Rita Azevedo

SLC19A1 80G allele as a biomarker of methotrexate-related gastrointestinal toxicity in Portuguese rheumatoid arthritis patients

AIM The aim of our study was to characterize the association of clinicopathological variables and the SLC19A1/RFC-1 G80A polymorphism in methotrexate (MTX)-related toxicity in Portuguese patients with rheumatoid arthritis. PATIENTS & METHODS The study included 233 consecutively recruited patients with rheumatoid arthritis under MTX treatment. The SLC19A1 G80A polymorphism was evaluated by PCR-RFLP. RESULTS Statistical analysis revealed that SLC19A1 80G carriers had increased risk of gastrointestinal toxicity (odds ratio [OR]: 2.61, p = 0.019) and that regular folic acid supplementation was associated with both overall and gastrointestinal toxicity protection (OR: 0.15, p < 0.001 and OR: 0.19, p < 0.001, respectively). Multivariate analysis confirmed the association of SLC19A1 80G and regular folic acid supplementation to gastrointestinal toxicity (OR: 5.53 and 0.13, respectively). Moreover, a multivariate Cox regression model demonstrated a higher risk of earlier gastrointestinal toxicity in SLC19A1 80G carriers (hazard ratio: 3.63, p = 0.002). CONCLUSION SLC19A1 G80A genotyping may be a useful tool for clinicians to identify patients at higher risk for developing gastrointestinal toxicity related to MTX treatment.

Role of Key TYMS Polymorphisms on Methotrexate Therapeutic Outcome in Portuguese Rheumatoid Arthritis Patients

Background Therapeutic outcome of rheumatoid arthritis (RA) patients treated with methotrexate (MTX) can be modulated by thymidylate synthase (TS) levels, which may be altered by genetic polymorphisms in TS gene (TYMS). This study aims to elucidate the influence of TYMS polymorphisms in MTX therapeutic outcome (regarding both clinical response and toxicity) in Portuguese RA patients. Methods Clinicopathological data from 233 Caucasian RA patients treated with MTX were collected, outcomes were defined and patients were genotyped for the following TYMS polymorphisms: 1) 28 base pairs (bp) variable number tandem repeat (rs34743033); 2) single nucleotide polymorphism C>G (rs2853542); and 3) 6 bp sequence deletion (1494del6, rs34489327). Chi-square and binary logistic regression analyses were performed, using genotype and haplotype-based approaches. Results Considering TYMS genotypes, 3R3R (p = 0.005, OR = 2.34), 3RC3RG (p = 0.016, OR = 3.52) and 6bp− carriers (p = 0.011, OR = 1.96) were associated with non-response to MTX. Multivariate analysis confirmed the increased risk for non-response to MTX in 6bp− carriers (p = 0.016, OR = 2.74). Data demonstrated that TYMS polymorphisms were in linkage disequilibrium (p<0.00001). Haplotype multivariate analysis revealed that haplotypes harboring both 3R and 6bp− alleles were associated with non-response to MTX. Regarding MTX-related toxicity, no statistically significant differences were observed in relation to TYMS genotypes and haplotypes. Conclusion Our study reveals that TYMS polymorphisms could be important to help predicting clinical response to MTX in RA patients. Despite the potential of these findings, translation into clinical practice needs larger studies to confirm these evidences.

SLC19A1, SLC46A1 and SLCO1B1 Polymorphisms as Predictors of Methotrexate-Related Toxicity in Portuguese Rheumatoid Arthritis Patients

