Predict Breast Cancer Metastasis Research Articles

Value of the NF-κB signalling pathway and the DNA repair gene PARP1 in predicting distant metastasis after breast cancer surgery

The DNA repair gene PARP1 and NF-κB signalling pathway affect the metastasis of breast cancer by influencing the drug resistance of cancer cells. Therefore, this study focused on the value of the DNA repair gene PARP1 and NF-κB pathway proteins in predicting the postoperative metastasis of breast cancer. A nested case‒control study was performed. Immunohistochemical methods were used to detect the expression of these genes in patients. ROC curves were used to analyse the predictive effect of these factors on distant metastasis. The COX model was used to evaluate the effects of PARP1 and TNF-α on distant metastasis. The results showed that the expression levels of PARP1, IKKβ, p50, p65 and TNF-α were significantly increased in the metastasis group (P < 0.001). PARP1 was correlated with IKKβ, p50, p65 and TNF-α proteins (P < 0.001). There was a correlation between IKKβ, p50, p65 and TNF-α proteins (P < 0.001). ROC curve analysis showed that immunohistochemical scores for PARP1 of > 6, IKKβ of > 4, p65 of > 4, p50 of > 2, and TNF-α of > 4 had value in predicting distant metastasis (SePARP1 = 78.35%, SpPARP1 = 79.38%, AUCPARP1 = 0.843; Sep50 = 64.95%, Spp50 = 70.10%, AUCp50 = 0.709; SeTNF-α = 60.82%, SpTNF-α = 69.07%, AUCTNF-α = 0.6884). Cox regression analysis showed that high expression levels of PARP1 and TNF-α were a risk factor for distant metastasis after breast cancer surgery (RRPARP1 = 4.092, 95% CI 2.475–6.766, P < 0.001; RRTNF-α = 1.825, 95% CI 1.189–2.799, P = 0.006). Taken together, PARP1 > 6, p50 > 2, and TNF-α > 4 have a certain value in predicting breast cancer metastasis, and the predictive value is better when they are combined for diagnosis (Secombine = 97.94%, Spcombine = 71.13%).

An Explainable AI Approach for Breast Cancer Metastasis Prediction Based on Clinicopathological Data.

Breast Cancer is the most prevalent cancer and the first cause of cancer deaths among women worldwide. In 90% of the cases, mortality is related to distant metastasis. Computer-aided prognosis systems using machine learning models have been widely used to predict breast cancer metastasis. Despite that, these systems still face several challenges. First, the models are generally biased toward the majority class due to datasets unbalance. Second, their increased complexity is associated with decreased interpretability which causes clinicians to distrust their prognosis. To tackle these issues, we have proposed an explainable approach for predicting breast cancer metastasis using clinicopathological data. Our approach is based on cost-sensitive CatBoost classifier and utilises LIME explainer to provide patient-level explanations. We used a public dataset of 716 breast cancer patients to assess our approach. The results demonstrate the superiority of cost-sensitive CatBoost in precision (76.5%), recall (79.5%), and f1-score (77%) over classical and boosting models. The LIME explainer was used to quantify the impact of patient and treatment characteristics on breast cancer metastasis, revealing that they have different impacts ranging from high impact like the non-use of adjuvant chemotherapy, and moderate impact including carcinoma with medullary features histological type, to low impact like oral contraception use. The code is available at https://github.com/IkramMaouche/CS-CatBoost Conclusion: Our approach serves as a first step toward introducing more efficient and explainable computer-aided prognosis systems for breast cancer metastasis prediction. This approach could help clinicians understand the factors behind metastasis and assist them in proposing more patient-specific therapeutic decisions.

Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data

Breast cancer has the highest incidence and death rate among women; moreover, its metastasis to other organs increases the mortality rate. Since several studies have reported gene expression and cancer prognosis to be related, the study of breast cancer metastasis using gene expression is crucial. To this end, a novel deep neural network architecture, deep learning-based cancer metastasis estimator (DeepCME), is proposed in this paper for predicting breast cancer metastasis. However, the problem of overfitting occurs frequently while training deep learning models using gene expression data because they contain a large number of genes and the sample size is rather small. To address overfitting, several regularization methods are implemented, such as L1 penalty, batch normalization, and dropout. To demonstrate the superior performance of our model, area under curve (AUC) scores are evaluated and then compared with five baseline models: logistic regression, support vector classifier (SVC), random forest, decision tree, and k-nearest neighbor. Considering results, DeepCME demonstrates the highest average AUC scores in most cross-validation cases, and the average AUC score of DeepCME is 0.754, which is approximately 12.9% higher than SVC, the second-best model. In addition, the 30 most significant genes related to breast cancer metastasis are identified based on DeepCME results and some are discussed in further detail considering the reports from some previous medical studies. Considering the high expense involved in measuring the expression of a single gene, the ability to develop the cost-effective and time-efficient tests using only a few key genes is valuable. Based on this study, we expect DeepCME to be utilized clinically for predicting breast cancer metastasis and be applied to other types of cancer as well after further research.

Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis

Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer metastasis to study the overfitting of deep feedforward neural networks (FNNs) prediction models. We studied how each hyperparameter and some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied were activate function, weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch size, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs.

A Robust Personalized Classification Method for Breast Cancer Metastasis Prediction.

Accurate prediction of breast cancer metastasis in the early stages of cancer diagnosis is crucial to reduce cancer-related deaths. With the availability of gene expression datasets, many machine-learning models have been proposed to predict breast cancer metastasis using thousands of genes simultaneously. However, the prediction accuracy of the models using gene expression often suffers from the diverse molecular characteristics across different datasets. Additionally, breast cancer is known to have many subtypes, which hinders the performance of the models aimed at all subtypes. To overcome the heterogeneous nature of breast cancer, we propose a method to obtain personalized classifiers that are trained on subsets of patients selected using the similarities between training and testing patients. Results on multiple independent datasets showed that our proposed approach significantly improved prediction accuracy compared to the models trained on the complete training dataset and models trained on specific cancer subtypes. Our results also showed that personalized classifiers trained on positively and negatively correlated patients outperformed classifiers trained only on positively correlated patients, highlighting the importance of selecting proper patient subsets for constructing personalized classifiers. Additionally, our proposed approach obtained more robust features than the other models and identified different features for different patients, making it a promising tool for designing personalized medicine for cancer patients.

A Novel Necroptosis-Related miRNA Signature for Predicting the Prognosis of Breast Cancer Metastasis.

Objective Necroptosis was recently identified as a form of programmed cell death that plays an essential role in breast cancer metastasis. MicroRNAs (miRNAs) have long been recognized to affect cell death and tumor growth. In this study, we aimed to screen for necroptosis-associated miRNAs that predict breast cancer metastasis. Method This study used The Cancer Genome Atlas (TCGA) public database to obtain miRNA expression data and associated clinical data from breast cancer patients and then retrieved miRNA data related to necrosis and apoptosis. Next, using Cox regression model analysis (univariate or multivariate) as well as a comparison analysis (differential analysis), a prognostic multi-miRNA molecular marker was established. Finally, prognosis-related miRNAs were utilized to identify target genes, and the functions of the target genes were analyzed for enrichment to investigate the probable mechanisms of the miRNAs. Results Ten miRNAs were screened through differential analysis to build models: hsa-miR-148a-3p, hsa-miR-223-3p, hsa-miR-331-3p, has-miR-181a-5p, hsa-miR-181b-5p, hsa-miR-181c-5p, hsa-miR-181d-5p, hsa-miR-200a-5p, hsa-miR-141-3p, and hsa-miR-425-5p. The multivariate Cox regression model was an independent prognostic factor (univariate Cox regression results: HR = 3.2642, 95%CI = 1.5773 − 6.7554, P = 0.0014; multivariate Cox regression results: HR = 3.1578, 95%CI = 1.5083 − 6, P = 0.0023). The survival curve of the risk score also revealed that patients with a high risk score had a poor prognosis (P = 2e − 04). The receiver operating characteristic (ROC) curve showed that the model has a certain prediction ability. Batch survival analysis of the miRNAs in the model was conducted and showed that hsa-miR-331-3p (P = 0.0182) was strongly associated with prognosis. Twenty-three predicted target genes were obtained, and Gene Ontology (GO) enrichment analysis showed that these target genes were strongly enriched in transcriptional initiation and cell membrane trafficking. Conclusion Our research identified a novel miRNA marker for predicting breast cancer patient prognosis and lays the groundwork for future research on necroptosis-related genes.

