Studying Myeloid Cell Heterogeneity After Spinal Cord Injury via Time-Resolved Single-Cell RNA Sequencing

Spinal cord injury (SCI) is a devastating pathology that affects thousands of individuals annually, resulting in the requirement for long-term physical and medical care and thus significant personal, societal, and economic burdens. The SCI pathology is characterised by an initial mechanical insult, followed by a spatiotemporally dynamic secondary injury. Decades of research have worked to assemble a general picture of this secondary pathology. We now understand that compared to the normal wound healing observed in the periphery, tissue recovery after SCI is dysregulated and results in a chronic wound state characterized by persistent inflammation and functional deficits. The primary drivers of this inflammation are central nervous system (CNS) resident microglia and infiltrating myeloid cells. However, the precise role of these myeloid cell subsets remains unclear as upon crossing the blood-spinal cord barrier (BSCB), infiltrating monocyte-derived macrophages may take on the morphology of microglia, and upregulate canonical microglia markers, making the two populations difficult to distinguish. In this PhD project, I employed single-cell RNA sequencing (scRNAseq) to deconvolute the complex heterogeneity of infiltrating and resident myeloid cells in mouse models of thoracic contusion SCI at an unprecedented resolution. To fully appreciate the temporal dynamics of the pathology, I collected samples across the acute, subacute, and early chronic phases of SCI, plus a sham-injured control. Recent experiments have demonstrated that CNS infiltrating macrophages also take on the transcriptional profiles of microglia, which led me to question whether I had accurately annotated infiltrating macrophages in the dataset. To address this, I repeated the experiment with a transgenic fate-mapping mouse line then integrated these two datasets to generate a time-resolved SCI myeloid cell atlas with definitive ontogeny labelling. With this dataset I generated a putative time resolved map of myeloid cell dynamics across the SCI pathology. Through collaboration, I was also able to verify the expression of select genes via single-molecule fluorescent in situ hybridization (smFISH) and immunofluorescence (IF). A key observation was the persistence of a pro-inflammatory foam cell-like state in both microglia and macrophages, which may contribute to the non-resolving chronic injury. Future studies might investigate the functional relevance of this population, and its suitability as a therapeutic target to reduce the long-term disabilities of SCI patients.Regan Hamel was supported by the Cambridge Trust and is the recipient of a Canadian Scholarship Trust Foundation, an MNI-Cambridge Douglas Avrith Graduate Studentship, and a Rosetrees Trust Studentship

Systems Biology Approaches For The Analysis Of High-Throughput Biological Data

The identification of biological processes involved with a certain phenotype, such as a disease or drug treatment, is the goal of the majority of life sciences experiments. Pathway analysis methods are used to interpret high-throughput biological data to identify such processes by incorporating information on biological systems to translate data into biological knowledge. Although widely used, current methods share a number of limitations. First, they do not take into account the individual contribution of each gene to the phenotype in analysis. Second, most of the methods include parameters of difficult interpretation, often arbitrarily set. Third, the results of all methods are affected by the fact that pathways are not independent entities, but communicate with each other by a phenomenon referred to as crosstalk. Crosstalk effects heavily influence the results of pathway analysis methods, adding a number of false positives and false negatives, making them difficult to interpret. We developed methods that address these limitations by i) allowing for the incorporation of individual gene contributions, ii) developing objective methods for the estimation of parameters of pathway analysis methods, and iii) developing an approach able to detect, quantify, and correct for crosstalk effects. We show on a number of real and simulated data that our approaches increase specificity and sensitivity of pathway analysis, allowing for a more effective identification of the processes and mechanisms underlying biological phenomena

Deciphering transcriptional regulation in cancer cells and development of a new method to identify key transcriptional regulators and their target genes

