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.

IL-27 acts on DCs to suppress the T cell response and autoimmunity by inducing expression of the immunoregulatory molecule CD39.

Dendritic cells (DCs) control the balance between effector T cells and regulatory T cells in vivo. Hence, the study of DCs might identify mechanisms of disease pathogenesis and guide new therapeutic approaches for disorders mediated by the immune system. We found that interleukin 27 (IL-27) signaling in mouse DCs limited the generation of effector cells of the TH1 and TH17 subsets of helper T cells and the development of experimental autoimmune encephalomyelitis (EAE). The effects of IL-27 were mediated at least in part through induction of the immunoregulatory molecule CD39 in DCs. IL-27-induced CD39 decreased the extracellular concentration of ATP and downregulated nucleotide-dependent activation of the NLRP3 inflammasome. Finally, therapeutic vaccination with IL-27-conditioned DCs suppressed established relapsing-remitting EAE. Thus, IL-27 signaling in DCs limited pathogenic T cell responses and the development of autoimmunity.

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 .

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.

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.

Immune Computation and COVID-19 Mortality: A Rationale for IVIg.

COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).

Comparing Transcriptome Profiles of Neurons Interfacing Adjacent Cells and Nanopatterned Substrates Reveals Fundamental Neuronal Interactions.

Developing neuronal axons are directed by chemical and physical signals toward a myriad of target cells. According to current dogma, the resulting network architecture is critically shaped by electrical interconnections, the synapses; however, key mechanisms translating neuronal interactions into neuronal growth behavior during network formation are still unresolved. To elucidate these mechanisms, we examined neurons interfacing nanopatterned substrates and compared them to natural interneuron interactions. We grew similar neuronal populations under three connectivity conditions, (1) the neurons are isolated, (2) the neurons are interconnected, and (3) the neurons are connected only to artificial substrates, then quantitatively compared both the cell morphologies and the transcriptome-expression profiles. Our analysis shows that whereas axon-guidance signaling pathways in isolated neurons are predominant, in isolated neurons interfacing nanotopography, these pathways are downregulated, similar to the interconnected neurons. Moreover, in nanotopography, interfacing neuron genes related to synaptogenesis and synaptic regulation are highly expressed, that is, again resembling the behavior of interconnected neurons. These molecular findings demonstrate that interactions with nanotopographies, although not leading to electrical coupling, play a comparable functional role in two major routes, neuronal guidance and network formation, with high relevance to the design of regenerative interfaces.

Astrocyte-specific transcriptome analysis using the ALDH1L1 bacTRAP mouse reveals novel biomarkers of astrogliosis in response to neurotoxicity.

Neurotoxicology is hampered by the inability to predict regional and cellular targets of toxicant-induced damage. Evaluating astrogliosis overcomes this problem because reactive astrocytes highlight the location of toxicant-induced damage. While enhanced expression of glial fibrillary acidic protein is a hallmark of astrogliosis, few other biomarkers have been identified. However, bacterial artificial chromosome - translating ribosome affinity purification (bacTRAP) technology allows for characterization of the actively translating transcriptome of a particular cell type; use of this technology in aldehyde dehydrogenase 1 family member L1 (ALDH1L1) bacTRAP mice can identify genes selectively expressed in astrocytes. The aim of this study was to characterize additional biomarkers of neurotoxicity-induced astrogliosis using ALDH1L1 bacTRAP mice. The known dopaminergic neurotoxicant 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP; 12.5 mg/kg s.c.) was used to induce astrogliosis. Striatal tissue was obtained 12, 24, and 48 h following exposure for the isolation of actively translating RNA. Subsequently, MPTP-induced changes in this RNA pool were analyzed by microarray and 184 statistically significant, differentially expressed genes were identified. The dataset was interrogated by gene ontology, pathway, and co-expression network analyses, which identified novel genes, as well as those with known immune and inflammatory functions. Using these analyses, we were directed to several genes associated with reactive astrocytes. Of these, TIMP1 and miR-147 were identified as candidate biomarkers because of their robust increased expression following both MPTP and trimethyl tin exposures. Thus, we have demonstrated that bacTRAP can be used to identify new biomarkers of astrogliosis and aid in the characterization of astrocyte phenotypes induced by toxicant exposures. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/. Cover Image for this issue: doi: 10.1111/jnc.14518.

