IGSF8 is an innate immune checkpoint and cancer immunotherapy target.

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo. It is overexpressed and associated with low antigen presentation, low immune infiltration, and worse clinical outcomes in many tumors. An antibody that blocks IGSF8-NK receptor interaction enhances NK cell killing of malignant cells in vitro and upregulates antigen presentation, NK cell-mediated cytotoxicity, and T cell signaling in vivo. In syngeneic tumor models, anti-IGSF8 alone, or in combination with anti-PD1, inhibits tumor growth. Our results indicate that IGSF8 is an innate immune checkpoint that could be exploited as a therapeutic target.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data.

Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes. Therefore, although many tools have been developed to genotype HLA genes using common sequencing data, none of them work for KIR genes. Even specialized KIR genotypers could not resolve all the KIR genes. Here we describe T1K, a novel computational method for the efficient and accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, or whole-exome sequencing data. T1K jointly considers alleles across all genotyped genes, so it can reliably identify present genes and distinguish homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. Moreover, T1K can call novel single-nucleotide variants and process single-cell data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 expression was enriched in tumor-specific CD8+ T cells. T1K may open the opportunity for HLA and KIR genotyping across various sequencing applications.

Transfer learning enables predictions in network biology.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha.

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms. SIGNIFICANCE: BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell-Secreted Cytokines.

Tumor heterogeneity is a major barrier to cancer therapy, including immunotherapy. Activated T cells can efficiently kill tumor cells following recognition of MHC class I (MHC-I)-bound peptides, but this selection pressure favors outgrowth of MHC-I-deficient tumor cells. We performed a genome-scale screen to discover alternative pathways for T cell-mediated killing of MHC-I-deficient tumor cells. Autophagy and TNF signaling emerged as top pathways, and inactivation of Rnf31 (TNF signaling) and Atg5 (autophagy) sensitized MHC-I-deficient tumor cells to apoptosis by T cell-derived cytokines. Mechanistic studies demonstrated that inhibition of autophagy amplified proapoptotic effects of cytokines in tumor cells. Antigens from apoptotic MHC-I-deficient tumor cells were efficiently cross-presented by dendritic cells, resulting in heightened tumor infiltration by IFNγ-and TNFα-producing T cells. Tumors with a substantial population of MHC-I-deficient cancer cells could be controlled by T cells when both pathways were targeted using genetic or pharmacologic approaches. SIGNIFICANCE: Tumor heterogeneity is a major barrier to immunotherapy. We show that MHC-I-deficient tumor cells are forced into apoptosis by T cell-derived cytokines when TNF signaling and autophagy pathways are targeted. This approach enables T cell-mediated elimination of tumors with a substantial population of resistant, MHC-I-deficient tumor cells. This article is highlighted in the In This Issue feature, p. 1027.

A Molecular Switch between Mammalian MLL Complexes Dictates Response to Menin-MLL Inhibition.

Menin interacts with oncogenic MLL1-fusion proteins, and small molecules that disrupt these associations are in clinical trials for leukemia treatment. By integrating chromatin-focused and genome-wide CRISPR screens with genetic, pharmacologic, and biochemical approaches, we discovered a conserved molecular switch between the MLL1-Menin and MLL3/4-UTX chromatin-modifying complexes that dictates response to Menin-MLL inhibitors. MLL1-Menin safeguards leukemia survival by impeding the binding of the MLL3/4-UTX complex at a subset of target gene promoters. Disrupting the Menin-MLL1 interaction triggers UTX-dependent transcriptional activation of a tumor-suppressive program that dictates therapeutic responses in murine and human leukemia. Therapeutic reactivation of this program using CDK4/6 inhibitors mitigates treatment resistance in leukemia cells that are insensitive to Menin inhibitors. These findings shed light on novel functions of evolutionarily conserved epigenetic mediators like MLL1-Menin and MLL3/4-UTX and are relevant to understand and target molecular pathways determining therapeutic responses in ongoing clinical trials. SIGNIFICANCE: Menin-MLL inhibitors silence a canonical HOX- and MEIS1-dependent oncogenic gene expression program in leukemia. We discovered a parallel, noncanonical transcriptional program involving tumor suppressor genes that are repressed in Menin-MLL inhibitor-resistant leukemia cells but that can be reactivated upon combinatorial treatment with CDK4/6 inhibitors to augment therapy responses. This article is highlighted in the In This Issue feature, p. 1.

Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression.

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Finally, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of preimmunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.

Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response.

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells.

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here we present MIRA, probabilistic multimodal models for integrated regulatory analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along cell state trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high-fidelity cell state trees, determines key regulators of fate decisions at branch points and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

Topical therapy for regression and melanoma prevention of congenital giant nevi.