Cancer cells accumulate genetic changes during carcinogenesis. The dimension of these changes range from point mutations to large chromosomal aberrations. It has been widely accepted that essential genetic programs are thereby dysregulated that normally would prevent uncontrolled cellular division and growth. Transcription factors (TFs) are key proteins of gene regulation and are frequently associated with genetic pathologies, e.g. MYCN in neuroblastomas (NBs). Research on gene regulation -in general or condition-specific- thus is a central aspect in cancer research, and it is also the focus of my work. In a carcinogenesis model of NBs without MYCN-amplification, mutations of chromosome 11q (11q-CNA) are suspected to critically influence tumor development. We were able to refine this model by means of gene expression analysis on 11q-CNA in NBs with different clinical outcome. Gene expression profiles of NBs with unfavorable progression differed significantly between tumors with and without 11q-CNA, whereas 11q-CNA in NBs with favorable outcome is apparently compensated by a yet unknown mechanism. The TF-encoding gene CAMTA1 is located on the chromosomal region 1p, which is frequently deleted in NBs. In vitro experiments with ectopic induction of CAMTA1 yielded CAMTA1-regulated genes with different gene expression profiles that were functionally associated by enrichment analyses with cell cycle regulation and neuronal differentiation. The suggested role of CAMTA1 as a tumor suppressor gene was confirmed by additional in vivo experiments. Furthermore, we studied the effect of MYC and MYCN in NBs without MYCN-amplification and found that these TF also strongly regulate a large number of common target genes according to their own gene expression in these tumors. Promoter analyses and chromatin immunoprecipitation additionally supported the regulation of the determined target genes by MYC/MYCN. The genome-wide application of promoter and enrichment analyses on gene expression data from mouse models enabled us to predict target TFs of Rage signaling. E2f1 and E2f4 were validated experimentally as components of the Rage-dependent gene regulatory network. Finally, we used our experience from gene expression analysis to develop a novel machine learning method to precisely predict TF target gene relationships in human. We combined results from a genome-wide correlation meta-analysis on 4064 microarray gene expression profiles and promoter analyses on TF binding sites with known regulatory interactions between TFs and target genes in our approach. Our method outperformed other comparable methods in human, as we improved shortcomings of other algorithms specifically for higher eukaryotes, in particular the frequently (erroneously) assumed correlation between the mRNA expression of TFs and their target genes. We made our method freely available as a software package with multiple applications like the identification of key TFs in a multiplicity of cellular systems (e.g. cancer cells)

Improving & applying single-cell RNA sequencing

The cell is the fundamental building block of life. With the advent of single-cell RNA sequencing (scRNA-seq), we can for the first time assess the transcriptome of many individual cells. This has profound implications for biological and medical questions and is especially important to characterize heterogeneous cell populations and rare cells. However, the technology is technically and computationally challenging as complementary DNA (cDNA) needs to be generated and amplified from minute amounts of mRNA and sequenceable libraries need to be efficiently generated from many cells. This requires to establish different protocols, identify important caveats, benchmark various methods and improve them if possible. To this end, we analysed amplification bias and its effect on detecting differentially expressed genes in several bulk and a single-cell RNA sequencing methods. We found that correcting for amplification bias is not possible computationally but improves the power of scRNA-seq considerably, though neglectable for bulk-RNA-seq. In the second study we compared six prominent scRNA-seq protocols as more and more single-cell RNA-sequencing are becoming available, but an independent benchmark of methods is lacking. By using the same mouse embryonic stem cells (mESCs) and exogenous mRNA spike-ins as common reference, we compared six important scRNA-seq protocols in their sensitivity, accuracy and precision to quantify mRNA levels. In agreement with our previous study, we find that the precision, i.e. the technical variance, of scRNA-seq methods is driven by amplification bias and drastically reduced when using unique molecular identifiers to remove amplification duplicates. To assess the combined effects of sensitivity and precision and to compare the cost-efficiency of methods we compared the power to detect differentially expressed genes among the tested scRNA-seq protocols using a novel simulation framework. We find that some methods are prohibitively inefficient and others show trade-offs depending on the number of cells per sample that need to be analysed. Our study also provides a framework for benchmarking further improvements of scRNA-seq protocol and we published an improved version of our simulation framework powsimR. It uniquely recapitulates the specific characteristics of scRNA-seq data to enable streamlined simulations for benchmarking both wet lab protocols and analysis algorithms. Furthermore, we compile our experience in processing different types of scRNA-seq data, in particular with barcoded libraries and UMIs, and developed zUMIs, a fast and flexible scRNA-seq data processing software overcoming shortcomings of existing pipelines. In addition, we used the in-depth characterization of scRNA-seq technology to optimize an already powerful scRNA-seq protocol even further. According to data generated from exogenous mRNA spike-ins, this new mcSCRB-seq protocol is currently the most sensitive scRNA-seq protocol available. Single-cell resolution makes scRNA-seq uniquely suited for the understanding of complex diseases, such as leukemia. In acute lymphoblastic leukemia (ALL), rare chemotherapy-resistant cells persist as minimal residual disease (MRD) and may cause relapse. However, biological mechanisms of these relapse-inducing cells remain largely unclear because characterisation of this rare population was lacking so far. In order to contribute to the understanding of MRD, we leveraged scRNA-seq to study minimal residual disease cells from ALL. We obtained and characterised rare, chemotherapy-resistant cell populations from primary patients and patient cells grown in xenograft mouse models. We found that MRD cells are dormant and feature high expression of adhesion molecules in order to persist in the hematopoietic niche. Furthermore, we could show that there is plasticity between resting, resistant MRD cells and cycling, therapy-sensitive cells, indicating that patients could benefit from strategies that release MRD cells from the niche. Importantly, we show that our data derived from xenograft models closely resemble rare primary patient samples. In conclusion, my work of the last years contributes towards the development of experimental and computational single-cell RNA sequencing methods enabling their widespread application to biomedical problems such as leukemia

