Michela Mattioli

A community computational challenge generates algorithms to predict activity of drug combinations. 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.

REV-ERBα and REV-ERBβ nuclear receptors regulate several physiological processes, including circadian rhythm and metabolism. A previous study reported the REV-ERBα gene to be co-overexpressed with ERBB2 in breast cancer cell lines. Surprisingly, we found that several tumor types, including a number of breast cancer cell lines, predominantly express the REV-ERBβ variant. This pattern was independent of ERBB2 and ER status, and opposite to that of non-cancer mammary epithelial HMEC cells, in which REV-ERBα was the major variant. Consistent with this molecular profile, REV-ERB target genes in both circadian and metabolic pathways were derepressed upon silencing of REV-ERBβ, but not REV-ERBα. Strikingly, we found that REV-ERBβ is a determinant of sensitivity to chloroquine, a clinically relevant lysosomotropic agent that suppresses autophagy. The cytoprotective function of REV-ERBβ appears to operate downstream of autophagy blockade. Through compound screening, we identified ARN5187, a novel lysosomotropic REV-ERBβ ligand with a dual inhibitory activity toward REV-ERB-mediated transcriptional regulation and autophagy. Remarkably, although ARN5187 and chloroquine share similar lysosomotropic potency and have a similar effect on autophagy inhibition, ARN5187 is significantly more cytotoxic. Collectively, our results reveal that dual inhibition of REV-ERBβ and autophagy is an effective strategy for eliciting cytotoxicity in cancer cells. Furthermore, our discovery of a novel inhibitor compound of both REV-ERB and autophagy may provide a scaffold for the discovery of new multifunctional anticancer agents.

The goal of combination therapy is to reduce the onset of drug resistance, while inducing a synergistic therapeutic effect, akin to synthetic lethality. Specifically, the measured reduction in tumor viability induced by a synergistic compound combination should be higher than expected based on additive behavior. Yet, assessing the synthetic lethality potential of a large collections of compounds is impractical, and the ability to predict drug synergy and synthetic lethality using quantitative approaches has not been experimentally validated. In addition, synthetic lethality has been shown to be cell-context dependent. To address these issues, we propose and experimentally validate a novel computational framework to efficiently explore the space of synthetic lethality in context specific fashion, to predict synergistic compound combinations. The reconstruction of accurate, context-specific molecular regulatory networks has shown significant potential in the rational elucidation of synergistic regulatory mechanisms in the cell. Our method leverages cell context-specific regulatory networks dissected by the ARACNe algorithm to infer changes in the activity of regulatory proteins (transcription factors and signaling proteins) based on the expression of their target genes. Specifically, we infer changes in protein activity, rather than in their corresponding gene expression, because most small molecule compounds modulate the activity but not the expression of their target proteins. We define a compound's functional Mode of Action (fMoA) as the specific protein activity signature induced by the compound in a specific cell type. To predict synergistic compounds, we propose two alternative approaches. (1) Target Molecular Phenotype (TMP): we first define a desired TMP to be induced by compound combination (e.g. cell death). We then leverage large collections of expression profiles following compound perturbations, such as the Connectivity Map (CMAP), to identify compounds whose combined fMoA most closely matches the TMP. We further prioritize compound pairs based on (a) the orthogonality of their individual fMoA, on the assumption that compounds with the same fMoA will affect the same pathways and thus will have a more additive behavior and (b) the complementary coverage of the TMP by their fMoA (i.e. compounds that implement distinct and complementary components of the TMP). (2) Sensitivity Induction (SI): we first obtain a protein activity signature of sensitivity to a given compound C_A, by comparing the gene expression profiles of C_A-sensitive vs C_A-resistant cells. We then search for other compounds, whose fMoA most closely matches the C_A's sensitivity signature, which are thus likely to act as sensitizers. We test the methods by using CMAP, as well as a dataset generated in our lab by profiling a set of three diffuse large B cell lymphoma (DLBCL) cell lines at 6h, 12h and 24h following perturbation with 92 drugs. We found that fMoA accurately captures the molecular targets of the assayed compounds. We then tested the synergistic predictions by measuring their overlap with all the pairs of 14 distinct compounds assessed in DLBCL cells (DREAM8 challenge data). For this analysis, we used both a general B-cell toxicity signature and an ABC-DLBCL-specific tumor progression signature as TMP. Finally, we studied the use of the SI approach to identify compounds that are candidate sensitizer of estrogen therapy (tamoxifen) in luminal ER+ breast tumors. This abstract is also presented as Poster A38. Citation Format: Mariano J. Alvarez, Yao Shen, Charles Karan, Mukesh Bansal, Michela Mattioli, Sergey Pampou, Andrea Califano. A network-based approach for drug synergy prediction from gene expression data. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR04.

