Evidence map›Paper›PMID 30072489›Full record

ReviewEssays in biochemistry2018

Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance.

Peter T Harrison, Paul H Huang

Abstract readReview
In one paragraph

Review in Essays in biochemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  18. TargetingSignal transduction and targeted therapy
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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.

Peter T HarrisonDivision of Molecular Pathology, The Institute of Cancer Research, London, U.K.
Paul H HuangDivision of Molecular Pathology, The Institute of Cancer Research, London, U.K. paul.huang@icr.ac.uk.

Funding

Cancer Research UK 19281Cancer Research UK C36478/A19281
6 · The paper itself

Abstract

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.

Indexed as

Drug Resistance, NeoplasmSignal TransductionSystems BiologyAntineoplastic Combined Chemotherapy ProtocolsHSP90 Heat-Shock ProteinsHumansNeoplasmsProtein Kinase InhibitorsSalvage TherapyHSP90 Heat-Shock ProteinsProtein Kinase InhibitorsCancer EvolutionCell SignallingCombination therapiesDrug resistancekinase inhibitorsTargeted therapy

Identifiers

PMID30072489
PMCPMC6204552

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.