LncRNAs signature defining major subtypes of B-cell acute lymphoblastic leukemia

Introduction: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most prevalent heterogeneous cancer in children and adults, with multiple subtypes. Emerging evidence suggests that long non-coding RNAs (lncRNAs) might play a key role in the development and progression of leukemia. Thus, we performed a transcriptional and DNA methylation survey to explore the lncRNA landscape on three BCP-ALL subtypes (82 samples) and demonstrated their functions and epigenetic profile. Methodology: The primary BCP-ALL samples from bone marrow material were collected from diagnosis (ID) and relapse (REL) stages of adult (n = 21) and pediatric (n = 24) BCP-ALL patients, using RNA-seq and DNA methylation array technology. The subtype-specific and relapse-specific lncRNAs were analyzed by differential expression (DE) analysis method using LIMMA Voom. By analyzing the co-expression of the subtype-specific lncRNAs and protein-coding (PC) genes from all subtypes, we inferred potential functions of these lncRNAs by applying “guilt-by-association” approach. Additionally, we validated our subtype-specific lncRNAs on an independent cohort of 47 BCP-ALL samples. The epigenetic regulation of subtype-specific lncRNAs were identified using the Bumphunter package. The correlation analysis was performed between DM and DE lncRNAs from three subtypes to determine the epigenetically facilitated and silenced lncRNAs. Results: We present a comprehensive landscape of lncRNAs signatures which classifies three molecular subtypes of BCP-ALL on DNA methylation and RNA expression levels. The principle component analysis (PCA) on most variable lncRNAs on RNA and DNA methylation level confirmed robust separation of DUX4, Ph-like and NH-HeH BCP-ALL subtypes. Using integrative bioinformatics analysis, subtype-specific and relapse-specific lncRNAs signature together determine 1564 subtype-specific and 941 relapse-specific lncRNAs from three subtypes. The unsupervised hierarchical clustering on these subtype-specific lncRNAs validated their specificity on the independent validation cohort. For the first time, our study demonstrates that BCP-ALL subtype specific as well as relapse-specific lncRNAs may contribute to the activation of key pathways including TGF-β, PI3K-Akt, mTOR and activation of JAK-STAT signaling pathways from DUX4 and Ph-like subtypes. Finally, the significantly hyper-methylated and hypo-methylated subtype-specific lncRNAs were profiled. In addition to that, we identified 23 subtypes specific lncRNAs showing hypo and hyper-methylation pattern in their promoter region that significantly correlates with their diminished and increased expression in respective subtypes. Conclusions: Overall, our work provides the most comprehensive analyses for lncRNAs in BCP-ALL subtypes. Our findings suggest a wide range of biological functions associated with lncRNAs and epigenetically facilitated lncRNAs in BCP-ALL and provide a foundation for functional investigations that could lead to novel therapeutic approaches.Einführung: Die B-Vorläufer