Multiomics Topic Modeling for Breast Cancer Classification.

The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.

The Dynamics of Aerotaxis in a Simple Eukaryotic Model.

In aerobic organisms, oxygen is essential for efficient energy production, and it acts as the last acceptor of the mitochondrial electron transport chain and as regulator of gene expression. However, excessive oxygen can lead to production of deleterious reactive oxygen species. Therefore, the directed migration of single cells or cell clumps from hypoxic areas toward a region of optimal oxygen concentration, named aerotaxis, can be considered an adaptive mechanism that plays a major role in biological and pathological processes. One relevant example is the development of O2 gradients when tumors grow beyond their vascular supply, leading frequently to metastasis. In higher eukaryotic organisms, aerotaxis has only recently begun to be explored, but genetically amenable model organisms suitable to dissect this process remain an unmet need. In this regard, we sought to assess whether Dictyostelium cells, which are an established model for chemotaxis and other motility processes, could sense oxygen gradients and move directionally in their response. By assessing different physical parameters, our findings indicate that both growing and starving Dictyostelium cells under hypoxic conditions migrate directionally toward regions of higher O2 concentration. This migration is characterized by a specific pattern of cell arrangement. A thickened circular front of high cell density (corona) forms in the cell cluster and persistently moves following the oxygen gradient. Cells in the colony center, where hypoxia is more severe, are less motile and display a rounded shape. Aggregation-competent cells forming streams by chemotaxis, when confined under hypoxic conditions, undergo stream or aggregate fragmentation, giving rise to multiple small loose aggregates that coordinately move toward regions of higher O2 concentration. By testing a panel of mutants defective in chemotactic signaling, and a catalase-deficient strain, we found that the latter and the pkbR1 null exhibited altered migration patterns. Our results suggest that in Dictyostelium, like in mammalian cells, an intracellular accumulation of hydrogen peroxide favors the migration toward optimal oxygen concentration. Furthermore, differently from chemotaxis, this oxygen-driven migration is a G protein-independent process.

Initial cell density encodes proliferative potential in cancer cell populations.

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.

Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

Investigating the epi-miRNome: identification of epi-miRNAs using transfection experiments.

Aim: Growing evidence shows a strong interplay between post-transcriptional regulation, mediated by miRNAs (miRs) and epigenetic regulation. Nevertheless, the number of experimentally validated miRs (called epi-miRs) involved in these regulatory circuitries is still very small. Material & methods: We propose a pipeline to prioritize candidate epi-miRs and to identify potential epigenetic interactors of any given miR starting from miR transfection experiment datasets. Results & conclusion: We identified 34 candidate epi-miRs: 19 of them are known epi-miRs, while 15 are new. Moreover, using an in-house generated gene expression dataset, we experimentally proved that a component of the polycomb-repressive complex 2, the histone methyltransferase enhancer of zeste homolog 2 (EZH2), interacts with miR-214, a well-known prometastatic miR in melanoma and breast cancer, highlighting a miR-214-EZH2 regulatory axis potentially relevant in tumor progression.

A curated database of miRNA mediated feed-forward loops involving MYC as master regulator.

BACKGROUND: The MYC transcription factors are known to be involved in the biology of many human cancer types. But little is known about the Myc/microRNAs cooperation in the regulation of genes at the transcriptional and post-transcriptional level. METHODOLOGY/PRINCIPAL FINDINGS: Employing independent databases with experimentally validated data, we identified several mixed microRNA/Transcription Factor Feed-Forward Loops regulated by Myc and characterized completely by experimentally supported regulatory interactions, in human. We then studied the statistical and functional properties of these circuits and discussed in more detail a few interesting examples involving E2F1, PTEN, RB1 and VEGF. CONCLUSIONS/SIGNIFICANCE: We have assembled and characterized a catalogue of human mixed Transcription Factor/microRNA Feed-Forward Loops, having Myc as master regulator and completely defined by experimentally verified regulatory interactions.