Molecular markers for cancer prognosis and treatment: have we struck gold?

Associations of ultra-processed food consumption with mortality among participants with a history of cancer: a prospective cohort analysis

BACKGROUND

Although high ultra-processed food (UPF) consumption has been linked with increased mortality risk in the general population, whether UPFs harm participants with a history of cancer remains unclear.

OBJECTIVE

This study aimed to evaluate the association of UPF consumption with mortality among participants with a history of cancer.

METHODS

Prospective cohort analysis was conducted on 13640 participants with a history of cancer from the UK Biobank. UPFs were defined by the Nova classification. UPF consumption was calculated as the weight proportion of UPFs in the total food consumption. Cox proportional hazard models were used to assess the association between UPF consumption and mortality among participants with a history of cancer.

RESULTS

The median UPF consumption was 29.25% (interquartile range: 19.46%-40.62%) for males and 25.81% (interquartile range: 16.61%-36.35%) for females in the total diet among participants with a history of cancer. During a median follow-up of 10.77 years, 1611 deaths were documented. Multivariable-adjusted hazard ratios (95% confidence intervals) among participants in the highest quartile of UPF consumption relative to the lowest were 1.17 (1.02, 1.35) for all-cause mortality and 1.22 (1.03, 1.44) for cancer mortality.

CONCLUSIONS

Higher UPF consumption after the diagnosis among participants with a history of cancer is associated with higher risk of mortality.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

BCL-2 (-938C>A), BAX (-248G>A), and HER2 Ile655Val Polymorphisms and Breast Cancer Risk in Indian Population

Breast cancer is the most common carcinoma in women worldwide. The present case-control study was aimed to examine the association of BCL-2 (-938C> A), BAX (-248G > A), and HER2 (I655V i.e. A > G) polymorphisms with breast cancer risk in Indian population. This study enrolled 117 breast cancer cases and 104 controls. BCL-2 (-938C > A), BAX (-248G > A), and HER2 Ile655Val polymorphisms were screened by PCR-RFLP method. There was no significance difference in the allelic and genotype frequency of the BCL-2 (-938C > A) and BAX (-248G > A) polymorphisms between cases and controls. In relation to HER2 Ile655Val polymorphism, the statistical analysis of observed genotypic frequencies showed significant association (p-0.0059). Compared to Ile/Ile (A/A) genotype, frequency of Ile/Val (A/G) genotype was significantly higher among cases than in control group and observed to increase the breast cancer risk (OR, 2.43; 95%CI, 1.32-4.46; p-0.004). The frequency of Val (G) allele was significantly higher in cases as compared to controls (6.83% vs 2.88%, resp.). Compared to Ile (A) allele, significant increase in the risk of breast cancer was observed with Val (G) allele (OR, 2.21; 95% CI, 1.35-3.63; p-0.0016). We observed significant association between HER2 Ile655Val polymorphism and breast cancer risk under the dominant (OR = 2.52; 95% CI: 1.41-4.51; p-0.001) and codominant (OR, 2.24; 95% CI: 1.23-4.09; p-0.008) model. In our study, BCL-2 (-938C > A) and BAX (-248G > A) polymorphism were not found to be associated with breast cancer risk. This present study for the first time shows significant association of HER2 Ile655Val polymorphism with risk of breast cancer in Indian population. Therefore, we suggest that each population need to evaluate its own genetic profile for breast cancer risk that may be helpful for better understanding the racial and geographic differences reported for breast cancer incidence and mortality.

Prognostic implication of glycolysis related gene signature in non-small cell lung cancer

Abnormal glycolysis is one of the hallmarks of cancer and plays an important role in its development. This study was devoted to identify glycolysis related genes as prognostic biomarkers for non-small cell lung cancer (NSCLC). The mRNA expression profile and clinical follow-up data were obtained using The Cancer Genome Atlas (TCGA) database. The validation set was obtained by bootstrap method of random repeated sampling. A total of 200 glycolysis-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and 46 genes were significantly associated with overall survival (OS). Five genes (PKP2, LDHA, HMMR, COL5A1 and B3GNT3) were eventually identified to calculate risk score of NSCLC patients.

