A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

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.

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.

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.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

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.

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.

MYC drives aggressive prostate cancer by disrupting transcriptional pause release at androgen receptor targets.

c-MYC (MYC) is a major driver of prostate cancer tumorigenesis and progression. Although MYC is overexpressed in both early and metastatic disease and associated with poor survival, its impact on prostate transcriptional reprogramming remains elusive. We demonstrate that MYC overexpression significantly diminishes the androgen receptor (AR) transcriptional program (the set of genes directly targeted by the AR protein) in luminal prostate cells without altering AR expression. Analyses of clinical specimens reveal that concurrent low AR and high MYC transcriptional programs accelerate prostate cancer progression toward a metastatic, castration-resistant disease. Data integration of single-cell transcriptomics together with ChIP-seq uncover an increase in RNA polymerase II (Pol II) promoter-proximal pausing at AR-dependent genes following MYC overexpression without an accompanying deactivation of AR-bound enhancers. Altogether, our findings suggest that MYC overexpression antagonizes the canonical AR transcriptional program and contributes to prostate tumor initiation and progression by disrupting transcriptional pause release at AR-regulated genes.

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.

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.

Pan-ERBB kinase inhibition augments CDK4/6 inhibitor efficacy in oesophageal squamous cell carcinoma.

OBJECTIVE: Oesophageal squamous cell carcinoma (OSCC), like other squamous carcinomas, harbour highly recurrent cell cycle pathway alterations, especially hyperactivation of the CCND1/CDK4/6 axis, raising the potential for use of existing CDK4/6 inhibitors in these cancers. Although CDK4/6 inhibition has shown striking success when combined with endocrine therapy in oestrogen receptor positive breast cancer, CDK4/6 inhibitor palbociclib monotherapy has not revealed evidence of efficacy to date in OSCC clinical studies. Herein, we sought to elucidate the identification of key dependencies in OSCC as a foundation for the selection of targets whose blockade could be combined with CDK4/6 inhibition. DESIGN: We combined large-scale genomic dependency and pharmaceutical screening datasets with preclinical cell line models, to identified potential combination therapies in squamous cell cancer. RESULTS: We identified sensitivity to inhibitors to the ERBB family of receptor kinases, results clearly extending beyond the previously described minority of tumours with EGFR amplification/dependence, specifically finding a subset of OSCCs with dual dependence on ERBB3 and ERBB2. Subsequently. we demonstrated marked efficacy of combined pan-ERBB and CDK4/6 inhibition in vitro and in vivo. Furthermore, we demonstrated that squamous lineage transcription factor KLF5 facilitated activation of ERBBs in OSCC. CONCLUSION: These results provide clear rationale for development of combined ERBB and CDK4/6 inhibition in these cancers and raises the potential for KLF5 expression as a candidate biomarker to guide the use of these agents. These data suggested that by combining existing Food and Drug Administration (FDA)-approved agents, we have the capacity to improve therapy for OSCC and other squamous cancer.

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.

In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target.

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

CoBRA: Containerized Bioinformatics Workflow for Reproducible ChIP/ATAC-seq Analysis.

Chromatin immunoprecipitation sequencing (ChIP-seq) and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) have become essential technologies to effectively measure protein-DNA interactions and chromatin accessibility. However, there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples, correction of copy number variations, and integration of new downstream analysis tools. Here we present Containerized Bioinformatics workflow for Reproducible ChIP/ATAC-seq Analysis (CoBRA), a modularized computational workflow which quantifies ChIP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses. CoBRA provides a comprehensive state-of-the-art ChIP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience. This enables researchers to gain rapid insight into protein-DNA interactions and chromatin accessibility through sample clustering, differential peak calling, motif enrichment, comparison of sites to a reference database, and pathway analysis. CoBRA is publicly available online at https://bitbucket.org/cfce/cobra.

Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening.

High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.

Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade.

Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NFκB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout gene expression signature is associated with better survival in ICB-naïve patients with cancer and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified Second Mitochondria-derived Activator of Caspase (SMAC) mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T cell-dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy. SIGNIFICANCE: MHC-I loss or downregulation in cancer cells is a major mechanism of resistance to T cell-based immunotherapies. Our study reveals that birinapant may be used for patients with low baseline MHC-I to enhance ICB response. This represents promising immunotherapy opportunities given the biosafety profile of birinapant from multiple clinical trials.This article is highlighted in the In This Issue feature, p. 1307.

