Circulating tumor DNA for triple-negative breast cancer diagnosis and treatment decisions

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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Association of Circulating Tumor DNA and Circulating Tumor Cells After Neoadjuvant Chemotherapy With Disease Recurrence in Patients With Triple-Negative Breast Cancer

JAMA Oncology ◽

10.1001/jamaoncol.2020.2295 ◽

2020 ◽

Vol 6 (9) ◽

pp. 1410 ◽

Cited By ~ 8

Author(s):

Milan Radovich ◽

Guanglong Jiang ◽

Bradley A. Hancock ◽

Christopher Chitambar ◽

Rita Nanda ◽

...

Keyword(s):

Breast Cancer ◽

Neoadjuvant Chemotherapy ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Disease Recurrence ◽

Circulating Tumor Dna ◽

Tumor Dna

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Monitoring of circulating tumor DNA in metastatic triple-negative breast cancer patients undergoing chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.15_suppl.e22056 ◽

2014 ◽

Vol 32 (15_suppl) ◽

pp. e22056-e22056

Author(s):

Jordan Madic ◽

Anna Kiialainen ◽

Francois-Clement Bidard ◽

Fabian Birzele ◽

Guillemette Ramey ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Circulating Tumor Dna ◽

Breast Cancer Patients ◽

Tumor Dna

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Concurrent Detection of Circulating Tumor Cells and Circulating Tumor DNA in Triple-negative Breast Cancer

Asian Pacific Journal of Cancer Care ◽

10.31557/apjcc.2021.6.4.373-377 ◽

2021 ◽

Vol 6 (4) ◽

pp. 373-377

Author(s):

Henry L Gomez ◽

Carlos A. Castaneda ◽

Miluska Castillo ◽

James Reuben ◽

Hui Gao ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Pik3ca Mutation ◽

Digital Pcr ◽

Circulating Tumor Dna ◽

Pik3ca Mutations ◽

Tumor Dna

Objective: Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) provide tumor information in breast cancer. Our objective was to characterize CTCs, and contrasted them with ctDNA PIK3CA mutation in 24 triple-negative breast cancer (TNBC). Methods: CTCs genes were characterized by AdnaTest protocol and ctDNA by digital PCR. Results: We found CTCs genes in 37.5% and ctDNA PIK3CA mutations in 29.16%. Three cases with CTCs genes had concurrent ctDNA PIK3CA mutations. MUC1 or GA733-2 were found in 4 cases, and 3 of them had concurrent ctDNA PIK3CA. CTCs ALDH1/TWIST1 were found in 2 cases, AKT2 in one and PI3Kα in another, and none had concurrent ctDNA PIK3CA mutations. There was no correlation between CTCs and ctDNA detection. All 3 cases with CTC & cDNA concurrent finding underwent death during follow-up. Conclusion: Infrequent concurrent detection of CTC and ctDNA presence suggests that both represent independent processes in TNBC patients, and could identify worst prognosis cases.

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Detection of TP53 and PIK3CA Mutations in Circulating Tumor DNA Using Next-Generation Sequencing in the Screening Process for Early Breast Cancer Diagnosis

Journal of Clinical Medicine ◽

10.3390/jcm8081183 ◽

2019 ◽

Vol 8 (8) ◽

pp. 1183 ◽

Cited By ~ 9

Author(s):

Rodriguez ◽

Córdoba ◽

Aranda ◽

Álvarez ◽

Vicioso ◽

...

Keyword(s):

Breast Cancer ◽

Early Breast Cancer ◽

Cancer Diagnosis ◽

Breast Imaging ◽

Breast Cancer Diagnosis ◽

Circulating Tumor Dna ◽

Sequencing Analysis ◽

Pik3ca Mutations ◽

Ctdna Analysis ◽

Tumor Dna

Circulating tumor DNA (ctDNA) has emerged as a non-invasive “liquid biopsy” for early breast cancer diagnosis. We evaluated the suitability of ctDNA analysis in the diagnosis of early breast cancer after mammography findings, comparing PIK3CA and TP53 mutations between tumor biopsies and pre-biopsy circulating DNA. Matched plasma and frozen fresh tissue biopsies from patients with Breast Imaging-Reporting and Data System (BIRADS) 4c/5 mammography findings and subsequent diagnosis of primary breast cancer were analyzed using NGS TruSeq Custom Amplicon Low Input Panel (Illumina) and plasma SafeSEQ (Sysmex Inostics). The same plasma and tumor mutations were observed in eight of 29 patients (27.6%) with four in TP53 and five in PIK3CA mutations. Sequencing analysis also revealed four additional ctDNA mutations (three in TP53 and one in PIK3CA) previously not identified in three patients tissue biopsy. One of these patients had mutations in both genes. Age, tumor grade and size, immunohistochemical (IHC) subtype, BIRADS category, and lymph node positivity were significantly associated with the detectability of these blood tumor-derived mutations. In conclusion, ctDNA analysis could be used in early breast cancer diagnosis, providing critical clinical information to improve patient diagnosis.

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Prognostic and predictive value of circulating tumor DNA during neoadjuvant chemotherapy for triple negative breast cancer

Scientific Reports ◽

10.1038/s41598-020-71236-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Luca Cavallone ◽

Adriana Aguilar-Mahecha ◽

Josiane Lafleur ◽

Susie Brousse ◽

Mohammed Aldamry ◽

...

Keyword(s):

Breast Cancer ◽

Neoadjuvant Chemotherapy ◽

Triple Negative Breast Cancer ◽

Predictive Value ◽

Triple Negative ◽

Residual Tumor ◽

Complete Response ◽

Circulating Tumor Dna ◽

Droplet Pcr ◽

Tumor Dna

Abstract Response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC) is highly prognostic and determines whether adjuvant chemotherapy is needed if residual tumor is found at surgery. To evaluate the predictive and prognostic values of circulating tumor DNA (ctDNA) in this setting, we analyzed tumor and serial bloods from 26 TNBC patients collected prior, during, and after NAC. Individual digital droplet PCR assays were developed for 121 variants (average 5/patient) identified from tumor sequencing, enabling ctDNA detection in 96% of patients at baseline. Mutant allele frequency at baseline was associated with clinical characteristics. Levels drastically fell after one cycle of NAC, especially in patients whose tumors would go on to have a pathological complete response (pCR), but then rose significantly before surgery in patients with significant residual tumor at surgery (p = 0.0001). The detection of ctDNA early during treatment and also late at the end of NAC before surgery was strongly predictive of residual tumor at surgery, but its absence was less predictive of pCR, especially when only TP53 variants are considered. ctDNA detection at the end of neoadjuvant chemotherapy indicated significantly worse relapse-free survival (HR = 0.29 (95% CI 0.08–0.98), p = 0.046), and overall survival (HR = 0.27 95% CI 0.075–0.96), p = 0.043). Hence, individualized multi-variant ctDNA testing during and after NAC prior to surgery has prognostic and predictive value in early TNBC patients.

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A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189174 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8573-8586

Author(s):

Sudhakar Sengan ◽

V. Priya ◽

A. Syed Musthafa ◽

Logesh Ravi ◽

Saravanan Palani ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

High Resolution ◽

Cancer Diagnosis ◽

Medical Images ◽

Breast Cancer Diagnosis ◽

Treatment Decisions ◽

Diagnosis And Treatment ◽

Automated System ◽

Svm Classifier

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

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