Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.     

Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery

Disease subtype discovery is an essential step in delivering personalized medicine. Disease subtyping via omics data has become a common approach for this purpose. With the advancement of technology and the lower price for generating omics data, multi-level and multi-cohort omics data are prevalent in the public domain, providing unprecedented opportunities to decrypt disease mechanisms. How to fully utilize multi-level/multi-cohort omics data and incorporate established biological knowledge toward disease subtyping remains a challenging problem. In this paper, we propose a meta-analytic integrative sparse Kmeans (MISKmeans) algorithm for integrating multi-cohort/multi-level omics data and prior biological knowledge. Compared with previous methods, MISKmeans shows better clustering accuracy and feature selection relevancy. An efficient R package, “MIS-Kmeans”, calling C++ is freely available on GitHub (https://github.com/Caleb-Huo/MIS-Kmeans).

     

Multi‐modal meta‐analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory‐specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta‐analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi‐modal meta‐analysis approach also identified synthetic lethal partners of cancer drivers, including a co‐dependency of PTEN deficient endometrial cancer cells on RNA helicases.     

Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA,DNA Methylation,microRNA, and mRNA across Five Human Cancers

IntroductionAdvances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored.ResultsUsing the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.ConclusionOur study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.     

AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments

In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, information contained in one data type pertaining to the other data type may be complex and in nonlinear form. Deep learning provides a convenient alternative to extract low-dimensional nonlinear data embedding. In addition, the deep learning setup can naturally incorporate the effects of clinical confounding factors into the integrative analysis. Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME), to extract data representation for omics data integrative analysis. The method can adjust for confounder variables, achieve informative data embedding, rank features in terms of their contributions, and find pairs of features from the two data types that are related to each other through the data embedding. In simulation studies, the method was highly effective in the extraction of major contributing features between data types. Using two real microRNA-gene expression datasets, one with confounder variables and one without, we show that AIME excluded the influence of confounders, and extracted biologically plausible novel information. The R package based on Keras and the TensorFlow backend is available at https://github.com/tianwei-yu/AIME.     

Ancient genes can be served as pan‐cancer diagnostic and prognostic biomarkers

One important challenge for cancer is efficient biomarkers monitoring its formation and developments remain greatly limited. Although the accumulated big omics data provide great opportunities to the above purpose, the biomarkers identified by the data‐driven strategy often do not work well in new datasets, which is one of the main bottlenecks limiting their utilities. Given that atavistic phenotype is generally observed in cancer cells, we have been suggested that the activity of progenitor genes in tumour could serve as an efficient cancer biomarker. For doing so, we first curated 77 progenitor genes and then proposed a quantitative score to evaluate cancer progenitorness. After applying progenitorness score to ~ 22 000 samples, 33 types of cancers from 81 datasets, this method generally performs well in the diagnosis, prognosis and therapy monitoring of cancers. This study proposed a potential pan‐cancer biomarker and revealed a significant role of atavism in the formation and development of cancers.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.     

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data

Background

Large-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.

Methods

We propose a novel framework for multi-omics data integration using multi-view feature selection. We introduce a novel multi-view feature selection algorithm, MRMR-mv, an adaptation of the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm to the multi-view setting.

Results

We report results of experiments using an ovarian cancer multi-omics dataset derived from the TCGA database on the task of predicting ovarian cancer survival. Our results suggest that multi-view models outperform both view-specific models (i.e., models trained and tested using a single type of omics data) and models based on two baseline data fusion methods.

Conclusions

Our results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.

     

Dimension reduction for integrative survival analysis

We propose a constrained maximum partial likelihood estimator for dimension reduction in integrative (e.g., pan-cancer) survival analysis with high-dimensional predictors. We assume that for each population in the study, the hazard function follows a distinct Cox proportional hazards model. To borrow information across populations, we assume that each of the hazard functions depend only on a small number of linear combinations of the predictors (i.e., “factors”). We estimate these linear combinations using an algorithm based on “distance-to-set” penalties. This allows us to impose both low-rankness and sparsity on the regression coefficient matrix estimator. We derive asymptotic results that reveal that our estimator is more efficient than fitting a separate proportional hazards model for each population. Numerical experiments suggest that our method outperforms competitors under various data generating models. We use our method to perform a pan-cancer survival analysis relating protein expression to survival across 18 distinct cancer types. Our approach identifies six linear combinations, depending on only 20 proteins, which explain survival across the cancer types. Finally, to validate our fitted model, we show that our estimated factors can lead to better prediction than competitors on four external datasets.     

Towards precise reconstruction of gene regulatory networks by data integration

Background: More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks. Results: In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences. Conclusions: We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.     

An eScience-Bayes strategy for analyzing omics data

Background  

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.     

肿瘤突变特征与病理分型的关联研究

准确评估肿瘤的病理亚型对诊断、治疗和预后至关重要。以往病理亚型的诊断主要依赖HE染色法和免疫组织化学法,而随着测序技术的不断发展,对患者进行基因型和表型特点的个体分析成为可能,将肿瘤病理分型与基因分型结合用于疾病分型、诊治选择和疗效判断的精准医学研究逐渐兴起。不同病理亚型的肿瘤细胞来源、致癌因素和临床表型均不尽相同,其在基因组上会留下特异“印迹”,即突变特征。本研究通过整合癌症基因组数据库(The Cancer Genome Atlas, TCGA)中肾癌、肺癌和食管癌的外显子测序数据,分别对3种肿瘤通过肿瘤基因突变特征进行肿瘤病理分型聚类和预测。首先通过非监督聚类方法将3种肿瘤分别按照24种突变特征进行聚类分析,其次通过随机森林法从24种突变特征中进一步选择对于区分不同病理亚型有显著性的突变特征并进行聚类分析,构建突变特征对3种肿瘤病理亚型的分型模型。在肾癌中,该模型准确率达到了100% (95% confidence interval (CI): 0.93~1.00),肺癌和食管癌中分别达到了78% (95% CI: 0.66~0.86)和84% (95% CI: 0.60~0.97)。以上研究结果表明,突变特征作为新型分子标记物,对肿瘤的病理分型、诊断,尤其是早诊具有一定的参考意义。     

整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

Background

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.

Methods

To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).

Results

We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).

Conclusions

Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.