The Metabolic Landscape of RAS-Driven Cancers from biology to therapy

Abstract

Our understanding of how the RAS protein family, and in particular mutant KRAS promote metabolic dysregulation in cancer cells has advanced significantly over the last decade. In this Review, we discuss the metabolic reprogramming mediated by oncogenic RAS in cancer, and elucidating the underlying mechanisms could translate to novel therapeutic opportunities to target metabolic vulnerabilities in RAS-driven cancers.

Keywords: KRAS; autophagy; cancer therapeutics; chemoresistance; ferroptosis; glutaminolysis; glycolysis; macropinocytosis; metabolism.

Figure 1.. Frequency and distribution of RAS mutations in human cancers.

Human cancers differ by mutated RAS isoform, codon, and amino acid substitution. a. Distribution of RAS isoform (KRAS, NRAS, and HRAS) mutations across tumor types and frequency (%) of RAS mutations by isoform in each tumor type. Detailed information in Supplementary Table 1. b. Types of cancers commonly associated with RAS mutations. The frequency of the most commonly mutated RAS genes is listed by tumor type. For each tumor type, the amino acid substitutions that occur most frequently in the RAS isoform are shown. The color of the mutation refers to the mutated RAS gene (KRAS, blue; NRAS, purple; and HRAS, pink). Detailed information in Supplementary Tables 2 and 3. All human cancers that had a sample size greater than or equal to 300 and a total RAS mutation rate greater than or equal to 15% are listed from data resources. Death rate/ year (%) is based on the death rate per 100,000 men and women. Data collected from the National Cancer Institute SEER cancer statistics 2020 database.

Figure 2.. Oncogenic RAS-dependent control of cellular metabolism.

Mutant RAS deregulates key metabolic processes, including glutaminolysis, glycolysis, autophagy, and macropinocytosis. Oncogenic KRAS directs glucose metabolism into hexosamine biosynthetic pathways by upregulating several key glycolytic enzymes, and induces the nonoxidative pentose phosphate pathway to support increased nucleic acid biosynthesis. RAS-driven cancer cells alter glutaminolysis to support rewired metabolism. Altered glutaminolysis is a key feature of KRAS-dependent cancer cells. KRAS regulated glutamine metabolic rewiring influenced the TCA cycle, which is critical for nucleotide biosynthesis to support cell growth and survival. KRAS-driven tumors require glutaminolysis for redox balance. KRAS-mediated activated NRF2 is depicted as a key transcription factor that modulates redox homeostasis for the survival of cells under oxidative stress. Cells harboring mutant RAS are characterized by increased macropinocytosis, autophagy, and mitophagy, processes which help generate the energy and macromolecules needed for accelerated tumor proliferation. Mutant RAS also regulates stress granule formation, which helps mediate chemoresistance. Yellow box indicates RAS-dependent gene and/or protein expression, with arrows indicating increased or decreased expression. Purple box indicates oncogenic KRAS. GDH1: Glutamate DeHydrogenase 1; TCA: Tricarboxylic Acid; COX2: Cyclooxygenase 2; HK1,2: Hexokinase 1 and 2; GLUT1: Glucose Transporter-1; PFK1: Phosphofructokinase-1; ENO1: alpha-enolase-1; LDHA: Lactate Dehydrogenase; ME1: Malic Enzyme-1; ROS: Reactive Oxygen Species; GOT1,2: Glutamate Oxaloacetate Transaminase 1,2.

Figure 3.. Metabolic alterations and vulnerabilities of RAS-driven cancers

a. Schematic representation of the impact of oncogenic KRAS on cancer metabolism. Various metabolic pathways involving KRAS-mutants critical in cancer cell proliferation and cell survival. Increased glucose uptake, induced glutaminolysis, autophagy and micropinocytosis are involved in deregulating energetics and nutrient scavenging which results in RAS-driven cancer cells’ metabolic adaption for the benefit of cell growth. b. The delicate balance of nutrient supply and demand dynamics in RAS-driven cancers. In a balanced state, nutrient supply is sufficient to maintain energy demand (left). Excessive supply of nutrient availability, in the absence of a parallel increase in energy demand, represents a situation in which the energy required to satisfy energy demand is lower than the available energy (middle). A nutrient-deprived condition provokes metabolic inequity (energy supply < energy demand), leading to energetic stress and, ultimately, metabolic vulnerability (right). Nutrient-replete mutant RAS cells utilize rewired metabolism in their favor (middle panel). In the absence of glucose or glutamine, the metabolic vulnerabilities of mutant RAS cells intensify, leading to energetic stress and ultimately to cell death. Interventions that decrease nutrient consumption abolish redox defense and lead the cells to metabolic imbalance. This results in metabolic vulnerability, a potential therapeutic approach for RAS-driven cancer cells. (right panel).

Figure 4.. Oncogenic driver genes and their involvement in metabolic pathways

a. Pathway-level heatmap showing four KEGG composite metabolic pathways with the most hits of cancer driver mutation genes involved in various metabolic processes. Values are ListHits or numbers of cancer driver mutation genes from the tissue types involved in the composite metabolic pathways from the KEGG database. Red shows the number of driver genes from each tissue involved in each corresponding pathway. b. Gene-level heatmaps showing the three most common driver genes across tumor types for the KEGG metabolic pathways and the RAS signaling pathway. Red indicates an involved driver gene, while black indicates that the gene is not involved. c. Proposed RAS’ connection with oncogenic driver genes of metabolic pathways. KRAS, other top driver genes in the RAS pathway, and the top driver genes in KEGG metabolism pathways in (b) were used as seeds to retrieve their direct relations with each other or their indirect relations with other genes from the MetaCore™ database. Green lines indicate positive/activation relations, red lines indicate negative/inhibition relations, and gray lines indicate unspecified relations. The arrows indicate the relations’ directions. The nodes with blue circles are the original seed genes, whereas other genes were added based on evidence to help connect these genes, if needed. For further details on how analyses were performed see the Supplementary Note.