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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.

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Related to Figure 1 and STAR Methods(A) Copy number profiles based on the cis-features that define the 10 Integrative Clusters (Curtis et al., 2012) in the METABRIC discovery dataset (n = 997 breast tumors) and the PDTX biobank (n = 121 samples from 36 models).(B) Left panel: Goodness of fit scores for copy number-based classification into IntClust subtypes (Metabric cohort n = 905 tumors, PDTX cohort n = 87 samples from 36 models). Right panel: Kaplan-Meier (disease-specific survival) of IntClust subtypes for the METABRIC cohort. IntClust4 was further stratified into ER-+ and ER-.(C) Hierarchical clustering (Ward’s method) of cancer pathway activation scores across samples. Included are 15 randomly picked samples belonging to each of the 10 IntClusts from the METABRIC cohort and 19 PDTX models (plus 5 technical replicates).(D) Scatter plot of reproducibility and prediction accuracy of cancer pathway activation scores in PDTXs (n = 78 tumor/PDTX pairs from 16 models). Reproducibility was assessed using Spearman correlation of cancer pathway scores in matched tumors and PDTXs. Prediction accuracy was determined by fitting a generalized additive model and computing the deviance explained.
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figs1: Related to Figure 1 and STAR Methods(A) Copy number profiles based on the cis-features that define the 10 Integrative Clusters (Curtis et al., 2012) in the METABRIC discovery dataset (n = 997 breast tumors) and the PDTX biobank (n = 121 samples from 36 models).(B) Left panel: Goodness of fit scores for copy number-based classification into IntClust subtypes (Metabric cohort n = 905 tumors, PDTX cohort n = 87 samples from 36 models). Right panel: Kaplan-Meier (disease-specific survival) of IntClust subtypes for the METABRIC cohort. IntClust4 was further stratified into ER-+ and ER-.(C) Hierarchical clustering (Ward’s method) of cancer pathway activation scores across samples. Included are 15 randomly picked samples belonging to each of the 10 IntClusts from the METABRIC cohort and 19 PDTX models (plus 5 technical replicates).(D) Scatter plot of reproducibility and prediction accuracy of cancer pathway activation scores in PDTXs (n = 78 tumor/PDTX pairs from 16 models). Reproducibility was assessed using Spearman correlation of cancer pathway scores in matched tumors and PDTXs. Prediction accuracy was determined by fitting a generalized additive model and computing the deviance explained.

Mentions: In order to be classified into one of the IntClust using the method we described (Ali et al., 2014), the PDTXs were subject to copy number profiling, by shallow whole-genome sequencing (“sWGS”), and expression profiling, by microarrays (“RNAexp”). The copy number profiles of PDTXs classified into each IntClust were similar to those reported in primary tumors (Curtis et al., 2012; Figure S1A). The goodness of fit scores of IntClust assignment were computed (for IntClusts with more than one xenograft model: IntClust1; IntClust3; IntClust4; IntClust5; IntClust6; IntClust9; and IntClust10), and with one exception (IntClust3), these scores were very similar to classifying primary tumors (Figure S1B).


A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds
Related to Figure 1 and STAR Methods(A) Copy number profiles based on the cis-features that define the 10 Integrative Clusters (Curtis et al., 2012) in the METABRIC discovery dataset (n = 997 breast tumors) and the PDTX biobank (n = 121 samples from 36 models).(B) Left panel: Goodness of fit scores for copy number-based classification into IntClust subtypes (Metabric cohort n = 905 tumors, PDTX cohort n = 87 samples from 36 models). Right panel: Kaplan-Meier (disease-specific survival) of IntClust subtypes for the METABRIC cohort. IntClust4 was further stratified into ER-+ and ER-.(C) Hierarchical clustering (Ward’s method) of cancer pathway activation scores across samples. Included are 15 randomly picked samples belonging to each of the 10 IntClusts from the METABRIC cohort and 19 PDTX models (plus 5 technical replicates).(D) Scatter plot of reproducibility and prediction accuracy of cancer pathway activation scores in PDTXs (n = 78 tumor/PDTX pairs from 16 models). Reproducibility was assessed using Spearman correlation of cancer pathway scores in matched tumors and PDTXs. Prediction accuracy was determined by fitting a generalized additive model and computing the deviance explained.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5037319&req=5

figs1: Related to Figure 1 and STAR Methods(A) Copy number profiles based on the cis-features that define the 10 Integrative Clusters (Curtis et al., 2012) in the METABRIC discovery dataset (n = 997 breast tumors) and the PDTX biobank (n = 121 samples from 36 models).(B) Left panel: Goodness of fit scores for copy number-based classification into IntClust subtypes (Metabric cohort n = 905 tumors, PDTX cohort n = 87 samples from 36 models). Right panel: Kaplan-Meier (disease-specific survival) of IntClust subtypes for the METABRIC cohort. IntClust4 was further stratified into ER-+ and ER-.(C) Hierarchical clustering (Ward’s method) of cancer pathway activation scores across samples. Included are 15 randomly picked samples belonging to each of the 10 IntClusts from the METABRIC cohort and 19 PDTX models (plus 5 technical replicates).(D) Scatter plot of reproducibility and prediction accuracy of cancer pathway activation scores in PDTXs (n = 78 tumor/PDTX pairs from 16 models). Reproducibility was assessed using Spearman correlation of cancer pathway scores in matched tumors and PDTXs. Prediction accuracy was determined by fitting a generalized additive model and computing the deviance explained.
Mentions: In order to be classified into one of the IntClust using the method we described (Ali et al., 2014), the PDTXs were subject to copy number profiling, by shallow whole-genome sequencing (“sWGS”), and expression profiling, by microarrays (“RNAexp”). The copy number profiles of PDTXs classified into each IntClust were similar to those reported in primary tumors (Curtis et al., 2012; Figure S1A). The goodness of fit scores of IntClust assignment were computed (for IntClusts with more than one xenograft model: IntClust1; IntClust3; IntClust4; IntClust5; IntClust6; IntClust9; and IntClust10), and with one exception (IntClust3), these scores were very similar to classifying primary tumors (Figure S1B).

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