Clinical and molecular prognostic factors in renal cell carcinoma: what we know so far

Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer

Purpose

We aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.

Materials and Methods

Preoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Non-parametric principal component decomposition (PCD) and unsupervised hierarchical clustering were applied to build the radiomics signature models. The factors confounding the radiomics signature were investigated and controlled sequentially. Kaplan-Meier curves and Cox regression analyses were performed to test the association between radiomics signatures and survival outcomes.

Results

183 patients of TCGA-KIRC cohort with available imaging, pathological, and clinical outcomes were included in this study. All 1,160 radiomics features were included in the first radiomics signature. Three additional radiomics signatures were then modelled in successive steps removing redundant radiomics features first, removing radiomics features biased by CT slice thickness second, and removing radiomics features dependent on tumor size third. The final radiomics signature model was the most parsimonious, unbiased by CT slice thickness, and independent of tumor size. This final radiomics signature stratified the cohort into radiomics phenotypes that are different by cancer-specific and recurrence-free survival; HR (95% CI) = 3.0 (1.5-5.7), p <0.05 and HR (95% CI) = 6.6 (3.1-14.1), p <0.05, respectively.

Conclusion

Radiomics signature can be confounded by multiple factors, including feature redundancy, image acquisition parameters like slice thickness, and tumor size. Attention to and proper control for these potential confounders are necessary for a reliable and clinically valuable radiomics signature.

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma

Objectives: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC).

Methods: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics).

Results: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001).

Conclusions: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.

Comparison of three risk stratification models for non-clear cell renal cell carcinoma patients treated with temsirolimus as first-line therapy

BACKGROUND/AIMS

For metastatic renal cell carcinoma (RCC), various prognostic scoring systems have been developed. However, owing to the low prevalence of nonclear cell RCC, the three most commonly used tools were mainly developed based on patients with clear cell histology. Accordingly, this study applied three prognostic models to Korean non-clear cell RCC patients treated with first-line temsirolimus.

METHODS

This study analyzed data for 74 patients with non-clear cell RCC who were treated with temsirolimus as the first-line therapy at eight medical centers between 2011 and 2016. The receiver-operating characteristic (ROC) curves for the different prognostic models were analyzed.

RESULTS

Twenty-seven (36.5%), 24 (32.4%), and 44 patients (59.5%) were assigned to the poor prognosis groups of the Memorial Sloan-Kettering Cancer Center (MSKCC), International Metastatic RCC Database Consortium (IMDC), and Advanced Renal Cell Carcinoma (ARCC) risk stratification models, respectively. All three prognostic models reliably discriminated the risk groups to predict progression-free survival and overall survival (p < 0.001). The area under the ROC curve (AUC) for progression and survival was highest for the ARCC model (0.777; 0.734), followed by the IMDC (0.756; 0.724) and the MSKCC (0.742; 0.712) models. Furthermore, the sensitivity and specificity for predicting progression were highest with the ARCC model (sensitivity 63.6%, specificity 85.7%), followed by the MSKCC (sensitivity 58.2%, specificity 86.5%) and the IMDC models (sensitivity 56.4%, specificity 85.7%).

CONCLUSION

All three prognostic models accurately predicted the survival of the non-clear cell RCC patients treated with temsirolimus as the first-line therapy. Furthermore, the ARCC risk model performed better than the other risk models in predicting survival.

Nonenhancing Component of Clear Cell Renal Cell Carcinoma on Computed Tomography Correlates With Tumor Necrosis and Stage and Serves as a Size-Independent Prognostic Biomarker

OBJECTIVES

This study aimed to quantify nonenhancing tumor (NT) component in clear cell renal cell carcinoma (ccRCC) and assess its association with histologically defined tumor necrosis, stage, and survival outcomes.

METHODS

Among 183 patients with ccRCC, multi-institutional changes in computed tomography attenuation of tumor voxels were used to quantify percent of NT. Associations of NT with histologic tumor necrosis and tumor stage/grade were tested using Wilcoxon signed rank test and with survival outcomes using Kaplan-Meier curves/Cox regression analysis.

