RESUMO
Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.
Assuntos
Neoplasias de Próstata Resistentes à Castração , Masculino , Humanos , Neoplasias de Próstata Resistentes à Castração/patologia , Nitrilas/farmacologia , Descoberta de Drogas , Castração , Resistencia a Medicamentos Antineoplásicos , Receptores Androgênicos/metabolismoRESUMO
Cancer survivors may experience long-term cardiovascular complications due to chemotherapeutic drugs such as doxorubicin (DOX). The exact mechanism of delayed DOX-induced cardiotoxicity has not been fully elucidated. Sex is an important risk factor for DOX-induced cardiotoxicity. In the current study, we identified sex differences in delayed DOX-induced cardiotoxicity and determined the underlying molecular determinants of the observed sexual dimorphism. Five-week-old male and female mice were administered intraperitoneal injections of DOX (4 mg/kg/week) or saline for 6 weeks. Echocardiography was performed 5 weeks after the last dose of DOX to evaluate cardiac function. Thereafter, mice were sacrificed and gene expression of markers of apoptosis, senescence, and inflammation was measured by PCR in hearts and livers. Proteomic profiling of the heart from both sexes was conducted to determine differentially expressed proteins (DEPs). Only DOX-treated male, but not female, mice demonstrated cardiac dysfunction, cardiac atrophy, and upregulated cardiac expression of Nppb and Myh7. No sex-related differences were observed in DOX-induced expression of most apoptotic, senescence, and pro-inflammatory markers. However, the gene expression of Trp53 was significantly reduced in hearts of DOX-treated female mice only. The anti-inflammatory marker Il-10 was significantly reduced in hearts of DOX-treated male mice only, while the pro-inflammatory marker Il-1α was significantly reduced in livers of DOX-treated female mice only. Gene expression of Tnf-α was reduced in hearts of both DOX-treated male and female mice. Proteomic analysis identified several DEPs after DOX treatment in a sex-specific manner, including anti-inflammatory acute phase proteins. This is the first study to assess sex-specific proteomic changes in a mouse model of delayed DOX-induced cardiotoxicity. Our proteomic analysis identified several sexually dimorphic DEPs, many of which are associated with the anti-inflammatory marker Il-10.
Assuntos
Cardiotoxicidade , Cardiopatias , Feminino , Masculino , Camundongos , Animais , Cardiotoxicidade/etiologia , Caracteres Sexuais , Interleucina-10/toxicidade , Antibióticos Antineoplásicos/toxicidade , Proteômica , Camundongos Endogâmicos C57BL , Doxorrubicina , Cardiopatias/induzido quimicamente , Cardiopatias/genética , Apoptose , Anti-Inflamatórios/farmacologia , Miócitos Cardíacos , Estresse OxidativoRESUMO
Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.
Assuntos
Perfilação da Expressão Gênica , Proteínas/genética , RNA Longo não Codificante/genética , Transcriptoma , Algoritmos , Linhagem Celular , Conjuntos de Dados como Assunto , Humanos , Aprendizado de MáquinaRESUMO
Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.
Assuntos
Antineoplásicos/farmacologia , Ensaios de Seleção de Medicamentos Antitumorais/métodos , RNA Longo não Codificante/química , Antineoplásicos/uso terapêutico , Linhagem Celular Tumoral , Cloridrato de Erlotinib/farmacologia , Cloridrato de Erlotinib/uso terapêutico , Regulação Neoplásica da Expressão Gênica , Genoma Humano , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Mutação , Análise de Sobrevida , TranscriptomaRESUMO
SUMMARY: MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. AVAILABILITY AND IMPLEMENTATION: The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
MicroRNAs , Algoritmos , Perfilação da Expressão Gênica , Aprendizado de Máquina , MicroRNAs/genética , TranscriptomaRESUMO
Osteosarcoma has a poor prognosis due to chemo-resistance and/or metastases. Increasing evidence shows that long non-coding RNAs (lncRNAs) can play an important role in drug sensitivity and cancer metastasis. Using osteosarcoma cell lines, we identified a positive correlation between the expression of a lncRNA and ANRIL, and resistance to two of the three standard-of-care agents for treating osteosarcoma-cisplatin and doxorubicin. To confirm the potential role of ANRIL in chemosensitivity, we independently inhibited and over-expressed ANRIL in osteosarcoma cell lines followed by treatment with either cisplatin or doxorubicin. Knocking-down ANRIL in SAOS2 resulted in a significant increase in cellular sensitivity to both cisplatin and doxorubicin, while the over-expression of ANRIL in both HOS and U2OS cells led to an increased resistance to both agents. To investigate the clinical significance of ANRIL in osteosarcoma, we assessed ANRIL expression in relation to clinical phenotypes using the osteosarcoma data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. Higher ANRIL expression was significantly associated with increased rates of metastases at diagnosis and death and was a significant predictor of reduced overall survival rate. Collectively, our results suggest that the lncRNA ANRIL can be a chemosensitivity and prognosis biomarker in osteosarcoma. Furthermore, reducing ANRIL expression may be a therapeutic strategy to overcome current standard-of-care treatment resistance.
