Correlating p53 immunostaining patterns with somatic TP53 mutation and functional properties of mutant p53 in triple-negative breast cancer

Correlating p53 immunostaining patterns with somatic TP53 mutation and functional properties of mutant p53 in triple-negative breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Correlating p53 immunostaining patterns with somatic TP53 mutation and functional properties of mutant p53 in triple-negative breast cancer Jun Kang, Meejeong Kim, Miseon Lee, Ahwon Lee, Byung-Ock Choi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4645054/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Immunohistochemical (IHC) staining pattern of p53 has been recognized as a potential surrogate marker for TP53 mutations across diverse cancer types. Nevertheless, interpretative criteria for p53 IHC expression as a predictive tool for TP53 mutation in triple-negative breast cancer (TNBC) have not been established yet. A total of 113 TNBC cases were analyzed for p53 IHC pattern and somatic TP53 mutation using whole exome sequencing. Functional properties of TP53 mutations were determined using the National Cancer Institute (NCI) TP53 database. P53 IHC staining patterns fell into three distinct categories: nuclear overexpression (n = 58), absence of staining indicating null pattern (n = 40), and wild-type expression (n = 15). The cutoff for predictive p53 nuclear overexpression was determined to be 80%. This threshold correlated strongly with TP53 mutations. Notably, nuclear overexpression had a positive predictive value (PPV) of 83% for missense or in-frame mutations, while the null pattern showed a PPV of 85% for detecting nonsense, frameshift, or splicing mutations. Furthermore, p53 overexpression was significantly linked to missense mutations within the DNA-binding domain (DBD) exhibiting gain-of-functon(GOF) or dominant-negative effect (DNE). Cases exhibiting both nuclear overexpression and cytoplasmic expression (n = 5) were correlated with nonsense or frameshift mutations affecting the DBD, NLS, or splice sites. Subclonal p53 staining patterns observed in 7 cases were found to be associated with TP53 mutations. Our study proposes newly defined criteria for interpreting p53 immunostaining patterns in TNBC, potentially allowing for the prediction of TP53 mutation types and their functional implications. Health sciences/Biomarkers/Diagnostic markers Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Tumour biomarkers p53 immunohistochemistry p53 expression mutant p53 gain-of-function TP53 mutation triple-negative breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Triple-negative breast cancer (TNBC) represents a distinct subtype characterized by the absence of expression of estrogen receptor (ER) and progesterone receptor (PR) and the lack of expression or amplification of human epidermal growth factor receptor 2 (HER2). It comprises a heterogeneous group of tumors with aggressive clinical behavior, limited treatment options, and a higher likelihood of early relapse compared to other subtypes of breast cancer 1 . TP53 mutations frequently occur in TNBC with a high prevalence (60 ‒ 80%), making them an attractive therapeutic target or marker for predicting chemotherapeutic outcomes 2-5 . The TP53 gene located at 17p13.1 locus, encodes the tumor suppressor protein p53, critical guardian of genomic integrity. Structurally, the p53 protein comprises four major functional domains: the N-terminal transactivation domain (TAD), the proline-rich domain (PRD), the DNA-binding domain (DBD), which is often the focus of mutations, and the unstructured C-terminal domain (CTD) including a nuclear localization signal (NLS) and a nuclear export signal (NES) 6 . As a regulator of gene expression, p53 governs cellular responses under stress conditions, activating mechanisms such as cell cycle arrest, apoptosis, and senescence 7 . Mutations in TP53 are known to disrupt normal functioning of p53, leading to loss of its tumor-suppressive activities and contributing to the development and progression of malignancies. In parallel, assessment of p53 protein expression using immunohistochemistry (IHC) serves as a valuable diagnostic tool with sensitivity and specificity more than 95 % to infer TP53 mutation status in various cancers 8,9 . The relationship between TP53 mutations and p53 IHC patterns has been extensively studied, particularly in high-grade serous ovarian carcinomas and, more recently, endometrial carcinomas 10-13 . However, the relationship between p53 expression and TP53 mutation prediction in TNBC remains unclear. A specific threshold for p53 overexpression has not been established yet. While functional significance of individual TP53 mutations is not fully understood, changes in particular p53 domains are anticipated to influence the protein's gain-of-function (GOF) oncogenic activities, which can affect processes such as metastasis and chemoresistance in breast cancer 14-16 . However, how these alterations correlate with p53 IHC staining patterns and various functional consequences of mutant p53 in breast cancer remains poorly understood. Thus, this study aimed to define abnormal p53 IHC patterns indicative of aberrant or mutation-driven expressions associated with TP53 mutations in TNBC, an area not well studied yet. By systematically analyzing functional attributes of mutant p53 within the database, this study aims to elucidate correlations between TP53 mutation functionalities and aberrant p53 IHC expression patterns. Results P53 immunohistochemistry patterns P53 overexpression for TP53 mutation prediction achieved an area under the curve (AUC) of 0.88 (95% CI: 0.76 ‒ 0.98). We established a new cutoff value for p53 overexpression at 80% to optimally predict TP53 mutation, corresponding to the highest Youden index. This threshold achieved a sensitivity of 0.95 (95% CI: 0.88 ‒ 0.99), a specificity of 0.78 (95% CI: 0.52 ‒ 0.94), and an accuracy of 0.91 (95% CI: 0.84 ‒ 0.97). We observed that an increase in p53 expression rate improved the accuracy of TP53 mutation prediction, as evidenced by a rising Youden index, which peaked at 80% (Supplementary Figs. 1a and 1b). Cases with TP53 mutations predominantly showed p53 nuclear expression rates above 75%, indicating a strong correlation between a high p53 expression rate and the presence of TP53 mutations (Supplementary Fig. 1c). IHC staining patterns of p53 were classified into three categories: (1) nuclear overexpression, characterized by strong and diffuse nuclear staining in at least 80% of tumor cells (Fig. 1a), (2) complete absence of staining (null pattern; Fig. 1b), (3) wild-type, identified by a mix of positive and negative cells at varying intensities, constituting less than 80% of tumor cells (Fig. 1c). In a total of 113 TNBC cases, p53 IHC expression patterns included overexpression in 58 cases, null in 40 cases, and wild-type in 15 cases. Clinicopathologic characteristics of patients are summarized in Supplementary Table 1. Although established criteria for classifying p53 immunostaining patterns in breast cancer are lacking, these categories we presented were adapted from widely used standards in various cancers, particularly endometrial and ovarian carcinomas 8,11,17 . Correlations between p53 immunostaining patterns and TP53 alterations Somatic TP53 mutation was detected in 89 (79%) of 113 TNBC cases. Two cases exhibiting double mutations: R280T + G245D in one patient and p.S127Lfs*42 + splicing mutation in another patient. These two cases harboring two types of TP53 mutations simultaneously were counted as one for mutation type in the classification. Therefore, 91 TP53 mutations were classified as missense (n = 47), in-frame (n = 3), frameshift (n = 18), nonsense (n = 15), and splicing (n = 8) mutations. Types of TP53 mutations according to p53 IHC patterns are presented in Table 1. The positive predictive value (PPV) for p53 