- Letter to the Editor
- Open Access
Pan-cancer analysis of tumor metabolic landscape associated with genomic alterations
© The Author(s). 2018
- Received: 17 July 2018
- Accepted: 25 September 2018
- Published: 17 October 2018
Although metabolic alterations are one of the hallmarks of cancer, there is a lack of understanding of how metabolic landscape is reconstituted according to cancer progression and which genetic alterations underlie its heterogeneity within cancer cells. Here, the configuration of the metabolic landscape according to genetic alteration is examined across 7648 subjects representing 29 cancers. The metabolic landscape and its reconfiguration according to the accumulated mutation maintained characteristics of their tissue of origin. However, there were some common patterns across cancers in terms of the association with cancer progression. Carbohydrate and pyrimidine metabolism showed the highest positive correlation with tumor metabolic burden and they were also common poor prognostic pathways in several cancer types. We additionally examined whether genetic alterations associated with the heterogeneity of metabolic landscape. Genetic alterations associated with each metabolic pathway differed between cancers, however, they were a part of cancer drivers in most cancer types.
- Tumor mutation burden
- Metabolic landscape
- Cancer metabolism
- Pan-cancer analysis
- Driver gene mutation
Cancer cells reorganize their metabolism to fulfill their biosynthetic requirements for tumor growth and proliferation . Besides activation of aerobic glycolysis, other metabolic pathways including nucleotide, amino acid, and lipid metabolism are also activated in cancer cells to produce biosynthetic building blocks for malignant cellular proliferation although the degree of metabolic activation is diverse [2, 3]. Although recent studies have elucidated the metabolic reprogramming of cancer cells compared with normal tissues and its biological implications [2, 4], the configuration of metabolic landscape according to cancer progression in terms of tumor mutational burden (TMB) and clinical outcome is yet unclear. Moreover, the diversity of cancer cell metabolism is hardly explained by the conventional analogy of genetic alteration model that tumor metabolism simply supports tumor growth and proliferation [5, 6]. A comprehensive understanding of genetic alterations which underlie the heterogeneity of the metabolic landscape of cancer cells can elucidate therapeutic targets in terms of cancer metabolism.
Here, we aim to investigate the metabolic landscape of multiple cancer types and its configuration according to the progression. We also aim to find answers for whether there are common features in genetic alterations related to the metabolic landscape across cancer types. An integrated analysis of genomic and transcriptomic profiles of 29 different solid cancers was performed. We demonstrate that some common metabolic pathways exhibit the close relationship with TMB and clinical outcome across several cancer types. We also show that genetic alterations associated with cancer metabolic heterogeneity were a part of cancer drivers in most cancer types.
Metabolic landscape specific for cancer types
We compared the median values of metabolic signatures manually curated considering cancer-related pathways for each cancer type (Additional file 1: Figure S1A). Metabolic profiles of all cancers were visualized by t-SNE  (Additional file 1: Figure S1B). Metabolic profiles of cancer tissues were clustered according to each cancer type. Additionally, metabolic profiles were closely mapped with those of same histologic types (e.g. HNSC, LUSC, and CESC clusters.) Of note, metabolic profiles of TCGT and CHOL showed heterogeneous metabolic characteristics. TGCT showed two large clusters, which correspond to histologic subtypes, and CHOL showed more heterogeneous metabolic profiles than others (Additional file 1: Figure S2).
Metabolic reconstitution according to mutational burden
Recently, TMB has been regarded as an important predictive biomarker for cancer immunotherapy, based on the idea that highly mutated tumors harboring more neoantigens can be targets of activated immune cells [8, 9]. In this regard, metabolic reconfiguration according to TMB can be an additional biomarker for cancer immunotherapy as tumor metabolism can be noninvasively and macroscopically estimated by PET. Moreover, the important metabolic signatures of each cancer type could help us find appropriate surrogate markers for PET radiotracers.
Metabolic landscape reveals prognostic pathways
Metabolic signatures significantly associated with prognosis for each cancer type were presented in Fig. 2b. The most common metabolic signatures significantly associated with poor prognosis were ribonucleotide synthesis (7 of 29 cancers), followed by pyrimidine, purine, and carbohydrate metabolism (6 or 29 cancers) (Fig. 2c). The metabolic signatures significantly associated with good prognosis were fatty acid oxidation (6 of 29 cancers), peroxisomal lipid metabolism, and branched chain amino acids catabolism (5 of 29 cancers) (Fig. 2d).
