Ovarian cancer is detectable from peripheral blood using machine learning over T-cell receptor repertoires.

The extraordinary diversity of T cells and B cells is critical for body maintenance. This diversity has an important role in protecting against tumor formation. In humans, the T-cell receptor (TCR) repertoire is generated through a striking stochastic process called V(D)J recombination, in which different gene segments are assembled and modified, leading to extensive variety. In ovarian cancer (OC), an unfortunate 80% of cases are detected late, leading to poor survival outcomes. However, when detected early, approximately 94% of patients live longer than 5 years after diagnosis. Thus, early detection is critical for patient survival. To determine whether the TCR repertoire obtained from peripheral blood is associated with tumor status, we collected blood samples from 85 women with or without OC and obtained TCR information. We then used machine learning to learn the characteristics of samples and to finally predict, over a set of unseen samples, whether the person is with or without OC. We successfully stratified the two groups, thereby associating the peripheral blood TCR repertoire with the formation of OC tumors. A careful study of the origin of the set of T cells most informative for the signature indicated the involvement of a specific invariant natural killer T (iNKT) clone and a specific mucosal-associated invariant T (MAIT) clone. Our findings here support the proposition that tumor-relevant signal is maintained by the immune system and is coded in the T-cell repertoire available in peripheral blood. It is also possible that the immune system detects tumors early enough for repertoire technologies to inform us near the beginning of tumor formation. Although such detection is made by the immune system, we might be able to identify it, using repertoire data from peripheral blood, to offer a pragmatic way to search for early signs of cancer with minimal patient burden, possibly with enhanced sensitivity.

The temporal behavior of the murine T cell receptor repertoire following Immunotherapy.

Immunotherapy is now an essential tool for cancer treatment, and the unique features of an individual's T cell receptor repertoire are known to play a key role in its effectiveness. The repertoire, famously vast due to a cascade of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire over several time points following treatment with anti-CTLA-4, in a syngeniec mouse model for colorectal cancer, generating a longitudinal dataset of T cell clones and their abundance. The dynamics of the repertoire can be observed in response to treatment over a period of four weeks, as clonal expansion of specific clones ascends and descends. The data made available here can be used to determine treatment and predict its effect, while also providing a unique look at the behavior of the immune system over time.

GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data.

BACKGROUND: In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential. RESULTS: This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire. CONCLUSION: The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py .

A T cell repertoire timestamp is at the core of responsiveness to CTLA-4 blockade.

Biology of the response to anti-CTLA-4 involves the dynamics of specific T cell clones. Reasons for clinical success and failure of this treatment are still largely unknown. Here, we quantified the dynamics of the T cell receptor (TCR) repertoire, throughout 4 weeks involving treatment with anti-CTLA-4, in a syngeneic mouse model for colorectal cancer. These dynamics show an initial increase in clonality in tandem with a decrease in diversity, effects which gradually subside. Furthermore, response to treatment is tightly connected to the shared and public parts of the T cell repertoire. We were able to recognize time-dependent behaviors of specific TCR sequences and cell types and to show the response is dominated by specific motifs. We see that a single, specific time point might be useful to inform a physician of the true response to treatmentThe research further highlights the importance of temporal analyses of the immune response.

CDR3 and V genes show distinct reconstitution patterns in T cell repertoire post-allogeneic bone marrow transplantation.

Restoration of T cell repertoire diversity after allogeneic bone marrow transplantation (allo-BMT) is crucial for immune recovery. T cell diversity is produced by rearrangements of germline gene segments (V (D) and J) of the T cell receptor (TCR) α and ß chains, and selection induced by binding of TCRs to MHC-peptide complexes. Multiple measures were proposed for this diversity. We here focus on the V-gene usage and the CDR3 sequences of the beta chain. We compared multiple T cell repertoires to follow T cell repertoire changes post-allo-BMT in HLA-matched related donor and recipient pairs. Our analyses of the differences between donor and recipient complementarity determining region 3 (CDR3) beta composition and V-gene profile show that the CDR3 sequence composition does not change during restoration, implying its dependence on the HLA typing. In contrast, V-gene usage followed a time-dependent pattern, initially following the donor profile and then shifting back to the recipients' profile. The final long-term repertoire was more similar to that of the recipient's original one than the donor's; some recipients converged within months, while others took multiple years. Based on the results of our analyses, we propose that donor-recipient V-gene distribution differences may serve as clinical biomarkers for monitoring immune recovery.

