Single-cell T-cell receptor repertoire profiling in dogs.

Spontaneous cancers in companion dogs are robust models of human disease. Tracking tumor-specific immune responses in these models requires reagents to perform species-specific single cell T cell receptor sequencing (scTCRseq). scTCRseq and integration with scRNA data have not been demonstrated on companion dogs with cancer. Here, five healthy dogs, two dogs with T cell lymphoma and four dogs with melanoma are selected to demonstrate applicability of scTCRseq in a cancer immunotherapy setting. Single-cell suspensions of PBMCs or lymph node aspirates are profiled using scRNA and dog-specific scTCRseq primers. In total, 77,809 V(D)J-expressing cells are detected, with an average of 3498 (348 - 5,971) unique clonotypes identified per sample. In total, 29/34, 40/40, 22/22 and 9/9 known functional TRAV, TRAJ, TRBV and TRBJ gene segments are observed respectively. Pseudogene or otherwise defective gene segments are also detected supporting re-annotation of several as functional. Healthy dogs exhibit highly diverse repertoires, T cell lymphomas exhibit clonal repertoires, and vaccine-treated melanoma dogs are dominated by a small number of highly abundant clonotypes. scRNA libraries define large clusters of V(D)J-expressing CD8+ and CD4 + T cells. Dominant clonotypes observed in melanoma PBMCs are predominantly CD8 + T cells, with activated phenotypes, suggesting possible anti-tumor T cell populations.

A macrophage-cell model of HIV latency reveals the unusual importance of the bromodomain axis.

BACKGROUND: Although macrophages are now recognized as an essential part of the HIV latent reservoir, whether and how viral latency is established and reactivated in these cell types is poorly understood. To understand the fundamental mechanisms of viral latency in macrophages, there is an urgent need to develop latency models amenable to genetic manipulations and screening for appropriate latency-reversing agents (LRAs). Given that differentiated THP-1 cells resemble monocyte-derived macrophages in HIV replication mechanisms, we set out to establish a macrophage cell model for HIV latency using THP-1 cells. METHODS: We created single-cell clones of THP-1 cells infected with a single copy of the dual-labeled HIVGKO in which a codon switched eGFP (csGFP) is under the control of the HIV-1 5' LTR promoter, and a monomeric Kusabira orange 2 (mKO2) under the control of cellular elongation factor one alpha promoter (EF1α). Latently infected cells are csGFP-, mKO2+, while cells with actively replicating HIV (or reactivated virus) are csGFP+,mKO2+. After sorting for latently infected cells, each of the THP-1 clones with unique integration sites for HIV was differentiated into macrophage-like cells with phorbol 12-myristate 13-acetate (PMA) and treated with established LRAs to stimulate HIV reactivation. Monocyte-derived macrophages (MDMs) harboring single copies of HIVGKO were used to confirm our findings. RESULTS: We obtained clones of THP-1 cells with latently infected HIV with unique integration sites. When the differentiated THP-1 or primary MDMs cells were treated with various LRAs, the bromodomain inhibitors JQ1 and I-BET151 were the most potent compounds. Knockdown of BRD4, the target of JQ1, resulted in increased reactivation, thus confirming the pharmacological effect. The DYRK1A inhibitor Harmine and lipopolysaccharide (LPS) also showed significant reactivation across all three MDM donors. Remarkably, LRAs like PMA/ionomycin, bryostatin-1, and histone deacetylase inhibitors known to potently reactivate latent HIV in CD4 + T cells showed little activity in macrophages. CONCLUSIONS: Our results indicate that this model could be used to screen for appropriate LRAs for macrophages and show that HIV latency and reactivation mechanisms in macrophages may be distinct from those of CD4 + T cells.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.

Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma.

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL. SIGNIFICANCE: Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.

Normalization of drug and therapeutic concepts with Thera-Py.

Objective: The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation. Materials and Methods: For this study, we created Thera-Py, a Python package and web API that constructs searchable concepts for drugs and therapeutic terminologies using 9 public resources and thesauri. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and combines them under a single concept record. Results: We highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources using Thera-Py and observe an increase in overlap of therapeutic concepts in 2 or more knowledge bases after harmonization using Thera-Py (9.8%-41.8%). Conclusion: We observe that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Mutations associated with progression in follicular lymphoma predict inferior outcomes at diagnosis: Alliance A151303.

