A case for the use of receiver operating characteristic analysis of potential clinical efficacy biomarkers in advanced renal cell carcinoma

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Exploring the Potential of Exosomes as Biomarkers in Tuberculosis and Other Diseases

Tuberculosis (TB) is a major cause of morbidity and mortality and remains an important public health issue in developing countries worldwide. The existing methods and techniques available for the diagnosis of TB are based on combinations of laboratory (chemical and biological), radiological, and clinical tests. These methods are sophisticated and laborious and have limitations in terms of sensitivity, specificity, and accuracy. Clinical settings need improved diagnostic biomarkers to accurately detect biological changes due to pathogen invasion and pharmacological responses. Exosomes are membrane-bound vesicles and mediators of intercellular signaling processes that play a significant role in the pathogenesis of various diseases, such as tuberculosis, and can act as promising biomarkers for the monitoring of TB infection. Compared to conventional biomarkers, exosome-derived biomarkers are advantageous because they are easier to detect in different biofluids, are more sensitive and specific, and may be useful in tracking patients' reactions to therapy. This review provides insights into the types of biomarkers, methods of exosome isolation, and roles of the cargo (proteins) present in exosomes isolated from patients through omics studies, such as proteomics. These findings will aid in developing new prognostic and diagnostic biomarkers and could lead to the identification of new therapeutic targets in the clinical setting.

Development of a genetic risk score to predict the risk of hypertension in European adolescents from the HELENA study

Introduction

From genome wide association study (GWAS) a large number of single nucleotide polymorphisms (SNPs) have previously been associated with blood pressure (BP) levels. A combination of SNPs, forming a genetic risk score (GRS) could be considered as a useful genetic tool to identify individuals at risk of developing hypertension from early stages in life. Therefore, the aim of our study was to build a GRS being able to predict the genetic predisposition to hypertension (HTN) in European adolescents.

Methods

Data were extracted from the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study. A total of 869 adolescents (53% female), aged 12.5-17.5, with complete genetic and BP information were included. The sample was divided into altered (≥130 mmHg for systolic and/or ≥80 mmHg for diastolic) or normal BP. Based on the literature, a total of 1.534 SNPs from 57 candidate genes related with BP were selected from the HELENA GWAS database.

Results

From 1,534 SNPs available, An initial screening of SNPs univariately associated with HTN (p < 0.10) was established, to finally obtain a number of 16 SNPs significantly associated with HTN (p < 0.05) in the multivariate model. The unweighted GRS (uGRS) and weighted GRS (wGRS) were estimated. To validate the GRSs, the area under the curve (AUC) was explored using ten-fold internal cross-validation for uGRS (0.802) and wGRS (0.777). Further covariates of interest were added to the analyses, obtaining a higher predictive ability (AUC values of uGRS: 0.879; wGRS: 0.881 for BMI z-score). Furthermore, the differences between AUCs obtained with and without the addition of covariates were statistically significant (p < 0.05).

Conclusions

Both GRSs, the uGRS and wGRS, could be useful to evaluate the predisposition to hypertension in European adolescents.

Circulating and extracellular vesicle-derived microRNAs as biomarkers in bone-related diseases

MicroRNAs (miRNA) are small non-coding RNA molecules that regulate posttranscriptional gene expression by repressing messengerRNA-targets. MiRNAs are abundant in many cell types and are secreted into extracellular fluids, protected from degradation by packaging in extracellular vesicles. These circulating miRNAs are easily accessible, disease-specific and sensitive to small changes, which makes them ideal biomarkers for diagnostic, prognostic, predictive or monitoring purposes. Specific miRNA signatures can be reflective of disease status and development or indicators of poor treatment response. This is especially important in malignant diseases, as the ease of accessibility of circulating miRNAs circumvents the need for invasive tissue biopsy. In osteogenesis, miRNAs can act either osteo-enhancing or osteo-repressing by targeting key transcription factors and signaling pathways. This review highlights the role of circulating and extracellular vesicle-derived miRNAs as biomarkers in bone-related diseases, with a specific focus on osteoporosis and osteosarcoma. To this end, a comprehensive literature search has been performed. The first part of the review discusses the history and biology of miRNAs, followed by a description of different types of biomarkers and an update of the current knowledge of miRNAs as biomarkers in bone related diseases. Finally, limitations of miRNAs biomarker research and future perspectives will be presented.

