Predictors of disease aggressiveness influence outcome from immunotherapy treatment in renal clear cell carcinoma

Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer

Background: Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. Methods: In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. Results: The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. Conclusions: The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.

Senescent cell-derived vaccines: a new concept towards an immune response against cancer and aging?

Two recent seminal works have untangled the intricate role of tumor-associated senescent cells in cancer progression, or regression, by guiding our immune system against cancer cells. The characterization of these unique, yet diverse cell populations, should be considered, particularly when contemplating the use of senolytics, which are drugs that selectively eliminate senescent cells, in a cancer framework. Here, we will describe the current knowledge in this field. In particular, we will discuss how the presence of senescent cells in tumors could be used as a therapeutic target in immunogenic cancers and how we may hypothetically design an adaptive anti-aging vaccine.

Cellular senescence of renal tubular epithelial cells in acute kidney injury

Cellular senescence represents an irreversible state of cell-cycle arrest during which cells secrete senescence-associated secretory phenotypes, including inflammatory factors and chemokines. Additionally, these cells exhibit an apoptotic resistance phenotype. Cellular senescence serves a pivotal role not only in embryonic development, tissue regeneration, and tumor suppression but also in the pathogenesis of age-related degenerative diseases, malignancies, metabolic diseases, and kidney diseases. The senescence of renal tubular epithelial cells (RTEC) constitutes a critical cellular event in the progression of acute kidney injury (AKI). RTEC senescence inhibits renal regeneration and repair processes and, concurrently, promotes the transition of AKI to chronic kidney disease via the senescence-associated secretory phenotype. The mechanisms underlying cellular senescence are multifaceted and include telomere shortening or damage, DNA damage, mitochondrial autophagy deficiency, cellular metabolic disorders, endoplasmic reticulum stress, and epigenetic regulation. Strategies aimed at inhibiting RTEC senescence, targeting the clearance of senescent RTEC, or promoting the apoptosis of senescent RTEC hold promise for enhancing the renal prognosis of AKI. This review primarily focuses on the characteristics and mechanisms of RTEC senescence, and the impact of intervening RTEC senescence on the prognosis of AKI, aiming to provide a foundation for understanding the pathogenesis and providing potentially effective approaches for AKI treatment.

Non‑small cell lung cancer carrying PBRM1 mutation suggests an immunologically cold phenotype leading to immunotherapy failure even with high TMB

High tumor mutation load (TMB-H, or TMB ≥ 10) has been approved by the U.S. FDA as a biomarker for pembrolizumab treatment of solid tumors, including non‑small cell lung cancer (NSCLC). Patients with cancer who have immunotherapy-resistant gene mutations cannot achieve clinical benefits even in TMB-H. In this study, we aimed to identify gene mutations associated with immunotherapy resistance and further informed mechanisms in NSCLC. A combined cohort of 350 immune checkpoint blockade-treated patients from Memorial Sloan Kettering Cancer Center (MSKCC) was used to identify genes whose mutations could negatively influence immunotherapy efficacy. An external NSCLC cohort for which profession-free survival (PFS) data were available was used for independent validation. CIBERSORT algorithms were used to characterize tumor immune infiltrating patterns. Immunogenomic features were analysed in the TCGA NSCLC cohort. We observed that PBRM1 mutations independently and negatively influence immunotherapy efficacy. Survival analysis showed that the overall survival (OS) and PFS of patients with PBRM1 mutations (MT) were significantly shorter than the wild type (WT). Moreover, compared with PBRM1-WT/TMB-H group, OS was worse in the PBRM1-MT/TMB-H group. Notably, in patients with TMB-H/PBRM1-MT, it was equal to that in the low-TMB group. The CIBERSORT algorithm further confirmed that the immune infiltration abundance of CD8+ T cells and activated CD4+ memory T was significantly lower in the MT group. Immunogenomic differences were observed in terms of immune signatures, T-cell receptor repertoire, and immune-related genes between WT and MT groups. Nevertheless, we noticed an inverse relationship, given that MT tumors had a higher TMB than the WT group in MSKCC and TCGA cohort. In conclusion, our study revealed that NSCLC with PBRM1 mutation might be an immunologically cold phenotype and exhibited immunotherapy resistance. NSCLC with PBRM1 mutation might be misclassified as immunoresponsive based on TMB.

