Prognostic Value of Plasma and Urine Glycosaminoglycan Scores in Clear Cell Renal Cell Carcinoma

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

Identification of potential non-invasive biomarkers in diastrophic dysplasia

Diastrophic dysplasia (DTD) is a recessive chondrodysplasia caused by pathogenic variants in the SLC26A2 gene encoding for a cell membrane sulfate/chloride antiporter crucial for sulfate uptake and glycosaminoglycan (GAG) sulfation. Research on a DTD animal model has suggested possible pharmacological treatment approaches. In view of future clinical trials, the identification of non-invasive biomarkers is crucial to assess the efficacy of treatments. Urinary GAG composition has been analyzed in several metabolic disorders including mucopolysaccharidoses. Moreover, the N-terminal fragment of collagen X, known as collagen X marker (CXM), is considered a real-time marker of endochondral ossification and growth velocity and was studied in individuals with achondroplasia and osteogenesis imperfecta. In this work, urinary GAG sulfation and blood CXM levels were investigated as potential biomarkers for individuals affected by DTD. Chondroitin sulfate disaccharide analysis was performed on GAGs isolated from urine by HPLC after GAG digestion with chondroitinase ABC and ACII, while CXM was assessed in dried blood spots. Results from DTD patients were compared with an age-matched control population. Undersulfation of urinary GAGs was observed in DTD patients with some relationship to the clinical severity and underlying SLC26A2 variants. Lower than normal CXM levels were observed in most patients, even if the marker did not show a clear pattern in our small patient cohort because CXM values are highly dependent on age, gender and growth velocity. In summary, both non-invasive biomarkers are promising assays targeting various aspects of the disorder including overall metabolism of sulfated GAGs and endochondral ossification.

Plasma and Urine Free Glycosaminoglycans as Monitoring and Predictive Biomarkers in Metastatic Renal Cell Carcinoma: A Prospective Cohort Study

PURPOSE

No liquid biomarkers are approved in metastatic renal cell carcinoma (mRCC) despite the need to predict and monitor response noninvasively to tailor treatment choices. Urine and plasma free glycosaminoglycan profiles (GAGomes) are promising metabolic biomarkers in mRCC. The objective of this study was to explore if GAGomes could predict and monitor response in mRCC.

PATIENTS AND METHODS

We enrolled a single-center prospective cohort of patients with mRCC elected for first-line therapy (ClinicalTrials.gov identifier: NCT02732665) plus three retrospective cohorts (ClinicalTrials.gov identifiers: NCT00715442 and NCT00126594) for external validation. Response was dichotomized as progressive disease (PD) versus non-PD every 8-12 weeks. GAGomes were measured at treatment start, after 6-8 weeks, and every third month in a blinded laboratory. We correlated GAGomes with response and developed scores to classify PD versus non-PD, which were used to predict response at treatment start or after 6-8 weeks.

RESULTS

Fifty patients with mRCC were prospectively included, and all received tyrosine kinase inhibitors (TKIs). PD correlated with alterations in 40% of GAGome features. We developed plasma, urine, and combined glycosaminoglycan progression scores that monitored PD at each response evaluation visit with the area under the receiving operating characteristic curve (AUC) of 0.93, 0.97, and 0.98, respectively. For internal validation, the scores predicted PD at treatment start with the AUC of 0.66, 0.68, and 0.74 and after 6-8 weeks with the AUC of 0.76, 0.66, and 0.75. For external validation, 70 patients with mRCC were retrospectively included and all received TKI-containing regimens. The plasma score predicted PD at treatment start with the AUC of 0.90 and at 6-8 weeks with the AUC of 0.89. The pooled sensitivity and specificity were 58% and 79% at treatment start. Limitations include the exploratory study design.

CONCLUSION

GAGomes changed in association with mRCC response to TKIs and may provide biologic insights into mRCC mechanisms of response.