RESULTS

Nonenhancing tumor was higher in ccRCC with tumor necrosis (11% vs 7%; P = 0.040) and higher pathological stage (P = 0.042 and P < 0.001, respectively). Patients with greater NT had higher incidence of cancer recurrence after resection (P < 0.001) and cancer-specific mortality (P < 0.001).

CONCLUSION

Nonenhancing tumor on preoperative computed tomographic scans in patients with ccRCC correlates with tumor necrosis and stage and may serve as an independent imaging prognostic biomarker for cancer recurrence and cancer-specific survival.

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. ©2017 AACR.

Cytoreductive nephrectomy in patients with metastatic renal cell carcinoma in the era of targeted therapy: a bibliographic review

PURPOSE

To evaluate the role of cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC), against a background of lack of evidence following the introduction of targeted therapy.

METHODS

A literature review was performed in January 2017 using the MEDLINE/PubMed and EMBASE databases. The PRISMA guidelines were followed for conduct of the study. Two authors independently screened the 270 papers retrieved from the search, and the finally selected publications were identified by consensus between the two reviewers. A total of 55 studies were included in the present review.

RESULTS

Globally, the indications for CN have decreased over recent years. Although current guidelines consider CN an adequate option in selected patients based on prospective studies in the cytokine era, evidence for CN in the era of targeted therapy is based on retrospective studies only.

CONCLUSIONS

The results of ongoing prospective studies are still awaited. Retrospective data suggest that young male patients with oligometastatic disease and a good performance status can be considered suitable surgical candidates who may benefit from CN.

Bone Metastasis from Renal Cell Carcinoma

About one-third of patients with advanced renal cell carcinoma (RCC) have bone metastasis that are often osteolytic and cause substantial morbidity, such as pain, pathologic fracture, spinal cord compression and hypercalcemia. The presence of bone metastasis in RCC is also associated with poor prognosis. Bone-targeted treatment using bisphosphonate and denosumab can reduce skeletal complications in RCC, but does not cure the disease or improve survival. Elucidating the molecular mechanisms of tumor-induced changes in the bone microenvironment is needed to develop effective treatment. The “vicious cycle” hypothesis has been used to describe how tumor cells interact with the bone microenvironment to drive bone destruction and tumor growth. Tumor cells secrete factors like parathyroid hormone-related peptide, transforming growth factor-β and vascular endothelial growth factor, which stimulate osteoblasts and increase the production of the receptor activator of nuclear factor κB ligand (RANKL). In turn, the overexpression of RANKL leads to increased osteoclast formation, activation and survival, thereby enhancing bone resorption. This review presents a general survey on bone metastasis in RCC by natural history, interaction among the immune system, bone and tumor, molecular mechanisms, bone turnover markers, therapies and healthcare burden.

The evolving landscape of metastatic renal cell carcinoma

The treatment paradigm in metastatic renal cell carcinoma (mRCC) has evolved over the last 5 years. There are now seven approved targeted therapies against the vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways. The use of targeted therapy, sequences, combinations, and investigational compounds will be discussed. Prognostic and predictive tools are detailed, although much work must be done to find predictive biomarkers in an effort to individualize therapy for patients.

Increased Insulin mRNA Binding Protein-3 Expression Correlates with Vascular Enhancement of Renal Cell Carcinoma by Intravenous Contrast-CT and is Associated with Bone Metastasis

Purpose

To: 1) assess the correlation between CT vascularity and a candidate molecular marker of RCC metastasis (insulin-like mRNA binding protein-3 (IMP3)); and 2) demonstrate the differential expression of IMP3 in high vs. low vascular tumors.

