Only a fraction of patients with metastatic colorectal cancer receive clinical benefit from therapy with anti-epidermal growth factor receptor (EGFR) antibodies, which calls for the identification of novel biomarkers for better personalized medicine. We produced large xenograft cohorts from 85 patient-derived, genetically characterized metastatic colorectal cancer samples (“xenopatients”) to discover novel determinants of therapeutic response and new oncoprotein targets. Serially passaged tumors retained the morphologic and genomic features of their original counterparts. A validation trial confirmed the robustness of this approach: xenopatients responded to the anti-EGFR antibody cetuximab with rates and extents analogous to those observed in the clinic and could be prospectively stratified as responders or nonresponders on the basis of several predictive biomarkers. Genotype–response correlations indicated HER2 amplification specifically in a subset of cetuximab-resistant, KRAS/NRAS/BRAF/PIK3CA wild-type cases. Importantly, HER2 amplification was also enriched in clinically nonresponsive KRAS wild-type patients. A proof-of-concept, multiarm study in HER2-amplified xenopatients revealed that the combined inhibition of HER2 and EGFR induced overt, long-lasting tumor regression. Our results suggest promising therapeutic opportunities in cetuximab-resistant patients with metastatic colorectal cancer, whose medical treatment in the chemorefractory setting remains an unmet clinical need. Significance: Direct transfer xenografts of tumor surgical specimens conserve the interindividual diversity and the genetic heterogeneity typical of the tumors of origin, combining the flexibility of preclinical analysis with the informative value of population-based studies. Our suite of patient-derived xenografts from metastatic colorectal carcinomas reliably mimicked disease response in humans, prospectively recapitulated biomarker-based case stratification, and identified HER2 as a predictor of resistance to anti-epidermal growth factor receptor antibodies and of response to combination therapies against HER2 and epidermal growth factor receptor in this tumor setting. Cancer Discovery; 1(6); 508–23. ©2011 AACR. Read the Commentary on this article by Ciardiello and Normanno, p. 472 This article is highlighted in the In This Issue feature, p. 457

MicroRNAs are endogenous non-coding RNAs which negatively regulate the expression of protein-coding genes in plants and animals. They are known to play an important role in several biological processes and, together with transcription factors, form a complex and highly interconnected regulatory network. Looking at the structure of this network, it is possible to recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions. Among them, a special role is played by the microRNA-mediated feedforward loop in which a master transcription factor regulates a microRNA and, together with it, a set of target genes. In this paper we show analytically and through simulations that the incoherent version of this motif can couple the fine-tuning of a target protein level with an efficient noise control, thus conferring precision and stability to the overall gene expression program, especially in the presence of fluctuations in upstream regulators. Among the other results, a nontrivial prediction of our model is that the optimal attenuation of fluctuations coincides with a modest repression of the target expression. This feature is coherent with the expected fine-tuning function and in agreement with experimental observations of the actual impact of a wide class of microRNAs on the protein output of their targets. Finally, we describe the impact on noise-buffering efficiency of the cross-talk between microRNA targets that can naturally arise if the microRNA-mediated circuit is not considered as isolated, but embedded in a larger network of regulations. The expression of protein-coding genes is controlled by a complex network of regulatory interactions. It is becoming increasingly appreciated that the post-transcriptional repression by microRNAs, a class of small non-coding RNAs, is a key layer of regulation in several biological processes. Since gene expression is a fundamentally stochastic process, the mixed network (comprising transcriptional and microRNA-mediated regulations) has to reliably perform its functions in the presence of noise. In this paper we investigate the function of one of the recurrent architectures of this network, the microRNA-mediated feedforward loops, using a detailed analytical model and simulations. With this approach we show that these regulatory circuits are appropriately designed so as to control noise, giving a rigorous mathematical proof of a previously proposed biological intuition. Moreover the theoretical framework introduced in this paper allows us to make nontrivial predictions that are presently in agreement with observed features of microRNA regulation and that could be more specifically tested experimentally in the future.

