Transfer learning between preclinical models and human tumors identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumors

Natural killer cells are required for the recruitment of CD8+ T cells and the efficacy of immune checkpoint blockade in melanoma brain metastases

Background Brain metastases (BrM) affect up to 60% of patients with metastatic melanoma and are associated with poor prognosis. While combined immune checkpoint blockade of programmed death-1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) demonstrates intracranial efficacy in a proportion of patients with melanoma, the responses are rarely durable, particularly in patients with symptomatic BrM. The brain is an immune-specialized organ and immune responses are regulated differently to the periphery.Methods Using our previously established two-site model of melanoma BrM with concomitant intracranial and extracranial tumors, in which clinically observed efficacy of the combined PD-1/CTLA-4 (PC) blockade can be reproduced, we here explored the role of natural killer (NK) cells in BrM, using functional studies, immunophenotyping and molecular profiling.Results We demonstrate that NK cells are required for the intracranial efficacy of PC blockade. While both perforin and interferon gamma were necessary for the PC blockade-dependent control of intracranial tumor growth, NK cells isolated from intracranial tumors demonstrated only a limited cancer cell killing ability, and PC blockade did not alter the abundance of NK cells within tumors. However, the depletion of NK cells in PC blockade-treated mice led to tumor molecular profiles reminiscent of those observed in intracranial tumors that failed to respond to therapy. Furthermore, the depletion of NK cells resulted in a strikingly reduced abundance of CD8+ T cells within intracranial tumors, while the abundance of other immune cell populations including CD4+ T cells, macrophages and microglia remained unaltered. Adoptive T cell transfer experiments demonstrated that PC blockade-induced trafficking of CD8+ T cells to intracranial tumors was chemokine-dependent. In line with this, PC blockade enhanced intratumoral expression of several T cell-attracting chemokines and we observed high expression levels of cognate chemokine receptors on BrM-infiltrating CD8+ T cells in mice, as well as in human BrM. Importantly, the depletion of NK cells strikingly reduced the intratumoral expression levels of T cell attracting chemokines and vascular T cell entry receptors that were upregulated following PC blockade.Conclusion Our data demonstrate that NK cells underpin the efficacy of PC blockade in BrM by orchestrating the "responder" molecular profile in tumors, and by controlling the intratumoral abundance of CD8+ T cells through regulation of multiple key molecular mediators of T cell trafficking.

Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors

Background

Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome.

Methods

We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets.

Results

Through our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity.

Conclusion

The dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion.

Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.

Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation.

SIGNIFICANCE

Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Antipodoplanin antibody enhances the antitumor effects of CTLA-4 blockade against malignant mesothelioma by natural killer cells

Combination immunotherapy with multiple immune checkpoint inhibitors (ICIs) has been approved for various types of malignancies, including malignant pleural mesothelioma (MPM). Podoplanin (PDPN), a transmembrane sialomucin-like glycoprotein, has been investigated as a diagnostic marker and therapeutic target for MPM. We previously generated and developed a PDPN-targeting Ab reagent with high Ab-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). However, the effects of anti-PDPN Abs on various tumor-infiltrating immune cells and their synergistic effects with ICIs have remained unclear. In the present study, we established a novel rat-mouse chimeric anti-mouse PDPN IgG2a mAb (PMab-1-mG2a ) and its core-fucose-deficient Ab (PMab-1-mG2a -f) to address these limitations. We identified the ADCC and CDC activity of PMab-1-mG2a -f against the PDPN-expressing mesothelioma cell line AB1-HA. The antitumor effect of monotherapy with PMab-1-mG2a -f was not sufficient to overcome tumor progression in AB1-HA-bearing immunocompetent mice. However, PMab-1-mG2a -f enhanced the antitumor effects of CTLA-4 blockade. Combination therapy with anti-PDPN Ab and anti-CTLA-4 Ab increased tumor-infiltrating natural killer (NK) cells. The depletion of NK cells inhibited the synergistic effects of PMab-1-mG2a -f and CTLA-4 blockade in vivo. These findings indicated the essential role of NK cells in novel combination immunotherapy targeting PDPN and shed light on the therapeutic strategy in advanced MPM.