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.

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.

Autotaxin-Lysophosphatidic Acid Axis Acts Downstream of Apoprotein B Lipoproteins in Endothelial Cells.

OBJECTIVE: As they travel through the blood stream, plasma lipoproteins interact continuously with endothelial cells (ECs). Although the focus of research has mostly been guided by the importance of lipoproteins as risk factors for atherosclerosis, thrombosis, and other cardiovascular diseases, little is known about the mechanisms linking lipoproteins and angiogenesis under physiological conditions, and particularly, during embryonic development. In this work, we performed global mRNA expression profiling of endothelial cells from hypo-, and hyperlipidemic zebrafish embryos with the goal of uncovering novel mediators of lipoprotein signaling in the endothelium. APPROACH AND RESULTS: Microarray analysis was conducted on fluorescence-activated cell sorting-isolated fli1:EGFP(+) ECs from normal, hypo-, and hyperlipidemic zebrafish embryos. We found that opposed levels of apoprotein B lipoproteins result in differential expression of the secreted enzyme autotaxin in ECs, which in turn affects EC sprouting and angiogenesis. We further demonstrate that the effects of autotaxin in vivo are mediated by lysophosphatidic acid (LPA)-a well-known autotaxin activity product-and that LPA and LPA receptors participate as well in the response of ECs to lipoprotein levels. CONCLUSIONS: Our findings provide the first in vivo gene expression profiling of ECs facing different levels of plasma apoprotein B lipoproteins and uncover a novel lipoprotein-autotaxin-LPA axis as regulator of EC behavior. These results highlight new roles for lipoproteins as signaling molecules, which are independent of their canonical function as cholesterol transporters.

Spatial regulation dominates gene function in the ganglia chain.

MOTIVATION: To understand the molecular mechanisms of neurons, it is imperative to identify genomic dissimilarities within the heterogeneity of the neural system. This is especially true for neuronal disorders in which spatial considerations are of critical nature. For this purpose, Hirudo medicinalis provides here an ideal system in which we are able to follow gene expression along the central nervous system, to affiliate location with gene behavior. RESULTS: In all, 221.1 million high-quality short reads were sequenced on the Illumina Hiseq2000 platform at the single ganglion level. Thereafter, a de novo assembly was performed using two state-of-the-art assemblers, Trinity and Trans-ABySS, to reconstruct a comprehensive de novo transcriptome. Classification of Trinity and Trans-ABySS transcripts produced a non-redundant set of 76 845 and 268 355 transcripts (>200 bp), respectively. Remarkably, using Trinity, 82% of the published medicinal leech messenger RNAs was identified. For the innexin family, all of the 21 recently reported genes were identified. Spatial regulation analysis across three ganglia throughout the entire central nervous system revealed distinct patterns of gene expression. These transcriptome data were combined with expression distribution to produce a spatio-transcripto map along the ganglia chain. This study provides a resource for gene discovery and gene regulation in future studies.

PhenoNet: identification of key networks associated with disease phenotype.

MOTIVATION: At the core of transcriptome analyses of cancer is a challenge to detect molecular differences affiliated with disease phenotypes. This approach has led to remarkable progress in identifying molecular signatures and in stratifying patients into clinical groups. Yet, despite this progress, many of the identified signatures are not robust enough to be clinically used and not consistent enough to provide a follow-up on molecular mechanisms. RESULTS: To address these issues, we introduce PhenoNet, a novel algorithm for the identification of pathways and networks associated with different phenotypes. PhenoNet uses two types of input data: gene expression data (RMA, RPKM, FPKM, etc.) and phenotypic information, and integrates these data with curated pathways and protein-protein interaction information. Comprehensive iterations across all possible pathways and subnetworks result in the identification of key pathways or subnetworks that distinguish between the two phenotypes. AVAILABILITY AND IMPLEMENTATION: Matlab code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct inhibitory effects on mTOR signaling by ethanol and INK128 in diffuse large B-cell lymphoma.

