14-bp ins/del polymorphism and +3142C>G SNP of the HLA-G gene have a significant impact on acute rejection after liver transplantation

Expression of human leukocyte antigen G (HLA-G) has been associated with increased graft survival and decreased rejection episodes. It has been described that the HLA-G 14-base pair (bp) insertion/deletion (ins/del) (rs66554220) and +3142C>G (rs1063320) gene polymorphisms modify the expression level of HLA-G. The aim of the study was to investigate whether these HLA-G polymorphisms have an impact on acute rejection after liver transplantation. In total, 146 liver transplant recipients (57 with acute rejection and 89 without acute rejection) and 99 corresponding liver donors were genotyped for both polymorphisms. In liver transplantation the 14-bp ins/ins and the +3142GG genotypes are more frequent in recipients without rejection compared to recipients with rejection (3.5% vs. 31.5%, p = <0.001; 12.3% vs. 41.6%, p = <0.001) demonstrating an association with protection from acute rejection. In contrast, in liver donors we could not reveal an association. We conclude that 14-bp ins/ins and +3142GG genotypes of HLA-G in liver transplant recipients are of importance for prediction of acute rejection after liver transplantation. Thus genotyping of liver recipients for both polymorphisms might be useful to stratify liver transplant recipients according to the risk of acute liver transplant rejection.     

精准医疗时代肝细胞肝癌诊疗策略

组学和临床数据的更新及检测技术的进步为精准医疗的实施提供了信息和技术支持。在这种理念下,通过对异质性疾病进行精细的亚组分类,可实现诊疗策略的精准应用,提高治疗效果,减少不必要的副作用。精准医疗理念在肿瘤领域发展较快,并对高度异质的肝细胞肝癌(hepatocellular carcinoma,HCC)的诊疗策略产生一定影响。本文针对精准医疗理念下HCC诊疗策略的更新及存在的问题进行概述,包括HCC的人群预防、高危人群筛查、早期诊断、分期、治疗方案的选择、疗效及预后评估等,为HCC的精准医疗提供新的思路。     

Genetics and diagnostic refinement

For many psychiatric conditions it is speculated that, rather than being single disease entities, they are a set of several disorders sharing clinical features but having (partly) different underlying causes. The possibility of measuring genetic variation on a large scale has given researchers new hope of identifying these disease subtypes that may differ with respect to prognosis, course, and response to treatment. However, although a considerable number of articles have been published suggesting that we may even be on the verge of making genotype-based diagnoses, the reality is that we do not have a good answer to even the most basic question of how measured genes could best be used to refine diagnostic categories. In this article, we show that for common psychiatric disorders, it may not be possible to simply look for similar genetic profiles in groups of patients. Instead, we propose a model assuming that genotypes affect phenotypes through more or less coherent etiological systems or pathogenic processes and argue that these etiological systems may provide a more fruitful basis for defining disease subtypes. Several examples from the literature that support the face validity of different aspects of our model are given. Finally, we argue that, given our limited knowledge of disease etiology, the use of discovery-oriented techniques requiring extensive data collection and (artificial) intelligent computer searches may be imperative, and discuss the prospect of model-based diagnosis to classify etiologically different disease subtypes.     

Time-dependent ROC analysis for censored biomarker data due to limit of detection

Receiver operating characteristic (ROC) curve is a well-established analysis method to evaluate biomarker’s discrimination accuracy for binary outcomes. When the endpoint of interest is time to event outcome such as time to cancer recurrence, a biomarker’s time-varying discriminatory performance is often assessed by time-dependent ROC analysis. In practice, biomarkers are often imprecisely measured due to the limitation of assay sensitivity. The values below the limit of detection are not detectable. Ignorance of such data characteristic may lead to inaccurate estimation of marker’s potential discriminatory power. The objective of this article is to extend time-dependent ROC method to censored biomarker data by using parameter estimates from the Cox regression model that accommodates censored biomarker measurements. In the simulation study, the proposed methods are shown to outperform the simple substitution method that has been conventionally adopted for handling censored data. Application data are also given to illustrate our methods.     

Urinary peptidomics in kidney disease and drug research

Introduction: Due to its close connection with the renal system, urine is considered a valuable source of information in kidney disease research. Peptidomics methods focus on the discovery of endogenous peptides, given their wide range of biological functions and diagnostic and therapeutic potential. Representing a non-invasive and sensitive method, technological prospects of urinary peptidomics should be evaluated in the context of drug discovery and research.

Areas covered: This review describes urinary peptidomics with focus on its application in drug research in the field of kidney diseases. The authors provide an overview of current achievements and potential future applications.

Expert opinion: The urinary peptidome is a dynamically changing source of information, able to reflect sudden and long-term changes affecting the renal system. Studies utilizing urinary peptidomics techniques have demonstrated their value in patient stratification and detection of early pathological changes in kidney disease. Serving as a liquid biopsy, urinary peptides are an invaluable tool for drug response monitoring. Nevertheless, peptidomics is largely underexplored in drug research in general, as evidenced by the scarce number of scientific publications on this topic. Further progress will be driven by the successful validation of current discoveries and continued efforts to improve the translation of results into therapeutic applications.     

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

ObjectiveA systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.MethodsWe searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.ResultsA total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n = 8), urine (n = 1) and synovial fluid (n = 1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.ConclusionsMetabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.     

ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

Background

Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.

Objective

To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.

Design, setting, and participants

A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.

Outcome measurements and statistical analysis

Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.

Results and limitations

The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.

Conclusions

The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.

Patient summary

We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.     

Blood- and tissue-based biomarkers for prediction of outcomes in urothelial carcinoma of the bladder

ObjectivesUrothelial carcinoma of the bladder (UCB) is a highly heterogeneous malignancy that causes significant morbidity and mortality. Standard pathologic features (stage, grade, and nodal status) are insufficient to predict accurately a patient's outcome. Biomarkers could help clinicians provide individualized prognostications and allow risk-stratified clinical decision making regarding surgical and medical treatment. This review summarizes the existing tissue- and blood-based biomarkers in UCB.Material and methodsA PubMed/Medline search was conducted to identify original articles regarding molecular biomarkers and UCB. Searches were limited to papers published in English. Keywords included urothelial carcinoma, bladder cancer, transitional cell, biomarker, marker, staining, cystectomy, recurrence or progression, survival, prediction, and prognosis.ResultsThe articles with the highest level of evidence were selected and reviewed, with the consensus of all the authors of this paper.ConclusionsThere is no doubt that a panel of biomarkers would eventually improve our clinical decision making regarding treatment and follow-up. However, to date, no biomarker panel is yet validated for daily clinical practice.