Utilization of genomic signatures to identify phenotype-specific drugs

The effects of metformin on the hippo pathway in the proliferation of melanoma cancer cells: a preclinical study

YAP and TAZ, two closely related transcriptional regulators, have crucial roles in tissue repair upon injury, organ size control, and cancer treatment. Some drugs, such as metformin, that alter cell metabolism can play a role in the regulation of the Hippo pathway. The cells were treated with various concentrations of metformin, dacarbazine (IC₅₀), and both of them. The evaluation of the biomarker and proteins was performed by FACS and immunoblotting, respectively. Cell viability was reduced by 50% after 24 h. Data showed that metformin treatment down-regulated YAP and TAZ (p = .002) expressions and enhanced YAP phosphorylation (p < .001). Metformin, alone and in combination, inhibited the growth and viability of melanoma cells in vitro. The increase in the phosphorylation of YAP renders it a potential target in the development of anticancer drugs. This study showed the effects of metformin on the inhibition of oncogenic YAP and TAZ in the proliferation of melanoma cells.

Molecular Signatures of Radiation Response in Breast Cancer: Towards Personalized Decision-Making in Radiation Treatment

Recent advances in gene expression profiling have allowed for a more sophisticated understanding of the biology of breast cancers. These advances led to the development of molecular signatures that now allow clinicians to more individually tailor recommendations regarding the utility and necessity of systemic therapies for women with breast cancer. Indeed, these molecularly based tests have been incorporated into national and international best practice guidelines and are now part of routine practice. Similar, though slower, progress is being made in the development of molecular signatures predictive of radiation response and necessity for women with breast cancer. This article will discuss the history of radiation response signature development, the current state of these signatures under ongoing clinical development, the barriers to their clinical adoption, and upcoming changes and opportunities that may allow for the personalized radiation treatment recommendations enabled by the development of these signatures.

MDP, a database linking drug response data to genomic information, identifies dasatinib and statins as a combinatorial strategy to inhibit YAP/TAZ in cancer cells

Targeted anticancer therapies represent the most effective pharmacological strategies in terms of clinical responses. In this context, genetic alteration of several oncogenes represents an optimal predictor of response to targeted therapy. Integration of large-scale molecular and pharmacological data from cancer cell lines promises to be effective in the discovery of new genetic markers of drug sensitivity and of clinically relevant anticancer compounds. To define novel pharmacogenomic dependencies in cancer, we created the Mutations and Drugs Portal (MDP, http://mdp.unimore.it), a web accessible database that combines the cell-based NCI60 screening of more than 50,000 compounds with genomic data extracted from the Cancer Cell Line Encyclopedia and the NCI60 DTP projects. MDP can be queried for drugs active in cancer cell lines carrying mutations in specific cancer genes or for genetic markers associated to sensitivity or resistance to a given compound. As proof of performance, we interrogated MDP to identify both known and novel pharmacogenomics associations and unveiled an unpredicted combination of two FDA-approved compounds, namely statins and Dasatinib, as an effective strategy to potently inhibit YAP/TAZ in cancer cells.

Control of YAP/TAZ Activity by Metabolic and Nutrient-Sensing Pathways

Metabolism is a fundamental cellular function that can be reprogrammed by signaling pathways and oncogenes to meet cellular requirements. An emerging paradigm is that signaling and transcriptional networks can be in turn regulated by metabolism, allowing cells to coordinate their metabolism and behavior in an integrated manner. The activity of the YAP/TAZ transcriptional coactivators, downstream transducers of the Hippo cascade and powerful pro-oncogenic factors, was recently found to be regulated by metabolic pathways, such as aerobic glycolysis and mevalonate synthesis, and by the nutrient-sensing LKB1-AMPK and TSC-mTOR pathways. We discuss here current data linking YAP/TAZ to metabolism and suggest how this coupling might coordinate nutrient availability with genetic programs that sustain tissue growth, neoplastic cell proliferation, and tumor malignancy.

Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

Improving Drug Sensitivity Prediction Using Different Types of Data

The algorithms and models used to address the two subchallenges that are part of the NCI-DREAM (Dialogue for Reverse Engineering Assessments and Methods) Drug Sensitivity Prediction Challenge (2012) are presented. In subchallenge 1, a bidirectional search algorithm is introduced and optimized using an ensemble scheme and a nonlinear support vector machine (SVM) is then applied to predict the effects of the drug compounds on breast cancer cell lines. In subchallenge 2, a weighted Euclidean distance method is introduced to predict and rank the drug combinations from the most to the least effective in reducing the viability of a diffuse large B-cell lymphoma (DLBCL) cell line.

