Abstract
Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.
Highlights
Dysregulation of cellular metabolism has been implicated in the progression of several cancers as a consequence of oncogenic mutations (Cairns et al, 2011; Pavlova and Thompson, 2016; DeBerardinis and Chandel, 2016; Luengo et al, 2017)
In order to explore the landscape of metabolic reactions in cancer, we aimed to reconstruct the metabolic network in each primary tumor of a cohort consisting of 1082 patients, spanning 13 cancer types
Each metabolic network was reconstructed in the form of a genome-scale metabolic model (GEM) (O'Brien et al, 2015), here-on referred to as model
Summary
Dysregulation of cellular metabolism has been implicated in the progression of several cancers as a consequence of oncogenic mutations (Cairns et al, 2011; Pavlova and Thompson, 2016; DeBerardinis and Chandel, 2016; Luengo et al, 2017). Even when shown to be selectively essential to cancer cells, the diversity of metabolic phenotypes associated with cancer questions the extent to which these regulatory programs are context-dependent rather than tumor-specific (Boroughs and Deberardinis 2015; Gatto and Nielsen, 2016). Glucose metabolism was shown to vary within tumor regions and between human tumors in lung cancer patients (Hensley et al, 2016) or to depend strongly on the initiating oncogenic mutation and the tumor tissue of origin in genetically engineered mice (Yuneva et al, 2012). It is plausible that metabolic shifts so far associated with different cancers are yet another expression of the plasticity of these cells to ever-changing conditions in their genome and their environment (Meacham and Morrison, 2013), with the advantage that in metabolism this adaptation can leverage on the high redundancy and complexity of the human metabolic network. The aim of this study was to characterize the landscape of metabolic reactions expressed in different cancers, to define their occurrence depending on the cancer type or mutations in key cancer genes, and to identify any difference from metabolic reactions normally expressed in human tissues
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