Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach.

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Since reactive oxygen species (ROS) play diverse roles in cancer, modulating the redox status of cancerous cells seems to be a promising therapeutic approach. Oxidant-targeted therapy appears logical for intervention with the acquired adaptive response to oxidative stress in cancer. In this study, we investigated the cytotoxic effects of juglone (J) and tamoxifen (T) and also the combination of each with ascorbate (A): tamoxifen/ascorbate (TA) and/or juglone/ascorbate (JA) on MCF7 cancerous cells. The results revealed that the growth inhibitory effects of juglone and tamoxifen were each associated with enhanced levels of ROS production and lipid peroxidation. These effects were markedly intensified in tamoxifen/ascorbate and juglone/ascorbate co-treatments. On the other hand, the intracellular anti-oxidant components such as reduced glutathione (GSH), catalase, superoxide dismutase (SOD), and glutathione peroxidase significantly declined in cells subjected to combination treatments compared to that in cells exposed solely to tamoxifen, juglone, and the untreated control cells. In addition, ascorbate association induced more apoptotic and necrotic or necrotic-like cell death than cells treated with each drug alone. These results were further confirmed by comparing the Bax/Bcl2 ratio in combination-treated cells. Additionally, ascorbate was able to potentiate the cytotoxic effects of combination therapy via activation of ROS-responsive factors including Foxo family members.

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.