Prediction of cancer drug resistance and implications for personalized medicine

Publication type:
Journal article
Metadata:
Autoren
  • Manfred Volm
  • Thomas Efferth
Sammlungen
  • metadata
ISSN
2234-943X
Zeitschrift
Frontiers in oncology
Schlüsselwörter
  • 570 Biowissenschaften
  • 570 Life sciences
Sprache
eng
Paginierung
Art. 282
Datum der Veröffentlichung
2015
Herausgeber
Frontiers Media
Herausgeber URL
http://dx.doi.org/10.3389/fonc.2015.00282
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
Prediction of cancer drug resistance and implications for personalized medicine
Ausgabe der Zeitschrift
5

Data source: METADATA.UB

Other metadata sources:
Autoren
  • Manfred Volm
  • Thomas Efferth
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000366810900001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.3389/fonc.2015.00282
Externe Identifier
  • Clarivate Analytics Document Solution ID: CZ0QL
  • PubMed Identifier: 26734568
ISSN
2234-943X
Zeitschrift
FRONTIERS IN ONCOLOGY
Schlüsselwörter
  • chemotherapy
  • drug resistance
  • individualized therapy
  • survival times
Artikelnummer
ARTN 282
Datum der Veröffentlichung
2015
Status
Published
Titel
Prediction of Cancer Drug Resistance and implications for Personalized Medicine
Sub types
  • Review
Ausgabe der Zeitschrift
5

Data source: Web of Science (Lite)

Autoren
  • Manfred Volm
  • Thomas Efferth
DOI
10.3389/fonc.2015.00282
eISSN
2234-943X
Zeitschrift
Frontiers in Oncology
Online publication date
2015
Status
Published online
Herausgeber
Frontiers Media SA
Herausgeber URL
http://dx.doi.org/10.3389/fonc.2015.00282
Datum der Datenerfassung
2020
Titel
Prediction of Cancer Drug Resistance and Implications for Personalized Medicine
Ausgabe der Zeitschrift
5

Data source: Crossref

Abstract
Drug resistance still impedes successful cancer chemotherapy. A major goal of early concepts in individualized therapy was to develop in vitro tests to predict tumors' drug responsiveness. We have developed an in vitro short-term test based on nucleic acid precursor incorporation to determine clinical drug resistance. This test detects inherent and acquired resistance in vitro and transplantable syngeneic and xenografted tumors in vivo. In several clinical trials, clinical resistance was predictable with more than 90% accuracy, while drug sensitivity was detected with less accuracy (~60%). Remarkably, clinical cross-resistance to numerous drugs (multidrug resistance, broad spectrum resistance) was detectable by a single compound, doxorubicin, due to its multifactorial modes of action. The results of this predictive test were in good agreement with predictive assays of other authors. As no predictive test has been established as yet for clinical diagnostics, the identification of sensitive drugs may not reach sufficiently high reliability for clinical routine. A meta-analysis of the literature published during the past four decades considering test results of more than 15,000 tumor patients unambiguously demonstrated that, in the majority of studies, resistance was correctly predicted with an accuracy between 80 and 100%, while drug sensitivity could only be predicted with an accuracy of 50-80%. This synopsis of the published literature impressively illustrates that prediction of drug resistance could be validated. The determination of drug resistance was reliable independent of tumor type, test assay, and drug used in these in vitro tests. By contrast, chemosensitivity could not be predicted with high reliability. Therefore, we propose a rethinking of the "chemosensitivity" concept. Instead, predictive in vitro tests may reliably identify drug-resistant tumors. The clinical consequence imply to subject resistant tumors not to chemotherapy, but to other new treatment options, such as antibody therapy, adoptive immune therapy, hyperthermia, gene therapy, etc. The high accuracy to predict resistant tumors may be exploited to develop new strategies for individualized cancer therapy. This new concept bears the potential of a revival of predictive tests for personalized medicine.
Addresses
  • Faculty of Medicine, Ruprecht Karls University , Heidelberg , Germany.
Autoren
DOI
10.3389/fonc.2015.00282
eISSN
2234-943X
Externe Identifier
  • PubMed Identifier: 26734568
  • PubMed Central ID: PMC4681783
Open access
true
ISSN
2234-943X
Zeitschrift
Frontiers in oncology
Sprache
eng
Medium
Electronic-eCollection
Online publication date
2015
Open access status
Open Access
Paginierung
282
Datum der Veröffentlichung
2015
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2016
Titel
Prediction of Cancer Drug Resistance and Implications for Personalized Medicine.
Sub types
  • review-article
  • Review
  • Journal Article
Ausgabe der Zeitschrift
5