overexpression in detecting missense or in-frame mutations was 83%. The PPV for p53 null pattern in predicting nonsense, frameshift, or splicing mutations was 85% (Table 2). The majority (91%, 51 of 56) of cases with p53 overexpression had TP53 mutations in the DBD (Fig. 2a). In contrast, 68% (23 of 34) cases with p53 null patterns showed DBD site mutations, which were fewer than those with p53 overexpression. Instead, cases with p53 null patterns had more frequent mutations at other sites: splice site (18%), TAD (3%), PRD (9%), and NES (6%) (Fig. 2b). In 89 TNBC cases with TP53 mutations, 63 (71%) exhibited heterozygous deletion and 5 (6%) showed low-level gain. There were no cases of homozygous deletion or amplification. All 89 cases with TP53 mutations had loss of heterozygosity (LOH), including 21 (23%) cases with copy neutral LOH. There was no significant relationship between p53 IHC pattern and TP53 copy number variation (CNV) or LOH. Functional properties of mutant p53 and their correlations with p53 immunostaining patterns Using the National Cancer Institute (NCI) TP53 database, assessment of functional properties of mutant p53 was available for 72 of 91 TP53 mutations. Functional properties were not reported for several mutations, including splicing mutations (n = 8), frameshift mutations (n = 6), in-frame deletions (n = 3), nonsense mutation (n = 1), and missense mutation (n = 1). Consequently, these were classified into a no evidence (NE) group. Functional heterogeneity enabled a mutant p53 protein to concomitantly exhibit loss of function (LOF), dominant-negative effect (DNE), and GOF as described in the discussion section. All 72 TP53 mutations were classified as LOF. Of these, 35 exhibited DNE, 31 of which also demonstrated GOF. Figure 3 summarizes characteristics of TP53 alteration and the associated mutant p53 function according to p53 IHC expression patterns in 113 TNBC cases. The majority of p53 overexpression exhibited GOF predominantly comprising missense mutations mainly located in the DBD, while none of the cases with null or wild-type pattern showed GOF ( P < 0.001). Additionally, p53 overexpression (compared to null or wild-type pattern) was significantly associated with hotspot mutations ( P < 0.001). GOF mutations were more likely to exhibit hotspots than NE-GOF mutations ( P < 0.001). GOF properties for 31 TP53 mutations representing 17 unique mutations are summarized in Supplementary Table 2. There was no relationship between the functionality of mutant p53 and CNV or LOH. Cytoplasmic or subclonal p53 immunostaining pattern with TP53 mutation Apart from the p53 IHC pattern classification based on nuclear expression, a minority of cases exhibited cytoplasmic or subclonal staining patterns. These cases showed unique features of TP53 mutations, which are separately detailed in our findings. Cytoplasmic p53 expression defined as at least 80% of tumor cells in this study with variable nuclear staining was observed in 5 (4%) of 113 of TNBCs, including 3 cases with p53 nuclear overexpression exhibiting weak to moderate cytoplasmic staining pattern (Fig. 4a) and two cases with complete absence of p53 nuclear expression but weak cytoplasmic expression (Fig. 4b). None of these cases showed cytoplasmic expression with a wild-type p53 nuclear pattern. All cases with cytoplasmic pattern harbored TP53 mutations, including nonsense mutations in the NES (n = 2), frameshift mutations in the DBD and NLS, and a splicing mutation (Fig. 4c). We found seven cases with a subclonal p53 staining pattern characterized by an abrupt transition between wild-type and aberrant expression (defined as nuclear overexpression, null pattern, or cytoplasmic expression). These cases were mainly characterized by a combination of p53 overexpression and a wild-type pattern (5 of 7; Figs. 5a and 5b). Other cases showed wild-type and null patterns (Fig. 5c) or a mixture of two mutant patterns and a wild-type pattern (Fig. 5d). There was no difference in histologic features among parts of heterogeneously staining tumor cells. TP53 mutations were identified in 5 of the 7 TNBC cases with subclonal p53 staining (Table 3). Two (cases 1 and 3) with a mixture of overexpression and wild-type patterns harbored missense mutations at the DBD. Other patterns (cases 2, 4, and 5) harbored frameshift or nonsense mutation located at the NLS, NES, or PRD. TP53 mutation was not identified in case 6 or 7, which exhibited focal subclonal staining (less than 5% of the entire tumor area). This was an expected result, as sequencing might not detect such low levels of subclonal staining. Discussion While p53 IHC has been a useful surrogate marker for TP53 mutation, its correlation with specific TP53 mutations in TNBC has not been well understood yet. Its effectiveness in predicting mutation subtypes or functional properties remains unclear. The p53 IHC pattern classification proposed in this study enables accurate prediction of TP53 mutations and categorization of mutation subtypes. Our findings indicate that an overexpression pattern of p53 IHC can reliably predict missense or in-frame mutations and that it is significantly associated with GOF or DNE properties. Conversely, a p53 null pattern strongly indicates the presence of nonsense, frameshift, or splicing mutations, typically with NE-GOF characteristics. Given these predictive capabilities, p53 IHC could be incorporated into clinical research designs to improve the selection of patients for therapies targeting specific TP53 mutations. The p53 overexpression pattern showed a high PPV for predicting TP53 missense or in-frame mutations in the DBD. These findings aligned with previous studies conducted on endometrial or ovarian carcinomas 8,10,17-19 . It was noteworthy that all GOF mutations in our dataset exhibited p53 overexpression patterns. A GOF mutation may result in increased levels of both mutant p53 mRNA and protein, leading to accumulation of degradation-resistant mutant p53 in the tumor nucleus 20,21 . These finding implicate a significant role of p53 IHC in predicting GOF mutations. The GOF of mutant p53 has been associated with tumorigenesis, cancer invasion, and metastasis 20 . Recent studies have suggested that GOF mutations play a poor prognostic role in malignancies 20,21 . Multiple strategies targeting mutant p53 with GOF have been proposed in therapies. These studies continue to attract sustained interest, with some progressing to clinical trials 22,23 . In accordance with this background, our results suggest that the p53 IHC overexpression pattern might be used as a screening tool to predict poor prognosis or identify potential candidates for targeted therapy that might be developed in the near future. Compared to the p53 overexpression pattern, the null pattern consistently showed nonsense, frameshift, or splicing mutation with a high predictive value. Therefore, the null pattern in TNBC represents a unique molecular characteristic, distinct from cases with wild-type p53. In line with findings from other solid tumors, p53 null pattern in TNBC should not be simply classified as 'p53-negative' based on usual cutoffs (e.g., less than 1% or 10% expression commonly used in breast cancer) 10,24,25 . Cytoplasmic p53 expression has been reported to be occurring in only 2-3% of tumors, with varying definitions across studies 8,10,18 . We considered cytoplasmic expression in over 80% of tumor cells as aberrant cytoplasmic expression, consistent with criteria from previous results of endometrial carcinoma 19 . In this study, all cases with aberrant cytoplasmic expression were associated with nonsense or frameshift mutations in the C-terminal side. Mutations at the C-terminal end of p53 (residues 311 – 367) tend to result in non-tetrameric structures, such as monomers or dimers, which can accumulate aberrantly in the cytoplasm, whereas the functional tetramer is predominantly localized in the nucleus 