A Distinct Transcriptional Program in Human CAR T Cells Bearing the 4-1BB Signaling Domain Revealed by scRNA-Seq.

T cells engineered to express chimeric antigen receptors (CARs) targeting CD19 have produced impressive outcomes for the treatment of B cell malignancies, but different products vary in kinetics, persistence, and toxicity profiles based on the co-stimulatory domains included in the CAR. In this study, we performed transcriptional profiling of bulk CAR T cell populations and single cells to characterize the transcriptional states of human T cells transduced with CD3ζ, 4-1BB-CD3ζ (BBζ), or CD28-CD3ζ (28ζ) co-stimulatory domains at rest and after activation by triggering their CAR or their endogenous T cell receptor (TCR). We identified a transcriptional signature common across CARs with the CD3ζ signaling domain, as well as a distinct program associated with the 4-1BB co-stimulatory domain at rest and after activation. CAR T cells bearing BBζ had increased expression of human leukocyte antigen (HLA) class II genes, ENPP2, and interleukin (IL)-21 axis genes, and decreased PD1 compared to 28ζ CAR T cells. Similar to previous studies, we also found BBζ CAR CD8 T cells to be enriched in a central memory cell phenotype and fatty acid metabolism genes. Our data uncovered transcriptional signatures related to costimulatory domains and demonstrated that signaling domains included in CARs uniquely shape the transcriptional programs of T cells.

Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase.

Fumarate hydratase (FH) is an enzyme of the tricarboxylic acid cycle (TCA cycle) that catalyses the hydration of fumarate into malate. Germline mutations of FH are responsible for hereditary leiomyomatosis and renal-cell cancer (HLRCC). It has previously been demonstrated that the absence of FH leads to the accumulation of fumarate, which activates hypoxia-inducible factors (HIFs) at normal oxygen tensions. However, so far no mechanism that explains the ability of cells to survive without a functional TCA cycle has been provided. Here we use newly characterized genetically modified kidney mouse cells in which Fh1 has been deleted, and apply a newly developed computer model of the metabolism of these cells to predict and experimentally validate a linear metabolic pathway beginning with glutamine uptake and ending with bilirubin excretion from Fh1-deficient cells. This pathway, which involves the biosynthesis and degradation of haem, enables Fh1-deficient cells to use the accumulated TCA cycle metabolites and permits partial mitochondrial NADH production. We predicted and confirmed that targeting this pathway would render Fh1-deficient cells non-viable, while sparing wild-type Fh1-containing cells. This work goes beyond identifying a metabolic pathway that is induced in Fh1-deficient cells to demonstrate that inhibition of haem oxygenation is synthetically lethal when combined with Fh1 deficiency, providing a new potential target for treating HLRCC patients.

Pan-cancer mapping of single CD8+ T cell profiles reveals a TCF1:CXCR6 axis regulating CD28 co-stimulation and anti-tumor immunity.

CD8+ T cells must persist and function in diverse tumor microenvironments to exert their effects. Thus, understanding common underlying expression programs could better inform the next generation of immunotherapies. We apply a generalizable matrix factorization algorithm that recovers both shared and context-specific expression programs from diverse datasets to a single-cell RNA sequencing (scRNA-seq) compendium of 33,161 CD8+ T cells from 132 patients with seven human cancers. Our meta-single-cell analyses uncover a pan-cancer T cell dysfunction program that predicts clinical non-response to checkpoint blockade in melanoma and highlights CXCR6 as a pan-cancer marker of chronically activated T cells. Cxcr6 is trans-activated by AP-1 and repressed by TCF1. Using mouse models, we show that Cxcr6 deletion in CD8+ T cells increases apoptosis of PD1+TIM3+ cells, dampens CD28 signaling, and compromises tumor growth control. Our study uncovers a TCF1:CXCR6 axis that counterbalances PD1-mediated suppression of CD8+ cell responses and is essential for effective anti-tumor immunity.

Predicting selective drug targets in cancer through metabolic networks.

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.

Spatial transcriptomics.

Spatial transcriptomics, with other spatial technologies, has enabled scientists to dissect the organization and interaction of different cell types within the tumor microenvironment. We asked experts to discuss some aspects of this technology from revealing the tumor microenvironment and heterogeneity, to tracking tumor evolution, to guiding tumor therapy, to current technical challenges.

Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.

The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. Here we chart liver metabolic activity and functionality in NAFLD by integrating global transcriptomic data, from human liver biopsies, and metabolic flux data, measured across the human splanchnic vascular bed, within a genome-scale model of human metabolism. We show that an increased amount of liver fat induces mitochondrial metabolism, lipolysis, glyceroneogenesis and a switch from lactate to glycerol as substrate for gluconeogenesis, indicating an intricate balance of exacerbated opposite metabolic processes in glycemic regulation. These changes were associated with reduced metabolic adaptability on a network level in the sense that liver fat accumulation puts increasing demands on the liver to adaptively regulate metabolic responses to maintain basic liver functions. We propose that failure to meet excessive metabolic challenges coupled with reduced metabolic adaptability may lead to a vicious pathogenic cycle leading to the co-morbidities of NAFLD.

Fumarate induces redox-dependent senescence by modifying glutathione metabolism.

Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) are associated with a highly malignant form of renal cancer. We combined analytical chemistry and metabolic computational modelling to investigate the metabolic implications of FH loss in immortalized and primary mouse kidney cells. Here, we show that the accumulation of fumarate caused by the inactivation of FH leads to oxidative stress that is mediated by the formation of succinicGSH, a covalent adduct between fumarate and glutathione. Chronic succination of GSH, caused by the loss of FH, or by exogenous fumarate, leads to persistent oxidative stress and cellular senescence in vitro and in vivo. Importantly, the ablation of p21, a key mediator of senescence, in Fh1-deficient mice resulted in the transformation of benign renal cysts into a hyperplastic lesion, suggesting that fumarate-induced senescence needs to be bypassed for the initiation of renal cancers.

Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling.

The metabolism of cancer cells is reprogrammed in various ways to support their growth and survival. Studying these phenomena to develop noninvasive diagnostic tools and selective treatments is a promising avenue. Metabolic modeling has recently emerged as a new way to study human metabolism in a systematic, genome-scale manner by using pertinent high-throughput omics data. This method has been shown in various studies to provide fairly accurate estimates of the metabolic phenotype and its modifications following genetic and environmental perturbations. Here, we provide an overview of genome-scale metabolic modeling and its current use to model human metabolism in health and disease. We then describe the initial steps made using it to study cancer metabolism and how it may be harnessed to enhance ongoing experimental efforts to identify drug targets and biomarkers for cancer in a rationale-based manner.

Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer.

Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)(+) versus ER(-) tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.