journal contribution
posted on 2023-03-30, 23:40 authored by Ashwini Kumar Sharma, Roland Eils, Rainer König <p>Fig. 1 - Description of genomic datasets, list of cancer causing genes and metabolic genes used in this study. Fig. 2 - The distribution of cancer causing genes, metabolic genes and the corresponding gene pairs in the genome. Fig. 3 - The distribution of RnpMI scores across all cancer types. Fig. 4 - Correlation between RnpMI scores and co-occurrence frequency. Fig. 5 - Gene counts after the iMetCG prioritization steps. Fig. 6 - (see additional file Supp. Fig. 6). Heatmap of prioritized cancer metabolic gene set. Fig. 7 - (see additional file Supp. Fig. 7). Heatmap of core metabolic gene set. Fig. 8 - Co-alteration of proximal cancer-metabolic gene pairs across tumor types. Fig. 9 - Tumor classification.</p>
Funding
BMBF
theHelmholtz International Graduate School for Cancer Research
History
ARTICLE ABSTRACT
Somatic copy number alterations frequently occur in the cancer genome affecting not only oncogenic or tumor suppressive genes, but also passenger and potential codriver genes. An intrinsic feature resulting from such genomic perturbations is the deregulation in the metabolism of tumor cells. In this study, we have shown that metabolic and cancer-causing genes are unexpectedly often proximally positioned in the chromosome and share loci with coaltered copy numbers across multiple cancers (19 cancer types from The Cancer Genome Atlas). We have developed an analysis pipeline, Identification of Metabolic Cancer Genes (iMetCG), to infer the functional impact on metabolic remodeling from such coamplifications and codeletions and delineate genes driving cancer metabolism from those that are neutral. Using our identified metabolic genes, we were able to classify tumors based on their tissue and developmental origins. These metabolic genes were similar to known cancer genes in terms of their network connectivity, isoform frequency, and evolutionary features. We further validated these identified metabolic genes by (i) using gene essentiality data from several tumor cell lines, (ii) showing that these identified metabolic genes are strong indicators for patient survival, and (iii) observing a significant overlap between our identified metabolic genes and known cancer-metabolic genes. Our analyses revealed a hitherto unknown generic mechanism for large-scale metabolic reprogramming in cancer cells based on linear gene proximities between cancer-causing and -metabolic genes. We have identified 119 new metabolic cancer genes likely to be involved in rewiring cancer cell metabolism. Cancer Res; 76(14); 4058–67. ©2016 AACR.