10,26 . Thus, cytoplasmic expression may hold distinct molecular significance regardless a coexistence of aberrant nuclear expression. However, mechanisms underlying the simultaneous nuclear and cytoplasmic accumulation of mutant p53 are not completely understood yet. During nucleocytoplasmic transport and degradation, localization of mutant p53 protein is regulated by diverse posttranslational modifications such as ubiquitination, acetylation, and phosphorylation. Varying activation of regulatory proteins involved in these modifications, such as MDM2 and deubiquitinases, might lead to different levels of mutant p53 nuclear accumulation 27 . We observed a subclonal p53 pattern in 6% of the TNBC cases. Their mutation types corresponded to areas of aberrant p53 staining patterns observed. Recent studies on endometrial carcinoma have reported that cases exhibiting a subclonal p53 IHC pattern often harbor TP53 mutations, supporting that this pattern represents true mutant subclones 19,28 . Our findings also indicate that a subclonal p53 staining pattern might represent a mutant-type expression pattern, although the clinicobiologic significance of the cutoff has not been defined yet. The mechanism behind wild-type staining in TP53 -mutated tumor cells might be explained by the absence of LOH, although we sequenced the entire tumor area without distinguishing each component showing subclonal expression. We encountered 11 discordant cases between IHC and sequencing results: aberrant p53 expression but no TP53 mutation (IHC false positivity; n = 10, consisting of 4 cases of overexpression and 6 cases of null pattern) and p53 wild-type expression but with TP53 mutation (IHC false negativity; n = 1). On reviewing IHC false-positive cases, we found that two cases exhibited intratumoral heterogeneity, with less than 5% of cells showing p53 overexpression, which might have been undetected by sequencing. This discrepancy in the null pattern might be due to large TP53 deletions undetectable by sequencing variant allele frequency. However, all cases showed LOH with shallow copy number alterations, and we could not determine the reason. In the false negative case, there was a splicing mutation with a variant allele frequency of 0.42 but it exhibited a wild-type pattern on p53 IHC. This type of discrepancy can be observed in splicing mutations or truncation mutations at the C-terminal stop-gain, resulting in a detectable but nonfunctional p53 protein that mimics the wild-type staining pattern 17 . Mutant p53 proteins exhibit diverse interactions with cellular components, yielding varied functional outcomes, wherein both LOF and GOF can occur concurrently 29 . Some TP53 mutations may act in a dominantly negative manner, with mutant p53 protein disrupting activity of the wild-type p53 protein. This disruption leads to the loss of normal tumor-suppressive function while simultaneously acquiring new functions that contribute to tumorigenesis, representing a GOF. In our study, all DNE mutations exhibited p53 overexpression pattern, with the majority also displaying GOF properties. These support that DNE of mutant p53 is associated with elevated levels of mutant p53 in tumor cells, raising the risk of forming inactive p53 complexes that compromise wild-type p53 function. However, DNE has emerged as a challenging phenotype to characterize. The influence of mutant p53 on wild-type appears to vary across tissues and contexts 26 . In breast cancer research, a definitive cutoff value for p53 nuclear overexpression has not been established yet. The threshold for assessing p53 overexpression varies widely, with reported values ranging from 1% to 90% 30 . Previous studies have suggested that a suitable cutoff for p53 in breast cancer might be around 10%, as TP53 mutations are commonly observed beyond this level 31-33 . For TNBC, we identified an optimal cutoff at 80% based on TP53 mutation prediction, higher than previously reported cutoffs for general breast cancer. However, this cutoff may not apply across all breast cancer types. Further research is necessary to explore p53 IHC interpretations in other molecular subtypes such as luminal or HER2-enriched subtypes. Another limitation may arise from interpreting cytoplasmic expression of p53, as it can be complicated by potential overlap with nuclear expression and variations in IHC performance. To reduce these ambiguities, we used a high cutoff of 80% staining area for cytoplasmic expression based on the prior study 19 . Nonetheless, additional research is required to elucidate the significance of cytoplasmic expression, even in cases where it is focal. In conclusion, our study introduces newly defined criteria for interpreting p53 IHC expression in TNBC. These criteria could enable the prediction of TP53 mutation types and their functional implications, providing valuable insights for future clinical research and potentially improving stratification of treatment options for patients. Materials And Methods Case selection This retrospective study included 113 consecutively collected cases of TNBC who underwent surgery at Seoul St. Mary's Hospital between 2018 and 2022. This study was approved by the Institutional Research Ethics Board of the Catholic University of Korea with a waiver of patient’s informed consent (IRB No. KC22SISI0716). Cases who underwent neoadjuvant therapy or had a tumor size of less than 1.5 cm in its greatest dimension were excluded. Confirmation of TNBC cases involved IHC for ER (clone SP1; cat. no. 790-4324; Roche/Ventana, ready to use), PR (clone 1E2; cat. No. 790-4296; Roche/Ventana, ready to use), and HER2 (clone 4B5; cat. No. 790-4493; Roche/Ventana, ready to use). Additional HER2 silver in situ hybridization (SISH), when necessary, was conducted using the VENTANA HER2 Dual ISH DNA Probe Cocktail. Classification of histological types for cases was based on the 5th edition of the WHO classification of breast tumors 34 . The histological type, histological grade, and tumor stage, as per the 8th edition of the American Joint Committee on Cancer (AJCC) TNM classification, were retrieved from their pathology reports. Immunohistochemistry Tissue acquisition to fixation time was minimized to within 2 hours. Samples were then fixed in 10% neutral buffered formalin (NBF) for 6 – 72 hours and sliced at 5 mm intervals. Sections of 4-µm-thick were obtained from paraffin-embedded blocks, deparaffinized in xylene, and rehydrated through a graded series of ethanol. P53 IHC was performed for 113 TNBC cases with DO7 antibody (cat. No. 800-2912; Roche Diagnostics, IN, USA; ready to use) and a Leica Bond III system (Leica Biosystems, Inc., Buffalo Grove, IL, USA) following the manufacturer’s protocol. One of four experienced pathologists reviewed p53 IHC whole slides while blinded to tumor's TP53 mutation status. The p53 IHC staining pattern was manually assessed based on nuclear expression in tumor cells. The proportion of expression across all tumor cells was manually quantified in 5% increments for each case, ranging from 0 to 100%. Three other pathologists reviewed assessments. Cases with discordant measures were discussed until a consensus was reached. To define a cutoff for p53 IHC overexpression, a p53 expression rate-dependent receiver operating characteristic (ROC) curve was analyzed using TP53 mutation status as the endpoint to optimize the Youden index. Whole exome sequencing Whole exome sequencing was conducted for formalin-fixed, paraffin-embedded tissue blocks of a representative normal and tumor slide for the 113 TNBC cases. Paired-end sequences were generated with a NovaSeq6000 (Illumina, San Diego, CA, USA) platform following the manufacturer's protocols. The sequencing quality was checked using FastQC. Sequence reads were aligned to human genome assembly hg19 (GRCh37) using Burrows-Wheeler Aligner (BWA MEM, http://bio-bwa.sourceforge.net/), specifically the BWA-MEM algorithm 