Combinatorial CRISPR-Cas9 Metabolic Screens Reveal Critical Redox Control Points Dependent on the KEAP1-NRF2 Regulatory Axis

Tumor Cell-Intrinsic Immunometabolism and Precision Nutrition in Cancer Immunotherapy

One of the greatest challenges in the cancer immunotherapy field is the need to biologically rationalize and broaden the clinical utility of immune checkpoint inhibitors (ICIs). The balance between metabolism and immune response has critical implications for overcoming the major weaknesses of ICIs, including their lack of universality and durability. The last decade has seen tremendous advances in understanding how the immune system's ability to kill tumor cells requires the conspicuous metabolic specialization of T-cells. We have learned that cancer cell-associated metabolic activities trigger shifts in the abundance of some metabolites with immunosuppressory roles in the tumor microenvironment. Yet very little is known about the tumor cell-intrinsic metabolic traits that control the immune checkpoint contexture in cancer cells. Likewise, we lack a comprehensive understanding of how systemic metabolic perturbations in response to dietary interventions can reprogram the immune checkpoint landscape of tumor cells. We here review state-of-the-art molecular- and functional-level interrogation approaches to uncover how cell-autonomous metabolic traits and diet-mediated changes in nutrient availability and utilization might delineate new cancer cell-intrinsic metabolic dependencies of tumor immunogenicity. We propose that clinical monitoring and in-depth molecular evaluation of the cancer cell-intrinsic metabolic traits involved in primary, adaptive, and acquired resistance to cancer immunotherapy can provide the basis for improvements in therapeutic responses to ICIs. Overall, these approaches might guide the use of metabolic therapeutics and dietary approaches as novel strategies to broaden the spectrum of cancer patients and indications that can be effectively treated with ICI-based cancer immunotherapy.

Niche-Selective Inhibition of Pathogenic Th17 Cells by Targeting Metabolic Redundancy

Targeting glycolysis has been considered therapeutically intractable owing to its essential housekeeping role. However, the context-dependent requirement for individual glycolytic steps has not been fully explored. We show that CRISPR-mediated targeting of glycolysis in T cells in mice results in global loss of Th17 cells, whereas deficiency of the glycolytic enzyme glucose phosphate isomerase (Gpi1) selectively eliminates inflammatory encephalitogenic and colitogenic Th17 cells, without substantially affecting homeostatic microbiota-specific Th17 cells. In homeostatic Th17 cells, partial blockade of glycolysis upon Gpi1 inactivation was compensated by pentose phosphate pathway flux and increased mitochondrial respiration. In contrast, inflammatory Th17 cells experience a hypoxic microenvironment known to limit mitochondrial respiration, which is incompatible with loss of Gpi1. Our study suggests that inhibiting glycolysis by targeting Gpi1 could be an effective therapeutic strategy with minimum toxicity for Th17-mediated autoimmune diseases, and, more generally, that metabolic redundancies can be exploited for selective targeting of disease processes.

The NRF2/KEAP1 Axis in the Regulation of Tumor Metabolism: Mechanisms and Therapeutic Perspectives

The NRF2/KEAP1 pathway is a fundamental signaling cascade that controls multiple cytoprotective responses through the induction of a complex transcriptional program that ultimately renders cancer cells resistant to oxidative, metabolic and therapeutic stress. Interestingly, accumulating evidence in recent years has indicated that metabolic reprogramming is closely interrelated with the regulation of redox homeostasis, suggesting that the disruption of NRF2 signaling might represent a valid therapeutic strategy against a variety of solid and hematologic cancers. These aspects will be the focus of the present review.

A census of pathway maps in cancer systems biology

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.

Tracing insights into de novo lipogenesis in liver and adipose tissues

Lipids play important roles in biology that include structural compartmentation as membranes, energy storage, and regulatory functions as signaling molecules. These molecules can be obtained via the surrounding environment (e.g. diet) or synthesized de novo. Fatty acid synthesis is an energetically demanding process and must therefore be tightly regulated to balance fatty acid availability with the functional and energetic needs of cells and tissues. Here we review key aspects of de novo lipogenesis (DNL) in mammalian systems. We highlight key nodes in the pathway that are used for quantitation of lipogenic fluxes and regulation of fatty acid diversity across tissues. Next, we discuss key aspects of DNL function in the major lipogenic tissues of mammals: liver, white adipose tissue (WAT), and brown adipose tissue (BAT), highlighting recent molecular discoveries that suggest potential roles for tissue specific DNL. Finally, we propose critical questions that will be important to address using the advanced approaches for DNL quantitation described herein.

Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9-Cas12a platform

Systematic mapping of genetic interactions (GIs) and interrogation of the functions of sizable genomic segments in mammalian cells represent important goals of biomedical research. To advance these goals, we present a CRISPR (clustered regularly interspaced short palindromic repeats)-based screening system for combinatorial genetic manipulation that employs coexpression of CRISPR-associated nucleases 9 and 12a (Cas9 and Cas12a) and machine-learning-optimized libraries of hybrid Cas9-Cas12a guide RNAs. This system, named Cas Hybrid for Multiplexed Editing and screening Applications (CHyMErA), outperforms genetic screens using Cas9 or Cas12a editing alone. Application of CHyMErA to the ablation of mammalian paralog gene pairs reveals extensive GIs and uncovers phenotypes normally masked by functional redundancy. Application of CHyMErA in a chemogenetic interaction screen identifies genes that impact cell growth in response to mTOR pathway inhibition. Moreover, by systematically targeting thousands of alternative splicing events, CHyMErA identifies exons underlying human cell line fitness. CHyMErA thus represents an effective screening approach for GI mapping and the functional analysis of sizable genomic regions, such as alternative exons.

EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction

Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.

Targeting Cell Metabolism as Cancer Therapy

Significance: Cancer cells exhibit altered metabolic pathways to keep up with biosynthetic and reduction-oxidation needs during tumor proliferation and metastasis. The common induction of metabolic pathways during cancer progression, regardless of cancer histio- or genotype, makes cancer metabolism an attractive target for therapeutic exploitation. Recent Advances: Emerging data suggest that these altered pathways may even result in resistance to anticancer therapies. Identifying specific metabolic dependencies that are unique to cancer cells has proved challenging in this field, limiting the therapeutic window for many candidate drug approaches. Critical Issues: Cancer cells display significant metabolic flexibility in nutrient-limited environments, hampering the longevity of suppressing cancer metabolism through any singular approach. Combinatorial "synthetic lethal" approaches may have a better chance for success and promising strategies are reviewed here. The dynamism of the immune system adds a level of complexity, as various immune populations in the tumor microenvironment often share metabolic pathways with cancer, with successive alterations during immune activation and quiescence. Decoding the reprogramming of metabolic pathways within cancer cells and stem cells, as well as examining metabolic symbiosis between components of the tumor microenvironment, would be essential to further meaningful drug development within the tumor's metabolic ecosystem. Future Directions: In this article, we examine evidence for the therapeutic potential of targeting metabolic alterations in cancer, and we discuss the drawbacks and successes that have stimulated this field.

Inhibition of TXNRD or SOD1 overcomes NRF2-mediated resistance to β-lapachone

Alterations in the NRF2/KEAP1 pathway result in the constitutive activation of NRF2, leading to the aberrant induction of antioxidant and detoxification enzymes, including NQO1. The NQO1 bioactivatable agent β-lapachone can target cells with high NQO1 expression but relies in the generation of reactive oxygen species (ROS), which are actively scavenged in cells with NRF2/KEAP1 mutations. However, whether NRF2/KEAP1 mutations influence the response to β-lapachone treatment remains unknown. To address this question, we assessed the cytotoxicity of β-lapachone in a panel of NSCLC cell lines bearing either wild-type or mutant KEAP1. We found that, despite overexpression of NQO1, KEAP1 mutant cells were resistant to β-lapachone due to enhanced detoxification of ROS, which prevented DNA damage and cell death. To evaluate whether specific inhibition of the NRF2-regulated antioxidant enzymes could abrogate resistance to β-lapachone, we systematically inhibited the four major antioxidant cellular systems using genetic and/or pharmacologic approaches. We demonstrated that inhibition of the thioredoxin-dependent system or copper-zinc superoxide dismutase (SOD1) could abrogate NRF2-mediated resistance to β-lapachone, while depletion of catalase or glutathione was ineffective. Interestingly, inhibition of SOD1 selectively sensitized KEAP1 mutant cells to β-lapachone exposure. Our results suggest that NRF2/KEAP1 mutational status might serve as a predictive biomarker for response to NQO1-bioactivatable quinones in patients. Further, our results suggest SOD1 inhibition may have potential utility in combination with other ROS inducers in patients with KEAP1/NRF2 mutations.