Cyclooxygenase-2 Blockade Is Crucial to Restore Natural Killer Cell Activity before Anti-CTLA-4 Therapy against High-Grade Serous Ovarian Cancer

Chronic inflammation influences the tumor immune microenvironment (TIME) in high-grade serous ovarian cancer (HGSOC). Specifically, cyclooxygenase-2 (COX-2) overexpression promotes cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) expression. Notably, elevated COX-2 levels in the TIME have been associated with reduced response to anti-CTLA-4 immunotherapy. However, the precise impact of COX-2, encoded by PTGS2, on the immune profile remains unknown. To address this, we performed an integrated bioinformatics analysis using data from the HGSOC cohorts (TCGA-OV, n = 368; Australian cohort AOCS, n = 80; GSE26193, n = 62; and GSE30161, n = 45). Employing Gene Set Variation Analysis (GSVA), MIXTURE and Ecotyper cell deconvolution algorithms, we concluded that COX-2 was linked to immune cell ecosystems associated with shorter survival, cell dysfunction and lower NK cell effector cytotoxicity capacity. Next, we validated these results by characterizing circulating NK cells from HGSOC patients through flow cytometry and cytotoxic assays while undergoing COX-2 and CTLA-4 blockade. The blockade of COX-2 improved the cytotoxic capacity of NK cells against HGSOC cell lines. Our findings underscore the relevance of COX-2 in shaping the TIME and suggest its potential as a prognostic indicator and therapeutic target. Increased COX-2 expression may hamper the effectivity of immunotherapies that require NK cell effector function. These results provide a foundation for experimental validation and clinical trials investigating combined therapies targeting COX-2 and CTLA-4 in HGSOC.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Killer instincts: natural killer cells as multifactorial cancer immunotherapy

Natural killer (NK) cells integrate heterogeneous signals for activation and inhibition using germline-encoded receptors. These receptors are stochastically co-expressed, and their concurrent engagement and signaling can adjust the sensitivity of individual cells to putative targets. Against cancers, which mutate and evolve under therapeutic and immunologic pressure, the diversity for recognition provided by NK cells may be key to comprehensive cancer control. NK cells are already being trialled as adoptive cell therapy and targets for immunotherapeutic agents. However, strategies to leverage their naturally occurring diversity and agility have not yet been developed. In this review, we discuss the receptors and signaling pathways through which signals for activation or inhibition are generated in NK cells, focusing on their roles in cancer and potential as targets for immunotherapies. Finally, we consider the impacts of receptor co-expression and the potential to engage multiple pathways of NK cell reactivity to maximize the scope and strength of antitumor activities.

An online clinical model and risk stratification system to predict progression-free survival for advanced non-small-cell lung cancer patients treated with PD-(L)1 inhibitor

Background

Our study aimed to build a risk stratification system predicting the progression-free survival (PFS) to classify patients into diverse prognostic subgroups for advanced non-small-cell lung cancer patients treated with PD-(L)1 inhibitor.

Methods

404 patients from our center were enrolled in this study and 70% patients (n = 282) were randomly assigned into the training cohort and other 30% patients (n = 122) into the validation cohort. A testing cohort contained 81 patients from other centers were used to assess the generalizability of model. Cox regression analyses were used to identify the most significant clinical parameters. The model's performance was assessed by using concordance index (C-index), calibration curves, Decision Curve Analyses (DCAs), net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and survival curve.

Results

Five clinical parameters were identified as the most significant predictors by using cox regression. We then integrated them into a Nomogram to Evaluate the relative PFS of ICIs Treatment (NEPIT). The C-index of NEPIT in the training cohort, the validation cohort and testing cohort was 0.789 (95%CI: 0.750-0.828), 0.745 (95%CI: 0.706-0.784), and 0.766 (95%CI: 0.744-0.788), respectively. The calibration curves presented a good congruence between the predictions and actual observations. The Decision Curve Analyses (DCAs) reflected positive net benefits can be obtained for NEPIT. The results from NRI and IDI analyses showed that the NEPIT could improve predictive power of TPS. In addition, the further constructed risk stratification system could effectively categorize patients into different risk subgroups.

Conclusion

The tools developed in this study would have value in guiding the optimal patient selection for precision care.

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy

Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.