Noninvasive detection of any-stage cancer using free glycosaminoglycans

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (N_urine = 220 cancer vs. 360 healthy) and plasma (N_plasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.

Plasma and Urine Free Glycosaminoglycans as Monitoring Biomarkers in Nonmetastatic Renal Cell Carcinoma-A Prospective Cohort Study

Background

No liquid biomarkers are approved in renal cell carcinoma (RCC), making early detection of recurrence in surgically treated nonmetastatic (M0) patients dependent on radiological imaging. Urine- and plasma free glycosaminoglycan profiles-or free GAGomes-are promising biomarkers reflective of RCC metabolism.

Objective

To explore whether free GAGomes could detect M0 RCC recurrence noninvasively.

Design setting and participants

Between June 2016 and February 2021, we enrolled a prospective consecutive series of patients elected for (1) partial or radical nephrectomy for clinical M0 RCC (cohort 1) or (2) first-line therapy following RCC metachronous metastatic recurrence (cohort 2) at Sahlgrenska University Hospital, Gothenburg, Sweden. The study population included M0 RCC patients with recurrent disease (RD) versus no evidence of disease (NED) in at least one follow-up visit. Plasma and urine free GAGomes-consisting of 40 chondroitin sulfate (CS), heparan sulfate, and hyaluronic acid (HA) features-were measured in a blinded central laboratory preoperatively and at each postoperative follow-up visit until recurrence or end of follow-up in cohort 1, or before treatment start in cohort 2.

Outcome measurements and statistical analysis

We used Bayesian logistic regression to correlate GAGome features with RD versus NED and with various histopathological variables. We developed three recurrence scores (plasma, urine, and combined) proportional to the predicted probability of RD. We internally validated the area under the curve (AUC) using bootstrap resampling. We performed a decision curve analysis to select a cutoff and report the corresponding net benefit, sensitivity, and specificity of each score. We used univariable analyses to correlate each preoperative score with recurrence-free survival (RFS).

Results and limitations

Of 127 enrolled patients in total, 62 M0 RCC patients were in the study population (median age: 63 year, 35% female, and 82% clear cell). The median follow-up time was 3 months, totaling 72 postoperative visits -17 RD and 55 NED cases. RD was compatible with alterations in 14 (52%) of the detectable GAGome features, mostly free CS. Eleven (79%) of these correlated with at least one histopathological variable. We developed a plasma, a urine, and a combined free CS RCC recurrence score to diagnose RD versus NED with AUCs 0.91, 0.93, and 0.94, respectively. At a cutoff equivalent to ≥30% predicted probability of RD, the sensitivity and specificity were, respectively, 69% and 84% in plasma, 81% and 80% in urine, and 80% and 82% when combined, and the net benefit was equivalent to finding an extra ten, 13, and 12 cases of RD per hundred patients without any unnecessary imaging for plasma, urine, and combined, respectively. The combined score was prognostic of RFS in univariable analysis (hazard ratio = 1.90, p = 0.02). Limitations include a lack of external validation.

Conclusions

Free CS scores detected postsurgical recurrence noninvasively in M0 RCC with substantial net benefit. External validity is required before wider clinical implementation.

Patient summary

In this study, we examined a new noninvasive blood and urine test to detect whether renal cell carcinoma recurred after surgery.

Analysis of Normal Levels of Free Glycosaminoglycans in Urine and Plasma in Adults

Plasma and urine glycosaminoglycans (GAGs) are long, linear sulfated polysaccharides that have been proposed as potential noninvasive biomarkers for several diseases. However, owing to the analytical complexity associated with the measurement of GAG concentration and disaccharide composition (the so-called GAGome), a reference study of the normal healthy GAGome is currently missing. Here, we prospectively enrolled 308 healthy adults and analyzed their free GAGomes in urine and plasma using a standardized ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry method together with comprehensive demographic and blood chemistry biomarker data. Of 25 blood chemistry biomarkers, we mainly observed weak correlations between the free GAGome and creatinine in urine and hemoglobin or erythrocyte counts in plasma. We found a higher free GAGome concentration – but not a more diverse composition - in males. Partitioned by gender, we also established reference intervals for all detectable free GAGome features in urine and plasma. Finally, we carried out a transference analysis in healthy individuals from two distinct geographical sites, including data from the Lifelines Cohort Study, which validated the reference intervals in urine. Our study is the first large-scale determination of normal free GAGomes reference intervals in plasma and urine and represents a critical resource for future physiology and biomarker research.