Assuntos
Biomarcadores Tumorais/metabolismo , Cisplatino/farmacologia , Doxorrubicina/farmacologia , Osteossarcoma/tratamento farmacológico , Osteossarcoma/metabolismo , RNA Longo não Codificante/metabolismo , Antineoplásicos/farmacologia , Biomarcadores Tumorais/genética , Neoplasias Ósseas/tratamento farmacológico , Neoplasias Ósseas/genética , Neoplasias Ósseas/metabolismo , Linhagem Celular Tumoral , Resistencia a Medicamentos Antineoplásicos , Regulação Neoplásica da Expressão Gênica , Técnicas de Silenciamento de Genes , Humanos , Osteossarcoma/genética , Prognóstico , RNA Longo não Codificante/genéticaRESUMO
Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.
Assuntos
Antineoplásicos/farmacologia , Biomarcadores Tumorais/genética , Genoma Humano , Genômica/métodos , Neoplasias , Testes Farmacogenômicos/métodos , Feminino , Humanos , Masculino , Neoplasias/tratamento farmacológico , Neoplasias/genéticaRESUMO
PURPOSE: Capecitabine is important in breast cancer treatment but causes diarrhea and hand-foot syndrome (HFS), affecting adherence and quality of life. We sought to identify pharmacogenomic predictors of capecitabine toxicity using a novel monitoring tool. METHODS: Patients with metastatic breast cancer were prospectively treated with capecitabine (2000 mg/m2/day, 14 days on/7 off). Patients completed in-person toxicity questionnaires (day 1/cycle) and automated phone-in assessments (days 8, 15). Correlation of genotypes with early and overall toxicity was the primary endpoint. RESULTS: Two hundred and fifty-nine patients were enrolled (14 institutions). Diarrhea and HFS occurred in 52% (17% grade 3) and 69% (9% grade 3), respectively. Only 29% of patients completed four cycles without dose reduction/interruption. In 39%, the highest toxicity grade was captured via phone. Three single nucleotide polymorphisms (SNPs) associated with diarrhea-DPYD*5 (odds ratio [OR] 4.9; P = 0.0005), a MTHFR missense SNP (OR 3.3; P = 0.02), and a SNP upstream of MTRR (OR 3.0; P = 0.03). GWAS elucidated a novel HFS SNP (OR 3.0; P = 0.0007) near TNFSF4 (OX40L), a gene implicated in autoimmunity including autoimmune skin diseases never before implicated in HFS. Genotype-gene expression analyses of skin tissues identified rs11158568 (associated with HFS via GWAS) with expression of CHURC1, a transcriptional activator controlling fibroblast growth factor (beta = - 0.74; P = 1.46 × 10-23), representing a previously unidentified mechanism for HFS. CONCLUSIONS: This is the first cancer pharmacogenomic study to use phone-in self-reporting, permitting augmented toxicity characterization. Three germline toxicity SNPs were replicated, and several novel SNPs/genes having strong functional relevance were discovered. If further validated, these markers could permit personalized capecitabine dosing.
Assuntos
Antimetabólitos Antineoplásicos/efeitos adversos , Biomarcadores Tumorais/genética , Neoplasias da Mama/patologia , Capecitabina/efeitos adversos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/genética , Feminino , Ferredoxina-NADP Redutase/genética , Seguimentos , Genótipo , Mutação em Linhagem Germinativa , Humanos , Metilenotetra-Hidrofolato Redutase (NADPH2)/genética , Pessoa de Meia-Idade , Polimorfismo de Nucleotídeo Único , Prognóstico , Estudos Prospectivos , Qualidade de VidaRESUMO
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).
