Optimisation of a primary human CAR-NK cell manufacturing pipeline.

Objectives: Autologous chimeric antigen receptor (CAR) T-cell therapy of B-cell malignancies achieves long-term disease remission in a high fraction of patients and has triggered intense research into translating this successful approach into additional cancer types. However, the complex logistics involved in autologous CAR-T manufacturing, the compromised fitness of patient-derived T cells, the high rates of serious toxicities and the overall cost involved with product manufacturing and hospitalisation have driven innovation to overcome such hurdles. One alternative approach is the use of allogeneic natural killer (NK) cells as a source for CAR-NK cell therapy. However, this source has traditionally faced numerous manufacturing challenges. Methods: To address this, we have developed an optimised expansion and transduction protocol for primary human NK cells primed for manufacturing scaling and clinical evaluation. We have performed an in-depth comparison of primary human NK cell sources as a starting material by characterising their phenotype, functionality, expansion potential and transduction efficiency at crucial timepoints of our CAR-NK manufacturing pipeline. Results: We identified adult peripheral blood-derived NK cells to be the superior source for generating a CAR-NK cell product because of a higher maximum yield of CAR-expressing NK cells combined with potent natural, as well as CAR-mediated anti-tumor effector functions. Conclusions: Our optimised manufacturing pipeline dramatically improves lentiviral transduction efficiency of primary human NK cells. We conclude that the exponential expansion pre- and post-transduction and high on-target cytotoxicity make peripheral blood-derived NK cells a feasible and attractive CAR-NK cell product for clinical utility.

PRMT5 and CDK4/6 inhibition result in distinctive patterns of alternative splicing in melanoma.

Drugs targeting cyclin-dependent kinases 4 and 6 (CDK4/6) are promising new treatments for melanoma and other solid malignancies. In studies on CDK4/6 inhibitor resistance, protein arginine methyltransferase 5 (PRMT5) regulation of alternative splicing was shown to be an important downstream component of the CDK4/6 pathway. However, the full effects of inhibition of CDK4/6 on splicing events in melanoma and the extent to which they are dependent on PRMT5 has not been established. We performed full-length mRNA sequencing on CHL1 and A375 melanoma cell lines treated with the CDK4/6 inhibitor palbociclib and the PRMT5 inhibitor GSK3326595 and analysed data for differential gene expression and differential pre-mRNA splicing induced by these agents. Changes in gene expression and RNA splicing were more extensive under PRMT5 inhibition than under CDK4/6 inhibition. Although PRMT5 inhibition and CDK4/6 inhibition induced common RNA splicing events and gene expression profiles, the majority of events induced by CDK4/6 inhibition were distinct. Our findings indicate CDK4/6 has the ability to regulate alternative splicing in a manner that is distinct from PRMT5 inhibition, resulting in divergent changes in gene expression under each therapy.

Discovery of a highly potent, selective, orally bioavailable inhibitor of KAT6A/B histone acetyltransferases with efficacy against KAT6A-high ER+ breast cancer.

KAT6A, and its paralog KAT6B, are histone lysine acetyltransferases (HAT) that acetylate histone H3K23 and exert an oncogenic role in several tumor types including breast cancer where KAT6A is frequently amplified/overexpressed. However, pharmacologic targeting of KAT6A to achieve therapeutic benefit has been a challenge. Here we describe identification of a highly potent, selective, and orally bioavailable KAT6A/KAT6B inhibitor CTx-648 (PF-9363), derived from a benzisoxazole series, which demonstrates anti-tumor activity in correlation with H3K23Ac inhibition in KAT6A over-expressing breast cancer. Transcriptional and epigenetic profiling studies show reduced RNA Pol II binding and downregulation of genes involved in estrogen signaling, cell cycle, Myc and stem cell pathways associated with CTx-648 anti-tumor activity in ER-positive (ER+) breast cancer. CTx-648 treatment leads to potent tumor growth inhibition in ER+ breast cancer in vivo models, including models refractory to endocrine therapy, highlighting the potential for targeting KAT6A in ER+ breast cancer.

