Evidence map›Paper›PMID 34283054›Full record

ReviewCancers2021

Metabolic Reprogramming: A Friend or Foe to Cancer Therapy?

Christopher McCann, Emma M Kerr

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

15 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  5. 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

2 authors.

Christopher McCannPatrick G. Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Rd, BT9 7AE Belfast, Ireland.
Emma M KerrPatrick G. Johnston Centre for Cancer Research, Queen's University Belfast, 97 Lisburn Rd, BT9 7AE Belfast, Ireland.

Funding

Cancer Research UK 26045Cancer Research UK A26045
6 · The paper itself

Abstract

Drug resistance is a major cause of cancer treatment failure, effectively driven by processes that promote escape from therapy-induced cell death. The mechanisms driving evasion of apoptosis have been widely studied across multiple cancer types, and have facilitated new and exciting therapeutic discoveries with the potential to improve cancer patient care. However, an increasing understanding of the crosstalk between cancer hallmarks has highlighted the complexity of the mechanisms of drug resistance, co-opting pathways outside of the canonical "cell death" machinery to facilitate cell survival in the face of cytotoxic stress. Rewiring of cellular metabolism is vital to drive and support increased proliferative demands in cancer cells, and recent discoveries in the field of cancer metabolism have uncovered a novel role for these programs in facilitating drug resistance. As a key organelle in both metabolic and apoptotic homeostasis, the mitochondria are at the forefront of these mechanisms of resistance, coordinating crosstalk in the event of cellular stress, and promoting cellular survival. Importantly, the appreciation of this role metabolism plays in the cytotoxic response to therapy, and the ability to profile metabolic adaptions in response to treatment, has encouraged new avenues of investigation into the potential of exploiting metabolic addictions to improve therapeutic efficacy and overcome drug resistance in cancer. Here, we review the role cancer metabolism can play in mediating drug resistance, and the exciting opportunities presented by imposed metabolic vulnerabilities.

Indexed as

cancer metabolismcell deathdrug resistancemitochondria

Identifiers

PMID34283054
PMCPMC8267696
OpenAlexW3178363348

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.