ABI1-based expression signature predicts breast cancer metastasis and survival.

Despite the current standard of care, breast cancer remains one of the leading causes of mortality in women worldwide, thus emphasizing the need for better predictive and therapeutic targets. ABI1 is associated with poor survival and an aggressive breast cancer phenotype, although its role in tumorigenesis, metastasis, and the disease outcome remains to be elucidated. Here, we define the ABI1‐based seven‐gene prognostic signature that predicts survival of metastatic breast cancer patients; ABI1 is an essential component of the signature. Genetic disruption of Abi1 in primary breast cancer tumors of PyMT mice led to significant reduction of the number and size of lung metastases in a gene dose‐dependent manner. The disruption of Abi1 resulted in deregulation of the WAVE complex at the mRNA and protein levels in mouse tumors. In conclusion, ABI1 is a prognostic metastatic biomarker in breast cancer. We demonstrate, for the first time, that lung metastasis is associated with an Abi1 gene dose and specific gene expression aberrations in primary breast cancer tumors. These results indicate that targeting ABI1 may provide a therapeutic advantage in breast cancer patients.

Aberrant Methylation of 21 MicroRNA Genes in Breast Cancer: Sets of Genes Associated with Progression and a System of Markers for Predicting Metastasis.

Systemic analysis of the relationship between the levels of methylation of 21 microRNA genes and the parameters of breast cancer progression was performed on a representative sample of 91 paired specimens of breast cancer and histologically normal tissues and a system of markers for prediction of metastasis was proposed. A significant association of hypermethylation of 11 genes with late (III-IV) clinical stages was found, and for 6 genes (MIR124-1, MIR127, MIR34B/C, MIR9-3, MIR1258, and MIR339) this association was highly significant (p≤0.001, FDR=0.01). For MIR9-3 and MIR339, an association with tumor size was demonstrated (p<0.001, FDR=0.01). No association of the levels of methylation of the analyzed microRNA genes with the degree of differentiation were found. An association with lymph node metastasis was established for 9 microRNA genes; the most significant association was shown for 6 genes MIR125B-1, MIR127, MIR9-3, MIR339, MIR124-3, and MIR1258 (p<0.005, FDR=0.05). Based on these 6 genes, a marker system for predicting breast cancer metastasis was developed by ROC analysis. This system is characterized by 87% sensitivity and 77% specificity (AUC=0.894). The proposed system may have clinical application in the personalized treatment of breast cancer patients.

Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes

Breast cancer metastasis can have a fatal outcome, with the prediction of metastasis being critical for establishing effective treatment strategies. RNA-sequencing (RNA-seq) is a good tool for identifying genes that promote and support metastasis development. The hub gene analysis method is a bioinformatics method that can effectively analyze RNA sequencing results. This can be used to specify the set of genes most relevant to the function of the cell involved in metastasis. Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used for the selection of gene modules and hub genes (function in mitochondrial metabolism). A machine learning prediction model using the hub gene set was devised and its accuracy was evaluated. A prediction model comprising 54-functional-gene modules and the hub gene set (NDUFA9, NDUFB5, and NDUFB3) showed an accuracy of 0.769 ± 0.02, 0.782 ± 0.012, and 0.945 ± 0.016, respectively. The test accuracy of the hub gene set was over 93% and that of the prediction model with random forest and hub genes was over 91%. A breast cancer metastasis dataset from The Cancer Genome Atlas was used for external validation, showing an accuracy of over 91%. The hub gene assay can be used to predict breast cancer metastasis by machine learning.

Correlation-centred variable selection of a gene expression signature to predict breast cancer metastasis

Predictions of distant cancer metastasis based on gene signatures are studied intensively to realise precise diagnosis and treatments. Gene selection i.e. feature selection is a cornerstone to both establish accurate predictions and understand underlying pathologies. Here, we developed a simple but robust feature selection method using a correlation-centred approach to select minimal gene sets that have both high predictive and generalisation abilities. A multiple logistic regression model was used to predict 5-year metastases of patients with breast cancer. Gene expression data obtained from tumour samples of lymph node-negative breast cancer patients were randomly split into training and validation data. Our method selected 12 genes using training data and this showed a higher area under the receiver operating characteristic curve of 0.730 compared with 0.579 yielded by previously reported 76 genes. The signature with the predictive model was validated in an independent dataset, and its higher generalization ability was observed. Gene ontology analyses revealed that our method consistently selected genes with identical functions which frequently selected by the 76 genes. Taken together, our method identifies fewer gene sets bearing high predictive abilities, which would be versatile and applicable to predict other factors such as the outcomes of medical treatments and prognoses of other cancer types.