Methotrexate (MTX) is used for rheumatoid arthritis (RA) treatment showing a wide toxicity profile. This study aimed to evaluate the influence of single nucleotide polymorphisms (SNPs) in genes encoding for MTX transporters with the occurrence of MTX-related toxicity (overall and gastrointestinal). A total of 233 Portuguese RA patients were genotyped for 23 SNPs. Haplotype analyses were performed and a toxicogenetic risk index (TRI) was created for SNPs that revealed to be statistically significant. Regarding MTX overall toxicity, an increased risk was associated to SLC19A1 rs7499 G carriers (p = 0.017), SLC46A1 rs2239907 GG (p = 0.030) and, SLCO1B1 rs4149056 T carriers (p = 0.040) and TT (p = 0.019). TRI revealed that patients with Index 3 were 18-fold more likely to present an adverse drug reaction when compared to those with Index 1 (p = 0.001). For MTX gastrointestinal toxicity, results demonstrated an increased risk associated with SLC19A1 rs7499 G carriers (p = 0.012) and GG (p = 0.045), SLC19A1 rs1051266 G carriers (p = 0.034), SLC19A1 rs2838956 A carriers (p = 0.049) and, SLCO1B1 rs4149056 T carriers (p = 0.042) and TT (p = 0.025). Haplotype analyses showed association between GGAG haplotype for SLC19A1 rs7499, rs1051266, rs2838956 and rs3788200 with MTX gastrointestinal toxicity (p = 0.029). TRI revealed that patients with Index 4 were 9-fold more likely to present a gastrointestinal disorder when compared to those with Index 1 (p = 0.020). This study demonstrated that SLC19A1, SLC46A1 and SLCO1B1 genotypes may help to identify patients with increased risk of MTX-related overall toxicity and that SLC19A1 and SLCO1B1 genotypes, and SLC19A1 haplotypes may help to identify patients with increased risk of MTX-related gastrointestinal toxicity.

Prediction of methotrexate clinical response in Portuguese rheumatoid arthritis patients: implication of MTHFR rs1801133 and ATIC rs4673993 polymorphisms.

Objective. Methotrexate (MTX), the most used drug in rheumatoid arthritis (RA) treatment, showing variability in clinical response, is often associated with genetic polymorphisms. This study aimed to elucidate the role of methylenetetrahydrofolate reductase (MTHFR) C677T and aminoimidazole carboxamide adenosine ribonucleotide transformylase (ATIC) T675C polymorphisms and clinicopathological variables in clinical response to MTX in Portuguese RA patients. Methods. Study included 233 RA patients treated with MTX for at least six months. MTHFR C677T and ATIC T675C polymorphisms were genotyped and clinicopathological variables were collected. Statistical analyses were performed and binary logistic regression method adjusted to possible confounding variables. Results. Multivariate analyses demonstrated that MTHFR 677TT (OR = 4.63; P = 0.013) and ATIC 675T carriers (OR = 5.16; P = 0.013) were associated with over 4-fold increased risk for nonresponse. For clinicopathological variables, noncurrent smokers (OR = 7.98; P = 0.001), patients positive to anti-cyclic citrullinated peptide (OR = 3.53; P = 0.004) and antinuclear antibodies (OR = 2.28; P = 0.045), with higher health assessment questionnaire score (OR = 2.42; P = 0.007), and nonsteroidal anti-inflammatory drug users (OR = 2.77; P = 0.018) were also associated with nonresponse. Contrarily, subcutaneous administration route (OR = 0.11; P < 0.001) was associated with response. Conclusion. Our study suggests that MTHFR C677T and ATIC T675C genotyping combined with clinicopathological data may help to identify patients whom will not benefit from MTX treatment and, therefore, assist clinicians in personalizing RA treatment.

Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

Background: Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours

Bladder carcinogenesis and tumour progression is accompanied by profound alterations in protein glycosylation on the cell surface, which may be explored for improving disease management. In a search for prognosis biomarkers and novel therapeutic targets we have screened, using immunohistochemistry, a series of bladder tumours with differing clinicopathology for short‐chain O‐glycans commonly found in glycoproteins of human solid tumours. These included the Tn and T antigens and their sialylated counterparts sialyl‐Tn(STn) and sialyl‐T(ST), which are generally associated with poor prognosis. We have also explored the nature of T antigen sialylation, namely the sialyl‐3‐T(S3T) and sialyl‐6‐T(S6T) sialoforms, based on combinations of enzymatic treatments. We observed a predominance of sialoglycans over neutral glycoforms (Tn and T antigens) in bladder tumours. In particular, the STn antigen was associated with high‐grade disease and muscle invasion, in accordance with our previous observations. The S3T and S6T antigens were detected for the first time in bladder tumours, but not in healthy urothelia, highlighting their cancer‐specific nature. These glycans were also overexpressed in advanced lesions, especially in cases showing muscle invasion. Glycoproteomic analyses of advanced bladder tumours based on enzymatic treatments, Vicia villosa lectin‐affinity chromatography enrichment and nanoLC‐ESI‐MS/MS analysis resulted in the identification of several key cancer‐associated glycoproteins (MUC16, CD44, integrins) carrying altered glycosylation. Of particular interest were MUC16 STn+‐glycoforms, characteristic of ovarian cancers, which were found in a subset of advanced‐stage bladder tumours facing the worst prognosis. In summary, significant alterations in the O‐glycome and O‐glycoproteome of bladder tumours hold promise for the development of novel noninvasive diagnostic tools and targeted therapeutics. Furthermore, abnormal MUC16 glycoforms hold potential as surrogate biomarkers of poor prognosis and unique molecular signatures for designing highly specific targeted therapeutics.