Itaconate modulates tricarboxylic acid and redox metabolism to mitigate reperfusion injury

OBJECTIVES

Cerebral ischemia/reperfusion (IR) drives oxidative stress and injurious metabolic processes that lead to redox imbalance, inflammation, and tissue damage. However, the key mediators of reperfusion injury remain unclear, and therefore, there is considerable interest in therapeutically targeting metabolism and the cellular response to oxidative stress.

METHODS

The objective of this study was to investigate the molecular, metabolic, and physiological impact of itaconate treatment to mitigate reperfusion injuries in in vitro and in vivo model systems. We conducted metabolic flux and bioenergetic studies in response to exogenous itaconate treatment in cultures of primary rat cortical neurons and astrocytes. In addition, we administered itaconate to mouse models of cerebral reperfusion injury with ischemia or traumatic brain injury followed by hemorrhagic shock resuscitation. We quantitatively characterized the metabolite levels, neurological behavior, markers of redox stress, leukocyte adhesion, arterial blood flow, and arteriolar diameter in the brains of the treated/untreated mice.

RESULTS

We demonstrate that the "immunometabolite" itaconate slowed tricarboxylic acid (TCA) cycle metabolism and buffered redox imbalance via succinate dehydrogenase (SDH) inhibition and induction of anti-oxidative stress response in primary cultures of astrocytes and neurons. The addition of itaconate to reperfusion fluids after mouse cerebral IR injury increased glutathione levels and reduced reactive oxygen/nitrogen species (ROS/RNS) to improve neurological function. Plasma organic acids increased post-reperfusion injury, while administration of itaconate normalized these metabolites. In mouse cranial window models, itaconate significantly improved hemodynamics while reducing leukocyte adhesion. Further, itaconate supplementation increased survival in mice experiencing traumatic brain injury (TBI) and hemorrhagic shock.

CONCLUSIONS

We hypothesize that itaconate transiently inhibits SDH to gradually "awaken" mitochondrial function upon reperfusion that minimizes ROS and tissue damage. Collectively, our data indicate that itaconate acts as a mitochondrial regulator that controls redox metabolism to improve physiological outcomes associated with IR injury.

Oncogenic R132 IDH1 Mutations Limit NADPH for De Novo Lipogenesis through (D)2-Hydroxyglutarate Production in Fibrosarcoma Sells

Neomorphic mutations in NADP-dependent isocitrate dehydrogenases (IDH1 and IDH2) contribute to tumorigenesis in several cancers. Although significant research has focused on the hypermethylation phenotypes associated with (D)2-hydroxyglutarate (D2HG) accumulation, the metabolic consequences of these mutations may also provide therapeutic opportunities. Here we apply flux-based approaches to genetically engineered cell lines with an endogenous IDH1 mutation to examine the metabolic impacts of increased D2HG production and altered IDH flux as a function of IDH1 mutation or expression. D2HG synthesis in IDH1-mutant cells consumes NADPH at rates similar to de novo lipogenesis. IDH1-mutant cells exhibit increased dependence on exogenous lipid sources for in vitro growth, as removal of medium lipids slows growth more dramatically in IDH1-mutant cells compared with those expressing wild-type or enzymatically inactive alleles. NADPH regeneration may be limiting for lipogenesis and potentially redox homeostasis in IDH1-mutant cells, highlighting critical links between cellular biosynthesis and redox metabolism.

Badur et al. apply metabolic flux analysis to understand how oncogenic mutations in IDH1 alter redox metabolism. Production of (D)2-hydroxyglutarate (D2HG) consumes NADPH at levels similar to de novo lipogenesis, and removal of lipids compromises in vitro growth of IDH1-mutant cells.