Circulating miR-1246 and miR-485-3p as Promising Biomarkers of Clinical Response and Outcome in Melanoma Patients Treated with Targeted Therapy

Despite the significant improvements in advanced melanoma therapy, there is still a pressing need for biomarkers that can predict patient response and prognosis, and therefore support rational treatment decisions. Here, we investigated whether circulating miRNAs could be biomarkers of clinical outcomes in patients treated with targeted therapy. Using next-generation sequencing, we profiled plasma miRNAs at baseline and at progression in patients treated with BRAF inhibitors (BRAFi) or BRAFi + MEKi. Selected miRNAs associated with response to therapy were subjected to validation by real-time quantitative RT-PCR . Receiver Operating Characteristics (ROC), Kaplan–Meier and univariate and multivariate Cox regression analyses were performed on the validated miR-1246 and miR-485-3p baseline levels. The median baseline levels of miR-1246 and miR-485-3p were significantly higher and lower, respectively, in the group of patients not responding to therapy (NRs) as compared with the group of responding patients (Rs). In Rs, a trend toward an increase in miR-1246 and a decrease in miR-485-3p was observed at progression. Baseline miR-1246 level and the miR-1246/miR-485-3p ratio showed a good ability to discriminate between Rs and NRs. Poorer PFS and OS were observed in patients with unfavorable levels of at least one miRNA. In multivariate analysis, a low level of miR-485-3p and a high miR-1246/miR-485-3p ratio remained independent negative prognostic factors for PFS, while a high miR-1246/miR-485-3p ratio was associated with an increased risk of mortality, although statistical significance was not reached. Evaluation of miR-1246 and miR-485-3p baseline plasma levels might help clinicians to identify melanoma patients most likely to be unresponsive to targeted therapy or at higher risk for short-term PFS and mortality, thus improving their management.

Identification of Crucial Gene Modules Related to the Efficiency of Anti-PD-1/PD-L1 Therapy and Comprehensive Analyses of a Novel Signature Based on These Modules

Biomarker development for clinical checkpoint inhibition is still in its early stages. It is critical to determine the cause of the lack of a long-term response in patients after immune checkpoint blockade (ICB) treatment and to develop composite biomarkers or signatures to improve personalized approaches. Three modules that were significantly correlated with the immunotherapeutic response were identified. Stimulatory pathways of cellular immunity, extracellular matrix formation-related pathways, and ATP metabolism-related pathways were enriched. Two distinct transcriptional subtypes were determined. Tumor microenvironment (TME) characteristics were highly correlated with “hot” and “cold” tumors. The ICB score was significantly correlated with clinical characteristics including age, Breslow depth, Clerk level, AJCC stage, and T stage. Meanwhile, a low ICB score is characterized by increased activation of immunity, a higher level of immune infiltration, and immune molecule expression. The ICB score showed a robust ability to predict melanoma prognosis in the discovery, internal validation, and external validation cohorts. In addition, a low ICB score was linked to a higher CR/PR rate in the immunotherapeutic cohort. The ICB score could reflect the pre-existing immune features and the expression pattern of “Cold” versus “Hot” tumors in melanoma patients. Thus, it has the potential to serve as a reliable predictor of melanoma prognosis and response to ICB therapy.

The meaning of significant mean group differences for biomarker discovery

Over the past decade, biomarker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterogeneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between "cases" and "controls," which tends to ignore within-group variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research-autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen's d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the "on average" when summarising their findings in their abstracts ("autistic people have deficits in X"), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers.

