Deep learning of pharmacogenomics resources: moving towards precision oncology.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Lineage of origin in rhabdomyosarcoma informs pharmacological response.

Lineage or cell of origin of cancers is often unknown and thus is not a consideration in therapeutic approaches. Alveolar rhabdomyosarcoma (aRMS) is an aggressive childhood cancer for which the cell of origin remains debated. We used conditional genetic mouse models of aRMS to activate the pathognomonic Pax3:Foxo1 fusion oncogene and inactivate p53 in several stages of prenatal and postnatal muscle development. We reveal that lineage of origin significantly influences tumor histomorphology and sensitivity to targeted therapeutics. Furthermore, we uncovered differential transcriptional regulation of the Pax3:Foxo1 locus by tumor lineage of origin, which led us to identify the histone deacetylase inhibitor entinostat as a pharmacological agent for the potential conversion of Pax3:Foxo1-positive aRMS to a state akin to fusion-negative RMS through direct transcriptional suppression of Pax3:Foxo1.

CeL-ID: cell line identification using RNA-seq data.

BACKGROUND: Cell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. However, it is commonly acknowledged that contamination of cell lines is a prevalent problem affecting biomedical science and available methods for cell line authentication suffer from limited access as well as being too daunting and time-consuming for many researchers. Therefore, a new and cost effective approach for authentication and quality control of cell lines is needed. RESULTS: We have developed a new RNA-seq based approach named CeL-ID for cell line authentication. CeL-ID uses RNA-seq data to identify variants and compare with variant profiles of other cell lines. RNA-seq data for 934 CCLE cell lines downloaded from NCI GDC were used to generate cell line specific variant profiles and pair-wise correlations were calculated using frequencies and depth of coverage values of all the variants. Comparative analysis of variant profiles revealed that variant profiles differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and are highly correlated (ρ > 0.9). Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool of cell line authentication in biomedical science. Finally, CeL-ID estimates the possible cross contamination using linear mixture model if no perfect match was detected. CONCLUSIONS: In this study, we show the utility of an RNA-seq based approach for cell line authentication. Our comparative analysis of variant profiles derived from RNA-seq data revealed that variant profiles of each cell line are distinct and overall share low variant identity with other cell lines whereas identical or synonymous cell lines show significantly high variant identity and hence variant profiles can be used as a discriminatory/identifying feature in cell authentication model.

Detection of high variability in gene expression from single-cell RNA-seq profiling.

BACKGROUND: The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). RESULTS: We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. CONCLUSIONS: In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.

A novel significance score for gene selection and ranking.

MOTIVATION: When identifying differentially expressed (DE) genes from high-throughput gene expression measurements, we would like to take both statistical significance (such as P-value) and biological relevance (such as fold change) into consideration. In gene set enrichment analysis (GSEA), a score that can combine fold change and P-value together is needed for better gene ranking. RESULTS: We defined a gene significance score π-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, π-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied π-value to GSEA and found it comparable to P-value and t-statistic based methods, with added protection against false discovery in certain situations. Finally, in a gene functional study of breast cancer profiles, we showed that using π-value helps elucidating otherwise overlooked important biological functions. AVAILABILITY: http://gccri.uthscsa.edu/Pi_Value_Supplementary.asp CONTACT: xy@ieee.org, cheny8@uthscsa.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ILT2 and ILT4 Drive Myeloid Suppression via Both Overlapping and Distinct Mechanisms.

Solid tumors are dense three-dimensional (3D) multicellular structures that enable efficient receptor-ligand trans interactions via close cell-cell contact. Immunoglobulin-like transcript (ILT)2 and ILT4 are related immune-suppressive receptors that play a role in the inhibition of myeloid cells within the tumor microenvironment. The relative contribution of ILT2 and ILT4 to immune inhibition in the context of solid tumor tissue has not been fully explored. We present evidence that both ILT2 and ILT4 contribute to myeloid inhibition. We found that although ILT2 inhibits myeloid cell activation in the context of trans-engagement by MHC-I, ILT4 efficiently inhibits myeloid cells in the presence of either cis- or trans-engagement. In a 3D spheroid tumor model, dual ILT2/ILT4 blockade was required for the optimal activation of myeloid cells, including the secretion of CXCL9 and CCL5, upregulation of CD86 on dendritic cells, and downregulation of CD163 on macrophages. Humanized mouse tumor models showed increased immune activation and cytolytic T-cell activity with combined ILT2 and ILT4 blockade, including evidence of the generation of immune niches, which have been shown to correlate with clinical response to immune-checkpoint blockade. In a human tumor explant histoculture system, dual ILT2/ILT4 blockade increased CXCL9 secretion, downregulated CD163 expression, and increased the expression of M1 macrophage, IFNγ, and cytolytic T-cell gene signatures. Thus, we have revealed distinct contributions of ILT2 and ILT4 to myeloid cell biology and provide proof-of-concept data supporting the combined blockade of ILT2 and ILT4 to therapeutically induce optimal myeloid cell reprogramming in the tumor microenvironment.