Comparative evaluation of network features for the prediction of breast cancer metastasis

BackgroundDiscovering a highly accurate and robust gene signature for the prediction of breast cancer metastasis from gene expression profiling of primary tumors is one of the most challenging tasks to reduce the number of deaths in women. Due to the limited success of gene-based features in achieving satisfactory prediction accuracy, many methodologies have been proposed in recent years to develop network-based features by integrating network information with gene expression. However, evaluation results are inconsistent to confirm the effectiveness of network-based features, because of many confounding factors involved in classification model learning process, such as data normalization, dimension reduction, and feature selection. An unbiased comparative evaluation is essential for uncovering the strength of network-based features.MethodsIn this study, we compared several types of network-based features obtained using different mathematical operators (Mean, Maximum, Minimum, Median, Variance) on geneset (i.e., a gene and its’ neighbors in the network) in protein-protein interaction network and gene co-expression network for their ability in predicting breast cancer metastasis using gene expression data from more than 10 patient cohorts.ResultsWhile network-based features are usually statistically more significant than gene-based feature, a consistent improvement of prediction performance using network-based features requires a substantial number of patients in the dataset. In contrary to many previous reports, no evidence was found to support the robustness of network-based features and we argue some of the robustness may be due to the inherent bias associated with node degree in the network. In addition, different types of network features seem to cover different pathways and are complementary to each other. Consequently, an ensemble classifier combining different network features was proposed and was found to significantly outperform classifiers based on gene-based feature or any single type of network-based features.ConclusionsNetwork-based features and their combination show promise for improving the prediction of breast cancer metastasis but may require a large amount of training data. Robustness claim of network-based features needs to be re-examined with network node degree and other confounding factors in consideration.

Abstract 2783: OMICS analysis of breast cancer PDX tumors to determine CTC-cluster-specific signature in predicting breast cancer metastasis

Abstract Circulating tumor cell clusters (CTCcl) have been shown to have a significantly higher metastatic potential compared to single CTCs and to predict long-term outcomes in breast cancer patients. A characterization of primary tumors that give rise to CTCcl hold significant promises for better diagnosis and may uncover targets for prevention and treatment of metastatic breast cancer. In our study, we utilized the available transcriptomic and proteomic data (RNA-Seq and RPPA) of 10 triple-negative breast cancer patient-derived xenograft (TNBC-PDX) transplantable models. The sample set consisted of 6 CTCcl-negative (-) and 4 CTCcl-positive (+) models. Genome-wide transcriptomic analysis revealed 549 differentially expressed genes in CTCcl+ TNBC-PDX models vs. CTCcl- models (316 up-regulated and 233 down-regulated, p&amp;lt;0.05). Gene set enrichment analysis (GSEA) terms significantly enriched (FDR&amp;lt;0.05) in CTCcl+ TNBC-PDXs included Cytoplasmic Ribosomal Proteins (NES=2.53), Angiogenesis (NES=1.95), Type II Interferon signaling (NES=-2.52), Antigen processing and presentation (NES=-2.38), TNF signaling pathway (NES=-2.04), Jak-STAT signaling pathway (NES=-1.91), and apoptosis (NES=-1.69). The proteomic analysis revealed elevated levels of Bcl2 expression in PDX tumors associated with CTCcl positivity, which substantiated the inhibited apoptosis pathway as seen in GSEA analysis. The increase in Bcl2 was also validated independently by IHC staining in primary PDX tumors that were CTCcl+. The distribution of EMT score based on the inferential EMT metric using canonical epithelial and mesenchymal markers was not significantly different between CTCcl+ and CTCcl- TNBC-PDX tumors suggesting no differential regulation of EMT in tumors that give rise to clusters versus only single cells. In summary, our results suggest that primary tumors with active anti-apoptotic and/or survival pathways may promote CTC clusters formation and increase the risk of distant metastasis. Citation Format: Hariprasad Thangavel, Carmine De Angelis, Suhas Vasaikar, Raksha Bhat, Mohit Kumar Jolly, Lacey Elizabeth Dobrolecki, Jason T. George, Mario Giuliano, Michael Lewis, Herbert Levine, Bing Zhang, Rachel Schiff, Meghana V. Trivedi. OMICS analysis of breast cancer PDX tumors to determine CTC-cluster-specific signature in predicting breast cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2783.