Hypoxia enhances the malignant nature of bladder cancer cells and concomitantly antagonizes protein O -glycosylation extension

Invasive bladder tumours express the cell-surface Sialyl-Tn (STn) antigen, which stems from a premature stop in protein O-glycosylation. The STn antigen favours invasion, immune escape, and possibly chemotherapy resistance, making it attractive for target therapeutics. However, the events leading to such deregulation in protein glycosylation are mostly unknown. Since hypoxia is a salient feature of advanced stage tumours, we searched into how it influences bladder cancer cells glycophenotype, with emphasis on STn expression. Therefore, three bladder cancer cell lines with distinct genetic and molecular backgrounds (T24, 5637 and HT1376) were submitted to hypoxia. To disclose HIF-1α-mediated events, experiments were also conducted in the presence of Deferoxamine Mesilate (Dfx), an inhibitor of HIF-1α proteasomal degradation. In both conditions all cell lines overexpressed HIF-1α and its transcriptionally-regulated protein CA-IX. This was accompanied by increased lactate biosynthesis, denoting a shift toward anaerobic metabolism. Concomitantly, T24 and 5637 cells acquired a more motile phenotype, consistent with their more mesenchymal characteristics. Moreover, hypoxia promoted STn antigen overexpression in all cell lines and enhanced the migration and invasion of those presenting more mesenchymal characteristics, in an HIF-1α-dependent manner. These effects were reversed by reoxygenation, demonstrating that oxygen affects O-glycan extension. Glycoproteomics studies highlighted that STn was mainly present in integrins and cadherins, suggesting a possible role for this glycan in adhesion, cell motility and invasion. The association between HIF-1α and STn overexpressions and tumour invasion was further confirmed in bladder cancer patient samples. In conclusion, STn overexpression may, in part, result from a HIF-1α mediated cell-survival strategy to adapt to the hypoxic challenge, favouring cell invasion. In addition, targeting STn-expressing glycoproteins may offer potential to treat tumour hypoxic niches harbouring more malignant cells.

In silico approaches for unveiling novel glycobiomarkers in cancer

Glycosylation is one of the most common and dynamic post-translational modification of cell surface and secreted proteins. Cancer cells display unique glycosylation patterns that decisively contribute to drive oncogenic behavior, including disease progression and dissemination. Moreover, alterations in glycosylation are often responsible for the creation of protein signatures holding significant biomarker value and potential for targeted therapeutics. Accordingly, many analytical protocols have been outlined for the identification of abnormally glycosylated proteins by mass spectrometry. Nevertheless, very few studies undergo a comprehensive mining of the generated data. Herein, we build on bladder cancer O-glycoproteomics datasets resulting from a hyphenated technique comprising enrichment by Vicia villosa agglutinin (VVA) lectin and nanoLC-ESI-MS/MS to propose an in silico step-by-step tutorial (Panther, UniProtKB, NetOGlyc, NetNGlyc, Oncomine, Cytoscape) for biomarker discovery in cancer. We envisage that this approach may be generalized to other mass spectrometry-based analytical approaches, including N-glycoproteomics studies, and different types of cancers. SIGNIFICANCE The glycoproteome is an important source of cancer biomarkers holding tremendous potential for targeted therapeutics. We now present an in silico roadmap for comprehensive interpretation of big data generated by mass spectrometry-based glycoproteomics envisaging the identification of clinically relevant glycobiomarkers.