Follicular lymphoma (FL) is clinically heterogeneous, with select patients tolerating extended watch-and-wait, whereas others require prompt treatment, suffer progression of disease within 24 months of treatment (POD24), and/or experience aggressive histologic transformation (t-FL). Because our understanding of the relationship between genetic alterations in FL and patient outcomes remains limited, we conducted a clinicogenomic analysis of 370 patients with FL or t-FL (from Cancer and Leukemia Group B/Alliance trials 50402/50701/50803, or real-world cohorts from Washington University School of Medicine, Cleveland Clinic, or University of Miami). FL subsets by grade, stage, watch-and-wait, or POD24 status did not differ by mutation burden, whereas mutation burden was significantly higher in relapsed/refractory (rel/ref) FL and t-FL than in newly diagnosed (dx) FL. Nonetheless, mutation burden in dx FL was not associated with frontline progression-free survival (PFS). CREBBP was the only gene more commonly mutated in FL than in t-FL yet mutated CREBBP was associated with shorter frontline PFS in FL. Mutations in 20 genes were more common in rel/ref FL or t-FL than in dx FL, including 6 significantly mutated genes (SMGs): STAT6, TP53, IGLL5, B2M, SOCS1, and MYD88. We defined a mutations associated with progression (MAP) signature as ≥2 mutations in these 7 genes (6 rel/ref FL or t-FL SMGs plus CREBBP). Patients with dx FL possessing a MAP signature had shorter frontline PFS, revealing a 7-gene set offering insight into FL progression risk potentially more generalizable than the m7-Follicular Lymphoma International Prognostic Index (m7-FLIPI), which had modest prognostic value in our cohort. Future studies are warranted to validate the poor prognosis associated with a MAP signature in dx FL, potentially facilitating novel trials specifically in this high-risk subset of patients.

Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment.

BACKGROUND: Interactions between immune and tumor cells are critical to determining cancer progression and response. In addition, preclinical prediction of immune-related drug efficacy is limited by interspecies differences between human and mouse, as well as inter-person germline and somatic variation. To address these gaps, we developed an autologous system that models the tumor microenvironment (TME) from individual patients with solid tumors. METHOD: With patient-derived bone marrow hematopoietic stem and progenitor cells (HSPCs), we engrafted a patient's hematopoietic system in MISTRG6 mice, followed by transfer of patient-derived xenograft (PDX) tissue, providing a fully genetically matched model to recapitulate the individual's TME. We used this system to prospectively study tumor-immune interactions in patients with solid tumor. RESULTS: Autologous PDX mice generated innate and adaptive immune populations; these cells populated the TME; and tumors from autologously engrafted mice grew larger than tumors from non-engrafted littermate controls. Single-cell transcriptomics revealed a prominent vascular endothelial growth factor A (VEGFA) signature in TME myeloid cells, and inhibition of human VEGF-A abrogated enhanced growth. CONCLUSIONS: Humanization of the interleukin 6 locus in MISTRG6 mice enhances HSPC engraftment, making it feasible to model tumor-immune interactions in an autologous manner from a bedside bone marrow aspirate. The TME from these autologous tumors display hallmarks of the human TME including innate and adaptive immune activation and provide a platform for preclinical drug testing.

T-BET and EOMES sustain mature human NK cell identity and antitumor function.

Since the T-box transcription factors (TFs) T-BET and EOMES are necessary for initiation of NK cell development, their ongoing requirement for mature NK cell homeostasis, function, and molecular programming remains unclear. To address this, T-BET and EOMES were deleted in unexpanded primary human NK cells using CRISPR/Cas9. Deleting these TFs compromised in vivo antitumor response of human NK cells. Mechanistically, T-BET and EOMES were required for normal NK cell proliferation and persistence in vivo. NK cells lacking T-BET and EOMES also exhibited defective responses to cytokine stimulation. Single-cell RNA-Seq revealed a specific T-box transcriptional program in human NK cells, which was rapidly lost following T-BET and EOMES deletion. Further, T-BET- and EOMES-deleted CD56bright NK cells acquired an innate lymphoid cell precursor-like (ILCP-like) profile with increased expression of the ILC-3-associated TFs RORC and AHR, revealing a role for T-box TFs in maintaining mature NK cell phenotypes and an unexpected role of suppressing alternative ILC lineages. Our study reveals the critical importance of sustained EOMES and T-BET expression to orchestrate mature NK cell function and identity.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer.

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

Distinct clonal identities of B-ALLs arising after lenolidomide therapy for multiple myeloma.

Patients with multiple myeloma (MM) who are treated with lenalidomide rarely develop a secondary B-cell acute lymphoblastic leukemia (B-ALL). The clonal and biological relationship between these sequential malignancies is not yet clear. We identified 17 patients with MM treated with lenalidomide, who subsequently developed B-ALL. Patient samples were evaluated through sequencing, cytogenetics/fluorescence in situ hybridization (FISH), immunohistochemical (IHC) staining, and immunoglobulin heavy chain (IgH) clonality assessment. Samples were assessed for shared mutations and recurrently mutated genes. Through whole exome sequencing and cytogenetics/FISH analysis of 7 paired samples (MM vs matched B-ALL), no mutational overlap between samples was observed. Unique dominant IgH clonotypes between the tumors were observed in 5 paired MM/B-ALL samples. Across all 17 B-ALL samples, 14 (83%) had a TP53 variant detected. Three MM samples with sufficient sequencing depth (>500×) revealed rare cells (average of 0.6% variant allele frequency, or 1.2% of cells) with the same TP53 variant identified in the subsequent B-ALL sample. A lack of mutational overlap between MM and B-ALL samples shows that B-ALL developed as a second malignancy arising from a founding population of cells that likely represented unrelated clonal hematopoiesis caused by a TP53 mutation. The recurrent variants in TP53 in the B-ALL samples suggest a common path for malignant transformation that may be similar to that of TP53-mutant, treatment-related acute myeloid leukemia. The presence of rare cells containing TP53 variants in bone marrow at the initiation of lenalidomide treatment suggests that cellular populations containing TP53 variants expand in the presence of lenalidomide to increase the likelihood of B-ALL development.

