Anna Sherwood

Using synthetic templates to design an unbiased multiplex PCR assay

T and B cell receptor loci undergo combinatorial rearrangement, generating a diverse immune receptor repertoire, which is vital for recognition of potential antigens. Here we use a multiplex PCR with a mixture of primers targeting the rearranged variable and joining segments to capture receptor diversity. Differential hybridization kinetics can introduce significant amplification biases that alter the composition of sequence libraries prepared by multiplex PCR. Using a synthetic immune receptor repertoire, we identify and minimize such biases and computationally remove residual bias after sequencing. We apply this method to a multiplex T cell receptor gamma sequencing assay. To demonstrate accuracy in a biological setting, we apply the method to monitor minimal residual disease in acute lymphoblastic leukaemia patients. A similar methodology can be extended to any adaptive immune locus.

High-Throughput Sequencing Detects Minimal Residual Disease in Acute T Lymphoblastic Leukemia

High-throughput sequencing can detect minimal residual disease comparable to multiparametric flow cytometry in T-ALL patients. Finding a Needle in a Haystack Even in seemingly successful cancer therapy, a small number of cells can survive treatment and persist in patients in remission. This minimal residual disease (MRD) is a major cause of cancer relapse, and until recently was undetectable. New ways to track MRD can determine whether cancer has been eradicated, compare the efficacy of different treatments, monitor patient remission status, and aid in treatment selection. Wu et al. use high-throughput sequencing (HTS) of lymphoid receptor genes to track MRD in T-lineage acute lymphoblastic leukemia/lymphoma (T-ALL). The authors sequence the variable regions of two T cell antigen receptor genes (TCRB and TCRG) using multiplexed polymerase chain reaction. First, they identified clonal T cell receptor (TCR) sequences in individual T-ALL patients and then looked in the same patients after treatment. Their strategy identified clonality at diagnosis in most cases and also detected subsequent MRD. In a subset of cases, HTS detected MRD in patients where it was not detected by flow cytometry, which is currently used in the clinic. Thus, HTS may lower the threshold of detection for MRD and affect treatment decisions. High-throughput sequencing (HTS) of lymphoid receptor genes is an emerging technology that can comprehensively assess the diversity of the immune system. Here, we applied HTS to the diagnosis of T-lineage acute lymphoblastic leukemia/lymphoma. Using 43 paired patient samples, we then assessed minimal residual disease (MRD) at day 29 after treatment. The variable regions of TCRB and TCRG were sequenced using an Illumina HiSeq platform after performance of multiplexed polymerase chain reaction, which targeted all potential V-J rearrangement combinations. Pretreatment samples were used to define clonal T cell receptor (TCR) complementarity-determining region 3 (CDR3) sequences, and paired posttreatment samples were evaluated for MRD. Abnormal T lymphoblast identification by multiparametric flow cytometry was concurrently performed for comparison. We found that TCRB and TCRG HTS not only identified clonality at diagnosis in most cases (31 of 43 for TCRB and 27 of 43 for TCRG) but also detected subsequent MRD. As expected, HTS of TCRB and TCRG identified MRD that was not detected by flow cytometry in a subset of cases (25 of 35 HTS compared with 13 of 35, respectively), which highlights the potential of this technology to define lower detection thresholds for MRD that could affect clinical treatment decisions. Thus, next-generation sequencing of lymphoid receptor gene repertoire may improve clinical diagnosis and subsequent MRD monitoring of lymphoproliferative disorders.

Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue

Tumors from colorectal cancer (CRC) are generally immunogenic and commonly infiltrated with T lymphocytes. However, the details of the adaptive immune reaction to these tumors are poorly understood. We have accrued both colon tumor samples and adjacent healthy mucosal samples from 15 CRC patients to study lymphocytes infiltrating these tissues. We apply a method for detailed sequencing of T-cell receptor (TCR) sequences from tumor-infiltrating lymphocytes (TILs) in CRC tumors at high throughput to probe T-cell clones in comparison with the TCRs from adjacent healthy mucosal tissue. In parallel, we captured TIL counts using standard immunohistochemistry. The variation in diversity of the TIL repertoire was far wider than the variation of T-cell clones in the healthy mucosa, and the oligoclonality was higher on average in the tumors. However, the diversity of the T-cell repertoire in both CRC tumors and healthy mucosa was on average 100-fold lower than in peripheral blood. Using the TCR sequences to identify and track clones between mucosal and tumor samples, we determined that the immune response in the tumor is different than in the adjacent mucosal tissue, and the number of shared clones is not dependent on distance between the samples. Together, these data imply that CRC tumors induce a specific adaptive immune response, but that this response differs widely in strength and breadth between patients.