Giant congenital melanocytic nevi are NRAS-driven proliferations that may cover up to 80% of the body surface. Their most dangerous consequence is progression to melanoma. This risk often triggers preemptive extensive surgical excisions in childhood, producing severe lifelong challenges. We have presented preclinical models, including multiple genetically engineered mice and xenografted human lesions, which enabled testing locally applied pharmacologic agents to avoid surgery. The murine models permitted the identification of proliferative versus senescent nevus phases and treatments targeting both. These nevi recapitulated the histologic and molecular features of human giant congenital nevi, including the risk of melanoma transformation. Cutaneously delivered MEK, PI3K, and c-KIT inhibitors or proinflammatory squaric acid dibutylester (SADBE) achieved major regressions. SADBE triggered innate immunity that ablated detectable nevocytes, fully prevented melanoma, and regressed human giant nevus xenografts. These findings reveal nevus mechanistic vulnerabilities and suggest opportunities for topical interventions that may alter the therapeutic options for children with congenital giant nevi.

Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies.

We applied our computational algorithm TRUST4 to assemble immune receptor (T-cell receptor/B-cell receptor) repertoires from approximately 12,000 RNA sequencing samples from The Cancer Genome Atlas and seven immunotherapy studies. From over 35 million assembled complete complementary-determining region 3 sequences, we observed that the expression of CCL5 and MZB1 is the most positively correlated genes with T-cell clonal expansion and B-cell clonal expansion, respectively. We analyzed amino acid evolution during B-cell receptor somatic hypermutation and identified tyrosine as the preferred residue. We found that IgG1+IgG3 antibodies together with FcRn were associated with complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity or phagocytosis. In addition to B-cell infiltration, we discovered that B-cell clonal expansion and IgG1+IgG3 antibodies are also correlated with better patient outcomes. Finally, we created a website, VisualizIRR, for users to interactively explore and visualize the immune repertoires in this study. See related Spotlight by Liu and Han, p. 786.

IKAROS and MENIN coordinate therapeutically actionable leukemogenic gene expression in MLL-r acute myeloid leukemia.

Acute myeloid leukemia (AML) remains difficult to treat and requires new therapeutic approaches. Potent inhibitors of the chromatin-associated protein MENIN have recently entered human clinical trials, opening new therapeutic opportunities for some genetic subtypes of this disease. Using genome-scale functional genetic screens, we identified IKAROS (encoded by IKZF1) as an essential transcription factor in KMT2A (MLL1)-rearranged (MLL-r) AML that maintains leukemogenic gene expression while also repressing pathways for tumor suppression, immune regulation and cellular differentiation. Furthermore, IKAROS displays an unexpected functional cooperativity and extensive chromatin co-occupancy with mixed lineage leukemia (MLL)1-MENIN and the regulator MEIS1 and an extensive hematopoietic transcriptional complex involving homeobox (HOX)A10, MEIS1 and IKAROS. This dependency could be therapeutically exploited by inducing IKAROS protein degradation with immunomodulatory imide drugs (IMiDs). Finally, we demonstrate that combined IKAROS degradation and MENIN inhibition effectively disrupts leukemogenic transcriptional networks, resulting in synergistic killing of leukemia cells and providing a paradigm for improved drug targeting of transcription and an opportunity for rapid clinical translation.

Immunostimulatory Cancer-Associated Fibroblast Subpopulations Can Predict Immunotherapy Response in Head and Neck Cancer.

PURPOSE: Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment. EXPERIMENTAL DESIGN: We performed high-dimensional single-cell RNA sequencing (scRNA-seq) on four patient tumors pretreatment and posttreatment from a neoadjuvant trial of patients with advanced-stage head and neck squamous cell carcinoma that were treated with the αPD-1 therapy, nivolumab. The head and neck CAF (HNCAF) protein activity profiles, derived from this cohort of paired scRNA-seq, were used to perform protein activity enrichment analysis on the 28-patient parental cohort of clinically annotated bulk transcriptomic profiles. Ex vivo coculture assays were used to test functional relevance of HNCAF subtypes. RESULTS: Fourteen distinct cell types were identified with the fibroblast population showing significant changes in abundance following nivolumab treatment. Among the fibroblast subtypes, HNCAF-0/3 emerged as predictive of nivolumab response, while HNCAF-1 was associated with immunosuppression. Functionally, HNCAF-0/3 were found to reduce TGFß-dependent PD-1+TIM-3+ exhaustion of CD8 T cells, increase CD103+NKG2A+ resident memory phenotypes, and enhance the overall cytolytic profile of T cells. CONCLUSIONS: Our findings demonstrate the functional importance of distinct HNCAF subsets in modulating the immunoregulatory milieu of human HNSCC. In addition, we have identified clinically actionable HNCAF subtypes that can be used as a biomarker of response and resistance in future clinical trials.