Personalized ctDNA micro-panels can monitor and predict clinical outcomes for patients with triple-negative breast cancer.

Circulating tumor DNA (ctDNA) in peripheral blood has been used to predict prognosis and therapeutic response for triple-negative breast cancer (TNBC) patients. However, previous approaches typically use large comprehensive panels of genes commonly mutated across all breast cancers. Given the reduction in sequencing costs and decreased turnaround times associated with panel generation, the objective of this study was to assess the use of custom micro-panels for tracking disease and predicting clinical outcomes for patients with TNBC. Paired tumor-normal samples from patients with TNBC were obtained at diagnosis (T0) and whole exome sequencing (WES) was performed to identify somatic variants associated with individual tumors. Custom micro-panels of 4-6 variants were created for each individual enrolled in the study. Peripheral blood was obtained at baseline, during Cycle 1 Day 3, at time of surgery, and in 3-6 month intervals after surgery to assess variant allele fraction (VAF) at different timepoints during disease course. The VAF was compared to clinical outcomes to evaluate the ability of custom micro-panels to predict pathological response, disease-free intervals, and patient relapse. A cohort of 50 individuals were evaluated for up to 48 months post-diagnosis of TNBC. In total, there were 33 patients who did not achieve pathological complete response (pCR) and seven patients developed clinical relapse. For all patients who developed clinical relapse and had peripheral blood obtained ≤ 6 months prior to relapse (n = 4), the custom ctDNA micro-panels identified molecular relapse at an average of 4.3 months prior to clinical relapse. The custom ctDNA panel results were moderately associated with pCR such that during disease monitoring, only 11% of patients with pCR had a molecular relapse, whereas 47% of patients without pCR had a molecular relapse (Chi-Square; p-value = 0.10). In this study, we show that a custom micro-panel of 4-6 markers can be effectively used to predict outcomes and monitor remission for patients with TNBC. These custom micro-panels show high sensitivity for detecting molecular relapse in advance of clinical relapse. The use of these panels could improve patient outcomes through early detection of relapse with preemptive intervention prior to symptom onset.

Integrative genomic analysis reveals low T-cell infiltration as the primary feature of tobacco use in HPV-positive oropharyngeal cancer.

Although tobacco use is an independent adverse prognostic feature in HPV(+) oropharyngeal squamous cell carcinoma (OPSCC), the biologic features associated with tobacco use have not been systematically investigated. We characterized genomic and immunologic features associated with tobacco use through whole exome sequencing, mRNA hybridization, and immunohistochemical staining in 47 HPV(+) OPSCC tumors. Low expression of transcripts in a T cell-inflamed gene expression profile (TGEP) was associated with tobacco use at diagnosis and lower overall and disease-free survival. Tobacco use was associated with an increased proportion of T > C substitutions and a lower proportion of expected mutational signatures, but not with increases in mutational burden or recurrent oncogenic mutations. Our findings suggest that rather than increased mutational burden, tobacco's primary and clinically relevant association in HPV(+) OPSCC is immunosuppression of the tumor immune microenvironment. Quantitative assays of T cell infiltration merit further study as prognostic markers in HPV(+) OPSCC.

Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome where individuals are predisposed to tumor development in the brain, adrenal gland, kidney, and other organs. It is caused by pathogenic variants in the VHL tumor suppressor gene. Standardized disease information has been difficult to collect due to the rarity and diversity of VHL patients. Over 4100 unique articles published until October 2019 were screened for germline genotype-phenotype data. Patient data were translated into standardized descriptions using Human Genome Variation Society gene variant nomenclature and Human Phenotype Ontology terms and has been manually curated into an open-access knowledgebase called Clinical Interpretation of Variants in Cancer. In total, 634 unique VHL variants, 2882 patients, and 1991 families from 427 papers were captured. We identified relationship trends between phenotype and genotype data using classic statistical methods and spectral clustering unsupervised learning. Our analyses reveal earlier onset of pheochromocytoma/paraganglioma and retinal angiomas, phenotype co-occurrences and genotype-phenotype correlations including hotspots. It confirms existing VHL associations and can be used to identify new patterns and associations in VHL disease. Our database serves as an aggregate knowledge translation tool to facilitate sharing information about the pathogenicity of VHL variants.