Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.     

大白猪和通城猪全基因组选择性清扫分析

长期的人工选择使猪的生产性能得到显著提高,与选择相关的基因组区域也随之发生特定遗传变异表征(选择信号)。不同类型品种所受到选择强度不一,选择信号亦不相同,选择性清扫分析已逐渐成为选择信号的主要检测手段。文章基于商用型大白猪(n=45)和地方猪品种通城猪(n=45)的猪60K SNP芯片分型数据,借助遗传分化系数Fst法进行选择信号检测分析。利用gPLINK软件设定质控标准,共计34 304个SNPs被筛选出用于统计分析。使用Genepop软件包计算两个猪品种之间的遗传分化参数Fst,所得Fst平均值为0.3209。选取Fst>0.7036(即占总Fst值数目的 1%),共计344个SNPs被选择出来。SNP位置注释显示这些位点涉及到79个候选基因(Sus scrofa Build 9)。利用在线软件Ingenuity Pathway Analysis对候选基因的生物学通路进行网络分析,发现它们多与生长繁殖及免疫应答有关,如NCOA6、ERBB4、RUNX2和APOB等基因。研究结果为进行猪产肉、抗病等性状候选基因和致因突变深入挖掘提供了有益参考。     

Quantification of Imatinib Plasma Levels in Patients with Chronic Myeloid Leukemia: Comparison Between HPLC–UV and LC–MS/MS

Measurement of imatinib plasma concentration can be useful to evaluate patient adherence to daily oral therapy, potential drug–drug interaction, treatment efficacy, and severe drug-related adverse events. The aim of this study was to correlate the two different blood level test methods, HPLC–UV and LC–MS/MS. We analyzed 162 plasma samples from patients treated with imatinib. We estimated the correlation between the two analytical methods on total data set and on five sets of patients grouped into different categories based on the drug-dose therapy. Finally, imatinib quantification was correlated with genetic data on the molecular response in monitoring follow-up of CML patients.     

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.     

Sex differences and non-additivity in the effects of genes on personality.

New large-sample data show that non-additive genetic effects, probably epistatic interactions between loci, and sex-limited gene expression are significant features of the genetic architecture of human personality as measured by questionnaire scales of extraversion and neuroticism. Three large data sets--new data on large samples (n = 20,554) of US twins, their spouses, parents, siblings and children, correlations for Australian twins (n = 7,532), and previously published twin data from Finland (n = 14,288)--are subjected to an integrated analysis to test alternative hypotheses about the genetic causes of family resemblance in personality. When allowance is made for differences in reliability of the scales, the combined data are consistent with the same model for variation. There are significant amounts of genetic non-additivity for both dimensions of personality. The evidence favours additive x additive epistatic interactions rather than dominance. In the case of neuroticism, there is especially strong evidence of sex differences in genetic architecture favouring a greater relative contribution of non-additive genetic effects in males. The data confirm previous claims to find no major contribution of the shared environment of twins and siblings to these dimensions of personality. Correlations between spouses are zero, and the correlations for very large samples of siblings and non-identical twins do not differ significantly.     

A pharmacogenomic method for individualized prediction of drug sensitivity

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (M erging genomic and pharmacologic A nalyses for T herapy CH oice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof‐of‐principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta‐analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three‐dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.     

From Description to Causality: Mechanisms of Gene Expression Signatures in Cancer

Global gene expression profiles of thousands of cancer samples have been completed, giving rise to hundreds of gene expression signatures (GES). Although many expression signatures show promise in predicting patient prognosis or response to therapies, the usefulness of the signatures in understanding the underlying mechanisms of cancer has not been fully exploited. While “reverse genomic” methods can test specific hypotheses of gene regulation, they fare less well in deciphering novel or combinatorial mechanisms of gene regulation. Recently we described SLAMS (stepwise linkage analysis of microarray signatures), a novel method that can prospectively identify genetic regulators of gene expression signatures in cancer. Applying SLAMS on a poor-prognosis wound signature in human breast cancer, we identified CSN5-mediated ubiquitination of MYC as a novel mechanism to activate a biological program favoring metastasis.