The univariate and multivariate Cox regression analysis indicated that the risk score was an independent prognostic factor (training set: HR=2.126, 95% CI [1.605, 2.815], p<0.001; validation set: HR=2.298, 95%CI [1.450, 3.640], p<0.001). Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (training set: P=7.946e-06; validation set: P=6.368e-07). Receiver operating characteristic (ROC) curve and stratification analysis also demonstrated the potential prognostic performance. In conclusion, we constructed a novel glycolysis related risk signature which might contribute to predicting the prognosis of NSCLC.

The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes

ADME genes are a group of genes that are involved in drug absorption, distribution, metabolism, and excretion (ADME). The expression profiles of ADME genes within tumours is proposed to impact on cancer patient survival; however, this has not been systematically examined. In this study, our comprehensive analyses of pan-cancer datasets from the Cancer Genome Atlas (TCGA) revealed differential intratumoral expression profiles for ADME genes in 21 different cancer types. Most genes also showed high interindividual variability within cancer-specific patient cohorts. Using Kaplan-Meier plots and logrank tests, we showed that intratumoral expression levels of twenty of the thirty-two core ADME genes were associated with overall survival (OS) in these cancers. Of these genes, five showed significant association with unfavourable OS in three cancers, including SKCM (ABCC2, GSTP1), KIRC (CYP2D6, CYP2E1), PAAD (UGT2B7); sixteen showed significant associations with favourable OS in twelve cancers, including BLCA (UGT2B15), BRCA (CYP2D6), COAD (NAT1), HNSC (ABCB1), KIRC (ABCG2, CYP3A4, SLC22A2, SLC22A6), KIRP (SLC22A2), LIHC (CYP2C19, CYP2C8, CYP2C9, CYP3A5, SLC22A1), LUAD (SLC15A2), LUSC (UGT1A1), PAAD (ABCB1), SARC (ABCB1), and SKCM (ABCB1, DYPD). Overall, these data provide compelling evidence supporting ADME genes as prognostic biomarkers and potential therapeutic targets. We propose that intratumoral expression of ADME genes may impact cancer patient survival by multiple mechanisms that can include metabolizing/transporting anticancer drugs, activating anticancer drugs, and metabolizing/transporting a variety of endogenous molecules involved in metabolically fuelling cancer cells and/or controlling pro-growth signalling pathways.

Fluorinated benzylidene indanone exhibits antiproliferative activity through modulation of microtubule dynamics and antiangiogenic activity

The application of fluorine in drug design has been understood significantly by the medicinal chemists in recent years. Modulation of tubulin-microtubule dynamics is one of the most effective targets for cancer chemotherapeutics. A logically designed and identified lead compound, fluorinated benzylidene indanone 1 has been extensively evaluated for cancer pharmacology. It occupied colchicine binding pocket acting as microtubule destabilizer and induced a G2/M phase arrest in MCF-7 cells. Compound 1 exerted an antiangiogenic effect in MCF-7 cells by down-regulating Vascular Endothelial Growth Factor (VEGF) and Hypoxia Inducible Factor-α (HIF-α). In in-vivo efficacy in C3H/Jax mice mammary carcinoma model, benzylidene indanone 1 reduced tumour volumes by 48.2%. Further in acute oral toxicity studies compound 1 was well tolerated and safe up to 1000 mg/kg dose in Swiss albino mice. The fluorinated benzylidene indanone 1, a new chemical entity (NCE) can further be optimized for better efficacy against breast adenocarcinoma.¹.

Proteomic analysis of circulating extracellular vesicles identifies potential markers of breast cancer progression, recurrence, and response

Proteomic profiling of circulating small extracellular vesicles (sEVs) represents a promising, noninvasive approach for early detection and therapeutic monitoring of breast cancer (BC). We describe a relatively low-cost, fast, and reliable method to isolate sEVs from plasma of BC patients and analyze their protein content by semiquantitative proteomics. sEV-enriched fractions were isolated from plasma of healthy controls and BC patients at different disease stages before and after surgery. Proteomic analysis of sEV-enriched fractions using reverse phase protein array revealed a signature of seven proteins that differentiated BC patients from healthy individuals, of which FAK and fibronectin displayed high diagnostic accuracy. The size of sEVs was significantly reduced in advanced disease stage, concomitant with a stage-specific protein signature. Furthermore, we observed protein-based distinct clusters of healthy controls, chemotherapy-treated and untreated postsurgery samples, as well as a predictor of high risk of cancer relapse, suggesting that the applied methods warrant development for advanced diagnostics.

