Livnat Jerby is an Assistant Professor in the Department of Genetics at Stanford University, a Chan Zuckerberg Biohub Investigator, and a Paul Allen Distinguished Investigator. Her research focuses on multicellular processes as a disease driver and therapeutic avenue, particularly in the context basic immune functions and properties. Using engineering-based, high-throughput approaches, she aims to gain insights into mechanisms and principles that govern immune responses and develop new "hybrid" platforms across in-vitro/in-vivo/in-silico systems that will allow us to identify tissue remodeling and immunomodulating interventions at an accelerated pace.
As a postdoctoral fellow in Aviv Regev's lab at the Broad Institute of MIT and Harvard, she studied T cell exclusion and dysfunction. Her work identified new mechanisms controlling cellular and tissue immunogenicity and demonstrated the potential of epigenetic reprogramming as a therapeutic modality to overcome immunotherapy resistance in cancer. Dr. Jerby holds a B.Sc. in Computer Science and Biology and obtained her PhD in 2016 from Tel Aviv University, where she worked with Prof. Eytan Ruppin, studying non-linear genetic interactions.
Dr. Jerby joined Stanford University in November 2020. Integrating genetic engineering, single-cell genomics, imaging, and machine learning, her laboratory develops high-throughput systems to study cellular circuits at greater scale, resolution, and depth, aiming to identify new and more diverse immunomodulating mechanisms and interventions and develop strategies to target and engineer endogenous and synthetic immune circuits, with potential implications for disease treatment and prevention.
Dr. Jerby’s research has been generously supported by the Schmidt Family Foundation, Rothschild Foundation, the Cancer Research Institute (CRI), the Burroughs Wellcome Fund (BWF), Ovarian Cancer Research Alliance (OCRA), Paul G. Allen Family Foundation, Bill and Melinda Gates Foundation, and Chan Zuckerberg Biohub initiative.
Honors & Awards
Allen Distinguished Investigator, Paul G. Allen Family Foundation (2022 - 2025)
Liz Tilberis Early Career Award, Ovarian Cancer Research Alliance (2022 - 2025)
Investigator award, Chan Zuckerberg biohub (2020 - 2025)
Career Awards at the Scientific Interface (CASI), Burroughs Wellcome Fund (BWF) (2019 - 2024)
Postdoctoral training fellowship, Cancer Research Institute (CRI) (2016-2019)
Postdoctoral award, Eric and Wendy Schmidt Foundation (2016-2017)
Postdoctoral fellowship, Rothschild, Yad Hanadiv (2015-2016)
Aviv Regev, Pratiksha Thakore, John Doench, JT Neal, Jesse Boehm, Oana Ursu, Livnat Jerby-Arnon. "United States Patent 16/809,458 Methods and Compositions for Massively Parallel Variant and Small Molecule Phenotyping"
Aviv Regev, Livnat Jerby-Arnon, Ana Anderson, Katherine Tooley, Vijay K. Kuchroo. "United States Patent 17/083,235 Pan-Cancer T Cell Exhaustion Genes"
A. Regev, O. Rozenblatt-Rosen, B. Izar, and L. Jerby. "United States Patent PCT/US2018/054020 and PCT/US2018/025507 Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer"
Livnat Jerby, A. Regev, L. Jerby, M. Suva, N. Riggi. "United States Patent PCT/US2020/022466 Detection means, Compositions and Methods for Modulating Synovial Sarcoma Cells"
Current Research and Scholarly Interests
Overview. We develop high-throughput, engineering-based approaches to: (1) dissect and target multicellular regulation at greater scale, resolution, and depth, focusing on the molecular mechanics that govern innate and adaptive immune responses in cancer, (2) broaden the spectrum of immunomodulating interventions to trigger targeted immune responses in more effective and targeted ways, including antigen-independent modalities, (3) modify cells and groups of cells to augment or create new "synthetic" (multi)cellular circuits – allowing us to probe the “inner-workings” of multicellular processes and potentially form a basis for new types of disease treatment and prevention strategies.
Current efforts. Bringing together advances in genetic engineering, machine learning, and single-cell/spatial genomics, we study the interplay between cancer cells and cytotoxic lymphocytes, namely, CD8 T cells and NK cells. These types of cells are a very convenient model system, as we can modify them ex vivo, and then study them in vivo. We develop new multidisciplinary methods to identify regulators of cellular and tissue immunogenicity, and uncover mechanisms controlling lymphocyte activation/ suppression, recruitment, and infiltration.
