Astrocyte immunometabolic regulation of the tumour microenvironment drives glioblastoma pathogenicity.

Malignant brain tumours are the cause of a disproportionate level of morbidity and mortality among cancer patients, an unfortunate statistic that has remained constant for decades. Despite considerable advances in the molecular characterization of these tumours, targeting the cancer cells has yet to produce significant advances in treatment. An alternative strategy is to target cells in the glioblastoma microenvironment, such as tumour-associated astrocytes. Astrocytes control multiple processes in health and disease, ranging from maintaining the brain's metabolic homeostasis, to modulating neuroinflammation. However, their role in glioblastoma pathogenicity is not well understood. Here we report that depletion of reactive astrocytes regresses glioblastoma and prolongs mouse survival. Analysis of the tumour-associated astrocyte translatome revealed astrocytes initiate transcriptional programmes that shape the immune and metabolic compartments in the glioma microenvironment. Specifically, their expression of CCL2 and CSF1 governs the recruitment of tumour-associated macrophages and promotes a pro-tumourigenic macrophage phenotype. Concomitantly, we demonstrate that astrocyte-derived cholesterol is key to glioma cell survival, and that targeting astrocytic cholesterol efflux, via ABCA1, halts tumour progression. In summary, astrocytes control glioblastoma pathogenicity by reprogramming the immunological properties of the tumour microenvironment and supporting the non-oncogenic metabolic dependency of glioblastoma on cholesterol. These findings suggest that targeting astrocyte immunometabolic signalling may be useful in treating this uniformly lethal brain tumour.

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample.

Detection of somatic mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). We have previously developed a tool for detection of somatic mutations using tumor RNA and a matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA sequencing data without a matched-normal sample. This is accomplished via a machine-learning approach that classifies mutations as either somatic or germline based on various features. When applied to RNA-sequencing of >450 melanoma samples high precision and recall are achieved, and both mutational signatures and driver genes are correctly identified. Finally, we show that RNA-based TMB is significantly associated with patient survival, showing similar or higher significance level as compared to DNA-based TMB. Our pipeline can be utilized in many future applications, analyzing novel and existing datasets where only RNA is available.

Targeting purine synthesis in ASS1-expressing tumors enhances the response to immune checkpoint inhibitors.

Argininosuccinate synthase (ASS1) downregulation in different tumors has been shown to support cell proliferation and yet, in several common cancer subsets ASS1 expression associates with poor patient prognosis. Here we demonstrate that ASS1 expression under glucose deprivation is induced by c-MYC, providing survival benefit by increasing nitric oxide synthesis and activating the gluconeogenic enzymes pyruvate carboxylase and phosphoenolpyruvate carboxykinase by S-nitrosylation. The resulting increased flux through gluconeogenesis enhances serine, glycine and subsequently purine synthesis. Notably, high ASS1-expressing breast cancer mice do not respond to immune checkpoint inhibitors and patients with breast cancer with high ASS1 have more metastases. We further find that inhibiting purine synthesis increases pyrimidine to purine ratio, elevates expression of the immunoproteasome and significantly enhances the response of autologous primary CD8+ T cells to anti-PD-1. These results suggest that treating patients with high-ASS1 cancers with purine synthesis inhibition is beneficial and may also sensitize them to immune checkpoint inhibition therapy.

The landscape of tiered regulation of breast cancer cell metabolism.

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.

System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.

The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.