Antibody-mediated inhibition of GDF15-GFRAL activity reverses cancer cachexia in mice.

Cancer cachexia is a highly prevalent condition associated with poor quality of life and reduced survival1. Tumor-induced perturbations in the endocrine, immune and nervous systems drive anorexia and catabolic changes in adipose tissue and skeletal muscle, hallmarks of cancer cachexia2-4. However, the molecular mechanisms driving cachexia remain poorly defined, and there are currently no approved drugs for the condition. Elevation in circulating growth differentiation factor 15 (GDF15) correlates with cachexia and reduced survival in patients with cancer5-8, and a GDNF family receptor alpha like (GFRAL)-Ret proto-oncogene (RET) signaling complex in brainstem neurons that mediates GDF15-induced weight loss in mice has recently been described9-12. Here we report a therapeutic antagonistic monoclonal antibody, 3P10, that targets GFRAL and inhibits RET signaling by preventing the GDF15-driven interaction of RET with GFRAL on the cell surface. Treatment with 3P10 reverses excessive lipid oxidation in tumor-bearing mice and prevents cancer cachexia, even under calorie-restricted conditions. Mechanistically, activation of the GFRAL-RET pathway induces expression of genes involved in lipid metabolism in adipose tissues, and both peripheral chemical sympathectomy and loss of adipose triglyceride lipase protect mice from GDF15-induced weight loss. These data uncover a peripheral sympathetic axis by which GDF15 elicits a lipolytic response in adipose tissue independently of anorexia, leading to reduced adipose and muscle mass and function in tumor-bearing mice.

A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.

MOTIVATION: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results. RESULTS: We demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines. SUPPLEMENTARY INFORMATION: Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. RESULTS: We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. CONCLUSIONS: Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

Following publication of the original article [1], the authors provided an updated funding statement to the article. The updated statement is as follows.

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization.

BACKGROUND: Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. RESULTS: In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. CONCLUSIONS: Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer.

AIM AND OBJECTIVE: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, "repositioning" of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. MATERIALS AND METHODS: First, a "gene set-compound" network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A "cancer-functional gene set- compound" network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. RESULTS: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. CONCLUSION: Our method can effectively identify compounds with a potential to be "repositioned" for cancer treatment according to their active mechanisms and their association with patients' survival time.

IGFBP7 Deletion Promotes Hepatocellular Carcinoma.

Activation of IGF signaling is a major oncogenic event in diverse cancers, including hepatocellular carcinoma (HCC). In this setting, the insulin-like growth factor binding protein IGFBP7 inhibits IGF signaling by binding the IGF1 receptor (IGF1R), functioning as a candidate tumor suppressor. IGFBP7 abrogates tumors by inhibiting angiogenesis and inducing cancer-specific senescence and apoptosis. Here, we report that Igfbp7-deficient mice exhibit constitutively active IGF signaling, presenting with proinflammatory and immunosuppressive microenvironments and spontaneous liver and lung tumors occurring with increased incidence in carcinogen-treated subjects. Igfbp7 deletion increased proliferation and decreased senescence of hepatocytes and mouse embryonic fibroblasts, effects that were blocked by treatment with IGF1 receptor inhibitor. Significant inhibition of genes regulating immune surveillance was observed in Igfbp7-/- murine livers, which was associated with a marked inhibition in antigen cross-presentation by Igfbp7-/- dendritic cells. Conversely, IGFBP7 overexpression inhibited growth of HCC cells in syngeneic immunocompetent mice. Depletion of CD4+ or CD8+ T lymphocytes abolished this growth inhibition, identifying it as an immune-mediated response. Our findings define an immune component of the pleiotropic mechanisms through which IGFBP7 suppresses HCC. Furthermore, they offer a genetically based preclinical proof of concept for IGFBP7 as a therapeutic target for immune management of HCC. Cancer Res; 77(15); 4014-25. ©2017 AACR.