Specific projects currently include: developing a multimodal perturb-seq system to identify key regulators of cancer-T-cell and cancer-NK interactions; mapping cancer-immune dynamics across time and space using emerging single-cell and spatial profiling technologies; and hybrid CRISPR-ML systems to identify combinatorial genetic perturbations to induce cytotoxic lymphocyte recruitment, infiltration and effector functions in the tumor microenvironment.
- Biology and Applications of CRISPR/Cas9: Genome Editing and Epigenome Modifications
BIOS 268, GENE 268 (Spr)
GENE 211 (Win)
Independent Studies (7)
- Directed Reading and Research
BIOMEDIN 299 (Aut, Win, Spr, Sum)
- Directed Reading in Cancer Biology
CBIO 299 (Aut)
- Directed Reading in Genetics
GENE 299 (Aut, Win, Spr, Sum)
- Graduate Research
CBIO 399 (Win, Spr, Sum)
- Graduate Research
GENE 399 (Aut, Win, Spr, Sum)
- Supervised Study
GENE 260 (Aut, Win, Spr, Sum)
- Undergraduate Research
GENE 199 (Aut, Win, Spr, Sum)
- Directed Reading and Research
Prior Year Courses
- Cancer Biology Journal Club
CBIO 280 (Spr)
- Cancer Biology Journal Club
Doctoral Dissertation Reader (AC)
Peter Du, Mingxin Gu, Ann Lin, Yiran Liu, Bhek Morton, Jarod Rutledge
Postdoctoral Faculty Sponsor
Youngmin Kim, Jeehyun Yoe
Doctoral Dissertation Advisor (AC)
Kevin Aguirre, Reece Akana, Celeste Diaz, Mike Tsai
Doctoral Dissertation Co-Advisor (AC)
Christine Yiwen Yeh
Graduate and Fellowship Programs
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data.
Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)-combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level-from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to single-cell RNA sequencing data across individuals or regions, DIALOGUE uncovered MCPs marking Alzheimer's disease, ulcerative colitis and resistance to cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies. DIALOGUE enables the analysis of multicellular regulation in health and disease.
View details for DOI 10.1038/s41587-022-01288-0
View details for PubMedID 35513526
Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma.
Synovial sarcoma (SyS) is an aggressive neoplasm driven by the SS18-SSX fusion, and is characterized by low T cell infiltration. Here, we studied the cancer-immune interplay in SyS using an integrative approach that combines single-cell RNA sequencing (scRNA-seq), spatial profiling and genetic and pharmacological perturbations. scRNA-seq of 16,872 cells from 12 human SyS tumors uncovered a malignant subpopulation that marks immune-deprived niches in situ and is predictive of poor clinical outcomes in two independent cohorts. Functional analyses revealed that this malignant cell state is controlled by the SS18-SSX fusion, is repressed by cytokines secreted by macrophages and T cells, and can be synergistically targeted with a combination of HDAC and CDK4/CDK6 inhibitors. This drug combination enhanced malignant-cell immunogenicity in SyS models, leading to induced T cell reactivity and T cell-mediated killing. Our study provides a blueprint for investigating heterogeneity in fusion-driven malignancies and demonstrates an interplay between immune evasion and oncogenic processes that can be co-targeted in SyS and potentially in other malignancies.
View details for DOI 10.1038/s41591-020-01212-6
View details for PubMedID 33495604
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade.
2018; 175 (4): 984-997.e24
Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.
View details for DOI 10.1016/j.cell.2018.09.006
View details for PubMedID 30388455
View details for PubMedCentralID PMC6410377
Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality
2014; 158 (5): 1199–1209
Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.
View details for DOI 10.1016/j.cell.2014.07.027
View details for Web of Science ID 000340945000023
View details for PubMedID 25171417
Inter-cellular CRISPR screens reveal regulators of cancer cell phagocytosis.
Monoclonal antibody therapies targeting tumour antigens drive cancer cell elimination in large part by triggering macrophage phagocytosis of cancer cells1-7. However, cancer cells evade phagocytosis using mechanisms that are incompletely understood. Here we develop a platform for unbiased identification of factors that impede antibody-dependent cellular phagocytosis (ADCP) using complementary genome-wide CRISPR knockout and overexpression screens in both cancer cells and macrophages. In cancer cells, beyond known factors such as CD47, we identify many regulators of susceptibility to ADCP, including the poorly characterized enzyme adipocyte plasma membrane-associated protein (APMAP). We find that loss of APMAP synergizes with tumour antigen-targeting monoclonal antibodies and/or CD47-blocking monoclonal antibodies to drive markedly increased phagocytosis across a wide range of cancer cell types, including those that are otherwise resistant to ADCP. Additionally, we show that APMAP loss synergizes with several different tumour-targeting monoclonal antibodies to inhibit tumour growth in mice. Using genome-wide counterscreens in macrophages, we find that the G-protein-coupled receptor GPR84 mediates enhanced phagocytosis of APMAP-deficient cancer cells. This work reveals a cancer-intrinsic regulator of susceptibility to antibody-driven phagocytosis and, more broadly, expands our knowledge of the mechanisms governing cancer resistance to macrophage phagocytosis.
