scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

scholarly journals Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning

2021 ◽  
Vol 11 (9) ◽  
pp. 881
Author(s):  
Rassanee Bissanum ◽  
Sitthichok Chaichulee ◽  
Rawikant Kamolphiwong ◽  
Raphatphorn Navakanitworakul ◽  
Kanyanatt Kanokwiroon
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Gene Expression Pattern ◽  
Classification Models ◽  
Gene Set ◽  
Svm Algorithm

Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95–98.8%; AUC 0.99–1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.

scholarly journals TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer

2012 ◽  
Vol 11 ◽  
pp. CIN.S9983 ◽  
Author(s):  
Xi Chen ◽  
Jiang Li ◽  
William H. Gray ◽  
Brian D. Lehmann ◽  
Joshua A. Bauer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Genomic Research ◽  
Data Matrix ◽  
Cancer Data ◽  
Subtyping Tool

Motivation Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer group, and identification of molecular subtypes is essential for understanding the biological characteristics and clinical behaviors of TNBC as well as for developing personalized treatments. Based on 3,247 gene expression profiles from 21 breast cancer data sets, we discovered six TNBC subtypes from 587 TNBC samples with unique gene expression patterns and ontologies. Cell line models representing each of the TNBC subtypes also displayed different sensitivities to targeted therapeutic agents. Classification of TNBC into subtypes will advance further genomic research and clinical applications. Result We developed a web-based subtyping tool TNBCtype for candidate TNBC samples using our gene expression meta data and classification methods. Given a gene expression data matrix, this tool will display for each candidate sample the predicted subtype, the corresponding correlation coefficient, and the permutation P-value. We offer a user-friendly web interface to predict the subtypes for new TNBC samples that may facilitate diagnostics, biomarker selection, drug discovery, and the more tailored treatment of breast cancer.

scholarly journals Machine learning predicts rapid relapse of triple negative breast cancer

2019 ◽  
Author(s):  
Yiqing Zhang ◽  
William Nock ◽  
Meghan Wyse ◽  
Zachary Weber ◽  
Elizabeth Adams ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Support Vector ◽  
Chi Square ◽  
Genomic Features ◽  
Immune Signatures ◽  
Metastatic Relapse ◽  
Significant Difference

ABSTRACTPurposeMetastatic relapse of triple-negative breast cancer (TNBC) within 2 years of diagnosis is associated with particularly aggressive disease and a distinct clinical course relative to TNBCs that relapse beyond 2 years. We hypothesized that rapid relapse TNBCs (rrTNBC; metastatic relapse or death <2 years) reflect unique genomic features relative to late relapse (lrTNBC; >2 years).Patients and MethodsWe identified 453 primary TNBCs from three publicly-available datasets and characterized each as rrTNBc, lrTNBC, or ‘no relapse’ (nrTNBC: no relapse/death with at least 5 years follow-up). We compiled primary tumor clinical and multi-omic data, including transcriptome (n=453), copy number alterations (CNAs; n=317), and mutations in 171 cancer-related genes (n=317), then calculated published gene expression and immune signatures.ResultsPatients with rrTNBC were higher stage at diagnosis (Chi-square p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (Chi-square p=0.03). Among 125 expression signatures, five immune signatures were significantly higher in nrTNBCs while lrTNBC were enriched for eight estrogen/luminal signatures (all FDR p<0.05). There was no significant difference in tumor mutation burden or percent genome altered across the groups. Among mutations, onlyTP53mutations were significantly more frequent in rrTNBC compared to lrTNBC (Fisher exact FDR p=0.009). To develop an optimal classifier, we used 77 significant clinical and ‘omic features to evaluate six modeling approaches encompassing simple, machine learning, and artificial neural network (ANN). Support vector machine outperformed other models with average receiver-operator characteristic area under curve >0.75.ConclusionsWe provide a new approach to define TNBCs based on timing of relapse. We identify distinct clinical and genomic features that can be incorporated into machine learning models to predict rapid relapse of TNBC.