Metabolic Signatures of Human Breast Cancer

Metabolomics has emerged as a new discovery tool with the promise of identifying therapeutic targets in cancer. Recent discoveries have described essential metabolomic pathways in breast cancer and characterized oncometabolites that drive tumor growth and progression. Oncogenes like MYC and tumor suppressor genes like TP53 prominently affect breast cancer biology through regulation of cell metabolism and mitochondrial biogenesis. These findings indicate that tumors with dominant mutations could be susceptible to inhibitors of disease metabolism. Moreover, various preclinical and clinical studies have linked tumor metabolism to therapeutic response and patient survival. Thus, recent advances suggest that metabolic profiling provides new opportunities to improve outcomes in breast cancer. In this review we summarize some of the identified roles of oncometabolites in breast cancer biology and highlight their clinical utility.

Statin induces inhibition of triple negative breast cancer (TNBC) cells via PI3K pathway

Primary TNBCs are treated as if they were a single disease entity, yet it is clear they do not behave as a single entity in response to current therapies. Recently, we reported that statins might have a potential benefit for TNBCs associated with ets-1 overexpression. The aim of this study is to investigate the role of PTEN loss in the effects of statin on TNBC cells. In addition, we analyze the relationship between AKT downstream pathways and the effects of statin on TNBC cells. We investigated the effect of a statin on TNBC cells and analyzed the association of PI3K pathways using various TNBC cells in terms of PTEN loss and AKT pathways. Simvastatin treatments resulted in decreased cell viabilities in various TNBC cell lines. Compared with PTEN wild-type TNBC cells, PTEN mutant-type TNBC cells showed a decreased response to simvastatin. Expressions of phosphorylated Akt and total Akt showed an inverse relationship with PTEN expression. The TNBC cell lines, which showed increased expression of p-Akt, appeared to attenuate the expression of p-Akt by PTEN loss in simvastatin-treated TNBC cells. The Akt inhibitor, LY294002, augmented the effect of simvastatin on PTEN wild-type TNBC cells. Simvastatin induces inhibition of TNBC cells via PI3K pathway activation.

Construction of cell type-specific logic models of signaling networks using CellNOpt

Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.

Novel oncogene-induced metastatic prostate cancer cell lines define human prostate cancer progression signatures

Herein, murine prostate cancer cell lines, generated via selective transduction with a single oncogene (c-Myc, Ha-Ras, and v-Src), showed oncogene-specific prostate cancer molecular signatures that were recapitulated in human prostate cancer and developed lung metastasis in immune-competent mice. Interrogation of two independent retrospective cohorts of patient samples using the oncogene signature showed an ability to distinguish tumor from normal prostate with a predictive value for prostate cancer of 98% to 99%. In a blinded study, the signature algorithm showed independent substratification of reduced recurrence-free survival by Kaplan-Meier analysis. The generation of new oncogene-specific prostate cancer cell lines that recapitulate human prostate cancer gene expression, which metastasize in immune-competent mice, are a valuable new resource for testing targeted therapy, whereas the molecular signatures identified herein provides further value over current gene signature markers of prediction and outcome.

Behavioral screening for neuroactive drugs in zebrafish

The larval zebrafish has emerged asa vertebrate model system amenable to small molecule screens for probing diverse biological pathways. Two large-scale small molecule screens examined the effects of thousands of drugs on larval zebrafish sleep/wake and photomotor response behaviors. Both screens identified hundreds of molecules that altered zebrafish behavior in distinct ways. The behavioral profiles induced by these small molecules enabled the clustering of compounds according to shared phenotypes. This approach identified regulators of sleep/wake behavior and revealed the biological targets for poorly characterized compounds. Behavioral screening for neuroactive small molecules in zebrafish is an attractive complement to in vitro screening efforts, because the complex interactions in the vertebrate brain can only be revealed in vivo.

Gene signature critical to cancer phenotype as a paradigm for anticancer drug discovery

Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called 'cooperation response genes' (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here, we show that CRGs have an essential role in drug-mediated anticancer activity and that anticancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anticancer drug mechanisms.

Mutant p53 disrupts mammary tissue architecture via the mevalonate pathway

p53 is a frequent target for mutation in human tumors, and mutant p53 proteins can actively contribute to tumorigenesis. We employed a three-dimensional culture model in which nonmalignant breast epithelial cells form spheroids reminiscent of acinar structures found in vivo, whereas breast cancer cells display highly disorganized morphology. We found that mutant p53 depletion is sufficient to phenotypically revert breast cancer cells to a more acinar-like morphology. Genome-wide expression analysis identified the mevalonate pathway as significantly upregulated by mutant p53. Statins and sterol biosynthesis intermediates reveal that this pathway is both necessary and sufficient for the phenotypic effects of mutant p53 on breast tissue architecture. Mutant p53 associates with sterol gene promoters at least partly via SREBP transcription factors. Finally, p53 mutation correlates with highly expressed sterol biosynthesis genes in human breast tumors. These findings implicate the mevalonate pathway as a therapeutic target for tumors bearing mutations in p53.