akute lymphatischen Leukämie (BCP-ALL) ist eine heterogene Krebserkrankung mit mehreren definierten Subgruppen. Neue Daten deuten darauf hin, dass lange nicht-kodierende RNAs (long noncoding RNAs - lncRNAs) eine Schlüsselrolle bei der Entwicklung und Progression der BCP-ALL spielen könnten. Daher führten wir eine Transkriptions- und DNA-Methylierungsstudie durch, um die lncRNA-Landschaft von drei BCP-ALL-Subgruppen (82 Proben) zu charakterisieren und potentielle regulative Konsequenzen zu analysieren. Methodik: Material wurde zum Zeitpunkt der Erstdiagnose (ID) und im Rezidiv (REL) von erwachenen (n = 21) und pädiatrischen (n = 24) BCP-ALL-Patienten entnommen und unter Verwendung von RNA-Seq und DNA-Methylierungs-Array-Technologien untersucht. Die Subgruppen-spezifischen und rezidiv-spezifischen lncRNAs wurden durch differentielle Expressions (DE) Analysen mit LIMMA Voom analysiert. Durch die Analyse der Koexpression von lncRNAs mit Protein-kodierenden (PC) Genen aus allen Subgruppen schlossen wir unter Verwendung eines ‚Guilt-by-association‘ -Ansatzes auf potentielle Funktionen der DE lncRNAs. Zudem haben wir die Subgruppen-spezifischen lncRNAs auf einem unabhängigen Datenset von 47 BCP-ALL-Proben validiert. Die epigenetische. Die epigenetische Regulation von Subgruppen-spezifischen lncRNAs wurde durch eine differentielle Methylierungs (DM) analyse identifiziert. Die Korrelation zwischen DM und DE lncRNAs aus drei Subgruppen wurde ermittelt, um den Einfluss der epigenetischen Regulation auf die Expression von lncRNAs zu analysieren. Ergebnisse: Wir präsentieren eine umfassende Landschaft von lncRNA-Signaturen, die drei molekulare Subtypen von BCP-ALL auf DNA-Methylierungs- und RNA-Expressionslevel klassifiziert. Die Hauptkomponentenanalyse (PCA) auf den top variablen lncRNAs auf RNA und DNA-Methylierungsniveau bestätigte eine robuste Trennung von Ph-like, DUX4 und NH-NeH BCP-ALL Subtypen. Mit integrativer bioinformatischer Analyse, zusammen 1564 subtyp-spezifische und 941 rezidiv-spezifische lncRNAs aus den drei Subtypen. Das unüberwachte hierarchische Clustering auf diesen Subtyp-spezifischen lncRNAs validierte ihre Spezifität in der unabhängigen Validierungskohorte. Unsere Studie zeigt erstmals, dass BCP-ALL-Subtyp-spezifische sowie Rezidiv-spezifische lncRNAs zur Aktivierung von Signalwegen wie TGF-β, PI3K-Akt, mTOR und Aktivierung von JAK-STAT-Signalwegen von DUX4 und Ph-like Subtypen. Endlich wurden die signifikant DM subtyp-spezifische lncRNAs profiliert. Darüber hinaus identifizierten wir 23 Subtyp-spezifische lncRNAs, die ein Hypo- und Hypermethylierungsmuster in ihrer Promotorregion zeigen, das signifikant mit ihrer verringerten und erhöhten Expression in den jeweiligen Subtypen korreliert. Schlussfolgerungen: Insgesamt liefert unsere Arbeit die umfassendsten Analysen für lncRNAs in BCP-ALL-Subtypen. Unsere Ergebnisse weisen auf eine Vielzahl von biologischen Funktionen im Zusammenhang mit lncRNAs und epigenetisch erleichterten lncRNAs in BCP-ALL hin und bieten eine Grundlage für funktionelle Untersuchungen, die zu neuen therapeutischen Ansätzen führen könnten

