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 (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 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.

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.

The specificity of the malarial VAR2CSA protein for chondroitin sulfate depends on 4-O-sulfation and ligand accessibility.

Placental malaria infection is mediated by the binding of the malarial VAR2CSA protein to the placental glycosaminoglycan, chondroitin sulfate. Recombinant subfragments of VAR2CSA (rVAR2) have also been shown to bind specifically and with high affinity to cancer cells and tissues, suggesting the presence of a shared type of oncofetal chondroitin sulfate (ofCS) in the placenta and in tumors. However, the exact structure of ofCS and what determines the selective tropism of VAR2CSA remains poorly understood. In this study, ofCS was purified by affinity chromatography using rVAR2 and subjected to detailed structural analysis. We found high levels of N-acetylgalactosamine 4-O-sulfation (∼80-85%) in placenta- and tumor-derived ofCS. This level of 4-O-sulfation was also found in other tissues that do not support parasite sequestration, suggesting that VAR2CSA tropism is not exclusively determined by placenta- and tumor-specific sulfation. Here, we show that both placenta and tumors contain significantly more chondroitin sulfate moieties of higher molecular weight than other tissues. In line with this, CHPF and CHPF2, which encode proteins required for chondroitin polymerization, are significantly upregulated in most cancer types. CRISPR/Cas9 targeting of CHPF and CHPF2 in tumor cells reduced the average molecular weight of cell-surface chondroitin sulfate and resulted in a marked reduction of rVAR2 binding. Finally, utilizing a cell-based glycocalyx model, we showed that rVAR2 binding correlates with the length of the chondroitin sulfate chains in the cellular glycocalyx. These data demonstrate that the total amount and cellular accessibility of chondroitin sulfate chains impact rVAR2 binding and thus malaria infection.

Expression of GM content in mass fraction from digital PCR data.

Nowadays the quantification of the content of genetically modified (GM) constituents in food or feed products is performed by using either quantitative real-time PCR (qPCR) or digital PCR (dPCR). The latter is increasingly used. Therefore, experimental protocols for the quantification of 52 GM events authorised in the EU have been converted into a digital format and minimum performance characteristics for dPCR methods are detailed. Because of the need to harmonise the transformation of PCR results between two different measurement scales, 50 conversion factors for Certified Reference Materials (CFCRM) have been experimentally determined by three and sometimes four independent expert laboratories. The uncertainty of each CFCRM has been estimated to express dPCR results in mass fraction with a consistent uncertainty contribution. In 38 out of 58 cases, the validated qPCR methods (for 52 event-specific and 6 taxon-specific measurements) could easily be transferred into dPCR methods by using the same oligo sequences, final oligo concentration or annealing temperatures for the dPCR procedure. Laboratories have nevertheless used different strategies to improve the resolution or to reduce the so-called rain in their dPCR outcome. Those modifications were needed for PCR procedures that could not be converted without changes into a digital format. Therefore, exclusion/quality criteria such as the maximum rate of partitions with intermediate fluorescence "rain", the minimum resolution and repeatability are suggested for dPCR methods. The CFCRM determined in this study were generally in agreement with the declared zygosity of the GM parental donor for hemizygous maize events. In a limited number of GM events the CFCRM values were significantly different when measured with different maize-specific (ZmAdh1 or hmgA) genes.

Single and multi-laboratory validation of a droplet digital PCR method.

The authorisation of genetically modified food and feed in the EU is subject to the provision of evidence of safety and of the availability of reliable analytical methods. These methods represent an essential tool for official laboratories to enforce a harmonised market control. Here the validation of droplet digital PCR (dPCR) methods has been performed for studying if the performance and acceptance parameters set by EU and other international guidelines for the analysis of genetically modified organisms (GMO) in food and feed are suitable and achievable also with such methods. The single-laboratory validation study showed that performance requirements set for GMO analysis by real time PCR can also be used to assess dPCR-based methods. Moreover, trueness and precision were assessed for both simplex and duplex formats in a multi-laboratory validation study organised according to international standards. Overall, the data on trueness, repeatability and reproducibility precision resulting from the collaborative study are satisfying the acceptance criteria for the respective parameters as stipulated in the EU and other international guidance such as the Codex Committee on Methods of Analysis and Sampling (CCMAS). For instance, the duplex droplet dPCR method for MON810 showed relative repeatability standard deviations from 1.8% to 15.7%, while the relative reproducibility standard deviation was found to be between 2.1% and 16.5% over the dynamic range studied. Moreover, the relative bias of the dPCR methods was well below 25% across the entire dynamic range. In addition, other aspects supporting the application of digital PCR for the control of GMOs on the market were experimentally assessed such as the conversion of the measurement results from copy number ratio to mass fraction, the influence of the DNA extraction step and of the ingredient content. It was found that the DNA extraction step added only a limited contribution to the variability of the measurement results under the studied conditions. The decreasing amount of the target ingredient content may decrease the level of precision of the method, although within the acceptance range of GMO performance parameters.