Association between ESR1, ESR2, HER2, UGT1A4, and UGT2B7 polymorphisms and breast Cancer in Jordan: a case-control study

Background

Breast cancer risk, development, and treatment are influenced by genetic variation in certain genes, namely those involved in cell proliferation, tumor suppression, and drug metabolism. In turn, the relevance of the aforementioned genetic variation to cancer depends on the ethnic group in question, highlighting the need for population-specific association studies. Therefore, the objective of the present study was to investigate the association between certain ESR1, ESR2, HER2, UGT1A4, and UGT2B7 single nucleotide polymorphisms and breast cancer.

Methods

Blood samples were collected from 437 Jordanian-Arab breast cancer patients and healthy volunteers and subject to genotyping using the Sequenom MassARRAY® system (iPLEX GOLD).

Results

Our findings show a significant association between breast cancer and the allelic (P = 0.02486879) and genotypic (P = 0.04793066) frequencies of the ESR1 polymorphism rs3798577, a result which was confirmed in different genetic models. No other investigated polymorphism showed a significant association with breast cancer itself in Jordanian Arabs, but the Rare Hz (GG) vs Het (AG) genetic model revealed an association of the disease with the ESR1 polymorphism rs3798577. However, several associations were found between certain polymorphisms and breast cancer’s prognostic factors.

Conclusion

This study suggests that certain polymorphisms may increase the risk of breast cancer in the Jordanian-Arab population. Future research and clinical translation could incorporate the current results in preventative breast cancer approaches tailored for Jordanian-Arab patients.

Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel

BACKGROUND

One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin).

METHODS

A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets.

RESULTS

Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC.

CONCLUSION

MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecologic malignancies. Over 70 % of ovarian cancer cases are high-grade serous ovarian cancers (HGSC) and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good and accurate prediction of prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve patient's prognosis through proper treatment, we present a prognostic prediction model by integrating the high dimensional RNA sequencing data with their clinical data through the following steps: (1) gene filtration, (2) pre-screening, (3) gene marker selection (4) integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

Production and Characterization of Monoclonal and Polyclonal Antibodies Against Truncated Recombinant Dickkopf-1 as a Candidate Biomarker

Several studies have reported an increased serum level of Dickkopf (DKK-1) protein in a variety of cancers, including multiple myeloma, lung, colorectal, bone loss, and Alzheimer's disease. This protein has potential to be used as a biomarker for the diagnosis of some cancers, especially bone loss in multiple myeloma. In the present study, to measure the concentration level of DKK-1 protein, rabbit polyclonal antibody (pAb) and mouse monoclonal antibodies (mAbs) were produced against this protein. New Zealand white rabbits and BALB/c mice were immunized with the chimeric recombinant DKK-1 antigen. Immunized mouse spleen cells were fused with SP2/0 cells to generate anti-rDKK-1 antibody-producing hybridoma cells. Antibodies were purified by protein A affinity chromatography and assessed using sodium dodecyl sulfate polyacrylamide gel, western blotting and enzyme-linked immunosorbent assay. These results implied that the pAb and mAb were produced against the DKK-1 protein. The Kd value of 5 × 10^-9 M was recorded for the mAb MR6F3 toward native DKK-1, and the Ig isotype was identified as IgG2b. No cross-reactivity was shown with DKK-2 by MR6F3. Collectively, our results revealed that the produced pAb and mAb could be used in the measurement of DKK-1 protein.

Prognostic value of serum alkaline phosphatase in the survival of prostate cancer: evidence from a meta-analysis

BACKGROUND

Many studies have evaluated the relationship between alkaline phosphatase (ALP) and the prognosis for prostate cancer (PCa). But they have not reached a widespread consensus yet. Therefore, we completed a meta-analysis to ascertain the significance of ALP and the prognosis for PCa.

METHODS

A literature search was performed in the PubMed, Embase, and Web of Science databases. HRs concerning overall survival (OS), progression-free survival (PFS), and cancer-specific survival (CSS) were extracted to evaluate the impacts of ALP on the prognosis for PCa. Subgroup analyses were conducted on different study types, regions, sample sizes, and cutoff values. Sensitivity analysis was performed by removing one study in sequence.