35 . Paired normal and tumor sequence reads were aligned and processed together to identify somatic TP53 mutations. MuTect2 was used to detect somatic mutations. Mutations were filtered using the filtration tool of the Genome Analysis Toolkit (GATK). Functional annotation of filtered mutations was performed with SnpEff. Interpretation was performed with dbSNP and SNPs from the 1000 Genome Project. Further annotation with databases including ESP6500, ClinVar, dbNSFP, and American College of Medical Genetics and Genomics (ACMG) information was performed using an in-house program and SnpEff. VCF files of somatic mutations were processed to MAF files using the vcf2maf program. The ‘maftools’ R package was used to filter out synonymous mutations. Copy number and loss of heterozygosity analysis Copy number analysis of TP53 was performed using GISTIC 2.0 (Genomic Identification of Significant Targets in Cancer, RRID:SCR_000151) applied to segmentation files obtained from Sequenza analysis 36,37 . The analysis was undertaken to pinpoint significant loci of somatic CNVs. The analysis was configured with a focal length cutoff of 0.5 to focus solely on focal alterations. A confidence level of 0.9 was set to ensure high reliability of findings, along with a q -value threshold of 0.05 to distinguish between significant and non-significant results. Additionally, the X chromosome was excluded to eliminate variations related to sex chromosomes. The algorithm calculated G-scores for each region, taking into account the amplitude of copy number changes and their frequency across the sample set. Subsequently, it categorized these changes into homozygous deletions, heterozygous deletions, low-level gains, and amplifications. To characterize the LOH at the TP53 locus, the GenomicRanges package in R was employed to process segmented CNV data from whole-exome sequencing. We annotated segmented data for traceability and determined LOH status for each segment by assessing the absence of heterozygosity. This binary information was then integrated into a dataset. Specifically, segments overlapping with the TP53 gene on chromosome 17, indicating LOH at the TP53 locus, were identified. These data were compiled into a TP53 -specific LOH dataset for further analysis. If LOH occurred without a change in copy number in TP53 , it was designated as copy neutral LOH. Database for functional classification of TP53 mutation Information on functional properties of TP53 mutations was compiled in the NCI TP53 database (R20 version; https://tp53.isb-cgc.org) 38 . Using this database, we systematically explored and analyzed functional properties of mutant p53. In this dataset, data were extracted from publications that reported functional assessment of mutant p53 proteins in human or yeast cells covering various aspects, such as transcriptional activities on p53-response elements, DNE on activities of wild-type p53, the ability to transactivate promoters not induced by wild-type p53, and the capability to promote cell growth and confer tumorigenicity. This assessment was conducted either by transfection and overexpression of mutant proteins or by evaluating endogenous mutants. Based on functional outcomes in the experimental database, TP53 mutations in our cases were categorized into either LOF, DNE, or GOF. Specifically, we considered LOF when the mutant protein resulted in the loss of functional properties of wild-type p53. DNE was considered when mutant proteins inhibited the wild-type protein in transactivation or cell growth assays. GOF encompassed functional properties exhibited by the mutant protein but not by the wild-type counterpart. In the current study, a mutant p53 was classified as having GOF when it met at least one of the following categories: 1) tumorigenic property (in nude mice) in transfected cells; 2) interference with p73 activity; 3) transactivation of genes repressed by wild-type p53; 4) resistance to a cytotoxic drug; 5) increase growth rate; 6) cooperation with an oncogene for the transformation of rat embryonic fibroblast or mouse embryonic fibroblast cells; and 7) alteration of mutant p53 stability and activity by impairing regulators, including HSC70 and MDM2 21,39 . Statistical analysis The Youden Index was utilized to determine the optimal cutoff value for p53 overexpression, defined as sensitivity + specificity − 1. The performance of the p53 expression cutoff point in predicting TP53 mutation was assessed using AUC, sensitivity, specificity, PPV, negative predictive value, and accuracy. The relationship between p53 IHC pattern and TP53 mutation type was assessed using the Chi-square test or Fisher's exact test, as appropriate. Results were considered significant if the p- value was equal to or less than 0.05. All statistical analyses were conducted using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Code availability Data analysis was conducted using R version 4.3.1. The code used in the study is available from the corresponding author upon reasonable request. Acknowledgements This study was supported by Research Fund of Seoul St.Mary’s Hospital, The Catholic University of Korea (M.K., No. ZC22TISI0880) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (J.K., No. 2021R1I1A1A01043754) Author contributions M.K., A.L., and J.K. designed the study. M.K. and M.L. collected patient materials, while B.C., W.P., and J.L. provided the patient data necessary for this collection. M.K., M.L., A.L., and J.K. reviewed the slides and manually quantified the immunoexpression. B.C., W.P., and J.L. analyzed and interpreted the patient data. J.K. provided the sequencing data. M.K and M.L performed the statistical analyses and interpreted the data. M.K. wrote the manuscript under the supervision of A.L. and J.K. All authors read and approved the final manuscript. Competing interests All authors declare no financial or non-financial competing interests. References de Ruijter, T. 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Heterogeneity of TP53 Mutations and P53 Protein Residual Function in Cancer: Does It Matter? Front Oncol 10 , 593383 (2020). Mueller, S. et al. p53 Expression in Luminal Breast Cancer Correlates With TP53 Mutation and Primary Endocrine Resistance. Mod Pathol 36 , 100100 (2023). Hasebe, T. et al. p53 expression in tumor-stromal fibroblasts forming and not forming fibrotic foci in invasive ductal carcinoma of the breast. Mod Pathol 23 , 662-672 (2010). Guarneri, V. et al. Predictive and prognostic role of p53 according to tumor phenotype in breast cancer patients treated with preoperative chemotherapy: a single-institution analysis. Int J Biol Markers 25 , 104-111 (2010). Kim, H. S. et al. Overexpression of p53 is correlated with poor outcome in premenopausal women with breast cancer treated with tamoxifen after chemotherapy. Breast Cancer Res Treat 121 , 777-788 (2010). Lyon, F. I. WHO Classification of Tumours Editorial Board. Breast tumours. 5th ed. , (2019). Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics 25 , 1754-1760 (2009). Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biology 12 , R41 (2011). Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol 26 , 64-70 (2015). de Andrade, K. C. et al. The TP53 Database: transition from the International Agency for Research on Cancer to the US National Cancer Institute. Cell Death Differ 29 , 1071-1073 (2022). Petitjean, A. et al. