Background: CTCs offer a relatively non-invasive source of metastatic tissue for molecular analysis. To elucidate the underlying biology of blood-borne metastasis, we profiled CTCs from MBC patients (pts). Methods: CTCs were isolated by IE/FACS (immunomagnetic enrichment/fluorescence-activated cell sorting). Expression of 64 cancer-related genes in CTCs was analyzed via microfluidic-based multiplex QPCR. Genome-wide copy number (CN) analysis by array comparative genomic hybridization (ACGH) was performed on CTCs isolated from the same tumor-enriched blood samples. The Illumina platform was utilized for next generation sequencing and data was analyzed using NantOmics analysis pipeline and Nexus 8.0 software. Mutations were confirmed by Sanger sequencing or by digital droplet PCR. Results: Expression profiles of CTCs from 105 MBC pts clustered away from those of blood, indicating high-purity isolation of CTCs by IE/FACS. In addition to EPCAM, tumor-related genes, e.g., CCND1, MUC1, and TTF3 were upregulated in CTCs. Approximately 70% of the CTC samples were considered ER-positive, of which 47% were ER+HER2+, and 22% ER+HER2-. Among the ER+HER2- samples, about two-thirds (68%) had low proliferative (MKI67) status. HER2-positive and triple-negative CTCs accounted for 27% and 30% of the samples, respectively. Furthermore, 30% of the samples were assigned to luminal A, 6% to luminal B, 13% to Her2-enriched, 33% to basal-like, and 12% to normal-like subtypes. Expression profiling of CTCs in 74 serial blood samples from 28 pts showed fluctuations in expression at the gene-level, while subtype calls were mostly consistent across time points. CTCs from 49 of the 105 pts analyzed by ACGH revealed numerous genomic aberrations such as 1q/8q gains and 8p/16q losses, consistent with breast cancer origin. CN profiles grouped into three major clusters: CTCs exhibiting low genomic instability, 8q gain, and 1q gain/11q loss. ERBB2 and CCND1 were upregulated in CTCs showing increased genomic alterations. Changes in ER (n=102) and HER2 (n=130) status between CTCs and matched primary tumors (PT) were observed in 27% and 23% of the pts, respectively, indicating that biomarker status may change during disease progression. Comparative analysis of CN data from low-pass whole genome sequencing (WGS) of CTCs vs. matched PTs (n=7 pairs) demonstrated clonal-relatedness as well as some genetic divergence. WGS (38x) and whole exome sequencing (140x) analysis of CTCs from an index pt diagnosed with invasive lobular carcinoma detected numerous genomic aberrations, including a copy loss and a frameshift mutation in E-cadherin (CDH1). Interestingly, analysis of CN and mutation data revealed that CTCs were more closely related to the lymph node metastases than to the PT. Single-cell sequencing of CTCs revealed uniformity in genome-wide CN alterations, while cell-to-cell heterogeneity was observed only when single-cell expression profiles were analyzed. Conclusions: Comprehensive molecular characterization provided novel insights into the biology of breast CTCs. Further CTC profiling may open avenues for discovery and development of novel biomarkers for personalized medicine and strategies to prevent metastasis. Citation Format: Magbanua MJ, Hauranieh L, Roy R, Wolf D, Benz S, Vaske C, Pendyala P, Sosa E, Scott J, Lee JS, Ordonez A, Ho B, Solanki T, VantVeer L, Rugo H, Park J. Comprehensive genomic characterization of circulating tumor cells (CTCs) in metastatic breast cancer (MBC) sheds light on the biology of blood-borne metastasis [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-01-04.
Cancer treatments act on a population of cells, each of which may experience different individual responses to treatment. Such differential response will result in resistance to treatment even if a majority of cancerous cells are eliminated. To examine differential cell response, we simultaneously profiled the gene expression and mutation spectrum of individual cells from the MDAMB231 cell line using next generation sequencing of isolated RNA. A total of 23 transcriptomes were characterized from paclitaxel-treated and paclitaxel-surviving cells. We found significant different changes in mutation rates between paclitaxel treated cells, with a dose-dependent increase in single nucleotide changes in RNA in paclitaxel-treated cells. Cells undergoing exposure to paclitaxel also showed higher pathway activity in SRC, as well as an integrin switch from ITGB1 to ITGB3. In contrast, cells that survived a high dose of paclitaxel showed an insignificant number of single nucleotide changes, suggesting that these cells either evaded initial paclitaxel exposure or were better able to repair the effects of paclitaxel exposure. Despite the RNA sequence similarity between surviving and untreated cells, there were changes in gene expression and pathway activities including higher PI3K activity. Paclitaxel-surviving cells also showed activation of pathways associated with higher proliferation. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-06-05.
Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms behind why particular patients respond differently to treatment is not well understood. Genomics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) attempt to address this issue by collecting large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Common analysis of these measurements integrates data across multiple samples to distinguish signal from noise. However, serious challenges remain in identifying genomic features and pathways significant for prognosis and clinical treatment classifications. We have created the Five3 Analysis Pipeline to streamline discovery of individual samples’ mutations, small indels, copy number alterations, genome rearrangements, expression changes, and resulting pathway activities. This pipeline is capable of processing and integrating data from both next generation sequencing and microarray platforms in the analysis of single or multiple tumor samples. Our sequence analysis corrects for both tumor sample impurity and germline variation to accurately identify somatic mutations present in the tumor. Our pathway analysis incorporates gene copy number, mutations, expression, and promoter methylation on a superimposed pathway constructed from several curated pathway databases in a sample-specific manner. By applying this pipeline to the TCGA breast cancer datasets, we recapitulate established breast subtypes at a pathway-dependent level. For example, basal tumors appear enriched for proliferation pathways compared to luminal samples within this cohort. Expanding the pathway analysis to include TCGA lung cancer samples, we find similar subnetworks activated between basal and squamous lung and between luminal and lung adenocarcinomas. This hints at similar genomic mechanisms for these subtypes independent of tissue of origin. Finally, by analyzing genomic alterations across all breast cancers we see mutational clusters in PIK3CA that correspond with publicly-available hotspots [1]. As suggested by previous reports [2], we find that samples with mutations clustered in exon 10 exhibit differential pathway activities relative to those samples with mutations clustered in exon 21, independent of subtype and TP53 mutation status. These results show the power of this integrated genomic platform in elucidating pathway signatures and the need to consider cross cancer analyses to identify shared tumorigenic mechanisms that may suggest common therapeutic targets. [1] Forbes, S.A et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucl. Acids Res. (2011) 39: D945-D950 [2] Vasudevan KM et al. AKT-independent signaling downstream of oncogenic PIK3CA mutations in human cancer. Cancer Cell 2009 Jul.;16(1):21–32. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-06-07.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.