Synthetic Epigenetic Reprogramming of Mesenchymal to Epithelial States Using the CRISPR/dCas9 Platform in Triple Negative Breast Cancer.

Epithelial-mesenchymal transition (EMT) is a reversible transcriptional program invoked by cancer cells to drive cancer progression. Transcription factor ZEB1 is a master regulator of EMT, driving disease recurrence in poor-outcome triple negative breast cancers (TNBCs). Here, this work silences ZEB1 in TNBC models by CRISPR/dCas9-mediated epigenetic editing, resulting in highly-specific and nearly complete suppression of ZEB1 in vivo, accompanied by long-lasting tumor inhibition. Integrated "omic" changes promoted by dCas9 linked to the KRAB domain (dCas9-KRAB) enabled the discovery of a ZEB1-dependent-signature of 26 genes differentially-expressed and -methylated, including the reactivation and enhanced chromatin accessibility in cell adhesion loci, outlining epigenetic reprogramming toward a more epithelial state. In the ZEB1 locus transcriptional silencing is associated with induction of locally-spread heterochromatin, significant changes in DNA methylation at specific CpGs, gain of H3K9me3, and a near complete erasure of H3K4me3 in the ZEB1 promoter. Epigenetic shifts induced by ZEB1-silencing are enriched in a subset of human breast tumors, illuminating a clinically-relevant hybrid-like state. Thus, the synthetic epi-silencing of ZEB1 induces stable "lock-in" epigenetic reprogramming of mesenchymal tumors associated with a distinct and stable epigenetic landscape. This work outlines epigenome-engineering approaches for reversing EMT and customizable precision molecular oncology approaches for targeting poor outcome breast cancers.

Epigenetic reactivation of tumor suppressor genes with CRISPRa technologies as precision therapy for hepatocellular carcinoma.

BACKGROUND: Epigenetic silencing of tumor suppressor genes (TSGs) is a key feature of oncogenesis in hepatocellular carcinoma (HCC). Liver-targeted delivery of CRISPR-activation (CRISPRa) systems makes it possible to exploit chromatin plasticity, by reprogramming transcriptional dysregulation. RESULTS: Using The Cancer Genome Atlas HCC data, we identify 12 putative TSGs with negative associations between promoter DNA methylation and transcript abundance, with limited genetic alterations. All HCC samples harbor at least one silenced TSG, suggesting that combining a specific panel of genomic targets could maximize efficacy, and potentially improve outcomes as a personalized treatment strategy for HCC patients. Unlike epigenetic modifying drugs lacking locus selectivity, CRISPRa systems enable potent and precise reactivation of at least 4 TSGs tailored to representative HCC lines. Concerted reactivation of HHIP, MT1M, PZP, and TTC36 in Hep3B cells inhibits multiple facets of HCC pathogenesis, such as cell viability, proliferation, and migration. CONCLUSIONS: By combining multiple effector domains, we demonstrate the utility of a CRISPRa toolbox of epigenetic effectors and gRNAs for patient-specific treatment of aggressive HCC.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin.

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

The Ratio of Exhausted to Resident Infiltrating Lymphocytes Is Prognostic for Colorectal Cancer Patient Outcome.

Immunotherapy success in colorectal cancer is mainly limited to patients whose tumors exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumor-infiltrating immune cells. Indeed, the presence of specific infiltrating immune-cell subsets has been shown to correlate with immunotherapy response and is in many cases prognostic of treatment outcome. Tumor-infiltrating lymphocytes (TIL) can undergo distinct differentiation programs, acquiring features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors, such as PD-1, and lose functionality. Although residency and exhaustion programs of CD8+ T cells are relatively well studied, these programs have only recently been appreciated in CD4+ T cells and remain largely unknown in tumor-infiltrating natural killer (NK) cells. In this study, we used single-cell RNA sequencing (RNA-seq) data to identify signatures of residency and exhaustion in colorectal cancer-infiltrating lymphocytes, including CD8+, CD4+, and NK cells. We then tested these signatures in independent single-cell data from tumor and normal tissue-infiltrating immune cells. Furthermore, we used versions of these signatures designed for bulk RNA-seq data to explore tumor-intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic datasets from patients with colon adenocarcinoma, we showed that combinations of these signatures, in particular combinations of NK-cell activity signatures, together with tumor-associated signatures, such as TGFß signaling, were associated with distinct survival outcomes in patients with colon adenocarcinoma.