Experimental design

Retrospectively obtained contrast CT from 72 patients with primary RCC were used to establish threshold values for Low, Intermediate and High tumor vascularity. Paired histopathology specimens from 33 of these patients were used for immunohistochemistry (IHC) to correlate CT with IMP-3 expression. IMP-3 gene expression studies were performed on RCC and poorly vascular prostate cancer (PC) human bone metastases samples to confirm presence of IMP3 in metastatic samples from RCC. Gene expression studies were performed on RCC 786-O and PC3 cell lines to confirm the presence of high expression of IMP3 in the RCC cell line.

Results

IMP-3 expression positively correlated with CT vascular enhancement (p<0.01). IMP3 expression by IHC was strongly positive in all RCC, but weak in PC bone metastases. Real time RT-PCR demonstrated a significant 4-fold increase in imp-3 expression in RCC 786-O vs. PC3 cells in vitro (p<0.001).

Conclusion

Quantitation of pre-operative CT is a feasible method to phenotype primary RCC vascularity, which correlates with IMP-3 expression. In situ and cell line studies demonstrate an association between high IMP-3 expression and RCC bone metastasis. Studies aimed at defining the diagnostic potential of biomarkers for RCC bone metastasis, and functional significance of IMP-3 in RCC vascularity and tumor progression are warranted.

Phase 2 trial of sunitinib and gemcitabine in patients with sarcomatoid and/or poor-risk metastatic renal cell carcinoma

BACKGROUND

Sarcomatoid renal cell carcinoma (RCC) is associated with an aggressive biology and a poor prognosis. Poor-risk RCC is defined by clinical prognostic factors and demonstrates similarly aggressive behavior. No standard treatment exists for patients with sarcomatoid RCC, and treatment options for patients with poor-risk disease are of limited benefit. The objective of this study was to investigate the efficacy of antiangiogenic therapy in combination with cytotoxic chemotherapy in clinically aggressive RCC.

METHODS

This was a phase 2, single-arm trial of sunitinib and gemcitabine in patients with sarcomatoid or poor-risk RCC. The primary endpoint was the objective response rate (ORR). Secondary endpoints included the time to progression (TTP), overall survival (OS), safety, and biomarker correlatives.

RESULTS

Overall, 39 patients had sarcomatoid RCC, and 33 had poor-risk RCC. The ORR was 26% for patients with sarcomatoid RCC and 24% for patients with poor-risk RCC. The median TTP and OS for patients with sarcomatoid RCC were 5 and 10 months, respectively. For patients with poor-risk disease, the median TTP and OS were 5.5 and 15 months, respectively. Patients whose tumors had >10% sarcomatoid histology had a higher clinical benefit rate (ORR plus stable disease) than those with ≤10% sarcomatoid histology (P = .04). The most common grade 3 or higher treatment-related adverse events included neutropenia (n = 20), anemia (n = 10), and fatigue (n = 7).

CONCLUSIONS

These results suggest that antiangiogenic therapy and cytotoxic chemotherapy are an active and well-tolerated combination for patients with aggressive RCC. The combination may be more efficacious than either therapy alone and is currently under further investigation.

Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers

BACKGROUND

Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.

OBJECTIVE

To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation.

DESIGN, SETTING, AND PARTICIPANTS

Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Biomarker association with CSS was analysed by univariate and multivariate analyses.

RESULTS AND LIMITATIONS

A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.

CONCLUSIONS

The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.

PATIENT SUMMARY

We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Biomarkers in renal cancer

Treatment options for primary and metastatic renal cancer are increasing. Accurate data from the pathological examination of renal cancer specimens aid clinicians in stratifying patients for surveillance and adjuvant therapies. This review focuses on biomarkers in diagnosis, prognosis and prediction of the biologic behavior of renal tumors which should be recorded in pathology reports and which are under investigation. Special emphasis is given to the use of immunohistochemical markers in differential diagnosis of various renal tumor subtypes. The relevance of cytogenetic and molecular findings is also discussed. The review includes the 2012 International Society for Urological Pathology Consensus conference recommendations.

