Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information

Cerebrospinal Fluid Metabolomics and Proteomics Integration in Neurological Syndromes

The integration of multi-omics data has increasingly been recognized as an effective approach to addressing complex problems and advancing precision medicine. Metabolomics and proteomics are closely related, and their integration provides complementary insights and enables cross-validation of experimental results. The integration of proteomics and metabolomics in cerebrospinal fluid is expected to reconstruct the complex biological networks underlying nervous system diseases, enhance understanding of molecular mechanisms, and aid in disease classification and prognosis prediction. However, integrating multi-omics data still faces numerous challenges, limiting the application of combined proteomic and metabolomic analyses in neurological diseases. Based on the advantages of integrated proteomics and metabolomics, this chapter introduces, for the first time, common strategies for the integrated analyses of omics data. Furthermore, we review advances in cerebrospinal fluid proteomics and metabolomics for neurological syndromes, highlighting current challenges and future directions.

Protein Kinase C Epsilon Overexpression Is Associated With Poor Patient Outcomes in AML and Promotes Daunorubicin Resistance Through p-Glycoprotein-Mediated Drug Efflux

The protein kinase C (PKC) family of serine/threonine kinases are pleiotropic signaling regulators and are implicated in hematopoietic signaling and development. Only one isoform however, PKCϵ, has oncogenic properties in solid cancers where it is associated with poor outcomes. Here we show that PKCϵ protein is significantly overexpressed in acute myeloid leukemia (AML; 37% of patients). In addition, PKCϵ expression in AML was associated with a significant reduction in complete remission induction and disease-free survival. Examination of the functional consequences of PKCϵ overexpression in normal human hematopoiesis, showed that PKCϵ promotes myeloid differentiation, particularly of the monocytic lineage, and decreased colony formation, suggesting that PKCϵ does not act as an oncogene in hematopoietic cells. Rather, in AML cell lines, PKCϵ overexpression selectively conferred resistance to the chemotherapeutic agent, daunorubicin, by reducing intracellular concentrations of this agent. Mechanistic analysis showed that PKCϵ promoted the expression of the efflux pump, P-GP (ABCB1), and that drug efflux mediated by this transporter fully accounted for the daunorubicin resistance associated with PKCϵ overexpression. Analysis of AML patient samples also showed a link between PKCϵ and P-GP protein expression suggesting that PKCϵ expression drives treatment resistance in AML by upregulating P-GP expression.

A multivariable approach for risk markers from pooled molecular data with only partial overlap

Background

Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers – even after imputation.

Methods

Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection.

Results

Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals.

Conclusions

Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request.

Trial registration

All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0849-0) contains supplementary material, which is available to authorized users.

A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors

Background

Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high‐dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them.

Methods

We assembled questionnaire, diagnostic, genotype, microarray, RT‐qPCR, flow cytometry, and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild‐to‐moderate allergic asthmatics, and nonallergic asthmatics. Based on data from 260 German children aged 4‐14 from our university outpatient clinic, we built a novel multilevel prediction approach for asthma outcome which could deal with a present complex missing data structure.

Results

The optimal learning method was boosting based on all data sets, achieving an area underneath the receiver operating characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%‐confidence interval (CI): 0.65‐0.94) using leave‐one‐out cross‐validation. Besides improving the AUC, our integrative multilevel learning approach led to tighter CIs than using smaller complete predictor data sets (AUC = 0.82 [0.66‐0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental).

Conclusion

Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multilevel data‐based risk prediction settings, which typically suffer from incomplete data.

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA

BACKGROUND

Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.

METHODS

In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation.

RESULTS

Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results.

CONCLUSION

The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.

Integrative Analysis of Omics Big Data

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.