Cited by 5,926Cancer, Hypoxia, and MetabolismMetabolism, Diabetes, and CancerCancer, Lipids, and Metabolism

The DOI matches its Crossref record; 241 of the 254 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 254 sources its claims cite is checked for support yet.

The Emerging Hallmarks of Cancer Metabolism

Open access

Abstract

Tumorigenesis is dependent on the reprogramming of cellular metabolism as both direct and indirect consequence of oncogenic mutations. A common feature of cancer cell metabolism is the ability to acquire necessary nutrients from a frequently nutrient-poor environment and utilize these nutrients to both maintain viability and build new biomass. The alterations in intracellular and extracellular metabolites that can accompany cancer-associated metabolic reprogramming have profound effects on gene expression, cellular differentiation and the tumor microenvironment. In this Review, we have organized known cancer-associated metabolic changes into six hallmarks: (1) deregulated uptake of glucose and amino acids, (2) use of opportunistic modes of nutrient acquisition, (3) use of glycolysis/TCA cycle intermediates for biosynthesis and NADPH production, (4) increased demand for nitrogen, (5) alterations in metabolite-driven gene regulation, and (6) metabolic interactions with the microenvironment. While few tumors display all six hallmarks, most display several. The specific hallmarks exhibited by an individual tumor may ultimately contribute to better tumor classification and aid in directing treatment.

Checked 2026-10-09 · 1 reference not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review organizes known cancer-associated metabolic changes into six hallmarks: deregulated uptake of glucose and amino acids, opportunistic modes of nutrient acquisition, use of glycolysis/TCA cycle intermediates for biosynthesis and NADPH production, increased demand for nitrogen, alterations in metabolite-driven gene regulation, and metabolic interactions with the microenvironment. The authors argue that while few tumors display all six hallmarks, most display several, and the specific hallmarks exhibited may aid tumor classification and treatment direction.

Key findings

  • Cancer cells acquire nutrients through oncogene-driven constitutive uptake: PI3K/Akt signaling promotes GLUT1 expression and translocation and potentiates hexokinase and phosphofructokinase, while c-myc drives glutamine transporter (ASCT2, SN2) and glutaminase (GLS1) expression; Rb loss upregulates ASCT2 and GLS1 via E2F.
  • Opportunistic nutrient acquisition allows survival under scarcity: Ras- or c-Src-driven macropinocytosis enables lysosomal degradation of extracellular protein (albumin at 20–30 mg/mL supports KRASG12D MEF proliferation without leucine), and mTORC1 inhibitors enhance this route — rapamycin suppresses proliferation at the tumor margin but enhances it in the poorly vascularized tumor core.
  • Proliferating cells divert glycolytic intermediates into biosynthesis: cancer cells may use as much as 50% of glucose-derived carbon for serine biosynthesis, and stepwise oxidation of one-carbon-THF species can produce up to 50% of cellular NADPH; PKM2 (rather than PKM1) predominates in essentially all cancer cells and is inhibited by tyrosine phosphorylation and activated by serine.
  • Metabolite-driven gene regulation links metabolism to epigenetics: mutant IDH1/IDH2 produce 2-HG that competitively inhibits α-ketoglutarate-dependent dioxygenases, and SDH/FH loss accumulates succinate/fumarate, all producing hypermethylation phenotypes; lactate accumulation promotes M2 macrophage polarization, angiogenesis, and immune-permissive microenvironments.

Methods

  • Narrative review/Perspective synthesizing published literature on cancer metabolism; the authors state they organize known cancer-associated metabolic changes into six hallmarks.
  • No primary data, sample, or statistical analysis is reported; evidence is drawn from cited studies across cell lines, mouse models, patient samples, and clinical imaging (e.g., 18F-FDG and 18F-glutamine PET).
  • Not stated in the paper for any systematic search strategy, inclusion criteria, or quantitative synthesis.

Limitations

  • The paper states that most studies to date have focused on glucose and glutamine metabolism, and that the contributions of other nutrients (sulfur-containing amino acids, essential fatty acids, choline, trace metals, vitamins, and microbiota-derived metabolites) are only beginning to be understood.
  • The paper notes that cell lineage context and potential secondary effects of adaptation to tissue culture conditions may influence findings such as the choice of anaplerotic substrate (glutamine vs. glucose-derived pyruvate carboxylation).
  • Evident from the text: as a narrative review without primary data or systematic methodology, the six-hallmark framework is an organizing proposal rather than an empirically validated classification, and the authors state that few tumors display all six hallmarks.

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

Integrity signals

Registry

Crossref · 10.1016/j.cmet.2015.12.006

Cites

241 of 242 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 254 cited sources checked · 0 supported · 0 flagged

Show all 187 claims

  • It may state two separate claims as one. 87
  • Probably not a substantial claim. 15
98 of 98
  • The paper relies on this without support.

    Most tumors display several of the six metabolic hallmarks, while few display all six.

    “While few tumors display all six hallmarks, most display several.”
  • The paper relies on this without support.

    Findings from animal models about dietary methyl donors and epigenetic marks apply to human tumorigenesis.

    “Furthermore, diets deficient in choline and betaine were shown to directly affect histone and DNA methylation in a number animal models (Du et al., 2009; Mehedint et al., 2010; Niculescu et al., 2006), suggesting that alterations in exogenous methyl donor availability may have strong effects on cellular epigenetic state and thus contribute to tumorigenesis.”
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?

Citations

Cites

Flagged first, then by citations

Show all 254 references

12 of the 254 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Items 701–800 of 1,029

Metadata

Authors
Pavlova, N. N., & Thompson, C. B.
Affiliations
Memorial Sloan Kettering Cancer Center (US)
Corresponding author
Thompson, Craig B.
Venue
Cell Metabolism
Publisher
Elsevier BV
Published
2016-01
Volume
23
Issue
1
Pages
27-47
ISSN
1550-4131 (ISSN-L)
OpenAlex
W2234115940
Cited by
5,926
References
254
Citations by year
2026: 576 · 2025: 556 · 2024: 661 · 2023: 678 · 2022: 819 · 2021: 846 · 2020: 601 · 2019: 477 · 2018: 352 · 2017: 252 · 2016: 103
FWCI
36.25
Type
journal article
Language
en
Open access
Locations
  • Cell Metabolism (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Cancer Institute
  • Terri Brodeur Breast Cancer Foundation
  • National Institutes of Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Lipids, and Metabolism
Keywords
cancer metabolism, metabolic reprogramming, carcinogenesis, glucose uptake, amino acid uptake, nutrient acquisition, glycolysis, TCA cycle intermediates, NADPH production, nitrogen requirements, tumor microenvironment, tumor classification
MeSH terms
Animals, Antineoplastic Agents, Biological Transport, Cell Transformation, Neoplastic, Energy Metabolism, Humans, Mutation, Neoplasms, Signal Transduction, Metabolic Networks and Pathways

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 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

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