Melanoma cells have the ability to switch to a dedifferentiated, invasive phenotype in response t... more Melanoma cells have the ability to switch to a dedifferentiated, invasive phenotype in response to multiple stimuli. Here, we show that exposure of melanomas to multiple stresses including BRAF–MEK inhibitor therapy, hypoxia, and UV irradiation leads to an increase in histone deacetylase 8 (HDAC8) activity and the adoption of a drug-resistant phenotype. Mass spectrometry–based phosphoproteomics implicated HDAC8 in the regulation of MAPK and AP-1 signaling. Introduction of HDAC8 into drug-naïve melanoma cells conveyed resistance both in vitro and in vivo. HDAC8-mediated BRAF inhibitor resistance was mediated via receptor tyrosine kinase activation, leading to MAPK signaling. Although HDACs function at the histone level, they also regulate nonhistone substrates, and introduction of HDAC8 decreased the acetylation of c-Jun, increasing its transcriptional activity and enriching for an AP-1 gene signature. Mutation of the putative c-Jun acetylation site at lysine 273 increased transcript...
To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a ... more To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a target-agnostic phenotypic screen of kinase inhibitors with target identification using activity-based protein profiling (ABPP) in which a desthiobiotin-ATP probe was used. We screened 21 SCLC cell lines with known c-MYC amplification status for alterations in viability using a chemical library of 235 small molecule kinase inhibitors. One screen hit compound was interrogated with ABPP, and through this approach we re-identified aurora kinase B as a critical kinase in MYC-amplified SCLC cells. We next extended the platform to a second compound that had activity in SCLC cell lines lacking c-MYC amplification and identified TANK-binding kinase 1 (TBK1), a kinase that affects cell viability, polo-like kinase-1 signaling, G2/M arrest, and apoptosis in SCLC cells lacking MYC amplification. These results demonstrate that phenotypic screening combined with activity-based protein profiling can id...
The mechanisms by which some melanoma cells adapt to Serine/threonine-protein kinase B-Raf (BRAF)... more The mechanisms by which some melanoma cells adapt to Serine/threonine-protein kinase B-Raf (BRAF) inhibitor therapy are incompletely understood. In the present study, we used mass spectrometry-based phosphoproteomics to determine how BRAF inhibition remodeled the signaling network of melanoma cell lines that were BRAF mutant and PTEN null. Short-term BRAF inhibition was associated with marked changes in fibronectin-based adhesion signaling that were PTEN dependent. These effects were recapitulated through BRAF siRNA knockdown and following treatment with chemotherapeutic drugs. Increased fibronectin expression was also observed in mouse xenograft models as well as specimens from melanoma patients undergoing BRAF inhibitor treatment. Analysis of a melanoma tissue microarray showed loss of PTEN expression to predict for a lower overall survival, with a trend for even lower survival being seen when loss of fibronectin was included in the analysis. Mechanistically, the induction of fibr...
Expression microarrays are increasingly used to characterize environmental responses and host-par... more Expression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes. Sequence similarity between ESTs, quantified by the E-value of pair-wise alignment, was used as a surrogate for expected hybridization between corresponding sequences. Using this value as a measure of dissimilarity, sequence ...
The Marine Genomics project is a functional genomics initiative developed to provide a pipeline f... more The Marine Genomics project is a functional genomics initiative developed to provide a pipeline for the curation of Expressed Sequence Tags (ESTs) and gene expression microarray data for marine organisms. It provides a unique clearing-house for marine specific EST and microarray data and is currently available at http://www.marinegenomics.org. The Marine Genomics pipeline automates the processing, maintenance, storage and analysis of EST and microarray data for an increasing number of marine species. It currently contains 19 species databases (over 46,000 EST sequences) that are maintained by registered users from local and remote locations in Europe and South America in addition to the USA. A collection of analysis tools are implemented. These include a pipeline upload tool for EST FASTA file, sequence trace file and microarray data, an annotative text search, automated sequence trimming, sequence quality control (QA/QC) editing, sequence BLAST capabilities and a tool for interacti...
