FOXC1 identifies basal-like breast cancer in a hereditary breast cancer cohort

NF-κB signaling in therapy resistance of breast cancer: Mechanisms, approaches, and challenges

Breast cancer is the most common type of cancer and is the second leading cause of cancer-related mortality in women. Chemotherapy, targeted therapy, endocrine therapy, and radiotherapy are all effective in destroying tumor cells, but they also activate the defense and protection systems of cancer cells, leading to treatment resistance. Breast cancer is characterized by a highly inflammatory tumor microenvironment. The NF-κB pathway is essential for connecting inflammation and cancer, as well as for tumor growth and therapy resistance. An increase in NF-κB signaling boosts the growth potential of breast cancer cells and facilitates the spread of tumors to bone, lymph nodes, lungs, and liver. This review focuses on the mechanisms by which chemotherapy, targeted therapy, endocrine therapy, and radiotherapy induce breast cancer resistance through NF-κB signaling. Additionally, we investigate therapeutic regimens, including single agents or in combination with target inhibitors, plant extracts, nanomedicines, and miRNAs, that have been reported in clinical trials, in vivo, and in vitro to reverse resistance. In particular, NF-κB inhibitors combined with tamoxifen were shown to significantly increase the sensitivity of breast cancer cells to tamoxifen. Combination therapy of miRNA-34a with doxorubicin was also found to synergistically inhibit the progression of doxorubicin-resistant breast cancer by inhibiting Notch/NF-κB signaling.

Prediction of Benefit From Adjuvant Pertuzumab by 80-Gene Signature in the APHINITY (BIG 4-11) Trial

PURPOSE

At the primary analysis, the APHINITY trial reported a statistically significant but modest benefit of adding pertuzumab to standard adjuvant chemotherapy plus trastuzumab in patients with histologically confirmed human epidermal growth factor receptor 2 (HER2)-positive early-stage breast cancer. This study evaluated whether the 80-gene molecular subtyping signature (80-GS) could identify patients within the APHINITY population who derive the most benefit from dual anti-HER2 therapy.

METHODS

In a nested case-control study design of 1,023 patients (matched event to control ratio of 3:1), the 80-GS classified breast tumors into functional luminal type, HER2 type, or basal type. Additionally, 80-GS distinguished tumor subtypes that exhibited a single-dominant functional pathway versus tumors with multiple activated pathways. The primary end point was invasive disease-free survival (IDFS). Hazard ratios (HRs) were evaluated by Cox regression. After excluding patients without appropriate consent and those with missing data, 964 patients were included.

RESULTS

The 80-GS classified 50% (n = 479) of tumors as luminal type, 28% (n = 275) as HER2 type, and 22% (n = 209) as basal type. Most luminal-type tumors (86%) displayed a single-activated pathway, whereas 49% of HER2-type and 42% of basal-type tumors were dual activated. There was no significant difference in IDFS among different conventional 80-GS subtypes (single- and dual-activated subtypes combined). However, basal single-subtype tumors were significantly more likely to have an IDFS event (hazard ratio, 1.69 [95% CI, 1.12 to 2.54]) compared with other subtypes. HER2 single-subtype tumors displayed a trend toward greater beneficial effect on the addition of pertuzumab (hazard ratio, 0.56 [95% CI, 0.27 to 1.16]) compared with all other subtypes.

CONCLUSION

The 80-GS identified subgroups of histologically confirmed HER2-positive tumors with distinct biological characteristics. Basal single-subtype tumors exhibit an inferior prognosis compared with other subgroups and may be candidates for additional therapeutic strategies. Preliminary results suggest patients with HER2-positive, genomically HER2 single-subtype tumors may particularly benefit from added pertuzumab, which warrants further investigation.

FOXC1

CONTEXT.—

Few studies have investigated the features of FOXC1 protein expression in invasive breast cancer subtypes as defined by immunohistochemistry (IHC)-based surrogate molecular classification.

OBJECTIVE.—

To investigate the diagnostic utility of the IHC-based FOXC1 test in breast cancer subtyping and to evaluate the correlation between FOXC1 expression and clinicopathologic parameters in triple-negative breast cancer (TNBC).

