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High levels of IL1β can result in chronic inflammation, which in turn can promote tumor growth and metastasis. Inhibition of IL1β could therefore be a promising therapeutic option in the treatment of cancer. Here, the effects of IL1β blockade induced by the mAbs canakinumab and gevokizumab were evaluated alone or in combination with docetaxel, anti–programmed cell death protein 1 (anti–PD-1), anti-VEGFα, and anti-TGFβ treatment in syngeneic and humanized mouse models of cancers of different origin. Canakinumab and gevokizumab did not show notable efficacy as single-agent therapies; however, IL1β blockade enhanced the effectiveness of docetaxel and anti–PD-1. Accompanying these effects, blockade of IL1β alone or in combination induced significant remodeling of the tumor microenvironment (TME), with decreased numbers of immune suppressive cells and increased tumor infiltration by dendritic cells (DC) and effector T cells. Further investigation revealed that cancer-associated fibroblasts (CAF) were the cell type most affected by treatment with canakinumab or gevokizumab in terms of change in gene expression. IL1β inhibition drove phenotypic changes in CAF populations, particularly those with the ability to influence immune cell recruitment. These results suggest that the observed remodeling of the TME following IL1β blockade may stem from changes in CAF populations. Overall, the results presented here support the potential use of IL1β inhibition in cancer treatment. Further exploration in ongoing clinical studies will help identify the best combination partners for different cancer types, cancer stages, and lines of treatment.

Supplementary tables S1-S5 and figures S1-S7

High levels of IL1β can result in chronic inflammation, which in turn can promote tumor growth and metastasis. Inhibition of IL1β could therefore be a promising therapeutic option in the treatment of cancer. Here, the effects of IL1β blockade induced by the mAbs canakinumab and gevokizumab were evaluated alone or in combination with docetaxel, anti–programmed cell death protein 1 (anti–PD-1), anti-VEGFα, and anti-TGFβ treatment in syngeneic and humanized mouse models of cancers of different origin. Canakinumab and gevokizumab did not show notable efficacy as single-agent therapies; however, IL1β blockade enhanced the effectiveness of docetaxel and anti–PD-1. Accompanying these effects, blockade of IL1β alone or in combination induced significant remodeling of the tumor microenvironment (TME), with decreased numbers of immune suppressive cells and increased tumor infiltration by dendritic cells (DC) and effector T cells. Further investigation revealed that cancer-associated fibroblasts (CAF) were the cell type most affected by treatment with canakinumab or gevokizumab in terms of change in gene expression. IL1β inhibition drove phenotypic changes in CAF populations, particularly those with the ability to influence immune cell recruitment. These results suggest that the observed remodeling of the TME following IL1β blockade may stem from changes in CAF populations. Overall, the results presented here support the potential use of IL1β inhibition in cancer treatment. Further exploration in ongoing clinical studies will help identify the best combination partners for different cancer types, cancer stages, and lines of treatment.

The anti-CD19 chimeric antigen receptor (CAR)–T cell therapy tisagenlecleucel was evaluated in the global, phase 2 JULIET study in adult patients with relapsed/refractory diffuse large B-cell lymphoma (DLBCL). We correlated tisagenlecleucel cellular kinetics with clinical/product parameters in 111 patients treated in JULIET. Tisagenlecleucel persistence in responders and nonresponders, respectively, was demonstrated for 554 and 400 days maximum by flow cytometry and for 693 and 374 days maximum by quantitative polymerase chain reaction (qPCR). No relationships were identified between cellular kinetics (qPCR) and product characteristics, intrinsic/extrinsic factors, dose, or immunogenicity. Most patients with 3-month response had detectable transgene at time of response and continued persistence for ≥6 months. Expansion (maximal expansion of transgene/CAR-positive T-cell levels in vivo postinfusion [Cmax]) was potentially associated with response duration but this did not reach statistical significance (hazard ratio for a twofold increase in Cmax, 0.79; 95% confidence interval, 0.61-1.01). Tisagenlecleucel expansion was associated with cytokine-release syndrome (CRS) severity and tocilizumab use; no relationships were observed with neurologic events. Transgene levels were associated with B-cell levels. Dose was associated with CRS severity, but this was not statistically significant after adjusting for baseline tumor burden. In contrast to the results from B-cell precursor acute lymphoblastic leukemia (B-ALL) and chronic lymphocytic leukemia, similar exposure was observed in DLBCL in this study regardless of response and expansion was lower in DLBCL than B-ALL, likely from differences in cancer location and/or T-cell intrinsic factors. Relationships between expansion and CRS severity, and lack of relationships between dose and exposure, were similar between DLBCL and B-ALL. Tisagenlecleucel cellular kinetics in adult relapsed/refractory DLBCL improve current understanding of in vivo expansion and its relationships with safety/efficacy endpoints. This trial was registered at www.clinicaltrials.gov as #NCT02445248.

