Limits...
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

Clonal Architecture and Clonal Dynamics of Breast Cancer PDTXs(A) Example plots of AB551 (left panel), HCI002 (middle panel), and STG282 (right panel). (Left graph) The mean cellular prevalence estimates of mutation clusters in originating patient samples (T) and subsequent xenograft passages (Xn; n for passage number) or PDTCs (XnCy; y for days in culture) are shown. PyClone was used to infer clusters and cellular prevalence using WES data. Line widths indicate the number of SNVs comprising each mutation cluster (numbers in brackets adjacent to each plot). Asterisks indicate clonal clusters with significant changes in cellular prevalence. (Right graph) Plots of distribution of variant allele frequency for selected genes within clusters.(B) PyClone plots (as in A) and cellular prevalence heatmap plots for STG139 and AB521 samples.(C) PyClone plot and plots of distribution of variant allele frequency for selected genes within clusters (as in A) of five spatially separated biopsies and their matched xenografts in CAMBMT1.See also Figure S3.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5037319&req=5

fig3: Clonal Architecture and Clonal Dynamics of Breast Cancer PDTXs(A) Example plots of AB551 (left panel), HCI002 (middle panel), and STG282 (right panel). (Left graph) The mean cellular prevalence estimates of mutation clusters in originating patient samples (T) and subsequent xenograft passages (Xn; n for passage number) or PDTCs (XnCy; y for days in culture) are shown. PyClone was used to infer clusters and cellular prevalence using WES data. Line widths indicate the number of SNVs comprising each mutation cluster (numbers in brackets adjacent to each plot). Asterisks indicate clonal clusters with significant changes in cellular prevalence. (Right graph) Plots of distribution of variant allele frequency for selected genes within clusters.(B) PyClone plots (as in A) and cellular prevalence heatmap plots for STG139 and AB521 samples.(C) PyClone plot and plots of distribution of variant allele frequency for selected genes within clusters (as in A) of five spatially separated biopsies and their matched xenografts in CAMBMT1.See also Figure S3.

Mentions: Clonal architecture in individual samples and clonal dynamics upon engraftment and across serial passaging were assessed on 104 samples from 22 models using PyClone (Roth et al., 2014), as we recently described (Eirew et al., 2015). PyClone identified 190 clonal clusters across the samples analyzed, but only 38 clonal clusters (20%) had significant changes in cellular prevalence estimates (Table S3 for extended information from PyClone analysis in all models tested). Clonal selection was seen upon initial engraftment (average change in clonal prevalence 0.21) but minimal through serial transplantation (average change in clonal prevalence 0.07; Figure S3B). We next asked whether clonal clusters showing engraftment-associated dynamics were enriched for cancer drivers. Recently, our group used a ratiometric method (Vogelstein et al., 2013) to identify 40 breast cancer mutation driver genes in 2,433 breast cancers (Pereira et al., 2016). Remarkably, in only 4 of the 38 clonal clusters that changed significantly after engraftment or during passaging could we identify a mutation driver: BAP1 in STG139 (cluster 12); KDM6A in HCI004 (cluster 3); MAP3K1 in STG143 (cluster 3); and PIK3CA in HCI008 (cluster 2; Table S3). These data strongly suggest that most of the clonal dynamics within xenografts are not associated with known driver genes. Figure 3A shows examples both of individual clonal cluster plots and of variant allele frequency distributions for individual genes within these clusters. Figure S3C shows all individual clonal cluster plots generated from the 22 models analyzed to illustrate the full diversity of clonal architectures observed in the PDTX biobank.


A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds
Clonal Architecture and Clonal Dynamics of Breast Cancer PDTXs(A) Example plots of AB551 (left panel), HCI002 (middle panel), and STG282 (right panel). (Left graph) The mean cellular prevalence estimates of mutation clusters in originating patient samples (T) and subsequent xenograft passages (Xn; n for passage number) or PDTCs (XnCy; y for days in culture) are shown. PyClone was used to infer clusters and cellular prevalence using WES data. Line widths indicate the number of SNVs comprising each mutation cluster (numbers in brackets adjacent to each plot). Asterisks indicate clonal clusters with significant changes in cellular prevalence. (Right graph) Plots of distribution of variant allele frequency for selected genes within clusters.(B) PyClone plots (as in A) and cellular prevalence heatmap plots for STG139 and AB521 samples.(C) PyClone plot and plots of distribution of variant allele frequency for selected genes within clusters (as in A) of five spatially separated biopsies and their matched xenografts in CAMBMT1.See also Figure S3.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

fig3: Clonal Architecture and Clonal Dynamics of Breast Cancer PDTXs(A) Example plots of AB551 (left panel), HCI002 (middle panel), and STG282 (right panel). (Left graph) The mean cellular prevalence estimates of mutation clusters in originating patient samples (T) and subsequent xenograft passages (Xn; n for passage number) or PDTCs (XnCy; y for days in culture) are shown. PyClone was used to infer clusters and cellular prevalence using WES data. Line widths indicate the number of SNVs comprising each mutation cluster (numbers in brackets adjacent to each plot). Asterisks indicate clonal clusters with significant changes in cellular prevalence. (Right graph) Plots of distribution of variant allele frequency for selected genes within clusters.(B) PyClone plots (as in A) and cellular prevalence heatmap plots for STG139 and AB521 samples.(C) PyClone plot and plots of distribution of variant allele frequency for selected genes within clusters (as in A) of five spatially separated biopsies and their matched xenografts in CAMBMT1.See also Figure S3.
Mentions: Clonal architecture in individual samples and clonal dynamics upon engraftment and across serial passaging were assessed on 104 samples from 22 models using PyClone (Roth et al., 2014), as we recently described (Eirew et al., 2015). PyClone identified 190 clonal clusters across the samples analyzed, but only 38 clonal clusters (20%) had significant changes in cellular prevalence estimates (Table S3 for extended information from PyClone analysis in all models tested). Clonal selection was seen upon initial engraftment (average change in clonal prevalence 0.21) but minimal through serial transplantation (average change in clonal prevalence 0.07; Figure S3B). We next asked whether clonal clusters showing engraftment-associated dynamics were enriched for cancer drivers. Recently, our group used a ratiometric method (Vogelstein et al., 2013) to identify 40 breast cancer mutation driver genes in 2,433 breast cancers (Pereira et al., 2016). Remarkably, in only 4 of the 38 clonal clusters that changed significantly after engraftment or during passaging could we identify a mutation driver: BAP1 in STG139 (cluster 12); KDM6A in HCI004 (cluster 3); MAP3K1 in STG143 (cluster 3); and PIK3CA in HCI008 (cluster 2; Table S3). These data strongly suggest that most of the clonal dynamics within xenografts are not associated with known driver genes. Figure 3A shows examples both of individual clonal cluster plots and of variant allele frequency distributions for individual genes within these clusters. Figure S3C shows all individual clonal cluster plots generated from the 22 models analyzed to illustrate the full diversity of clonal architectures observed in the PDTX biobank.

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