Blood Volume as a new functional image-based biomarker of progression in metastatic renal cell carcinoma

RECIST v1.1 has limitations in evaluating progression. We assessed Dynamic Constrast Enhanced Computed Tomography (DCE-CT) identified Blood Volume (BV) for the evaluation of progressive disease (PD) in patients with metastatic renal cell carcinoma (mRCC). BV was quantified prospectively at baseline, after one month, then every three months until PD. Relative changes (ΔBV) were assessed at each timepoint compared with baseline values. The primary endpoint was Time to PD (TTP), the secondary endpoint was Time to the scan prior to PD (PDminus1). Cox proportional hazard models adjusted ΔBV for treatments and International mRCC Database Consortium factors. A total of 62 patients had analyzable scans at the PD timepoint. Median BV was 23.92 mL × 100 g^-1 (range 4.40-399.04) at PD and 26.39 mL × 100 g^-1 (range 8.70-77.44) at PDminus1. In the final multivariate analysis higher ΔBV was statistically significantly associated with shorter Time to PD, HR 1.11 (95% CI 1.07-1.15, P < 0.001). Also assessed at PDminus1, higher ΔBV was significantly associated with shorter time to PD, HR 1.14 (95% CI 1.01-1.28, P = 0.031). In conclusion, DCE-CT identified BV is a new image-based biomarker of therapy progression in patients with mRCC.

Establishment of a post-race biomarkers database and application of pathway analysis to identify potential biomarkers in post-race equine plasma

In the context of doping control, conventional direct chemical testing detects only a limited scope of target substances in equine biological samples. To expand the ability to detect doping agents and their detection windows, metabolomics has recently become a common approach for monitoring alteration of biomarkers caused by doping agents in relevant metabolic pathways. In horse racing, remarkable changes in metabolic profiles between the rest state and racing are likely to affect the identification of doping biomarkers. Previously, we reported a limited number of significantly upregulated metabolites after racing, based on a non-targeted metabolomics approach using out-of-competition and post-race equine plasma samples. In this study, we performed a more thorough analysis of the data set, using pathway analysis to establish a post-race biomarkers database (PBD) that includes upregulated and downregulated metabolites, their fold changes, and relevant pathways, with the main objective of improving our understanding of changes in physiological status related to horse racing. Statistical analysis of the PBD revealed that two peak combinations of pentadecanoyl carnitine/galactosylglycerol (P/G) and heptadecanoyl carnitine/galactosylglycerol (H/G) could be used as potential biomarkers for the discrimination of the rest and post-race groups. To demonstrate the applicability of the PBD, we validated the post-race biomarkers P/G and H/G (highly involved in lipid metabolism) by a single-blind test. This strategy, which combines establishment of a biomarker database with pathway analysis, represents a powerful tool for discovering potential doping biomarkers in the future.

Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study

Obesity is the result of interactions between genes and environmental factors. Since monogenic etiology is only known in some obesity-related genes, a genetic risk score (GRS) could be useful to determine the genetic predisposition to obesity. Therefore, the aim of our study was to build a GRS able to predict genetic predisposition to overweight and obesity in European adolescents. A total of 1069 adolescents (51.3% female), aged 11-19 years participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study were genotyped. The sample was divided in non-overweight (non-OW) and overweight/obesity (OW/OB). From 611 single nucleotide polymorphisms (SNP) available, a first screening of 104 SNPs univariately associated with obesity (p < 0.20) was established selecting 21 significant SNPs (p < 0.05) in the multivariate model. Unweighted GRS (uGRS) was calculated by summing the number of risk alleles and weighted GRS (wGRS) by multiplying the risk alleles to each estimated coefficient. The area under curve (AUC) was calculated in uGRS (0.723) and wGRS (0.734) using tenfold internal cross-validation. Both uGRS and wGRS were significantly associated with body mass index (BMI) (p < .001). Both GRSs could potentially be considered as useful genetic tools to evaluate individual's predisposition to overweight/obesity in European adolescents.