Breast cancer is marked by specific, Public T-cell receptor CDR3 regions shared by mice and humans.

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires.

Predicting and affecting response to cancer therapy based on pathway-level biomarkers.

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

A single nucleotide variant of human PARP1 determines response to PARP inhibitors.

The introduction of novel cancer drugs and innovative treatments brings great hope for cancer patients, but also an urgent need to match drugs to suitable patients, since certain drugs that benefit one patient may actually harm others. The newly developed poly-ADP ribose polymerase (PARP) inhibitors (PARPis) are a group of pharmacological enzyme inhibitors used clinically for multiple indications. Several forms of cancer tend to be PARP dependent, making PARP an attractive target for cancer therapy. Specifically, PARPis are commonly used in BRCA-associated breast cancers patients, since unrepaired single-strand breaks are converted into double-strand breaks and BRCA-associated tumors cannot repair them by homologous recombination so that PARPi leads to tumor cell death, by a mechanism called "Synthetic Lethality". Unfortunately, not all patients respond to PARPi, and it is not currently possible to predict who will or will not respond. Here, we present a specific genomic marker, which reflects a single-nucleotide polymorphism of human PARP1 and correlates in vitro with response to PARPi, throughout all indications. In addition, we report that this SNP is associated with re-shaping mRNA, and mRNA levels, and influences the final protein structure to expose new binding sites while hiding others. The status of the SNP is therefore critical to patients' care, as it relates responses to PARPi to the PARP1-SNP carried.

Resistance to paclitaxel is associated with a variant of the gene BCL2 in multiple tumor types.

Paclitaxel, the most commonly used form of chemotherapy, is utilized in curative protocols in different types of cancer. The response to treatment differs among patients. Biological interpretation of a mechanism to explain this personalized response is still unavailable. Since paclitaxel is known to target BCL2 and TUBB1, we used pan-cancer genomic data from hundreds of patients to show that a single-nucleotide variant in the BCL2 sequence can predict a patient's response to paclitaxel. Here, we show a connection between this BCL2 genomic variant, its transcript structure, and protein abundance. We demonstrate these findings in silico, in vitro, in formalin-fixed paraffin-embedded (FFPE) tissue, and in patient lymphocytes. We show that tumors with the specific variant are more resistant to paclitaxel. We also show that tumor and normal cells with the variant express higher levels of BCL2 protein, a phenomenon that we validated in an independent cohort of patients. Our results indicate BCL2 sequence variations as determinants of chemotherapy resistance. The knowledge of individual BCL2 genomic sequences prior to the choice of chemotherapy may improve patient survival. The current work also demonstrates the benefit of community-wide, integrative omics data sources combined with in-lab experimentation and validation sets.

The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation.

Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points: The immune system classifies healthy and pathologic states of the body-including both self and foreign elements-by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions-such collective inflammatory responses are dynamically responsive.Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms. We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions.

Immunological analysis of phase II glioblastoma dendritic cell vaccine (Audencel) trial: immune system characteristics influence outcome and Audencel up-regulates Th1-related immunovariables.

