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Establishment of cross-resistance profiles for new agents

F M Schabel, H E Skipper, M W Trader

    Cancer Treatment Reports
    |October 1, 1983
    PubMed

    Insights

    Developing drug-resistant cancer models in mice helps predict patient treatment outcomes. Understanding cross-resistance patterns guides the selection of effective anticancer drugs for clinical trials and combination therapies.

    Area of Science:

    • Oncology
    • Pharmacology
    • Translational Research

    Background:

    • Acquired resistance to anticancer drugs is a major challenge in cancer therapy.
    • Drug-resistant tumor sublines are crucial for preclinical studies.
    • Understanding resistance mechanisms informs clinical trial design and patient selection.

    Purpose of the Study:

    • To establish and characterize drug-resistant murine leukemia and solid tumor sublines.
    • To investigate cross-resistance and collateral sensitivity patterns against a broad spectrum of anticancer drugs.
    • To evaluate the utility of these models in predicting clinical outcomes and guiding new drug development.

    Main Methods:

    • Selection of L1210 and P388 murine leukemia sublines for resistance to diverse anticancer agents.
    • Serial in vivo passage and in vitro culture of resistant tumor sublines.
    • Comprehensive drug sensitivity and cross-resistance profiling using over 100 anticancer drugs.

    Main Results:

    • Established multiple drug-resistant sublines of murine leukemias and solid tumors.
    • Characterized extensive resistance, cross-resistance, and collateral sensitivity profiles.
    • Demonstrated the predictive value of murine models for clinical drug response.

    Conclusions:

    • Drug-resistant murine tumor models provide valuable insights into clinical drug resistance.
    • Cross-resistance profiles aid in patient selection for clinical trials.
    • These models facilitate the identification of mechanisms of action and guide combination chemotherapy strategies.

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