Low Expression of Single-stranded DNA Binding Protein 2 (SSBP2) Predicts Unfavourable Postoperative Outcomes in Patients With Clear Cell Renal Cell Carcinoma

BACKGROUND

Single-stranded DNA binding protein 2 (SSBP2) is a subunit of a single-stranded DNA binding complex, which is involved in the maintenance of hematopoietic stem cells and stress responses. Numerous studies have suggested that SSBP2 functions as a tumor suppressor and is silenced through a pathway mediated by promoter hypermethylation. However, the role of SSBP2 in human renal cell carcinoma has not been reported, to date. Herein, we investigated the clinicopathological significance of SSBP2 expression in clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We constructed tissue micro arrays consisting of 173 ccRCC tissues, and SSBP2 expression was evaluated semi-quantitatively based on the staining intensity and the proportion of stained cells. Regarding statistical analysis, the tissues were divided into two groups according to SSBP2 expression, and correlation of SSBP2 expression with various clinicopathological characteristics and patient outcomes was evaluated.

RESULTS

Low SSBP2 expression was observed in 114 of 175 (65.9%) of ccRCC cases, and low SSBP2 expression was significantly correlated with larger tumor size (p=0.005, Chi-square test), higher WHO/ISUP histological grade (p<0.001, Chi-square test), tumor necrosis (p=0.008, Chi-square test), sarcomatoid change (p=0.021, Chi-square test), and higher pT AJCC stage (p=0.002, Chi-square test). Kaplan-Meier survival curves revealed that patients with low SSBP2 expression had worse recurrence-free survival (p=0.041, log-rank test).

CONCLUSION

ccRCC with low SSBP2 expression was associated with adverse clinicopathological characteristics and poor patient outcomes.

Potential Salivary mRNA Biomarkers for Early Detection of Oral Cancer

We evaluated potential biomarkers in human whole saliva for the early diagnosis of oral squamous cell carcinoma (OSCC). We selected 30 candidate genes with relevance to cancer from recent reports in PubMed. Saliva samples were obtained from 34 non-tumor control and 33 OSCC patients. Real-time PCR was performed, and mRNA levels were compared. Normalized mRNA levels of six genes (NGFI-A binding protein 2 (NAB2), cytochrome P450, family 27, subfamily A, polypeptide 1 (CYP27A1), nuclear pore complex interacting protein family, member B4 (NPIPB4), monoamine oxidase B (MAOB), sialic acid acetyltransferase (SIAE), and collagen, type III, alpha 1 (COL3A1)) were significantly lower in saliva of OSCC patients. Receiver operating characteristics (ROC) analysis was used to individually evaluate the predictive power of the potential biomarkers for OSCC diagnosis. The area under the curve (AUC) values were evaluated for the OSCC vs. non-tumor groups via univariate ROC analyses, as well as multivariate ROC analyses of combinations of multiple potential biomarkers. The combination of CYP27A1 + SIAE showed a favorable AUC value of 0.84. When we divided saliva samples into two groups according to age using a 60-year cut-off, with OSCC patients and controls evaluated together, the AUC of MAOB-NAB2 was more predictive of OSCC in the under-60 group (AUC, 0.91; sensitivity, 0.92; and specificity, 0.86) than any other gene combination. These results are expected to aid the early diagnosis of OSCC, especially in patients under 60 years of age. While more studies with larger numbers of patients are necessary, our result suggest that salivary mRNA would be a potent biomarker for early OSCC diagnosis.