ZBTB7A Suppresses Melanoma Metastasis by Transcriptionally Repressing MCAM.

UNLABELLED: The excessive metastatic propensity of melanoma makes it the most deadly form of skin cancer, yet the underlying mechanism of metastasis remains elusive. Here, mining of cancer genome datasets discovered a frequent loss of chromosome 19p13.3 and associated downregulation of the zinc finger transcription factor ZBTB7A in metastatic melanoma. Functional assessment of ZBTB7A-regulated genes identified MCAM, which encodes an adhesion protein key to melanoma metastasis. Using an integrated approach, it is demonstrated that ZBTB7A directly binds to the promoter and transcriptionally represses the expression of MCAM, establishing ZBTB7A as a bona fide transcriptional repressor of MCAM. Consistently, downregulation of ZBTB7A results in marked upregulation of MCAM and enhanced melanoma cell invasion and metastasis. An inverse correlation of ZBTB7A and MCAM expression in association with melanoma metastasis is further validated with data from analysis of human melanoma specimens. IMPLICATIONS: Together, these results uncover a previously unrecognized role of ZBTB7A in negative regulation of melanoma metastasis and have important clinical implications.

Identification of genomic functional hotspots with copy number alteration in liver cancer.

Copy number alterations (CNAs) can be observed in most of cancer patients. Several oncogenes and tumor suppressor genes with CNAs have been identified in different kinds of tumor. However, the systematic survey of CNA-affected functions is still lack. By employing systems biology approaches, instead of examining individual genes, we directly identified the functional hotspots on human genome. A total of 838 hotspots on human genome with 540 enriched Gene Ontology functions were identified. Seventy-six aCGH array data of hepatocellular carcinoma (HCC) tumors were employed in this study. A total of 150 regions which putatively affected by CNAs and the encoded functions were identified. Our results indicate that two immune related hotspots had copy number alterations in most of patients. In addition, our data implied that these immune-related regions might be involved in HCC oncogenesis. Also, we identified 39 hotspots of which copy number status were associated with patient survival. Our data implied that copy number alterations of the regions may contribute in the dysregulation of the encoded functions. These results further demonstrated that our method enables researchers to survey biological functions of CNAs and to construct regulation hypothesis at pathway and functional levels.

Utilizing signature-score to identify oncogenic pathways of cholangiocarcinoma.

Extracting maximal information from gene signature sets (GSSs) via microarray-based transcriptional profiling involves assigning function to up and down regulated genes. Here we present a novel sample scoring method called Signature-score (S-score) which can be used to quantify the expression pattern of tumor samples from previously identified gene signature sets. A simulation result demonstrated an improved accuracy and robustness by S-score method comparing with other scoring methods. By applying the S-score method to cholangiocarcinoma (CAC), an aggressive hepatic cancer that arises from bile ducts cells, we identified enriched oncogenic pathways in two large CAC data sets. Thirteen pathways were enriched in CAC compared with normal liver and bile duct. Moreover, using S-score, we were able to dissect correlations between CAC-associated oncogenic pathways and Gene Ontology function. Two major oncogenic clusters and associated functions were identified. Cluster 1, which included beta-catenin and Ras, showed a positive correlation with the cell cycle, while cluster 2, which included TGF-beta, cytokeratin 19 and EpCAM was inversely correlated with immune function. We also used S-score to identify pathways that are differentially expressed in CAC and hepatocellular carcinoma (HCC), the more common subtype of liver cancer. Our results demonstrate the utility and effectiveness of S-score in assigning functional roles to tumor-associated gene signature sets and in identifying potential therapeutic targets for specific liver cancer subtypes.

De-regulated microRNAs in pediatric cancer stem cells target pathways involved in cell proliferation, cell cycle and development.

BACKGROUND: microRNAs (miRNAs) have been implicated in the control of many biological processes and their deregulation has been associated with many cancers. In recent years, the cancer stem cell (CSC) concept has been applied to many cancers including pediatric. We hypothesized that a common signature of deregulated miRNAs in the CSCs fraction may explain the disrupted signaling pathways in CSCs. METHODOLOGY/RESULTS: Using a high throughput qPCR approach we identified 26 CSC associated differentially expressed miRNAs (DEmiRs). Using BCmicrO algorithm 865 potential CSC associated DEmiR targets were obtained. These potential targets were subjected to KEGG, Biocarta and Gene Ontology pathway and biological processes analysis. Four annotated pathways were enriched: cell cycle, cell proliferation, p53 and TGF-beta/BMP. Knocking down hsa-miR-21-5p, hsa-miR-181c-5p and hsa-miR-135b-5p using antisense oligonucleotides and small interfering RNA in cell lines led to the depletion of the CSC fraction and impairment of sphere formation (CSC surrogate assays). CONCLUSION: Our findings indicated that CSC associated DEmiRs and the putative pathways they regulate may have potential therapeutic applications in pediatric cancers.