Monoclonal Antibodies and Their Functional Fragments in Research, Diagnosis and Therapy

This book is a compendium of scientific articles submitted to a Special Issue of International Journal of Molecular Sciences, fostered by MDPI and curated by Dr. Annamaria Sandomenico and Dr. Menotti Ruvo from the Institute of Biostructure and Bioimaging of the National Research Council. All articles underwent a rigorous peer review and were selected to highlight the properties that make monoclonal antibodies and their functional fragments some of the most useful and versatile assets in therapy and diagnosis

Functional genomics of brain development and developmentally related brain disease in "Drosophila"

One of the fundamental challenges in basic neuroscience is to understand the molecular genetic networks associated with building the brain. As malfunction in these genetic pathways can lead to disorders like cancer, brain development is also a crucial research area for clinical neuroscience. In the course of this thesis, different molecular aspects of Drosophila brain development and related neoplastic disease were analyzed using high-density oligonucleotide arrays. The homeotic selector gene labial (lab) plays an important role in specification of neuronal identity in the embryonic brain of Drosophila. In labial mutants presumptive neurons in the posterior tritocerebrum fail to differentiate. This leads to severe defects in tritocerebral axon pathways. Using high density oligonucleotide arrays we identified downstream target genes of Labial and showed that only a limited and distinct set of genes expressed in the embryo is regulated by this homeoprotein. Furthermore, we performed genetic rescue experiments to analyze the functional equivalence of Drosophila Hox gene products in specification of the tritocerebral neuromere. Surprisingly, all tested homeotic proteins, with the exception of Abd-B, were able to rescue the labial mutant phenotype in the tritocerebrum. These results indicate that the specificity of homeotic gene action in embryonic brain development has to be modulated by cis-acting regulatory elements. Another study circled around the homeobox transcription factor otd and its human homolog Otx2. Cross-phylum rescue experiments have shown that these genes are functionally equivalent. We used quantitative transcript imaging to analyze otd and Otx gene action in the Drosophila embryo at a genomic level. Our experiments suggest that about one third of the Otd-regulated transcripts in Drosophila can also be controlled by the human Otx2. These common otd/Otx2 downstream genes are likely to represent the molecular basis for the functional equivalence of otd and Otx2 gene action in Drosophila. glial cells missing (gcm) is a key control gene of gliogenesis. gcm loss-of-function leads to a transformation of glial cells into neurons and, conversely, when gcm is ectopically misexpressed, presumptive neurons become glia. Since gcm encodes a transcription factor it is supposed that a set of downstream genes are regulated by GCM that in turn execute the glial differentiation program. Again, a set of full-genome transcript profiling experiments was conducted to identify gcm downstream genes in a comprehensive manner. A set of several hundred candidate gcm target genes were identified in this screen, giving new insights into neuroglial fate specification in Drosophila. Brain tumors have been extensively studied by looking at genetic alterations and mutations that lead to malignant growth. Still, the causes of brain tumorigenesis are largely unknown. Model systems like Drosophila can be of great help to shed light on altered transcriptional activity in brain tumor phenotypes. To investigate the in vivo transcriptional activity associated with a brain tumor, we conducted genome-wide microarray expression analyses of an adult brain tumor in Drosophila caused by homozygous mutation in the tumor suppressor gene brain tumor (brat). Two independent gene expression studies using two different oligonucleotide microarray platforms were used to compare the transcriptome of adult wildtype flies with mutants displaying the adult bratk06028 mutant brain tumor. Cross-validation and stringent statistical criteria identified a core transcriptional signature of bratk06028 neoplastic tissue. We found highly significant expression level changes for 321 annotated genes associated with the adult neoplastic bratk06028 tissue indicating elevated and aberrant metabolic and cell cycle activity, upregulation of the basal transcriptional machinery, as well as elevated and aberrant activity of ribosome synthesis and translation control. One fifth of these genes show homology to known mammalian genes involved in cancer formation. These results identify for the first time the genome-wide transcriptional alterations associated with an adult brain tumor in Drosophila and reveal insights into the possible mechanisms of tumor formation caused by homozygous mutation of the translational repressor brat

Promises and challenges of adoptive T-cell therapies for solid tumours

From Springer Nature via Jisc Publications RouterHistory: received 2020-11-09, rev-recd 2021-02-22, accepted 2021-03-04, registration 2021-03-04, pub-electronic 2021-03-29, online 2021-03-29, pub-print 2021-05-25Publication status: PublishedFunder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272; Grant(s): RCF18/046Funder: Ovarian Cancer Action; doi: https://doi.org/10.13039/501100000299; Grant(s): HER000762Abstract: Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas—cancer genomics, cancer immunology and cell-therapy manufacturing—that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours

A COMPARISON OF META-ANALYSIS METHODS FOR DETECTING DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY EXPERIMENTS: AN APPLICATION TO MALIGNANT PLEURAL MESOTHELIOMA DATA