Computational Prediction of Subjective Human Immunodeficiency Virus Status in Malawi Using a Random Forest Approach

An individual's subjective judgment about his or her Human Immunodeficiency Virus status depends on certain factors, behavioral, health, and sociodemographic alike. This paper aims to develop a model with good accuracy for predicting subjective HIV infection status using the random forest approach. A total of 12,796 responses of Malawians over a 12-year period were assessed. Fourteen risk factors including behavioral, health, and sociodemographic information were analysed as potential predictors of subjective Human Immunodeficiency Virus infection status in the general population and thirteen behavioral, health, and sociodemographic information were analysed among males and females. The random forest approach was adopted to build a comprehensive model comprising 14 risk factors in Malawi. It was revealed that age, worries about infection, and health rate were the most significant predictors as compared to use of condoms, marital status, and education which were the least important predictors of subjective Human Immunodeficiency Virus status in Malawi. However, the importance of infidelity on the part of a spouse and marital status as predictors of subjective Human Immunodeficiency Virus status alternated among males and females. The importance of infidelity and marital status was relatively high among females than among males. The model achieved a prediction accuracy of about 97%-99% measured by c-statistic with jack-knife cross validation and verified by Mathews correlation coefficient. As a result, RF based model has great potential to be an effective approach for analysing subjective health status.

Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis

IMPORTANCE

PD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti-PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established.

OBJECTIVE

To compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti-PD-1/PD-L1 therapy.

EVIDENCE REVIEW

A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti-PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti-PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study.

MAIN OUTCOMES AND MEASURES

Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR-) for each assay modality.

RESULTS

Tumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti-PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR-, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches.

CONCLUSIONS AND RELEVANCE

In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti-PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.

miR-126-3p down-regulation contributes to dabrafenib acquired resistance in melanoma by up-regulating ADAM9 and VEGF-A

Background

Development of resistance to inhibitors of BRAF (BRAFi) and MEK (MEKi) remains a great challenge for targeted therapy in patients with BRAF-mutant melanoma. Here, we explored the role of miRNAs in melanoma acquired resistance to BRAFi.

Methods

miRNA expression in two BRAF-mutant melanoma cell lines and their dabrafenib-resistant sublines was determined using Affymetrix GeneChip® miRNA 3.1 microarrays and/or qRT-PCR. The effects of miR-126-3p re-expression on proliferation, apoptosis, cell cycle, ERK1/2 and AKT phosphorylation, dabrafenib sensitivity, invasiveness and VEGF-A secretion were evaluated in the dabrafenib-resistant sublines using MTT assays, flow cytometry, immunoblotting, invasion assays in Boyden chambers and ELISA. ADAM9, PIK3R2, MMP7 and CXCR4 expression in the sensitive and dabrafenib-resistant cells was determined by immunoblotting. Small RNA interference was performed to investigate the consequence of VEGFA or ADAM9 silencing on proliferation, invasiveness or dabrafenib sensitivity of the resistant sublines. Long-term proliferation assays were carried out in dabrafenib-sensitive cells to assess the effects of enforced miR-126-3p expression or ADAM9 silencing on resistance development. VEGF-A serum levels in melanoma patients treated with BRAFi or BRAFi+MEKi were evaluated at baseline (T0), after two months of treatment (T2) and at progression (TP) by ELISA.

Results

miR-126-3p was significantly down-regulated in the dabrafenib-resistant sublines as compared with their parental counterparts. miR-126-3p replacement in the drug-resistant cells inhibited proliferation, cell cycle progression, phosphorylation of ERK1/2 and/or AKT, invasiveness, VEGF-A and ADAM9 expression, and increased dabrafenib sensitivity. VEGFA or ADAM9 silencing impaired proliferation and invasiveness of the drug-resistant sublines. ADAM9 knock-down in the resistant cells increased dabrafenib sensitivity, whereas miR-126-3p enforced expression or ADAM9 silencing in the drug-sensitive cells delayed the development of resistance. At T0 and T2, statistically significant differences were observed in VEGF-A serum levels between patients who responded to therapy and patients who did not. In responder patients, a significant increase of VEGF-A levels was observed at TP versus T2.