"NRF2 addiction" in lung cancer cells and its impact on cancer therapy

Nuclear factor erythroid 2-like factor 2 (NRF2) is a master regulator of the antioxidant enzymes and the detoxification proteins that play major roles in redox homeostasis. Although it plays a protective role against tumorigenesis, emerging evidence has shown that the NRF2 pathway is frequently altered in different types of cancer, including lung cancer. NRF2 activation influences many of the hallmarks of cancer and their signaling pathways, mainly apoptosis, proliferation, angiogenesis, metastasis, and metabolic reprogramming to establish cellular metabolic processes leading to "NRF2 addiction" in lung cancer cells. Intriguingly, constitutive activation of NRF2 promotes cancer development as well as resistance to chemotherapy and radiotherapy, and these malignant phenotypes lead to a poor prognosis in lung cancer patients. Therefore, targeted inhibition of the NRF2 together with traditional chemotherapy, radiotherapy, and immunotherapy, may be a promising approach to improving the survival rates of the NRF2-addicted lung cancer cases. Here we summarize the recent advances in NRF2-addicted lung cancer.

Invertebrate models of lamin diseases

Lamins are evolutionarily conserved nuclear intermediate filament proteins. They provide structural support for the nucleus and help regulate many other nuclear activities. Mutations in human lamin genes, and especially in the LMNA gene, cause numerous diseases, termed laminopathies, including muscle, cardiac, metabolic, neuronal and early aging diseases. Most laminopathies arise from autosomal dominant missense mutations. Many of the mutant residues are conserved in the lamin genes of the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster. Our current understanding of the mechanisms leading to these diseases is mostly based on patients cell lines and animal models including C. elegans and D. melanogaster. The simpler lamin system and the powerful genetic tools offered by these invertebrate organisms greatly contributed to such studies. Here we provide an overview of the studies of laminopathies in Drosophila and C. elegans models.

Learning the pattern of epistasis linking genotype and phenotype in a protein

Understanding the pattern of epistasis-the non-independence of mutations-is critical for relating genotype and phenotype. However, the combinatorial complexity of potential epistatic interactions has severely limited the analysis of this problem. Using new mutational approaches, we report a comprehensive experimental study of all 2¹³ mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein-an opportunity to examine epistasis up to the 13^th order. The data show the existence of many high-order epistatic interactions between mutations, but also reveal extraordinary sparsity, enabling novel experimental and computational strategies for learning the relevant epistasis. We demonstrate that such information, in turn, can be used to accurately predict phenotypes in practical situations where the number of measurements is limited. Finally, we show how the observed epistasis shapes the solution space of single-mutation trajectories between the parental fluorescent proteins, informative about the protein's evolutionary potential. This work provides conceptual and experimental strategies to profoundly characterize epistasis in a protein, relevant to both natural and laboratory evolution.

GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens

Systems for CRISPR-based combinatorial perturbation of two or more genes are emerging as powerful tools for uncovering genetic interactions. However, systematic identification of these relationships is complicated by sample, reagent, and biological variability. We develop a variational Bayes approach (GEMINI) that jointly analyzes all samples and reagents to identify genetic interactions in pairwise knockout screens. The improved accuracy and scalability of GEMINI enables the systematic analysis of combinatorial CRISPR knockout screens, regardless of design and dimension. GEMINI is available as an open source R package on GitHub at https://github.com/sellerslab/gemini.

Cysteine dioxygenase 1 is a metabolic liability for non-small cell lung cancer

NRF2 is emerging as a major regulator of cellular metabolism. However, most studies have been performed in cancer cells, where co-occurring mutations and tumor selective pressures complicate the influence of NRF2 on metabolism. Here we use genetically engineered, non-transformed primary murine cells to isolate the most immediate effects of NRF2 on cellular metabolism. We find that NRF2 promotes the accumulation of intracellular cysteine and engages the cysteine homeostatic control mechanism mediated by cysteine dioxygenase 1 (CDO1), which catalyzes the irreversible metabolism of cysteine to cysteine sulfinic acid (CSA). Notably, CDO1 is preferentially silenced by promoter methylation in human non-small cell lung cancers (NSCLC) harboring mutations in KEAP1, the negative regulator of NRF2. CDO1 silencing promotes proliferation of NSCLC by limiting the futile metabolism of cysteine to the wasteful and toxic byproducts CSA and sulfite (SO₃^2-), and depletion of cellular NADPH. Thus, CDO1 is a metabolic liability for NSCLC cells with high intracellular cysteine, particularly NRF2/KEAP1 mutant cells.

