently, clinical prognosis methods for the evaluation of the risk of recurrence includes multiple parameters, such as Breslow tumor thickness, mitotic rate, ulceration, local or nodal metastasis, which are at the basis of the American Joint Committee on Cancer (AJCC) pathologic tumor stage. [2][3][4] Despite routinely applied in clinical practice, these methods have some pitfalls. 5 Thus, predicting the risk of recurrence in melanoma patient is urgent. Methods In this study, we propose a deep learning model, that exploits convolutional neural networks, which mimic the functioning of human brain, to extract features from hematoxylin and eosin (H&E) slide images with the final goal of predicting 1-year disease-free survival (DFS) in patients with I-III stage cutaneous melanoma. H&E images referred to a cohort of 43 patients from Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database (31 DF cases, 12 non-DF cases) 6 were firstly analyzed to design the predictive model (table 1). Then, the model was validated on H&E images referred to a validation cohort of 11 cutaneous melanoma patients (table 2), which was provided by our Institute (8 DF cases, 3 non-DF cases). Basically, we developed a computerized system to automatically extract information that are usually evaluated manually and visually by pathologists. Results The median Area Under the Curve (AUC) and accuracy values in the patients from the CPTAC-CM public dataset were 69.5% and 72.7%, respectively, by implementing a 5-fold cross validation scheme for 5-rounds. AUC and accuracy values in the validation cohort of patients were 66.7% and 72.7%, respectively, by using the CPTAC-CM dataset as training set and the validation cohort as test set. Conclusions Our model proved to be robust and generalizable. The promising results obtained in this preliminary work suggest that our proposal, after further validation on a larger cohort of patients, may have the potential to better define the risk of recurrence for each patient and better tailor adjuvant therapy. Ethics ApprovalThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Scientific Board of Istituto Tumori 'Giovanni Paolo II' (Bari, Italy). Consent This study was determined by the Scientific Board to not require written consent from subjects, as it is retrospective and involves minimal risk.Abstract 1281 Table 1 Clinical data referred to CPTAC-CM public databaseFor categorical variables, absolute (abs.) and percentage (%) counts are reported. For continuous values, the median and standard deviation (sd.) values are indicated.Abstract 1281 Table 2 Clinical data referred to the validation cohort For categorical variables, absolute (abs.) and percentage (%) counts are reported. For continuous values, the median and...