Renal tumors: diagnostic and prognostic biomarkers

The International Society of Urological Pathology convened a consensus conference on renal cancer, preceded by an online survey, to address issues relating to the diagnosis and reporting of renal neoplasia. In this report, the role of biomarkers in the diagnosis and assessment of prognosis of renal tumors is addressed. In particular we focused upon the use of immunohistochemical markers and the approach to specific differential diagnostic scenarios. We enquired whether cytogenetic and molecular tools were applied in practice and asked for views on the perceived prognostic role of biomarkers. Both the survey and conference voting results demonstrated a high degree of consensus in participants' responses regarding prognostic/predictive markers and molecular techniques, whereas it was apparent that biomarkers for these purposes remained outside the diagnostic realm pending clinical validation. Although no individual antibody or panel of antibodies reached consensus for classifying renal tumors, or for confirming renal metastatic disease, it was noted from the online survey that 87% of respondents used immunohistochemistry to subtype renal tumors sometimes or occasionally, and a majority (87%) used immunohistochemical markers (Pax 2 or Pax 8, renal cell carcinoma [RCC] marker, panel of pan-CK, CK7, vimentin, and CD10) in confirming the diagnosis of metastatic RCC. There was consensus that immunohistochemistry should be used for histologic subtyping and applied before reaching a diagnosis of unclassified RCC. At the conference, there was consensus that TFE3 and TFEB analysis ought to be requested when RCC was diagnosed in a young patient or when histologic appearances were suggestive of the translocation subtype; whereas Pax 2 and/or Pax 8 were considered to be the most useful markers in the diagnosis of a renal primary.

Clinical applications of recent molecular advances in urologic malignancies: no longer chasing a "mirage"?

As our understanding of the molecular events leading to the development and progression of genitourologic malignancies, new markers of detection, prognostication, and therapy prediction can be exploited in the management of these prevalent tumors. The current review discusses the recent advances in prostate, bladder, renal, and testicular neoplasms that are pertinent to the anatomic pathologist.

External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study

BACKGROUND

The International Metastatic Renal-Cell Carcinoma Database Consortium model offers prognostic information for patients with metastatic renal-cell carcinoma. We tested the accuracy of the model in an external population and compared it with other prognostic models.

METHODS

We included patients with metastatic renal-cell carcinoma who were treated with first-line VEGF-targeted treatment at 13 international cancer centres and who were registered in the Consortium's database but had not contributed to the initial development of the Consortium Database model. The primary endpoint was overall survival. We compared the Database Consortium model with the Cleveland Clinic Foundation (CCF) model, the International Kidney Cancer Working Group (IKCWG) model, the French model, and the Memorial Sloan-Kettering Cancer Center (MSKCC) model by concordance indices and other measures of model fit.

FINDINGS

Overall, 1028 patients were included in this study, of whom 849 had complete data to assess the Database Consortium model. Median overall survival was 18·8 months (95% 17·6-21·4). The predefined Database Consortium risk factors (anaemia, thrombocytosis, neutrophilia, hypercalcaemia, Karnofsky performance status <80%, and <1 year from diagnosis to treatment) were independent predictors of poor overall survival in the external validation set (hazard ratios ranged between 1·27 and 2·08, concordance index 0·71, 95% CI 0·68-0·73). When patients were segregated into three risk categories, median overall survival was 43·2 months (95% CI 31·4-50·1) in the favourable risk group (no risk factors; 157 patients), 22·5 months (18·7-25·1) in the intermediate risk group (one to two risk factors; 440 patients), and 7·8 months (6·5-9·7) in the poor risk group (three or more risk factors; 252 patients; p<0·0001; concordance index 0·664, 95% CI 0·639-0·689). 672 patients had complete data to test all five models. The concordance index of the CCF model was 0·662 (95% CI 0·636-0·687), of the French model 0·640 (0·614-0·665), of the IKCWG model 0·668 (0·645-0·692), and of the MSKCC model 0·657 (0·632-0·682). The reported versus predicted number of deaths at 2 years was most similar in the Database Consortium model compared with the other models.