scholarly journals Metastatic Triple Negative Breast Cancer: The New Era of Thinking

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amrallah A. Mohammed ◽  
Mohamed A. Elbassuiony ◽  
Hanaa Rashied
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Classification ◽  
Molecular Techniques ◽  
Response To Therapy ◽  
New Era ◽  
Current Review

Abstract The heterogeneity of triple negative breast cancer (TNBC) is reflected in a bizarre response to therapy. Although it is chemotherapy sensitive, the failure is the usual pathway either in local or distance status. With progression in Gene Expression Profile (GEP) and other molecular techniques, TNBC is divided into sub-types with unique pathways. In the current review, we are trying to highlight based on the molecular classification of TNBC and the management based on every type.

scholarly journals An open-label, pilot study of veliparib and lapatinib in patients with metastatic, triple-negative breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Erica M. Stringer-Reasor ◽  
Jori E. May ◽  
Eva Olariu ◽  
Valerie Caterinicchia ◽  
Yufeng Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Repair ◽  
Pilot Study ◽  
Expression Analysis ◽  
Triple Negative Breast Cancer ◽  
Gene Expression Analysis ◽  
Triple Negative ◽  
Egfr Inhibition ◽  
Link Type

Abstract Background Poly (ADP-ribose)-polymerase inhibitors (PARPi) have been approved for cancer patients with germline BRCA1/2 (gBRCA1/2) mutations, and efforts to expand the utility of PARPi beyond BRCA1/2 are ongoing. In preclinical models of triple-negative breast cancer (TNBC) with intact DNA repair, we have previously shown an induced synthetic lethality with combined EGFR inhibition and PARPi. Here, we report the safety and clinical activity of lapatinib and veliparib in patients with metastatic TNBC. Methods A first-in-human, pilot study of lapatinib and veliparib was conducted in metastatic TNBC (NCT02158507). The primary endpoint was safety and tolerability. Secondary endpoints were objective response rates and pharmacokinetic evaluation. Gene expression analysis of pre-treatment tumor biopsies was performed. Key eligibility included TNBC patients with measurable disease and prior anthracycline-based and taxane chemotherapy. Patients with gBRCA1/2 mutations were excluded. Results Twenty patients were enrolled, of which 17 were evaluable for response. The median number of prior therapies in the metastatic setting was 1 (range 0–2). Fifty percent of patients were Caucasian, 45% African–American, and 5% Hispanic. Of evaluable patients, 4 demonstrated a partial response and 2 had stable disease. There were no dose-limiting toxicities. Most AEs were limited to grade 1 or 2 and no drug–drug interactions noted. Exploratory gene expression analysis suggested baseline DNA repair pathway score was lower and baseline immunogenicity was higher in the responders compared to non-responders. Conclusions Lapatinib plus veliparib therapy has a manageable safety profile and promising antitumor activity in advanced TNBC. Further investigation of dual therapy with EGFR inhibition and PARP inhibition is needed. Trial registration ClinicalTrials.gov, NCT02158507. Registered on 12 September 2014

scholarly journals The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

2021 ◽  
Vol 22 (4) ◽  
pp. 1820
Author(s):  
Anna Makuch-Kocka ◽  
Janusz Kocki ◽  
Anna Brzozowska ◽  
Jacek Bogucki ◽  
Przemysław Kołodziej ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Data ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Lymphatic Vessels ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Cancer Tissue ◽  
Expression Levels

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

scholarly journals Practical classification of triple-negative breast cancer: intratumoral heterogeneity, mechanisms of drug resistance, and novel therapies

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Antonio Marra ◽  
Dario Trapani ◽  
Giulia Viale ◽  
Carmen Criscitiello ◽  
Giuseppe Curigliano
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Residual Disease ◽  
Therapeutic Approaches ◽  
Minimal Residual Disease Monitoring ◽  
Anticancer Treatment ◽  
Immune Contexture

