A Molecular Model for Predicting Overall Survival in Patients with Metastatic Clear Cell Renal Carcinoma: Results from CALGB 90206 (Alliance)

Linking genotype to phenotype: bench to bedside

Over the past three decades, researchers in the NCI-funded cancer cooperative groups have routinely incorporated collection of biospecimens, quality of life assessments, diet and physical activity data and other health outcome variables from clinical trial participants to provide an expanding resource for correlative science in cancer clinical research.

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.

Methods

Clinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.

Results

A total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.

Conclusion

Our study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Identification of a 15 DNA Damage Repair-Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

BACKGROUND

Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment.

INTRODUCTION

To identify a reliable DDR-related gene signature as an independent predictor of LUAD.

METHODS

DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan-Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis.

RESULTS

Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 - 4, T2-T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients.

CONCLUSION

The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

On the design and the analysis of stratified biomarker trials in the presence of measurement error

A major emphasis in precision medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. And as such, enormous resources and innovative clinical trials designs in oncology are devoted to identifying predictive biomarkers. Predictive biomarkers are ones that will identify patients that are more likely to respond to specific therapies and they are usually discovered through retrospective analysis from large randomized phase II or phase III trials. One important design to consider is the stratified biomarker design, where patients will have their specimens obtained at baseline and the biomarker status will be assessed prior to random assignment. Regardless of their biomarker status, patients will be randomized to either an experimental arm or the standard of care arm. The stratified biomarker design can be used to test for a treatment-biomarker interaction in predicting a time-to event outcome. Many biomarkers, however, are derived from tissues from patients, and their levels may be heterogeneous. As a result, biomarker levels may be measured with error and this would have an adverse impact on the power of a stratified biomarker clinical trial. We present a trial design and an analysis framework for the stratified biomarker design. We show that the naïve test is biased and provide bias-corrected estimators for computing the sample size and the 95% confidence interval when testing for a treatment-biomarker interaction in predicting a time to event outcome. We propose a sample size formula that adjusts for misclassification and apply it in the design of a phase III clinical trial in renal cancer.

Prognostic value of downregulated 5-hydroxymethylcytosine expression in renal cell carcinoma: a 10 year follow-up retrospective study

5-hydroxymethylcytosine (5hmC) is converted from DNA methylation of cytosine (5mC) by the catalysis of TET proteins, and proposed to be involved in tumorigenesis. However, the prognostic value of 5hmC in renal cell carcinoma (RCC) is still unclear. This study aimed to define the clinical significance of 5hmC in RCC. We performed dot blot assays to measure the relative expression of 5hmC in RCC. We reviewed the clinical records of 310 RCC patients and performed immunohistochemical (IHC) staining of 5hmC. The overall survival (OS) and cancer specific survival (CSS) of all patients were recorded over a 10-year follow-up period. Effective prognostic nomograms which contained 5hmC were established to provide individualized OS and CSS in RCC. 5hmC expression level was significantly decreased in RCC tissues compared with those in the normal counterparts. Kaplan-Meier curves revealed that high 5hmC expression had a good prognostic impact on RCC patients. Cox multivariate survival analyses further indicated 5hmC was an independent prognostic factor for RCC survival. Nomograms constructed based on cox regression analysis were available to calculate the survival probability directly. Calibration curves displayed good agreements. The findings were validated with an independent external cohort included 77 RCC cases. Thus, we believe we have found a significative prognostic factor for RCC.

Comprehensive assessment gene signatures for clear cell renal cell carcinoma prognosis

There are many prognostic gene signature models in clear cell renal cell carcinoma (ccRCC). However, different results from various methods and samples are hard to contribute to clinical practice. It is necessary to develop a robust gene signature for improving clinical practice in ccRCC.A method was proposed to integrate least absolute shrinkage and selection operator and multiple Cox regression to obtain mRNA and microRNA signature from the cancer genomic atlas database for predicting prognosis of ccRCC. The gene signature model consisted by 5 mRNAs and 1 microRNA was identified. Prognosis index (PI) model was constructed from RNA expression and median value of PI is used to classified patients into high- and low-risk groups.The results showed that high-risk patients showed significantly decrease survival comparison with low-risk groups [hazard ratio (HR) =7.13, 95% confidence interval = 3.71-13.70, P < .001]. As the gene signature was mainly consisted by mRNA, the validation data can use transcriptomic data to verify. For comparison of the performance with previous works, other gene signature models and 4 datasets of ccRCC were retrieved from publications and public database. For estimating PI in each model, 3 indicators including HR, concordance index , and the area under the curve of receiver operating characteristic for 3 years were calculated across 4 independent datasets.The comparison results showed that the integrative model from our study was more robust than other models via comprehensive analysis. These findings provide some genes for further study their functions and mechanisms in ccRCC tumorigenesis and malignance, and may be useful for effective clinical decision making of ccRCC patients.

