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A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

View Article: PubMed Central - PubMed

ABSTRACT

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

No MeSH data available.


High-Throughput Drug Screening Using PDTCs(A) AUCs scatterplots showing reproducibility of PDTC drug testing. (Left panel) AUCs of technical replicates (n = 6,325; same sample, same compound) are shown. (Right plot) AUCs of biological replicates (n = 1,341; same model, different passages, same compound) are shown. r, Pearson correlation.(B) AUC scatterplots of all drugs targeting PI3K/AKT/mTOR pathway (n = 34 passages from 20 models). Red indicates Pearson correlation > 0.5.(C) AUC scatterplot for cisplatin and BMN-673 treatment across models tested (n = 15).(D) Illustration of the PI3K pathway with panels depicting difference in the AUC in models (n = 15) with versus without molecular alteration in pathway member. (Left panels) Inhibitors of PI3K alpha and PI3Kbeta are shown. (Right panels) Inhibitors of AKT1 and mTOR are shown.See also Figures S4, S5, and S6.
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fig4: High-Throughput Drug Screening Using PDTCs(A) AUCs scatterplots showing reproducibility of PDTC drug testing. (Left panel) AUCs of technical replicates (n = 6,325; same sample, same compound) are shown. (Right plot) AUCs of biological replicates (n = 1,341; same model, different passages, same compound) are shown. r, Pearson correlation.(B) AUC scatterplots of all drugs targeting PI3K/AKT/mTOR pathway (n = 34 passages from 20 models). Red indicates Pearson correlation > 0.5.(C) AUC scatterplot for cisplatin and BMN-673 treatment across models tested (n = 15).(D) Illustration of the PI3K pathway with panels depicting difference in the AUC in models (n = 15) with versus without molecular alteration in pathway member. (Left panels) Inhibitors of PI3K alpha and PI3Kbeta are shown. (Right panels) Inhibitors of AKT1 and mTOR are shown.See also Figures S4, S5, and S6.

Mentions: First, the observed AUC values across all drugs and models tested were highly correlated across technical (Pearson correlation of 0.94) and biological replicates (Pearson correlation of 0.78; Figure 4A). These results are highly similar to those we previously reported in established cell lines or tumor organoids (Garnett et al., 2012, van de Wetering et al., 2015). To further verify the robustness of these in vitro drug response data, we tested in eight PDTC models a set of 19 drugs using CyQUANT and Sytox endpoint assays, in addition to CTG (see STAR Methods). The results of these experiments revealed highly correlated drug responses independently of the assay used (Figure S4E; Table S5).


A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds
High-Throughput Drug Screening Using PDTCs(A) AUCs scatterplots showing reproducibility of PDTC drug testing. (Left panel) AUCs of technical replicates (n = 6,325; same sample, same compound) are shown. (Right plot) AUCs of biological replicates (n = 1,341; same model, different passages, same compound) are shown. r, Pearson correlation.(B) AUC scatterplots of all drugs targeting PI3K/AKT/mTOR pathway (n = 34 passages from 20 models). Red indicates Pearson correlation > 0.5.(C) AUC scatterplot for cisplatin and BMN-673 treatment across models tested (n = 15).(D) Illustration of the PI3K pathway with panels depicting difference in the AUC in models (n = 15) with versus without molecular alteration in pathway member. (Left panels) Inhibitors of PI3K alpha and PI3Kbeta are shown. (Right panels) Inhibitors of AKT1 and mTOR are shown.See also Figures S4, S5, and S6.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5037319&req=5

fig4: High-Throughput Drug Screening Using PDTCs(A) AUCs scatterplots showing reproducibility of PDTC drug testing. (Left panel) AUCs of technical replicates (n = 6,325; same sample, same compound) are shown. (Right plot) AUCs of biological replicates (n = 1,341; same model, different passages, same compound) are shown. r, Pearson correlation.(B) AUC scatterplots of all drugs targeting PI3K/AKT/mTOR pathway (n = 34 passages from 20 models). Red indicates Pearson correlation > 0.5.(C) AUC scatterplot for cisplatin and BMN-673 treatment across models tested (n = 15).(D) Illustration of the PI3K pathway with panels depicting difference in the AUC in models (n = 15) with versus without molecular alteration in pathway member. (Left panels) Inhibitors of PI3K alpha and PI3Kbeta are shown. (Right panels) Inhibitors of AKT1 and mTOR are shown.See also Figures S4, S5, and S6.
Mentions: First, the observed AUC values across all drugs and models tested were highly correlated across technical (Pearson correlation of 0.94) and biological replicates (Pearson correlation of 0.78; Figure 4A). These results are highly similar to those we previously reported in established cell lines or tumor organoids (Garnett et al., 2012, van de Wetering et al., 2015). To further verify the robustness of these in vitro drug response data, we tested in eight PDTC models a set of 19 drugs using CyQUANT and Sytox endpoint assays, in addition to CTG (see STAR Methods). The results of these experiments revealed highly correlated drug responses independently of the assay used (Figure S4E; Table S5).

View Article: PubMed Central - PubMed

ABSTRACT

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

No MeSH data available.