Mariano J. Alvarez

The transcriptional network for mesenchymal transformation of brain tumours

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPβ and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Assembly of a transcriptional and post‐translational molecular interaction network in B cells, the human B‐cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre‐replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

Genome-wide Identification of Post-translational Modulators of Transcription Factor Activity in Human B-Cells

The ability of a transcription factor (TF) to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. However, high-throughput methods for the identification of proteins that affect TF activity are still largely unavailable. Here we introduce an algorithm, modulator inference by network dynamics (MINDy), for the genome-wide identification of post-translational modulators of TF activity within a specific cellular context. When used to dissect the regulation of MYC activity in human B lymphocytes, the approach inferred novel modulators of MYC function, which act by distinct mechanisms, including protein turnover, transcription complex formation and selective enzyme recruitment. MINDy is generally applicable to study the post-translational modulation of mammalian TFs in any cellular context. As such it can be used to dissect context-specific signaling pathways and combinatorial transcriptional regulation.

Integrated biochemical and computational approach identifies BCL6 direct target genes controlling multiple pathways in normal germinal center B cells

BCL6 is a transcriptional repressor required for mature B-cell germinal center (GC) formation and implicated in lymphomagenesis. BCL6s physiologic function is only partially known because the complete set of its targets in GC B cells has not been identified. To address this issue, we used an integrated biochemical-computational-functional approach to identify BCL6 direct targets in normal GC B cells. This approach includes (1) identification of BCL6-bound promoters by genome-wide chromatin immunoprecipitation, (2) inference of transcriptional relationships by the use of a regulatory network reverse engineering approach (ARACNe), and (3) validation of physiologic relevance of the candidate targets down-regulated in GC B cells. Our approach demonstrated that a large set of promoters (> 4000) is physically bound by BCL6 but that only a fraction of them is repressed in GC B cells. This set of 1207 targets identifies several cellular functions directly controlled by BCL6 during GC development, including activation, survival, DNA-damage response, cell cycle arrest, cytokine signaling, Toll-like receptor signaling, and differentiation. These results define a broad role of BCL6 in preventing centroblasts from responding to signals leading to exit from the GC before they complete the phase of proliferative expansion and of antibody affinity maturation.

Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy

To identify regulatory drivers of prostate cancer malignancy, we have assembled genome-wide regulatory networks (interactomes) for human and mouse prostate cancer from expression profiles of human tumors and of genetically engineered mouse models, respectively. Cross-species computational analysis of these interactomes has identified FOXM1 and CENPF as synergistic master regulators of prostate cancer malignancy. Experimental validation shows that FOXM1 and CENPF function synergistically to promote tumor growth by coordinated regulation of target gene expression and activation of key signaling pathways associated with prostate cancer malignancy. Furthermore, co-expression of FOXM1 and CENPF is a robust prognostic indicator of poor survival and metastasis. Thus, genome-wide cross-species interrogation of regulatory networks represents a valuable strategy to identify causal mechanisms of human cancer.

Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a framework for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. We tested this framework by identifying the genetic determinants of the mesenchymal subtype of glioblastoma. Our analysis uncovered KLHL9 deletions as upstream activators of two previously established master regulators of the subtype, C/EBPβ and C/EBPδ. Rescue of KLHL9 expression induced proteasomal degradation of C/EBP proteins, abrogated the mesenchymal signature, and reduced tumor viability in vitro and in vivo. Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype. The method generalized to study other human diseases, including breast cancer and Alzheimers disease.

A community computational challenge to predict the activity of pairs of compounds

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

PFT1, the MED25 subunit of the plant Mediator complex, promotes flowering through CONSTANS dependent and independent mechanisms in Arabidopsis

Two aspects of light are very important for plant development: the length of the light phase or photoperiod and the quality of incoming light. Photoperiod detection allows plants to anticipate the arrival of the next season, whereas light quality, mainly the red to far-red ratio (R:FR), is an early signal of competition by neighbouring plants. phyB represses flowering by antagonising CO at the transcriptional and post-translational levels. A low R:FR decreases active phyB and consequently increases active CO, which in turn activates the expression of FT, the plant florigen. Other phytochromes like phyD and phyE seem to have redundant roles with phyB. PFT1, the MED25 subunit of the plant Mediator complex, has been proposed to act in the light-quality pathway that regulates flowering time downstream of phyB. However, whether PFT1 signals through CO and its specific mechanism are unclear. Here we show that CO-dependent and -independent mechanisms operate downstream of phyB, phyD and phyE to promote flowering, and that PFT1 is equally able to promote flowering by modulating both CO-dependent and -independent pathways. Our data are consistent with the role of PFT1 as an activator of CO transcription, and also of FT transcription, in a CO-independent manner. Our transcriptome analysis is also consistent with CO and FT genes being the most important flowering targets of PFT1. Furthermore, comparison of the pft1 transcriptome with transcriptomes after fungal and herbivore attack strongly suggests that PFT1 acts as a hub, integrating a variety of interdependent environmental stimuli, including light quality and jasmonic acid-dependent defences.

Functional characterization of somatic mutations in cancer using network-based inference of protein activity

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.