Background-Androgen receptor (AR) plays a critical role in the progression of both androgendepend... more Background-Androgen receptor (AR) plays a critical role in the progression of both androgendependent and androgen-independent prostate cancer (AIPC). Ligand-independent activation of AR in AIPC or castration resistant prostate cancer (CRPC) is often associated with poor prognosis. Recently, tyrosine kinase Ack1 has been shown to regulate AR activity by phosphorylating it at tyrosine 267 and this event was shown to be critical for AIPC growth. However, whether a small molecule inhibitor that can mitigate Ack1 activation is sufficient to abrogate AR activity on AR regulated promoters in androgen-depleted environment is not known. Methods-We have generated two key resources, antibodies that specifically recognize pTyr267-AR and synthesized a small molecule inhibitor of Ack1, 4-amino-5,6-biaryl-furo[2,3d]pyrimidine (named here as AIM-100) to test whether AIM-100 modulates ligand-independent AR activity and inhibits prostate cell growth. Results-Prostate tissue microarray analysis indicates that Ack1 Tyr284 phosphorylation correlates positively with disease progression and negatively with the survival of prostate cancer patients. Interestingly, neither pTyr267-AR expression nor its transcriptional activation was affected by anti-androgens in activated Ack1 expressing or EGF stimulated prostate cells. However, the Ack1 inhibitor, AIM-100, not only inhibited Ack1 activation but also able to suppress pTyr267-AR phosphorylation, binding of AR to PSA, NKX3.1, and TMPRSS2 promoters, and inhibit AR transcription activity. Conclusion-Ack1 Tyr284 phosphorylation is prognostic of progression of prostate cancer and inhibitors of Ack1 activity could be novel therapeutic agents to treat AIPC.
It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs ... more It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.
The vast amount of biological knowledge accumulated over the years has allowed researchers to ide... more The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.
MicroRNAs (miRNAs) are non-coding RNAs that function as post-transcriptional regulators of tumor ... more MicroRNAs (miRNAs) are non-coding RNAs that function as post-transcriptional regulators of tumor suppressors and oncogenes. Single nucleotide polymorphisms (SNPs) in miRNAs may contribute to carcinogenesis by altering expression of miRNAs and their targets. A G>C polymorphism (rs2910164) in the miR-146a precursor sequence leads to a functional change associated with the risk for numerous malignancies. A role for this SNP in glioma pathogenesis has not yet been examined. We investigated whether rs2910164 genotypes influence glioma risk and prognosis in a multi-center case-control study comprised of 593 Caucasian glioma cases and 614 community-based controls. Unconditional logistic regression was used to estimate odds ratios
Expression microarray analysis is one of the most popular molecular diagnostic techniques in the ... more Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multiv...
In this project I construct a statistical procedure to infer a biological network of very high di... more In this project I construct a statistical procedure to infer a biological network of very high dimensionality, where microRNAs, small RNAs, are supposed to down-regulate mRNAs, also called target genes. From a statistical point of view, I address this problem by building a network that represents the biological regulatory system, indicating which microRNA regulates which gene. In particular, this method provides a novel graphical modeling approach that includes constraints on the regression coefficients to take into account the down-regulatory e ffect of the network. This approach is able to select single connections in the network, unlike previous methods in the Bayesian variable selection literature, which only allow the selection of covariates (microRNAs) that a ffect either all the genes or none of them. The main challenge of this project is represented by the dimensionality of the data. The stochastic search variable selection algorithm is able to efficiently explore the space of all possible networks and to fi nd, for each gene, which microRNAs have high posterior probability of being down-regulating the gene. To help the selection, I also propose a new prior formulation which is able to integrate di fferent sources of data, by exploiting information from sequence and structure analyses. Because many sources of information are integrated, the model is also able to determine which information is consistent with the data via posterior inference on the parameters de fined in the data integration prior. The proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.