Senescence and the tumor-immune landscape: Implications for cancer immunotherapy

Cancer therapies, including conventional chemotherapy, radiation, and molecularly targeted agents, can lead to tumor eradication through a variety of mechanisms. In addition to their effects on tumor cell growth and survival, these regimens can also influence the surrounding tumor-immune microenvironment in ways that ultimately impact therapy responses. A unique biological outcome of cancer therapy is induction of cellular senescence. Senescence is a damage-induced stress program that leads to both the durable arrest of tumor cells and remodeling the tumor-immune microenvironment through activation of a collection pleiotropic cytokines, chemokines, growth factors, and proteinases known as the senescence-associated secretory phenotype (SASP). Depending on the cancer context and the mechanism of action of the therapy, the SASP produced following therapy-induced senescence (TIS) can promote anti-tumor immunity that enhances therapeutic efficacy, or alternatively chronic inflammation that leads to therapy failure and tumor relapse. Thus, a deeper understanding of the mechanisms regulating the SASP and components necessary for robust anti-tumor immune surveillance in different cancer and therapy contexts are key to harnessing senescence for tumor control. Here we draw a roadmap to modulate TIS and its immune-stimulating features for cancer immunotherapy.

Cellular senescence: the good, the bad and the unknown

Cellular senescence is a ubiquitous process with roles in tissue remodelling, including wound repair and embryogenesis. However, prolonged senescence can be maladaptive, leading to cancer development and age-related diseases. Cellular senescence involves cell-cycle arrest and the release of inflammatory cytokines with autocrine, paracrine and endocrine activities. Senescent cells also exhibit morphological alterations, including flattened cell bodies, vacuolization and granularity in the cytoplasm and abnormal organelles. Several biomarkers of cellular senescence have been identified, including SA-βgal, p16 and p21; however, few markers have high sensitivity and specificity. In addition to driving ageing, senescence of immune and parenchymal cells contributes to the development of a variety of diseases and metabolic disorders. In the kidney, senescence might have beneficial roles during development and recovery from injury, but can also contribute to the progression of acute kidney injury and chronic kidney disease. Therapies that target senescence, including senolytic and senomorphic drugs, stem cell therapies and other interventions, have been shown to extend lifespan and reduce tissue injury in various animal models. Early clinical trials confirm that senotherapeutic approaches could be beneficial in human disease. However, larger clinical trials are needed to translate these approaches to patient care.

Cuproptosis-related lncRNA: Prediction of prognosis and subtype determination in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, accounting for approximately 70% of all RCC cases. Cuproptosis, a novel mechanism of cell death, may be a potential target for intervention in tumor development.

Methods: Cuproptosis-related prognostic lncRNAs were identified by co-expression analysis and univariable Cox regression. Five lncRNA profiles were obtained by LASSO regression analysis, and a model with high accuracy was constructed to assess the prognosis of ccRCC patients based on these cuproptosis-related lncRNAs. Survival analysis and time-dependent ROC curves were performed for the α and β groups, and the results confirmed the high accuracy of the model in predicting the prognosis of ccRCC patients. Immunoassay, principal component analysis (PCA), and drug sensitivity analysis were also performed for different risk categories. Finally, we classified ccRCC patients into two different subtypes by consistent class clustering, and performed immune checkpoint activation, tumor microenvironment analysis, PCA, and drug sensitivity analysis for different subtypes.

Results: We developed a prognostic model using five cuproptosis-associated lncRNAs, which was found to be highly accurate in predicting ccRCC patients’ prognosis. Immunotherapy may be more beneficial to the hyper-risk category and the C2 subtype.

Conclusion: The results of this study confirm that five cuproptosis-associated lncRNAs can be used as potential prognostic markers for ccRCC.

Assessing PD-L1 status in mRCC treated with first-line immune-based combinations: a meta-analysis

Aims: We performed a meta-analysis to investigate the predictive value of PD-L1 in metastatic renal cell carcinoma (mRCC) patients receiving immune-based combinations versus sunitinib monotherapy. Materials & Methods: Outcomes of interest included OS and PFS; HRs and their 95% CIs were extracted. Results: The pooled HR for OS was 0.71 (95% CI: 0.61-0.84) and 0.75 (95% CI: 0.68-0.82) in PD-L1-positive and unselected patients, respectively. Similarly, HR for PFS was 0.68 (95% CI: 0.54-0.85) in PD-L1-positive patients and 0.63 (95% CI: 0.50-0.78) in the ITT population, respectively. Conclusion: Slight differences were observed between PD-L1-positive patients and the ITT population, and these findings support the limited role of PD-L1 as predictive biomarkers in this setting.

Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

Background

Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.

Methods

Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.

Results

The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.

Conclusions

The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

Exploration of the Associations of lncRNA Expression Patterns with Tumor Mutation Burden and Prognosis in Colon Cancer

Background

Tumor mutation burden (TMB) is emerging as a new biomarker to monitor the response of cancer patients to immunotherapy. Long non-coding RNAs (lncRNAs) are critical in regulating gene expression and play a significant role in cancer-associated immune responses. However, the association between lncRNA expression patterns and TMB levels and survival outcomes remains unknown in colon cancer.