The oncogene AAMDC links PI3K-AKT-mTOR signaling with metabolic reprograming in estrogen receptor-positive breast cancer.

Adipogenesis associated Mth938 domain containing (AAMDC) represents an uncharacterized oncogene amplified in aggressive estrogen receptor-positive breast cancers. We uncover that AAMDC regulates the expression of several metabolic enzymes involved in the one-carbon folate and methionine cycles, and lipid metabolism. We show that AAMDC controls PI3K-AKT-mTOR signaling, regulating the translation of ATF4 and MYC and modulating the transcriptional activity of AAMDC-dependent promoters. High AAMDC expression is associated with sensitization to dactolisib and everolimus, and these PI3K-mTOR inhibitors exhibit synergistic interactions with anti-estrogens in IntClust2 models. Ectopic AAMDC expression is sufficient to activate AKT signaling, resulting in estrogen-independent tumor growth. Thus, AAMDC-overexpressing tumors may be sensitive to PI3K-mTORC1 blockers in combination with anti-estrogens. Lastly, we provide evidence that AAMDC can interact with the RabGTPase-activating protein RabGAP1L, and that AAMDC, RabGAP1L, and Rab7a colocalize in endolysosomes. The discovery of the RabGAP1L-AAMDC assembly platform provides insights for the design of selective blockers to target malignancies having the AAMDC amplification.

Stable gene expression for normalisation and single-sample scoring.

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these 'stably-expressed genes'. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

The cancer-natural killer cell immunity cycle.

Immunotherapy with checkpoint blockade induces rapid and durable immune control of cancer in some patients and has driven a monumental shift in cancer treatment. Neoantigen-specific CD8+ T cells are at the forefront of current immunotherapy strategies, and the majority of drug discovery and clinical trials revolve around further harnessing these immune effectors. Yet the immune system contains a diverse range of antitumour effector cells, and these must function in a coordinated and synergistic manner to overcome the immune-evasion mechanisms used by tumours and achieve complete control with tumour eradication. A key antitumour effector is the natural killer (NK) cells, cytotoxic innate lymphocytes present at high frequency in the circulatory system and identified by their exquisite ability to spontaneously detect and lyse transformed or stressed cells. Emerging data show a role for intratumoural NK cells in driving immunotherapy response and, accordingly, there have been renewed efforts to further elucidate and target the pathways controlling NK cell antitumour function. In this Review, we discuss recent clinical evidence that NK cells are a key immune constituent in the protective antitumour immune response and highlight the major stages of the cancer-NK cell immunity cycle. We also perform a new analysis of publicly available transcriptomic data to provide an overview of the prognostic value of NK cell gene expression in 25 tumour types. Furthermore, we discuss how the role of NK cells evolves with tumour progression, presenting new opportunities to target NK cell function to enhance cancer immunotherapy response rates across a more diverse range of cancers.

Harnessing Natural Killer Immunity in Metastatic SCLC.