Over forty years of bladder cancer glycobiology: Where do glycans stand facing precision oncology?

The high molecular heterogeneity of bladder tumours is responsible for significant variations in disease course, as well as elevated recurrence and progression rates, thereby hampering the introduction of more effective targeted therapeutics. The implementation of precision oncology settings supported by robust molecular models for individualization of patient management is warranted. This effort requires a comprehensive integration of large sets of panomics data that is yet to be fully achieved. Contributing to this goal, over 40 years of bladder cancer glycobiology have disclosed a plethora of cancer-specific glycans and glycoconjugates (glycoproteins, glycolipids, proteoglycans) accompanying disease progressions and dissemination. This review comprehensively addresses the main structural findings in the field and consequent biological and clinical implications. Given the cell surface and secreted nature of these molecules, we further discuss their potential for non-invasive detection and therapeutic development. Moreover, we highlight novel mass-spectrometry-based high-throughput analytical and bioinformatics tools to interrogate the glycome in the postgenomic era. Ultimately, we outline a roadmap to guide future developments in glycomics envisaging clinical implementation.The high molecular heterogeneity of bladder tumours is responsible for significant variations in disease course, as well as elevated recurrence and progression rates, thereby hampering the introduction of more effective targeted therapeutics. The implementation of precision oncology settings supported by robust molecular models for individualization of patient management is warranted. This effort requires a comprehensive integration of large sets of panomics data that is yet to be fully achieved. Contributing to this goal, over 40 years of bladder cancer glycobiology have disclosed a plethora of cancer-specific glycans and glycoconjugates (glycoproteins, glycolipids, proteoglycans) accompanying disease progressions and dissemination. This review comprehensively addresses the main structural findings in the field and consequent biological and clinical implications. Given the cell surface and secreted nature of these molecules, we further discuss their potential for non-invasive detection and therapeutic development. Moreover, we highlight novel mass-spectrometry-based high-throughput analytical and bioinformatics tools to interrogate the glycome in the postgenomic era. Ultimately, we outline a roadmap to guide future developments in glycomics envisaging clinical implementation.

Glycan affinity magnetic nanoplatforms for urinary glycobiomarkers discovery in bladder cancer

Bladder Cancer (BC) presents one of the highest recurrence rates amongst solid tumours and constitutes the second deadliest disease of the genitourinary track. Non-invasive identification of patients facing disease recurrence and/or progression remains one of the most critical and challenging aspects in disease management. To contribute to this goal, we demonstrate the potential of glycan-affinity glycoproteomics nanoplatforms for urinary biomarkers discovery in bladder cancer. Briefly, magnetic nanoprobes (MNP) coated with three broad-spectrum lectins, namely Concanavalin A (ConA; MNP@ConA), Wheat Germ Agglutinin (WGA; MNP@WGA), and Sambucus nigra (SNA; MNP@SNA), were used to selectively capture glycoproteins from the urine of low-grade and high-grade non-muscle invasive as well as muscle-invasive BC patients. Proteins were identified by nano-LC MALDI-TOF/TOF and data was curated using bioinformatics tools (UniProt, NetOGlyc, NetNGlyc, ClueGO app for Cytoscape and Oncomine) to highlight clinically relevant species. Accordingly, 63 glycoproteins were exclusively identified in cancer samples compared with healthy controls matching in age and gender. Specific glycoprotein sets exclusively found in low-grade non-muscle invasive bladder tumours may aid early diagnosis, while those only found in high-grade non-invasive and muscle-invasive tumours hold potential for accessing progression. Amongst these proteins is bladder cancer stem-cell marker CD44, which has been associated with poor prognosis. Orthogonal validation studies by slot-blotting demonstrated an elevation in urine CD44 levels of high-grade patients, which became more pronounced upon muscle-invasion, in mimicry of the primary tumour. These observations demonstrate the potential of MNP@lectins for identification of clinically relevant glycoproteomics signatures in bladder cancer. Future clinical validation in a larger and well characterized patient subset is required envisaging clinical translation of the results.