Files

https://www.frontiersin.org/articles/10.3389/fonc.2015.00282/pdf https://europepmc.org/articles/PMC4681783?pdf=render

Data source: Europe PubMed Central

Abstract
Drug resistance still impedes successful cancer chemotherapy. A major goal of early concepts in individualized therapy was to develop in vitro tests to predict tumors' drug responsiveness. We have developed an in vitro short-term test based on nucleic acid precursor incorporation to determine clinical drug resistance. This test detects inherent and acquired resistance in vitro and transplantable syngeneic and xenografted tumors in vivo. In several clinical trials, clinical resistance was predictable with more than 90% accuracy, while drug sensitivity was detected with less accuracy (~60%). Remarkably, clinical cross-resistance to numerous drugs (multidrug resistance, broad spectrum resistance) was detectable by a single compound, doxorubicin, due to its multifactorial modes of action. The results of this predictive test were in good agreement with predictive assays of other authors. As no predictive test has been established as yet for clinical diagnostics, the identification of sensitive drugs may not reach sufficiently high reliability for clinical routine. A meta-analysis of the literature published during the past four decades considering test results of more than 15,000 tumor patients unambiguously demonstrated that, in the majority of studies, resistance was correctly predicted with an accuracy between 80 and 100%, while drug sensitivity could only be predicted with an accuracy of 50-80%. This synopsis of the published literature impressively illustrates that prediction of drug resistance could be validated. The determination of drug resistance was reliable independent of tumor type, test assay, and drug used in these in vitro tests. By contrast, chemosensitivity could not be predicted with high reliability. Therefore, we propose a rethinking of the "chemosensitivity" concept. Instead, predictive in vitro tests may reliably identify drug-resistant tumors. The clinical consequence imply to subject resistant tumors not to chemotherapy, but to other new treatment options, such as antibody therapy, adoptive immune therapy, hyperthermia, gene therapy, etc. The high accuracy to predict resistant tumors may be exploited to develop new strategies for individualized cancer therapy. This new concept bears the potential of a revival of predictive tests for personalized medicine.
Date of acceptance
2015
Autoren
  • Manfred Volm
  • Thomas Efferth
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/26734568
DOI
10.3389/fonc.2015.00282
Externe Identifier
  • PubMed Central ID: PMC4681783
ISSN
2234-943X
Zeitschrift
Front Oncol
Schlüsselwörter
  • chemotherapy
  • drug resistance
  • individualized therapy
  • survival times
Sprache
eng
Country
Switzerland
Paginierung
282
Datum der Veröffentlichung
2015
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2016
Titel
Prediction of Cancer Drug Resistance and Implications for Personalized Medicine.
Sub types
  • Journal Article
  • Review
Ausgabe der Zeitschrift
5

Data source: PubMed

Author's licence
CC-BY
Autoren
  • Manfred Volm
  • Thomas Efferth
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-OA-Publizieren (2012 - 2017)
Resource version
Published version
DOI
10.3389/fonc.2015.00282
Funding acknowledgements
  • DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
File(s) embargoed
false
Open access
true
ISSN
2234-943X
Zeitschrift
Frontiers in oncology
Schlüsselwörter
  • 570 Biowissenschaften
  • 570 Life sciences
Sprache
eng
Open access status
Open Access
Paginierung
Art. 282
Datum der Veröffentlichung
2015
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/7762
Herausgeber
Frontiers Media
Herausgeber URL
http://dx.doi.org/10.3389/fonc.2015.00282
Datum der Datenerfassung
2022
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Zugang
Public
Titel
Prediction of cancer drug resistance and implications for personalized medicine
Ausgabe der Zeitschrift
5

Files

prediction_of_cancer_drug_res-20220913190849926.pdf

Data source: OPENSCIENCE.UB

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