Assuntos
Biologia Computacional/métodos , Predisposição Genética para Doença/genética , Herança Multifatorial/genética , Teorema de Bayes , Estudos de Casos e Controles , Processos de Crescimento Celular/genética , LDL-Colesterol/sangue , Humanos , MicroRNAs/genética , Modelos Genéticos , Fenótipo , RNA Mensageiro/genética , Sinvastatina/farmacologia , Software , Biologia de Sistemas/métodos , Fatores de TempoRESUMO
We sought to comprehensively and systematically characterize the relationship between genetic variation, miRNA expression, and mRNA expression. Genome-wide expression profiling of samples of European and African ancestry identified in each population hundreds of miRNAs whose increased expression is correlated with correspondingly reduced expression of target mRNAs. We scanned 3' UTR SNPs with a potential functional effect on miRNA binding for cis-acting expression quantitative trait loci (eQTLs) for the corresponding proximal target genes. To extend sequence-based, localized analyses of SNP effect on miRNA binding, we proceeded to dissect the genetic basis of miRNA expression variation; we mapped miRNA expression levels-as quantitative traits-to loci in the genome as miRNA eQTLs, demonstrating that miRNA expression is under significant genetic control. We found that SNPs associated with miRNA expression are significantly enriched with those SNPs already shown to be associated with mRNA. Moreover, we discovered that many of the miRNA-associated genetic variations identified in our study are associated with a broad spectrum of human complex traits from the National Human Genome Research Institute catalog of published genome-wide association studies. Experimentally, we replicated miRNA-induced mRNA expression inhibition and the cis-eQTL relationship to the target gene for several identified relationships among SNPs, miRNAs, and mRNAs in an independent set of samples; furthermore, we conducted miRNA overexpression and inhibition experiments to functionally validate the miRNA-mRNA relationships. This study extends our understanding of the genetic regulation of the transcriptome and suggests that genetic variation might underlie observed relationships between miRNAs and mRNAs more commonly than has previously been appreciated.
Assuntos
Regulação da Expressão Gênica , MicroRNAs/metabolismo , Transcriptoma , Regiões 3' não Traduzidas , Algoritmos , Éxons , Perfilação da Expressão Gênica , Variação Genética , Genoma , Genoma Humano , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Modelos Genéticos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , RNA Mensageiro/metabolismo , Transcrição GênicaRESUMO
RATIONALE: Most genomic studies of lung function have used phenotypic data derived from a single time-point (e.g., presence/absence of disease) without considering the dynamic progression of a chronic disease. OBJECTIVES: To characterize lung function change over time in subjects with asthma and identify genetic contributors to a longitudinal phenotype. METHODS: We present a method that models longitudinal FEV1 data, collected from 1,041 children with asthma who participated in the Childhood Asthma Management Program. This longitudinal progression model was built using population-based nonlinear mixed-effects modeling with an exponential structure and the determinants of age and height. MEASUREMENTS AND MAIN RESULTS: We found ethnicity was a key covariate for FEV1 level. Budesonide-treated children with asthma had a slight but significant effect on FEV1 when compared with those treated with placebo or nedocromil (P < 0.001). A genome-wide association study identified seven single-nucleotide polymorphisms nominally associated with longitudinal lung function phenotypes in 581 white Childhood Asthma Management Program subjects (P < 10(-4) in the placebo ["discovery"] and P < 0.05 in the nedocromil treatment ["replication"] group). Using ChIP-seq and RNA-seq data, we found that some of the associated variants were in strong enhancer regions in human lung fibroblasts and may affect gene expression in human lung tissue. Genetic mapping restricted to genome-wide enhancer single-nucleotide polymorphisms in lung fibroblasts revealed a highly significant variant (rs6763931; P = 4 × 10(-6); false discovery rate < 0.05). CONCLUSIONS: This study offers a strategy to explore the genetic determinants of longitudinal phenotypes, provide a comprehensive picture of disease pathophysiology, and suggest potential treatment targets.
Assuntos
Antiasmáticos/uso terapêutico , Asma/tratamento farmacológico , Asma/genética , Fibroblastos/efeitos dos fármacos , Volume Expiratório Forçado/efeitos dos fármacos , Volume Expiratório Forçado/genética , Nedocromil/uso terapêutico , Fatores Etários , Asma/fisiopatologia , Budesonida/uso terapêutico , Criança , Feminino , Regulação da Expressão Gênica , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Estudos Longitudinais , Pulmão/efeitos dos fármacos , Masculino , Modelos Teóricos , Fenótipo , Polimorfismo Genético , Fatores de TempoRESUMO
The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.
Assuntos
Linhagem Celular Tumoral , Proliferação de Células , Expressão Gênica/genética , Ativação Linfocitária , Neoplasias/genética , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Projeto HapMap , Humanos , Neoplasias/metabolismo , Farmacogenética , Polimorfismo de Nucleotídeo Único , Locos de Características QuantitativasRESUMO
Commonly observed aberrations in epidermal growth factor receptor (EGFR) signaling have led to the development of EGFR-targeted therapies for various cancers, including non-small cell lung cancer (NSCLC). EGFR mutations and overexpression have further been shown to modulate sensitivity to these EGFR-targeted therapies in NSCLC and several other types of cancers. However, it is clear that mutations and/or genetic variations in EGFR alone cannot explain all of the variability in the responses of patients with NSCLC to EGFR-targeted therapies. For instance, in addition to EGFR genotype, genetic variations in other members of the signaling pathway downstream of EGFR or variations in parallel receptor tyrosine kinase (RTK) pathways are now recognized to have a significant impact on the efficacy of certain EGFR-targeted therapies. In this review, we highlight the mutations and genetic variations in such genes downstream of EGFR and in parallel RTK pathways. Specifically, the directional effects of these pharmacogenetic factors are discussed with a focus on two commonly prescribed EGFR inhibitors: cetuximab and erlotinib. The results of this comprehensive review can be used to optimize the treatment of NSCLC with EGFR inhibitors. Furthermore, they may provide the rationale for the design of subsequent combination therapies that involve the inhibition of EGFR.