Pan-cancer analysis of the metabolic reaction network.

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma.

Several common oncogenic pathways have been implicated in the emergence of renowned metabolic features in cancer, which in turn are deemed essential for cancer proliferation and survival. However, the extent to which different cancers coordinate their metabolism to meet these requirements is largely unexplored. Here we show that even in the heterogeneity of metabolic regulation a distinct signature encompassed most cancers. On the other hand, clear cell renal cell carcinoma (ccRCC) strongly deviated in terms of metabolic gene expression changes, showing widespread down-regulation. We observed a metabolic shift that associates differential regulation of enzymes in one-carbon metabolism with high tumor stage and poor clinical outcome. A significant yet limited set of metabolic genes that explained the partial divergence of ccRCC metabolism correlated with loss of von Hippel-Lindau tumor suppressor (VHL) and a potential activation of signal transducer and activator of transcription 1. Further network-dependent analyses revealed unique defects in nucleotide, one-carbon, and glycerophospholipid metabolism at the transcript and protein level, which contrasts findings in other tumors. Notably, this behavior is recapitulated by recurrent loss of heterozygosity in multiple metabolic genes adjacent to VHL. This study therefore shows how loss of heterozygosity, hallmarked by VHL deletion in ccRCC, may uniquely shape tumor metabolism.

Breast cyst fluid heparan sulphate is distinctively N-sulphated depending on apocrine or flattened type.

Breast cyst fluid (BCF) contained in gross cists is involved with its many biomolecules in different stages of breast cystic development. Type I apocrine and type II flattened cysts are classified based on biochemical, morphological and hormonal differences, and their different patterns of growth factors and active biocompounds may require different regulation. In a previous paper, hyaluronic acid in a very low content and chondroitin sulphate/dermatan sulphate were identified and characterized in BCF. In this new study, various apocrine and flattened BCFs were analyzed for HS concentration and disaccharide pattern. Apocrine HS was found specifically constituted of N-acetyl groups contrary to flattened HS richer in N-sulphate disaccharides with an overall N-acetylated/N-sulphated ratio significantly increased in apocrine compared with flattened (13.5 vs 3.7). Related to this different structural features, the charge density significantly decreased (~-30%) in apocrine versus flattened BCFs. Finally, no significant differences were observed for HS amount (~0.9-1.3 µg ml(-1) ) between the two BCF types even if a greater content was determined for flattened samples. The specifically N-sulphated sequences in flattened BCF HS can exert biologic capacity by regulating growth factors activity. On the other hand, we cannot exclude a peculiar regulation of the activity of biomolecules in apocrine BCF by HS richer in N-acetylated disaccharides. In fact, the different patterns of growth factors and active biocompounds in the two types of cysts may require different regulation by specific sequences in the HS backbone possessing specific structural characteristics and distinctive chemical groups.

Kiwi: a tool for integration and visualization of network topology and gene-set analysis.

BACKGROUND: The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures. RESULTS: Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network. CONCLUSIONS: Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies.

BACKGROUND: The polymerase chain reaction (PCR) is the current state of the art technique for DNA-based detection of Genetically Modified Organisms (GMOs). A typical control strategy starts by analyzing a sample for the presence of target sequences (GM-elements) known to be present in many GMOs. Positive findings from this "screening" are then confirmed with GM (event) specific test methods. A reliable knowledge of which GMOs are detected by combinations of GM-detection methods is thus crucial to minimize the verification efforts. DESCRIPTION: In this article, we describe a novel platform that links the information of two unique databases built and maintained by the European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) at the Joint Research Centre (JRC) of the European Commission, one containing the sequence information of known GM-events and the other validated PCR-based detection and identification methods. The new platform compiles in silico determinations of the detection of a wide range of GMOs by the available detection methods using existing scripts that simulate PCR amplification and, when present, probe binding. The correctness of the information has been verified by comparing the in silico conclusions to experimental results for a subset of forty-nine GM events and six methods. CONCLUSIONS: The JRC GMO-Matrix is unique for its reliance on DNA sequence data and its flexibility in integrating novel GMOs and new detection methods. Users can mine the database using a set of web interfaces that thus provide a valuable support to GMO control laboratories in planning and evaluating their GMO screening strategies. The platform is accessible at http://gmo-crl.jrc.ec.europa.eu/jrcgmomatrix/ .

Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans-A Diagnostic Prediction Model.

