Molecular Classification of Triple-Negative Breast Cancer

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.

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

npj Breast Cancer ◽

10.1038/s41523-020-00197-2 ◽

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.

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Breast Cancer Type Classification Using Machine Learning

Journal of Personalized Medicine ◽

10.3390/jpm11020061 ◽

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.

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Essential amino acid metabolism-related molecular classification in triple-negative breast cancer

Epigenomics ◽

10.2217/epi-2021-0210 ◽

2021 ◽

Vol 13 (16) ◽

pp. 1247-1268

Author(s):

Yajie Zhao ◽

Chunrui Pu ◽

Dechuang Jiao ◽

Jiujun Zhu ◽

Xuhui Guo ◽

...

Keyword(s):

Breast Cancer ◽

Amino Acid ◽

Triple Negative Breast Cancer ◽

Immune Checkpoint ◽

Essential Amino Acid ◽

Triple Negative ◽

Expression Patterns ◽

Molecular Classification ◽

Biological Insight ◽

Metabolic Heterogeneity

Aim: To develop an approach to characterize and classify triple-negative breast cancer (TNBC) tumors based upon their essential amino acid (EAA) metabolic activity. Methods: We performed bioinformatic analyses of genomic, transcriptomic and clinical data in an integrated cohort of 740 TNBC patients from public databases. Results: Based on EAA metabolism-related gene expression patterns, two TNBC subtypes were identified with distinct prognoses and genomic alterations. Patients exhibiting an upregulated EAA metabolism phenotype were more prone to chemoresistance but also expressed higher levels of immune checkpoint genes and may be better candidates for immune checkpoint inhibitor therapy. Conclusion: Metabolic classification based upon EAA profiles offers a novel biological insight into previously established TNBC subtypes and advances current understanding of TNBC’s metabolic heterogeneity.

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