The adoptive transfer of T lymphocytes reprogrammed to target tumor cells has demonstrated significant potential in various malignancies. However, little is known about the long-term potential and the clonal stability of the infused cells. Here, we studied the longest persisting CD19 redirected chimeric antigen receptor (CAR) T cells to date in two chronic lymphocytic leukemia (CLL) patients who achieved a complete remission in 2010. CAR T-cells were still detectable up to 10+ years post-infusion, with sustained remission in both patients. Surprisingly, a prominent, highly activated CD4+ population developed in both patients during the years post-infusion, dominating the CAR T-cell population at the late time points. This transition was reflected in the stabilization of the clonal make-up of CAR T-cells with a repertoire dominated by few clones. Single cell multi-omics profiling via Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) with TCR sequencing of CAR T-cells obtained 9.3 years post-infusion demonstrated that these long-persisting CD4+ CAR T-cells exhibited cytotoxic characteristics along with strong evidence of ongoing functional activation and proliferation. Our data provide novel insight into the CAR T-cell characteristics associated with long-term remission in leukemia.

The adoptive transfer of T lymphocytes reprogrammed to target tumour cells has demonstrated potential for treatment of various cancers

High-dimensional flow cytometry experiments have become a method of choice for high-throughput integration and characterization of cell populations. Here, we present a summary of state-of-the-art R-based pipelines used for differential analyses of cytometry data, largely based on chimeric antigen receptor (CAR) T cell therapies. These pipelines are based on publicly available R libraries, put together in a systematic and functional fashion, therefore free of cost.In recent years, existing tools tailored to analyze complex high-dimensional data such as single-cell RNA sequencing (scRNAseq) have been successfully ported to cytometry studies due to the similar nature of flow cytometry and scRNAseq platforms. Existing environments like Cytobank (Kotecha et al., 2010), FlowJo (FlowJo™ Software) and FCS Express (https://denovosoftware.com) already offer a variety of these ported tools, but they either come at a premium or are fairly complicated to manage by an inexperienced user. To mitigate these limitations, experienced cytometrists and bioinformaticians usually incorporate these functions into an RShiny (https://shiny.rstudio.com) application that ultimately offers a user-friendly, intuitive environment that can be used to analyze flow cytometry data. Computational tools and Shiny-based tools are the perfect answer to the ever-growing dimensionality and complexity of flow cytometry data, by offering a dynamic, yet user-friendly exploratory space, tailored to bridge the space between the lab experimental world and the computational, machine learning space.

