Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer

Motivation: B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not. Results: We present here V'DJer, an assembly-based method that reconstructs adaptive immune receptor repertoires from short-read RNA sequencing data. This method captures expressed BCR loci from a standard RNA-seq assay. We applied this method to 473 Melanoma samples from The Cancer Genome Atlas and demonstrate V'DJer's ability to accurately reconstruct BCR repertoires from short read mRNA-seq data

Anti-PD-1 Checkpoint Therapy Can Promote the Function and Survival of Regulatory T Cells

We have previously shown in a model of claudin-low breast cancer that regulatory T cells (Tregs) are increased in the tumor microenvironment (TME) and express high levels of PD-1. In mouse models and patients with triple-negative breast cancer, it is postulated that one cause for the lack of activity of anti-PD-1 therapy is the activation of PD-1-expressing Tregs in the TME. We hypothesized that the expression of PD-1 on Tregs would lead to enhanced suppressive function of Tregs and worsen antitumor immunity during PD-1 blockade. To evaluate this, we isolated Tregs from claudin-low tumors and functionally evaluated them ex vivo. We compared transcriptional profiles of Tregs isolated from tumor-bearing mice with or without anti-PD-1 therapy using RNA sequencing. We found several genes associated with survival and proliferation pathways; for example, Jun, Fos, and Bcl2 were significantly upregulated in Tregs exposed to anti-PD-1 treatment. Based on these data, we hypothesized that anti-PD-1 treatment on Tregs results in a prosurvival phenotype. Indeed, Tregs exposed to PD-1 blockade had significantly higher levels of Bcl-2 expression, and this led to increased protection from glucocorticoid-induced apoptosis. In addition, we found in vitro and in vivo that Tregs in the presence of anti-PD-1 proliferated more than control Tregs. PD-1 blockade significantly increased the suppressive activity of Tregs at biologically relevant Treg/Tnaive cell ratios. Altogether, we show that this immunotherapy blockade increases proliferation, protection from apoptosis, and suppressive capabilities of Tregs, thus leading to enhanced immunosuppression in the TME

Machine-learning prediction of tumor antigen immunogenicity in the selection of therapeutic epitopes

Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclas-sic out-of-frame antigens capable of driving antitumor immunity

Targeting the Canonical Nuclear Factor-κB Pathway with a High-Potency IKK2 Inhibitor Improves Outcomes in a Mouse Model of Idiopathic Pneumonia Syndrome

Idiopathic pneumonia syndrome (IPS) is a noninfectious inflammatory disorder of the lungs that occurs most often after fully myeloablative allogeneic hematopoietic stem cell transplantation (HSCT). IPS can be severe and is associated with high 1-year mortality rates despite existing therapies. The canonical nuclear factor-(NF) κB signaling pathway has previously been linked to several inflammatory disorders of the lung, including asthma and lung allograft rejection. It has never been specifically targeted as a novel IPS treatment approach, however. Here, we report that the IκB kinase 2 (IKK2) antagonist BAY 65-5811 or “compound A,” a highly potent and specific inhibitor of the NF-κB pathway, was able to improve median survival times and recipient oxygenation in a well-described mouse model of IPS. Compound A impaired the production of the proinflammatory chemokines CCL2 and CCL5 within the host lung after transplantation. This resulted in significantly lower numbers of donor lung infiltrating CD4+ and CD8+ T cells and reduced pulmonary inflammatory cytokine production after allograft. Compound A's beneficial effects appeared to be specific for limiting pulmonary injury, as the drug was unable to improve outcomes in a B6 into B6D2 haplotype-matched murine HSCT model in which recipient mice succumb to lethal acute graft-versus-host disease of the gastrointestinal tract. Collectively, our data suggest that the targeting of the canonical NF-κB pathway with a small molecule IKK2 antagonist may represent an effective and novel therapy for the specific management of acute lung injury that can occur after allogeneic HSCT

