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.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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 prognosis
. 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 cells
. 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 landscape
and provides a resource for the development of novel therapeutic approaches.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
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•Genome-scale data-driven identification of synthetic lethality in cancer•Synthetic lethality networks successfully predict cancer gene essentiality•Synthetic lethality networks predict 15 year survival in breast cancer patients•Synthetic dosage lethality networks predict drug response in cancer
A data-driven computational pipeline named DAISY captures known and novel synthetic lethal (SL) interactions, enabling the utilization of genome-wide cancer SL networks to predict gene essentiality, drug response, and clinical prognosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
Synopsis
In silico prediction of synthetic rescue genetic interactions identifies gene involved in resistance to targeted immunotherapy, which determines patients’ clinical response. Inhibiting predicted genes sensitizes cancer cells to cancer therapies, laying a basis for developing new drug combinations.
An in silico method is presented that identifies synthetic rescue genetic interactions whereby the loss of fitness due to the deletion of one gene is compensated by the altered activity of another rescuer gene.
Genes are identified that mediate therapy resistance in cancer and that predict clinical response to targeted therapy in patients.
Synthetic rescue interactions predict resistance mechanism to immunotherapy and inhibition of rescuer genes sensitizes resistant cancer cells to therapies.
In silico prediction of synthetic rescue genetic interactions identifies gene involved in resistance to targeted immunotherapy, which determines patients’ clinical response. Inhibiting predicted genes sensitizes cancer cells to cancer therapies, laying a basis for developing new drug combinations.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
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.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
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.
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 phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase (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
and
. Altogether, our study creates a new link between two known cancer regulators and emphasizes PHGDH as a promising target for TNBC with IDH2 overexpression. SIGNIFICANCE: These findings highlight the metabolic dependence of IDH2 on the serine biosynthesis pathway, adding an important layer to the connection between TCA cycle and glycolysis, which can be translated into novel targeted therapies.
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.
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•Single-cell profiling of CD4 T cells along tumor development in a mouse model•Treg diversity shifts to a Klrg1+Areg+ (KA) effector phenotype in advanced tumors•Il1rl1 (encoding ST2)+ Tregs have higher expression of KA effector Treg genes•Treg-specific ST2 loss enhances CD8+ T cell infiltration and decreases tumor burden
Li et al. show in a genetic mouse model of lung adenocarcinoma that during tumor development regulatory T cell (Treg) diversity shifts from an interferon-responsive to a ST2-positive, Klrg1+Areg+ effector-like phenotype. Treg-specific deletion of ST2 alters Treg heterogeneity, increases tumor infiltration by CD8+ T cells, and decreases tumor burden.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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