Though extensive studies have been conducted, questions regarding the molecular effectors and pathways underlying the regulatory role of 1,25(OH)₂D₃ in human osteoblasts other than cell differentiation and matrix protein production remain unanswered. This study aims to identify genes and pathways that are modulated by 1,25(OH)₂D₃ treatment in human osteoblasts. Primary osteoblast cultures obtained from human bone tissue samples were treated with 1,25(OH)₂D₃ (10⁻⁷ M) for 24 h and their transcritptomes were profiled by microarray analysis using the Affymetrix GeneChip®. Statistical analysis was conducted to identify genes whose expression is significantly modulated following 1,25(OH)₂D₃ treatment. One hundred and fifty‐eight genes were found to be differentially expressed. Of these, 136 were upregulated, indicating clear transcriptional activation by 1,25(OH)₂D₃. Biostatistical evaluation of microarray data by Ingenuity Pathways Analysis (IPA) revealed a relevant modulation of genes involved in vitamin D metabolism (CYP24), immune functions (CD14), neurotransmitter transporters (SLC1A1, SLC22A3), and coagulation [thrombomodulin (THBD), tissue plasminogen activator (PLAT), endothelial protein C receptor (PROCR), thrombin receptor (F2R)]. We identified a restricted number of highly regulated genes and confirmed their differential expression by real‐time quantitative PCR (RT qPCR). The present genome‐wide microarray analysis on 1,25(OH)₂D₃‐treated human osteoblasts reveals an interplay of critical regulatory and metabolic pathways and supports the hypothesis that 1,25(OH)₂D₃ can modulate the coagulation process through osteoblasts, activates osteoclastogenesis through inflammation signaling, modulates the effects of monoamines by affecting their reuptake. J. Cell. Biochem. 113: 640–649, 2012.

Proliferation/survival pathways constitutively activated by genetic alterations in multiple myeloma (MM), or sustained by the bone marrow (BM) microenvironment, provide opportunities for the development of novel targeted therapies. Deregulated protein tyrosine kinases (TKs) involved in these pathways may play a critical role in driving the MM malignant phenotype thus representing attractive therapeutic targets. We have investigated the effects of a multi‐target tyrosine kinase inhibitor (1,3‐dihydro‐5,6‐dimethoxy‐3‐[(4‐hydrophenyl)methylene]1‐indol‐2‐one) (named RPI‐1 in our laboratory) in a panel of human MM cell lines, including t(4;14) positive cell lines expressing the TK receptor FGF‐R3. Cells harboring FGF‐R3 activating mutations (KMS11 and OPM2) displayed the highest sensitivity to the drug antiproliferative effect. However, proliferation induced by the FGF‐R3 ligand, aFGF, was abrogated even in cells harboring a non‐constitutively active receptor. Drug treatment inhibited activation and expression of the FGF‐R3^Y373C mutant as well as aFGF‐dependent signaling involving AKT and ERKs. Jak2 has been identified as an additional target of RPI‐1 in MM cells. Inhibition of Jak2 TK resulted in STAT3 inactivation. Blockade of these proliferation/survival pathways was associated with activation of caspase‐dependent apoptosis. Moreover, the TK inhibitor abrogated proliferative and pro‐invasive stimuli provided by conditioned medium from mesenchymal stromal cells. Gene expression analysis of KMS11 cells showed 22 upregulated and 52 downregulated genes upon RPI‐1 treatment, with an early modulation of genes implicated in MM pathobiology such as SAT‐1, MYC, MIP‐1α/β, FGF‐R3, and the growth factor receptor B‐cell maturation antigen (BCMA). Thus, concomitant blockade of FGF‐R3 and Jak2 results in inhibition of several MM‐promoting pathways, including BCMA‐regulated signaling, and downregulation of disease‐associated proteins. Overall, these data support the therapeutic interest for FGF‐R3/JAK2 multi‐target approaches in the treatment of t(4;14) MM. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B224.