Decision-making in active surveillance in kidney cancer: current trends and future urine and tissue markers

Surveillance for small renal masses is a growing choice of management amongst physicians and patients. These decisions, however, can be difficult as patient factors and tumor factors may blur the line between continued surveillance and intervention. Currently, there are no biomarkers that are readily available to aid in the decision making for patients with known renal cell carcinoma; however, many show promise. We herein review the literature of the adjunct tools that are currently available for decision making in small renal masses, but also new potential biomarkers that can potentially be of use.

Chondroitin Sulfate Disaccharides, a Serum Marker for Primary Serous Epithelial Ovarian Cancer

Glycosaminoglycans are long polysaccharidic chains, which are mostly present in connective tissues. Modified GAG expression in tissues surrounding malignant cells has been shown to contribute to tumor progression, aggressive status and metastasis in many types of cancer. Ovarian cancer is one of the most lethal gynecological malignancies due to its late diagnosis because of the absence of clear symptoms and unavailability of early disease markers. We investigated for the first time GAG changes at the molecular level as a novel biomarker for primary epithelial ovarian cancer. To this end, serum of a cohort of 68 samples was digested with chondroitinase ABC, which releases chondroitin sulfate into disaccharides. After labeling and purification, they were measured by HPLC, yielding a profile of eight disaccharides. We proposed a novel GAG-based score named "CS- bio" from the measured abundance of disaccharides present that were of statistical relevance. CS-bio's performance was compared with CA125, the clinically used serum tumor marker in routine diagnostics. CS-bio had a better sensitivity and specificity than CA125. It was more apt in differentiating early-stage patients from healthy controls, which is of high interest for oncologists.

Analytical performance of a standardized kit for mass spectrometry-based measurements of human glycosaminoglycans

Glycosaminoglycans (GAGs) are long linear sulfated polysaccharides implicated in processes linked to disease development such as mucopolysaccharidosis, respiratory failure, cancer, and viral infections, thereby serving as potential biomarkers. A successful clinical translation of GAGs as biomarkers depends on the availability of standardized GAG measurements. However, owing to the analytical complexity associated with the quantification of GAG concentration and structural composition, a standardized method to simultaneously measure multiple GAGs is missing. In this study, we sought to characterize the analytical performance of a ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UHPLC-MS/MS)-based kit for the quantification of 17 free GAG disaccharides. The kit showed acceptable linearity, selectivity and specificity, accuracy and precision, and analyte stability in the absolute quantification of 15 disaccharides. In native human samples, here using urine as a reference matrix, the analytical performance of the kit was acceptable for the quantification of CS disaccharides. Intra- and inter-laboratory tests performed in an external laboratory demonstrated robust reproducibility of GAG measurements showing that the kit was acceptably standardized. In conclusion, these results indicated that the UHPLC-MS/MS kit was standardized for the simultaneous measurement of free GAG disaccharides allowing for comparability of measurements and enabling translational research.

Urine and serum glycosaminoglycan levels in the diagnosis of urological diseases and conditions: A narrative review of the literature

Glycosaminoglycans (GAGs) are sulfated, negatively charged polysaccharides produced in almost every cell of the human body. As GAGs are extracellularly localized, the changes in body fluids such as blood and urine may reflect pathological changes in the urinary system as observed in other pathologies. In this review, we determined the potential of urinary and/or serum GAG levels as a marker for kidney and urothelial system diseases. We performed a search in the PubMed, MEDLINE, and ScienceDirect databases until September 30, 2019. A number of studies reported changes in the urinary and/or plasma GAG levels or composition in urological diseases and conditions, such as renal cell carcinoma, kidney stone, bladder carcinoma, and overactive bladder. GAGs were found to have a predictive biomarker potential that could be limited by generalizability concerns.