Background: Autologous chimeric antigen receptor T (CAR-T) cell therapies show significant clinical activity against hematologic malignancies including multiple myeloma (MM). Numerous factors influence the effectiveness of this therapeutic approach. The fitness of a CAR-T cell product is an important factor affecting T cell engraftment and persistence, a pre-requisite for effective CAR-T cell therapy. In an effort to deconstruct the cellular heterogeneity of CAR-T cell products and explore relationships between T cell states within the cellular product, gene expression and CAR-T cell engraftment following adoptive transfer, we performed single cell RNA-sequencing (scRNA-seq) on CAR-T cell products from a phase I trial of BCMA-specific CAR-T cells in relapsed/refractory MM patients that included cohorts receiving a similar CAR-T cell dose with and without preconditioning with cyclophosphamide (NCT02546167, Cohen et al, J Clin Invest 2019). Method: scRNA-seq was performed on 25 unique products with an average of 8823 cells captured per product at an average depth of 40779 reads per cell. Batch effects were controlled by integration using established metrics for quality control. The linked inference of genomic experimental relationships (LIGER) method exhibited the most effective integration of 228,752 single cells across the 25 products. Annotation was completed by comparing transcriptomes of cells to the Blueprint/ENCODE references of pure immune cells. Differential gene expression (DGE) analysis was conducted by both Wilcoxon rank sum test testing and negative binomial distribution. Gene enrichment analysis was performed by comparing DGE with the Molecular Signatures Database v7.4. Cell to cell communication utilized the KEGG signaling pathway maps and curated lists of interactions from the literature. The intercellular communication probability was estimated on the DE genes before statistically significant intercellular communications were calculated by a permutation test, with dominant senders, receivers, mediators, and influencers identified using graph theory. Patients were grouped based upon "good" or "poor" engraftment using a peak blood vector copies per cell cutoff of 10,000. Association of transcriptional variation with neurotoxicity was explored for all patients. Results: The CD4:CD8 ratio of 2.51 single cells in the data was comparable to the observed average CD4:CD8 ratio of total T cells in the product based upon flow cytometric analysis of the products at harvest. Cell annotation showed significant heterogeneity of CD4+ and CD8+ T cells with cells exhibiting a CD4 Tcm transcriptional profile comprising the largest subset of T cells within the product. DGE analysis found 205 genes that were up or down regulated between the "good" vs "poor" engraftment phenotypes with 75 genes being detected using both mathematical approaches. CAR-BCMA expression was increased in "good" engraftment groups which has not previously been shown in CAR sorted cells. Within both CD4+ and CD8+ T cell subsets, transcriptional pathways associated with the RXF5/RFXAP/RFXANK transcriptional activator complex and the IL-2/STAT5 signaling were identified as upregulated in the "good" engrafted products. Cell to cell communication analysis for both secreted signaling and cell to cell contact revealed similar ligand-receptor interaction differences between the engraftment groups. Within the neurotoxicity groups CAR-BCMA expression was not associated with either neurotoxicity or with high and low neurotoxicity effects in the product. High vs low neurotoxicity showed a shift with interferon gamma and cytokine-cytokine interactions. We also detected IL-6/JAK/STAT3 signaling activation increases in this group. Conclusion: The RFX5/RFXAP/RFXANK pathway associated with MHC class II expression and IL-2/STAT5 signaling show a significant association with engraftment of BCMA-specific CAR-T cells. Although IL-2 signaling is well known to be critical to T cell survival and a potential key driver for long-term persistence, the role of the RFX5 transcriptional activator complex in T cells, outside of its important role in regulating MHC expression, is largely unknown. Both pathways deserve further investigation. Cell to cell communication between engraftment groups suggests CD4/CD8 interactions that might be beneficial to engraftment at the product manufacturing stage. Figure 1 Figure 1. Disclosures Garfall: Amgen: Honoraria; CRISPR Therapeutics: Research Funding; GlaxoSmithKline: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Research Funding; Tmunity: Research Funding. Bu: Novartis: Current Employment, Patents & Royalties: Co-inventor on patent applications. Orlando: Novartis: Current Employment. Brogdon: Novartis Institutes for Biomedical Research: Current Employment. Pruteanu-Malinici: Novartis: Current Employment. Cohen: Janssen: Consultancy; Oncopeptides: Consultancy; Genentech/Roche: Consultancy; BMS/Celgene: Consultancy; AstraZeneca: Consultancy; Novartis: Research Funding; Takeda: Consultancy; GlaxoSmithKline: Consultancy, Research Funding.

