Genetic variation determines VEGF-A plasma levels in cancer patients

Contribution of plasma levels of VEGF-A and angiopoietin-2 in addition to a genetic variant in KCNAB1 to predict the risk of bevacizumab-induced hypertension

Bevacizumab-induced hypertension poses a therapeutic challenge and identifying biomarkers for hypertension can enhance therapy safety. Lower plasma levels of VEGF-A, angiopoietin-2, and rs6770663 in KCNAB1 were previously associated with increased risk of bevacizumab-induced hypertension. This study investigated whether these factors independently contribute to grade 2-3 bevacizumab-induced hypertension risk in 277 cancer patients (CALGB/Alliance 90401). Multivariable analyses assessed the independent association of each factor and hypertension. Likelihood ratio test (LRT) evaluated the explanatory significance of combining protein levels and rs6770663 in predicting hypertension. Boostrap was employed to assess the mediation effect of protein levels on the rs6770663 association with hypertension. Lower protein levels and rs6770663 were independently associated with increased hypertension risk. Adding rs6770663 to protein levels improved the prediction of hypertension (LRT p = 0.0002), with no mediation effect observed. Protein levels of VEGF-A, angiopoietin-2 and rs6770663 in KCNAB1 are independent risk factors and, when combined, may improve prediction of bevacizumab-induced hypertension. ClinicalTrials.gov Identifier: NCT00110214.

Applications of Biomolecular Nanostructures for Anti-Angiogenic Theranostics

Angiogenesis is a physiological process of forming new blood vessels that has pathological importance in seemingly unrelated illnesses like cancer, diabetes, and various inflammatory diseases. Treatment targeting angiogenesis has shown promise for these types of diseases, but current anti-angiogenic agents have critical limitations in delivery and side-effects. This necessitates exploration of alternative approaches like biomolecule-based drugs. Proteins, lipids, and oligonucleotides have recently become popular in biomedicine, specifically as biocompatible components of therapeutic drugs. Their excellent bioavailability and potential bioactive and immunogenic properties make them prime candidates for drug discovery or drug delivery systems. Lipid-based liposomes have become standard vehicles for targeted nanoparticle (NP) delivery, while protein and nucleotide NPs show promise for environment-sensitive delivery as smart NPs. Their therapeutic applications have initially been hampered by short circulation times and difficulty of fabrication but recent developments in nanofabrication and NP engineering have found ways to circumvent these disadvantages, vastly improving the practicality of biomolecular NPs. In this review, we are going to briefly discuss how biomolecule-based NPs have improved anti-angiogenesis-based therapy.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-β2 levels in metastatic colorectal cancer patients (Alliance)

BACKGROUND

Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC). The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings.

RESULTS

The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73.

CONCLUSIONS

This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-β2 levels in metastatic colorectal cancer patients (Alliance)

Background

Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC).The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings.

Results

The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73.

Conclusions

This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Investigation of Vascular Endothelial Growth Factor Polymorphisms on Risk, Metastasis, Laterality, and Prognosis of Colorectal Cancer in Turkish Subjects

Objectives: Tumor angiogenesis is known to support the spread and invasion of tumor cells, allow distant organ metastasis, resulting in worse prognosis and mortality. Since vascular endothelial growth factor-A (VEGF-A) is the major regulator of angiogenesis, in the present study, the associations of VEGF-A +405G>C and -460C>T polymorphisms with risk, primary tumor location, prognosis, and metastasis of colorectal cancer (CRC) were investigated in Turkish subjects. Material and Methods: A total of 153 subjects consisting of 74 controls and 79 CRC diagnosed patients were included in the study. VEGF-A +405G>C and -460C>T polymorphisms were analyzed using Agena MassARRAY platform. Results: VEGF +405GC+CC genotypes were found to be significantly associated with left colon cancer (unadjusted odds ratio [OR] = 5.208 confidence interval [95% CI]: 1.064-25.496, p = 0.04). VEGF -460TT and CT+TT genotypes were associated with reduced liver metastasis risk (OR = 0.080 95% CI: 0.009-0.689 p = 0.02 and OR = 0.191 95% CI: 0.039-0.925, p = 0.04, respectively). Patients with VEGF +405GG genotype showed longer progression-free survival as a response to bevacizumab treatment (Log rank = 6.92, p = 0.03). Conclusion: According to our results, VEGF +405G>C and -460C>T polymorphisms were found to be associated with CRC prognosis, sidedness, and metastasis. Our findings should be conducted in further studies.