The incidence of melanoma is increasing over the years with a still poor prognosis and the lack of a cure able to guarantee an adequate survival of patients. Although the new immuno-based coupled to target therapeutic strategy is encouraging, the appearance of targeted/cross-resistance and/or side effects such as autoimmune disorders could limit its clinical use. Alternative therapeutic strategies are therefore urgently needed to efficiently kill melanoma cells. Ferroptosis induction and execution were evaluated in metastasis-derived wild-type and oncogenic BRAF melanoma cells, and the process responsible for the resistance has been dissected at molecular level. Although efficiently induced in all cells, in an oncogenic BRAF- and ER stress-independent way, most cells were resistant to ferroptosis execution. At molecular level we found that: resistant cells efficiently activate NRF2 which in turn upregulates the early ferroptotic marker CHAC1, in an ER stress-independent manner, and the aldo-keto reductases AKR1C1 ÷ 3 which degrades the 12/15-LOX-generated lipid peroxides thus resulting in ferroptotic cell death resistance. However, inhibiting AKRs activity/expression completely resensitizes resistant melanoma cells to ferroptosis execution. Finally, we found that the ferroptotic susceptibility associated with the differentiation of melanoma cells cannot be applied to metastatic-derived cells, due to the EMT-associated gene expression reprogramming process. However, we identified SCL7A11 as a valuable marker to predict the susceptibility of metastatic melanoma cells to ferroptosis. Our results identify the use of pro-ferroptotic drugs coupled to AKRs inhibitors as a new valuable strategy to efficiently kill human skin melanoma cells.

Background: Transcription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them. Description: Here we describe a dynamic web-accessible database, , supporting a genome-wide transcriptional and post-transcriptional regulatory network integration, for the human and mouse genomes, based on a bioinformatic sequence-analysis approach. In particular, is currently focused on the study of mixed miRNA/TF Feed-Forward regulatory Loops (FFLs), i.e. elementary circuits in which a master TF regulates an miRNA and together with it a set of Joint Target protein-coding genes. The database was constructed using an ab-initio oligo analysis procedure for the identification of the transcriptional and post-transcriptional interactions. Several external sources of information were then pooled together to obtain the functional annotation of the proposed interactions. Results for human and mouse genomes are presented in an integrated web tool, that allows users to explore the circuits, investigate their sequence and functional properties and thus suggest possible biological experiments. Conclusions: We present , a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: http://biocluster.di.unito.it/circuits/

Purpose: We determined the gene copy numbers for MET, for its transcriptional activator MACC1 and for its ligand hepatocyte growth factor (HGF) in liver metastases from colorectal carcinoma (mCRC). We correlated copy numbers with mRNA levels and explored whether gain and/or overexpression of MET and MACC1 predict response to anti-Met therapies. Finally, we assessed whether their genomic or transcriptional deregulation correlates with pathologic and molecular parameters of aggressive disease. Experimental Design: One hundred three mCRCs were analyzed. Copy numbers and mRNA were determined by quantitative PCR (qPCR). Thirty nine samples were implanted and expanded in NOD (nonobese diabetic)/SCID (severe combined immunodeficient) mice to generate cohorts that were treated with the Met inhibitor JNJ-38877605. In silico analysis of MACC1 targets relied on genome-wide mapping of promoter regions and on expression data from two CRC datasets. Results: No focal, high-grade amplifications of MET, MACC1, or HGF were detected. Chromosome 7 polysomy and gain of the p-arm were observed in 21% and 8% of cases, respectively, and significantly correlated with higher expression of both Met and MACC1. Met inhibition in patient-derived xenografts did not modify tumor growth. Copy number gain and overexpression of MACC1 correlated with unfavorable pathologic features better than overexpression of Met. Bioinformatic analysis of putative MACC1 targets identified elements besides Met, whose overexpression cosegregated with aggressive forms of colorectal cancer. Conclusions: Experiments in patient-derived xenografts suggest that mCRCs do not rely on Met genomic gain and/or overexpression for growth. On the basis of pathologic correlations and bioinformatic analysis, MACC1 could contribute to CRC progression through mechanisms other than or additional to Met transcriptional upregulation. Clin Cancer Res; 17(10); 3146–56. ©2011 AACR.

In this work, we describe a computational framework for the genome-wide identification and characterization of mixed transcriptional/post-transcriptional regulatory circuits in humans. We concentrated in particular on feed-forward loops (FFL), in which a master transcription factor regulates a microRNA, and together with it, a set of joint target protein coding genes. The circuits were assembled with a two step procedure. We first constructed separately the transcriptional and post-transcriptional components of the human regulatory network by looking for conserved over-represented motifs in human and mouse promoters, and 3′-UTRs. Then, we combined the two subnetworks looking for mixed feed-forward regulatory interactions, finding a total of 638 putative (merged) FFLs. In order to investigate their biological relevance, we filtered these circuits using three selection criteria: (I) GeneOntology enrichment among the joint targets of the FFL, (II) independent computational evidence for the regulatory interactions of the FFL, extracted from external databases, and (III) relevance of the FFL in cancer. Most of the selected FFLs seem to be involved in various aspects of organism development and differentiation. We finally discuss a few of the most interesting cases in detail.