A simple method for detecting oncofetal chondroitin sulfate glycosaminoglycans in bladder cancer urine

Proteoglycans in bladder tumors are modified with a distinct oncofetal chondroitin sulfate (ofCS) glycosaminoglycan that is normally restricted to placental trophoblast cells. This ofCS-modification can be detected in bladder tumors by the malarial VAR2CSA protein, which in malaria pathogenesis mediates adherence of parasite-infected erythrocytes within the placenta. In bladder cancer, proteoglycans are constantly shed into the urine, and therefore have the potential to be used for detection of disease. In this study we investigated whether recombinant VAR2CSA (rVAR2) protein could be used to detect ofCS-modified proteoglycans (ofCSPGs) in the urine of bladder cancer patients as an indication of disease presence. We show that ofCSPGs in bladder cancer urine can be immobilized on cationic nitrocellulose membranes and subsequently probed for ofCS content by rVAR2 protein in a custom-made dot-blot assay. Patients with high-grade bladder tumors displayed a marked increase in urinary ofCSPGs as compared to healthy individuals. Urine ofCSPGs decreased significantly after complete tumor resection compared to matched urine collected preoperatively from patients with bladder cancer. Moreover, ofCSPGs in urine correlated with tumor size of bladder cancer patients. These findings demonstrate that rVAR2 can be utilized in a simple biochemical assay to detect cancer-specific ofCS-modifications in the urine of bladder cancer patients, which may be further developed as a noninvasive approach to detect and monitor the disease.

Structural analysis of urinary glycosaminoglycans from healthy human subjects

Urinary glycosaminoglycans (GAGs) can reflect the health condition of a human being, and the GAGs composition can be directly related to various diseases. In order to effectively utilize such information, a detailed understanding of urinary GAGs in healthy individuals can provide insight into the levels and structures of human urinary GAGs. In this study, urinary GAGs were collected and purified from healthy males and females of adults and young adults. The total creatinine-normalized urinary GAG content, molecular weight distribution and disaccharide compositions were determined. Using capillary zone electrophoresis (CZE)-mass spectrometry (MS) and CZE-MS/MS relying on negative electron transfer dissociation, the major components of healthy human urinary GAGs were determined. The structures of 10 GAG oligosaccharides representing the majority of human urinary GAGs were determined.

The extracellular matrix proteoglycan versican is strongly expressed in the urothelium of healthy rats

Objective: We have previously demonstrated protein expression of the extracellular matrix degrading protein ADAMTS5 in the nuclei of urothelial cells in healthy rats. The proteoglycan versican constitutes one of the main substrates for this protease. In this follow up study we investigated a potential co-localization of versican and ADAMTS5 in the urinary bladder wall.Material and Methods: The study was conducted with archive material (paraffin embedded bladder tissue from our previous study, i.e., 8 male Sprague-Dawley rats). Protein expression of versican was investigated by immunohistochemistry. Furthermore, the occurrence of versican mRNA was examined by in-situ hybridization.Results: Positive immunoreactivity for versican was evident in the urothelium but also, weakly, in the detrusor. This expression was localized only in the cytoplasm, leaving the nuclei devoid of reactivity. Interestingly, versican mRNA was only sparsely observed in the urothelial cells.Conclusions: We found by immunohistochemistry that the substrate for ADAMTS5, versican, was localized in the cytosol of urothelial cells. This demonstrates a difference regarding the expression of ADAMTS5, which was emphasized in the nuclei. This could imply an additional, non-enzymatic, function of ADAMTS5 in the urothelium.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Plasma Glycosaminoglycans as Diagnostic and Prognostic Biomarkers in Surgically Treated Renal Cell Carcinoma

BACKGROUND

Plasma glycosaminoglycan (GAG) measurements, when aggregated into diagnostic scores, accurately distinguish metastatic clear-cell renal cell carcinoma (RCC) from healthy samples and correlate with prognosis. However, it is unknown if GAG scores can detect RCC in earlier stages or if they correlate with prognosis after surgery.