Conclusions

Strategies restoring miR-126-3p expression or targeting VEGF-A or ADAM9 could restrain growth and metastasis of dabrafenib-resistant melanomas and increase their drug sensitivity. Circulating VEGF-A is a promising biomarker for predicting patients’ response to BRAFi or BRAFi+MEKi and for monitoring the onset of resistance.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1238-4) contains supplementary material, which is available to authorized users.

Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma

Renal cell carcinoma (RCC) is a malignant tumor that currently lacks clinically useful biomarkers indicative of early diagnosis or disease status. RCC has commonly been diagnosed based on imaging results. Metabolomics offers a potential technology for discovering biomarkers and therapeutic targets by comprehensive screening of metabolites from patients with various cancers. We aimed to identify metabolites associated with early diagnosis and clinicopathological factors in RCC using global metabolomics (G-Met). Tumor and nontumor tissues were sampled from 20 cases of surgically resected clear cell RCC. G-Met was performed by liquid chromatography mass spectrometry and important metabolites specific to RCC were analyzed by multivariate statistical analysis for cancer diagnostic ability based on area under the curve (AUC) and clinicopathological factors (tumor volume, pathological T stage, Fuhrman grade, presence of coagulation necrosis and distant metastasis). We identified 58 metabolites showing significantly increased levels in tumor tissues, 34 of which showed potential early diagnostic ability (AUC >0.8), but 24 did not discriminate between tumor and nontumor tissues (AUC ≤0.8). We recognized 6 pathways from 9 metabolites with AUC >0.8 and 7 pathways from 10 metabolites with AUC ≤0.8 about malignant status. Clinicopathological factors involving malignant status correlated significantly with metabolites showing AUC ≤0.8 (p = 0.0279). The tricarboxylic acid cycle (TCA) cycle, TCA cycle intermediates, nucleotide sugar pathway and inositol pathway were characteristic pathways for the malignant status of RCC. In conclusion, our study found that metabolites and their pathways allowed discrimination between early diagnosis and malignant status in RCC according to our G-Met protocol.

Sunitinib: Ten Years of Successful Clinical Use and Study in Advanced Renal Cell Carcinoma

The oral multikinase inhibitor sunitinib malate was approved by the U.S. Food and Drug Administration in January 2006 for use in patients with advanced renal cell carcinoma (RCC). Since then, it has been approved globally for this indication and for patients with imatinib-resistant or -intolerant gastrointestinal stromal tumors and advanced pancreatic neuroendocrine tumors. As we mark the 10-year anniversary of the beginning of the era of targeted therapy, and specifically the approval of sunitinib, it is worthwhile to highlight the progress that has been made in advanced RCC as it relates to the study of sunitinib. We present the key trials and data for sunitinib that established it as a reference standard of care for first-line advanced RCC therapy and, along with other targeted agents, significantly altered the treatment landscape in RCC. Moreover, we discuss the research with sunitinib that has sought to refine its role via patient selection and prognostic markers, improve dosing and adverse event management, and identify predictive efficacy biomarkers, plus the extent to which this research has contributed to the overall understanding and management of RCC. We also explore the key learnings regarding study design and data interpretation from the sunitinib studies and how these findings and the sunitinib development program, in general, can be a model for successful development of other agents. Finally, ongoing research into the continued and future role of sunitinib in RCC management is discussed.

THE ONCOLOGIST

2017;22:41-52 IMPLICATIONS FOR PRACTICE: Approved globally, sunitinib is established as a standard of care for first-line advanced renal cell carcinoma (RCC) therapy and, along with other targeted agents, has significantly altered the treatment landscape in RCC. Research with sunitinib that has sought to refine its role via patient selection and prognostic markers, improve dosing and adverse event management, and identify predictive efficacy biomarkers has contributed to the overall understanding and management of RCC. Key learnings regarding study design and data interpretation from the sunitinib studies and the sunitinib development program, in general, can be a model for the successful development of other agents.