Melanoma cells have the ability to switch to a dedifferentiated, invasive phenotype in response t... more Melanoma cells have the ability to switch to a dedifferentiated, invasive phenotype in response to multiple stimuli. Here, we show that exposure of melanomas to multiple stresses including BRAF–MEK inhibitor therapy, hypoxia, and UV irradiation leads to an increase in histone deacetylase 8 (HDAC8) activity and the adoption of a drug-resistant phenotype. Mass spectrometry–based phosphoproteomics implicated HDAC8 in the regulation of MAPK and AP-1 signaling. Introduction of HDAC8 into drug-naïve melanoma cells conveyed resistance both in vitro and in vivo. HDAC8-mediated BRAF inhibitor resistance was mediated via receptor tyrosine kinase activation, leading to MAPK signaling. Although HDACs function at the histone level, they also regulate nonhistone substrates, and introduction of HDAC8 decreased the acetylation of c-Jun, increasing its transcriptional activity and enriching for an AP-1 gene signature. Mutation of the putative c-Jun acetylation site at lysine 273 increased transcript...
To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a ... more To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a target-agnostic phenotypic screen of kinase inhibitors with target identification using activity-based protein profiling (ABPP) in which a desthiobiotin-ATP probe was used. We screened 21 SCLC cell lines with known c-MYC amplification status for alterations in viability using a chemical library of 235 small molecule kinase inhibitors. One screen hit compound was interrogated with ABPP, and through this approach we re-identified aurora kinase B as a critical kinase in MYC-amplified SCLC cells. We next extended the platform to a second compound that had activity in SCLC cell lines lacking c-MYC amplification and identified TANK-binding kinase 1 (TBK1), a kinase that affects cell viability, polo-like kinase-1 signaling, G2/M arrest, and apoptosis in SCLC cells lacking MYC amplification. These results demonstrate that phenotypic screening combined with activity-based protein profiling can id...
The mechanisms by which some melanoma cells adapt to Serine/threonine-protein kinase B-Raf (BRAF)... more The mechanisms by which some melanoma cells adapt to Serine/threonine-protein kinase B-Raf (BRAF) inhibitor therapy are incompletely understood. In the present study, we used mass spectrometry-based phosphoproteomics to determine how BRAF inhibition remodeled the signaling network of melanoma cell lines that were BRAF mutant and PTEN null. Short-term BRAF inhibition was associated with marked changes in fibronectin-based adhesion signaling that were PTEN dependent. These effects were recapitulated through BRAF siRNA knockdown and following treatment with chemotherapeutic drugs. Increased fibronectin expression was also observed in mouse xenograft models as well as specimens from melanoma patients undergoing BRAF inhibitor treatment. Analysis of a melanoma tissue microarray showed loss of PTEN expression to predict for a lower overall survival, with a trend for even lower survival being seen when loss of fibronectin was included in the analysis. Mechanistically, the induction of fibr...
Expression microarrays are increasingly used to characterize environmental responses and host-par... more Expression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes. Sequence similarity between ESTs, quantified by the E-value of pair-wise alignment, was used as a surrogate for expected hybridization between corresponding sequences. Using this value as a measure of dissimilarity, sequence ...
The Marine Genomics project is a functional genomics initiative developed to provide a pipeline f... more The Marine Genomics project is a functional genomics initiative developed to provide a pipeline for the curation of Expressed Sequence Tags (ESTs) and gene expression microarray data for marine organisms. It provides a unique clearing-house for marine specific EST and microarray data and is currently available at http://www.marinegenomics.org. The Marine Genomics pipeline automates the processing, maintenance, storage and analysis of EST and microarray data for an increasing number of marine species. It currently contains 19 species databases (over 46,000 EST sequences) that are maintained by registered users from local and remote locations in Europe and South America in addition to the USA. A collection of analysis tools are implemented. These include a pipeline upload tool for EST FASTA file, sequence trace file and microarray data, an annotative text search, automated sequence trimming, sequence quality control (QA/QC) editing, sequence BLAST capabilities and a tool for interacti...