INTERPRETATION

The Database Consortium model is now externally validated and can be applied to stratify patients by risk in clinical trials and to counsel patients about prognosis.

FUNDING

None.

Survival prediction in everolimus-treated patients with metastatic renal cell carcinoma incorporating tumor burden response in the RECORD-1 trial

BACKGROUND

The phase 3 RECORD-1 study demonstrated clinical benefit of everolimus over placebo (median progression-free survival: 4.9 mo compared with 1.9 mo, p<0.001) in treatment-resistant patients with metastatic renal cell carcinoma (mRCC). However, the Response Evaluation Criteria in Solid Tumors (RECIST) objective response rate was low.

OBJECTIVE

To explore the potential role of tumor burden response to everolimus in predicting patient survival.

DESIGN, SETTING, AND PARTICIPANTS

RECORD-1 patients with at least two tumor assessments (baseline and weeks 2-14) were included (n=246).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A multivariate Cox proportional hazard model was used to assess the impact of various prognostic factors on overall survival (OS). Components of RECIST progression were explored using univariate Cox regression.

RESULTS AND LIMITATIONS

The baseline sum of longest tumor diameters (SLD) and progression at weeks 2-14 were prognostic factors of OS by multivariate analysis. Univariate analysis at weeks 2-14 demonstrated that growth of nontarget lesions and appearance of new lesions were predictive of OS (p<0.001). This retrospective analysis used data from one arm of one trial; patients in the placebo arm were excluded because of confounding effects when they crossed over to everolimus.

CONCLUSIONS

This analysis identified baseline SLD as a predictive factor of OS, and the appearance of a new lesion or progression of a nontarget lesion at first assessment after baseline also affects OS in patients with mRCC treated with everolimus.

Altered expression of the clock gene machinery in kidney cancer patients

BACKGROUND AND AIM

Kidney cancer is associated with alteration in the pathways regulated by von Hippel-Lindau protein and hypoxia inducible factor α. Tight interrelationships have been evidenced between hypoxia response pathways and circadian pathways. The dysregulation of the circadian clock circuitry is involved in carcinogenesis. The aim of our study was to evaluate the clock gene machinery in kidney cancer.

METHODS

mRNA expression levels of the clock genes ARNTL1, ARNTL2, CLOCK, PER1, PER2, PER3, CRY1, CRY2, TIMELESS, TIPIN and CSNK1E and of the clock controlled gene SERPINE1 were evaluated by DNA microarray assays and by qRT-PCR in primary tumor and matched nontumorous tissue collected from a cohort of 11 consecutive kidney cancer patients.

RESULTS

In kidney tumor tissue, we found down-regulation of PER2 (median=0.658, Q1-Q3=0.562-0.744, P<0.01), TIMELESS (median=0.705, Q1-Q3=0.299-1.330, P=0.04) and TIPIN (median=0.556, Q1-Q3=0.385-1.945, P=0.01), up-regulation of SERPINE1 (median=1.628, Q1-Q3=0.339-4.071, P=0.04), whereas the expression of ARNTL2 (median=0.605, Q1-Q3=0.318-1.738, P=0.74) and CSNK1E (median=0.927, Q1-Q3=0.612-2.321, P=0.33) did not differ. A statistically significant correlation was evidenced between mRNA levels of PER2 and CSNKIE (r=0.791, P<0.01), PER2 and TIPIN (r=0.729, P=0.01), PER2 and SERPINE1 (r=0.704, P=0.01), TIMELESS and TIPIN (r=0.605, P=0.04), TIMELESS and CSNKIE (r=0.637, P=0.03), TIPIN and CSNKIE (r=0.940, P<0.01).

CONCLUSION

In kidney cancer, the circadian clock circuitry is deregulated and the altered expression of the clock genes might be involved in disease onset and progression.