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

The reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) upon overexpression of OCT4, KLF4, SOX2, and c-MYC (OKSM) provides a powerful system to interrogate basic mechanisms of cell fate change. However, iPSC formation with standard methods is protracted and inefficient, resulting in heterogeneous cell populations. Here we show that exposure of OKSM-expressing cells to both ascorbic acid and a GSK3-beta inhibitor (termed “AGi”) facilitates more synchronous and rapid iPSC formation from a variety of mouse cell types. AGi treatment restored the ability of refractory cell populations to yield iPSC colonies, and it attenuated the activation of developmental regulators commonly observed during the reprogramming process. Moreover, AGi supplementation gave rise to chimera-competent iPSCs after as little as 48 hours of OKSM expression. Our results offer a simple modification to the reprogramming protocol, facilitating iPSC induction at unparalleled efficiencies and enabling dissection of the underlying mechanisms in more homogeneous cell populations.

We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using ldquonormalrdquo/ldquotypicalrdquo video. The iHMM retains a full posterior density function on all model parameters, including the number of underlying HMM states. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via Markov chain Monte Carlo and using a variational Bayes formulation. Comparisons are made to modeling based on conventional maximum-likelihood-based HMMs, as well as to Dirichlet-process-based Gaussian-mixture models.

To fully describe gene expression dynamics requires the ability to quantitatively capture expression in individual cells over time. Automated systems for acquiring and analyzing real-time images are needed to obtain unbiased data across many samples and conditions. We developed a microfluidics device, the RootArray, in which 64 Arabidopsis thaliana seedlings can be grown and their roots imaged by confocal microscopy over several days without manual intervention. To achieve high throughput, we decoupled acquisition from analysis. In the acquisition phase, we obtain images at low resolution and segment to identify regions of interest. Coordinates are communicated to the microscope to record the regions of interest at high resolution. In the analysis phase, we reconstruct three-dimensional objects from stitched high-resolution images and extract quantitative measurements from a virtual medial section of the root. We tracked hundreds of roots to capture detailed expression patterns of 12 transgenic reporter lines under different conditions.

We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic and the topics are drawn from a mixture model. The proposed model infers the change in the topic mixture weights as a function of time. The details of this general framework may take different forms, depending on the specifics of the model. For the examples considered here, we examine base measures based on independent multinomial-Dirichlet measures for representation of topic-dependent word counts. The form of the hierarchical model allows efficient variational Bayesian inference, of interest for large-scale problems. We demonstrate results and make comparisons to the model when the dynamic character is removed, and also compare to latent Dirichlet allocation (LDA) and Topics over Time (TOT). We consider a database of Neural Information Processing Systems papers as well as the US Presidential State of the Union addresses from 1790 to 2008.

We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using "normal"/"typical" video data. The iHMM automatically determines the proper number of HMM states, and it retains a full posterior density function on all model parameters. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process (HDP) framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via MCMC and using a variational Bayes (VB) formulation.

A newer generation of anti-cancer drugs targeting underlying somatic genetic driver events have resulted in high single-agent or single-pathway response rates in selected patients, but few patients achieve complete responses and a sizeable fraction of patients relapse within a year. Thus, there is a pressing need for identification of combinations of targeted agents which induce more complete responses and prevent disease progression. We describe the results of a combination screen of an unprecedented scale in mammalian cells performed using a collection of targeted, clinically tractable agents across a large panel of melanoma cell lines. We find that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition. We identified drugs sensitizing cell lines that are BRAFV600E mutant but intrinsically resistant to BRAF inhibitor PLX4720, including the vascular endothelial growth factor receptor/kinase insert domain receptor (VEGFR/KDR) and platelet derived growth factor receptor (PDGFR) family inhibitor cediranib. The combination of cediranib and PLX4720 induced apoptosis in vitro and tumor regression in animal models. This synergistic interaction is likely due to engagement of multiple receptor tyrosine kinases (RTKs), demonstrating the potential of drug- rather than gene-specific combination discovery approaches. Patients with elevated biopsy KDR expression showed decreased progression free survival in trials of mitogen-activated protein kinase (MAPK) kinase pathway inhibitors. Thus, high-throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield clinically-actionable drug combinations.