Germline TP53 pathogenic variants and breast cancer: A narrative review

Blondeaux, Eva; Arecco, Luca; Punie, Kevin; Graffeo, Rossella; Toss, Angela; Angelis, Carmine De; Trevisan, Lucia; Buzzatti, Giulia; Linn, Sabine C.; Dubsky, Peter; Cruellas, Mara; Partridge, Ann H.; Balmañà, Judith; Paluch‐Shimon, Shani; Lambertini, Matteo

doi:10.1016/j.ctrv.2023.102522

Cited by 15 publications

(9 citation statements)

References 101 publications

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“…The low-risk group was characterized by low TMB and fewer mutated genes, whereas the high-risk group exhibited higher TMB and more mutations in genes such as TP53 and PIK3CA. TP53, a quintessential tumor suppressor gene, has been implicated in BC progression and poor prognosis due to its mutations ( 38 ). CNV analysis revealed that high-risk patients also displayed notable aberrations in gene amplifications and deletions.…”

Section: Discussionmentioning

confidence: 99%

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Li,

Yang

et al. 2024

Front. Immunol.

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BackgroundThis study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses.MethodsOur analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies.ResultsOur research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model’s broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib.ConclusionsThe GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

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Section: Discussionmentioning

confidence: 99%

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Li,

Yang

et al. 2024

Front. Immunol.

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“…It was concluded that low-risk BC patients were featured by lower TMB and low-mutated genes, while high-risk BC patients possessed more TMB and more mutated genes, such as TP53. Massive research had reported that its mutation promoted the progression of BC, resulting in an inferior prognosis ( 65 ). On the other hand, CNV analysis showed that the amplification of 3q26.32, 5p15.33 and 8q24.21, and the deletion of 5q21.3, 8p23.2 and 9p21.3 were primarily founded in high-risk patients.…”

Section: Discussionmentioning

confidence: 99%

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection

Yang,

Wang,

Yang

et al. 2024

Front. Immunol.

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BackgroundBreast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies.MethodsThrough comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy.ResultsThe developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC.ConclusionOur study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

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“…Notably, young patients displayed a higher incidence of mutations in the BRCA1 and TP53 genes, which were also evident in the corresponding pathways. Emerging evidence indicates that the TP53 mutation is inclined to reach a complete response (CR) after chemotherapy; however, it is also linked to worse OS and DFS in BC patients [ 23 , 24 , 25 ]. Furthermore, TP53-mutated patients showed a higher risk of tumor recurrence following breast-conserving therapy (BCT) than wild-type patients, thereby warranting a recommendation of mastectomy for TP53-mutated individuals [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Integrated Multi-Omics Profiling of Young Breast Cancer Patients Reveals a Correlation between Galactose Metabolism Pathway and Poor Disease-Free Survival

Han,

Luo

et al. 2023

Cancers

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In recent years, there has been a notable rise in the incidence of breast cancer among young patients, who exhibit worse survival outcomes and distinct characteristics compared to intermediate and elderly patients. Therefore, it is imperative to identify the specific features unique to young patients, which could offer insights into potential therapeutic strategies and improving survival outcomes. In our study, we performed an integrative analysis of bulk transcriptional and genomic data from extensive clinical cohorts to identify the prognostic factotrs. Additionally, we analyzed the single-cell transcriptional data and conducted in vitro experiments. Our work confirmed that young patients exhibited higher grading, worse disease-free survival (DFS), a higher frequency of mutations in TP53 and BRCA1, a lower frequency of mutations in PIK3CA, and upregulation of eight metabolic pathways. Notably, the galactose metabolism pathway showed upregulation in young patients and was associated with worse DFS. Further analysis and experiments indicated that the galactose metabolism pathway may regulate the stemness of cancer cells and ultimately contribute to worse survival outcomes. In summary, our finding identified distinct clinicopathological, transcriptional, and genomics features and revealed a correlation between the galactose metabolism pathway, stemness, and poor disease-free survival of breast cancer in young patients.

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Germline TP53 pathogenic variants and breast cancer: A narrative review

Cited by 15 publications

References 101 publications

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection

Integrated Multi-Omics Profiling of Young Breast Cancer Patients Reveals a Correlation between Galactose Metabolism Pathway and Poor Disease-Free Survival

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