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Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy requires therapeutic combinations that induce quality T cells. Tumor microenvironment (TME) analysis following therapeutic interventions can identify response mechanisms, informing design of effective combinations. We provide a reference single-cell dataset from tumor-infiltrating leukocytes (TILs) from a human neoadjuvant clinical trial comparing the granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting allogeneic PDAC vaccine GVAX alone, in combination with anti-PD1 or with both anti-PD1 and CD137 agonist. Treatment with GVAX and anti-PD-1 led to increased CD8 + T cell activation and expression of cytoskeletal and extracellular matrix (ECM)-interacting components. Addition of CD137 agonist increased abundance of clonally expanded CD8 + T cells and increased immunosuppressive TREM2 signaling in tumor associated macrophages (TAMs), identified by comparison of ligand-receptor networks, corresponding to changes in metabolism and ECM interactions. These findings associate therapy with GVAX, anti-PD1, and CD137 agonist with enhanced CD8 + T cell function while inducing alternative immunosuppressive pathways in patients with PDAC., Competing Interests: L.Z. receives grant support from Bristol Myers Squibb, Merck, Astrazeneca, iTeos, Amgen, NovaRock, Inxmed, and Halozyme. L.Z. is a paid consultant/advisory Board Member at Biosion, Alphamab, NovaRock, Ambrx, Akrevia/Xilio, QED, Natera, Novagenesis, Snow Lake Capitals, BioArdis, Amberstone Biosciences, Tempus, Pfizer, Tavotek Lab, Clinical Trial Options, LLC, and Mingruizhiyao. L.Z. holds shares at Alphamab, Amberstone, and Mingruizhiyao. E.M.J. reports other support from Abmeta and Adventris, personal fees from Achilles, Dragonfly, Mestag, The Medical Home Group, and Surgtx, other support from Parker Institute, grants and other support from the Lustgarten Foundation, Genentech, BMS, and Break Through Cancer outside the submitted work. E.S.C. receives grant support from Affimed GmbH, NextCure, Pfizer, Haystack Oncology, Regeneron and is a consultant for Seres Therapeutics and SIRTex. R.A.A. receives grant support from Bristol-Meyer Squibb, RAPT Therapeutics. R.A.A. is a paid consultant for Bristol-Meyer Squibb, Merck, and Astrazeneca. S.Y. reports grants from NIH and Maryland Cigarette Restitution Fund during the conduct of the study, grants and personal fees from Cepheid, other support from Digital Harmonic, and grants from Janssen and Bristol Myers Squibb outside the submitted work. J.H.E. was previously a consultant and holds equity in Unity Biotechnology, Aegeria Soft Tissue and is an advisor for Tessera Therapeutics, HapInScience, Regenity, and Font Bio. W.J.H. has patent royalties from Rodeo/Amgen, grants from Sanofi and NeoTX, and speaking/travel honoraria from Exelixis and Standard BioTools. J.W.Z. reports grant funding from Genentech.

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.

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., (© 2023. Springer Nature Limited.)

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., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)