A high-resolution view of intra-tumoral B cell immunity.

In this issue of Immunity, Meylan et al. (2022) uses spatial transcriptomics to examine B cell immunity within intratumoral tertiary lymphoid structures (TLSs). They find that B cells expand and mature into plasma cells (PCs) within the TLS, migrate along fibroblastic tracks to tumor beds, and produce IgG antibodies that target cancer cells.

Inhibition of EZH2 transactivation function sensitizes solid tumors to genotoxic stress.

Drugs that block the activity of the methyltransferase EZH2 are in clinical development for the treatment of non-Hodgkin lymphomas harboring EZH2 gain-of-function mutations that enhance its polycomb repressive function. We have previously reported that EZH2 can act as a transcriptional activator in castration-resistant prostate cancer (CRPC). Now we show that EZH2 inhibitors can also block the transactivation activity of EZH2 and inhibit the growth of CRPC cells. Gene expression and epigenomics profiling of cells treated with EZH2 inhibitors demonstrated that in addition to derepressing gene expression, these compounds also robustly down-regulate a set of DNA damage repair (DDR) genes, especially those involved in the base excision repair (BER) pathway. Methylation of the pioneer factor FOXA1 by EZH2 contributes to the activation of these genes, and interaction with the transcriptional coactivator P300 via the transactivation domain on EZH2 directly turns on the transcription. In addition, CRISPR-Cas9-mediated knockout screens in the presence of EZH2 inhibitors identified these BER genes as the determinants that underlie the growth-inhibitory effect of EZH2 inhibitors. Interrogation of public data from diverse types of solid tumors expressing wild-type EZH2 demonstrated that expression of DDR genes is significantly correlated with EZH2 dependency and cellular sensitivity to EZH2 inhibitors. Consistent with these findings, treatment of CRPC cells with EZH2 inhibitors dramatically enhances their sensitivity to genotoxic stress. These studies reveal a previously unappreciated mechanism of action of EZH2 inhibitors and provide a mechanistic basis for potential combination cancer therapies.

Checkpoint blockade-induced CD8+ T cell differentiation in head and neck cancer responders.

BACKGROUND: Immune checkpoint blockade (ICB) response in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) is limited to 15%-20% of patients and underpinnings of resistance remain undefined. METHODS: Starting with an anti-PD1 sensitive murine HNSCC cell line, we generated an isogenic anti-PD1 resistant model. Mass cytometry was used to delineate tumor microenvironments of both sensitive parental murine oral carcinoma (MOC1) and resistant MOC1esc1 tumors. To examine heterogeneity and clonal dynamics of tumor infiltrating lymphocytes (TILs), we applied paired single-cell RNA and TCR sequencing in three HNSCC models. RESULTS: Anti-PD1 resistant MOC1esc1 line displayed a conserved cell intrinsic immune evasion signature. Immunoprofiling showed distinct baseline tumor microenvironments of MOC1 and MOC1esc1, as well as the remodeling of immune compartments on ICB in MOC1esc1 tumors. Single cell sequencing analysis identified several CD8 +TIL subsets including Tcf7 +Pd1- (naïve/memory-like), Tcf7 +Pd1+ (progenitor), and Tcf7-Pd1+ (differentiated effector). Mapping TCR shared fractions identified that successful anti-PD1 or anti-CTLA4 therapy-induced higher post-treatment T cell lineage transitions. CONCLUSIONS: These data highlight critical aspects of CD8 +TIL heterogeneity and differentiation and suggest facilitation of CD8 +TIL differentiation as a strategy to improve HNSCC ICB response.

TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response.

Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

Genetic fusions favor tumorigenesis through degron loss in oncogenes.

Chromosomal rearrangements can generate genetic fusions composed of two distinct gene sequences, many of which have been implicated in tumorigenesis and progression. Our study proposes a model whereby oncogenic gene fusions frequently alter the protein stability of the resulting fusion products, via exchanging protein degradation signal (degron) between gene sequences. Computational analyses of The Cancer Genome Atlas (TCGA) identify 2,406 cases of degron exchange events and reveal an enrichment of oncogene stabilization due to loss of degrons from fusion. Furthermore, we identify and experimentally validate that some recurrent fusions, such as BCR-ABL, CCDC6-RET and PML-RARA fusions, perturb protein stability by exchanging internal degrons. Likewise, we also validate that EGFR or RAF1 fusions can be stabilized by losing a computationally-predicted C-terminal degron. Thus, complementary to enhanced oncogene transcription via promoter swapping, our model of degron loss illustrates another general mechanism for recurrent fusion proteins in driving tumorigenesis.