Predictive Value of the TP53/PIK3CA/ATM Mutation Classifier for Patients With Bladder Cancer Responding to Immune Checkpoint Inhibitor Therapy

Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Assessment of aggressive bladder cancer mutations in plasma cell-free DNA

Background and aims

The spatial and temporal genetic heterogeneity of bladder cancer (BC) makes challenging to find specific drivers of metastatic disease, thus preventing to determine those BC patients at high risk of tumor progression. Our aim was to identify DNA mutations providing aggressive behavior to bladder tumors and analyze them in patients' cell-free DNA (cfDNA) during their follow-up after radical cystectomy (RC) in order to monitor tumor evolution.

Methods

Six BC patients who underwent RC and presented disease progression during their follow-up were included. Next-generation sequencing was used to determine somatic mutations in several primary tumor and metastatic specimens from each patient. Shared DNA mutations between primary bladder tumor and metastatic sites were identified in cfDNA samples through droplet digital PCR.

Results

Besides BC genetic heterogeneity, specific mutations in at least one of these genes -TERT, ATM, RB1, and FGFR3- were found in primary tumors and their metastases in all patients. These mutations were also identified in the patients' cfDNA at different follow-up time points. Additionally, the dynamic changes of these mutations in cfDNA allowed us to determine tumor evolution in response to treatment.

Conclusion

The analysis of BC mutations associated with poor prognosis in plasma cfDNA could be a valuable tool to monitor tumor evolution, thus improving the clinical management of BC patients.

Targeting PI3Kα increases the efficacy of anti-PD-1 antibody in cervical cancer

Targeting programmed death 1(PD-1) has been approved for relapsed cervical cancer with unsatisfactory clinical efficacy. This study aims to analyse the impact of PI3K pathway activation on tumour immune microenvironment and evaluates the immune sensitization effect by PI3K inhibition in cervical cancer. The effect of PIK3CA mutation on PD-L1 expression and CD8+ T cells differentiation was determined in cervical cancer tissues. Luciferase and ChIP-qPCR/PCR assays were used to determine the transcriptional regulation of PD-L1 by PIK3CA-E545K. The effects of PI3K inhibitor treatment on immune environment in vitro and in vivo were evaluated by RNA sequencing (RNA-seq) and flow cytometry. The efficacy of PI3K inhibitor and anti-PD-1 therapy was assessed in cell-derived xenografts (CDX) and patients-derived xenografts (PDX). PD-L1 overexpression is more frequently observed in elder women with squamous cervical carcinoma. It predicts longer progress-free survival and overall survival. PIK3CA mutation results in increased mRNA and protein levels of PD-L1, the repression of CD8+ T cell differentiation in cervical cancer. Here, we report a case that continuous pembrolizumab monotherapy treatment induced complete remission of a recurrent cervical cancer patient with systemic metastasis and PIK3CA-E545K mutation, implying that PIK3CA mutation is potentially a biomarker for pembrolizumab treatment in cervical cancer. Specifically, this mutation promotes the expression of PD-L1 by upregulating the transcription factor IRF1. PI3Kα-specific inhibitor markedly activates immune microenvironment by regulating the PD-1/L1-related pathways and promoting CD8+ T cell differentiation and proliferation in Caski-CDXs with PIK3CA-E545K mutation. PI3Kα inhibitor significantly enhances the anti-tumour efficacy of PD-1 blockade in CDXs and PDXs. PIK3CA mutations may predict the response of cervical cancer to PD-1 blockade. The efficacy of PI3Kα inhibitors combined with PD-1 antibodies is promising in cervical cancer and warrants additional clinical and mechanistic investigations.

RB1 aberrations predict outcomes of immune checkpoint inhibitor combination therapy in NSCLC

Introduction

Immune checkpoint inhibitors (ICI) have changed the treatment of non-small cell lung cancer (NSCLC). Furthermore, compared with monotherapy, ICI combination therapy had better efficacy and partly different mechanism. Therefore, we aim to investigate and improve biomarkers specialized for ICI combination therapy.

Methods

We enrolled 53 NSCLC patients treated with ICI combination therapy and collected their tissue and plasma samples to perform next-generation sequencing (NGS) with a 425-gene panel.

Results

The line of treatment was the only clinical factor significantly affecting objective response rate (ORR) and progression-free survival (PFS). Surprisingly, classical markers PD-L1 and TMB only had limited predictive values in the ICI combination therapy. Instead, we found RB1 mutation was significantly associated with prognosis. Patients with mutated RB1 had shorter PFS than those with wild RB1 (134d vs 219d, p=0.018). Subsequent analysis showed the RB1 related mutated cell cycle and chromosomal instability were also deleterious to prognosis (103d vs 411d, p<0.001; 138d vs 505d, p=0.018). Additionally, patients with more circulating tumor DNA (ctDNA) had significantly shorter PFS (41d vs 194d, p=0.0043).

Conclusion

This study identified that NSCLC patients with mutated RB1 were less sensitive to ICI combination therapy. RB1 mutations and following cell cycle abnormalities and chromosomal instability can potentially guide clinical management.

