Network-based assessment of HDAC6 activity predicts preclinical and clinical responses to the HDAC6 inhibitor ricolinostat in breast cancer.

Inhibiting individual histone deacetylase (HDAC) is emerging as well-tolerated anticancer strategy compared with pan-HDAC inhibitors. Through preclinical studies, we demonstrated that the sensitivity to the leading HDAC6 inhibitor (HDAC6i) ricolinstat can be predicted by a computational network-based algorithm (HDAC6 score). Analysis of ~3,000 human breast cancers (BCs) showed that ~30% of them could benefice from HDAC6i therapy. Thus, we designed a phase 1b dose-escalation clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in patients with metastatic BC (MBC) (NCT02632071). Study results showed that the two agents can be safely combined, that clinical activity is identified in patients with HR+/HER2- disease and that the HDAC6 score has potential as predictive biomarker. Analysis of other tumor types also identified multiple cohorts with predicted sensitivity to HDAC6i's. Mechanistically, we have linked the anticancer activity of HDAC6i's to their ability to induce c-Myc hyperacetylation (ac-K148) promoting its proteasome-mediated degradation in sensitive cancer cells.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm.

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

Toward better benchmarking: challenge-based methods assessment in cancer genomics.

Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.

hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests.

A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.

Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.

Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher's exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.

Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference.

The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell-context-specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high-throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities.

Pharmacogenetics: ethical issues and policy options.

Pharmacogenetics offers the prospect of an era of safer and more effective drugs, as well as more individualized use of drug therapies. Before the benefits of pharmacogenetics can be realized, the ethical issues that arise in research and clinical application of pharmacogenetic technologies must be addressed. The ethical issues raised by pharmacogenetics can be addressed under six headings: (1) regulatory oversight, (2) confidentiality and privacy, (3) informed consent, (4) availability of drugs, (5) access, and (6) clinicians' changing responsibilities in the era of pharmacogenetic medicine. We analyze each of these categories of ethical issues and provide policy approaches for addressing them.