High-throughput sequencing of T-cell receptors reveals a homogeneous repertoire of tumour-infiltrating lymphocytes in ovarian cancer

The cellular adaptive immune system mounts a response to many solid tumours mediated by tumour‐infiltrating T lymphocytes (TILs). Basic measurements of these TILs, including total count, show promise as prognostic markers for a variety of cancers, including ovarian and colorectal. In addition, recent therapeutic advances are thought to exploit this immune response to effectively fight melanoma, with promising studies showing efficacy in additional cancers. However, many of the basic properties of TILs are poorly understood, including specificity, clonality, and spatial heterogeneity of the T‐cell response. We utilize deep sequencing of rearranged T‐cell receptor beta (TCRB) genes to characterize the basic properties of TILs in ovarian carcinoma. Due to somatic rearrangement during T‐cell development, the TCR beta chain sequence serves as a molecular tag for each T‐cell clone. Using these sequence tags, we assess similarities and differences between infiltrating T cells in discretely sampled sections of large tumours and compare to T cells from peripheral blood. Within the limits of sensitivity of our assay, the TIL repertoires show strong similarity throughout each tumour and are distinct from the circulating T‐cell repertoire. We conclude that the cellular adaptive immune response within ovarian carcinomas is spatially homogeneous and distinct from the T‐cell compartment of peripheral blood. Copyright

Deep Sequencing of the Human TCRγ and TCRβ Repertoires Suggests that TCRβ Rearranges After αβ and γδ T Cell Commitment

Deep sequencing provides new insights about T cell receptor rearrangement in humans. Feng Shui for T Cells In the ancient art of feng shui, buildings and even furniture are rearranged to more auspicious orientations. Lymphocytes are the feng shui masters of the immune system. Unlike other cells, which distinguish themselves by differentially expressing certain genes, T lymphocytes and other cells of the adaptive immune system actively rearrange their DNA, cutting and splicing different parts of their antigen receptor genes to respond to the vast number of pathogens that can attack the body. It has been long thought that for T lymphocytes, functional T cell receptor (TCR) chain rearrangement was required for T cell commitment to a particular lineage and function. Sherwood et al. now use deep sequencing of the repertoires of two chains of the TCR, TCRγ and TCRβ, to show that TCRβ may rearrange after lineage commitment. The two main lineages of T cells are classified based on their TCR usage: αβ and γδ T cells. These cells have different functions and are found in different locations in the body: αβ T cells respond to peptides presented in the context of human leukocyte antigen (HLA) molecules and are the predominant T cells in the blood, whereas γδ T cells may not bind HLA-peptide complexes and are found frequently in the lining of the gut. It had previously been thought that TCRβ, γ, and δ chains all rearranged before a T cell’s commitment to one or the other lineage, which suggests a decisive role for rearrangement in the commitment choice. However, Sherwood et al. show through deep sequencing of millions of TCRs in either γδ or αβ T cells that although TCRγ is rearranged in all T lymphocytes, TCRβ is rearranged only in less than 4% of γδ T cells. These data suggest that TCRβ rearrangement may not be required for lineage commitment after all. Moreover, they see both common and diverse TCR rearrangements in γδ T cells, which indicate possible adaptive- and innate-like roles for these two populations. Thus, TCR rearrangement may not only increase TCR diversity but also guide function as well. After all, what could be more auspicious than a healthy immune system? T lymphocytes respond to a broad array of pathogens with the combinatorial diversity of the T cell receptor (TCR). This adaptive response is possible because of the unique structure of the TCR, which is composed of two chains, either αβ or γδ, that undergo genetic rearrangement in the thymus. αβ and γδ T cells are functionally distinct within the host but are derived from a common multipotent precursor. The canonical model for T cell lineage commitment assumes that the γ, δ, and β chains rearrange before αβ or γδ T cell commitment. To test the standard model in humans, we used high-throughput sequencing to catalog millions of TCRγ and TCRβ chains from peripheral blood αβ and γδ T cells from three unrelated individuals. Almost all sampled αβ and γδ T cells had rearranged TCRγ sequences. Although sampled αβ T cells had a diverse repertoire of rearranged TCRβ chains, less than 4% of γδ T cells in peripheral blood had a rearranged TCRβ chain. Our data suggest that TCRγ rearranges in all T lymphocytes, consistent with TCRγ rearranging before T cell lineage commitment. However, rearrangement of the TCRβ locus appears to be restricted after T cell precursors commit to the αβ T cell lineage. Indeed, in T cell leukemias and lymphomas, TCRγ is almost always rearranged and TCRβ is only rearranged in a subset of cancers. Because high-throughput sequencing of TCRs is translated into the clinic for monitoring minimal residual for leukemia/lymphoma, our data suggest the sequencing target should be TCRγ.

