Evidence map›Paper›PMID 15980154›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2005

Drug resistance in cancer: principles of emergence and prevention.

Natalia L Komarova, Dominik Wodarz

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers.

0numbers the graph read from it
0cells of the map it votes in
162citing papers in PubMed
12.4field-weighted citation impact, top 1% 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

162 citing papers in PubMed, 429 citations in OpenAlex.

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  19. Alkaloid Derivative (Molecules (Basel, Switzerland) · 2022
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102 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Natalia L KomarovaDepartment of Mathematics, 103 MSTB, University of California, Irvine, CA 92697, USA. komarova@math.uci.edu
Dominik Wodarz
University of California, Irvine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although targeted therapy is yielding promising results in the treatment of specific cancers, drug resistance poses a problem. We develop a mathematical framework that can be used to study the principles underlying the emergence and prevention of resistance in cancers treated with targeted small-molecule drugs. We consider a stochastic dynamical system based on measurable parameters, such as the turnover rate of tumor cells and the rate at which resistant mutants are generated. We find that resistance arises mainly before the start of treatment and, for cancers with high turnover rates, combination therapy is less likely to yield an advantage over single-drug therapy. We apply the mathematical framework to chronic myeloid leukemia. Early-stage chronic myeloid leukemia was the first case to be treated successfully with a targeted drug, imatinib (Novartis, Basel). This drug specifically inhibits the BCR-ABL oncogene, which is required for progression. Although drug resistance prevents successful treatment at later stages of the disease, our calculations suggest that, within the model assumptions, a combination of three targeted drugs with different specificities might overcome the problem of resistance.

Indexed as

Drug Resistance, MultipleDrug Resistance, NeoplasmModels, BiologicalAntineoplastic AgentsBenzamidesHumansImatinib MesylateLeukemia, Myelogenous, Chronic, BCR-ABL PositivePiperazinesPyrimidinesAntineoplastic AgentsBenzamidesImatinib MesylatePiperazinesPyrimidines

Identifiers

PMID15980154
PMCPMC1172248
OpenAlexW2094556422

What OpenQuestion holds

Textmetadata
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.