Purpose: To determine a “gene/molecular fingerprint” of multiple myeloma endothelial cells and identify vascular mechanisms governing the malignant progression from quiescent monoclonal gammopathy of undetermined significance. Experimental Design: Comparative gene expression profiling of multiple myeloma endothelial cells and monoclonal gammopathy of undetermined significance endothelial cells with the Affymetrix U133A Arrays was carried out in patients at diagnosis; expression and function of selective vascular markers was validated by real-time reverse transcriptase-PCR, Western blot, and small interfering RNA analyses. Results: Twenty-two genes were found differentially expressed (14 down-regulated and eight up-regulated) at relatively high stringency in multiple myeloma endothelial cells compared with monoclonal gammopathy of undetermined significance endothelial cells. Functional annotation revealed a role of these genes in the regulation of extracellular matrix formation and bone remodeling, cell adhesion, chemotaxis, angiogenesis, resistance to apoptosis, and cell-cycle regulation. Validation was focused on six genes (DIRAS3, SERPINF1, SRPX, BNIP3, IER3, and SEPW1) not previously found to be functionally correlated to the overangiogenic phenotype of multiple myeloma endothelial cells in active disease. The small interfering RNA knockdown of BNIP3, IER3, and SEPW1 genes affected critical multiple myeloma endothelial cell functions correlated with the overangiogenic phenotype. Conclusions: The distinct endothelial cell gene expression profiles and vascular phenotypes detected in this study may influence remodeling of the bone marrow microenvironment in patients with active multiple myeloma. A better understanding of the linkage between plasma cells and endothelial cells in multiple myeloma could contribute to the molecular classification of the disease and thus pinpoint selective gene targets for more effective antiangiogenic treatments. (Clin Cancer Res 2009;15(17):5369–78)

To investigate the patterns of genetic lesions in a panel of 23 human multiple myeloma cell lines (HMCLs), we made a genomic integrative analysis involving FISH, and both gene expression and genome‐wide profiling approaches. The expression profiles of the genes targeted by the main IGH translocations showed that the WHSC1/MMSET gene involved in t(4;14)(p16;q32) was expressed at different levels in all of the HMCLs, and that the expression of the MAF gene was not restricted to the HMCLs carrying t(14;16)(q32;q23). Supervised analyses identified a limited number of genes specifically associated with t(4;14) and involved in different biological processes. The signature related to MAF/MAFB expression included the known MAF target genes CCND2 and ITGB7, as well as genes controlling cell shape and cell adhesion. Genome‐wide DNA profiling allowed the identification of a gain on chromosome arm 1q in 88% of the analyzed cell lines, together with recurrent gains on 8q, 18q, 7q, and 20q; the most frequent deletions affected 1p, 13q, 17p, and 14q; and almost all of the cell lines presented LOH on chromosome 13. Two hundred and twenty‐two genes were found to be simultaneously overexpressed and amplified in our panel, including the BCL2 locus at 18q21.33. Our data further support the evidence of the genomic complexity of multiple myeloma and reinforce the role of an integrated genomic approach in improving our understanding of the molecular pathogenesis of the disease. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045‐2257/suppmat.

Anaplastic large cell lymphomas (ALCLs) represent a subset of lymphomas in which the anaplastic lymphoma kinase (ALK) gene is frequently fused to the nucleophosmin (NPM) gene. We previously demonstrated that the constitutive phosphorylation of ALK chimeric proteins is sufficient to induce cellular transformation in vitro and in vivo and that ALK activity is strictly required for the survival of ALK-positive ALCL cells. To elucidate the signaling pathways required for ALK-mediated transformation and tumor maintenance, we analyzed the transcriptomes of multiple ALK-positive ALCL cell lines, abrogating their ALK-mediated signaling by inducible ALK RNA interference (RNAi) or with potent and cell-permeable ALK inhibitors. Transcripts derived from the gene expression profiling (GEP) analysis uncovered a reproducible signature, which included a novel group of ALK-regulated genes. Functional RNAi screening on a set of these ALK transcriptional targets revealed that the transcription factor C/EBPβ and the antiapoptotic protein BCL2A1 are absolutely necessary to induce cell transformation and/or to sustain the growth and survival of ALK-positive ALCL cells. Thus, we proved that an experimentally controlled and functionally validated GEP analysis represents a powerful tool to identify novel pathogenetic networks and validate biologically suitable target genes for therapeutic interventions.