Molecular profiling of renal cell carcinoma: building a bridge toward clinical impact

PURPOSE OF REVIEW

The daunting task of identifying key molecular drivers of renal cell carcinoma (RCC) has begun to reveal significant insights into tumor biology. This review provides an update on recent discoveries in this field and their possible clinical implications.

RECENT FINDINGS

Molecular profiles within the classic RCC histologic subtypes present distinctive appreciation of tumor biology and also allow for exploitation of targeted treatment regimens for patients with metastatic disease. Prognostic signatures have demonstrated the ability to accurately predict many clinical outcomes.

SUMMARY

The molecular and genomic profiling of RCC subtypes has identified a unique and diverse spectrum of alterations. Utilization of these characteristics to improve our prognostic and therapeutic outcomes in the clinical realm remains in its infancy but is rapidly advancing.

High expression of galectin-7 associates with poor overall survival in patients with non-metastatic clear-cell renal cell carcinoma

Background

Galectin-7, has a controversial role in tumor progression, can either suppress tumor growth or induce chemoresistance depends on different tumor histology types. The aim was to appraise Galectin-7 expression on the overall survival (OS) of patients with non-metastatic clear cell renal cell carcinoma (ccRCC) following surgery.

Results

High galectin-7 expression was specifically correlated with necrosis (P = 0.015). Multivariate analysis confirmed galectin-7 as an independent prognosticator for OS (P = 0.005). High galectin-7 expression suggested poor OS (P < 0.001), particularly with UISS intermediate and high score groups. Notably, the predictive accuracy of the traditional prognostic scores was improved when combined with galectin-7 expression.

Materials and Methods

We retrospectively enrolled 416 patients who underwent nephrectomy at a single institute between 2008 and 2009 and detected their intratumor galectin-7 expression by immunohistochemistry. Kaplan-Meier method was conducted to plot survival curves and multivariate cox regression analysis for potential independent prognostic factors on OS. A nomogram was constructed with concordance index (C-index) and Akaike's Information Criteria (AIC) to appraise prognostic accuracy of different models.

Conclusions

High galectin-7 expression is an independent adverse predictor for survival. Evaluation of galectin-7 could help guide postsurgical management for non-metastatic ccRCC patients.

Improving outcomes in high-risk, nonmetastatic renal cancer: new data and ongoing trials

High-risk, localized renal cancer is associated with recurrence rates of up to 75% at 10 years. The outcomes of patients at this disease stage depend on optimal patient stratification, surgical management and systemic therapy selection. Current evidence does not support the use of adjuvant therapy in patients with high-risk, localized disease. During the past 12 months, the results of large, randomized-controlled trials of adjuvant tyrosine kinase inhibitor (TKI) treatment, such as ASSURE and S-TRAC, have been published, but their findings are conflicting. Whether TKIs will become standard of care in the adjuvant setting depends on the long-term data from ongoing trials. In addition, several new trials that evaluate the utility of novel immune checkpoint inhibitors in this patient group are currently recruiting. The management of renal cancer is likely to evolve at a rapid pace over the next few years and matching patients with the appropriate therapeutic regimen is likely to be a focus of future research.

High expression of HMGA2 predicts poor survival in patients with clear cell renal cell carcinoma

High-mobility group AT-hook 2 (HMGA2) is involved in a wide spectrum of biological processes and is upregulated in several tumors, but its role in renal carcinoma remains unclear. The aim of this study was to examine the expression of HMGA2 and its relationship to the overall survival (OS) of patients with non-metastatic clear cell renal cell carcinoma (ccRCC) following surgery. The expression of HMGA2 was evaluated retrospectively by immunohistochemistry (IHC) in 162 patients with ccRCC who underwent nephrectomy in 2003 and 2004. An IHC analysis revealed that HMGA2 was expressed in the nuclei of tumor cells in 146 (90.1%) patients with ccRCC. The level of HMGA2 was positively correlated with tumor size, lymph node metastasis, and Fuhrman Grade. A Kaplan–Meier analysis with log-rank test found that patients with high HMGA2 expression had a poor outcome and that patients with low HMGA2 expression had better survival. Cox regression analysis showed that HMGA2 expression could serve as an independent prognostic factor for ccRCC patients. The efficacy of the following prognostic models was improved when HMGA2 expression was added: tumor node metastasis stage, UCLA Integrated Scoring System, Mayo Clinic stage, size, grade, and necrosis score. In summary, this study showed that HMGA2 expression is an independent prognostic factor for OS in patients with ccRCC. HMGA2 was found to be a valuable biomarker for ccRCC progression.