OBJECTIVE

To explore the sensitivity and specificity of plasma GAGs for detection of early-stage RCC and prediction of recurrence and death after RCC surgery.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective case-control study consisting of a consecutive series of 175 RCC patients surgically treated between May 2011 and February 2014 and 19 healthy controls.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Plasma GAGs in preoperative and postoperative RCC and healthy samples were measured using capillary electrophoresis with laser-induced fluorescence in a single blinded laboratory. A discovery set was first analyzed to update the historical GAG score. The sensitivity of the new GAG score for RCC detection versus healthy subjects was validated using the remaining samples. The correlation of the new GAG score to histopathologic variables, overall survival, and recurrence-free survival was evaluated using nonparametric and log-rank tests and multivariable Cox regression analyses.

RESULTS AND LIMITATIONS

The RCC cohort included 94 stage I, 58 stage II-III, and 22 stage IV cases. In the first discovery set (n=67), the new GAG score distinguished RCC from healthy samples with an area under the receiver operating characteristic curve (AUC) of 0.999. In the validation set (n=108), the GAG score achieved an AUC of 0.991, with 93.5% sensitivity. GAG scores were elevated in RCC compared to healthy samples, irrespective of and uncorrelated to stage, grade, histology, age, or gender. The total chondroitin sulfate concentration was an independent prognostic factor for both overall and recurrence-free survival (hazard ratios 1.51 and 1.25) with high concordance when combined with variables available at pathologic diagnosis (C-index 0.926 and 0.849) or preoperatively (C-index 0.846 and 0.736). Limitations of the study include its retrospective nature and moderate variability in GAG laboratory measurements.

CONCLUSIONS

Plasma GAGs are highly sensitive diagnostic and prognostic biomarkers in surgically treated RCC independent of stage, grade, or histology. Prospective validation studies on GAG scores for early detection, prediction, and surveillance for RCC recurrence are thus warranted.

PATIENT SUMMARY

In this study, we examined if a new molecular blood test can detect renal cell carcinoma in the early stages and predict if the cancer might relapse after surgery. The trial is registered on ClinicalTrial.gov as NCT03471897.

Renal cell carcinoma: the search for a reliable biomarker

One particular challenge in the treatment of kidney tumors is the range of histologies and tumor phenotypes a renal mass can represent. A kidney tumor can range from benign (e.g., oncocytoma) to a clinically indolent malignancy (e.g., papillary type I, chromophobe) to aggressive disease [e.g., papillary type II or high-grade clear cell renal cell carcinoma (ccRCC)]. Even among various subtypes, kidney cancers are genetically diverse with variable prognoses and treatment response rates. Therefore, the key to proper treatment is the differentiation of these subtypes. Currently, a wide array of diagnostic, prognostic, and predictive biomarkers exist that may help guide the individualized care of kidney cancer patients. This review will discuss the various serum, urine, imaging, and immunohistological biomarkers available in practice.

Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine

Systems biology uses mathematical models to analyze large datasets and simulate system behavior. It enables integrative analysis of different types of data and can thereby provide new insight into complex biological systems. Here will be discussed the challenges of using systems medicine for advancing the development of personalized and precision medicine to treat metabolic diseases like insulin resistance, obesity, NAFLD, NASH, and cancer. It will be illustrated how the concept of genome-scale metabolic models can be used for integrative analysis of big data with the objective of identifying novel biomarkers that are foundational for personalized and precision medicine.