INTRODUCTION: SCLC is the most aggressive subtype of lung cancer, and though most patients initially respond to platinum-based chemotherapy, resistance develops rapidly. Immunotherapy holds promise in the treatment of lung cancer; however, patients with SCLC exhibit poor overall responses highlighting the necessity for alternative approaches. Natural killer (NK) cells are an alternative to T cell-based immunotherapies that do not require sensitization to antigens presented on the surface of tumor cells. METHODS: We investigated the immunophenotype of human SCLC tumors by both flow cytometry on fresh samples and bioinformatic analysis. Cell lines generated from murine SCLC were transplanted into mice lacking key cytotoxic immune cells. Subcutaneous tumor growth, metastatic dissemination, and activation of CD8+ T and NK cells were evaluated by histology and flow cytometry. RESULTS: Transcriptomic analysis of human SCLC tumors revealed heterogeneous immune checkpoint and cytotoxic signature profiles. Using sophisticated, genetically engineered mouse models, we reported that the absence of NK cells, but not CD8+ T cells, substantially enhanced metastatic dissemination of SCLC tumor cells in vivo. Moreover, hyperactivation of NK cell activity through augmentation of interleukin-15 or transforming growth factor-ß signaling pathways ameliorated SCLC metastases, an effect that was enhanced when combined with antiprogrammed cell death-1 therapy. CONCLUSIONS: These proof-of-principle findings provide a rationale for exploiting the antitumor functions of NK cells in the treatment of patients with SCLC. Moreover, the distinct immune profiles of SCLC subtypes reveal an unappreciated level of heterogeneity that warrants further investigation in the stratification of patients for immunotherapy.

Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.

BACKGROUND: Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. RESULTS: In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. CONCLUSIONS: Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.

Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures.

Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.

Therapeutic blockade of activin-A improves NK cell function and antitumor immunity.

Natural killer (NK) cells are innate lymphocytes that play a major role in immunosurveillance against tumor initiation and metastatic spread. The signals and checkpoints that regulate NK cell fitness and function in the tumor microenvironment are not well defined. Transforming growth factor-ß (TGF-ß) is a suppressor of NK cells that inhibits interleukin-15 (IL-15)-dependent signaling events and increases the abundance of receptors that promote tissue residency. Here, we showed that NK cells express the type I activin receptor ALK4, which, upon binding to its ligand activin-A, phosphorylated SMAD2/3 to suppress IL-15-mediated NK cell metabolism. Activin-A impaired human and mouse NK cell proliferation and reduced the production of granzyme B to impair tumor killing. Similar to TGF-ß, activin-A also induced SMAD2/3 phosphorylation and stimulated NK cells to increase their cell surface expression of several markers of ILC1 cells. Activin-A also induced these changes in TGF-ß receptor-deficient NK cells, suggesting that activin-A and TGF-ß stimulate independent pathways that drive SMAD2/3-mediated NK cell suppression. Last, inhibition of activin-A by follistatin substantially slowed orthotopic melanoma growth in mice. These data highlight the relevance of examining TGF-ß-independent SMAD2/3 signaling mechanisms as a therapeutic axis to relieve NK cell suppression and promote antitumor immunity.

Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon-Intron Split Analysis (EISA).

Epithelial-mesenchymal transition (EMT) has been a subject of intense scrutiny as it facilitates metastasis and alters drug sensitivity. Although EMT-regulatory roles for numerous miRNAs and transcription factors are known, their functions can be difficult to disentangle, in part due to the difficulty in identifying direct miRNA targets from complex datasets and in deciding how to incorporate 'indirect' miRNA effects that may, or may not, represent biologically relevant information. To better understand how miRNAs exert effects throughout the transcriptome during EMT, we employed Exon-Intron Split Analysis (EISA), a bioinformatic technique that separates transcriptional and post-transcriptional effects through the separate analysis of RNA-Seq reads mapping to exons and introns. We find that in response to the manipulation of miRNAs, a major effect on gene expression is transcriptional. We also find extensive co-ordination of transcriptional and post-transcriptional regulatory mechanisms during both EMT and mesenchymal to epithelial transition (MET) in response to TGF-ß or miR-200c respectively. The prominent transcriptional influence of miRNAs was also observed in other datasets where miRNA levels were perturbed. This work cautions against a narrow approach that is limited to the analysis of direct targets, and demonstrates the utility of EISA to examine complex regulatory networks involving both transcriptional and post-transcriptional mechanisms.