View details for DOI 10.1038/s41586-021-03879-4
View details for PubMedID 34497417
Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion
Resistance to immune checkpoint inhibitors (ICIs) is a key challenge in cancer therapy. To elucidate underlying mechanisms, we developed Perturb-CITE-sequencing (Perturb-CITE-seq), enabling pooled clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 perturbations with single-cell transcriptome and protein readouts. In patient-derived melanoma cells and autologous tumor-infiltrating lymphocyte (TIL) co-cultures, we profiled transcriptomes and 20 proteins in ~218,000 cells under ~750 perturbations associated with cancer cell-intrinsic ICI resistance (ICR). We recover known mechanisms of resistance, including defects in the interferon-γ (IFN-γ)-JAK/STAT and antigen-presentation pathways in RNA, protein and perturbation space, and new ones, including loss/downregulation of CD58. Loss of CD58 conferred immune evasion in multiple co-culture models and was downregulated in tumors of melanoma patients with ICR. CD58 protein expression was not induced by IFN-γ signaling, and CD58 loss conferred immune evasion without compromising major histocompatibility complex (MHC) expression, suggesting that it acts orthogonally to known mechanisms of ICR. This work provides a framework for the deciphering of complex mechanisms by large-scale perturbation screens with multimodal, single-cell readouts, and discovers potentially clinically relevant mechanisms of immune evasion.
View details for DOI 10.1038/s41588-021-00779-1
View details for Web of Science ID 000623746200001
View details for PubMedID 33649592
Serine biosynthesis is a metabolic vulnerability in IDH2-driven breast cancer progression.
Cancer-specific metabolic phenotypes and their vulnerabilities represent a viable area of cancer research. In this study, we explored the association of breast cancer subtypes with different metabolic phenotypes and identified isocitrate dehydrogenase 2 (IDH2) as a key player in triple-negative breast cancer (TNBC) and HER2. Functional assays combined with mass spectrometry-based analyses revealed the oncogenic role of IDH2 in cell proliferation, anchorage-independent growth, glycolysis, mitochondrial respiration, and antioxidant defense. Genome-scale metabolic modeling identified PHGDH and PSAT1 as the synthetic dosage lethal (SDL) partners of IDH2. In agreement, CRISPR-Cas9 knockout of PHGDH and PSAT1 showed the essentiality of serine biosynthesis proteins in IDH2-high cells. The clinical significance of the SDL interaction was supported by patients with IDH2-high/PHGDH-low tumors, who exhibited longer survival than patients with IDH2-high/PHGDH-high tumors. Furthermore, PHGDH inhibitors were effective in treating IDH2-high cells in vitro and in vivo. Altogether, our study creates a new link between two known cancer regulators and emphasizes PHGDH as a promising target for TNBC with IDH2 overexpression.
View details for DOI 10.1158/0008-5472.CAN-19-3020
View details for PubMedID 33500247
Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis.
T cells are critical effectors of cancer immunotherapies, but little is known about their gene expression programs in diffuse gliomas. Here, we leverage single-cell RNA sequencing (RNA-seq) to chart the gene expression and clonal landscape of tumor-infiltrating T cells across 31 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and IDH mutant glioma. We identify potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes. Analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 (encoding CD161) as a candidate inhibitory receptor. Accordingly, genetic inactivation of KLRB1 or antibody-mediated CD161 blockade enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other human cancers. Our work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immunotherapy targets.
View details for DOI 10.1016/j.cell.2021.01.022
View details for PubMedID 33592174
A Distinct Transcriptional Program in Human CAR T Cells Bearing the 4-1BB Signaling Domain Revealed by scRNA-Seq.
Molecular therapy : the journal of the American Society of Gene Therapy
2020; 28 (12): 2577-2592
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.
View details for DOI 10.1016/j.ymthe.2020.07.023
View details for PubMedID 32755564
View details for PubMedCentralID PMC7704462
A single-cell landscape of high-grade serous ovarian cancer
2020; 26 (8): 1271-+
Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells2. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape3-5 and provides a resource for the development of novel therapeutic approaches.