Cancers form in humans and other animals when cells of the body develop mutations that allow them to grow and divide uncontrollably. The set of chemical reactions happening inside cancer cells, referred to as “metabolism”, can be very different to metabolism in the healthy cells they originate from. Some of these differences are directly caused by mutations, while others are a result of the environment surrounding the cancer cells as they develop into a tumor.

A protein called NRF2 is often overactive in human tumors due to mutations in its inhibitor protein KEAP1. Previous studies have shown that NRF2 changes the metabolism of cancer cells by switching specific genes on or off. However, since cancer cells also have other mutations that could mask or amplify some of the effects of NRF2, the precise role of this protein in metabolism remains unclear.

To address this question, Kang et al. generated mice that could switch between producing the normal KEAP1 protein or a mutant version that is unable to inhibit NRF2. The mouse model was then used to examine the immediate effects of activating the NRF2 protein. This revealed that NRF2 altered how mouse cells used a molecule called cysteine, which is required to make proteins and other cell components. When NRF2 was active, some of the cysteine molecules were converted into two wasteful and toxic particles by an enzyme called CDO1.

Kang et al. found that inactivating CDO1 in human lung cancer cells prevented these wasteful particles from being produced. This allows cancer cells to grow more rapidly, and may explain why human tumors generally evolve to shut down CDO1.

The findings of Kang et al. show that not all of the changes in metabolism caused by individual mutations in cancer cells help tumors to grow. As a tumor develops it may need to acquire further mutations to override the negative effects of these changes in metabolism. In the future these findings may help researchers develop new therapies that reactivate or mimic CDO1 to limit the growth of tumors.

The potential of breath analysis to improve outcome for patients with lung cancer

Lung cancer remains the most common cause of cancer related death in both the UK and USA. Development of diagnostic approaches that have the ability to detect lung cancer early are a research priority with potential to improve survival. Analysis of exhaled breath metabolites, or volatile organic compounds (VOCs) is an area of considerable interest as it could fulfil such requirements. Numerous studies have shown that VOC profiles are different in the breath of patients with lung cancer compared to healthy individuals or those with non-malignant lung diseases. This review provides a scientific and clinical assessment of the potential value of a breath test in lung cancer. It discusses the current understanding of metabolic pathways that contribute to exhaled VOC production in lung cancer and reviews the research conducted to date. Finally, we highlight important areas for future research and discuss how a breath test could be incorporated into various clinical pathways.

Functional Genomics via CRISPR-Cas

RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas proteins have recently emerged as versatile tools to investigate and engineer the genome. The programmability of CRISPR-Cas has proven especially useful for probing genomic function in high-throughput. Facile single-guide RNA library synthesis allows CRISPR-Cas screening to rapidly investigate the functional consequences of genomic, transcriptomic, and epigenomic perturbations. Furthermore, by combining CRISPR-Cas perturbations with downstream single-cell analyses (flow cytometry, expression profiling, etc.), forward screens can generate robust data sets linking genotypes to complex cellular phenotypes. In the following review, we highlight recent advances in CRISPR-Cas genomic screening while outlining protocols and pitfalls associated with screen implementation. Finally, we describe current challenges limiting the utility of CRISPR-Cas screening as well as future research needed to resolve these impediments. As CRISPR-Cas technologies develop, so too will their clinical applications. Looking ahead, patient centric functional screening in primary cells will likely play a greater role in disease management and therapeutic development.

Quantifying Intermediary Metabolism and Lipogenesis in Cultured Mammalian Cells Using Stable Isotope Tracing and Mass Spectrometry

Metabolism plays a central role in virtually all diseases, including diabetes, cancer, and neurodegeneration. Detailed analysis is required to identify the specific metabolic pathways dysregulated in the context of a given disease or biological perturbation. Measurement of metabolite concentrations can provide some insights into altered pathway activity or enzyme function, but since most biochemicals are metabolized by various enzymes in distinct pathways within cells and tissues, these approaches are somewhat limited. By applying metabolic tracers to a biological system, one can visualize pathway-specific information depending on the tracer used and analytes measured. To this end, stable isotope tracers and mass spectrometry are emerging as important tools for the examination of metabolic pathways and fluxes in cultured mammalian cells and other systems. Here, we describe a detailed workflow for quantifying metabolic processes in mammalian cell cultures using stable isotopes and gas chromatography coupled to mass spectrometry (GC-MS). As a case study, we apply ¹³C isotopic labeled glucose and glutamine to a cancer cell line to quantify substrate utilization for TCA metabolism and lipogenesis. Guidelines are also provided for interpretation of data and considerations for application to other cell systems. Ultimately, this approach provides a robust and precise method for quantifying stable isotope labeling in metabolite pools that can be applied to diverse biological systems.

