Data-driven characterization of molecular phenotypes across heterogeneous sample collections

Abstract

Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation, comparison of molecular disease subtypes identified from separate t-SNE representations, and characterization of the obtained clusters based on pathway databases and additional data. In this manner, we performed an integrative analysis across multi-omics acute myeloid leukemia studies. Our approach indicated new molecular subtypes with differential survival and drug responsiveness among samples lacking fusion genes, including a novel myelodysplastic syndrome-like cluster and a cluster characterized with CEBPA mutations and differential activity of the S-adenosylmethionine-dependent DNA methylation pathway. In summary, integration across multiple studies can help to identify novel molecular disease subtypes and generate insight into disease biology.

Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities

“…In this study, we examined the TF activities that may contribute in maintaining leukemic cell states in E/R+ cases and linked those to target genes, including modulators of leukemia-immune cell cross-talk. Previous bulk cancer genomics studies have established that repeated gene expression patterns also characterize cancer samples [ 99 ], including ALL where such studies have established several transcriptome-based subtypes [ 67 , 100 – 103 ]. They have also shed light on pathway activity and TF expression in E/R+ cells that could be utilized to design targeted therapies [ 6 , 104 , 105 ].…”

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

“…In particular, Koh et al ( 44 ) predicted breast cancer subtypes by applying a modified shrunken centroid method in the development of their network-based tool, iOmicsPASS. Further, breast cancer datasets in TGCA represent a benchmark for integrative models ( 92 – 94 ), as well as AML ( 95 ).…”

“…In particular, Koh et al (44) predicted breast cancer subtypes by applying a modified shrunken centroid method in the development of their networkbased tool, iOmicsPASS. Further, breast cancer datasets in TGCA represent a benchmark for integrative models (92)(93)(94), as well as AML (95). More recently, the success of deep learning algorithms in various bioinformatics fields (96) prompted the adoption of deep neural networks for omics-integration in precision oncology.…”

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

“…In particular, Koh and colleagues (41) predicted breast bioRxiv INF cancer subtypes by applying a modified shrunken centroid method in the development of their networkbased tool, iOmicsPASS. Further, breast cancer datasets in TGCA represent a benchmark for integrative models (87,88,89), as well as AML (90). More recently, the success of deep learning algorithms in various bioinformatics fields (91) prompted the adoption of deep neural network for omics-integration in precision oncology.…”