Unraveling the complexity of basal-like breast cancer

Marotta, Lauren L Campbell; Polyák, Kornélia

doi:10.18632/oncotarget.314

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…18 Stat3 has become a widely explored target for new drug development (reviewed in detail in refs. [45][46][47][48]. Agents targeting Stat3 include direct inhibitors of Stat3 at the SH2, DNA binding, N-terminal domains, or the upstream mediators of Stat3 activation.…”

Section: Discussionmentioning

confidence: 99%

Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers

Deng¹,

Wang²,

Deng³

et al. 2012

Cell Cycle

187

147

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A distinct group of breast cancers, called "basal" or "triple-negative" (TN) cancers express both basal cytokeratins and the epidermal growth factor receptor, but fail to express estrogen receptors, progesterone receptors or HER2 and have stem-like or mesenchymal features. They are particularly aggressive, are frequently chemo-resistant, with p53 mutation, up-regulation of IL-6 and Stat3. Because TN cells are particularly sensitive to the anti-diabetic agent metformin, we hypothesized that it may target JAK2/Stat3 signaling. The effects of metformin upon Stat3 expression and activation were examined in four human TN cell lines. Metformin's effects were also studied in sublines with forced over-expression of constitutively active (CA) Stat3, as well as lines with stable knockdown of Stat3. Metformin inhibited Stat3 activation (P-Stat3) at Tyr705 and Ser727 and downstream signaling in each of the four parental cell lines. CA-Stat3 transfection attenuated, whereas Stat3 knockdown enhanced, the effects of metformin upon growth inhibition and apoptosis induction. A Stat3 specific inhibitor acted synergistically with metformin in reducing cell growth and inducing apoptosis. An mTOR inhibitor showed no significant interaction with metformin. In summary, Stat3 is a critical regulator of metformin action in TN cancer cells, providing the potential for enhancing metformin's efficacy in the clinical setting.

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Section: Discussionmentioning

confidence: 99%

Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers

Deng¹,

Wang²,

Deng³

et al. 2012

Cell Cycle

187

147

View full text Add to dashboard Cite

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“…Global gene profiling has uncovered previously unrecognized subtype of human breast cancer, including the so-called “triple-negative” or “basal-like” tumors characterized by estrogen/progesterone receptor negativity, lack of HER2 amplification and high frequency of p53 mutation [ 1 – 6 ]. These refractory tumors are therefore insensitive to effective hormonal therapy or herceptin-based therapy and have a poor prognosis compared to other subtypes [ 1 , 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Fibroblast growth factor receptor 4 (FGFR4) and fibroblast growth factor 19 (FGF19) autocrine enhance breast cancer cells survival

et al. 2016

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Basal-like breast cancer is an aggressive tumor subtype with poor prognosis. The discovery of underlying mechanisms mediating tumor cell survival, and the development of novel agents to target these pathways, is a priority for patients with basal-like breast cancer. From a functional screen to identify key drivers of basal-like breast cancer cell growth, we identified fibroblast growth factor receptor 4 (FGFR4) as a potential mediator of cell survival. We found that FGFR4 mediates cancer cell survival predominantly via activation of PI3K/AKT. Importantly, a subset of basal-like breast cancer cells also secrete fibroblast growth factor 19 (FGF19), a canonical ligand specific for FGFR4. siRNA-mediated silencing of FGF19 or neutralization of extracellular FGF19 by anti-FGF19 antibody (1A6) decreases AKT phosphorylation, suppresses cancer cell growth and enhances doxorubicin sensitivity only in the FGFR4+/FGF19+ breast cancer cells. Consistently, FGFR4/FGF19 co-expression was also observed in 82 out of 287 (28.6%) primary breast tumors, and their expression is strongly associated with AKT phosphorylation, Ki-67 staining, higher tumor stage and basal-like phenotype. In summary, our results demonstrated the presence of an FGFR4/FGF19 autocrine signaling that mediates the survival of a subset of basal-like breast cancer cells and suggest that inactivation of this autocrine loop may potentially serve as a novel therapeutic intervention for future treatment of breast cancers.

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“…Almost all public cancer signatures including BRmet50 and PMID18271932Sig33 are derived from whole tumor samples. Intertumor and intratumor heterogeneity of whole tumors is evident in their histology, gene expression, genotype, and metastatic and proliferative potential [ 73 , 74 ]. The major subtype of whole primary tumors is ER-positive (ER+) (~60%).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value

et al. 2015

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BackgroundSystematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting.MethodsTo analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity.ResultsAmong 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer.ConclusionsWe have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1102-7) contains supplementary material, which is available to authorized users.

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Unraveling the complexity of basal-like breast cancer

Cited by 12 publications

References 7 publications

Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers

Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers

Fibroblast growth factor receptor 4 (FGFR4) and fibroblast growth factor 19 (FGF19) autocrine enhance breast cancer cells survival

Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value

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