CD4 T cell-activating neoantigens enhance personalized cancer vaccine efficacy.

Personalized cancer vaccines aim to activate and expand cytotoxic antitumor CD8+ T cells to recognize and kill tumor cells. However, the role of CD4+ T cell activation in the clinical benefit of these vaccines is not well defined. We previously established a personalized neoantigen vaccine (PancVAX) for the pancreatic cancer cell line Panc02, which activates tumor-specific CD8+ T cells but required combinatorial checkpoint modulators to achieve therapeutic efficacy. To determine the effects of neoantigen-specific CD4+ T cell activation, we generated a vaccine (PancVAX2) targeting both major histocompatibility complex class I- (MHCI-) and MHCII-specific neoantigens. Tumor-bearing mice vaccinated with PancVAX2 had significantly improved control of tumor growth and long-term survival benefit without concurrent administration of checkpoint inhibitors. PancVAX2 significantly enhanced priming and recruitment of neoantigen-specific CD8+ T cells into the tumor with lower PD-1 expression after reactivation compared with the CD8+ vaccine alone. Vaccine-induced neoantigen-specific Th1 CD4+ T cells in the tumor were associated with decreased Tregs. Consistent with this, PancVAX2 was associated with more proimmune myeloid-derived suppressor cells and M1-like macrophages in the tumor, demonstrating a less immunosuppressive tumor microenvironment. This study demonstrates the biological importance of prioritizing and including CD4+ T cell-specific neoantigens for personalized cancer vaccine modalities.

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.

Entinostat Decreases Immune Suppression to Promote Antitumor Responses in a HER2+ Breast Tumor Microenvironment.

Therapeutic combinations to alter immunosuppressive, solid tumor microenvironments (TME), such as in breast cancer, are essential to improve responses to immune checkpoint inhibitors (ICI). Entinostat, an oral histone deacetylase inhibitor, has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs. The precise and comprehensive alterations to the TME induced by entinostat remain unknown. Here, we employed single-cell RNA sequencing on HER2-overexpressing breast tumors from mice treated with entinostat and ICIs to fully characterize changes across multiple cell types within the TME. This analysis demonstrates that treatment with entinostat induced a shift from a protumor to an antitumor TME signature, characterized predominantly by changes in myeloid cells. We confirmed myeloid-derived suppressor cells (MDSC) within entinostat-treated tumors associated with a less suppressive granulocytic (G)-MDSC phenotype and exhibited altered suppressive signaling that involved the NFκB and STAT3 pathways. In addition to MDSCs, tumor-associated macrophages were epigenetically reprogrammed from a protumor M2-like phenotype toward an antitumor M1-like phenotype, which may be contributing to a more sensitized TME. Overall, our in-depth analysis suggests that entinostat-induced changes on multiple myeloid cell types reduce immunosuppression and increase antitumor responses, which, in turn, improve sensitivity to ICIs. Sensitization of the TME by entinostat could ultimately broaden the population of patients with breast cancer who could benefit from ICIs.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response.

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

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

BACKGROUND: Tumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun using single-cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in translational research. METHOD: We previously developed a computational transfer learning approach called projectR to identify shared biology between independent high-throughput single-cell RNA-sequencing (scRNA-seq) datasets. In the present study, we test this algorithm's ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and expand its application to the comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry. RESULTS: We found a conserved signature of NK cell activation in anti-CTLA-4 responsive mouse and human tumors. In human metastatic melanoma, we found that the NK cell activation signature associates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 antibodies independent of the antibody binding receptor (FcR) and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation. CONCLUSIONS: These data demonstrate a novel application of our transfer learning approach, which was able to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many questions in cancer therapeutics, enhance translational research, and enable better understanding and treatment of disease.

From bench to bedside: Single-cell analysis for cancer immunotherapy.

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Evaluation of both exonic and intronic variants for effects on RNA splicing allows for accurate assessment of the effectiveness of precision therapies.

Elucidating the functional consequence of molecular defects underlying genetic diseases enables appropriate design of therapeutic options. Treatment of cystic fibrosis (CF) is an exemplar of this paradigm as the development of CFTR modulator therapies has allowed for targeted and effective treatment of individuals harboring specific genetic variants. However, the mechanism of these drugs limits effectiveness to particular classes of variants that allow production of CFTR protein. Thus, assessment of the molecular mechanism of individual variants is imperative for proper assignment of these precision therapies. This is particularly important when considering variants that affect pre-mRNA splicing, thus limiting success of the existing protein-targeted therapies. Variants affecting splicing can occur throughout exons and introns and the complexity of the process of splicing lends itself to a variety of outcomes, both at the RNA and protein levels, further complicating assessment of disease liability and modulator response. To investigate the scope of this challenge, we evaluated splicing and downstream effects of 52 naturally occurring CFTR variants (exonic = 15, intronic = 37). Expression of constructs containing select CFTR intronic sequences and complete CFTR exonic sequences in cell line models allowed for assessment of RNA and protein-level effects on an allele by allele basis. Characterization of primary nasal epithelial cells obtained from individuals harboring splice variants corroborated in vitro data. Notably, we identified exonic variants that result in complete missplicing and thus a lack of modulator response (e.g. c.2908G>A, c.523A>G), as well as intronic variants that respond to modulators due to the presence of residual normally spliced transcript (e.g. c.4242+2T>C, c.3717+40A>G). Overall, our data reveals diverse molecular outcomes amongst both exonic and intronic variants emphasizing the need to delineate RNA, protein, and functional effects of each variant in order to accurately assign precision therapies.