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies

BackgroundApproximately 10%–15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and serum tests. However, evaluation of a combination of clinicopathological features may offer a more comprehensive overview for breast cancer prognosis. Materials and methodsWe evaluated serum human epidermal growth factor receptor 2 (sHER2) as part of a combination of clinicopathological features used to predict breast cancer metastasis using machine learning algorithms, namely random forest, support vector machine, logistic regression, and Bayesian classification algorithms. The sample cohort comprised 302 patients who were diagnosed with and treated for breast cancer and received at least one sHER2 test at Chang Gung Memorial Hospital at Linkou between 2003 and 2016. ResultsThe random-forest-based model was determined to be the optimal model to predict breast cancer metastasis at least 3 months in advance; the correspondingarea under the receiver operating characteristic curve value was 0. 75 (p < 0. 001). ConclusionThe random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system and may lead to early detection of recurrence, early treatment, and more favorable outcomes.

Protein S100-A8: A potential metastasis-associated protein for breast cancer determined via iTRAQ quantitative proteomic and clinicopathological analysis.

Breast cancer is the most common malignancy in females, with metastasis of this type of cancer frequently proving lethal. However, there are still no effective biomarkers to predict breast cancer metastasis. The aim of the present study was, therefore, to analyze breast cancer metastasis-associated proteins and evaluate the association between protein S100-A8 and the prognosis of breast cancer. The isobaric tags for relative and absolute quantitation (iTRAQ) proteomic technique was used to analyze the differential expression of proteins between fresh primary breast tumor (PBT) tissue and fresh paired metastatic lymph nodes (PMLN) tissue. Subsequently, immunohistochemical staining was used to locate and assess the expression of protein S100-A8 in benign breast disease (n=15), primary breast cancer with (n=109) or without (n=83) metastasis, and in paired metastatic lymph nodes (n=109) formalin fixed paraffin embedded (FFPE) tissue. Staining scores were evaluated and the association between protein S100-A8 expression levels and the clinicopathological characteristics of 192 patients with breast cancer were evaluated using the χ2 test. Kaplan-Meier and Cox hazards regression analyses were utilized to investigate the association between the expression of protein S100-A8 and the prognosis of patients with breast cancer. A total of 4,837 proteins were identified using the iTRAQ proteomic technique. Among these proteins, 643 differentially expressed proteins were revealed. Protein S100-A8 expression levels were identified to differ between PBT and PMLN tissues. Immunohistochemical staining suggested a significant difference between NMBT and PMLN (P=0.002), and also between PBT and PMLN (P<0.001). Cox hazards regression model analyses suggested that histological grade (P=0.031) and nodal status (P=0.001) were risk factors for lymph nodes metastasis of breast cancer. Kaplan-Meier analyses revealed no significant relationship between protein S100-A8 expression level and overall survival rate of patients with breast cancer. In conclusion, by using the iTRAQ proteomic technique and immunohistochemistry staining, it was identified that protein S100-A8 may be associated with lymph nodes metastasis of breast cancer and be a marker for progression of breast cancer.

Abstract LB-009: Collagen regulation in postpartum mammary gland involution, a novel breast cancer prevention target