Rb1 loss modifies but does not initiate alveolar rhabdomyosarcoma.

BACKGROUND: Alveolar rhabdomyosarcoma (aRMS) is a myogenic childhood sarcoma frequently associated with a translocation-mediated fusion gene, Pax3:Foxo1a. METHODS: We investigated the complementary role of Rb1 loss in aRMS tumor initiation and progression using conditional mouse models. RESULTS: Rb1 loss was not a necessary and sufficient mutational event for rhabdomyosarcomagenesis, nor a strong cooperative initiating mutation. Instead, Rb1 loss was a modifier of progression and increased anaplasia and pleomorphism. Whereas Pax3:Foxo1a expression was unaltered, biomarkers of aRMS versus embryonal rhabdomyosarcoma were both increased, questioning whether these diagnostic markers are reliable in the context of Rb1 loss. Genome-wide gene expression in Pax3:Foxo1a,Rb1 tumors more closely approximated aRMS than embryonal rhabdomyosarcoma. Intrinsic loss of pRb function in aRMS was evidenced by insensitivity to a Cdk4/6 inhibitor regardless of whether Rb1 was intact or null. This loss of function could be attributed to low baseline Rb1, pRb and phospho-pRb expression in aRMS tumors for which the Rb1 locus was intact. Pax3:Foxo1a RNA interference did not increase pRb or improve Cdk inhibitor sensitivity. Human aRMS shared the feature of low and/or heterogeneous tumor cell pRb expression. CONCLUSIONS: Rb1 loss from an already low pRb baseline is a significant disease modifier, raising the possibility that some cases of pleomorphic rhabdomyosarcoma may in fact be Pax3:Foxo1a-expressing aRMS with Rb1 or pRb loss of function.

Androgen-responsive microRNAs in mouse Sertoli cells.

Although decades of research have established that androgen is essential for spermatogenesis, androgen's mechanism of action remains elusive. This is in part because only a few androgen-responsive genes have been definitively identified in the testis. Here, we propose that microRNAs--small, non-coding RNAs--are one class of androgen-regulated trans-acting factors in the testis. Specifically, by using androgen suppression and androgen replacement in mice, we show that androgen regulates the expression of several microRNAs in Sertoli cells. Our results reveal that several of these microRNAs are preferentially expressed in the testis and regulate genes that are highly expressed in Sertoli cells. Because androgen receptor-mediated signaling is essential for the pre- and post-meiotic germ cell development, we propose that androgen controls these events by regulating Sertoli/germ cell-specific gene expression in a microRNA-dependent manner.

Genomic loss of tumor suppressor miRNA-204 promotes cancer cell migration and invasion by activating AKT/mTOR/Rac1 signaling and actin reorganization.

Increasing evidence suggests that chromosomal regions containing microRNAs are functionally important in cancers. Here, we show that genomic loci encoding miR-204 are frequently lost in multiple cancers, including ovarian cancers, pediatric renal tumors, and breast cancers. MiR-204 shows drastically reduced expression in several cancers and acts as a potent tumor suppressor, inhibiting tumor metastasis in vivo when systemically delivered. We demonstrated that miR-204 exerts its function by targeting genes involved in tumorigenesis including brain-derived neurotrophic factor (BDNF), a neurotrophin family member which is known to promote tumor angiogenesis and invasiveness. Analysis of primary tumors shows that increased expression of BDNF or its receptor tropomyosin-related kinase B (TrkB) parallel a markedly reduced expression of miR-204. Our results reveal that loss of miR-204 results in BDNF overexpression and subsequent activation of the small GTPase Rac1 and actin reorganization through the AKT/mTOR signaling pathway leading to cancer cell migration and invasion. These results suggest that microdeletion of genomic loci containing miR-204 is directly linked with the deregulation of key oncogenic pathways that provide crucial stimulus for tumor growth and metastasis. Our findings provide a strong rationale for manipulating miR-204 levels therapeutically to suppress tumor metastasis.