A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Sathyanarayanan, Anita; Gupta, Rohit; Thompson, Erik W.; Nyholt, Dale R.; Bauer, Denis C.; Nagaraj, Shivashankar H.

doi:10.1093/bib/bbz121

Cited by 57 publications

(47 citation statements)

References 64 publications

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“…We are aware of some limitations of our review. An important aspect that has not been covered by this review is the quantitative comparison among tools (76), which could highlight possible overfitting (77) and issues that may prevent the actual translation of multi-omics approaches from bench to bedside. Although, by indicating works that provide a usable tool ( Table 1), our review could be a starting point for a comprehensive quantitative comparison.…”

Section: Discussionmentioning

confidence: 99%

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

et al. 2020

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In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

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Section: Discussionmentioning

confidence: 99%

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

et al. 2020

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“…This assumption has been confirmed by multiple studies on diverse diseases, such as cardiovascular disease (60), diabetes (61), liver disease (62), or mitochondrial diseases (63), and also longitudinally (64), suggesting that the more complex the disease the more advantageous the integration. As the co-occurrence of multiple causes and correlated events is a well-known characteristic of tumorigenesis and cancer development, the integration of data generated from multiple sources can thus be particularly useful for the identification of cancer hallmarks (65)(66)(67)(68).…”

Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

et al. 2020

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“…This assumption has been confirmed by multiple studies on diverse diseases, such as cardiovascular disease (57), diabetes (58), liver disease (59), or mitochondrial diseases (60), and also longitudinally (61), suggesting that the more complex the disease the more advantageous the integration. As the co-occurence of multiple causes and correlated events is a well-known characteristic of tumorigenesis and cancer development, the integration of data generated from multiple sources can thus be particularly useful for the identification of cancer hallmarks (62,63,64,65).…”

Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Cited by 57 publications

References 64 publications

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Integrative Network Fusion: a multi-omics approach in molecular profiling

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