ATM Inhibition-Induced ISG15/IFI27/OASL Is Correlated with Immunotherapy Response and Inflamed Immunophenotype

Immune checkpoint blockade (ICB) therapy can improve the survival of cancer patients with a high tumor mutation burden (TMB-H) or deficiency in DNA mismatch repair (dMMR) in their tumors. However, most cancer patients without TMB-H and dMMR do not benefit from ICB therapy. The inhibition of ATM can increase DNA damage and activate the interferon response, thus modulating the tumor immune microenvironment (TIME) and the efficacy of ICB therapy. In this study, we showed that ATM inhibition activated interferon signaling and induced interferon-stimulated genes (ISGs) in cisplatin-resistant and parent cancer cells. The ISGs induced by ATM inhibition were correlated with survival in cancer patients who received ICB therapy. In oral cancer, high expressions of ISG15, IFI27, and OASL were associated with low expressions of ATM, the activation of inflamed immune pathways, and increased tumor-infiltrating scores of CD8+ T, natural killer, and dendritic cells. The high expressions of ISG15, IFI27, and OASL were also correlated with complete remission in patients with cervical cancer treated with cisplatin. These results suggest that ATM inhibition can induce the interferon response and inflamed TIME, which may benefit ICB therapy.

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

Purpose

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis.

Methods

We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P &lt; 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne’s. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs.

Results

In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy.

Conclusion

Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

Identification and validation of a genomic mutation signature as a predictor for immunotherapy in NSCLC

Currently, the benefits of immune checkpoint inhibitor (ICI) therapy prediction via emerging biomarkers have been identified, and the association between genomic mutation signatures (GMS) and immunotherapy benefits has been widely recognized as well. However, the evidence about non-small cell lung cancer (NSCLC) remains limited. We analyzed 310 immunotherapy patients with NSCLC from the Memorial Sloan Kettering Cancer Center (MSKCC) cohort. Lasso Cox regression was used to construct a GMS, and the prognostic value of GMS could be able to verify in the Rizvi cohort (N=240) and Hellmann cohort (N=75). We further conducted immunotherapy-related characteristics analysis in The Cancer Genome Atlas (TCGA) cohort (N=1052). A total of seven genes (ZFHX3, NTRK3, EPHA7, MGA, STK11, EPHA5, TP53) were identified for GMS model construction. Compared with GMS-high patients, patients with GMS-low had longer overall survival (OS; P<0.001) in the MSKCC cohort and progression-free survival (PFS; P<0.001) in the validation cohort. Multivariate Cox analysis revealed that GMS was an independent predictive factor for NSCLC patients in both the MSKCC and validation cohort. Meanwhile, we found that GMS-low patients reflected enhanced antitumor immunity in TCGA cohort. The results indicated that GMS had not only potential predictive value for the benefit of immunotherapy but also may serve as a potential biomarker to guide clinical ICI treatment decisions for NSCLC.

Prognostic implication of TERT promoter mutation and circulating tumor cells in muscle-invasive bladder cancer

PURPOSE

Current clinical prognostic factors are not accurate enough to identify and monitor those muscle-invasive bladder cancer (MIBC) patients at high risk of progression after radical cystectomy (RC). Here, we determined genetic alterations in the tumor and circulating tumor cell (CTC) enumeration to find biomarkers useful for the management of MIBC after RC.

METHODS

Thirty-nine MIBC patients undergoing RC were included. Tumoral tissue DNA was analyzed by next generation sequencing. CTCs were isolated from blood collected before RC and one, four and 12 months later.

RESULTS

Sixteen (41%) patients progressed in a median time of 8.5 months and 11 (69%) of these patients harbored the TERT c.-124C > T mutation. All progressive patients harboring the TERT c.-124C > T mutation presented a significant increase in CTC number 12 months after RC compared to those without the mutation. Additionally, CTC number at 12 months was identified as an independent prognostic biomarker for tumor progression and cancer specific survival (CSS). Ten (63%) progressive patients showed an increment of CTC number with a median anticipation period of four months compared with imaging techniques.

CONCLUSIONS

The TERT c.-124C > T mutation could be considered a biomarker of aggressivity. CTC enumeration is a useful tool for identifying MIBC patients at high risk of progression and CSS after RC and for detecting tumor progression earlier than imaging techniques.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma

Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.

Anticancer Activity of Erianin: Cancer-Specific Target Prediction Based on Network Pharmacology