RESULTS

A total of 63 studies from 54 articles with 16,135 patients were included in this meta-analysis. The pooled results indicated that high baseline ALP was associated with obviously poor OS (HR=1.74, 95% CI: 1.47-2.06) and PFS (HR=1.60, 95% CI: 1.13-2.26) in patients with PCa. The pooled HR for bone-specific ALP and OS was 1.76 (95% CI: 1.45-2.15). However, no association between ALP and CSS (HR=1.002, 95% CI: 0.998-1.005) was found for PCa. The results of subgroup analyses were all in accordance with the main findings. Sensitivity analysis suggested that no single study could affect the stability of the results.

CONCLUSION

High serum ALP is significantly associated with poor OS and PFS except for CSS in PCa. ALP is an efficient and convenient biomarker for PCa prognosis.

The prognostic value of decreased miR-101 in various cancers: a meta-analysis of 12 studies

BACKGROUND

A consensus regarding the prognostic value of decreased miR-101 in human cancers has not been reached. This study aimed to comprehensively investigate the internal associations between loss of miR-101 expression and prognostic implications in patients with cancer.

MATERIALS AND METHODS

All relevant literature in electronic databases, including PubMed, ISI Web of Science, and Embase, up to March 1, 2017 were searched. Correlations between decreased miR-101 and clinicopathological parameters were defined by odds ratios (ORs). The degree of association between reduced miR-101 and survival outcome was evaluated by pooled hazard ratios (HRs) and relevant 95% CIs.

RESULTS

Twelve eligible studies with 2,088 patients were included in this meta-analysis. Decreased miR-101 expression was closely connected with poor overall survival, with a pooled HR of 2.15 (95% CI 1.71-2.7, P<0.001). This correlation was also revealed when stratified analysis was conducted with respect to ethnicity, cancer type, sample size, specimen source, and analysis model. However, decreased miR-101 was not associated with disease-free survival, recurrence-free survival, or progression-free survival, with a pooled HR of 1.59 (95% CI 0.83-3.03, P=0.128), despite a positive trend. In addition, reduced miR-101 was intimately related to poorer tumor differentiation (OR 2.17, 95% CI 1.14-4.13; P=0.019), advanced tumor classification (OR 5.25, 95% CI 3.39-8.12; P<0.001), and higher TNM stage (OR 6.18, 95% CI 3.79-10.09; P<0.001).

CONCLUSION

Our findings suggest that loss of miR-101 expression is correlated with worse overall survival in a variety of cancers, and could serve as a predictive indicator for clinicopathological features. Furthermore, miR-101 may become a feasible therapeutic target in most human cancers.

Racial Variation in the Utility of Urinary Biomarkers PCA3 and T2ERG in a Large Multicenter Study

PURPOSE

To our knowledge it is unknown whether urinary biomarkers for prostate cancer have added utility to clinical risk calculators in different racial groups. We examined the utility of urinary biomarkers added to clinical risk calculators for predicting prostate cancer in African American and nonAfrican American men.

MATERIALS AND METHODS

Demographics, PCPT (Prostate Cancer Prevention Trial) risk scores, data on the biomarkers data PCA3 (prostate cancer antigen 3) and T2ERG (transmembrane protease serine 2 and v-ets erythroblastosis virus E26 oncogene homolog gene fusion), and biopsy pathology features were prospectively collected on 718 men as part of EDRN (Early Detection Research Network). Utility was determined by generating ROC curves and comparing AUC values for the baseline multivariable PCPT model and for models containing biomarker scores.

RESULTS

PCA3 and T2ERG added utility for the prediction of prostate cancer and clinically significant prostate cancer when combined with the PCPT Risk Calculator. This utility was seen in nonAfrican American men only for PCA3 (AUC 0.64 increased to 0.75 for prostate cancer and to 0.69-0.77 for clinically significant prostate cancer, both p <0.001) and for T2ERG (AUC 0.64-0.74 for prostate cancer, p <0.001, and 0.69-0.73 for clinically significant prostate cancer, p = 0.029). African American men did not have an added benefit with the addition of biomarkers, including PCA3 (AUC 0.75-0.77, p = 0.64, and 0.65-0.66, p = 0.74) and T2ERG (AUC 0.75-0.74, p = 0.74, and 0.65-0.64, p = 0.88), for prostate cancer and clinically significant prostate cancer, respectively. Limitations include the small number of African American men (72). The post hoc subgroup analysis nature of the study limited findings to being hypothesis generating.