Audencel is a dendritic cell (DC)-based cellular cancer immunotherapy against glioblastoma multiforme (GBM). It is characterized by loading of DCs with autologous whole tumor lysate and in vitro maturation via "danger signals". The recent phase II "GBM-Vax" trial showed no clinical efficacy for Audencel as assessed with progression-free and overall survival in all patients. Here we present immunological research accompanying the trial with a focus on immune system factors related to outcome and Audencel's effect on the immune system. Methodologically, peripheral blood samples (from apheresis before Audencel or venipuncture during Audencel) were subjected to functional characterization via enzyme-linked immunospot (ELISPOT) assays connected with cytokine bead assays (CBAs) as well as phenotypical characterization via flow cytometry and mRNA quantification. GBM tissue samples (from surgery) were subjected to T cell receptor sequencing and immunohistochemistry. As results we found: Patients with favorable pre-existing anti-tumor characteristics lived longer under Audencel than Audencel patients without them. Pre-vaccination blood CD8+ T cell count and ELISPOT Granzyme B production capacity in vitro upon tumor antigen exposure were significantly correlated with overall survival. Despite Audencel's general failure to induce a significant clinical response, it nevertheless seemed to have an effect on the immune system. For instance, Audencel led to a significant up-regulation of the Th1-related immunovariables ELISPOT IFNγ, the transcription factor T-bet in the blood and ELISPOT IL-2 in a dose-dependent manner upon vaccination. Post-vaccination levels of ELISPOT IFNγ and CD8+ cells in the blood were indicative of a significantly better survival. In summary, Audencel failed to reach an improvement of survival in the recent phase II clinical trial. No clinical efficacy was registered. Our concomitant immunological work presented here indicates that outcome under Audencel was influenced by the state of the immune system. On the other hand, Audencel also seemed to have stimulated the immune system. Overall, these immunological considerations suggest that DC immunotherapy against glioblastoma should be studied further - with the goal of translating an apparent immunological response into a clinical response. Future research should concentrate on investigating augmentation of immune reactions through combination therapies or on developing meaningful biomarkers.

Tamoxifen-resistant breast cancer cells are resistant to DNA-damaging chemotherapy because of upregulated BARD1 and BRCA1.

Tamoxifen resistance is accountable for relapse in many ER-positive breast cancer patients. Most of these recurrent patients receive chemotherapy, but their chemosensitivity is unknown. Here, we report that tamoxifen-resistant breast cancer cells express significantly more BARD1 and BRCA1, leading to resistance to DNA-damaging chemotherapy including cisplatin and adriamycin, but not to paclitaxel. Silencing BARD1 or BRCA1 expression or inhibition of BRCA1 phosphorylation by Dinaciclib restores the sensitivity to cisplatin in tamoxifen-resistant cells. Furthermore, we show that activated PI3K/AKT pathway is responsible for the upregulation of BARD1 and BRCA1. PI3K inhibitors decrease the expression of BARD1 and BRCA1 in tamoxifen-resistant cells and re-sensitize them to cisplatin both in vitro and in vivo. Higher BARD1 and BRCA1 expression is associated with worse prognosis of early breast cancer patients, especially the ones that received radiotherapy, indicating the potential use of PI3K inhibitors to reverse chemoresistance and radioresistance in ER-positive breast cancer patients.

Network Representation of T-Cell Repertoire- A Novel Tool to Analyze Immune Response to Cancer Formation.

The T cell repertoire potentially presents complexity compatible, or greater than, that of the human brain. T cell based immune response is involved with practically every part of human physiology, and high-throughput biology needed to follow the T-cell repertoire has made great leaps with the advent of massive parallel sequencing [1]. Nevertheless, tools to handle and observe the dynamics of this complexity have only recently started to emerge [e.g., 2, 3, 4] in parallel with sequencing technologies. Here, we present a network-based view of the dynamics of the T cell repertoire, during the course of mammary tumors development in a mouse model. The transition from the T cell receptor as a feature, to network-based clustering, followed by network-based temporal analyses, provides novel insights to the workings of the system and provides novel tools to observe cancer progression via the perspective of the immune system. The crux of the approach here is at the network-motivated clustering. The purpose of the clustering step is not merely data reduction and exposing structures, but rather to detect hubs, or attractors, within the T cell receptor repertoire that might shed light on the behavior of the immune system as a dynamic network. The Clone-Attractor is in fact an extension of the clone concept, i.e., instead of looking at particular clones we observe the extended clonal network by assigning clusters to graph nodes and edges to adjacent clusters (editing distance metric). Viewing the system as dynamical brings to the fore the notion of an attractors landscape, hence the possibility to chart this space and map the sample state at a given time to a vector in this large space. Based on this representation we applied two different methods to demonstrate its effectiveness in identifying changes in the repertoire that correlate with changes in the phenotype: (1) network analysis of the TCR repertoire in which two measures were calculated and demonstrated the ability to differentiate control from transgenic samples, and, (2) machine learning classifier capable of both stratifying control and trangenic samples, as well as to stratify pre-cancer and cancer samples.

