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A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

ABSTRACT: Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model "system" to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the system also exhibit the substantial genomic, transcriptional, and biological heterogeneity found in primary tumors. We show, using Trastuzumab (Herceptin) monotherapy as an example, that the system can be used to identify molecular features that predict or indicate response to targeted therapies or other physiological perturbations.

SUBMITTER: Neve RM

PROVIDER: S-EPMC2730521 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

Neve Richard M RM Chin Koei K Fridlyand Jane J Yeh Jennifer J Baehner Frederick L FL Fevr Tea T Clark Laura L Bayani Nora N Coppe Jean-Philippe JP Tong Frances F Speed Terry T Spellman Paul T PT DeVries Sandy S Lapuk Anna A Wang Nick J NJ Kuo Wen-Lin WL Stilwell Jackie L JL Pinkel Daniel D Albertson Donna G DG Waldman Frederic M FM McCormick Frank F Dickson Robert B RB Johnson Michael D MD Lippman Marc M Ethier Stephen S Gazdar Adi A Gray Joe W JW

Cancer cell 20061201 6

Recent studies suggest that thousands of genes may contribute to breast cancer pathophysiologies when deregulated by genomic or epigenomic events. Here, we describe a model "system" to appraise the functional contributions of these genes to breast cancer subsets. In general, the recurrent genomic and transcriptional characteristics of 51 breast cancer cell lines mirror those of 145 primary breast tumors, although some significant differences are documented. The cell lines that comprise the syste ...[more]

PMID: 17157791

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:IntroductionPrevious studies in mouse models and pilot epidemiology studies have demonstrated that inherited polymorphisms are associated with inherited risk of tumor progression and poor outcome in human breast cancer. To extend these studies and gain better understanding of the function of inherited polymorphism in breast cancer progression, a validation prognosis study was performed in a large independent breast cancer patient population.MethodsThe study population consisted of 1863 Dutch patients with operable primary breast cancer from Rotterdam, The Netherlands. Genomic DNA was genotyped for the missense Pro436Leu RRP1B single nucleotide polymorphism (SNP) rs9306160 and the intronic SIPA1 SNP rs2448490 by SNP-specific PCR.ResultsA significant association of variants in RRP1B with metastasis-free survival was observed (P = 0.012), validating the role of RRP1B with inherited metastatic susceptibility. Stratification of patients revealed that association with patients' survival was found to be specifically restricted to estrogen receptor positive, lymph node-negative (ER+/LN-) patients (P = 0.011). The specific association with metastasis-free survival only in ER+/LN- patients was replicated for SIPA1, a second metastasis susceptibility gene known to physically interact with RRP1B (P = 0.006). Combining the genotypes of these two genes resulted in the significant ability to discriminate patients with poor metastasis-free survival (HR: 0.40, 95% CI: 0.24 to 0.68, P = 0.001).ConclusionsThese results validate SIPA1 and RRP1B as metastasis susceptibility genes and suggest that genotyping assays may be a useful supplement to other clinical and molecular indicators of prognosis. The results also suggest that lymphatic and hematogeneous metastases are genetically distinct that may involve different mechanisms. If true, these results suggest that metastatic disease, like primary breast cancer, may be multiple diseases and that stratification of late stage patients may therefore be required to fully understand breast cancer progression and metastasis.

Dataset Information

A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

Publications

A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.

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