Predictive biomarkers for the treatment of resectable esophageal and esophago-gastric junction adenocarcinoma: from hypothesis generation to clinical validation

Introduction: Esophageal and esophago-gastric junction (EGJ) adenocarcinomas remain a major health problem worldwide with a worryingly increasing incidence. Recent trials indicate survivals benefit for preoperative or perioperative chemoradiotherapy compared to surgery alone. Beside standard chemoradiotherapy regimens, new therapeutic approaches with targeted therapies have been proposed for the treatment of resectable disease. However, clinical outcomes remain extremely poor due to drug resistance phenomena. The failure of these approaches could be partially ascribed to their incorrect application in patients. Therefore, the identification of strong biomarkers for optimal patient management is urgently needed.

Areas covered: This review aims to summarize and critically discuss the most relevant findings regarding predictive biomarker development for neoadjuvant treatment of resectable esophageal and esophago-gastric junction adenocarcinoma patients.

Expert commentary: Optimizing the currently available therapeutic modalities through a more accurate selection of patients may avoid the use of ineffective and potentially toxic treatments. During the last decade, the advent of high-throughput ‘-omics’ technologies has set the basis for a new biomarker discovery approach from ‘molecule by molecule’ screening towards a large-scale systematic screening process with exponential increases in putative biomarkers, which often failed to provide adequate clinical validation.     

A PRIM approach to predictive‐signature development for patient stratification

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real‐world data sets. We also compare the results with those obtained from a recent regression‐based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

The suitability of EBC-Pb as a new biomarker to assess occupational exposure to lead

Occupational exposure to lead (Pb) requires continuous surveillance to assure, as much as possible, safe and healthful working conditions. This study addresses the suitability of assessing Pb exposure in relevant workers using their exhaled breath condensate (EBC). This study enrolled workers of two different Pb processing industries characterized by moderate and high Pb exposure levels in the work environment, and a group of non-exposed individuals working in offices who served as baseline for Pb exposure. The EBC-Pb of workers reflected the Pb levels in the work environment of all three settings, although the relationship with B-Pb was not clear. The lack of correlation between EBC-Pb and B-Pb most probably indicates the time lag for Pb to enter in the two body pools. The EBC-Pb seems to reflect immediate exposure, providing a prompt signature of Pb in the environmental that may interact directly with the organ. By delivering short-term evaluation of exposure, EBC-Pb represents a clear advantage in biomonitoring and may become an interesting tool for estimating organ burden.     

LEAP: Biomarker Inference Through Learning and Evaluating Association Patterns

Single nucleotide polymorphism (SNP) high‐dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000‐SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high‐dimensional datasets and determining their probability.     

血清中磷酯酰肌醇蛋白聚糖-3及高尔基体蛋白-73异常表达对肝癌的诊断价值

目的 :探究肝组织及外周血磷酯酰肌醇蛋白聚糖-3(phosphatidylinositol proteinglycan 3,GPC3)及高尔基体糖蛋白-73(Golgi protein 73,GP73)高表达对肝癌的诊断价值。方法 :用ELISA法检测39例肝癌患者及31例肝硬化患者外周血GPC3及GP73浓度,RT-PCR法检测两组患者肝组织中GPC3 m RNA及GP73 m RNA表达水平,比较研究肝癌及肝硬化患者外周血、肝组织中GPC3及GP73的表达差异,分层分析此两种蛋白表达与肝癌不同临床分期、病理分级的相关性。结果:肝癌组外周血GPC3、GP73浓度分别为(16.81±0.56)μg/L、(115.92±7.01)ng/ml,肝硬化组外周血GPC3、GP73浓度分别为(7.41±0.25)μg/L、(64.63±3.07)ng/ml,肝癌组外周血GPC3、GP73浓度均显著高于肝硬化组(P<0.001)。GPC3值为9.3μg/L时诊断肝癌的灵敏度和特异度分别为89.74%和96.77%,阳性预测值97.20%,阴性预测值88.20%;GP73截断值为77.68 ng/ml时,其诊断肝癌的灵敏度92.31%,特异度83.87%,阳性预测值87.80%,阴性预测值89.70%。肝癌组GPC3 m RNA和GP73 m RNA相对表达量分别为8.91±3.70、68.41±32.86,肝硬化组GPC3 m RNA和GP73 m RNA相对表达量分别为3.04±0.58、2.32±0.25,肝癌组GPC3 m RNA、GP73 m RNA表达均高于肝硬化组(P均<0.05)。上述结果可知肝癌患者癌组织GPC3、GP73 m RNA表达水平与外周血相应蛋白浓度一致。肝癌患者中外周血GP73、GPC3蛋白浓度及组织m RNA表达水平与年龄、性别、肿瘤大小及肝癌临床分期间无统计学意义;而肝癌患者GPC3表达与病理分级间存在统计学差异,病理分级为3级的肝癌患者GPC3表达水平低于1~2级患者。结论:GP73、GPC3在肝癌患者中表达明显增高,且灵敏度高于AFP,而特异度与其相当,有望成为一种新的、较好早期诊断HCC的血清标志物;另外,GPC3表达对肝癌细胞的分化程度有一定的指示作用。     

Circular RNA hsa_circ_0000567 can be used as a promising diagnostic biomarker for human colorectal cancer

Background

Recent studies have revealed that circular RNAs are involved in the biological process of some kinds of human cancers. However, little is known about their diagnostic values and functions in colorectal cancer (CRC).

Methods

The expression levels of hsa_circ_0000567 in 102 paired CRC tissues and adjacent noncancerous tissues, 5 CRC cell lines, and a normal colorectal epithelial cell line were detected by quantitative real‐time polymerase chain reaction (qRT‐PCR). The correlations between hsa_circ_0000567 expression levels and the clinicopathological factors of patients with CRC were analyzed. Furthermore, the loss‐of‐function assay was performed to investigate the functions of hsa_circ_0000567 in vitro. Finally, a receiver operating characteristic (ROC) curve was established to evaluate the diagnostic value of hsa_circ_0000567.

Results

Hsa_circ_0000567 expression was significantly downregulated in CRC tissues and CRC cell lines. In addition, the decreased hsa_circ_0000567 expression in CRC was negatively correlated with tumor size (P = .011), lymph metastasis (P = .003), distal metastasis (P < .0001), and tumor–node–metastasis (TNM) stage (P = .003) in CRC. Moreover, knockdown of hsa_circ_0000567 promoted CRC cells proliferation and migration in vitro. Importantly, the area under the ROC curve (AUC) was 0.8653, which indicates hsa_circ_0000567 can serve as a diagnostic biomarker.

Conclusion

Hsa_circ_0000567 may be a novel suppressor and a potential diagnosis biomarker in CRC.