<i>TP53</i> somatic mutations in Asian breast cancer are associated with subtype-specific effects

Ragu, Mohana Eswari; Lim, Joanna; Ng, Pei-Sze; Yip, Cheng‐Har; Rajadurai, Pathmanathan; Teo, Soo‐Hwang; Pan, Jia-Wern

doi:10.1101/2022.03.31.486643

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…SNVs of interest also were assessed independently in two cohorts: 859 women with breast cancer from the Malaysian Breast Cancer Study (MyBrCa) [74,75] and 393 AFA women with TNBC from the Breast Cancer in African Americans: Understanding Somatic Mutations and Etiology (B-CAUSE) study (Supplemental Tables S19 and S20) [76]. Validation SNVs for the MyBrCa study were excluded from analyses if they mapped to the X chromosome or if they had a MAF less than 1% in Malaysian individuals.…”

Section: Independent Validation Studiesmentioning

confidence: 99%

Association ofESR1germline variants withTP53somatic variants in breast tumors in a genome-wide study

Tjader,

Beer,

Ramroop

et al. 2023

Preprint

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BackgroundIn breast tumors, somatic mutation frequencies inTP53andPIK3CAvary by tumor subtype and ancestry. HER2 positive and triple negative breast cancers (TNBC) have a higher frequency ofTP53somatic mutations than other subtypes.PIK3CAmutations are more frequently observed in hormone receptor positive tumors. Emerging data suggest tumor mutation status is associated with germline variants and genetic ancestry. We aimed to identify germline variants that are associated with somaticTP53orPIK3CAmutation status in breast tumors.MethodsA genome-wide association study was conducted using breast cancer mutation status ofTP53andPIK3CAand functional mutation categories includingTP53gain of function (GOF) and loss of function mutations andPIK3CAactivating/hotspot mutations. The discovery analysis consisted of 2850 European ancestry women from three datasets. Germline variants showing evidence of association with somatic mutations were selected for validation analyses based on predicted function, allele frequency, and proximity to known cancer genes or risk loci. Candidate variants were assessed for association with mutation status in a multi-ancestry validation study, a Malaysian study, and a study of African American/Black women with TNBC.ResultsThe discovery Germline x Mutation (GxM) association study found five variants associated with one or moreTP53phenotypes withPvalues <1×10-6, 33 variants associated with one or moreTP53phenotypes withPvalues <1×10-5, and 44 variants associated with one or morePIK3CAphenotypes withPvalues <1×10-5. In the multi-ancestry and Malaysian validation studies, germlineESR1locus variant, rs9383938, was associated with the presence ofTP53mutations overall (Pvalues 6.8×10-5and 9.8×10-8, respectively) andTP53GOF mutations (Pvalue 8.4×10-6). Multiple variants showed suggestive evidence of association withPIK3CAmutation status in the validation studies, but none were significant after correction for multiple comparisons.ConclusionsWe found evidence that germline variants were associated withTP53andPIK3CAmutation status in breast cancers. Variants near the estrogen receptor alpha gene,ESR1,were significantly associated with overallTP53mutations and GOF mutations. Larger multi-ancestry studies are needed to confirm these findings and determine if these variants contribute to ancestry-specific differences in mutation frequency.

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Section: Independent Validation Studiesmentioning

confidence: 99%

Association ofESR1germline variants withTP53somatic variants in breast tumors in a genome-wide study

Tjader,

Beer,

Ramroop

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

Alam

Seo

Abdul‐Ghafar

et al. 2023

Briefings in Bioinformatics

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Purpose Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. Methods A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. Results Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. Conclusion AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.

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Mutations of TP53 and genes related to homologous recombination repair in breast cancer with germline BRCA1/2 mutations

et al. 2023

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Background Germline mutations of breast cancer susceptibility gene BRCA1 and BRCA2 (gBRCA1/2) are associated with elevated risk of breast cancer in young women in Asia. BRCA1 and BRCA2 proteins contribute to genomic stability through homologous recombination (HR)-mediated double-strand DNA break repair in cooperation with other HR-related proteins. In this study, we analyzed the targeted sequencing data of Korean breast cancer patients with gBRCA1/2 mutations to investigate the alterations in HR-related genes and their clinical implications. Materials and methods Data of the breast cancer patients with pathogenic gBRCA1/2 mutations and qualified targeted next-generation sequencing, SNUH FiRST cancer panel, were analyzed. Single nucleotide polymorphisms, small insertions, and deletions were analyzed with functional annotations using ANNOVAR. HR-related genes were defined as ABL1, ATM, ATR, BARD1, BRCA1, BRCA2, CDKN1A, CDKN2A, CHEK1, CHEK2, FANCA, FANCD2, FANCG, FANCI, FANCL, KDR, MUTYH, PALB2, POLE, POLQ, RAD50, RAD51, RAD51D, RAD54L, and TP53. Mismatch-repair genes were MLH1, MSH2, and MSH6. Clinical data were analyzed with cox proportional hazard models and survival analyses. Results Fifty-five Korean breast cancer patients with known gBRCA1/2 mutations and qualified targeted NGS data were analyzed. Ethnically distinct mutations in gBRCA1/2 genes were noted, with higher frequencies of Val1833Ser (14.8%), Glu1210Arg (11.1%), and Tyr130Ter (11.1%) in gBRCA1 and Arg2494Ter (25.0%) and Lys467Ter (14.3%) in gBRCA2. Considering subtypes, gBRCA1 mutations were associated with triple-negative breast cancers (TNBC), while gBRCA2 mutations were more likely hormone receptor-positive breast cancers. At least one missense mutation of HR-related genes was observed in 44 cases (80.0%). The most frequently co-mutated gene was TP53 (38.1%). In patients with gBRCA1/2 mutations, however, genetic variations of TP53 occurred in locations different from the known hotspots of those with sporadic breast cancers. The patients with both gBRCA1/2 and TP53 mutations were more likely to have TNBC, high Ki-67 values, and increased genetic mutations, especially of HR-related genes. Survival benefit was observed in the TP53 mutants of patients with gBRCA2 mutations, compared to those with TP53 wild types. Conclusion Our study showed genetic heterogeneity of breast cancer patients with gBRCA1 and gBRCA2 mutations in the Korean populations. Further studies on precision medicine are needed for tailored treatments of patients with genetic diversity among different ethnic groups.

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TP53 somatic mutations in Asian breast cancer are associated with subtype-specific effects

Cited by 3 publications

References 43 publications

Association ofESR1germline variants withTP53somatic variants in breast tumors in a genome-wide study

Association ofESR1germline variants withTP53somatic variants in breast tumors in a genome-wide study

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

Mutations of TP53 and genes related to homologous recombination repair in breast cancer with germline BRCA1/2 mutations

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