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Review
. 2021 Feb 22;14(2):dmm048930.
doi: 10.1242/dmm.048930.

Modeling the developmental origins of pediatric cancer to improve patient outcomes

Affiliations
Review

Modeling the developmental origins of pediatric cancer to improve patient outcomes

James F Amatruda. Dis Model Mech. .

Abstract

In the treatment of children and adolescents with cancer, multimodal approaches combining surgery, chemotherapy and radiation can cure most patients, but may cause lifelong health problems in survivors. Current therapies only modestly reflect increased knowledge about the molecular mechanisms of these cancers. Advances in next-generation sequencing have provided unprecedented cataloging of genetic aberrations in tumors, but understanding how these genetic changes drive cellular transformation, and how they can be effectively targeted, will require multidisciplinary collaboration and preclinical models that are truly representative of the in vivo environment. Here, I discuss some of the key challenges in pediatric cancer from my perspective as a physician-scientist, and touch on some promising new approaches that have the potential to transform our understanding of these diseases.

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Conflict of interest statement

Competing interestsThe author declares no competing or financial interests.

Figures

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James F. Amatruda, MD, PhD. Head of Basic and Translational Research in the Cancer and Blood Disease Institute and the Division of Pediatric Hematology-Oncology at Children's Hospital Los Angeles; and Professor of Pediatrics and Medicine at the Keck School of Medicine of the University of Southern California.
Fig. 1.
Fig. 1.
Improving therapy of pediatric cancers through collaboration. Next-generation sequencing of tumor samples may directly identify candidate targeted therapies. In many cases, further investigation is required. Model systems such as organoids or genetically engineered animals can interrogate the function of candidate driver genes in a setting that recapitulates the complexity of the in vivo tumor environment. Such models can support drug screening and preclinical testing of novel therapies. Throughout, collaboration between clinicians and basic scientists is essential to define clinical challenges and to build and refine disease models.

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