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database. Hum Mutat 28 , 622-629 (2007). Tables Table 1. Correlation between p53 immunohistochemical staining pattern and TP53 mutational status P53 pattern TP53 mutation status, n (%) Total Missense In-frame Frameshift Nonsense Splicing Absent Overexpression 47 (78%) 3 (5%) 2 (3%) 3 (5%) 1(2%) 4 (7%) 60 a (100%) Null 0 0 16 (40%) 12 (31%) 6 (15%) 6 (15%) 40 (100%) Wild-type 0 0 0 0 1(7%) 14 (93%) 15 (100%) a The total number is based on the count of 60 mutant variants in 58 cases, including two cases with double mutations: R280T + G245D, and S127Lfs*42 + splicing mutation. Table 2. P53 immunostaining patterns and mutation type prediction Statistics P53 Overexpression (Missense/In-frame) P53 Null pattern (Nonsense /Frameshift/Splicing) Sensitivity (95% CI) 100% (93 – 100) 85% (70 – 94) Specificity (95% CI) 85% (74 – 92) 92% (83 – 97) Positive predictive value (95% CI) 83% (74 – 90) 85% (72 – 93) Negative predictive value (95% CI) 100% (94 – 100) 92% (84 – 96) Accuracy (95% CI) 91% (85 – 96) 89% (82 – 94) Table 3. Subclonal p53 immunostaining patterns and TP53 mutations Case no. Histologic type and grade P53 immunostaining pattern and proportion TP53 mutation Mutation type Domain Hotspot VAF Nucleotide change Amino acid change LOH 1 IBC-NST, G2 Overexpression (30%) and wild-type (70%) Present Missense DBD Yes 0.14 c.711G>A p.Met237Ile LOH 2 IBC-NST, G3 Overexpression with cytoplasmic staining (20%) and wild-type (80%) Present Frameshift deletion NLS No 0.32 c.902del p.Pro301GlnfsTer44 LOH 3 IBC-NST, G2 Overexpression (15%) and wild-type (85%) Present Missense DBD Yes 0.17 c.524G>A p.Arg175His LOH 4 IBC-NST, G3 Null pattern (90%) and wild-type (10%) Present Frameshift deletion NES No 0.38 c.1028del p.Glu343GlyfsTer2 LOH 5 IBC-NST, G3 Overexpression (40%), null pattern (30%), and wild-type (30%) Present Nonsense PRD No 0.13 c.281C>A p.Ser94Ter LOH 6 IBC-NST, G3 Overexpression (˂ 5%) and wild-type (> 95%) Absent - - - - - - LOH 7 APO, G2 Overexpression (˂ 5%) and wild-type (> 95%) Absent - - - - - - LOH IBC-NST, invasive breast carcinoma of no special type; G, grade; APO, carcinoma with apocrine differentiation; VAF, variant allele frequency; LOH, loss of heterozygosity Additional Declarations (Not answered) Supplementary Files supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4645054","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":325801594,"identity":"da5d53fe-0226-42cd-b7c0-c75e93462647","order_by":0,"name":"Jun Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACxh4ILcMGZD8AMnj4iNXCA9TCbABlEAA8CIpNAsQiqIW55/Czh18Y7Hj42HuPVX7NsQO6kPnhoxv4HNbbZm4sw5DMw8ZzLu227DYgg4HN2DgHn5Z+BjNpCQZmHjaJHLPbktuADKB3pPFrYf8G1FLPwyb/xqxYcls9EVp6e8wkPzAcBtrCY8b4cdthIrT0nCmTZjA4DvRLjrE04zYgg5mAXwx70rdJ/qiolpNvP2P48ee2ant+9uaHj/FqaQAGNI8BhMMMjiRmPMpBQB7kuB8wV/7Ap3QUjIJRMApGLAAAp6A5Yf/J4QIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7967-0917","institution":"Seoul St. Mary’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Kang","suffix":""},{"id":325801595,"identity":"1008808a-6e9c-4e6e-8bd5-90f2ced60443","order_by":1,"name":"Meejeong Kim","email":"","orcid":"","institution":"Seoul St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meejeong","middleName":"","lastName":"Kim","suffix":""},{"id":325801596,"identity":"f1c72219-7471-453f-9eac-0bb25591bfed","order_by":2,"name":"Miseon Lee","email":"","orcid":"https://orcid.org/0000-0002-6385-7621","institution":"Seoul St. Mary's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Miseon","middleName":"","lastName":"Lee","suffix":""},{"id":325801597,"identity":"9ef29853-3e1c-48bc-813d-e1cf66ffb718","order_by":3,"name":"Ahwon Lee","email":"","orcid":"","institution":"Seoul St. Mary's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ahwon","middleName":"","lastName":"Lee","suffix":""},{"id":325801598,"identity":"f4d2f62b-3e8f-4afe-a6aa-4483c63b9cdc","order_by":4,"name":"Byung-Ock Choi","email":"","orcid":"","institution":"Seoul St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Byung-Ock","middleName":"","lastName":"Choi","suffix":""},{"id":325801599,"identity":"f8be6bd8-ea92-498f-b7fb-82d812ffac5a","order_by":5,"name":"Woo-Chan Park","email":"","orcid":"","institution":"Seoul St Mary' Hospital, the Catholic University Medical College, Korea","correspondingAuthor":false,"prefix":"","firstName":"Woo-Chan","middleName":"","lastName":"Park","suffix":""},{"id":325801600,"identity":"e4dff5b1-f0bf-4c1f-89d0-428eca45f9db","order_by":6,"name":"Sung Hun Kim","email":"","orcid":"","institution":"Seoul St. Mary's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sung","middleName":"Hun","lastName":"Kim","suffix":""},{"id":325801601,"identity":"2b1c9f3b-c894-4c72-9e32-edbfe9510736","order_by":7,"name":"Jieun Lee","email":"","orcid":"","institution":"Seoul St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jieun","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-06-26 22:55:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4645054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4645054/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60704489,"identity":"1a6e6c9f-83da-4419-801b-4c9d55579c23","added_by":"auto","created_at":"2024-07-19 19:09:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":758544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative patterns of p53 immunohistochemical staining in triple-negative breast cancer \u003c/strong\u003e(a) Overexpression pattern of p53 in more than 80% of tumor cells with strong nuclear expression (magnification, x200). (b) Complete loss of staining (null pattern; magnification, x400). (c) Wild-type pattern with variable and patchy nuclear expression (magnification, x200).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/8d5d27a8fe021dbc5460ed38.jpg"},{"id":60704160,"identity":"43b558cd-67b9-4594-be85-79dd6f6a12b6","added_by":"auto","created_at":"2024-07-19 19:01:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":466004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLollipop plot depicting the frequency and position of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTP53 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emutations based on p53 immunohistochemical staining patterns. \u003c/strong\u003eLocalization of TP53 mutation in (a) p53 overexpression (n=60) and (b) p53 null pattern (n=40).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/4529ad351fd5cad19c1a81a0.jpg"},{"id":60703787,"identity":"655999e0-fa2b-46b1-9d17-b59937cfbb3c","added_by":"auto","created_at":"2024-07-19 18:53:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":802365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of p53 immunohistochemical staining patterns and TP53 alteration in triple-negative breast cancer. \u003c/strong\u003eThe oncoplot presents TP53 status, including mutation parameters, functional properties, and copy number variations, according to p53 staining patterns.\u003c/p\u003e\n\u003cp\u003eTAD, transactivation domain; PRD, proline-rich domain; DBD, DNA-binding domain; NLS, nuclear localization signal; NES, nuclear export signal; NA, not available; GOF, gain-of-function; NE, no evidence of functional property; CNV, copy number variation; LOH, loss of heterozygosity.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/d7fc87797983a883273a2997.jpg"},{"id":60703782,"identity":"fdb5f4fa-6273-4ce3-b234-d9ebe20b9523","added_by":"auto","created_at":"2024-07-19 18:53:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":623717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytoplasmic p53 staining patterns and a lollipop plot of TP53 mutations. \u003c/strong\u003e(a) Case with moderate to strong cytoplasmic and nuclear staining (magnification, x400) harboring TP53 nonsense mutation, R342*. (b) Case with weak cytoplasmic expression and absence of nuclear expression (magnification, x400) harboring TP53 frameshift deletion, S261Vfs*84. (c) TP53 mutation frequency and position in the four cases with cytoplasmic expression, excluding one splicing mutation for which the domain location was not available.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/9484ba069c6a07903c171885.jpg"},{"id":60703788,"identity":"b8c9a09c-3a00-4052-92ff-ed3d3e5330d8","added_by":"auto","created_at":"2024-07-19 18:53:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1087153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative cases with subclonal p53 staining patterns. \u003c/strong\u003e(a) Case exhibiting a subclonal pattern with a combination of p53 overexpression (arrow) and wild-type pattern (arrowhead) with a TP53 missense mutation, M237I in DNA-binding domain (magnification, x100). (b) Case displaying overexpression (arrow) and wild-type (arrowhead) patterns with cytoplasmic expression harboring a TP53 frameshift deletion, P301Qfs44, near the nuclear localization signal (magnification, x400). (c) Case with a subclonal pattern showing a combination of wild-type (arrowhead) and null pattern (arrow) harboring a TP53 frameshift deletion, E343Gfs2, near the nuclear export signal domain (magnification, x200). (d) Case with a mixture of wild-type (white arrow), null (black arrow), and overexpression (arrowhead) harboring a TP53 nonsense mutation, S94* near the proline-rich domain (magnification, x10).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/428a3e4055919bfca1f43eb7.jpg"},{"id":60705459,"identity":"e3755ba7-bb6b-474c-92e5-8d1655b95bd6","added_by":"auto","created_at":"2024-07-19 19:18:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4576193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/b13e7ca7-62d9-4691-96a5-5352a6705ae5.pdf"},{"id":60703785,"identity":"5fb0ad22-d018-4c52-add9-1c4c80999121","added_by":"auto","created_at":"2024-07-19 18:53:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":80350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4645054/v1/1b20ddda2406de619b9bd617.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Correlating p53 immunostaining patterns with somatic TP53 mutation and functional properties of mutant p53 in triple-negative breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) represents a distinct subtype characterized by the absence of expression of estrogen receptor (ER) and progesterone receptor (PR) and the lack of expression or amplification of human epidermal growth factor receptor 2 (HER2). It comprises a heterogeneous group of tumors with aggressive clinical behavior, limited treatment options, and a higher likelihood of early relapse compared to other subtypes of breast cancer\u003csup\u003e1\u003c/sup\u003e. \u003cem\u003eTP53\u003c/em\u003e mutations frequently occur in TNBC with a high prevalence (60 ‒ 80%), making them an attractive therapeutic target or marker for predicting chemotherapeutic outcomes\u003csup\u003e2-5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eTP53\u003c/em\u003e gene located at 17p13.1 locus, encodes the tumor suppressor protein p53, critical guardian of genomic integrity. Structurally, the p53 protein comprises four major functional domains: the N-terminal transactivation domain (TAD), the proline-rich domain (PRD), the DNA-binding domain (DBD), which is often the focus of mutations, and the unstructured C-terminal domain (CTD) including a nuclear localization signal (NLS) and a nuclear export signal (NES)\u003csup\u003e\u0026nbsp;6\u003c/sup\u003e. As a regulator of gene expression, p53 governs cellular responses under stress conditions, activating mechanisms such as cell cycle arrest, apoptosis, and senescence\u003csup\u003e7\u003c/sup\u003e. Mutations in \u003cem\u003eTP53\u003c/em\u003e are known to disrupt normal functioning of p53, leading to loss of its tumor-suppressive activities and contributing to the development and progression of malignancies. In parallel, assessment of p53 protein expression using immunohistochemistry (IHC) serves as a valuable diagnostic tool with sensitivity and specificity more than 95 % to infer \u003cem\u003eTP53\u003c/em\u003e mutation status in various cancers\u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe relationship between \u003cem\u003eTP53\u003c/em\u003e mutations and p53 IHC patterns has been extensively studied, particularly in high-grade serous ovarian carcinomas and, more recently, endometrial carcinomas\u003csup\u003e10-13\u003c/sup\u003e. However, the relationship between p53 expression and \u003cem\u003eTP53\u003c/em\u003e mutation prediction in TNBC remains unclear. A specific threshold for p53 overexpression has not been established yet. While functional significance of individual \u003cem\u003eTP53\u003c/em\u003e mutations is not fully understood, changes in particular p53 domains are anticipated to influence the protein\u0026apos;s gain-of-function (GOF) oncogenic activities, which can affect processes such as metastasis and chemoresistance in breast cancer\u003csup\u003e14-16\u003c/sup\u003e. However, how these alterations correlate with p53 IHC staining patterns and various functional consequences of mutant p53 in breast cancer remains poorly understood.\u003c/p\u003e\n\u003cp\u003eThus, this study aimed to define abnormal p53 IHC patterns indicative of aberrant or mutation-driven expressions associated with \u003cem\u003eTP53\u003c/em\u003e mutations in TNBC, an area not well studied yet. By systematically analyzing functional attributes of mutant p53 within the database, this study aims to elucidate correlations between \u003cem\u003eTP53\u003c/em\u003e mutation functionalities and aberrant p53 IHC expression patterns.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eP53 immunohistochemistry patterns\u003c/h2\u003e\n\u003cp\u003eP53 overexpression for \u003cem\u003eTP53\u003c/em\u003e mutation prediction achieved an area under the curve (AUC) of 0.88 (95% CI: 0.76 ‒ 0.98). We established a new cutoff value for p53 overexpression at 80% to optimally predict \u003cem\u003eTP53\u003c/em\u003e mutation, corresponding to the highest Youden index. This threshold achieved a sensitivity of 0.95 (95% CI: 0.88 ‒ 0.99), a specificity of 0.78 (95% CI: 0.52 ‒ 0.94), and an accuracy of 0.91 (95% CI: 0.84 ‒ 0.97).\u0026nbsp;We observed that an increase in p53 expression rate improved the accuracy of \u003cem\u003eTP53\u003c/em\u003e mutation prediction, as evidenced by a rising Youden index, which peaked at 80% (Supplementary Figs. 1a and 1b). Cases with \u003cem\u003eTP53\u003c/em\u003e mutations predominantly showed p53 nuclear expression rates above 75%, indicating a strong correlation between a high p53 expression rate and the presence of \u003cem\u003eTP53\u003c/em\u003e mutations (Supplementary Fig. 1c).\u003c/p\u003e\n\u003cp\u003eIHC staining patterns of p53 were classified into three categories: (1) nuclear overexpression, characterized by strong and diffuse nuclear staining in at least 80% of tumor cells (Fig. 1a), (2) complete absence of staining (null pattern; Fig. 1b), (3) wild-type, identified by a mix of positive and negative cells at varying intensities, constituting less than 80% of tumor cells (Fig. 1c). In a total of 113 TNBC cases, p53 IHC expression patterns included overexpression in 58 cases, null in 40 cases, and wild-type in 15 cases. Clinicopathologic characteristics of patients are summarized in Supplementary Table 1. Although established criteria for classifying p53 immunostaining patterns in breast cancer are lacking, these categories we presented were adapted from widely used standards in various cancers, particularly endometrial and ovarian carcinomas\u003csup\u003e8,11,17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCorrelations between p53 immunostaining patterns and TP53 alterations\u003c/h2\u003e\n\u003cp\u003eSomatic \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutation was detected in 89 (79%) of 113 TNBC cases. Two cases exhibiting double mutations: R280T + G245D in one patient and p.S127Lfs*42 + splicing mutation in another patient. These two cases harboring two types of \u003cem\u003eTP53\u003c/em\u003e mutations simultaneously were counted as one for mutation type in the classification. Therefore, 91 \u003cem\u003eTP53\u003c/em\u003e mutations were classified as missense (n = 47), in-frame (n = 3), frameshift (n = 18), nonsense (n = 15), and splicing (n = 8) mutations. Types of \u003cem\u003eTP53\u003c/em\u003e mutations according to p53 IHC patterns are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;positive predictive value (PPV) for p53 overexpression in detecting missense or in-frame mutations was 83%. The PPV for p53 null pattern in predicting nonsense, frameshift, or splicing mutations was 85% (Table 2). The majority (91%, 51 of 56) of cases with p53 overexpression had \u003cem\u003eTP53\u003c/em\u003e mutations in the DBD (Fig. 2a). In contrast, 68% (23 of 34) cases with p53 null patterns showed DBD site mutations, which were fewer than those with p53 overexpression. Instead, cases with p53 null patterns had more frequent mutations at other sites: splice site (18%), TAD (3%), PRD (9%), and NES (6%) (Fig. 2b).