B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer

This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors

STING agonist promotes CAR T cell trafficking and persistence in breast cancer

CAR T therapy targeting solid tumors is restrained by limited infiltration and persistence of those cells in the tumor microenvironment (TME). Here, we developed approaches to enhance the activity of CAR T cells using an orthotopic model of locally advanced breast cancer. CAR T cells generated from Th/Tc17 cells given with the STING agonists DMXAA or cGAMP greatly enhanced tumor control, which was associated with enhanced CAR T cell persistence in the TME. Using single-cell RNA sequencing, we demonstrate that DMXAA promoted CAR T cell trafficking and persistence, supported by the generation of a chemokine milieu that promoted CAR T cell recruitment and modulation of the immunosuppressive TME through alterations in the balance of immune-stimulatory and suppressive myeloid cells. However, sustained tumor regression was accomplished only with the addition of anti-PD-1 and anti-GR-1 mAb to Th/Tc17 CAR T cell therapy given with STING agonists. This study provides new approaches to enhance adoptive T cell therapy in solid tumors

Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma

Human endogenous retroviruses (hERVs) are remnants of exogenous retroviruses that have integrated into the genome throughout evolution. We developed a computational workflow, hervQuant, which identified more than 3,000 transcriptionally active hERVs within The Cancer Genome Atlas (TCGA) pan-cancer RNA-Seq database. hERV expression was associated with clinical prognosis in several tumor types, most significantly clear cell renal cell carcinoma (ccRCC). We explored two mechanisms by which hERV expression may influence the tumor immune microenvironment in ccRCC: (i) RIG-I-like signaling and (ii) retroviral antigen activation of adaptive immunity. We demonstrated the ability of hERV signatures associated with these immune mechanisms to predict patient survival in ccRCC, independent of clinical staging and molecular subtyping. We identified potential tumor-specific hERV epitopes with evidence of translational activity through the use of a ccRCC ribosome profiling (Ribo-Seq) dataset, validated their ability to bind HLA in vitro, and identified the presence of MHC tetramer-positive T cells against predicted epitopes. hERV sequences identified through this screening approach were significantly more highly expressed in ccRCC tumors responsive to treatment with programmed death receptor 1 (PD-1) inhibition. hervQuant provides insights into the role of hERVs within the tumor immune microenvironment, as well as evidence that hERV expression could serve as a biomarker for patient prognosis and response to immunotherapy. © 2018 American Society for Clinical Investigation. All rights reserved

The Landscape of Immune Microenvironments in Racially Diverse Breast Cancer Patients

Background: Immunotherapy is a rapidly evolving treatment option in breast cancer; However, the breast cancer immune microenvironment is understudied in Black and younger (<50 years) patients. Methods: We used histologic and RNA-based immunoprofiling methods to characterize the breast cancer immune landscape in 1,952 tumors from the Carolina Breast Cancer Study (CBCS), a population-based study that oversampled Black (n ¼ 1,030) and young women (n ¼ 1,039). We evaluated immune response leveraging markers for 10 immune cell populations, compared profiles to those in The Cancer Genome Atlas (TCGA) Project [n ¼ 1,095 tumors, Black (n ¼ 183), and young women (n ¼ 295)], and evaluated in association with clinical and demographic variables, including recurrence. Results: Consensus clustering identified three immune clusters in CBCS (adaptive-enriched, innate-enriched, or immune-quiet) that varied in frequency by race, age, tumor grade and subtype; however, only two clusters were identified in TCGA, which were predominantly comprised of adaptive-enriched and innate-enriched tumors. In CBCS, the strongest adaptive immune response was observed for basal-like, HER2-positive (HER2þ), triple-negative breast cancer (TNBC), and high-grade tumors. Younger patients had higher proportions of adaptive-enriched tumors, particularly among estrogen receptor (ER)-negative (ER-) cases. Black patients had higher frequencies of both adaptive-enriched and innate-enriched tumors. Immune clusters were associated with recurrence among ER- tumors, with adaptive-enriched showing the best and innate-enriched showing the poorest 5-year recurrence-free survival. Conclusions: These data suggest that immune microenvironments are intricately related to race, age, tumor subtype, and grade. Impact: Given higher mortality among Black and young women, more defined immune classification using cell-type–specific panels could help explain higher recurrence and ultimately lead to target-able interventions