Functional impact of multi-omic interactions in breast cancer subtypes

Abstract

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

Integrated analysis of -omic landscapes in breast cancer subtypes

“…122 However, only a few studies focus on the analysis of intricate omic features of breast cancer subtypes 98 or try to understand the complexity of their interactions. 123,124 In this sense, our study offers a significant advancement in understanding the complexity of molecular events and their interactions in breast cancer subtypes with the implications for survival and clinical parameters.…”

Functional impact of multi-omic interactions in lung cancer

“…However, it is worth noting that SGCCA does have a drawback, mainly attributed to LASSO’s instability. To address this concern, we chose to retain only those features present in over 70% of subsamples, ensuring a more stable feature set ( Ochoa and Hernández-Lemus, 2023 ). We favor the sparse method for its reliability in feature selection ( Kang et al, 2013 ).…”

“…The analysis was performed by providing the algorithm with the different blocks of data, a corresponding sparsity parameter, along with the number of components to recover (ncomp), a design matrix, and a covariance-maximizing function. Cross validation with k = 5 was employed to select sparsity parameters for each omic taking the sequence [0.01, 0.02, …, 0.99] ( Ochoa and Hernández-Lemus, 2023 ).…”

“…The threshold was determined for the regulatory interactions by selecting the median MI values instead of the mean to prevent outliers from dominating from having a superior influence on the threshold determination. Considering MI values to vary across different types of pairs, we obtained different thresholds for CpG-transcript, CpG-miRNA, TF transcript-transcript, and miRNA-transcript edges in similar way as in ( Ochoa and Hernández-Lemus, 2023 ).…”

Molecular Subtyping and Survival Analysis of Osteosarcoma Reveals Prognostic Biomarkers and Key Canonical Pathways

“…Current advances in omics technologies, especially genomics, epigenomics, and transcriptomics, in combination with advanced data analytical tools have enabled molecular classification or subtyping of heterogeneous cancers that are increasingly preferred by physicians over the traditional and error-prone approaches based on clinical characteristics such as the histopathology, grade, and other visual observations [ 13 ]. To overcome the limitations of the previous approaches, recent methodologies have focused on the use of molecular profiling data for subtyping of cancers, including those of lung [ 14 , 15 , 16 ], colon [ 17 , 18 ], breast [ 19 , 20 , 21 ], and others [ 22 , 23 , 24 ]. In addition, new techniques such as NanoString and tissue microarray (TMA) approaches have also been evolved for subtype characterization of cancers [ 25 , 26 , 27 , 28 ].…”