Immunotherapy for genitourinary cancer: state of the art and new perspectives

Prognostic value and immunological role of AXL gene in clear cell renal cell carcinoma associated with identifying LncRNA/RBP/AXL mRNA networks

Backgrounds

This article aimed to explore the prognostic and immunological roles of AXL gene in clear cell renal cell carcinoma (ccRCC) for overall survival (OS) and to identify the LncRNA/RBP/AXL mRNA networks.

Methods

AXL-related gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset and AXL-related pathways were identified by gene set enrichment analysis (GSEA). We performed univariate/multivariate Cox regression analysis to evaluate independent prognostic factors and the relationships between AXL and immunity were also investigated.

Results

The outcomes of us indicated that the AXL mRNA expression was up-regulated in ccRCC samples and high expression of AXL was associated with worse OS in TCGA dataset (P < 0.01). Further external verification results from HPA, UALCAN, ICGC dataset, GSE6344, GSE14994, and qRT-PCR remained consistent (all P < 0.05). AXL was also identified as an independent prognostic factor for ccRCC by univariate/multivariate Cox regression analysis (both P < 0.05). A nomogram including AXL expression and clinicopathological factors was established by us and GSEA results found that elevated AXL expression was associated with the JAK-STAT, P53, WNT, VEGF and MAPK signaling pathways. In terms of immunity, AXL was dramatically linked to tumor microenvironment, immune cells, immune infiltration, immune checkpoint molecules and tumor mutational burden (TMB). As for its potential mechanisms, we also identified several LncRNA/RBP/AXL mRNA axes.

Conclusions

AXL was revealed to play prognostic and immunological roles in ccRCC and LncRNA/RBP/AXL mRNA axes were also identified by us for its potential mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02322-y.

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

Targeting androgen-independent pathways: new chances for patients with prostate cancer?

Androgen deprivation therapy (ADT) is the mainstay treatment for advanced prostate cancer (PC). Most patients eventually progress to a condition known as castration-resistant prostate cancer (CRPC), characterized by lack of response to ADT. Although new androgen receptor signaling (ARS) inhibitors and chemotherapeutic agents have been introduced to overcome resistance to ADT, many patients progress because of primary or acquired resistance to these agents. This comprehensive review aims at exploring the mechanisms of resistance and progression of PC, with specific focus on alterations which lead to the activation of androgen receptor (AR)-independent pathways of survival. Our work integrates available clinical and preclinical data on agents which target these pathways, assessing their potential clinical implication in specific settings of patients. Given the rising interest of the scientific community in cancer immunotherapy strategies, further attention is dedicated to the role of immune evasion in PC.