Setia Pramana

Affinity Proteomics Reveals Elevated Muscle Proteins in Plasma of Children with Cerebral Malaria

Systemic inflammation and sequestration of parasitized erythrocytes are central processes in the pathophysiology of severe Plasmodium falciparum childhood malaria. However, it is still not understood why some children are more at risks to develop malaria complications than others. To identify human proteins in plasma related to childhood malaria syndromes, multiplex antibody suspension bead arrays were employed. Out of the 1,015 proteins analyzed in plasma from more than 700 children, 41 differed between malaria infected children and community controls, whereas 13 discriminated uncomplicated malaria from severe malaria syndromes. Markers of oxidative stress were found related to severe malaria anemia while markers of endothelial activation, platelet adhesion and muscular damage were identified in relation to children with cerebral malaria. These findings suggest the presence of generalized vascular inflammation, vascular wall modulations, activation of endothelium and unbalanced glucose metabolism in severe malaria. The increased levels of specific muscle proteins in plasma implicate potential muscle damage and microvasculature lesions during the course of cerebral malaria.

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival

MOTIVATION Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. RESULTS An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. AVAILABILITY AND IMPLEMENTATION The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. CONTACT yudi.pawitan@ki.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

An expression signature at diagnosis to estimate prostate cancer patients’ overall survival

Background:This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis.Methods:To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan–Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival.Results:An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91–11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79–6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone.Conclusions:The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Molecular classification of breast cancer into clinically relevant subtypes helps improve prognosis and adjuvant-treatment decisions. The aim of this study is to provide a better characterization of the molecular subtypes by providing a comprehensive landscape of subtype-specific isoforms including coding, long non-coding RNA and microRNA transcripts. Isoform-level expression of all coding and non-coding RNAs is estimated from RNA-sequence data of 1168 breast samples obtained from The Cancer Genome Atlas (TCGA) project. We then search the whole transcriptome systematically for subtype-specific isoforms using a novel algorithm based on a robust quasi-Poisson model. We discover 5451 isoforms specific to single subtypes. A total of 27% of the subtype-specific isoforms have better accuracy in classifying the intrinsic subtypes than that of their corresponding genes. We find three subtype-specific miRNA and 707 subtype-specific long non-coding RNAs. The isoforms from long non-coding RNAs also show high performance for separation between Luminal A and Luminal B subtypes with an AUC of 0.97 in the discovery set and 0.90 in the validation set. In addition, we discover 1500 isoforms preferentially co-expressed in two subtypes, including 369 isoforms co-expressed in both Normal-like and Basal subtypes, which are commonly considered to have distinct ER-receptor status. Finally, analyses at protein level reveal four subtype-specific proteins and two subtype co-expression proteins that successfully validate results from the isoform level.

Operator Dependent Choice of Prostate Cancer Biopsy Has Limited Impact on a Gene Signature Analysis for the Highly Expressed Genes IGFBP3 and F3 in Prostate Cancer Epithelial Cells

Background Predicting the prognosis of prostate cancer disease through gene expression analysis is receiving increasing interest. In many cases, such analyses are based on formalin-fixed, paraffin embedded (FFPE) core needle biopsy material on which Gleason grading for diagnosis has been conducted. Since each patient typically has multiple biopsy samples, and since Gleason grading is an operator dependent procedure known to be difficult, the impact of the operators choice of biopsy was evaluated. Methods Multiple biopsy samples from 43 patients were evaluated using a previously reported gene signature of IGFBP3, F3 and VGLL3 with potential prognostic value in estimating overall survival at diagnosis of prostate cancer. A four multiplex one-step qRT-PCR test kit, designed and optimized for measuring the signature in FFPE core needle biopsy samples was used. Concordance of gene expression levels between primary and secondary Gleason tumor patterns, as well as benign tissue specimens, was analyzed. Results The gene expression levels of IGFBP3 and F3 in prostate cancer epithelial cell-containing tissue representing the primary and secondary Gleason patterns were high and consistent, while the low expressed VGLL3 showed more variation in its expression levels. Conclusion The assessment of IGFBP3 and F3 gene expression levels in prostate cancer tissue is independent of Gleason patterns, meaning that the impact of operators choice of biopsy is low.

Model-Based Approaches

The aim of the analysis presented in Chap. 14 is not to detect genes with a significant dose-response relationship but to use parametric dose-response models in order to compare between several compounds or between the characteristics of the dose-response curves for several genes in a single or multiple compound experiment. The basic methodology for parametric dose-response models presented in the chapter was introduced in Chap. 4. In contrast with Chap. 10 in which the classification post-selection procedure was applied to order-restricted ANOVA models, in this chapter, we focus on the case in which several parametric models are fitted to the gene expression data and we discuss model averaging techniques for the estimation of the ED50 parameter.