DESIGN.—

FOXC1 expression was evaluated with IHC in a large cohort of 2443 patients with breast cancer. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of FOXC1 expression to predict the triple-negative phenotype and to identify the best cutoff value. FOXC1 expression was correlated with the clinicopathologic parameters of TNBC.

RESULTS.—

The expression rate of FOXC1 in TNBC was significantly higher than in other subtypes. The area under the ROC curve confirmed the high diagnostic value of FOXC1 for the prediction of the triple-negative phenotype. The cutoff value of 1% showed a maximized sum of sensitivity and specificity. In TNBC, FOXC1 expression was significantly associated with aggressive tumor phenotypes. Furthermore, FOXC1 expression was primarily observed in invasive breast carcinoma of no special type and metaplastic carcinoma but rarely in invasive carcinoma with apocrine differentiation. Correspondingly, FOXC1 expression was significantly associated with the expression of basal markers but was negatively correlated with apocrine-related markers in TNBC.

CONCLUSIONS.—

In conclusion, FOXC1 is a highly specific marker for the triple-negative phenotype. Moreover, immunohistochemical detection of FOXC1 expression can be used as an additional diagnostic tool for the triple-negative phenotype and subclassification in TNBC.

Therapeutically Targeting Cancers That Overexpress FOXC1: A Transcriptional Driver of Cell Plasticity, Partial EMT, and Cancer Metastasis

Metastasis accounts for more than 90% of cancer related mortality, thus the most pressing need in the field of oncology today is the ability to accurately predict future onset of metastatic disease, ideally at the time of initial diagnosis. As opposed to current practice, what would be desirable is that prognostic, biomarker-based detection of metastatic propensity and heightened risk of cancer recurrence be performed long before overt metastasis has set in. Without such timely information it will be impossible to formulate a rational therapeutic treatment plan to favorably alter the trajectory of disease progression. In order to help inform rational selection of targeted therapeutics, any recurrence/metastasis risk prediction strategy must occur with the paired identification of novel prognostic biomarkers and their underlying molecular regulatory mechanisms that help drive cancer recurrence/metastasis (i.e. recurrence biomarkers). Traditional clinical factors alone (such as TNM staging criteria) are no longer adequately prognostic for this purpose in the current molecular era. FOXC1 is a pivotal transcription factor that has been functionally implicated to drive cancer metastasis and has been demonstrated to be an independent predictor of heightened metastatic risk, at the time of initial diagnosis. In this review, we present our viewpoints on the master regulatory role that FOXC1 plays in mediating cancer stem cell traits that include cellular plasticity, partial EMT, treatment resistance, cancer invasion and cancer migration during cancer progression and metastasis. We also highlight potential therapeutic strategies to target cancers that are, or have evolved to become, “transcriptionally addicted” to FOXC1. The potential role of FOXC1 expression status in predicting the efficacy of these identified therapeutic approaches merits evaluation in clinical trials.

Expression of IFN-induced 2'-5'-oligoadenylate synthetases correlates with immune infiltration, revealing potential targets and new biomarkers for basal-like breast cancer prognosis

Triple-negative breast cancer has been classified as basal-like immune activated (BLIA), basal-like immune-suppressed (BLIS), and two other subtypes, suggesting potential immune therapeutic targets for basal-like breast cancer (BLBC). 2'-5'-Oligoadenylate synthetases (OASs), identified from differentially expressed genes (DEGs) between BLIA and BLIS breast cancers (GSE76124), are involved in antiviral activity induced by interferons. However, the association between the four OASs and prognosis or tumor-infiltrating immune cells (TIICs) remains unclear. Expression, survival data, and immune correlations for OASs in BLBC were assessed using bioinformatics tools. We found that OASs were highly expressed in BLIA breast cancer. Survival analysis suggested that high transcriptional levels of OASs were associated with better overall survival, relapse-free survival, and distant metastasis-free survival in patients with BLBC. Moreover, the prognostic value of OASs with respect to different clinicopathological factors, and especially according to lymph node metastasis, in patients with BLBC was further assessed. Our findings elucidated the expression, prognostic role, and effect of OASs in TIICs on BLBC, which might promote the development of OAS-targeted immunotherapy for BLBC.

