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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.


Related in: MedlinePlus

Related to Figure 4 and STAR Methods(A) Drug responses classified into 8 different groups according to response curves. Left panel- the drug responses clustered into 8 groups. Right panel- plots with percentage of samples (n = 37 samples from 20 models) displaying each drug response pattern for each compound. Drug name (bold) and putative target indicated.(B) Unsupervised clustering of drug responses in tested models (n = 37 samples from 20 models) according to subtype of drug response pattern (color coded as in A). Vertical axis- drugs; horizontal axis- models.
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figs5: Related to Figure 4 and STAR Methods(A) Drug responses classified into 8 different groups according to response curves. Left panel- the drug responses clustered into 8 groups. Right panel- plots with percentage of samples (n = 37 samples from 20 models) displaying each drug response pattern for each compound. Drug name (bold) and putative target indicated.(B) Unsupervised clustering of drug responses in tested models (n = 37 samples from 20 models) according to subtype of drug response pattern (color coded as in A). Vertical axis- drugs; horizontal axis- models.

Mentions: Distinct PDTC models can sometimes share the same IC50 and AUC values for a compound and have very different dose-response curves. Hence, a new method, based on the pattern of the slope of the dose-response curve, was developed to classify drug sensitivity patterns into eight groups (see STAR Methods). Figure S5A shows for each compound the proportion of drug responses classified into each of the eight drug sensitivity patterns across all models tested with that drug. Clustering of drug sensitivity patterns (Figure S5B) confirmed the high reproducibility and biological robustness of the data: different passages of the same model and compounds with similar mechanisms of action and target specificities clustered together.


A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds
Related to Figure 4 and STAR Methods(A) Drug responses classified into 8 different groups according to response curves. Left panel- the drug responses clustered into 8 groups. Right panel- plots with percentage of samples (n = 37 samples from 20 models) displaying each drug response pattern for each compound. Drug name (bold) and putative target indicated.(B) Unsupervised clustering of drug responses in tested models (n = 37 samples from 20 models) according to subtype of drug response pattern (color coded as in A). Vertical axis- drugs; horizontal axis- models.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037319&req=5

figs5: Related to Figure 4 and STAR Methods(A) Drug responses classified into 8 different groups according to response curves. Left panel- the drug responses clustered into 8 groups. Right panel- plots with percentage of samples (n = 37 samples from 20 models) displaying each drug response pattern for each compound. Drug name (bold) and putative target indicated.(B) Unsupervised clustering of drug responses in tested models (n = 37 samples from 20 models) according to subtype of drug response pattern (color coded as in A). Vertical axis- drugs; horizontal axis- models.
Mentions: Distinct PDTC models can sometimes share the same IC50 and AUC values for a compound and have very different dose-response curves. Hence, a new method, based on the pattern of the slope of the dose-response curve, was developed to classify drug sensitivity patterns into eight groups (see STAR Methods). Figure S5A shows for each compound the proportion of drug responses classified into each of the eight drug sensitivity patterns across all models tested with that drug. Clustering of drug sensitivity patterns (Figure S5B) confirmed the high reproducibility and biological robustness of the data: different passages of the same model and compounds with similar mechanisms of action and target specificities clustered together.

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.


Related in: MedlinePlus