CONCLUSIONS

As novel biomarkers are discovered, clinical utility should be established across demographically diverse cohorts.

A new understanding towards the reactivity of DNA peroxy radicals

New radical reaction paths for the formation of DNA oxidation products are suggested, which are strongly different from the previously suggested paths with the tetraoxide intermediate.

Current state of biomarkers in ovarian cancer prognosis

High-grade serous ovarian cancer remains one of the most lethal malignancies in women. Despite recent advances in surgical and pharmaceutical therapies, survival rates remain poor. A major impediment in management of this disease, that continues to contribute to poor overall survival rates, is resistance to standard carboplatin-paclitaxel combination chemotherapies. In addition to tumor cell intrinsic mechanisms leading to drug resistance, there is increasing awareness of the crucial role of the tumor microenvironment in mediating natural immune defense mechanisms and selective pressures that appear to facilitate chemotherapy sensitivity. We provide an overview of some of the promising new genetic and immunological biomarkers in ovarian cancer and discuss their biology and their likely clinical utility in future ovarian cancer management.

Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo

Background

Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactions in vivo using murine lactation as a dynamic, physiological model system.

Results

Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested that in silico network perturbations can predict in vivo qualitative responses. In silico perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy).

Conclusion

These findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0226-3) contains supplementary material, which is available to authorized users.

Development and validation of protein microarray technology for simultaneous inflammatory mediator detection in human sera

Biomarkers, including cytokines, can help in the diagnosis, prognosis, and prediction of treatment response across a wide range of disease settings. Consequently, the recent emergence of protein microarray technology, which is able to quantify a range of inflammatory mediators in a large number of samples simultaneously, has become highly desirable. However, the cost of commercial systems remains somewhat prohibitive. Here we show the development, validation, and implementation of an in-house microarray platform which enables the simultaneous quantitative analysis of multiple protein biomarkers. The accuracy and precision of the in-house microarray system were investigated according to the Food and Drug Administration (FDA) guidelines for pharmacokinetic assay validation. The assay fell within these limits for all but the very low-abundant cytokines, such as interleukin- (IL-) 10. Additionally, there were no significant differences between cytokine detection using our microarray system and the “gold standard” ELISA format. Crucially, future biomarker detection need not be limited to the 16 cytokines shown here but could be expanded as required. In conclusion, we detail a bespoke protein microarray system, utilizing well-validated ELISA reagents, that allows accurate, precise, and reproducible multiplexed biomarker quantification, comparable with commercial ELISA, and allowing customization beyond that of similar commercial microarrays.

Mutations in SPRTN cause early onset hepatocellular carcinoma, genomic instability and progeroid features

Age-related degenerative and malignant diseases represent major challenges for health care systems. Elucidation of the molecular mechanisms underlying carcinogenesis and age-associated pathologies is thus of growing biomedical relevance. We identified biallelic germline mutations in SPRTN (also called C1orf124 or DVC1) in three patients from two unrelated families. All three patients are affected by a new segmental progeroid syndrome characterized by genomic instability and susceptibility toward early onset hepatocellular carcinoma. SPRTN was recently proposed to have a function in translesional DNA synthesis and the prevention of mutagenesis. Our in vivo and in vitro characterization of identified mutations has uncovered an essential role for SPRTN in the prevention of DNA replication stress during general DNA replication and in replication-related G2/M-checkpoint regulation. In addition to demonstrating the pathogenicity of identified SPRTN mutations, our findings provide a molecular explanation of how SPRTN dysfunction causes accelerated aging and susceptibility toward carcinoma.

Predictions for the future of kallikrein-related peptidases in molecular diagnostics

Kallikrein-related peptidases (KLKs) form a cancer-related ensemble of serine proteases. This multigene family hosts the most widely used cancer biomarker that is PSA-KLK3, with millions of tests performed annually worldwide. The present report provides an overview of the biomarker potential of the extended KLK family (KLK1-KLK15) in various disease settings and envisages approaches that could lead to additional KLK-driven applications in future molecular diagnostics. Particular focus is given on the inclusion of KLKs into multifaceted cancer biomarker panels that provide enhanced diagnostic, prognostic and/or predictive accuracy in several human malignancies. Such panels have been described so far for prostate, ovarian, lung and colorectal cancers. The role of KLKs as biomarkers in non-malignant disease settings, such as Alzheimer's disease and multiple sclerosis, is also commented upon. Predictions are given on the challenges and future directions regarding clinically oriented KLK research.