Assuntos
Antineoplásicos , Carcinoma Pulmonar de Células não Pequenas , Receptores ErbB , Genes erbB-1 , Farmacogenética , Cetuximab , Cloridrato de Erlotinib , Humanos , Neoplasias Pulmonares , Mutação , QuinazolinasRESUMO
As an important class of non-coding regulatory RNAs, microRNAs (miRNAs) play a key role in a range of biological processes. These molecules serve as post-transcriptional regulators of gene expression and their regulatory activity has been implicated in disease pathophysiology and pharmacological traits. We sought to investigate the impact of miRNAs on cellular proliferation to gain insight into the molecular basis of complex traits that depend on cellular growth, including, most prominently, cancer. We examined the relationship between miRNA expression and intrinsic cellular growth (iGrowth) in the HapMap lymphoblastoid cell lines derived from individuals of different ethnic backgrounds. We found a substantial enrichment for miRNAs (53 miRNAs, FDR < 0.05) correlated with cellular proliferation in pooled CEU (Caucasian of northern and western European descent) and YRI (individuals from Ibadan, Nigeria) samples. Specifically, 119 miRNAs (59 %) were significantly correlated with iGrowth in YRI; of these miRNAs, 18 were correlated with iGrowth in CEU. To gain further insight into the effect of miRNAs on cellular proliferation in cancer, we showed that over-expression of miR-22, one of the top iGrowth-associated miRNAs, leads to growth inhibition in an ovarian cancer cell line (SKOV3). Furthermore, over-expression of miR-22 down-regulates the expression of its target genes (MXI1 and SLC25A37) in this ovarian cancer cell line, highlighting an miRNA-mediated regulatory network potentially important for cellular proliferation. Importantly, our study identified miRNAs that can be used as molecular targets in cancer therapy.
Assuntos
Proliferação de Células , Regulação Neoplásica da Expressão Gênica , MicroRNAs/genética , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , População Negra/genética , Proteínas de Transporte de Cátions/genética , Linhagem Celular Transformada , Linhagem Celular Tumoral , Etnicidade , Europa (Continente) , Feminino , Estudo de Associação Genômica Ampla , Projeto HapMap , Humanos , Proteínas Mitocondriais/genética , Nigéria , Neoplasias Ovarianas/genética , Fenótipo , Análise de Regressão , Proteínas Supressoras de Tumor/genética , População Branca/genéticaRESUMO
INTRODUCTION: Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs. AREAS COVERED: Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination. EXPERT OPINION: Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.
Assuntos
Antineoplásicos , Descoberta de Drogas , Sequenciamento de Nucleotídeos em Larga Escala , Neoplasias da Próstata , Humanos , Neoplasias da Próstata/tratamento farmacológico , Neoplasias da Próstata/genética , Masculino , Descoberta de Drogas/métodos , Antineoplásicos/farmacologia , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Desenho de Fármacos , Desenho Assistido por Computador , AnimaisRESUMO
Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.
Assuntos
Antineoplásicos , Neoplasias , Humanos , Antineoplásicos/farmacologia , Neoplasias/tratamento farmacológico , Descoberta de Drogas , Medicina de PrecisãoRESUMO
Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. Our goal is to identify therapeutics specifically associated with high glycolysis. Here, we established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. First, using our established computational tool, OncoPredict, we imputed the likelihood of drug responses to approximately 1900 agents in each mCRPC tumor from two large clinical patient cohorts. We selected drugs with predicted sensitivity highly correlated with glycolysis scores. In total, 77 drugs predicted to be more sensitive in high glycolysis mCRPC tumors were identified. These drugs represent diverse mechanisms of action. Three of the candidates, ivermectin, CNF2024, and P276-00, were selected for subsequent vitro validation based on the highest measured drug responses associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected higher sensitivity of all three drugs under this condition (p < 0.0001 for all drugs). For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities and high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein-protein interaction network and topological methods to identify biomarkers for these drug candidates. EEF1B2 and CCNA2 were identified as key biomarkers for ivermectin and CNF2024, respectively, through multiple independent biomarker nomination pipelines. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen-deprivation therapies by precisely targeting mCRPC with high glycolysis.
RESUMO
Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation.