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Deciphering mechanisms of response and resistance in large-scale mouse cancer screens
Anirudh Prahallad1, Michael Rugaard Jensen1, Emilie Anne Chapeau1
1Oncology Disease Area, Novartis Institutes for BioMedical Research, Basel, Switzerland.
Abstract:
Acquired resistance is a major limitation for the successful treatment of cancer patients. Although numerous efficacious cancer therapeutics have been developed in the past decades, resistance arises due to a variety of reasons including tumoral genetic alterations, or modulation of factors in the tumor environment. Understanding the mechanistic reasons for tumor relapse supports the identification of novel combination therapies that could lead to more durable responses. Here, we will review large-scale in vivo screens in pre-clinical cancer models that employed genetic and pharmacological agents toward elucidating acquired drug resistance and informing on beneficial combinations to be tested in clinical trials.
Insights
Acquired resistance limits cancer treatment success. This review explores how large-scale screens in preclinical models identify mechanisms of drug resistance and guide combination therapies for better patient outcomes.
Area of Science:
- Oncology
- Cancer Research
- Pharmacology
Background:
- Acquired resistance to cancer therapeutics is a significant clinical challenge.
- Tumor genetic alterations and microenvironment modulation contribute to treatment failure.
- Understanding resistance mechanisms is crucial for developing durable cancer treatments.
Purpose of the Study:
- To review large-scale in vivo screening strategies for elucidating acquired drug resistance.
- To identify novel combination therapies based on resistance mechanisms.
- To inform the design of clinical trials for improved cancer treatment.
Main Methods:
- Review of large-scale in vivo screens in preclinical cancer models.
- Analysis of genetic and pharmacological agents used in resistance studies.
- Evaluation of identified resistance mechanisms and potential combination strategies.
Main Results:
- In vivo screens effectively identify key drivers of acquired drug resistance.
- Understanding tumor environment modulation aids in predicting resistance.
- Data from preclinical models can guide the selection of effective combination therapies.
Conclusions:
- Large-scale in vivo screens are powerful tools for dissecting cancer drug resistance.
- Elucidating resistance mechanisms is essential for developing next-generation cancer therapies.
- Findings support the clinical testing of novel combination strategies for durable cancer treatment.
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