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Abstract Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Abstract The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Abstract Background Compositional systems, represented as parts of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations. Results To resolve both of these issues, we provide a general and data-driven modeling tool for compositional systems called Compositional Maximum Entropy (CME). By integrating the prior geometric structure of compositions with sample-specific information, CME infers the underlying multivariate relationships between the constituent components. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer. Conclusions CME provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.

In this paper, we present a network-based clustering method, called vector Wasserstein clustering (vWCluster), based on the vector-valued Wasserstein distance derived from optimal mass transport (OMT) theory. This approach allows for the natural integration of multi-layer representations of data in a given network from which one derives clusters via a hierarchical clustering approach. In this study, we applied the methodology to multi-omics data from the two largest breast cancer studies. The resultant clusters showed significantly different survival rates in Kaplan-Meier analysis in both datasets. CIBERSORT scores were compared among the identified clusters. Out of the 22 CIBERSORT immune cell types, 9 were commonly significantly different in both datasets, suggesting the difference of tumor immune microenvironment in the clusters. vWCluster can aggregate multi-omics data represented as a vectorial form in a network with multiple layers, taking into account the concordant effect of heterogeneous data, and further identify subgroups of tumors in terms of mortality.

Unbalanced optimal mass transport (OMT) seeks to remove the conservation of mass constraint by adding a source term to the standard continuity equation in the Benamou-Brenier formulation of OMT. In this study, we show how the unbalanced case fits into the vector-valued OMT framework simply by adding an auxiliary source layer and taking the flow between the source layer and the original layer(s) as the source term. This allows for unbalanced models both in the scalar and vector-valued density settings. The results are demonstrated on a number of synthetic and real vector-valued data sets.

Background and Aim. The senescence of human bone marrow mesenchymal stem cells (hBMSCs) can be induced by oxidative stress, but the mechanism by which it occurs is not yet clear. Here, we performed an investigation on the pathogenesis of hypoxia-induced senescence through proteomic analyses and aimed to explore the mechanisms of stem cell senescence. Methods. Hypoxia in hBMSCs was induced for 0, 4, and 12 hours, and cellular senescence was evaluated by senescence-associated β-galactosidase (SA-β-gal) staining. Tandem mass tag (TMT) labeling was combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for differential proteomic analysis of hypoxia in hBMSCs. Parallel reaction monitoring (PRM) analysis was used to validate the candidate proteins. Verifications of signaling pathways were evaluated by western blotting. Cell apoptosis was evaluated using Annexin V/7-AAD staining by flow cytometry. The production of reactive oxygen species (ROS) was detected by the fluorescent probe 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA). Results. Cell senescence detected by SA-β-gal activity was higher in the 12-hour hypoxia-induced group. TMT analysis of 12-hour hypoxia-induced cells identified over 6000 proteins, including 686 differentially expressed proteins. Based on biological pathway analysis, we found that the senescence-associated proteins were predominantly enriched in the cancer pathways, PI3K-Akt pathway, and cellular senescence signaling pathways. CDK1, CDK2, and CCND1 were important nodes in PPI analyses. Moreover, the CCND1, UQCRH, and COX7C expressions were verified by PRM. Hypoxia induction for 12 hours in hBMSCs reduced CCND1 expression but promoted ROS production and cell apoptosis. Such effects were markedly reduced by the PI3K agonist, 740 Y-P, and attenuated by LY294002. Conclusions. Hypoxia of hBMSCs inhibited CCND1 expression but promoted ROS production and cell apoptosis through activating the PI3K-dependent signaling pathway. These findings provided a detailed characterization of the proteomic profiles related to hypoxia-induced senescence of hBMSCs and facilitated our understanding of the molecular mechanisms leading to stem cell senescence.