Methods

In colon cancer patients from The Cancer Genome Atlas Program (TCGA), a multi-lncRNAs based classifier for predicting TMB levels was established using the least absolute shrinkage and selection operator (LASSO) method. The association between classifier index and immune-related characteristics of patients was also investigated. Quantitative polymerase chain reaction (qPCR) was used to verify the expression levels of these lncRNAs in normal and CRC cell lines.

Results

The multi-lncRNAs based classifier had ability to predict TMB level of patients with accuracy (AUC= 0.70), and the general applicability of this classifier was proved in the validation set (AUC= 0.71) and the pooled set (AUC= 0.70). The classifier index was related to three immune checkpoints (PD1, PD-L1, and CTLA-4), the infiltration level of immune cells, and immune response-related score (IFN-γ score, gene expression profiles (GEP) score, cytolytic activity (CYT) score and MHC score). A nomogram, which integrates classifier and some common clinical information, was able to predict the overall survival of colon cancer patients accurately.

Conclusion

LncRNA expression patterns are associated with TMB, which may serve as a classifier to predict the TMB in colon cancer patients. The nomogram could potentially evaluate survival outcomes and provide a reference to better manage colon cancer patients.

Comparative effectiveness of first-line immune checkpoint inhibitors plus tyrosine kinase inhibitors according to IMDC risk groups in metastatic renal cell carcinoma: a meta-analysis

Aim: Immune checkpoint inhibitor (ICI)-based combinations have become the new standard of primary systemic treatment for metastatic renal cell carcinoma patients. We performed a meta-analysis aimed at evaluating ICIs plus tyrosine kinase inhibitors (TKIs) combinations across International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk groups. Materials & methods: All the relevant randomized clinical trials were retrieved through Cochrane library, PubMed/Med and EMBASE; three Phase III randomized clinical trials were included. Results: ICI-TKI combinations significantly decreased the risk of death in IMDC poor- and intermediate-risk patients. Conversely, a nonstatistically significant benefit was observed in favorable-risk patients. Conclusion: Our results suggest that IMDC poor-risk patients benefit most from ICI-TKI combinations, while a proportion of metastatic renal cell carcinoma patients could respond to targeted agent monotherapy.

Comprehensive analyses of PBRM1 in multiple cancer types and its association with clinical response to immunotherapy and immune infiltrates

Background

The prognostic value of polybromo 1 (PBRM1) gene mutations in clear cell renal carcinoma (CCRCC) with anti-programmed death-ligand 1 (PD-L1) therapy remains controversial, and few studies have reported the impact of PBRM1 mutations in other cancer types.

Methods

The patient information was obtained from cBioPortal and the Tumor Immune Estimation Resource (TIMER) databases. Mann-Whitney U test were used for correlation analysis. For survival analyses, Kaplan-Meier survival curves were used and compared using the log-rank test. Cox’s regression model was used to perform univariable and multivariable analyses

Results

Our study, for the first time, performed comprehensive analyses of PBRM1 mutation frequency, PBRM1 expression, relationship of PBRM1 mutations with clinical benefit from immunotherapy, and PBRM1 expression with immune infiltrates in diverse cancer types. The results showed that the expression of PBRM1 was significantly lower in diverse cancer types compared with normal tissues. Based on multivariable analysis, PBRM1 mutations trended towards worse clinical outcomes from anti-PD-L1 in CCRCC, lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), and skin cutaneous melanoma (SKCM), and a significant association was observed in LUAD and BLCA. PBRM1 mutations were associated with higher TMB in diverse cancer types and significant associations were observed in LUAD and BLCA. The expression of PBRM1 was found to positively correlate with immune infiltrates in diverse cancer types.

Conclusions

Our findings suggested caution in starting immunotherapy alone in PBRM1 mutant patients. Further studies are needed to improve treatment for PBRM1 mutant patients.

Identification of a Metastasis-Associated Gene Signature of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most frequent pathological subtypes of kidney cancer, accounting for ~70-75%, and the major cause of mortality is metastatic disease. The difference in gene expression profiles between primary ccRCC tumors and metastatic tumors has not been determined. Thus, we report integrated genomic and transcriptomic analysis for identifying differentially expressed genes (DEGs) between primary and metastatic ccRCC tumors to understand the molecular mechanisms underlying the development of metastases. The microarray datasets GSE105261 and GSE85258 were obtained from the Gene Expression Omnibus (GEO) database, and the R package limma was used for DEG analyses. In summary, the results described herein provide important molecular evidence that metastatic ccRCC tumors are different from primary tumors. Enrichment analysis indicated that the DEGs were mainly enriched in ECM-receptor interaction, platelet activation, protein digestion, absorption, focal adhesion, and the PI3K-Akt signaling pathway. Moreover, we found that DEGs associated with a higher level of tumor immune infiltrates and tumor mutation burden were more susceptible to poor prognosis of ccRCC. Specifically, our study indicates that seven core genes, namely the collagen family (COL1A2, COL1A1, COL6A3, and COL5A1), DCN, FBLN1, and POSTN, were significantly upregulated in metastatic tumors compared with those in primary tumors and, thus, potentially offer insight into novel therapeutic and early diagnostic biomarkers of ccRCC.