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction

BACKGROUND

Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. However, most of these methods generally require a long processing time. In addition, information-theoretic methods discretize continuous features, which is a drawback that can lead to the loss of information.

RESULTS

In this paper, a novel supervised feature scoring method named ClearF is proposed. The proposed method is suitable for continuous-valued data, which is similar to the principle of feature selection using mutual information, with the added advantage of a reduced computation time. The proposed score calculation is motivated by the association between the reconstruction error and the information-theoretic measurement. Our method is based on class-wise low-dimensional embedding and the resulting reconstruction error. Given multi-class datasets such as a case-control study dataset, low-dimensional embedding is first applied to each class to obtain a compressed representation of the class, and also for the entire dataset. Reconstruction is then performed to calculate the error of each feature and the final score for each feature is defined in terms of the reconstruction errors. The correlation between the information theoretic measurement and the proposed method is demonstrated using a simulation. For performance validation, we compared the classification performance of the proposed method with those of various algorithms on benchmark datasets.

CONCLUSIONS

The proposed method showed higher accuracy and lower execution time than the other established methods. Moreover, an experiment was conducted on the TCGA breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer.

FOXC1 in cancer development and therapy: deciphering its emerging and divergent roles

Forkhead box C1 (FOXC1) is an essential member of the forkhead box transcription factors and has been highlighted as an important transcriptional regulator of crucial proteins associated with a wide variety of carcinomas. FOXC1 regulates tumor-associated genes and is regulated by multiple pathways that control its mRNA expression and protein activity. Aberrant FOXC1 expression is involved in diverse tumorigenic processes, such as abnormal cell proliferation, cancer stem cell maintenance, cancer migration, and angiogenesis. Herein, we review the correlation between the expression of FOXC1 and tumor behaviors. We also summarize the mechanisms of the regulation of FOXC1 expression and activity in physiological and pathological conditions. In particular, we focus on the pathological processes of cancer targeted by FOXC1 and discuss whether FOXC1 is good or detrimental during tumor progression. Moreover, FOXC1 is highlighted as a clinical biomarker for diagnosis or prognosis in various human cancers. The information reviewed here should assist in experimental designs and emphasize the potential of FOXC1 as a therapeutic target for cancer.

Analysis of breast cancer subtypes by AP-ISA biclustering

BACKGROUND

Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear.

RESULTS

In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies.

CONCLUSIONS

Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study.

Identification of EGF-NF-κB-FOXC1 signaling axis in basal-like breast cancer

Background

The pathogenesis of human basal-like breast cancer (BLBC) is not well understood and patients with BLBC have a poor prognosis. Expression of the epidermal growth factor receptor (EGFR) and nuclear factor-κB (NF-κB) is well-known to be upregulated in BLBC. The forkhead box C1 (FOXC1) transcription factor, an important prognostic biomarker specific for BLBC, has been shown to be induced by EGF and is critical for EGF effects in breast cancer cells. How FOXC1 is transcriptionally activated in BLBC is not clear.

Methods

Luciferase reporter assays were performed to show that NF-κB-p65 enhances FOXC1 promoter activity in BLBC cells (MDA-MB-468). Electrophoretic mobility shift assay, biotinylated oligonucleotide precipitation assay, and chromatin immunoprecipitation assay were used to show that NF-κB interacts and binds to the promoter region of FOXC1.

Results

In this study, we demonstrate that NF-κB is a pivotal mediator of the EGF/EGFR regulation of FOXC1 expression by binding to the FOXC1 promoter to activate FOXC1 transcription. Loss or inhibition of NF-κB diminished FOXC1 expression.

Conclusion

Collectively, our findings reveal a novel EGFR-NF-κB-FOXC1 signaling axis that is critical for BLBC cell function, supporting the notion that intervention in the FOXC1 pathway may provide potential modalities for BLBC treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12964-017-0180-3) contains supplementary material, which is available to authorized users.