Impact of<scp><i>TP53</i></scp>mutations in breast cancer: Clinicopathological features and prognosisImpact of<i>TP53</i>mutations in breast CA

Chen, Xiaoqing; Li, Wen; Wang, Yulei; Chen, Bin; Xue, Yunlian; Guo, Liping; Liao, Ning

doi:10.1111/1759-7714.13467

Cited by 10 publications

(9 citation statements)

References 30 publications

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“…TP53 alternations were more frequently detected among HER2‐enriched and TNBC patients in our baseline cohort. The enrichment of TP53 mutation was also observed in one European cohort of 1794 BC patients and one Chinese cohort of 411 BC patients [ 43 , 44 ]. ZNF703 , CTCF , and GNAS alterations were exclusively observed among HR‐positive/HER2‐negative patients, which was consistent with what was observed in a study on 11 616 breast tumors.…”

Section: Discussionmentioning

confidence: 89%

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Zhang

Sun

Zhao³

et al. 2022

Molecular Oncology

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Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA-monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next-generation sequencing of 425 cancer-relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/ GNAS mutations (P < 0.01) displayed inferior disease-free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild-type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

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Section: Discussionmentioning

confidence: 89%

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Zhang

Sun

Zhao³

et al. 2022

Molecular Oncology

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“…Furthermore, in order to examine if the protein products of the three protein-encoding DMGs included in the BrCa-specific biosignature were somehow implicated in BrCa pathophysiology, we analyzed the identified genes, using a literature mining tool UniReD, which predicts functional associations between proteins. As previously [ 17 ], for this analysis, we used the following list of 10 protein-coding genes with an established role in BrCa pathophysiology, namely, BRCA1 [ 26 ], BRCA2 [ 26 ], RASSF1 [ 27 ], ESR1 [ 28 ], TP53 [ 29 ], PIK3CA [ 30 ], BRMS1 [ 31 ], CDH1 [ 32 ], CST6 [ 33 ] and PTEN [ 34 ]. All genes were found to be associated with breast cancer pathways according to the KEGG pathway identification.…”

Section: Resultsmentioning

confidence: 99%

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Karaglani

Παναγοπούλου

Baltsavia

et al. 2022

IJMS

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Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

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“…Underweight patients in the postmenopausal group harbored more TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway. A higher TP53 mutation rate has been reported in breast cancer patients with aggressive characteristics ( 22 , 23 ). DNA amplification is a ubiquitous mechanism of oncogene activation in cancers ( 24 ).…”

Section: Discussionmentioning

confidence: 96%

Association of Body Mass Index With Somatic Mutations in Breast Cancer

Chen

Guo

et al. 2021

Front. Oncol.

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BackgroundThe relationship between body mass index (BMI) and the prognosis or treatment response in patients with breast cancer has been demonstrated in previous studies, but the somatic mutation profiles in breast cancer patients with different BMIs have not been explored.MethodsIn the present study, the somatic mutation profiles in 421 female breast cancer patients who were stratified into three subgroups based on BMI (normal weight, overweight/obese, and underweight) were investigated. Capture-based targeted sequencing was performed using a panel comprising 520 cancer-related genes.ResultsA total of 3547 mutations were detected in 390 genes. In breast cancer patients with different BMI statuses, the tumors exhibited high mutation frequency and burden. TP53 was the most common gene in the three groups, followed by PIK3CA, ERBB2, and CDK12. Meanwhile, the mutation hotspots in TP53 and PIK3CA were the same in the three BMI groups. More JAK1 mutations were identified in underweight patients than those in normal patients. Except for JAK1, differentially mutated genes in postmenopausal patients were completely different from those in premenopausal patients. The distribution of mutation types was significantly different among BMI groups in the postmenopausal group. Underweight patients in the postmenopausal group harbored more TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway.ConclusionsOur next-generation sequencing (NGS)-based gene panel analysis revealed the gene expression profiles of breast cancer patients with different BMI statuses. Although genes with high mutation frequency and burden were found in different BMI groups, some subtle differences could not be ignored. JAK1 mutations might play a vital role in the progression of breast cancer in underweight patients, and this needs further analysis. Postmenopausal underweight patients with breast cancer have more aggressive characteristics, such as TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway. This study provides new evidence for understanding the characteristics of breast cancer patients with different BMIs.

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Impact ofTP53mutations in breast cancer: Clinicopathological features and prognosisImpact ofTP53mutations in breast CA

Cited by 10 publications

References 30 publications

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Association of Body Mass Index With Somatic Mutations in Breast Cancer

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