A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients.

Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15 Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.

The Emergence of Natural Killer Cells as a Major Target in Cancer Immunotherapy.

Immune 'checkpoint' inhibitors can increase the activity of tumor-resident cytotoxic lymphocytes and have revolutionized cancer treatment. Current therapies block inhibitory pathways in tumor-infiltrating CD8+ T cells and recent studies have shown similar programs in other effector populations such as natural killer (NK) cells. NK cells are critical for immunosurveillance, particularly the control of metastatic cells or hematological cancers. However, how NK cells specifically recognize transformed cells and dominant negative feedback pathways, as well as how tumors escape NK cell control, remains undefined. This review summarizes recent advances that have illuminated inhibitory checkpoints in NK cells, some of which are shared with conventional cytotoxic T lymphocytes. It also outlines emerging approaches aimed at unleashing the potential of NK cells in immunotherapy.

Single sample scoring of molecular phenotypes.

BACKGROUND: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. CONCLUSIONS: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

A Transcriptional Program for Detecting TGFß-Induced EMT in Cancer.

Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. TGFß is one of the best-studied drivers of EMT, and many drugs are available to target this signaling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFß-induced EMT which can be used to score TGFß-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumor datasets, a number of cell lines (including basal B breast cancer and cancers of the central nervous system) show evidence for TGFß-driven EMT and carry a low mutational burden across the TGFß signaling pathway. Furthermore, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumor types with evidence of TGFß-induced EMT. Tumor types with high scores showed significantly lower survival rates than those with low scores and also carry a lower mutational burden in the TGFß pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFß.Implications: The TGFß-induced EMT signature may be useful to identify patients with mesenchymal-like tumors who could benefit from targeted therapeutics to inhibit promesenchymal TGFß signaling and disrupt the metastatic cascade. Mol Cancer Res; 15(5); 619-31. ©2017 AACR.

Systems analysis identifies miR-29b regulation of invasiveness in melanoma.

BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs. However, it remains a challenge to identify which miR-mRNA interactions are active at endogenous expression levels, and of biological consequence. METHODS: We developed a workflow to integrate TargetScan and DIANA-microT predictions into the analysis of data-driven associations calculated from transcript abundance (RNASeq) data, specifically the mutual information and Pearson's correlation metrics. We use this workflow to identify putative relationships of miR-mediated mRNA repression with strong support from both lines of evidence. Applying this approach systematically to a large, published collection of unique melanoma cell lines - the Ludwig Melbourne melanoma (LM-MEL) cell line panel - we identified putative miR-mRNA interactions that may contribute to invasiveness. This guided the selection of interactions of interest for further in vitro validation studies. RESULTS: Several miR-mRNA regulatory relationships supported by TargetScan and DIANA-microT demonstrated differential activity across cell lines of varying matrigel invasiveness. Strong negative statistical associations for these putative regulatory relationships were consistent with target mRNA inhibition by the miR, and suggest that differential activity of such miR-mRNA relationships contribute to differences in melanoma invasiveness. Many of these relationships were reflected across the skin cutaneous melanoma TCGA dataset, indicating that these observations also show graded activity across clinical samples. Several of these miRs are implicated in cancer progression (miR-211, -340, -125b, -221, and -29b). The specific role for miR-29b-3p in melanoma has not been well studied. We experimentally validated the predicted miR-29b-3p regulation of LAMC1 and PPIC and LASP1, and show that dysregulation of miR-29b-3p or these mRNA targets can influence cellular invasiveness in vitro. CONCLUSIONS: This analytic strategy provides a comprehensive, systems-level approach to identify miR-mRNA regulation in high-throughput cancer data, identifies novel putative interactions with functional phenotypic relevance, and can be used to direct experimental resources for subsequent experimental validation. Computational scripts are available: http://github.com/uomsystemsbiology/LMMEL-miR-miner.