Erianin is a major bisbenzyl compound extracted from Dendrobium chrysotoxum Lindl., an important traditional Chinese herb. In recent years, a growing body of evidence has proved the potential therapeutic effects of erianin on various cancers, including hepatoma, melanoma, non-small-cell lung carcinoma, myelogenous leukemia, breast cancer, and osteosarcoma. Especially, the pharmacological activities of erianin, such as antioxidant and anticancer activity, have been frequently demonstrated by plenty of studies. In this study, we firstly conducted a systematic review on reported anticancer activity of erianin. All updated valuable information regarding the underlying action mechanisms of erianin in specific cancer was recorded and summarized in this paper. Most importantly, based on the molecular structure of erianin, its potential molecular targets were analyzed and predicted by means of the SwissTargetPrediction online server (http://www.swisstargetprediction.ch). In the meantime, the potential therapeutic targets of 10 types of cancers in which erianin has been proved to have anticancer effects were also predicted via the Online Mendelian Inheritance in Man (OMIM) database (http://www.ncbi.nlm.nih.gov/omim). The overlapping targets may serve as valuable target candidates through which erianin exerts its anticancer activity. The clinical value of those targets was subsequently evaluated by analyzing their prognostic role in specific cancer using Kaplan-Meier plotter (http://Kmplot.com/analysis/) and Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/). To better assess and verify the binding ability of erianin with its potential targets, molecular flexible docking was performed using Discovery Studio (DS). The valuable targets obtained from the above analysis and verification were further mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/) to explore the possible signaling pathways disturbed/regulated by erianin. Furthermore, the in silico prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of erianin was also performed and provided in this paper. Overall, in this study, we aimed at 1) collecting all experiment-based important information regarding the anticancer effect and pharmacological mechanism of erianin, 2) providing the predicted therapeutic targets and signaling pathways that erianin might act on in cancers, and 3) especially providing in silico ADMET properties of erianin.

Checkpoint Inhibition in Bladder Cancer: Clinical Expectations, Current Evidence, and Proposal of Future Strategies Based on a Tumor-Specific Immunobiological Approach

Simple Summary

In contrast with other strategies, immunotherapy is a treatment aimed at empowering the patient’s immune system in order to increase immunity and the response against cancer. Recently, a new class of drugs, immune checkpoint inhibitors, has shown potential in increasing treatment chances for patients with bladder cancers, improving their survival. However, predicting the response to immune checkpoint inhibition is important, since only a group of patients develop a good response. Biomarkers to predict the response to checkpoint inhibition must identify tumors’ and patients’ specific profiles. This study reviews the current knowledge on this most relevant clinical topic, focusing on bladder cancer, going from basic science to ongoing clinical trials and available clinical evidence. Finally, a critical analysis of published data is provided, and an original panel of biomarkers, able to select the right patients for treatments, based on patient-specific immune profiling, is proposed.

Abstract

In contrast with other strategies, immunotherapy is the only treatment aimed at empowering the immune system to increase the response against tumor growth. Immunotherapy has a role in the treatment of bladder cancer (BC) due to these tumors’ high tumor mutational burden (TMB) and mostly prominent immune infiltrate. The therapy or combination has to be adjusted to the tumor’s immunobiology. Recently, a new class of immunotherapeutic agents, immune checkpoint inhibitors (ICI), has shown potential in increasing treatment chances for patients with genitourinary cancers, improving their oncological outcomes. The clinical efficacy of ICI has been shown in both the first-line treatment of cisplatin-ineligible patients, with programmed death ligand 1 (PD-L1)-positive tumors (atezolizumab, pembrolizumab), and in second-line settings, for progression after platinum-based chemotherapy (atezolizumab, pembrolizumab, and nivolumab for FDA and EMA; durvalumab and avelumab for FDA alone). Predicting the response to ICI is important since only a subset of patients undergoing ICI therapy develop a concrete and lasting response. Most of the patients require a different therapy or therapy combination to achieve tumor control. The cancer immunity cycle provides a conceptual framework to assist therapy selection. Biomarkers to predict response to ICI must identify where the cancer immunity cycle is disrupted. We reviewed the current knowledge on ICI treatment in BC, going from basic science to current data and available clinical evidence. Secondly, a critical analysis of published data is provided, and an original panel of biomarkers able to predict response to ICI treatment, based on tumor-specific immune profiling, is proposed.

Development and Validation of a Gene Mutation-Associated Nomogram for Hepatocellular Carcinoma Patients From Four Countries

Background: Genomic alteration is the basis of occurrence and development of carcinoma. Specific gene mutation may be associated with the prognosis of hepatocellular carcinoma (HCC) patients without distant or lymphatic metastases. Hence, we developed a nomogram based on prognostic gene mutations that could predict the overall survival of HCC patients at early stage and provide reference for immunotherapy.

Methods: HCC cohorts were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The total patient was randomly assigned to training and validation sets. Univariate and multivariate cox analysis were used to select significant variables for construction of nomogram. The support vector machine (SVM) and principal component analysis (PCA) were used to assess the distinguished effect of significant genes. Besides, the nomogram model was evaluated by concordance index, time-dependent receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS) were utilized to explore the potential mechanism of immune-related process and immunotherapy.

Results: A total of 695 HCC patients were selected in the process including 495 training patients and 200 validation patients. Nomogram was constructed based on T stage, age, country, mutation status of DOCK2, EYS, MACF1 and TP53. The assessment showed the nomogram has good discrimination and high consistence between predicted and actual data. Furthermore, we found T cell exclusion was the potential mechanism of malignant progression in high-risk group. Meanwhile, low-risk group might be sensitive to immunotherapy and benefit from CTLA-4 blocker treatment.

Conclusion: Our research established a nomogram based on mutant genes and clinical parameters, and revealed the underlying association between these risk factors and immune-related process.