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

Abstract

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.systems biology | synthetic dosage lethality | human metabolism | cancer | genetic interactions S ynthetic lethality (SL) occurs when the combined loss of two nonessential genes renders a lethal phenotype (1). SLs have been studied by using experimental (2, 3) and computational approaches (4-6) to address various questions of cell function and evolution. The potential of SLs for cancer therapy has been recognized and accelerated the development of many SL screens (7-11). (See refs. 12-14 for reviews of SLs applied in the context of cancer research.)Less studied are the so-called synthetic dosage lethality (SDL) interactions. An SDL is a genetic interaction between two genes whereby the underexpression of gene A (A ↓ ) together with the overexpression of gene B (B ↑ ) is lethal (15). The observation that an interaction with an overexpressed gene can be lethal makes it particularly interesting for targeting cancer cells with (over) expressed oncogenes. This is because many oncogenes that drive tumor growth are essential to cell function and thus difficult to target directly. Targeting the oncogenes' SDL partner, which is a nonessential gene in normal cells, may nevertheless kill cancer cells. That SDLs can have important implications for cancer research, for instance to aid in the design of new therapies, has also been recognized (12,(16)(17)(18). Moreover, it has been shown that the overexpression of specific genes can be detrimental to canc...

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

“…Although SDL has been used to identify protein targets of enzymes (82,100,101) and to identify specific subsets of genes within chromosome segregation mutants (29), it is underused to uncover genetic contexts that could selectively target cancer cells with elevated levels of a specific gene (26,27,102). SDL screens therefore represent a powerful approach for fast and easy identification of candidate chemotherapeutic drug targets within the context of dCIN gene overexpression that could enable targeted elimination of these cells.…”

“…Most SL approaches focus on exploiting specific somatic mutations or deletions in cancer driver genes; however, there are just as many amplified regions as deleted regions in cancer genomes (17). Thus, we propose using synthetic dosage lethality (SDL), which is SL with an amplified and/or overexpressed gene, as an approach to selectively target tumors that overexpress dCIN genes (26,27). SDL occurs when the overexpression of a gene is not lethal in a wild-type background but in conjunction with a second site nonlethal mutation causes lethality (28)(29)(30).…”

Identification of Cancer–associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

“…For instance, when glucose is completely oxidized to carbon dioxide to generate maximal amounts of ATP, it can no longer be utilized as biomass precursors for amino acid and nucleotide biosynthesis. Therefore, we reasoned that the distribution of metabolic flux can only be comprehensively determined by multiple biological objectives ( Fig 1A), including (1) maximization of biomass production, which is frequently considered as the only objective in previous FBA studies of cancer cells Gatto et al, 2015;Megchelenbrink et al, 2015;Yizhak et al, 2014a), (2) maximization of ATP hydrolysis, which is considered as the objective in some FBA studies of non-malignant cells Yizhak et al, 2014b), (3) minimization of total abundance of metabolic enzymes, which is an analogue of the solvent capacity constraint Vazquez et al, 2010), and (4) minimization of total carbon uptake (Savinell and Palsson, 1992). These four objectives reflect different aspects of metabolic demand, covering both maximization of biomass yield and minimization of energy cost.…”

“…While traditional methods are suited to dissect limited numbers of metabolic pathways, systems biology is a powerful tool to study metabolism from a global perspective . Within the field of cancer metabolism, analyses of genome-scale metabolic models (GSMMs) Thiele et al, 2013) enabled researchers to elucidate the plausible mechanism of Warburg effect , quantify efficacies and side effects of cancer therapeutics (Agren et al, 2014;Folger et al, 2011;Shaked et al, 2016;Yizhak et al, 2014a;Yizhak et al, 2014b), and unravel context-dependent functionality of metabolic enzymes during tumor progression Megchelenbrink et al, 2015;Rabinovich et al, 2015;Tardito et al, 2015). Among various strategies, flux balance analysis (FBA) exhibits itself as a highly effective approach to analyze GSMMs (Orth et al, 2010).…”

Beyond synthetic lethality: charting the landscape of clinically relevant genetic interactions in cancer

“…Indeed, previous investigations have shown that the overall numbers of functionally active SLs and SDLs 65 in a given tumor sample are highly predictive of patient survival (Megchelenbrink et al, 2015). These 66 three interaction types however represent just the 'tip of the GI iceberg', as there are many additional 67 types of GI that can be defined at a conceptual level, and whose systematic exploration may have 68 important functional ramifications for cancer therapy.…”