Metabolomic characterization of nipple aspirate fluid by (1)H NMR spectroscopy and GC-MS

Nipple aspirate fluid (NAF) is a noninvasively obtained biofluid from the duct openings of the breast. NAF components are constantly secreted, metabolized, and reabsorbed by the epithelial lining of the lactiferous ducts of the breast. NAF has been studied as a potential breast tissue surrogate for the discovery of novel breast cancer risk, early detection, and treatment response biomarkers. We report the first unsupervised metabolite characterization of nipple aspirate fluid using NMR and GC-MS using convenience samples previously collected from four premenopausal and four postmenopausal women. A total of 38 metabolites were identified using the two analytical techniques, including amino acids, organic acids, fatty acids, and carbohydrates. Analytical reproducibility of metabolites in NAF by GC-MS was high across different extraction and analysis days. Overall, 31 metabolites had a coefficient of variation below 20%. By GC-MS, there were eight metabolites unique to NAF, 19 unique to plasma, and 24 shared metabolites. Correlative analysis of shared metabolites between matched NAF and plasma samples from pre- and postmenopausal women shows almost no correlations, with the exception being lactic acid, which was significantly negatively correlated (R(2) = 0.57; P = 0.03). These results suggest that NAF is metabolically distinct from plasma and that the application of metabolomic strategies may be useful for future studies investigating breast cancer risk and intervention response biomarkers.

Role of glycosylation in the anticancer activity of antibacterial peptides against breast cancer cells

Antibacterial peptides (ABPs) with cancer-selective toxicity have received much more attention as alternative chemotherapeutic agents in recent years. However, the basis of their anticancer activity remains unclear. The modification of cell surface glycosylation is a characteristic of cancer cells. The present study investigated the effect of glycosylation, in particular sialic acid, on the anticancer activity of ABPs. We showed that aurein 1.2, buforin IIb and BMAP-28m exhibited selective cytotoxicity toward MX-1 and MCF-7 breast cancer cells. The binding activity, cytotoxicity and apoptotic activity of ABPs were enhanced by the presence of O-, N-glycoproteins, gangliosides and sialic acid on the surface of breast cancer cells. Among N-, O-glycoproteins and ganglioside, O-glycoproteins almost had the strongest effect on the binding and cytotoxicity of the three peptides. Further, up-regulation of hST6Gal1 in CHO-K1 cells enhanced the susceptibility of cells to these peptides. Finally, the growth of MX-1 xenograft tumors in mice was significantly suppressed by buforin IIb treatment, which was associated with induction of apoptosis and inhibition of vascularization. These data demonstrate that the three peptides bind to breast cancer cells via an interaction with surface O-, N-glycoproteins and gangliosides. Sialic acids act as key glycan binding sites for cationic ABP binding to glycoproteins and gangliosides. Therefore, glycosylation in breast cancer cells plays an important role in the anticancer activity of ABPs, which may partly explain their cancer-selective toxicity. Anticancer ABPs with cancer-selective cytotoxicity will be promising candidates for anticancer therapy in the future.

Early lung cancer diagnosis by biosensors

Lung cancer causes an extreme threat to human health, and the mortality rate due to lung cancer has not decreased during the last decade. Prognosis or early diagnosis could help reduce the mortality rate. If microRNA and tumor-associated antigens (TAAs), as well as the corresponding autoantibodies, can be detected prior to clinical diagnosis, such high sensitivity of biosensors makes the early diagnosis and prognosis of cancer realizable. This review provides an overview of tumor-associated biomarker identifying methods and the biosensor technology available today. Laboratorial researches utilizing biosensors for early lung cancer diagnosis will be highlighted.

Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome

BACKGROUND

One of the major goals in cancer research is to find and evaluate the early presence of biomarkers in human fluids and tissues. To resolve the complex cell heterogeneity of a tumor mass, it will be useful to characterize the intricate biomolecular composition of tumor microenvironment (the so called cancer secretome), validating secreted proteins as early biomarkers of cancer initiation and progression. This approach is not broadly applicable because of the paucity of well validated and FDA-approved biomarkers and because most of the candidate biomarkers are mainly organ-specific rather than tumor-specific. For these reasons, there is an urgent need to identify and validate a panel of biomarker combinations for early detection of human tumors. This is especially important for breast cancer, the cancer spread most worldwide among women. It is well known that patients with early diagnosed breast cancer live longer, require less extensive treatment and fare better than patients with more aggressive and/or advanced disease.

RESULTS

In the frame of searching breast cancer biomarkers (especially using nipple aspirate fluid mirroring breast microenvironment), studies have highlighted an optimal combination of well-known biomarkers: uPA + PAI-1 + TF. When individually investigated they did not show perfect accuracy in predicting the presence of breast cancer, whereas the triple combination has been demonstrated to be highly predictive of pre-cancer and/or cancerous conditions, approaching 97-100% accuracy.

CONCLUSION

Despite the heterogeneous composition of breast cancer and the difficulties to find specific breast cancer biomolecules, the noninvasive analysis of the nipple aspirate fluid secretome may significantly improve the discovery of promising biomarkers, helping also the differentiation among benign and invasive breast diseases, opening new frontiers in early oncoproteomics.

RhoGAPs attenuate cell proliferation by direct interaction with p53 tetramerization domain

Many Rho GTPase activation proteins (RhoGAPs) are deleted or downregulated in cancers, but the functional consequences are still unclear. Here, we show that the RhoGAP ArhGAP11A induces cell-cycle arrest and apoptosis by binding to the tumor suppressor p53. The RhoGAP domain of ArhGAP11A binds to the tetramerization domain of p53, but not to its family members p63 or p73. The interaction stabilizes the tetrameric conformation of p53 and enhances its DNA-binding activity, thereby inducing cell-cycle arrest and apoptosis. Upon DNA damage stress, ArhGAP11A accumulates in the nucleus and interacts with p53, whereas knockdown of ArhGAP11A partially blocks p53 transcriptional activity. These findings explain why RhoGAPs are frequently deleted in cancers and suggest that the RhoGAP family sits at the crossroads between the cell-migration and proliferation pathways.

Serum biomarkers for neurofibromatosis type 1 and early detection of malignant peripheral nerve-sheath tumors

BACKGROUND

Neurofibromatosis type 1 (NF1) is a hereditary tumor syndrome characterized by the development of benign nerve-sheath tumors, which transform to malignant peripheral nerve-sheath tumors (MPNST) in about 8 to 13% of patients with NF1. MPNST are invasive sarcomas with extremely poor prognosis, and their development may correlate with internal tumor load of patients with NF1. Because early identification of patients with NF1 at risk for developing MPNST should improve their clinical outcome, the aim of this study was to identify serum biomarkers for tumor progression in NF1, and to analyze their correlation with tumor type and internal tumor load.

METHODS

We selected candidate biomarkers for NF1 by manually mining published data sources, and conducted a systematic screen of 56 candidate serum biomarkers using customized antibody arrays. Serum from 104 patients with NF1 with and without MPNST, and from 41 healthy control subjects, was analyzed. Statistical analysis was performed using the non-parametric Mann-Whitney U-test, followed by Bonferroni correction.

RESULTS

Our analysis identified four markers (epidermal growth factor receptor, interferon-γ, interleukin-6, and tumor necrosis factor-α) for which significantly different serum concentrations were seen in patients with NF1 compared with healthy controls. Two markers (insulin-like growth factor binding protein 1 (IGFBP1) and regulated upon activation, normal T-cell expressed and secreted (RANTES)) showed significantly higher concentrations in patients with NF1 and MPNST compared with patients with NF1 without MPNST. A correlation with internal tumor load was found for IGFBP1.

CONCLUSION

Our study identified two serum markers with potential for early detection of patients with NF1 at risk for developing MPNST, and four markers that could distinguish between patients with NF1 and healthy subjects. Such markers may be useful as diagnostic tools to support the diagnosis of NF1 and for timely identification of MPNST. Moreover, the data suggest that there is a systemic increase in inflammatory cytokines independently of tumor load in patients with NF1.