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the 'immune response' and 'regulation of cell migration and proliferation'. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironment-associated genes that have powerful prognostic value in patients with RCC were identified in the present study.

Exploration of the relationships between tumor mutation burden with immune infiltrates in clear cell renal cell carcinoma

Background

Whether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to investigate the prognosis of TMB and the potential association with immune infiltrates in clear cell renal cell carcinoma (ccRCC).

Methods

We downloaded the somatic mutation data of 336 ccRCC patients from the Cancer Genome Atlas (TCGA) database, and analyzed the mutation profiles with "maftools" package. TMB was calculated and we classified the samples into high-TMB and low-TMB group. Differential analysis was conducted to compare the expression profiles between two groups using "limma" package, and we identified the 9 hub TMB-related signature from batch survival analysis. Gene ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were performed to screen significantly enriched pathways between two groups. Based on the TIMER database, we further assessed the relationships of the mutants of 9 TMB-related signature with immune infiltration levels in ccRCC. Besides, we utilized the "CIBERSORT" package to estimate the abundance of 22 immune fractions between low- and high-TMB groups, and the significant difference were determined by Wilcoxon rank-sum test. Furthermore, Cox regression model combined with survival analysis were used to evaluate the prognostic value of immune cells. Last, we constructed a Tumor Mutation Burden Prognostic Index (TMBPI) from multivariate Cox results and Receiver Operating Characteristic (ROC) curve was drawn to assess the predictive accuracy.

Results

Single nucleotide polymorphism (SNP) occurred more frequently than insertion or deletion, and C>T was the most common of SNV in ccRCC. Higher TMB levels conferred poor survival outcomes, associated with higher tumor grades and advanced pathological stages. A total of 1,265 differentially expressed genes were obtained and top 19 immune-related genes were identified in Venn diagram. GSEA revealed that patients in higher TMB groups correlated with MAPK signaling pathway, Wnt signaling pathway and pathway in cancers. Moreover, we identified 9 hub TMB-related immune genes related with survival and mutants of 9 signature were associated with lower immune infiltrates. In addition, infiltration levels of CD8+ T cell, CD4+ memory resting T cell, M1 and M2 macrophages, as well as dendritic resting cells in high-TMB group were lower than that in low-TMB group, especially the level of CD8+ T cell and macrophage correlated negatively with prognosis of ccRCC. Last, the TMBPI was constructed and the AUC of ROC curve was 0.666.

Conclusions

Higher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in ccRCC. The mutants of 9 hub TMB-related immune signature conferred lower immune cells infiltration which deserved further validation.

Multi-omics analysis of tumor mutation burden combined with immune infiltrates in bladder urothelial carcinoma

To explore the prognosis of tumor mutation burden (TMB) and underlying relationships with tumor-infiltrating immune cells in bladder cancer (BLCA). Transcriptome profiles and somatic mutation data from The Cancer Genome Atlas database by the GDC tool. A total of 437 samples were included, consisted of 412 BLCA patients and matched 25 normal samples. Specific mutation information was summarized and illustrated in waterfall plot. Higher TMB levels revealed improved overall survival (OS) and lower tumor recurrence. We found 68 differentially expressed genes in two TMB groups and identified eight independent hub TMB-related signature. Pathway analysis suggested that differential TMB-related signature correlated with multiple cancer-related crosstalk, including cell cycle, DNA replication, cellular senescence, and p53 signaling pathway. Besides, the tumor mutation burden related signature (TMBRS) model based on eight signature possessed well predictive value with area under curve (AUC) = 0.753, and patients with higher TMBRS scores showed worse OS outcomes (p < .001). Moreover, we exhibited the inferred immune cell fractions in box plot and differential abundance of immune cells were shown in the heatmap. The Wilcoxon rank-sum test suggested that CD8+ T cell (p = .001) and memory activated CD4+ T cell (p = .004) showed higher infiltrating levels in high-TMB group, while the density of resting mast cells showed lower infiltrating level in high-TMB group (p = .016). Finally, it is significant to note that CD8+ T cell and memory activated CD4+ T cell subsets not only revealed higher infiltrating abundance in high-TMB group but correlated with prolonged OS and lower risk of tumor recurrence, respectively.