The linker histone H1.0 generates epigenetic and functional intratumor heterogeneity.

Tumors comprise functionally diverse subpopulations of cells with distinct proliferative potential. Here, we show that dynamic epigenetic states defined by the linker histone H1.0 determine which cells within a tumor can sustain the long-term cancer growth. Numerous cancer types exhibit high inter- and intratumor heterogeneity of H1.0, with H1.0 levels correlating with tumor differentiation status, patient survival, and, at the single-cell level, cancer stem cell markers. Silencing of H1.0 promotes maintenance of self-renewing cells by inducing derepression of megabase-sized gene domains harboring downstream effectors of oncogenic pathways. Self-renewing epigenetic states are not stable, and reexpression of H1.0 in subsets of tumor cells establishes transcriptional programs that restrict cancer cells' long-term proliferative potential and drive their differentiation. Our results uncover epigenetic determinants of tumor-maintaining cells.

hsa-miR-9 controls the mobility behavior of glioblastoma cells via regulation of MAPK14 signaling elements.

BACKGROUND: Glioblastoma Multiforme (GBM) is the most common and lethal primary tumor of the brain. GBM is associated with one of the worst 5-year survival rates among all human cancers, despite much effort in different modes of treatment. RESULTS: Here, we demonstrate specific GBM cancer phenotypes that are governed by modifications to the MAPAKAP network. We then demonstrate a novel regulation mode by which a set of five key factors of the MAPKAP pathway are regulated by the same microRNA, hsa-miR-9.We demonstrate that hsa-miR-9 overexpression leads to MAPKAP signaling inhibition, partially by interfering with the MAPK14/MAPKAP3 complex. Further, hsa-miR-9 overexpression initiates re-arrangement of actin filaments, which leads us to hypothesize a mechanism for the observed phenotypic shift. CONCLUSION: The work presented here exposes novel microRNA features and situates hsa-miR-9 as a therapeutic target, which governs metastasis and thus determines prognosis in GBM through MAPKAP signaling.

Dendritic Cells in the Context of Human Tumors: Biology and Experimental Tools.

Dendritic cells (DC) are the most potent and versatile antigen-presenting cells (APC) in the immune system. DC have an exceptional ability to comprehend the immune context of a captured antigen based on molecular signals identified from its vicinity. The analyzed information is then conveyed to other immune effector cells. Such capability enables DC to play a pivotal role in mediating either an immunogenic response or immune tolerance towards an acquired antigen. This review summarizes current knowledge on DC in the context of human tumors. It covers the basics of human DC biology, elaborating on the different markers, morphology and function of the different subsets of human DC. Human blood-borne DC are comprised of at least three subsets consisting of one plasmacytoid DC (pDC) and two to three myeloid DC (mDC) subsets. Some tissues have unique DC. Each subset has a different phenotype and function and may induce pro-tumoral or anti-tumoral effects. The review also discusses two methods fundamental to the research of DC on the single-cell level: multicolor flow cytometry (FCM) and image-based cytometry (IC). These methods, along with new genomics and proteomics tools, can provide high-resolution information on specific DC subsets and on immune and tumor cells with which they interact. The different layers of collected biological data may then be integrated using Immune-Cytomics modeling approaches. Such novel integrated approaches may help unravel the complex network of cellular interactions that DC carry out within tumors, and may help harness this complex immunological information into the development of more effective treatments for cancer.

Experimental Support for the Ecoimmunity Theory: Distinct Phenotypes of Nonlymphocytic Cells in SCID and Wild-Type Mice.

