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A Model of Dormant-Emergent Metastatic Breast Cancer Progression Enabling Exploration of Biomarker Signatures.

ABSTRACT: Breast cancer mortality predominantly results from dormant micrometastases that emerge as fatal outgrowths years after initial diagnosis. In order to gain insights concerning factors associated with emergence of liver metastases, we recreated spontaneous dormancy in an all-human ex vivo hepatic microphysiological system (MPS). Seeding this MPS with small numbers (<0.05% by cell count) of the aggressive MDA-MB-231 breast cancer cell line, two populations formed: actively proliferating ("growing"; EdU+), and spontaneously quiescent ("dormant"; EdU-). Following treatment with a clinically standard chemotherapeutic, the proliferating cells were eliminated and only quiescent cells remained; this residual dormant population could then be induced to a proliferative state ("emergent"; EdU+) by physiologically-relevant inflammatory stimuli, lipopolysaccharide (LPS) and epidermal growth factor (EGF). Multiplexed proteomic analysis of the MPS effluent enabled elucidation of key factors and processes that correlated with the various tumor cell states, and candidate biomarkers for actively proliferating (either primary or secondary emergence) versus dormant metastatic cells in liver tissue. Dormancy was found to be associated with signaling reflective of cellular quiescence even more strongly than the original tumor-free liver tissue, whereas proliferative nodules presented inflammatory signatures. Given the minimal tumor burden, these markers likely represent changes in the tumor microenvironment rather than in the tumor cells. A computational decision tree algorithm applied to these signatures indicated the potential of this MPS for clinical discernment of each metastatic stage from blood protein analysis.

SUBMITTER: Clark AM

PROVIDER: S-EPMC5880110 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A Model of Dormant-Emergent Metastatic Breast Cancer Progression Enabling Exploration of Biomarker Signatures.

Clark Amanda M AM Kumar Manu P MP Wheeler Sarah E SE Young Carissa L CL Venkataramanan Raman R Stolz Donna B DB Griffith Linda G LG Lauffenburger Douglas A DA Wells Alan A

Molecular & cellular proteomics : MCP 20180120 4

Breast cancer mortality predominantly results from dormant micrometastases that emerge as fatal outgrowths years after initial diagnosis. In order to gain insights concerning factors associated with emergence of liver metastases, we recreated spontaneous dormancy in an all-human <i>ex vivo</i> hepatic microphysiological system (MPS). Seeding this MPS with small numbers (<0.05% by cell count) of the aggressive MDA-MB-231 breast cancer cell line, two populations formed: actively proliferating ("gr ...[more]

PMID: 29353230

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Project description:PURPOSE:This study evaluates the prognostic significance of MST1R (RON) expression in breast cancer with respect to disease progression, long-term survival, subtype, and association with conventional prognostic factors. METHODS:The approach includes interrogation of survival and tumor staging with paired MST1R RNA expression from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Protein expression evaluation was performed using immunohistochemistry (IHC) staining of MST1R on breast cancer tissue samples from the Cancer Diagnosis Program Breast Cancer Progression tissue microarray and locally obtained breast tumor tissue samples analyzed with paired survival, metastasis, and subtype. RESULTS:Data from TCGA (n?=?774) show poorer relapse-free survival (RFS) in patients with high MST1R expression (P?=?0.32) and no difference in MST1R expression based on tumor stage (P?=?0.77) or nodal status (P?=?0.94). Patients in the GEO-derived Kaplan-Meier Plotter microarray dataset demonstrate the association of MST1R and poorer overall survival (n?=?1402, P?=?0.018) and RFS in patients receiving chemotherapy (n?=?798, P?=?0.041). Patients with high MST1R expression display worse overall survival (P?=?0.01) and receiver operator characteristic (ROC) analysis demonstrate the predictive capacity of increased MST1R with early death (P?=?0.0017) in IHC-stained samples. Paired IHC-stained breast tumor samples from the primary versus metastatic site show MST1R expression is associated with metastatic progression (P?=?0.032), and ROC analysis supports the predictive capacity of MST1R in metastatic progression (P?=?0.031). No associations of MST1R with estrogen receptor (ER), progesterone receptor (PR), both ER and PR, HER2 positivity, or triple-negativity were found (P?=?0.386, P?=?0.766, P?=?0.746, P?=?0.457, P?=?0.947, respectively). CONCLUSIONS:MST1R expression has prognostic value in breast cancer with respect to survival and metastatic progression. MST1R expression is not associated with tumor stage, nodal status, or subtype.

Project description:Background: Biomarkers predicting response to bevacizumab in breast cancer are still missing. Since epigenetic modifications can contribute to an aberrant regulation of angiogenesis and treatment resistance, we investigated the influence of DNA methylation patterns on bevacizumab efficacy. Methods: Genome-wide methylation profiling using the Illumina Infinium HumanMethylation450 BeadChip was performed in archival FFPE specimens of 36 patients with HER2-negative metastatic breast cancer treated with chemotherapy in combination with bevacizumab as first-line therapy (learning set). Based on objective response and progression-free survival (PFS) and considering ER expression, patients were divided in responders (R) and non-responders (NR). Significantly differentially methylated gene loci (CpGs) with a strong change in methylation levels (??>0.15 or ??<-0.15) between R and NR were identified and further investigated in 80 bevacizumab-treated breast cancer patients (optimization set) and in 15 patients treated with chemotherapy alone (control set) using targeted deep amplicon bisulfite sequencing. Methylated gene loci were considered predictive if there was a significant association with outcome (PFS) in the optimization set but not in the control set using Spearman rank correlation, Cox regression, and logrank test. Results: Differentially methylated loci in 48 genes were identified, allowing a good separation between R and NR (odds ratio (OR) 101, p<0.0001). Methylation of at least one cytosine in 26 gene-regions was significantly associated with progression-free survival (PFS) in the optimization set, but not in the control set. Using information from the optimization set, the panel was reduced to a 9-gene signature, which could divide patients from the learning set into 2 clusters, thereby predicting response with an OR of 40 (p<0.001) and an AUC of 0.91 (LOOCV). A further restricted 3-gene methylation model showed a significant association of predicted responders with longer PFS in the learning and optimization set even in multivariate analysis with an excellent and good separation of R and NR with AUC=0.94 and AUC=0.86, respectively. Conclusion: Both a 9-gene and 3-gene methylation signature can discriminate between R and NR to a bevacizumab-based therapy in MBC and could help identify patients deriving greater benefit from bevacizumab.

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Dataset Information

A Model of Dormant-Emergent Metastatic Breast Cancer Progression Enabling Exploration of Biomarker Signatures.

Publications

A Model of Dormant-Emergent Metastatic Breast Cancer Progression Enabling Exploration of Biomarker Signatures.

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