\u003c/p\u003e\n\u003cp\u003eIn 89 TNBC cases with \u003cem\u003eTP53\u003c/em\u003e mutations, 63 (71%) exhibited heterozygous deletion and 5 (6%) showed low-level gain. There were no cases of homozygous deletion or amplification. All 89 cases with \u003cem\u003eTP53\u003c/em\u003e mutations had\u0026nbsp;loss of heterozygosity (LOH), including 21 (23%) cases with copy neutral LOH. There was no significant relationship between p53 IHC pattern and \u003cem\u003eTP53\u003c/em\u003e copy number variation (CNV) or LOH.\u003c/p\u003e\n\u003ch2\u003eFunctional properties of mutant p53 and their correlations with p53 immunostaining patterns\u003c/h2\u003e\n\u003cp\u003eUsing the National Cancer Institute (NCI) \u003cem\u003eTP53\u003c/em\u003e database, assessment of functional properties of mutant p53 was available for 72 of 91 \u003cem\u003eTP53\u003c/em\u003e mutations. Functional properties were not reported for several mutations, including splicing mutations (n = 8), frameshift mutations (n = 6), in-frame deletions (n = 3), nonsense mutation (n = 1), and missense mutation (n = 1). Consequently, these were classified into a no evidence (NE) group. Functional heterogeneity enabled a mutant p53 protein to concomitantly exhibit loss of function (LOF), dominant-negative effect (DNE), and GOF as described in the discussion section. All 72 \u003cem\u003eTP53\u003c/em\u003e mutations were classified as LOF. Of these, 35 exhibited DNE, 31 of which also demonstrated GOF.\u003c/p\u003e\n\u003cp\u003eFigure 3 summarizes characteristics of \u003cem\u003eTP53\u003c/em\u003e alteration and the associated mutant p53 function according to p53 IHC expression patterns in 113 TNBC cases. The majority of p53 overexpression exhibited GOF predominantly comprising missense mutations mainly located in the DBD, while none of the cases with null or wild-type pattern showed GOF (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Additionally, p53 overexpression (compared to null or wild-type pattern) was significantly associated with hotspot mutations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). GOF mutations were more likely to exhibit hotspots than NE-GOF mutations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). GOF properties for 31 \u003cem\u003eTP53\u003c/em\u003e mutations representing 17 unique mutations are summarized in Supplementary Table 2. There was no relationship between the functionality of mutant p53 and CNV or LOH.\u003c/p\u003e\n\u003ch2\u003eCytoplasmic or subclonal p53 immunostaining pattern with TP53 mutation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eApart from the p53 IHC pattern classification based on nuclear expression, a minority of cases exhibited cytoplasmic or subclonal staining patterns. These cases showed unique features of \u003cem\u003eTP53\u003c/em\u003e mutations, which are separately detailed in our findings. Cytoplasmic p53 expression defined as at least 80% of tumor cells in this study with variable nuclear staining was observed in 5 (4%) of 113 of TNBCs, including 3 cases with p53 nuclear overexpression exhibiting weak to moderate cytoplasmic staining pattern (Fig. 4a) and two cases with complete absence of p53 nuclear expression but weak cytoplasmic expression (Fig. 4b). None of these cases showed cytoplasmic expression with a wild-type p53 nuclear pattern. All cases with cytoplasmic pattern harbored \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutations, including nonsense mutations in the NES (n = 2), frameshift mutations in the DBD and NLS, and a splicing mutation (Fig. 4c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found seven cases with a subclonal p53 staining pattern characterized by an abrupt transition between wild-type and aberrant expression (defined as nuclear overexpression, null pattern, or cytoplasmic expression). These cases were mainly characterized by a combination of p53 overexpression and a wild-type pattern (5 of 7; Figs. 5a and 5b). Other cases showed wild-type and null patterns (Fig. 5c) or a mixture of two mutant patterns and a wild-type pattern (Fig. 5d).\u0026nbsp;There was no difference in histologic features among parts of heterogeneously staining tumor cells. \u003cem\u003eTP53\u003c/em\u003e mutations were identified in 5 of the 7 TNBC cases with subclonal p53 staining (Table 3). Two (cases 1 and 3) with a mixture of overexpression and wild-type patterns harbored missense mutations at the DBD. Other patterns (cases 2, 4, and 5) harbored frameshift or nonsense mutation located at the NLS, NES, or PRD. \u003cem\u003eTP53\u003c/em\u003e mutation was not identified in case 6 or 7, which exhibited focal subclonal staining (less than 5% of the entire tumor area). This was an expected result, as sequencing might not detect such low levels of subclonal staining.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile p53 IHC has been a useful surrogate marker for \u003cem\u003eTP53\u003c/em\u003e mutation, its correlation with specific \u003cem\u003eTP53\u003c/em\u003e mutations in TNBC has not been well understood yet. Its effectiveness in predicting mutation subtypes or functional properties remains unclear. The p53 IHC pattern classification proposed in this study enables accurate prediction of \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutations and categorization of mutation subtypes. Our findings indicate that an overexpression pattern of p53 IHC can reliably predict missense or in-frame mutations and that it is significantly associated with GOF or DNE properties. Conversely, a p53 null pattern strongly indicates the presence of nonsense, frameshift, or splicing mutations, typically with NE-GOF characteristics. Given these predictive capabilities, p53 IHC could be incorporated into clinical research designs to improve the selection of patients for therapies targeting specific \u003cem\u003eTP53\u003c/em\u003e mutations.\u003c/p\u003e\n\u003cp\u003eThe p53 overexpression pattern showed a high PPV for predicting \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emissense or in-frame mutations in the DBD. These findings aligned with previous studies conducted on endometrial or ovarian carcinomas\u003csup\u003e8,10,17-19\u003c/sup\u003e. It was noteworthy that all GOF mutations in our dataset exhibited p53 overexpression patterns. A GOF mutation may result in increased levels of both mutant p53 mRNA and protein, leading to accumulation of degradation-resistant mutant p53 in the tumor nucleus\u003csup\u003e20,21\u003c/sup\u003e. These finding implicate a significant role of p53 IHC in predicting GOF mutations. The GOF of mutant p53 has been associated with tumorigenesis, cancer invasion, and metastasis\u003csup\u003e20\u003c/sup\u003e. Recent studies have suggested that GOF mutations play a poor prognostic role in malignancies\u003csup\u003e20,21\u003c/sup\u003e. Multiple strategies targeting mutant p53 with GOF have been proposed in therapies. These studies continue to attract sustained interest, with some progressing to clinical trials\u003csup\u003e22,23\u003c/sup\u003e. In accordance with this background, our results suggest that the p53 IHC overexpression pattern might be used as a screening tool to predict poor prognosis or identify potential candidates for targeted therapy that might be developed in the near future.