View details for DOI 10.1038/s41591-020-0926-0
View details for Web of Science ID 000542094400003
View details for PubMedID 32572264
View details for PubMedCentralID PMC7723336
A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
2020; 26 (5): 792-+
Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
View details for DOI 10.1038/s41591-020-0844-1
View details for Web of Science ID 000549240200006
View details for PubMedID 32405060
View details for PubMedCentralID PMC7220853
Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma
2019; 25 (12): 1916-+
Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.
View details for DOI 10.1038/s41591-019-0654-5
View details for Web of Science ID 000500824900035
View details for PubMedID 31792460
View details for PubMedCentralID PMC6898788
Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
MOLECULAR SYSTEMS BIOLOGY
2019; 15 (3): e8323
Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.
View details for DOI 10.15252/msb.20188323
View details for Web of Science ID 000463969600007
View details for PubMedID 30858180
View details for PubMedCentralID PMC6413886
IL-33 Signaling Alters Regulatory T Cell Diversity in Support of Tumor Development.
2019; 29 (10): 2998–3008.e8
Regulatory T cells (Tregs) can impair anti-tumor immune responses and are associated with poor prognosis in multiple cancer types. Tregs in human tumors span diverse transcriptional states distinct from those of peripheral Tregs, but their contribution to tumor development remains unknown. Here, we use single-cell RNA sequencing (RNA-seq) to longitudinally profile dynamic shifts in the distribution of Tregs in a genetically engineered mouse model of lung adenocarcinoma. In this model, interferon-responsive Tregs are more prevalent early in tumor development, whereas a specialized effector phenotype characterized by enhanced expression of the interleukin-33 receptor ST2 is predominant in advanced disease. Treg-specific deletion of ST2 alters the evolution of effector Treg diversity, increases infiltration of CD8+ T cells into tumors, and decreases tumor burden. Our study shows that ST2 plays a critical role in Treg-mediated immunosuppression in cancer, highlighting potential paths for therapeutic intervention.
View details for DOI 10.1016/j.celrep.2019.10.120
View details for PubMedID 31801068
Harnessing synthetic lethality to predict the response to cancer treatment
2018; 9: 2546
While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.
View details for DOI 10.1038/s41467-018-04647-1
View details for Web of Science ID 000436791400007
View details for PubMedID 29959327
View details for PubMedCentralID PMC6026173
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
2016; 167 (7): 1853-+
Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.
View details for DOI 10.1016/j.cell.2016.11.038
View details for Web of Science ID 000393114700022
View details for PubMedID 27984732
View details for PubMedCentralID PMC5181115
Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease
2016; 7: 8994
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.
View details for DOI 10.1038/ncomms9994
View details for Web of Science ID 000371140200001
View details for PubMedID 26839171
View details for PubMedCentralID PMC4742839
Moving ahead on harnessing synthetic lethality to fight cancer
MOLECULAR & CELLULAR ONCOLOGY
2015; 2 (2): e977150
We have recently developed a data-mining pipeline that comprehensively identifies cancer unique susceptibilities, following the concept of Synthetic Lethality (SL). The approach enables, for the first time, to identify and harness genome-scale SL-networks to accurately predict gene essentiality, drug response, and clinical prognosis in cancer.
View details for DOI 10.4161/23723556.2014.977150
View details for Web of Science ID 000218553800022
View details for PubMedID 27308440
View details for PubMedCentralID PMC4904895
Fumarate induces redox-dependent senescence by modifying glutathione metabolism
2015; 6: 6001
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.
View details for DOI 10.1038/ncomms7001
View details for Web of Science ID 000348812400002
View details for PubMedID 25613188
View details for PubMedCentralID PMC4340546
Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer
2012; 72 (22): 5712–20
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.
View details for DOI 10.1158/0008-5472.CAN-12-2215
View details for Web of Science ID 000311141300009
View details for PubMedID 22986741
Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling
CLINICAL CANCER RESEARCH
2012; 18 (20): 5572–84
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.
View details for DOI 10.1158/1078-0432.CCR-12-1856
View details for Web of Science ID 000311908500009
View details for PubMedID 23071359
Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase
2011; 477 (7363): 225–U132
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.
View details for DOI 10.1038/nature10363
View details for Web of Science ID 000294603900039
View details for PubMedID 21849978
Predicting selective drug targets in cancer through metabolic networks
MOLECULAR SYSTEMS BIOLOGY
2011; 7: 501
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.
View details for DOI 10.1038/msb.2011.35
View details for Web of Science ID 000292463300005
View details for PubMedID 21694718
View details for PubMedCentralID PMC3159974
Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism
MOLECULAR SYSTEMS BIOLOGY
2010; 6: 401
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.
View details for DOI 10.1038/msb.2010.56
View details for Web of Science ID 000282766200001
View details for PubMedID 20823844
View details for PubMedCentralID PMC2964116