High-throughput pairing of T cell receptor α and β sequences

T cell receptor α and β sequences can be accurately paired from hundreds of thousands of T cell clones in parallel. T cell receptor chains pair off High-throughput immunosequencing can take a snapshot of the repertoire of immune cells, providing a broad picture of the immune response at any given time and tracking how the immune response changes as a result of perturbations such as vaccines, infection, or cancer. However, this approach has been limited by the inability to determine which TCR α and TCR β chains combine to form specific T cell receptors in a given cell. Now, Howie et al. report and validate a high-throughput method to pair TCR α and β segments without the need for single-cell technologies. They confirm that their method can be used for T cells from both blood and solid tissues. The T cell receptor (TCR) protein is a heterodimer composed of an α chain and a β chain. TCR genes undergo somatic DNA rearrangements to generate the diversity of T cell binding specificities needed for effective immunity. Recently, high-throughput immunosequencing methods have been developed to profile the TCR α (TCRA) and TCR β (TCRB) repertoires. However, these methods cannot determine which TCRA and TCRB chains combine to form a specific TCR, which is essential for many functional and therapeutic applications. We describe and validate a method called pairSEQ, which can leverage the diversity of TCR sequences to accurately pair hundreds of thousands of TCRA and TCRB sequences in a single experiment. Our TCR pairing method uses standard laboratory consumables and equipment without the need for single-cell technologies. We show that pairSEQ can be applied to T cells from both blood and solid tissues, such as tumors.

Detection of Minimal Residual Disease in B Lymphoblastic Leukemia by High-Throughput Sequencing of IGH

Purpose: High-throughput sequencing (HTS) of immunoglobulin heavy-chain genes (IGH) in unselected clinical samples for minimal residual disease (MRD) in B lymphoblastic leukemia (B-ALL) has not been tested. As current MRD-detecting methods such as flow cytometry or patient-specific qPCR are complex or difficult to standardize in the clinical laboratory, sequencing may enhance clinical prognostication. Experimental Design: We sequenced IGH in paired pretreatment and day 29 post-treatment samples using residual material from consecutive, unselected samples from the Childrens Oncology Group AALL0932 trial to measure MRD as compared with flow cytometry. We assessed the impact of ongoing recombination at IGH on MRD detection in post-treatment samples. Finally, we evaluated a subset of cases with discordant MRD results between flow cytometry and sequencing. Results: We found clonal IGH rearrangements in 92 of 98 pretreatment patient samples. Furthermore, while ongoing recombination of IGH was evident, index clones typically prevailed in MRD-positive post-treatment samples, suggesting that clonal evolution at IGH does not contribute substantively to tumor fitness. MRD was detected by sequencing in all flow cytometry–positive cases with no false-negative results. In addition, in a subset of patients, MRD was detected by sequencing, but not by flow cytometry, including a fraction with MRD levels within the sensitivity of flow cytometry. We provide data that suggest that this discordance in some patients may be due to the phenotypic maturation of the transformed cell. Conclusion: Our results provide strong support for HTS of IGH to enhance clinical prognostication in B-ALL. Clin Cancer Res; 20(17); 4540–8. ©2014 AACR.