Plasma levels of angiopoietin-2, VEGF-A, and VCAM-1 as markers of bevacizumab-induced hypertension: CALGB 80303 and 90401 (Alliance)

Hypertension is a common toxicity induced by bevacizumab and other antiangiogenic drugs. There are no biomarkers to predict the risk of bevacizumab-induced hypertension. This study aimed to identify plasma proteins related to the function of the vasculature to predict the risk of severe bevacizumab-induced hypertension. Using pretreated plasma samples from 398 bevacizumab-treated patients in two clinical trials (CALGB 80303 and 90401), the levels of 17 proteins were measured via ELISA. The association between proteins and grade 3 bevacizumab-induced hypertension was performed by calculating the odds ratio (OR) from logistic regression adjusting for age, sex, and clinical trial. Using the optimal cut-point of each protein, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for hypertension were estimated. Five proteins showed no difference in levels between clinical trials and were used for analyses. Lower levels of angiopoietin-2 (p = 0.0013, OR 3.41, 95% CI 1.67-7.55), VEGF-A (p = 0.0008, OR 4.25, 95% CI 1.93-10.72), and VCAM-1 (p = 0.0067, OR 2.68, 95% CI 1.34-5.63) were associated with an increased risk of grade 3 hypertension. The multivariable model suggests independent effects of angiopoietin-2 (p = 0.0111, OR 2.71, 95% CI 1.29-6.10), VEGF-A (p = 0.0051, OR 3.66, 95% CI 1.54-9.73), and VCAM-1 (p = 0.0308, OR 2.27, 95% CI 1.10-4.92). The presence of low levels of 2-3 proteins had an OR of 10.06 (95% CI 3.92-34.18, p = 1.80 × 10^-5) for the risk of hypertension, with sensitivity of 89.7%, specificity of 53.5%, PPV of 17.3%, and NPV of 97.9%. This is the first study providing evidence of plasma proteins with potential value to predict patients at risk of developing bevacizumab-induced hypertension.Clinical trial registration: ClinicalTrials.gov Identifier: NCT00088894 (CALGB 80303); and NCT00110214 (CALGB 90401).

A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma

Background

Microvascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI.

Methods

A total of 381 patients with resected HCC were enrolled and divided into a training cohort (n = 267) and a validation cohort (n = 114). Serum VEGF-A level was examined by enzyme-linked immunosorbent assay (ELISA). Risk factors for MVI were assessed based on univariate and multivariate analyses in the training cohort. A nomogram incorporating independent risk predictors was established and validated.

Result

The serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (P <0.05). Serum VEGF-A concentration ≥138.30 pg/ml was an independent risk factor of MVI (OR: 33.088; 95%CI: 12.871–85.057; P <0.001). Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery were identified as significant predictors for MVI. The nomogram indicated excellent predictive performance with an AUROC of 0.948 (95% CI: 0.923–0.973) and 0.881 (95% CI: 0.820–0.942) in the training and validation cohorts, respectively. The nomogram showed a good model fit and calibration.

Conclusions

Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.

KDR (VEGFR2) Genetic Variants and Serum Levels in Patients with Rheumatoid Arthritis

We investigated kinase insert domain-containing receptor (KDR) polymorphisms and protein levels in relation to susceptibility to and severity of Rheumatoid Arthritis (RA). 641 RA patients and 340 controls (HC) were examined for the rs1870377 KDR variant by the polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) method and for rs2305948 and rs2071559 KDR single nucleotide polymorphisms (SNPs) by TaqMan SNP genotyping assay. KDR serum levels were determined by enzyme-linked immunosorbent assay (ELISA). The rs1870377 KDR variant has shown association with RA under the codominant (p = 0.02, OR = 1.76, 95% CI = 1.09–2.85) and recessive models (p = 0.019, OR = 1.53, 95% CI = 1.07–2.20). KDR rs2305948 was associated with RA under the dominant model (p = 0.005, OR = 1.38, 95% CI = 1.10–1.73). Under the codominant model, the frequency of the rs2071559 TC and GG genotypes were lower in RA patients than in controls (p < 0.001, OR = 0.51, 95% CI = 0.37–0.69, and p = 0.002, OR = 0.57, 95% CI = 0.39–0.81). KDR rs2071559 T and rs2305948 A alleles were associated with RA (p = 0.001, OR = 0.60, 95% CI = 0.45–0.81 and p = 0.008, OR = 1.71, CI = 1.15–2.54). KDR rs2305948SNP was associated with Disease Activity Score (DAS)-28 score (p < 0.001), Visual Analog Scale (VAS) score (p < 0.001), number of swollen joints (p < 0.001), mean value of CRP (p < 0.001). A higher KDR serum level was found in RA patients than in HC (8018 pg/mL versus 7381 pg/mL, p = 0.002). Present results shed light on the role of KDR genetic variants in the severity of RA.

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

Background

Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts necessitates highly efficient computational algorithms for the robust evaluation of many features.

Results

We have developed an R extension package, fastJT, for conducting genome-wide association studies and feature selection for machine learning using the Jonckheere-Terpstra statistic for constrained hypotheses. The kernel of the package features an efficient algorithm for calculating the statistics, replacing the pairwise comparison and counting processes with a data sorting and searching procedure, reducing computational complexity from O(n²) to O(n log(n)). The computational efficiency is demonstrated through extensive benchmarking, and example applications to real data are presented.

Conclusions

fastJT is an open-source R extension package, applying the Jonckheere-Terpstra statistic for robust feature selection for machine learning and association studies. The package implements an efficient algorithm which leverages internal information among the samples to avoid unnecessary computations, and incorporates shared-memory parallel programming to further boost performance on multi-core machines.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2869-3) contains supplementary material, which is available to authorized users.