Enhanced Efficacy of Simultaneous PD-1 and PD-L1 Immune Checkpoint Blockade in High-Grade Serous Ovarian Cancer.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC. SIGNIFICANCE: This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

Genetically Defined, Syngeneic Organoid Platform for Developing Combination Therapies for Ovarian Cancer.

The paucity of genetically informed, immunocompetent tumor models impedes evaluation of conventional, targeted, and immune therapies. By engineering mouse fallopian tube epithelial organoids using lentiviral gene transduction and/or CRISPR/Cas9 mutagenesis, we generated multiple high-grade serous tubo-ovarian cancer (HGSC) models exhibiting mutational combinations seen in patients with HGSC. Detailed analysis of homologous recombination (HR)-proficient (Trp53-/-;Ccne1OE;Akt2OE;KrasOE ), HR-deficient (Trp53-/-;Brca1-/-;MycOE ), and unclassified (Trp53-/-;Pten-/-;Nf1-/- ) organoids revealed differences in in vitro properties (proliferation, differentiation, and "secretome"), copy-number aberrations, and tumorigenicity. Tumorigenic organoids had variable sensitivity to HGSC chemotherapeutics, and evoked distinct immune microenvironments that could be modulated by neutralizing organoid-produced chemokines/cytokines. These findings enabled development of a chemotherapy/immunotherapy regimen that yielded durable, T cell-dependent responses in Trp53-/-;Ccne1OE;Akt2OE;Kras HGSC; in contrast, Trp53-/-;Pten-/-;Nf1-/- tumors failed to respond. Mouse and human HGSC models showed genotype-dependent similarities in chemosensitivity, secretome, and immune microenvironment. Genotype-informed, syngeneic organoid models could provide a platform for the rapid evaluation of tumor biology and therapeutics. SIGNIFICANCE: The lack of genetically informed, diverse, immunocompetent models poses a major barrier to therapeutic development for many malignancies. Using engineered fallopian tube organoids to study the cell-autonomous and cell-nonautonomous effects of specific combinations of mutations found in HGSC, we suggest an effective combination treatment for the currently intractable CCNE1-amplified subgroup.This article is highlighted in the In This Issue feature, p. 211.

Clonal tracing reveals diverse patterns of response to immune checkpoint blockade.

BACKGROUND: Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment. RESULTS: We establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts. CONCLUSIONS: Our study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

Inhibition of MAN2A1 Enhances the Immune Response to Anti-PD-L1 in Human Tumors.

PURPOSE: Immune checkpoint blockade has shown remarkable efficacy, but in only a minority of patients with cancer, suggesting the need to develop additional treatment strategies. Aberrant glycosylation in tumors, resulting from the dysregulated expression of key enzymes in glycan biosynthesis, modulates the immune response. However, the role of glycan biosynthesis enzymes in antitumor immunity is poorly understood. We aimed to study the immunomodulatory effects of these enzymes. EXPERIMENTAL DESIGN: We integrated transcriptional profiles of treatment-naïve human tumors and functional CRISPR screens to identify glycometabolism genes with immunomodulatory effects. We further validated our findings using in vitro coculture and in vivo syngeneic tumor growth assays. RESULTS: We identified MAN2A1, encoding an enzyme in N-glycan maturation, as a key immunomodulatory gene. Analyses of public immune checkpoint blockade trial data also suggested a synergy between MAN2A1 inhibition and anti-PD-L1 treatment. Loss of Man2a1 in cancer cells increased their sensitivity to T-cell-mediated killing. Man2a1 knockout enhanced response to anti-PD-L1 treatment and facilitated higher cytotoxic T-cell infiltration in tumors under anti-PD-L1 treatment. Furthermore, a pharmacologic inhibitor of MAN2A1, swainsonine, synergized with anti-PD-L1 in syngeneic melanoma and lung cancer models, whereas each treatment alone had little effect. CONCLUSIONS: Man2a1 loss renders cancer cells more susceptible to T-cell-mediated killing. Swainsonine synergizes with anti-PD-L1 in suppressing tumor growth. In light of the limited efficacy of anti-PD-L1 and failed phase II clinical trial on swainsonine, our study reveals a potential therapy combining the two to overcome tumor immune evasion.See related commentary by Bhat and Kabelitz, p. 5778.