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

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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Patient-Specific Circulating Tumor DNA Detection during Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Clinical Chemistry ◽

10.1373/clinchem.2016.262337 ◽

2017 ◽

Vol 63 (3) ◽

pp. 691-699 ◽

Cited By ~ 72

Author(s):

Francesca Riva ◽

Francois-Clement Bidard ◽

Alexandre Houy ◽

Adrien Saliou ◽

Jordan Madic ◽

...

Keyword(s):

Breast Cancer ◽

Neoadjuvant Chemotherapy ◽

Minimal Residual Disease ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Residual Disease ◽

Circulating Tumor Dna ◽

Tp53 Mutations ◽

Tumor Dna ◽

Minimal Residual

Abstract BACKGROUND In nonmetastatic triple-negative breast cancer (TNBC) patients, we investigated whether circulating tumor DNA (ctDNA) detection can reflect the tumor response to neoadjuvant chemotherapy (NCT) and detect minimal residual disease after surgery. METHODS Ten milliliters of plasma were collected at 4 time points: before NCT; after 1 cycle; before surgery; after surgery. Customized droplet digital PCR (ddPCR) assays were used to track tumor protein p53 (TP53) mutations previously characterized in tumor tissue by massively parallel sequencing (MPS). RESULTS Forty-six patients with nonmetastatic TNBC were enrolled. TP53 mutations were identified in 40 of them. Customized ddPCR probes were validated for 38 patients, with excellent correlation with MPS (r = 0.99), specificity (≥2 droplets/assay), and sensitivity (at least 0.1%). At baseline, ctDNA was detected in 27/36 patients (75%). Its detection was associated with mitotic index (P = 0.003), tumor grade (P = 0.003), and stage (P = 0.03). During treatment, we observed a drop of ctDNA levels in all patients but 1. No patient had detectable ctDNA after surgery. The patient with rising ctDNA levels experienced tumor progression during NCT. Pathological complete response (16/38 patients) was not correlated with ctDNA detection at any time point. ctDNA positivity after 1 cycle of NCT was correlated with shorter disease-free (P < 0.001) and overall (P = 0.006) survival. CONCLUSIONS Customized ctDNA detection by ddPCR achieved a 75% detection rate at baseline. During NCT, ctDNA levels decreased quickly and minimal residual disease was not detected after surgery. However, a slow decrease of ctDNA level during NCT was strongly associated with shorter survival.

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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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Adjuvant Platinum Chemotherapy Does Not Improve Outcomes in Patients With Triple-Negative Breast Cancer With Residual Disease

Default Digital Object Group ◽

10.1200/adn.21.200663 ◽

2021 ◽

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Residual Disease ◽

Platinum Chemotherapy

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Latest changes in the molecular classification of breast cancer and triple negative breast cancer

Varna Medical Forum ◽

10.14748/vmf.v8i2.6046 ◽

2019 ◽

Vol 8 (2) ◽

pp. 80

Author(s):

Vassilena Tsvetkova

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Molecular Classification

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Systemic immune-inflammation index and ultrasonographic classification of breast imaging-reporting and data system predict outcomes of triple-negative breast cancer

Cancer Management and Research ◽

10.2147/cmar.s185890 ◽

2019 ◽

Vol Volume 11 ◽

pp. 813-819 ◽

Cited By ~ 3

Author(s):

Ping Wang ◽

Wensheng Yue ◽

Wenyan Li ◽

Yuqun Luo ◽

Zukun Li ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Breast Imaging ◽

Triple Negative ◽

Data System ◽

Immune Inflammation

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Targeted-Gene Sequencing to Catch Triple Negative Breast Cancer Heterogeneity before and after Neoadjuvant Chemotherapy

Cancers ◽

10.3390/cancers11111753 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1753 ◽

Cited By ~ 2

Author(s):

Serena Di Cosimo ◽

Valentina Appierto ◽

Marco Silvestri ◽

Giancarlo Pruneri ◽

Andrea Vingiani ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Primary Tumor ◽

Triple Negative ◽

Kinase Inhibitor ◽

Residual Disease ◽

Gene Sequencing ◽

Tumor Biopsy ◽

Recurrent Mutations ◽

Targeted Gene Sequencing

Triple negative breast cancer (TNBC) patients not attaining pathological Complete Response (pCR) after neo-adjuvant chemotherapy (NAC) have poor prognosis. We characterized 19 patients for somatic mutations in primary tumor biopsy and residual disease (RD) at surgery by 409 cancer-related gene sequencing (IonAmpliSeqTM Comprehensive Cancer Panel). A median of four (range 1–66) genes was mutated in each primary tumor biopsy, and the most common mutated gene was TP53 followed by a long tail of low frequency mutations. There were no recurrent mutations significantly associated with pCR. However, half of patients with RD had primary tumor biopsy with mutations in genes related to the immune system compared with none of those achieving pCR. Overall, the number of mutations showed a downward trend in post- as compared to pre-NAC samples. PIK3CA was the most common altered gene after NAC. The mutational profile of TNBC during treatment as inferred from patterns of mutant allele frequencies in matched pre-and post-NAC samples showed that RD harbored alterations of cell cycle progression, PI3K/Akt/mTOR, and EGFR tyrosine kinase inhibitor-resistance pathways. Our findings support the use of targeted-gene sequencing for TNBC therapeutic development, as patients without pCR may present mutations of immune-related pathways in their primary tumor biopsy, or actionable targets in the RD.

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