Evidence map›Paper›PMID 35203491›Full record

ReviewBiomedicines2022

Identification of Targetable Liabilities in the Dynamic Metabolic Profile of

Anastasios Gkountakos, Giovanni Centonze, Emanuele Vita, Lorenzo Belluomini, Michele Milella, Emilio Bria, Massimo Milione, Aldo Scarpa, Michele Simbolo

Open access · goldAbstract readReview
In one paragraph

Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Plasma metabolomic analysis in Thai EGFR-mutated non-small cell lung cancer patients.Computational and structural biotechnology journal · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Article
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  12. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Anastasios GkountakosARC-NET Applied Research on Cancer Center, University of Verona, 37134 Verona, Italy.ORCID 0000-0001-9001-0293
Giovanni Centonze1st Pathology Division, Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0003-2864-7941
Emanuele VitaComprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.
Lorenzo BelluominiMedical Oncology, Department of Medicine, University of Verona, 37134 Verona, Italy.ORCID 0000-0003-1981-5499
Michele MilellaMedical Oncology, Department of Medicine, University of Verona, 37134 Verona, Italy.
Emilio BriaComprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.ORCID 0000-0002-2333-704X
Massimo Milione1st Pathology Division, Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Aldo ScarpaARC-NET Applied Research on Cancer Center, University of Verona, 37134 Verona, Italy.ORCID 0000-0003-1678-739X
Michele SimboloDepartment of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy.ORCID 0000-0002-0866-4499
University of Verona · ITFondazione IRCCS Istituto Nazionale dei Tumori · ITUniversità Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) as first-line treatment in patients with lung adenocarcinoma (LUAD) harboring EGFR-activating mutations has resulted in a dramatic improvement in the management of the disease. However, the long-term clinical benefit is inevitably compromised by multiple resistance mechanisms. Accumulating evidence suggests that metabolic landscape remodeling is one of the mechanisms that EGFR-mutant LUAD cells activate, thus acquiring higher plasticity, tolerating EGFR TKI-mediated cytotoxic stress, and sustaining their oncogenic phenotype. Several metabolic pathways are upregulated in EGFR TKI-resistant models modulating the levels of numerous metabolites such as lipids, carbohydrates, and metabolic enzymes which have been suggested as potential mediators of resistance to EGFR TKIs. Moreover, metabolites have been shown to carry signals and stimulate oncogenic pathways and tumor microenvironment (TME) components such as fibroblasts, facilitating resistance to EGFR TKIs in various ways. Interestingly, metabolic signatures could function as predictive biomarkers of EGFR TKI efficacy, accurately classifying patients with EGFR-mutant LUAD. In this review, we present the identified metabolic rewiring mechanisms and how these act either independently or in concert with epigenetic or TME elements to orchestrate EGFR TKI resistance. Moreover, we discuss potential nutrient dependencies that emerge, highlighting them as candidate druggable metabolic vulnerabilities with already approved drugs which, in combination with EGFR TKIs, might counteract the solid challenge of resistance, hopefully prolonging the clinical benefit.

Indexed as

epidermal growth factor receptorlung adenocarcinomametabolismresistancetyrosine kinase inhibitor

Identifiers

PMID35203491
PMCPMC8869286
OpenAlexW4210474451

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.