Targeting the cell signaling pathway Keap1-Nrf2 as a therapeutic strategy for adenocarcinomas of the lung

INTRODUCTION

Kelch-like ECH associated protein 1/Nuclear factor erythroid 2-like factor 2 (Keap1-Nrf2) signaling plays a pivotal role in response to oxidative stress in lung cancer. Mutations in KEAP1/NFE2L2 genes always cause persistent Nrf2 activation in lung cancer cells that confer therapeutic resistance and aggressive tumorigenic activity, dictating either poor prognosis or short duration of response to chemotherapy in clinical observations. Areas covered: We provide a review of the mechanisms underlying the regulation of Keap1-Nrf2 at different stages, including genetic mutations, epigenetic modifications, translational/post-translational alterations, and protein-protein interactions. Based on the current knowledge, we discuss the possibilities of intervening Keap1-Nrf2 in lung adenocarcinoma as a therapeutic target. Expert opinion: It is prevalently conceived that Keap1-Nrf2 signaling plays different roles at diverse stages of cancer. Although various Nrf2 or Keap1 inhibitors have been reported during the last decades, none of these inhibitors are currently under clinical studies or in clinical applications, suggesting that sole inhibition of Nrf2 might not be sufficient to suppress tumor growth. On the basis of current studies, we suggest that the rational combination of Nrf2 suppression with chemical agents which cause enhanced oxidative imbalance or abnormal metabolism would be promising in the treatment of lung adenocarcinoma.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Pan-Cancer Metabolic Signature Predicts Co-Dependency on Glutaminase and De Novo Glutathione Synthesis Linked to a High-Mesenchymal Cell State

The enzyme glutaminase (GLS1) is currently in clinical trials for oncology, yet there are no clear diagnostic criteria to identify responders. The evaluation of 25 basal breast lines expressing GLS1, predominantly through its splice isoform GAC, demonstrated that only GLS1-dependent basal B lines required it for maintaining de novo glutathione synthesis in addition to mitochondrial bioenergetics. Drug sensitivity profiling of 407 tumor lines with GLS1 and gamma-glutamylcysteine synthetase (GCS) inhibitors revealed a high degree of co-dependency on both enzymes across indications, suggesting that redox balance is a key function of GLS1 in tumors. To leverage these findings, we derived a pan-cancer metabolic signature predictive of GLS1/GCS co-dependency and validated it in vivo using four lung patient-derived xenograft models, revealing the additional requirement for expression of GAC above a threshold (log₂RPKM + 1 ≥ 4.5, where RPKM is reads per kilobase per million mapped reads). Analysis of the pan-TCGA dataset with our signature identified multiple indications, including mesenchymal tumors, as putative responders to GLS1 inhibitors.

Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies

Cancers have an altered metabolism, and there is interest in understanding precisely how oncogenic transformation alters cellular metabolism and how these metabolic alterations can translate into therapeutic opportunities. Researchers are developing increasingly powerful experimental techniques to study cellular metabolism, and these techniques have allowed for the analysis of cancer cell metabolism, both in tumors and in ex vivo cancer models. These analyses show that, while factors intrinsic to cancer cells such as oncogenic mutations, alter cellular metabolism, cell-extrinsic microenvironmental factors also substantially contribute to the metabolic phenotype of cancer cells. These findings highlight that microenvironmental factors within the tumor, such as nutrient availability, physical properties of the extracellular matrix, and interactions with stromal cells, can influence the metabolic phenotype of cancer cells and might ultimately dictate the response to metabolically targeted therapies. In an effort to better understand and target cancer metabolism, this Review focuses on the experimental evidence that microenvironmental factors regulate tumor metabolism, and on the implications of these findings for choosing appropriate model systems and experimental approaches.