Correction: Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines.

BACKGROUND: Identifying potential resistance mechanisms while tumour cells still respond to therapy is critical to delay acquired resistance. METHODS: We generated the first comprehensive multi-omics, bulk and single-cell data in sensitive head and neck squamous cell carcinoma (HNSCC) cells to identify immediate responses to cetuximab. Two pathways potentially associated with resistance were focus of the study: regulation of receptor tyrosine kinases by TFAP2A transcription factor, and epithelial-to-mesenchymal transition (EMT). RESULTS: Single-cell RNA-seq demonstrates heterogeneity, with cell-specific TFAP2A and VIM expression profiles in response to treatment and also with global changes to various signalling pathways. RNA-seq and ATAC-seq reveal global changes within 5 days of therapy, suggesting early onset of mechanisms of resistance; and corroborates cell line heterogeneity, with different TFAP2A targets or EMT markers affected by therapy. Lack of TFAP2A expression is associated with HNSCC decreased growth, with cetuximab and JQ1 increasing the inhibitory effect. Regarding the EMT process, short-term cetuximab therapy has the strongest effect on inhibiting migration. TFAP2A silencing does not affect cell migration, supporting an independent role for both mechanisms in resistance. CONCLUSION: Overall, we show that immediate adaptive transcriptional and epigenetic changes induced by cetuximab are heterogeneous and cell type dependent; and independent mechanisms of resistance arise while tumour cells are still sensitive to therapy.

Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Single-Cell RNA-Seq Analysis of Retinal Development Identifies NFI Factors as Regulating Mitotic Exit and Late-Born Cell Specification.

Precise temporal control of gene expression in neuronal progenitors is necessary for correct regulation of neurogenesis and cell fate specification. However, the cellular heterogeneity of the developing CNS has posed a major obstacle to identifying the gene regulatory networks that control these processes. To address this, we used single-cell RNA sequencing to profile ten developmental stages encompassing the full course of retinal neurogenesis. This allowed us to comprehensively characterize changes in gene expression that occur during initiation of neurogenesis, changes in developmental competence, and specification and differentiation of each major retinal cell type. We identify the NFI transcription factors (Nfia, Nfib, and Nfix) as selectively expressed in late retinal progenitor cells and show that they control bipolar interneuron and Müller glia cell fate specification and promote proliferative quiescence.

Correlating Cystic Fibrosis Transmembrane Conductance Regulator Function with Clinical Features to Inform Precision Treatment of Cystic Fibrosis.

Rationale: The advent of precision treatment for cystic fibrosis using small-molecule therapeutics has created a need to estimate potential clinical improvements attributable to increases in cystic fibrosis transmembrane conductance regulator (CFTR) function. Objectives: To derive CFTR function of a variety of CFTR genotypes and correlate with key clinical features (sweat chloride concentration, pancreatic exocrine status, and lung function) to develop benchmarks for assessing response to CFTR modulators. Methods: CFTR function assigned to 226 unique CFTR genotypes was correlated with the clinical data of 54,671 individuals enrolled in the Clinical and Functional Translation of CFTR (CFTR2) project. Cross-sectional FEV1% predicted measurements were plotted by age at which measurement was obtained. Shifts in sweat chloride concentration and lung function reported in CFTR modulator trials were compared with function-phenotype correlations to assess potential efficacy of therapies. Measurements and Main Results: CFTR genotype function exhibited a logarithmic relationship with each clinical feature. Modest increases in CFTR function related to differing genotypes were associated with clinically relevant improvements in cross-sectional FEV1% predicted over a range of ages (6-82 yr). Therapeutic responses to modulators corresponded closely to predictions from the CFTR2-derived relationship between CFTR genotype function and phenotype. Conclusions: Increasing CFTR function in individuals with severe disease will have a proportionally greater effect on outcomes than similar increases in CFTR function in individuals with mild disease and should reverse a substantial fraction of the disease process. This study provides reference standards for clinical outcomes that may be achieved by increasing CFTR function.