Background-Androgen receptor (AR) plays a critical role in the progression of both androgendepend... more Background-Androgen receptor (AR) plays a critical role in the progression of both androgendependent and androgen-independent prostate cancer (AIPC). Ligand-independent activation of AR in AIPC or castration resistant prostate cancer (CRPC) is often associated with poor prognosis. Recently, tyrosine kinase Ack1 has been shown to regulate AR activity by phosphorylating it at tyrosine 267 and this event was shown to be critical for AIPC growth. However, whether a small molecule inhibitor that can mitigate Ack1 activation is sufficient to abrogate AR activity on AR regulated promoters in androgen-depleted environment is not known. Methods-We have generated two key resources, antibodies that specifically recognize pTyr267-AR and synthesized a small molecule inhibitor of Ack1, 4-amino-5,6-biaryl-furo[2,3d]pyrimidine (named here as AIM-100) to test whether AIM-100 modulates ligand-independent AR activity and inhibits prostate cell growth. Results-Prostate tissue microarray analysis indicates that Ack1 Tyr284 phosphorylation correlates positively with disease progression and negatively with the survival of prostate cancer patients. Interestingly, neither pTyr267-AR expression nor its transcriptional activation was affected by anti-androgens in activated Ack1 expressing or EGF stimulated prostate cells. However, the Ack1 inhibitor, AIM-100, not only inhibited Ack1 activation but also able to suppress pTyr267-AR phosphorylation, binding of AR to PSA, NKX3.1, and TMPRSS2 promoters, and inhibit AR transcription activity. Conclusion-Ack1 Tyr284 phosphorylation is prognostic of progression of prostate cancer and inhibitors of Ack1 activity could be novel therapeutic agents to treat AIPC.
It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs ... more It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.
The vast amount of biological knowledge accumulated over the years has allowed researchers to ide... more The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.
MicroRNAs (miRNAs) are non-coding RNAs that function as post-transcriptional regulators of tumor ... more MicroRNAs (miRNAs) are non-coding RNAs that function as post-transcriptional regulators of tumor suppressors and oncogenes. Single nucleotide polymorphisms (SNPs) in miRNAs may contribute to carcinogenesis by altering expression of miRNAs and their targets. A G>C polymorphism (rs2910164) in the miR-146a precursor sequence leads to a functional change associated with the risk for numerous malignancies. A role for this SNP in glioma pathogenesis has not yet been examined. We investigated whether rs2910164 genotypes influence glioma risk and prognosis in a multi-center case-control study comprised of 593 Caucasian glioma cases and 614 community-based controls. Unconditional logistic regression was used to estimate odds ratios
Expression microarray analysis is one of the most popular molecular diagnostic techniques in the ... more Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization. We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multiv...
In this project I construct a statistical procedure to infer a biological network of very high di... more In this project I construct a statistical procedure to infer a biological network of very high dimensionality, where microRNAs, small RNAs, are supposed to down-regulate mRNAs, also called target genes. From a statistical point of view, I address this problem by building a network that represents the biological regulatory system, indicating which microRNA regulates which gene. In particular, this method provides a novel graphical modeling approach that includes constraints on the regression coefficients to take into account the down-regulatory e ffect of the network. This approach is able to select single connections in the network, unlike previous methods in the Bayesian variable selection literature, which only allow the selection of covariates (microRNAs) that a ffect either all the genes or none of them. The main challenge of this project is represented by the dimensionality of the data. The stochastic search variable selection algorithm is able to efficiently explore the space of all possible networks and to fi nd, for each gene, which microRNAs have high posterior probability of being down-regulating the gene. To help the selection, I also propose a new prior formulation which is able to integrate di fferent sources of data, by exploiting information from sequence and structure analyses. Because many sources of information are integrated, the model is also able to determine which information is consistent with the data via posterior inference on the parameters de fined in the data integration prior. The proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.
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