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August 16, 2024Open Access

Figure 1 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium

Paul C. Lott Katherine Chiu Juanita Elizabeth Quino April Pangia Vang Michael W. Lloyd Anuj Srivastava Jeffrey H. Chuang Luis G. Carvajal‐Carmona

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Abstract

Diversity of genetic ancestry estimates from PDXNet models. A, Inferred genetic ancestry for 960 models across all cancer types. B, Top two categories for 57 “MIXED” samples with no category >70%. Numbers and percentages in each box reflect the models assigned to that category based on estimates from the SNPweights analysis.

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Lott et al. (2024) studied this question.

synapsesocial.com/papers/68e5bfb4b6db643587557cb1 https://doi.org/10.1158/2767-9764.26764424

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Figure S2 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024
2Figure S1 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024
3Table S4 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024
4Table S4 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024
5Table S4 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024

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Table S4 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024
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Table S2 from Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium2024