Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction

Abstract

In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model. However, cancer is dysregulated by multiple levels in the biological system through genomic, epigenomic, transcriptomic, and proteomic level. Thus, genomic features are likely to interact with other genomic features in the different genomic levels. In order to deepen our knowledge, it would be desirable to incorporate such inter-relationship information when integrating multi-omics data for cancer clinical outcome prediction. In this study, we propose a new graph-based framework that integrates not only multi-omics data but inter-relationship between them for better elucidating cancer clinical outcomes. In order to highlight the validity of the proposed framework, serous cystadenocarcinoma data from TCGA was adopted as a pilot task. The proposed model incorporating inter-relationship between different genomic features showed significantly improved performance compared to the model that does not consider inter-relationship when integrating multi-omics data. For the pair between miRNA and gene expression data, the model integrating miRNA, for example, gene expression, and inter-relationship between them with an AUC of 0.8476 (REI) outperformed the model combining miRNA and gene expression data with an AUC of 0.8404. Similar results were also obtained for other pairs between different levels of genomic data. Integration of different levels of data and inter-relationship between them can aid in extracting new biological knowledge by drawing an integrative conclusion from many pieces of information collected from diverse types of genomic data, eventually leading to more effective screening strategies and alternative therapies that may improve outcomes.

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

“…One of the common approaches is that patients can be divided into two groups, such as high-risk survival and low-risk survival group, according to a survival-time threshold, and then a binary classification algorithm can be applied to predict the survival group for each individual patient in a test dataset [24, 26, 27, 52, 57]. This approach has an advantage of providing natural performance metrics from two by two contingency tables, along with positive and negative predictive values, to enable unambiguous assessments for survival prediction.…”

“…The explosion of these unprecedented dataset has provided many opportunities to examine the complex genetic architecture of several cancers and improve the diagnosis, treatment, and ultimately prevention of cancer [21, 35, 45-47]. Despite these efforts, it is crucial to develop a novel data integration method to better predict cancer clinical outcome, further exploring a global view on the interactions within/between meta-dimensional genomic data [23, 24, 27, 28, 39, 44, 56]. …”

“…Previously, we proposed many methodological frameworks that predict clinical outcomes by integrating multi-omics data [23, 24, 27, 28]. However, these binary classification approaches have difficulties to directly predict survival data due to the problems of setting threshold and the characteristics of censored observations.…”

Taking Bioinformatics to Systems Medicine

“…Further improvement of these profi les may be obtained through the explicit incorporation of interrelationships between various types of measurements such as microRNA-mRNA target, or gene methylation-microRNA (based on a common target gene). This was demonstrated for the prediction of short-term and long-term survival from serous cystadenocarcinoma TCGA data [ 83 ].…”

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

“…Because different types of omics data have been shown to complement each other [8], there is a growing interest in effective methods for integrative analyses of multi-omics data [9–11]. The resulting methods have been successfully used to predict the molecular abnormalities that impact both clinical outcomes and therapeutic targets [5, 10, 12–16].…”