Abstract Collagen is the most abundant extracellular protein in breast tissue and plays a critical role in breast cancer progression. Intravital imaging shows collagen fibers as “highways” for tumor cell invasion, and collagen fibers arranged perpendicular to the tumor border (radial alignment) independently predict breast cancer metastasis. To understand the role of collagen in breast cancer, we argue it is necessary to understand collagen biology in the normal breast. While collagen production has been well studied in the context of wound healing, little is known about its regulation in normal breast physiology. Here we utilize a murine, weaning-induced mammary gland involution model, which is characterized by robust collagen deposition and remodeling, to investigate collagen regulation in the normal mammary gland. During mammary gland involution, we find increased collagen-I, -III, and -V gene expression and high levels of lysyl-oxidase (LOX), a major collagen cross-linker. By SHG analysis, we find collagen fibers around mammary ducts in the involuting gland are more tightly organized than nulliparous gland, with “hot spots” of radial alignment. Collagen is predominantly produced by fibroblasts and we identified mammary fibroblasts as PDGFRα+. Using flow cytometry-based cell sorting and gene expression analysis, PDGFRα+ fibroblasts isolated from the involuting mammary gland have increased gene expression of collagen-I,-III, and LOX. Further, there is ∼2 fold increase in number of PDGFRα+ cells/mg weight of tissue in the involuting gland, suggesting that fibroblasts may be recruited and activated during involution. Increased production of collagen by fibroblasts and radial alignment of collagen are consistent with a tumor promotional microenvironment during mammary gland involution. In fact, our lab has demonstrated pro-tumorigenic attributes of involution in rodent models of postpartum breast cancer, which can be mitigated by non-steroidal anti-inflammatory drug (NSAID) treatment. To investigate the impact of NSAID treatment on collagen during involution, we isolated fibroblasts from mammary glands of animals undergoing involution in the presence and absence of NSAID intervention. Mammary fibroblasts flow sorted from NSAID treated hosts display reduced collagen I, TGFβ1, and MMP-3 gene expression by 30∼60% compared to involution-fibroblasts isolated from untreated mice. Taken together, these data demonstrate that fibroblasts are responsive to COX-2 inhibition and suggest a novel mechanism of NSAIDs action that may be mediated through suppression of collagen deposition. Collagen and breast cancer are inseparable topics, as collagen abundance, architecture and tension impact breast cancer risk and prognosis. Weaning induced mammary gland involution provides a unique physiological window to study collagen regulation. Studying the mechanism of how NSAIDs regulate collagen may provide insight into the use of NSAIDs as a preventative and therapeutic intervention in breast cancer. Citation Format: Qiuchen Guo, Pepper J. Schedin. Collagen regulation in postpartum mammary gland involution, a novel breast cancer prevention target. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-009. doi:10.1158/1538-7445.AM2015-LB-009

Effects of the ninein-like protein centrosomal protein on breast cancer cell invasion and migration.

To investigate the effects of the centrosomal protein, ninein-like protein (Nlp), on the proliferation, invasion and metastasis of MCF-7 breast cancer cells, the present study established green fluorescent protein (GFP)-containing MCF7 plasmids with steady and overexpression of Nlp (MCG7-GFP-N1p) and blank plasmids (MCF7-GFP) using lentiviral transfection technology in MCF7 the breast cancer cell line. The expression of Nlp was determined by reverse transcription-quantitative polymerase chain reaction and western blott analysis. Differences in levels of proliferation, invasion and metastasis between the MCF7-GFP-Nlp group and MCF-GFP group were compared using MTT, plate colony formation and Transwell migration assays. The cell growth was more rapid and the colony forming rate was markedly increased in the MCF7-GFP-Nlp group (P<0.05) compared with the MCF7-GFP group. The number of cells in the MCF-GFP-Nlp and MCF7-GFP groups transferred across membranes were 878±18.22 and 398±8.02, respectively, in the migration assay. The invasive capacity was significantly increased in the MCF7-GFP-Nlp group (P<0.05) compared with the MCF7-GFP group. The western blotting results demonstrated high expression levels of C-X-C chemokine receptor type 4 in the MCF7-GFP-Nlp group. The increased expression of Nlp was associated with an increase in MCF7 cell proliferation, invasion and metastasis, which indicated that Nlp promoted breast tumorigenesis and may be used as a potent biological index to predict breast cancer metastasis and develop therapeutic regimes.

A cell-ECM screening method to predict breast cancer metastasis.