The proliferation of microarray experiments and the increasing availability of relevant amount of data in public repositories have created a need for meta-analysis methods to efficiently integrate and validate microarray results from independent but related studies. Despite its increasing popularity, meta-analysis of microarray data is not without problems. In fact, although it shares many features with traditional meta-analysis, most classical meta-analysis methods cannot be directly applied to microarray experiments because of their unique issues. Several meta-analysis techniques have been proposed in the context of microarrays. However, only recently a comprehensive framework to carry out microarray data meta-analysis has been proposed. Moreover very few software packages for microarray meta-analysis implementation exist and most of them either have unclear manuals or are not easy to apply. We applied four meta-analysis methods, the Stouffer’s method, the moderated effect size combination approach, the t-based hierarchical modeling and the rank product method, to a set of three microarray studies on malignant pleural mesothelioma. We focused on differential expression analysis between normal and malignant mesothelioma pleural tissues. Both unfiltered and filtered data were analyzed. The lists of differentially expressed genes provided by each method for either kind of data were compared, also by pathway analysis. These comparisons highlighted a poor overlap between the lists of differentially expressed genes and the related pathways obtained using the unfiltered data. Conversely, a higher concordance of the results, both at the gene and the pathway level, was observed when filtered data were considered. The fact that a significant number of genes were identified by only one of the tested methods shows that the gene ranking is based on different perspectives. In fact, the analyzed methods are based on different assumptions and focus on diverse aspects in selecting significant genes. Since so far there is no consensus on what is (are) the ‘best’ meta-analysis method(s), it may be useful to select candidate genes for further analysis using a combination of different meta-analysis methods. In particular, differentially expressed genes detected by more than one method may be considered as the most reliable ones while genes identified by only a single method may be further explored to expand the knowledge of the biological phenomenon of interest

Biased Constitutive Activity in the Uveal Melanoma Oncogene CYSLTR2 is Unique in CYSLTR2 Germline and Pan-Cancer Human Variome

Uveal melanoma is the most common eye cancer in adults and is clinically and genetically distinct from skin cutaneous melanoma. In a subset of cases, the oncogenic driver is an activating mutation in CYSLTR2, the gene encoding the G protein-coupled receptor (GPCR) cysteinylleukotriene receptor 2. The mutant CYSLTR2 encodes for CysLTR2-L129Q receptor, with the substitution of Leu to Gln at position 129 (3.43). The ability of CysLTR2-L129Q to cause malignant transformation has been hypothesized to result from constitutive activity, but how the receptor could escape desensitization is unknown. In this work, we characterized the functional properties of CysLTR2-L129Q. CysLTR2 signals through the Gq/11/PLC-β pathways, so using a homogenous time resolved fluorescence (HTRF) IP1 accumulation assay, we show that CysLTR2-L129Q is a constitutively active mutant that strongly drives Gq/11 signaling pathways. However, CysLTR2-L129Q only poorly recruits β-arrestin as shown by a bioluminescence resonance energy transfer 2 (BRET2) based β-arrestin recruitment assay. Using a modified Slack-Hall operational model, we quantified the constitutive activity for both pathways and conclude that CysLTR2-L129Q displays profound signaling bias for Gq/11 signaling pathways while escaping β-arrestin-mediated downregulation. CYSLTR2 is the first known example of a GPCR driver oncogene that encodes a highly biased constitutively active mutant receptor. These results provide new insights into the mechanism of CysLTR2-L129Q oncoprotein signaling and suggest CYSLTR2 as a promising potential therapeutic target in uveal melanoma. Furthermore, we learned that CysLTR2 is a significantly mutated GPCR in several other cancers as well. We identified \u3e100 CYSLTR2 missense variants of unknown significance (VUS) in human cancer genomes from available cancer databases, as well as another \u3e100 CYSLTR2 single-nucleotide polymorphisms (SNPs) from exome sequence data. Here, we introduce a proof-of-concept, experimental, activity-based profiling pipeline to systematically assess the mutational landscape of CYSLTR2. We use a single transfection mixture of receptor-encoding DNA and HEK293T cells is used to characterize all variants for expression level, basal and agonist-stimulated G protein signaling, and basal and agonist-stimulated β-arrestin recruitment. The CysLTR2-L129Q mutation causing uveal melanoma has a unique phenotype among all cancer-associated variants. It is highly constitutively active with gain-of-function (GoF) in basal Gq/11-PLC-β signaling and loss-of-function (LoF) in agonist-dependent signaling, while only poorly recruiting β-arrestin. Furthermore, we found that about 21% of the variants show no detectable activity and are basically indistinguishable from mock-transfected controls, suggesting that a large portion of these mutations are damaging. A further 21% lose 50% of activity as normalized to WT (100%), and another ten percent are nonsense and frameshift variants. This means that about 50% of total somatic mutations of CYSLTR2 have a LoF phenotype, which points to a tumor suppressor function following the famous “20/20” rule