Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism.

BACKGROUND: Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. METHODS: We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. RESULTS: We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. CONCLUSIONS: These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment.

Assessment of ctDNA in CSF may be a more rapid means of assessing surgical outcomes than plasma ctDNA in glioblastoma.

We aimed to develop a high-throughput deep DNA sequencing assay of cerebrospinal fluid (CSF) to identify clinically relevant oncogenic mutations that contribute to the development of glioblastoma (GBM) and serve as biomarkers to predict patients' responses to surgery. For this purpose, we recruited five patients diagnosed with highly suspicious GBM according to preoperative magnet resonance imaging. Subsequently, patients were histologically diagnosed with GBM. CSF was obtained through routine lumbar puncture, and plasma from peripheral blood was collected before surgery and 7 days after. Fresh tumor samples were collected using routine surgical procedures. Targeted deep sequencing was used to characterize the genomic landscape and identify mutational profile that differed between pre-surgical and post-surgical samples. Sequence analysis was designed to detect protein-coding exons, exon-intron boundaries, and the untranslated regions of 50 genes associated with cancers of the central nervous system. Circulating tumor DNAs (ctDNAs) were prepared from the CSF and plasma from peripheral blood. For comparison, DNA was isolated from fresh tumor tissues. Non-silent coding variants were detected in CSF and plasma ctDNAs, and the overall minor allele frequency (MAF) of the former corresponded to an earlier disease stage compared with that of plasma when the tumor burden was released (surgical removal). Gene mutation loads of GBMs significantly correlated with overall survival (OS, days) (Pearson correlation = -0.95, P = 0.01). We conclude that CSF ctDNAs better reflected the sequential mutational changes of driver genes compared with those of plasma ctDNAs. Deep sequencing of the CSF of patients with GBM may therefore serve as an alternative clinical assay to improve patients' outcomes.

Necrocide 1 mediates necrotic cell death and immunogenic response in human cancer cells.

Many anticancer agents induce apoptosis, mitotic catastrophe or cellular senescence. Here, we report the functional characterization of an experimental inducer of tumor necrosis factor (TNF)-independent necrosis, necrocide-1 (NC1). NC1 (but not its stereoisomer) killed a panel of human cancer cells (but not normal cells) at nanomolar concentrations and with a non-apoptotic, necrotic morphotype, both in vitro and in vivo. NC1-induced killing was not inhibited by caspase blockers, anti-apoptotic BCL2 overexpression or TNFα neutralization, suggesting that NC1 elicits a bona fide necrotic pathway. However, pharmacological or genetic inhibition of necroptosis, pyroptosis and ferroptosis failed to block NC1-mediated cell death. Instead, NC1 elicited reactive oxygen species (ROS) production by mitochondria, and elimination of mitochondrial DNA, quenching of mitochondrial ROS, as well as blockade of mitochondrial permeability transition with cyclosporine A, interfered with NC1-induced cell death. NC1 induced hallmarks of immunogenic cell death incurring calreticulin (CALR) exposure, ATP secretion and high mobility group box 1 (HMGB1) release. Taken together, these data identify a previously uncharacterized signaling cascade leading to an immunogenic variant of mitochondrion-regulated necrosis, supporting the notion that eliciting regulated necrosis may constitute a valid approach for anticancer therapy.