Breast cancer preferentially spreads to the bone, brain, liver, and lung. The clinical patterns of this tissue-specific spread (tropism) cannot be explained by blood flow alone, yet our understanding of what mediates tropism to these physically and chemically diverse tissues is limited. While the microenvironment has been recognized as a critical factor in governing metastatic colonization, the role of the extracellular matrix (ECM) in mediating tropism has not been thoroughly explored. We created a simple biomaterial platform with systematic control over the ECM protein density and composition to determine if integrin binding governs how metastatic cells differentiate between secondary tissue sites. Instead of examining individual behaviors, we compiled large patterns of phenotypes associated with adhesion to and migration on these controlled ECMs. In combining this novel analysis with a simple biomaterial platform, we created an in vitro fingerprint that is predictive of in vivo metastasis. This rapid biomaterial screen also provided information on how β1, α2, and α6 integrins might mediate metastasis in patients, providing insights beyond a purely genetic analysis. We propose that this approach of screening many cell-ECM interactions, across many different heterogeneous cell lines, is predictive of in vivo behavior, and is much simpler, faster, and more economical than complex 3D environments or mouse models. We also propose that when specifically applied toward the question of tissue tropism in breast cancer, it can be used to provide insight into certain integrin subunits as therapeutic targets.

NPY1R is a novel peripheral blood marker predictive of metastasis and prognosis in breast cancer patients.

The aim of the current study was to evaluate a novel tumor marker, neuropeptide Y receptor Y1 (NPY1R), for the detection of circulating cancer cells and to investigate its clinical significance in breast cancer patients. The Digital Gene Expression Displayer tool of the Cancer Genome Anatomy Project was used to identify the marker gene NPY1R, which is able to detect circulating cancer cells. Nested quantitative polymerase chain reaction was performed to correlate the NPY1R expression levels with the clinicopathological features of 142 breast cancer patients. A follow-up study of 131 of the breast cancer patients was conducted for 38 months. Compared with the 60 normal control individuals, NPY1R was highly expressed in the cancer patients (P<0.01). These high levels of NPY1R expression were positively correlated with the clinical stage and lymph node metastasis status of the disease, as well as with the status of the estrogen and progesterone receptors (P<0.05). Breast cancer patients with circulating cancer cells that expressed NPY1R exhibited shorter tumor-specific survival when compared with those with no NPY1R expression (P<0.01). Additionally, the mortality rate was associated with HER2 expression in the NPY1R positive and negative groups. These results indicate that NPY1R may serve as a useful marker to predict breast cancer metastasis and to evaluate the prognosis of breast cancer patients.

Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes.

BackgroundAxillary lymph node (ALN) status is an important prognostic factor for breast cancer. We retrospectively used contrast-enhanced computed tomography (CE-CT) to evaluate the presence of ALN, metastasis based on size, shape, and contrasting effects.MethodsOf 131 consecutive patients who underwent CE-CT followed by surgery for breast cancer between 2005 and 2012 in our institution, 49 were histologically diagnosed with lymph node metastasis. Maximum Hounsfield units (HU) and mean HU were measured in non-contrasting CT (NC-CT) and CE-CT of ALNs.ResultsOf 12 examined measurements, we found significant differences between negative and metastatic ALNs in mean and maximum NC-CT HU, and mean and maximum CE-CT HU (P < 0.05). We used a receiver operating curve, to determine cut-off values of four items in which significant differences were observed. The highest accuracy rate was noted for the cut-off value of 54 as maximum NC-CT HU for which sensitivity, specificity, and accuracy rate were 79.6%, 80.5% and 80.2%, respectively.ConclusionsCT HU of a patient with breast cancer are absolute values that offer objective disease management data that are not influenced by the screener’s ability.

Identification of Robust Pathway Markers for Cancer through Rank-Based Pathway Activity Inference

One important problem in translational genomics is the identification of reliable and reproducible markers that can be used to discriminate between different classes of a complex disease, such as cancer. The typical small sample setting makes the prediction of such markers very challenging, and various approaches have been proposed to address this problem. For example, it has been shown that pathway markers, which aggregate the gene activities in the same pathway, tend to be more robust than gene markers. Furthermore, the use of gene expression ranking has been demonstrated to be robust to batch effects and that it can lead to more interpretable results. In this paper, we propose an enhanced pathway activity inference method that uses gene ranking to predict the pathway activity in a probabilistic manner. The main focus of this work is on identifying robust pathway markers that can ultimately lead to robust classifiers with reproducible performance across datasets. Simulation results based on multiple breast cancer datasets show that the proposed inference method identifies better pathway markers that can predict breast cancer metastasis with higher accuracy. Moreover, the identified pathway markers can lead to better classifiers with more consistent classification performance across independent datasets.