Immune tolerance toward "self" is critical in multiple immune disorders. While there are several mechanisms to describe the involvement of immune cells in the process, the role of peripheral tissue cells in that context is not yet clear. The theory of ecoimmunity postulates that interactions between immune and tissue cells represent a predator-prey relationship. A lifelong interaction, shaped mainly during early ontogeny, leads to selection of nonimmune cell phenotypes. Normally, therefore, nonimmune cells that evolve alongside an intact immune system would be phenotypically capable of evading immune responses, and cells whose phenotype falls short of satisfying this steady state would expire under hostile immune responses. This view was supported until recently by experimental evidence showing an inferior endurance of severe combined immunodeficiency (SCID)-derived pancreatic islets when engrafted into syngeneic immune-intact wild-type (WT) mice, relative to islets from WT. Here we extend the experimental exploration of ecoimmunity by searching for the presence of the phenotypic changes suggested by the theory. Immune-related phenotypes of islets, spleen, and bone marrow immune cells were determined, as well as SCID and WT nonlymphocytic cells. Islet submass grafting was performed to depict syngeneic graft functionality. Islet cultures were examined under both resting and inflamed conditions for expression of CD40 and major histocompatibility complex (MHC) class I/II and release of interleukin-1α (IL-1α), IL-1ß, IL-6, tumor necrosis factor-α (TNF-α), IL-10, and insulin. Results depict multiple pathways that appear to be related to the sculpting of nonimmune cells by immune cells; 59 SCID islet genes displayed relative expression changes compared with WT islets. SCID cells expressed lower tolerability to inflammation and higher levels of immune-related molecules, including MHC class I. Accordingly, islets exhibited a marked increase in insulin release upon immunocyte depletion, in effect resuming endocrine function that was otherwise suppressed by resident immunocytes. This work provides further support of the ecoimmunity theory and encourages subsequent studies to identify its role in the emergence and treatment of autoimmune pathologies, transplant rejection, and cancer.

Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation.

The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.

Reciprocal Regulation between SIRT6 and miR-122 Controls Liver Metabolism and Predicts Hepatocarcinoma Prognosis.

Mice overexpressing the longevity protein SIRT6 or deficient for the liver's most prevalent microRNA miR-122 display a similar set of phenotypes, including improved lipid profile and protection against damage linked to obesity. Here, we show that miR-122 and SIRT6 negatively regulate each other's expression. SIRT6 downregulates miR-122 by deacetylating H3K56 in the promoter region. MiR-122 binds to three sites on the SIRT6 3' UTR and reduces its levels. The interplay between SIRT6 and miR-122 is manifested in two physiologically relevant ways in the liver. First, they oppositely regulate a similar set of metabolic genes and fatty acid ß-oxidation. Second, in hepatocellular carcinoma patients, loss of a negative correlation between SIRT6 and miR-122 expression is significantly associated with better prognosis. These findings show that SIRT6 and miR-122 negatively regulate each other to control various aspects of liver physiology and SIRT6-miR-122 correlation may serve as a biomarker for hepatocarcinoma prognosis.

Fmrp Interacts with Adar and Regulates RNA Editing, Synaptic Density and Locomotor Activity in Zebrafish.

Fragile X syndrome (FXS) is the most frequent inherited form of mental retardation. The cause for this X-linked disorder is the silencing of the fragile X mental retardation 1 (fmr1) gene and the absence of the fragile X mental retardation protein (Fmrp). The RNA-binding protein Fmrp represses protein translation, particularly in synapses. In Drosophila, Fmrp interacts with the adenosine deaminase acting on RNA (Adar) enzymes. Adar enzymes convert adenosine to inosine (A-to-I) and modify the sequence of RNA transcripts. Utilizing the fmr1 zebrafish mutant (fmr1-/-), we studied Fmrp-dependent neuronal circuit formation, behavior, and Adar-mediated RNA editing. By combining behavior analyses and live imaging of single axons and synapses, we showed hyperlocomotor activity, as well as increased axonal branching and synaptic density, in fmr1-/- larvae. We identified thousands of clustered RNA editing sites in the zebrafish transcriptome and showed that Fmrp biochemically interacts with the Adar2a protein. The expression levels of the adar genes and Adar2 protein increased in fmr1-/- zebrafish. Microfluidic-based multiplex PCR coupled with deep sequencing showed a mild increase in A-to-I RNA editing levels in evolutionarily conserved neuronal and synaptic Adar-targets in fmr1-/- larvae. These findings suggest that loss of Fmrp results in increased Adar-mediated RNA editing activity on target-specific RNAs, which, in turn, might alter neuronal circuit formation and behavior in FXS.