Integration and analysis of genome-scale data from gliomas

Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.

Effect of simvastatin on cetuximab resistance in human colorectal cancer with KRAS mutations

BACKGROUND

Metastatic colorectal cancer (CRC) patients with v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations are resistant to treatment with cetuximab, a monoclonal antibody that targets the epidermal growth factor receptor. Statins have reported antitumor activity, but it is unknown whether simvastatin can reverse cetuximab resistance in KRAS mutant CRC.

METHODS

Human CRC cell lines with KRAS mutations (LS153, LS174T, DLD1, LoVo, SW403, SW480, SNU175, and LS1034) or with v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations (DiFi, SW48, HT29, and RKO) were used to test the effect of cetuximab, simvastatin, and cetuximab plus simvastatin on cell proliferation and apoptosis in vitro. Because BRAF(V600E) mutant may be responsible for cetuximab resistance in KRAS wild-type cells, we measured the growth of xenograft tumors originating from KRAS mutant and BRAF mutant cells in mice treated with cetuximab alone or plus simvastatin (n = 5 mice per treatment group). We used immunoblot assays to study RAS-regulated activation of BRAF protein after simvastatin treatment. All statistical tests were two-sided.

RESULTS

Addition of simvastatin (0.2 μM) to cetuximab (0.03-1.0 μM) reduced cell proliferation of KRAS mutant (P < .001) but not of BRAF mutant CRC cells in vitro. Treatment of KRAS mutant cells with simvastatin reduced BRAF activity and induced apoptosis. Treatment with cetuximab and simvastatin reduced the growth of xenograft tumors originating from KRAS mutant cells compared with cetuximab alone (eg, for tumors originating from DLD1 cells, cetuximab vs cetuximab + simvastatin, mean tumor volume = 49.4 vs 20.2 cm(3), mean difference = 29.2 cm(3), 95% confidence interval = 19.7 to 38.5, P < .001); treatment with cetuximab alone or in combination with simvastatin had no effect on the growth of BRAF mutant tumors.

CONCLUSION

Simvastatin may overcome cetuximab resistance in colon cancer cells with KRAS mutations by modulating BRAF activity and inducing apoptosis.

Using a stem cell-based signature to guide therapeutic selection in cancer

Given the very substantial heterogeneity of most human cancers, it is likely that most cancer therapeutics will be active in only a small fraction of any population of patients. As such, the development of new therapeutics, coupled with methods to match a therapy with the individual patient, will be critical to achieving significant gains in disease outcome. One such opportunity is the use of expression signatures to identify key oncogenic phenotypes that can serve not only as biomarkers but also as a means of identifying therapeutic compounds that might specifically target these phenotypes. Given the potential importance of targeting tumors exhibiting a stem-like phenotype, we have developed an expression signature that reflects common biological aspects of various stem-like characteristics. The consensus stemness ranking (CSR) signature is upregulated in cancer stem cell-enriched samples at advanced tumor stages and is associated with poor prognosis in multiple cancer types. Using two independent computational approaches we utilized the CSR signature to identify clinically useful compounds that could target the CSR phenotype. In vitro assays confirmed selectivity of several predicted compounds including topoisomerase inhibitors and resveratrol towards breast cancer cell lines that exhibit a high-CSR phenotype. Importantly, the CSR signature could predict clinical response of breast cancer patients to a neoadjuvant regimen that included a CSR-specific agent. Collectively, these results suggest therapeutic opportunities to target the CSR phenotype in a relevant cohort of cancer patients.

Predicting in vitro drug sensitivity using Random Forests

MOTIVATION

Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART).

RESULTS

This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable.

IMPLEMENTATION

Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.

Breast cancer genomes--form and function

This review summarizes advances in our understanding of the genomic and epigenomic abnormalities in breast cancers that are being revealed by the increasingly powerful suite of genomic analysis technologies. It summarizes the remarkable genomic heterogeneity that characterizes the disease, describes mechanisms that shape cancer genomes as they evolve toward metastasis, summarizes important recurrent aberrations that exist in spite of the genomic chaos and that contribute to breast cancer pathophysiology, and describes the use of preclinical models to identify drugs that will be effective against subsets of breast cancers carrying specific genomic and epigenomic abnormalities.