Abstract Triple-negative breast cancer (TNBC) is not a unique disease, encompassing multiple entities with marked histopathological, transcriptomic and genomic heterogeneity. Despite several efforts, transcriptomic and genomic classifications have remained merely theoretic and most of the patients are being treated with chemotherapy. Driver alterations in potentially targetable genes, including PIK3CA and AKT, have been identified across TNBC subtypes, prompting the implementation of biomarker-driven therapeutic approaches. However, biomarker-based treatments as well as immune checkpoint inhibitor-based immunotherapy have provided contrasting and limited results so far. Accordingly, a better characterization of the genomic and immune contexture underpinning TNBC, as well as the translation of the lessons learnt in the metastatic disease to the early setting would improve patients’ outcomes. The application of multi-omics technologies, biocomputational algorithms, assays for minimal residual disease monitoring and novel clinical trial designs are strongly warranted to pave the way toward personalized anticancer treatment for patients with TNBC.

Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

scholarly journals CBP/β-Catenin/FOXM1 Is a Novel Therapeutic Target in Triple Negative Breast Cancer

Cancers ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 525 ◽  
Author(s):  
Alexander Ring ◽  
Cu Nguyen ◽  
Goar Smbatyan ◽  
Debu Tripathy ◽  
Min Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Drug Resistance ◽  
Triple Negative Breast Cancer ◽  
Therapeutic Target ◽  
Triple Negative ◽  
Creb Binding Protein ◽  
Novel Therapeutic

Background: Triple negative breast cancers (TNBCs) are an aggressive BC subtype, characterized by high rates of drug resistance and a high proportion of cancer stem cells (CSC). CSCs are thought to be responsible for tumor initiation and drug resistance. cAMP-response element-binding (CREB) binding protein (CREBBP or CBP) has been implicated in CSC biology and may provide a novel therapeutic target in TNBC. Methods: RNA Seq pre- and post treatment with the CBP-binding small molecule ICG-001 was used to characterize CBP-driven gene expression in TNBC cells. In vitro and in vivo TNBC models were used to determine the therapeutic effect of CBP inhibition via ICG-001. Tissue microarrays (TMAs) were used to investigate the potential of CBP and associated proteins as biomarkers in TNBC. Results: The CBP/ß-catenin/FOXM1 transcriptional complex drives gene expression in TNBC and is associated with increased CSC numbers, drug resistance and poor survival outcome. Targeting of CBP/β-catenin/FOXM1 with ICG-001 eliminated CSCs and sensitized TNBC tumors to chemotherapy. Immunohistochemistry of TMAs demonstrated a significant correlation between FOXM1 expression and TNBC subtype. Conclusion: CBP/β-catenin/FOXM1 transcriptional activity plays an important role in TNBC drug resistance and CSC phenotype. CBP/β-catenin/FOXM1 provides a molecular target for precision therapy in triple negative breast cancer and could form a rationale for potential clinical trials.

scholarly journals Response to FEC Chemotherapy and Oncolytic HSV-1 Is Associated with Macrophage Polarization and Increased Expression of S100A8/A9 in Triple Negative Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5590
Author(s):  
Alyssa Vito ◽  
Nader El-Sayes ◽  
Omar Salem ◽  
Yonghong Wan ◽  
Karen L. Mossman
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Macrophage Polarization ◽  
Gene Signature ◽  
Immune Checkpoint Blockade ◽  
Differential Gene ◽  
Microarray Analyses ◽  
Tumor Cell Killing

The era of immunotherapy has seen an insurgence of novel therapies driving oncologic research and the clinical management of the disease. We have previously reported that a combination of chemotherapy (FEC) and oncolytic virotherapy (oHSV-1) can be used to sensitize otherwise non-responsive tumors to immune checkpoint blockade and that tumor-infiltrating B cells are required for the efficacy of our therapeutic regimen in a murine model of triple-negative breast cancer. In the studies herein, we have performed gene expression profiling using microarray analyses and have investigated the differential gene expression between tumors treated with FEC + oHSV-1 versus untreated tumors. In this work, we uncovered a therapeutically driven switch of the myeloid phenotype and a gene signature driving increased tumor cell killing.

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