Impaired glycosaminoglycans (GAGs) catabolism may lead to a cluster of rare metabolic and genetic disorders called mucopolysaccharidoses (MPSs). Each subtype is caused by the deficiency of one of the lysosomal hydrolases normally degrading GAGs. Affected tissues accumulate undegraded GAGs in cell lysosomes and in the extracellular matrix, thus leading to the MPS complex clinical phenotype. Although each MPS may present with recognizable signs and symptoms, these may often overlap between subtypes, rendering the diagnosis difficult and delayed. Here, we performed an exploratory analysis to develop a model that predicts MPS subtypes based on UHPLC-MS/MS measurement of a urine free GAG profile (or GAGome). We analyzed the GAGome of 78 subjects (38 MPS, 37 healthy and 3 with other MPS symptom-overlapping disorders) using a standardized kit in a central-blinded laboratory. We observed several MPS subtype-specific GAGome changes. We developed a multivariable penalized Lasso logistic regression model that attained 91.2% balanced accuracy to distinguish MPS type II vs. III vs. any other subtype vs. not MPS, with sensitivity and specificity ranging from 73.3% to 91.7% and from 98.4% to 100%, depending on the predicted subtype. In conclusion, the urine GAGome was revealed to be useful in accurately discriminating the different MPS subtypes with a single UHPLC-MS/MS run and could serve as a reliable diagnostic test for a more rapid MPS biochemical diagnosis.

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.

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.

Accelerating the Development and Validation of Liquid Biopsy for Early Cancer Screening and Treatment Tailoring.

Liquid biopsy (LB) is a minimally invasive method which aims to detect circulating tumor-derived components in body fluids. It provides an alternative to current cancer screening methods that use tissue biopsies for the confirmation of diagnosis. This paper attempts to determine how far the regulatory, policy, and governance framework provide support to LB implementation into healthcare systems and how the situation can be improved. For that reason, the European Alliance for Personalised Medicine (EAPM) organized series of expert panels including different key stakeholders to identify different steps, challenges, and opportunities that need to be taken to effectively implement LB technology at the country level across Europe. To accomplish a change of patient care with an LB approach, it is required to establish collaboration between multiple stakeholders, including payers, policymakers, the medical and scientific community, and patient organizations, both at the national and international level. Regulators, pharma companies, and payers could have a major impact in their own domain. Linking national efforts to EU efforts and vice versa could help in implementation of LB across Europe, while patients, scientists, physicians, and kit manufacturers can generate a pull by undertaking more research into biomarkers.

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.

Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma.

BACKGROUNDIdentifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored.METHODSWe performed a meta-analysis of 4 published shotgun metagenomic studies (Ntot = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI.RESULTSOur analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348-0.899) and was predictive of prognosis (HR = 0.35, P = 0.081).CONCLUSIONThese results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes.FUNDINGThis work was funded by the Knut and Alice Wallenberg Foundation, BioGaia AB, and Cancerfonden.

Exercise-Mediated Lowering of Glutamine Availability Suppresses Tumor Growth and Attenuates Muscle Wasting.

Glutamine is a central nutrient for many cancers, contributing to the generation of building blocks and energy-promoting signaling necessary for neoplastic proliferation. In this study, we hypothesized that lowering systemic glutamine levels by exercise may starve tumors, thereby contributing to the inhibitory effect of exercise on tumor growth. We demonstrate that limiting glutamine availability, either pharmacologically or physiologically by voluntary wheel running, significantly attenuated the growth of two syngeneic murine tumor models of breast cancer and lung cancer, respectively, and decreased markers of atrophic signaling in muscles from tumor-bearing mice. In continuation, wheel running completely abolished tumor-induced loss of weight and lean body mass, independently of the effect of wheel running on tumor growth. Moreover, wheel running abolished tumor-induced upregulation of muscular glutamine transporters and myostatin signaling. In conclusion, our data suggest that voluntary wheel running preserves muscle mass by counteracting muscular glutamine release and tumor-induced atrophic signaling.

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.

Purine metabolism regulates DNA repair and therapy resistance in glioblastoma.

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming therapy resistance. Treatments that are effective independent of genotype are urgently needed. By correlating intracellular metabolite levels with radiation resistance across dozens of genomically-distinct models of GBM, we find that purine metabolites, especially guanylates, strongly correlate with radiation resistance. Inhibiting GTP synthesis radiosensitizes GBM cells and patient-derived neurospheres by impairing DNA repair. Likewise, administration of exogenous purine nucleosides protects sensitive GBM models from radiation by promoting DNA repair. Neither modulating pyrimidine metabolism nor purine salvage has similar effects. An FDA-approved inhibitor of GTP synthesis potentiates the effects of radiation in flank and orthotopic patient-derived xenograft models of GBM. High expression of the rate-limiting enzyme of de novo GTP synthesis is associated with shorter survival in GBM patients. These findings indicate that inhibiting purine synthesis may be a promising strategy to overcome therapy resistance in this genomically heterogeneous disease.