Cited by 326Cancer, Hypoxia, and MetabolismProtein Kinase Regulation and GTPase SignalingMetabolism, Diabetes, and Cancer

The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index; 13 of the 93 sources its claims cite were checked for support.

The metabolic landscape of RAS-driven cancers from biology to therapy

Open access
Mukhopadhyay, Suman, Vander Heiden, Matthew G., McCormick, Frank·2021·Nature Cancer

Abstract

Our understanding of how the RAS protein family, and in particular mutant KRAS, promotes metabolic dysregulation in cancer cells has advanced substantially over the last decade. In this Review, we discuss the metabolic reprogramming mediated by oncogenic RAS in cancer and elucidate the underlying mechanisms that could translate to novel therapeutic opportunities to target metabolic vulnerabilities in RAS-driven cancers. Mukhopadhyay, Vander Heiden and McCormick review the metabolic landscape of RAS-driven cancers, the effects of RAS-directed metabolic reprogramming and opportunities for targeting these cancers therapeutically.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that oncogenic RAS, especially mutant KRAS, rewires glucose, glutamine, lipid, redox, and nutrient-scavenging metabolism to support tumor growth and therapy resistance. It surveys preclinical and early clinical strategies for exploiting these metabolic dependencies, while stressing that effective treatments will likely need to be context-specific and combinatorial.

Key findings

  • RAS mutations contribute to roughly one third of human cancers, with KRAS predominant in many adenocarcinomas, NRAS prominent in melanoma and myeloid cancers, and HRAS mutated less often but mainly in bladder and head/neck squamous carcinoma. Mutation patterns vary by tissue: about 22% of oncogenic lung adenocarcinoma mutations are KRAS, while nearly 24% of skin cutaneous melanoma mutations are NRAS; G12C dominates in lung adenocarcinoma and G12D in pancreatic adenocarcinoma.
  • Mutant KRAS increases glucose uptake and glycolytic flux through GLUT1, HK1/HK2, PFK1, ENO1, and LDHA, and also supports the hexosamine biosynthesis pathway and non-oxidative pentose phosphate pathway. KRAS4A can directly regulate HK1. Proposed glycolytic vulnerabilities include GAPDH inhibition by vitamin C, methylglyoxal scavengers combined with AKT inhibitors, and GLUT inhibitors such as silybin and BAY-876.
  • KRAS-driven tumors often depend on glutaminolysis, the GOT1/2–ME1 pathway, NRF2-driven antioxidant programs, and xCT/cysteine metabolism; glutaminase inhibition may be especially relevant in tumors with KEAP1 or LKB1 co-mutations, and cysteine depletion can trigger ferroptosis. Lipid-related targets include ACLY, ACSL3, FASN, and fatty acid oxidation, while nutrient scavenging involves autophagy, NIX-mediated mitophagy, and macropinocytosis, with allele-specific differences such as KRAS G12R being impaired in PI3K signaling and macropinocytosis.
  • Therapy resistance is linked to ROS management, stress granule formation via 15-d-PGJ2/NRF2, and compensatory signaling. KRAS G12C inhibitors show promise, but resistance suggests combinations with mTOR, SHP2, CDK4/6, or metabolic inhibitors; clinical trials are targeting glutaminolysis and autophagy.

Methods

  • Narrative review, not a primary experimental study; it synthesizes literature on RAS biology and cancer metabolism, focusing mainly on KRAS and noting other isoforms where known.
  • Mutation data for figures were compiled from AACR Genie Release 6.1-public, COSMIC v90, cBioPortal, TCGA, and NCI GDC; Figure 4 used previously published driver mutation data from ICGC/TCGA controlled access and an in-house pathway pattern extraction pipeline implemented in R.
  • No systematic review, meta-analysis, or new experimental data are reported.

Limitations

  • The authors state that many open questions remain: the interplay among KRAS isoforms, RAS alleles, tissue of origin, and metabolic alterations is poorly understood; whether RAS-mediated metabolic changes are common across all RAS-driven cancers is unanswered; and HRAS/NRAS metabolic landscapes are less explored.
  • The authors note that metabolic flexibility and compensatory pathways can limit single-pathway targeting, that toxicity to normal cells and immune cells is a major concern, and that in vivo glutamine dependencies can differ from cell-culture findings, making model choice important.
  • Evident from the text: as a review, it provides no primary validation or systematic search criteria, and many proposed metabolic therapies remain preclinical or early clinical and unapproved.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s43018-021-00184-x

Cites

158 of 158 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

48 Not checked yet · 4 Partly supported · 2 Supported · 2 Own interpretation · accuracy not yet measured

Verify all 80 references — free account

Show all 29 claims

  • It may state two separate claims as one. 26
  • Probably not a substantial claim. 1
27 of 27
  • The paper relies on this without support.

    Metabolic changes caused by oncogenes can be broad, with several pathways altered at once within one cancer type.

    “Oncogene-directed metabolic alterations can have extensive impact, with multiple metabolic pathways being altered simultaneously in a single cancer type.”
  • The paper relies on this without support.

    Metabolic phenotypes differ between tumor types and also vary among tumors of the same type.

    “The elucidation of these events should also take into account the fact that metabolic phenotype is not uniform across different tumor types, and variability also exists between different tumors of the same type.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Show all 158 references

Cited by

Sorted by citations

Items 201–300 of 326

Metadata

Authors
Mukhopadhyay, S., Vander Heiden, M. G., & McCormick, F.
Affiliations
  • NYU Langone Health (US)
  • University of California, San Francisco (US)
Corresponding author
McCormick, Frank
Venue
Nature Cancer
Published
2021-03-24
Volume
2
Issue
3
Pages
271-283
ISSN
2662-1347 (ISSN-L)
OpenAlex
W3138007187
Cited by
315
References
158
Citations by year
2026: 27 · 2025: 48 · 2024: 81 · 2023: 73 · 2022: 56 · 2021: 30
FWCI
7.58
Type
journal article
Language
en
Open access
Locations
  • Nature Cancer (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
Funders
Frederick National Laboratory for Cancer Research (HHSN261200800001E)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
Keywords
RAS-driven cancers, KRAS mutation, oncogenic Ras, tumor metabolic reprogramming, cancer metabolism, metabolic vulnerability, metabolic cancer therapy, Ras signaling
MeSH terms
Biology, Humans, Neoplasms, Genes, ras, ras Proteins

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review