In brief, the increased metabolic activity of carbohydrate and nucleotides in cancer was associated with poor prognosis as well as the progression of cancer in terms of TMB as found in previous results. Furthermore, our results were similar with the previous report, which showed that glycolysis, ribonucleotide, pyrimidine, and purine metabolism were associated with poor prognosis, and mitochondrial fatty acid oxidation and peroxisomal lipid metabolism were associated with good prognosis . These results support the idea that the link between biological stepwise mutational progression, metabolic landscape reconfiguration, and clinical progression of cancer. Accordingly, aggressive malignant phenotypes of cancer cells obtained by accumulated mutations change metabolic phenotypes to demand energy source represented by carbohydrate and elements for proliferation represented by nucleotide.
Metabolism-related genetic alterations are mostly cancer drivers
We further examined whether the metabolism-related genes were cancer driver genes. All metabolism-related genes of 12 cancer types (ACC, BLCA, GBM, KICH, LIHC, LUSC, PCPG, PRAD, SARC, THCA, THYM, and UVM) were included in cancer drivers (Fig. 3c). In other cancer types, most of the metabolism-related genes overlapped with cancer drivers except STAD, COAD, and CHOL. The metabolism-related genes of CHOL did not overlap with cancer drivers. Notably, metabolic profiles of CHOL were highly heterogeneous (Additional file 1: Figure S2) as a previous result. STAD and COAD showed a large number of the metabolism-related genes than other cancer types. Since they are known to include tumors with MSI which is associated with hypermutation [10, 11], we examined the relationship between metabolic profiles of the two cancer types and MSI status. As shown in Additional file 1: Figure S7, tumors with high metabolic signatures including pyrimidine metabolism and glucose metabolism were clustered in those with MSI.
According to the results, cancer transcripts and protein networks consisting of metabolic pathways may be changed by cancer drivers instead of alterations of genes participated in the networks. It corresponds to the Darwinian selection of cancer cells which carry driver mutations facilitate cellular survival and growth by changing a favorable metabolic landscape . The results also suggest that the metabolic change as a hallmark of cancer may be one of the tumorigenesis processes caused by genetic alterations rather than an independent process.
Among several metabolic signatures, carbohydrate metabolism representing overall nutrient demand and nucleotide metabolism showed the closest association with TMB and clinical outcome. Genetic alterations closely associated with metabolic heterogeneity were a part of cancer drivers in most cancers rather than genes affiliated to metabolic pathways. It supports the role of cancer drivers in the evolution process in constituting metabolic landscape appropriate for tumor growth. Our database of the metabolic landscape (https://choih.shinyapps.io/metabolicsignatures) according to cancer type and its reconstitution according to cancer progression can contribute to developing appropriate diagnostics and therapeutics targeting metabolism for cancer subtypes.
The Cancer Genome Atlas
Tumor mutation burden
T-Distributed Stochastic Neighbor Embedding
Abbreviations for cancer type
Bladder urothelial carcinoma
Cervical and endocervical cancers
Diffuse Large B-cell Lymphoma
Head and Neck Squamous Cell Carcinoma
Kidney renal papillary cell carcinoma
Brain low grade glioma
Liver hepatocellular carcinoma
Lung Squamous Cell Carcinoma
Ovarian serous cystadenocarcinoma
Pheochromocytoma and Paraganglioma
Testicular Germ Cell Tumors
Abbreviations for genes
Capicua Transcriptional Repressor
Epidermal growth factor receptor
Far Upstream Element Binding Protein 1
Isocitrate dehydrogenase 1
The results here are in whole based upon data generated by the TCGA Research Network: “http://cancergenome.nih.gov/”.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Availability of data and materials
The raw sequencing and clinical data can be found at the GDC portal (https://portal.gdc.cancer.gov/). The mutation data can be downloaded from the R library, ‘TCGAmutations’ (https://github.com/PoisonAlien/TCGAmutations). Our processed results are available at a web-based resource (https://choih.shinyapps.io/metabolicsignatures). Software and resources used for the analyses are described in each method section.
CH and NKJ designed the study, analyzed and interpreted the data, wrote and edited manuscript, and read and approved the manuscript.
Ethics approval and consent to participate
Patient data we used were acquired by a publicly available dataset that removed patient identifiers. The publicly available data were collected with patients’ informed consent approved by the institutional review boards of all participating institutions following 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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