Estimating the ratio of CD4+ to CD8+ T cells using high-throughput sequence data.

Mature T cells express either CD8 or CD4, defining two physiologically distinct populations of T cells. CD8+ T cells, or killer T-cells, and CD4+ T cells, or helper T cells, effect different aspects of T cell mediated adaptive immunity. Currently, determining the ratio of CD4+ to CD8+ T cells requires flow cytometry or immunohistochemistry. The genomic T cell receptor locus is rearranged during T cell maturation, generating a highly variable T cell receptor locus in each mature T cell. As part of thymic maturation, T cells that will become CD4+ versus CD8+ are subjected to different selective pressures. In this study, we apply high-throughput next-generation sequencing to T cells from both a healthy cohort and a cohort with an autoimmune disease (multiple sclerosis) to identify sequence features in the variable CDR3 region of the rearranged T cell receptor gene that distinguish CD4+ from CD8+ T cells. We identify sequence features that differ between CD4+ and CD8+ T cells, including Variable gene usage and CDR3 region length. We implement a likelihood model to estimate relative proportions of CD4+ and CD8+ T cells using these features. Our model accurately estimates the proportion of CD4+ and CD8+ T cell sequences in samples from healthy and diseased immune systems, and simulations indicate that it can be applied to as few as 1000 T cell receptor sequences; we validate this model using in vitro mixtures of T cell sequences, and by comparing the results of our method to flow cytometry using peripheral blood samples. We believe our computational method for determining the CD4:CD8 ratio in T cell samples from sequence data will provide additional useful information for any samples on which high-throughput TCR sequencing is performed, potentially including some solid tumors.

A Public Database of Memory and Naive B-Cell Receptor Sequences.

The vast diversity of B-cell receptors (BCR) and secreted antibodies enables the recognition of, and response to, a wide range of epitopes, but this diversity has also limited our understanding of humoral immunity. We present a public database of more than 37 million unique BCR sequences from three healthy adult donors that is many fold deeper than any existing resource, together with a set of online tools designed to facilitate the visualization and analysis of the annotated data. We estimate the clonal diversity of the naive and memory B-cell repertoires of healthy individuals, and provide a set of examples that illustrate the utility of the database, including several views of the basic properties of immunoglobulin heavy chain sequences, such as rearrangement length, subunit usage, and somatic hypermutation positions and dynamics.

T Cell Receptor Sequencing Reveals the Clonal Diversity and Overlap of Colonic Effector and FOXP3+ T Cells in Ulcerative Colitis

Background:FOXP3+ regulatory T cell prevent inflammation but are paradoxically increased in ulcerative colitis (UC). Local T-cell activation has been hypothesized to account for increased FOXP3 expression in colon lamina propria (LP) T cells. Methods:To see if human FOXP3+ LP T cells are an activated fraction of otherwise FOXP3− effector T cells and explore their clonal diversity in health and disease, we deep sequenced clonally unique T-cell receptor hypervariable regions of FOXP3+ and FOXP3−CD4+ T-cell subpopulations from inflamed versus noninflamed colon LP or mesenteric lymph nodes of patients with or without UC. Results:The clonal diversity of each LP T-cell population was not different between patients with versus without UC. Repertoire overlap was only seen between a minority of FOXP3+ and FOXP3− cells, including recently activated CD38+ cells and Th17-like CD161+ effector T cells, but this repertoire overlap was not different between patients with versus without UC and was no larger than the overlap between Helios− and Helios+ FOXP3+ cells. Conclusions:Thus, at steady state, only a minority of FOXP3+, and particularly Helios+, T cells share a T-cell receptor sequence with FOXP3− effector populations in the colon LP, even in UC, revealing distinct clonal origins for LP regulatory T cell and effector T cells in humans.