BACKGROUND: The mechanistic target of rapamycin, (mTOR) kinase plays a pivotal role in controlling critical cellular growth and survival pathways, and its aberrant induction is implicated in cancer pathogenesis. Therefore, suppression of active mTOR signaling has been of great interest to researchers; several mTOR inhibitors have been discovered to date. Ethanol (EtOH), similar to pharmacologic mTOR inhibitors, has been shown to suppress the mTOR signaling pathway, though in a non-catalytic manner. Despite population studies showing that the consumption of EtOH has a protective effect against hematological malignancies, the mechanisms behind EtOH's modulation of mTOR activity in cells and its downstream consequences are largely unknown. Here we evaluated the effects of EtOH on the mTOR pathway, in comparison to the active-site mTOR inhibitor INK128, and compared translatome analysis of their downstream effects in diffuse large B-cell lymphoma (DLBCL). RESULTS: Treatment of DLBCL cells with EtOH suppressed mTORC1 complex formation while increasing AKT phosphorylation and mTORC2 complex assembly. INK128 completely abrogated AKT phosphorylation without affecting the structure of mTORC1/2 complexes. Accordingly, EtOH less profoundly suppressed cap-dependent translation and global protein synthesis, compared to a remarkable inhibitory effect of INK128 treatment. Importantly, EtOH treatment induced the formation of stress granules, while INK128 suppressed their formation. Microarray analysis of polysomal RNA revealed that although both agents primarily affected cell growth and survival, EtOH and INK128 regulated the synthesis of mostly distinct genes involved in these processes. Though both EtOH and INK128 inhibited cell cycle, proliferation and autophagy, EtOH, in contrast to INK128, did not induce cell apoptosis. CONCLUSION: Given that EtOH, similar to pharmacologic mTOR inhibitors, inhibits mTOR signaling, we systematically explored the effect of EtOH and INK128 on mTOR signal transduction, components of the mTORC1/2 interaction and their downstream effectors in DLBCL malignancy. We found that EtOH partially inhibits mTOR signaling and protein translation, compared to INK128's complete mTOR inhibition. Translatome analysis of mTOR downstream target genes established that differential inhibition of mTOR by EtOH and INK128 distinctly modulates translation of specific subsets of mRNAs involved in cell growth and survival, leading to differential cellular response and survival.

Shift in GATA3 functions, and GATA3 mutations, control progression and clinical presentation in breast cancer.

INTRODUCTION: GATA binding protein 3 (GATA3) is a regulator of mammary luminal cell differentiation, and an estrogen receptor (ER) associated marker in breast cancer. Tumor suppressor functions of GATA3 have been demonstrated primarily in basal-like breast cancers. Here, we focused on its function in luminal breast cancer, where GATA3 is frequently mutated, and its levels are significantly elevated. METHODS: GATA3 target genes were identified in normal- and luminal cancer- mammary cells by ChIP-seq, followed by examination of the effects of GATA3 expressions and mutations on tumorigenesis-associated genes and processes. Additionally, mutations and expression data of luminal breast cancer patients from The Cancer Genome Atlas were analyzed to characterize genetic signatures associated with GATA3 mutations. RESULTS: We show that some GATA3 effects shift from tumor suppressing to tumor promoting during tumorigenesis, with deregulation of three genes, BCL2, DACH1, THSD4, representing major GATA3-controlled processes in cancer progression. In addition, we identify an altered activity of mutant GATA3, and distinct associated genetic signatures. These signatures depend on the functional domain mutated; and, for a specific subgroup, are shared with basal-like breast cancer patients, who are a clinical group with regard to considerations of mode of treatment. CONCLUSIONS: The GATA3 dependent mechanisms may call for special considerations for proper prognosis and treatment of patients.