One CNV Discordance in NRXN1 Observed Upon Genome-wide Screening in 38 Pairs of Adult Healthy Monozygotic Twins

Monozygotic (MZ) twins stem from the same single fertilized egg and therefore share all their inherited genetic variation. This is one of the unequivocal facts on which genetic epidemiology and twin studies are based. To what extent this also implies that MZ twins share genotypes in adult tissues is not precisely established, but a common pragmatic assumption is that MZ twins are 100% genetically identical also in adult tissues. During the past decade, this view has been challenged by several reports, with observations of differences in post-zygotic copy number variations (CNVs) between members of the same MZ pair. In this study, we performed a systematic search for differences of CNVs within 38 adult MZ pairs who had been misclassified as dizygotic (DZ) twins by questionnaire-based assessment. Initial scoring by PennCNV suggested a total of 967 CNV discordances. The within-pair correlation in number of CNVs detected was strongly dependent on confidence score filtering and reached a plateau of r = 0.8 when restricting to CNVs detected with confidence score larger than 50. The top-ranked discordances were subsequently selected for validation by quantitative polymerase chain reaction (qPCR), from which one single ~120kb deletion in NRXN1 on chromosome 2 (bp 51017111-51136802) was validated. Despite involving an exon, no sign of cognitive/mental consequences was apparent in the affected twin pair, potentially reflecting limited or lack of expression of the transcripts containing this exon in nerve/brain.

Improving the Prediction of Prostate Cancer Overall Survival by Supplementing Readily Available Clinical Data with Gene Expression Levels of IGFBP3 and F3 in Formalin-Fixed Paraffin Embedded Core Needle Biopsy Material

Background A previously reported expression signature of three genes (IGFBP3, F3 and VGLL3) was shown to have potential prognostic value in estimating overall and cancer-specific survivals at diagnosis of prostate cancer in a pilot cohort study using freshly frozen Fine Needle Aspiration (FNA) samples. Methods We carried out a new cohort study with 241 prostate cancer patients diagnosed from 2004–2007 with a follow-up exceeding 6 years in order to verify the prognostic value of gene expression signature in formalin fixed paraffin embedded (FFPE) prostate core needle biopsy tissue samples. The cohort consisted of four patient groups with different survival times and death causes. A four multiplex one-step RT-qPCR test kit, designed and optimized for measuring the expression signature in FFPE core needle biopsy samples, was used. In archive FFPE biopsy samples the expression differences of two genes (IGFBP3 and F3) were measured. The survival time predictions using the current clinical parameters only, such as age at diagnosis, Gleason score, PSA value and tumor stage, and clinical parameters supplemented with the expression levels of IGFBP3 and F3, were compared. Results When combined with currently used clinical parameters, the gene expression levels of IGFBP3 and F3 are improving the prediction of survival time as compared to using clinical parameters alone. Conclusion The assessment of IGFBP3 and F3 gene expression levels in FFPE prostate cancer tissue would provide an improved survival prediction for prostate cancer patients at the time of diagnosis.

A gene expression signature to predicit overall, prostate cancer, and non–prostate cancer survival.

51 Background: For prostate cancer patients, prostate cancer specific and non-prostate cancer specific survival have the same importance. This study aimed at identifying expression biomarkers that can predict prostate cancer specific, non-prostate cancer specific and overall survival at diagnosis. METHODS Selected ESCGPs (embryonic stem cell gene predictors) and control genes were analyzed by multiplex quantitative PCR using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 prostate cancer patients diagnosed between 1986 and 2000. Of all patients, 97.9% had overall and cancer-specific survival data and 77.9% were primarily treated only by hormone therapy. The cohort was divided into one discovery and two validation subsets. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis. A published dataset was used for external validation. RESULTS An expression signature of F3, VGLL3 and IGFBP3, was sufficient to categorize the patients into high-risk, intermediate-risk and low-risk subtypes. The median overall survival of the subtypes was 3.23, 4.00 and 9.85 years respectively. The difference corresponded to HRs of 5.86 (95% CI 2.91-11.78, P<0.001) for the high-risk and 3.45 (95% CI 1.79-6.66, P<0.001) for the intermediate-risk compared to the low-risk subtype. This signature is significant in correlation to overall, cancer-specific and non-cancer specific survival in both univariate and multivariate analyses including common clinical parameters. CONCLUSIONS These results suggest that these novel expression biomarkers and the expression signature could be used to improve the accuracy of the currently available clinical tools for predicting overall, cancer-specific and non-cancer specific survival and selecting patients with potential survival benefit from hormone treatment.

Interfaces for Analyzing Dose–Response Studies in Microarray Experiments: IsoGeneGUI and ORIOGEN

In this chapter, we present two interfaces for the analysis of dose–response microarray data. The first, the IsoGeneGUI is a biocunductor menu-based R package. The IsoGeneGUI has the same graphical supports as the IsoGene package, discussed previously in the book, and therefore all the output provided by the IsoGene library can be produced with the IsoGeneGUI as well. The second, the ORIOGEN package (Peddada, Harris, and Harvey 2003) is a java-based interface which can be used to produce the order-restricted analysis presented in Chap. 11.