Immune checkpoints on T and NK cells in the context of HBV infection: Landscape, pathophysiology and therapeutic exploitation

In hepatitis B virus (HBV) infection, the interplay between the virus and the host immune system is crucial in determining the pathogenesis of the disease. Patients who fail to mount a sufficient and sustained anti-viral immune response develop chronic hepatitis B (CHB). T cells and natural killer (NK) cells play decisive role in viral clearance, but they are defective in chronic HBV infection. The activation of immune cells is tightly controlled by a combination of activating and inhibitory receptors, called immune checkpoints (ICs), allowing the maintenance of immune homeostasis. Chronic exposure to viral antigens and the subsequent dysregulation of ICs actively contribute to the exhaustion of effector cells and viral persistence. The present review aims to summarize the function of various ICs and their expression in T lymphocytes and NK cells in the course of HBV infection as well as the use of immunotherapeutic strategies targeting ICs in chronic HBV infection.

New Insights on the Role of Anti-PD-L1 and Anti-CTLA-4 mAbs on Different Lymphocytes Subpopulations in TNBC

Antibody-based cancer immunotherapy includes monoclonals against immune checkpoints (ICs), to modulate specific T cell responses against cancer. NK cells are a newly emerging target for immune checkpoint receptor inhibition in cancer immunotherapy, as ICs are also expressed on NK cells in various cancers. The latter cells are becoming attractive targets for cancer immunotherapy, as they are effector cells similar to CTLs, exerting natural cytotoxicity against primary tumor cells and metastasis, and they are able to distinguish tumor cells from healthy ones, leading to more specific anti-tumor cytotoxicity and reduced off-target effects. Thus, we decided to test the effects on isolated NK cells and T cell subpopulations of novel immunomodulatory mAbs, recently generated in our lab, in comparison with those in clinical use, such as ipilimumab and atezolizumab. Interestingly, we found that the novel anti-CTLA-4 (ID-1) and anti-PD-L1 (PD-L1_1) antibodies are able to induce NK cell activation and exert anti-tumor effects on TNBC cells co-cultured with NK cells more efficiently than the clinically validated ones, either when used as single agents or in combinatorial treatments. On the other hand, ipilimumab was found to be more effective in activating T cells with respect to ID-1. These findings indicate that antibodies targeting different epitopes can have differential effects on different lymphocytes subpopulations and that novel combinations of mAbs could be suitable for therapeutic approaches aimed at activating not only T cells but also NK cells, especially for tumors lacking MHC.

Inhibitory Receptors and Immune Checkpoints Regulating Natural Killer Cell Responses to Cancer

Simple Summary

Recent years marked the discovery and increased understanding of the role immune checkpoints play in immunity against cancer. This has revolutionized cancer treatment, saving the lives of many patients. For numerous years the spotlight of success has been directed towards T cells; however, it is now appreciated that other cells play vital roles in this protection. In this review we focused on cytotoxic lymphocytes Natural Killer (NK) cells, which are known to be well equipped in the fight against cancer. We explored the role of well-described and newly emerging inhibitory receptors, including immune checkpoints in regulating NK cell activity against cancer. The knowledge summarized in this review should guide the development of immunotherapies targeting inhibitory receptors with the aim of restoring NK cell responses in cancer patients.

Abstract

The discovery of immune checkpoints provided a breakthrough for cancer therapy. Immune checkpoints are inhibitory receptors that are up-regulated on chronically stimulated lymphocytes and have been shown to hinder immune responses to cancer. Monoclonal antibodies against the checkpoint molecules PD-1 and CTLA-4 have shown early clinical success against melanoma and are now approved to treat various cancers. Since then, the list of potential candidates for immune checkpoint blockade has dramatically increased. The current paradigm stipulates that immune checkpoint blockade therapy unleashes pre-existing T cell responses. However, there is accumulating evidence that some of these immune checkpoint molecules are also expressed on Natural Killer (NK) cells. In this review, we summarize our latest knowledge about targetable NK cell inhibitory receptors. We discuss the HLA-binding receptors KIRS and NKG2A, receptors binding to nectin and nectin-like molecules including TIGIT, CD96, and CD112R, and immune checkpoints commonly associated with T cells such as PD-1, TIM-3, and LAG-3. We also discuss newly discovered pathways such as IL-1R8 and often overlooked receptors such as CD161 and Siglecs. We detail how these inhibitory receptors might regulate NK cell responses to cancer, and, where relevant, we discuss their implications for therapeutic intervention.