MicroRNA-gene association as a prognostic biomarker in cancer exposes disease mechanisms.

The transcriptional networks that regulate gene expression and modifications to this network are at the core of the cancer phenotype. MicroRNAs, a well-studied species of small non-coding RNA molecules, have been shown to have a central role in regulating gene expression as part of this transcriptional network. Further, microRNA deregulation is associated with cancer development and with tumor progression. Glioblastoma Multiform (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. To study the transcriptional network and its modifications in GBM, we utilized gene expression, microRNA sequencing, whole genome sequencing and clinical data from hundreds of patients from different datasets. Using these data and a novel microRNA-gene association approach we introduce, we have identified unique microRNAs and their associated genes. This unique behavior is composed of the ability of the quantifiable association of the microRNA and the gene expression levels, which we show stratify patients into clinical subgroups of high statistical significance. Importantly, this stratification goes unobserved by other methods and is not affiliated by other subsets or phenotypes within the data. To investigate the robustness of the introduced approach, we demonstrate, in unrelated datasets, robustness of findings. Among the set of identified microRNA-gene associations, we closely study the example of MAF and hsa-miR-330-3p, and show how their co-behavior stratifies patients into prognosis clinical groups and how whole genome sequences tells us more about a specific genomic variation as a possible basis for patient variances. We argue that these identified associations may indicate previously unexplored specific disease control mechanisms and may be used as basis for further study and for possible therapeutic intervention.

Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership.

The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.

Altered immune pathway activity under exercise challenge in Gulf War Illness: an exploratory analysis.

Though potentially linked to the basic physiology of stress response we still have no clear understanding of Gulf War Illness (GWI), a debilitating illness presenting with a complex constellation of immune, endocrine and neurological symptoms. Here we compared male GWI (n=20) with healthy veterans (n=22) and subjects with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) (n=7). Blood was drawn during a Graded eXercise Test (GXT) prior to exercise, at peak effort (VO2 max) and 4-h post exercise. Affymetrix HG U133 plus 2.0 microarray gene expression profiling in peripheral blood mononuclear cells (PBMCs) was used to estimate activation of over 500 documented pathways. This was cast against ELISA-based measurement of 16 cytokines in plasma and flow cytometric assessment of lymphocyte populations and cytotoxicity. A 2-way ANOVA corrected for multiple comparisons (q statistic <0.05) indicated significant increases in neuroendocrine-immune signaling and inflammatory activity in GWI, with decreased apoptotic signaling. Conversely, cell cycle progression and immune signaling were broadly subdued in CFS. Partial correlation networks linking pathways with symptom severity via changes in immune cell abundance, function and signaling were constructed. Central to these were changes in IL-10 and CD2+ cell abundance and their link to two pathway clusters. The first consisted of pathways supporting neuronal development and migration whereas the second was related to androgen-mediated activation of NF-κB. These exploratory results suggest an over-expression of known exercise response mechanisms as well as illness-specific changes that may involve an overlapping stress-potentiated neuro-inflammatory response.

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.

Rep-Seq: uncovering the immunological repertoire through next-generation sequencing.

Recent scientific discoveries fuelled by the application of next-generation DNA and RNA sequencing technologies highlight the striking impact of these platforms in characterizing multiple aspects in genomics research. This technology has been used in the study of the B-cell and T-cell receptor repertoire. The novelty of immunosequencing comes from the recent rapid development of techniques and the exponential reduction in cost of sequencing. Here, we describe some of the technologies, which we collectively refer to as Rep-Seq (repertoire sequencing), to portray achievements in the field and to present the essential and inseparable role of next-generation sequencing to the understanding of entities in immune response. The large Rep-Seq data sets that should be available in the near future call for new computational algorithms to segue the transition from 'classic' molecular-based analysis to system-wide analysis. The combination of new algorithms with high-throughput data will form the basis for possible new clinical implications in personalized medicine and deeper understanding of immune behaviour and immune response.