Pre-clinical modelling of ROS1+ non-small cell lung cancer

Marc Terrones1, Ken Op de Beeck1, Guy Van Camp1

  • 1Center of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43/6, 2650 Edegem, Belgium; Center for Oncological Research, University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610 Wilrijk, Belgium.

Insights

New preclinical models are crucial for developing effective treatments for ROS1-positive non-small cell lung cancer (NSCLC). These models aim to overcome resistance mechanisms and improve patient survival in this rare cancer subset.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Non-small cell lung cancer (NSCLC) is a major cause of cancer death, with ROS1 rearrangements found in about 2% of cases.
  • ROS1-positive NSCLC typically affects younger, non-smoking individuals with adenocarcinoma.
  • Current tyrosine kinase inhibitors (TKIs) offer benefits but face challenges due to resistance and limited availability.

Purpose of the Study:

  • To review existing preclinical models for ROS1-positive NSCLC.
  • To highlight the strengths and limitations of these models in addressing clinical needs.
  • To emphasize the importance of novel models for developing next-generation TKIs.

Main Methods:

  • Review of existing literature on preclinical models for ROS1-positive NSCLC.
  • Analysis of model development, including gene-editing tools and cell culture techniques.
  • Evaluation of model utility in studying TKI resistance mechanisms.

Main Results:

  • Limited preclinical models exist for ROS1-positive NSCLC, often based on initial TKI approvals.
  • Disease relapse occurs in about 50% of patients due to resistance mechanisms.
  • Existing models have limitations in fully recapitulating clinical resistance scenarios.

Conclusions:

  • Advanced preclinical models are essential for understanding and overcoming TKI resistance in ROS1-positive NSCLC.
  • Combining gene-editing and novel cell culture approaches can create more robust models.
  • These improved models will accelerate the development of next-generation TKIs for improved patient outcomes.

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