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Functional viability profiles of breast cancer.

ABSTRACT: UNLABELLED:The design of targeted therapeutic strategies for cancer has largely been driven by the identification of tumor-specific genetic changes. However, the large number of genetic alterations present in tumor cells means that it is difficult to discriminate between genes that are critical for maintaining the disease state and those that are merely coincidental. Even when critical genes can be identified, directly targeting these is often challenging, meaning that alternative strategies such as exploiting synthetic lethality may be beneficial. To address these issues, we have carried out a functional genetic screen in >30 commonly used models of breast cancer to identify genes critical to the growth of specific breast cancer subtypes. In particular, we describe potential new therapeutic targets for PTEN-mutated cancers and for estrogen receptor-positive breast cancers. We also show that large-scale functional profiling allows the classification of breast cancers into subgroups distinct from established subtypes. SIGNIFICANCE:Despite the wealth of molecular profiling data that describe breast tumors and breast tumor cell models, our understanding of the fundamental genetic dependencies in this disease is relatively poor. Using high-throughput RNA interference screening of a series of pharmacologically tractable genes, we have generated comprehensive functional viability profiles for a wide panel of commonly used breast tumor cell models. Analysis of these profiles identifies a series of novel genetic dependencies, including that of PTEN-null breast tumor cells upon mitotic checkpoint kinases, and provides a framework upon which additional dependencies and candidate therapeutic targets may be identified.

SUBMITTER: Brough R

PROVIDER: S-EPMC3188379 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Functional viability profiles of breast cancer.

Brough Rachel R Frankum Jessica R JR Sims David D Mackay Alan A Mendes-Pereira Ana M AM Bajrami Ilirjana I Costa-Cabral Sara S Rafiq Rumana R Ahmad Amar S AS Cerone Maria Antonietta MA Natrajan Rachael R Sharpe Rachel R Shiu Kai-Keen KK Wetterskog Daniel D Dedes Konstantine J KJ Lambros Maryou B MB Rawjee Teeara T Linardopoulos Spiros S Reis-Filho Jorge S JS Turner Nicholas C NC Lord Christopher J CJ Ashworth Alan A

Cancer discovery 20110802 3

<h4>Unlabelled</h4>The design of targeted therapeutic strategies for cancer has largely been driven by the identification of tumor-specific genetic changes. However, the large number of genetic alterations present in tumor cells means that it is difficult to discriminate between genes that are critical for maintaining the disease state and those that are merely coincidental. Even when critical genes can be identified, directly targeting these is often challenging, meaning that alternative strate ...[more]

PMID: 21984977

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Project description:The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

Project description:BackgroundBreast cancer accounts for nearly a quarter of all cancers in women worldwide, and more than 90% of women diagnosed with breast cancer undergo mastectomy or breast-conserving surgery. Retrospective clinical studies have suggested that use of regional anesthesia leads to improved patient outcomes. Laboratory studies have reported that breast cancer cells are inhibited by some local anesthetics at millimolar concentration. Here, we present a comprehensive analysis of the effects of six common local anesthetics on two human breast cancer cell lines. We used concentrations ranging from those corresponding to plasma levels during regional block by local anesthetic (plasma concentration) to those corresponding to direct infiltration of local anesthetic.MethodsHuman breast cancer cell lines, MDA-MB-231 and MCF7, were incubated with each of six local anesthetics (lidocaine, mepivacaine, ropivacaine, bupivacaine, levobupivacaine, and chloroprocaine) (10 μM ~ 10 mM) for 6 to 72 h. Assays for cell viability, cytotoxicity, migration, and cell cycle were performed.ResultsHigh concentrations (> 1 mM) of local anesthetics applied to either MDA-MB-231 or MCF7 cells for 48 h significantly inhibited cell viability and induced cytotoxicity. At plasma concentrations (~ 10 μM) for 72 h, none of the local anesthetics affected cell viability or migration in either cell line. However, at 10 × plasma concentrations, 72-h exposure to bupivacaine, levobupivacaine or chloroprocaine inhibited the viability of MDA-MB-231 cells by > 40% (p < 0.001). Levobupivacaine also inhibited the viability of MCF7 cells by 50% (p < 0.001). None of the local anesthetics affected the viability of a non-cancerous breast cell line, MCF10A. MDA-MB-231 cell migration was inhibited by 10 × plasma concentrations of levobupivacaine, ropivacaine or chloroprocaine and MCF7 cell migration was inhibited by mepivacaine and levobupivacaine (p < 0.05). Cell cycle analysis showed that the local anesthetics arrest MDA-MB-231 cells in the S phase at both 1 × and 10 × plasma concentrations.ConclusionsLocal anesthetics at high concentrations significantly inhibited breast cancer cell survival. At 10 × plasma concentrations, the effect of local anesthetics on cancer cell viability and migration depended on the exposure time, specific local anesthetic, specific measurement endpoint and specific cell line.

Project description:Importance:Higher overall leukocyte counts in women may be associated with increased risk of breast cancer, but the association of specific leukocyte subtypes with breast cancer risk remains unknown. Objective:To determine associations between circulating leukocyte subtypes and risk of breast cancer. Design, Setting, and Participants:Between 2003 and 2009, the Sister Study enrolled 50 884 women who had a sister previously diagnosed with breast cancer but were themselves breast cancer free. A case-cohort subsample was selected in July 2014 from the full Sister Study cohort. Blood samples were obtained at baseline, and women were followed up through October 2016. Data analysis was performed in April 2019. Main Outcomes and Measures:The main outcome was the development of breast cancer in women. Whole-blood DNA methylation was measured, and methylation values were deconvoluted using the Houseman method to estimate proportions of 6 leukocyte subtypes (B cells, natural killer cells, CD8+ and CD4+ T cells, monocytes, and granulocytes). Leukocyte subtype proportions were dichotomized at their population median value, and Cox proportional hazard models were used to estimate associations with breast cancer. Results:Among 2774 non-Hispanic white women included in the analysis (mean [SD] age at enrollment, 56.6 [8.8] years), 1295 women were randomly selected from the full cohort (of whom 91 developed breast cancer) along with an additional 1479 women who developed breast cancer during follow-up (mean [SD] time to diagnosis, 3.9 [2.2] years). Circulating proportions of B cells were positively associated with later breast cancer (hazard ratio [HR], 1.17; 95% CI, 1.01-1.36; P = .04). Among women who were premenopausal at blood collection, the association between B cells and breast cancer was significant (HR, 1.38; 95% CI, 1.05-1.82; P = .02), and an inverse association for circulating proportions of monocytes was found (HR, 0.75; 95% CI, 0.57-0.99; P = .05). Among all women, associations between leukocyte subtypes and breast cancer were time dependent: higher monocyte proportions were associated with decreased near-term risk (within 1 year of blood collection, HR, 0.62; 95% CI, 0.43-0.89; P = .01), whereas higher B cell proportions were associated with increased risk 4 or more years after blood collection (HR, 1.38; 95% CI, 1.15-1.67; P = .001). Conclusions and Relevance:Circulating leukocyte profiles may be altered before clinical diagnoses of breast cancer and may be time-dependent markers for breast cancer risk, particularly among premenopausal women.

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Functional viability profiles of breast cancer.

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Functional viability profiles of breast cancer.

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