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to the p53 overexpression pattern, the null pattern consistently showed nonsense, frameshift, or splicing mutation with a high predictive value. Therefore, the null pattern in TNBC represents a unique molecular characteristic, distinct from cases with wild-type p53. In line with findings from other solid tumors, p53 null pattern in TNBC should not be simply classified as \u0026apos;p53-negative\u0026apos; based on usual cutoffs (e.g., less than 1% or 10% expression commonly used in breast cancer)\u003csup\u003e\u0026nbsp;10,24,25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCytoplasmic p53 expression has been reported to be occurring in only 2-3% of tumors, with varying definitions across studies\u003csup\u003e8,10,18\u003c/sup\u003e. We considered cytoplasmic expression in over 80% of tumor cells as aberrant cytoplasmic expression, consistent with criteria from previous results of endometrial carcinoma\u003csup\u003e19\u003c/sup\u003e. In this study, all cases with aberrant cytoplasmic expression were associated with nonsense or frameshift mutations in the C-terminal side. Mutations at the C-terminal end of p53 (residues 311 \u0026ndash; 367) tend to result in non-tetrameric structures, such as monomers or dimers, which can accumulate aberrantly in the cytoplasm, whereas the functional tetramer is predominantly localized in the nucleus\u003csup\u003e10,26\u003c/sup\u003e. Thus, cytoplasmic expression may hold distinct molecular significance regardless a coexistence of aberrant nuclear expression. However, mechanisms underlying the simultaneous nuclear and cytoplasmic accumulation of mutant p53 are not completely understood yet. During nucleocytoplasmic transport and degradation, localization of mutant p53 protein is regulated by diverse posttranslational modifications such as ubiquitination, acetylation, and phosphorylation. Varying activation of regulatory proteins involved in these modifications, such as MDM2 and deubiquitinases, might lead to different levels of mutant p53 nuclear accumulation\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe observed a subclonal p53 pattern in 6% of the TNBC cases. Their mutation types corresponded to areas of aberrant p53 staining patterns observed. Recent studies on endometrial carcinoma have reported that cases exhibiting a subclonal p53 IHC pattern often harbor \u003cem\u003eTP53\u003c/em\u003e mutations, supporting that this pattern represents true mutant subclones\u003csup\u003e19,28\u003c/sup\u003e. Our findings also indicate that a subclonal p53 staining pattern might represent a mutant-type expression pattern, although the clinicobiologic significance of the cutoff has not been defined yet. The mechanism behind wild-type staining in \u003cem\u003eTP53\u003c/em\u003e-mutated tumor cells might be explained by the absence of LOH, although we sequenced the entire tumor area without distinguishing each component showing subclonal expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe encountered 11 discordant cases between IHC and sequencing results: aberrant p53 expression but no \u003cem\u003eTP53\u003c/em\u003e mutation (IHC false positivity; n = 10, consisting of 4 cases of overexpression and 6 cases of null pattern) and p53 wild-type expression but with \u003cem\u003eTP53\u003c/em\u003e mutation (IHC false negativity; n = 1). On reviewing IHC false-positive cases, we found that two cases exhibited intratumoral heterogeneity, with less than 5% of cells showing p53 overexpression, which might have been undetected by sequencing. This discrepancy in the null pattern might be due to large \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003edeletions undetectable by sequencing variant allele frequency. However, all cases showed LOH with shallow copy number alterations, and we could not determine the reason. In the false negative case, there was a splicing mutation with a variant allele frequency of 0.42 but it exhibited a wild-type pattern on p53 IHC. This type of discrepancy can be observed in splicing mutations or truncation mutations at the C-terminal stop-gain, resulting in a detectable but nonfunctional p53 protein that mimics the wild-type staining pattern\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMutant p53\u003cem\u003e\u0026nbsp;\u003c/em\u003eproteins exhibit diverse interactions with cellular components, yielding varied functional outcomes, wherein both LOF and GOF can occur concurrently\u003csup\u003e29\u003c/sup\u003e. Some \u003cem\u003eTP53\u003c/em\u003e mutations may act in a dominantly negative manner, with mutant p53 protein disrupting activity of the wild-type p53 protein. This disruption leads to the loss of normal tumor-suppressive function while simultaneously acquiring new functions that contribute to tumorigenesis, representing a GOF. In our study, all DNE mutations exhibited p53 overexpression pattern, with the majority also displaying GOF properties. These support that DNE of mutant p53 is associated with elevated levels of mutant p53 in tumor cells, raising the risk of forming inactive p53 complexes that compromise wild-type p53 function. However, DNE has emerged as a challenging phenotype to characterize. The influence of mutant p53 on wild-type appears to vary across tissues and contexts\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn breast cancer research, a definitive cutoff value for p53 nuclear overexpression has not been established yet. The threshold for assessing p53 overexpression varies widely, with reported values ranging from 1% to 90%\u003csup\u003e30\u003c/sup\u003e. Previous studies have suggested that a suitable cutoff for p53 in breast cancer might be around 10%, as \u003cem\u003eTP53\u003c/em\u003e mutations are commonly observed beyond this level\u003csup\u003e31-33\u003c/sup\u003e. For TNBC, we identified an optimal cutoff at 80% based on \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutation prediction, higher than previously reported cutoffs for general breast cancer. However, this cutoff may not apply across all breast cancer types. Further research is necessary to explore p53 IHC interpretations in other molecular subtypes such as luminal or HER2-enriched subtypes. Another limitation may arise from interpreting cytoplasmic expression of p53, as it can be complicated by potential overlap with nuclear expression and variations in IHC performance. To reduce these ambiguities, we used a high cutoff of 80% staining area for cytoplasmic expression based on the prior study\u003csup\u003e19\u003c/sup\u003e. Nonetheless, additional research is required to elucidate the significance of cytoplasmic expression, even in cases where it is focal.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study introduces newly defined criteria for interpreting p53 IHC expression in TNBC. These criteria could enable the prediction of \u003cem\u003eTP53\u003c/em\u003e mutation types and their functional implications, providing valuable insights for future clinical research and potentially improving stratification of treatment options for patients.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eCase selection\u003c/h2\u003e\n\u003cp\u003eThis retrospective study included 113 consecutively collected cases of TNBC who underwent surgery at Seoul St. Mary\u0026apos;s Hospital between 2018 and 2022. This study was approved by the Institutional Research Ethics Board of the Catholic University of Korea with a waiver of patient\u0026rsquo;s informed consent (IRB No. KC22SISI0716). Cases who underwent neoadjuvant therapy or had a tumor size of less than 1.5 cm in its greatest dimension were excluded.\u003c/p\u003e\n\u003cp\u003eConfirmation of TNBC cases involved IHC for ER (clone SP1; cat. no. 790-4324; Roche/Ventana, ready to use), PR (clone 1E2; cat. No. 790-4296; Roche/Ventana, ready to use), and HER2 (clone 4B5; cat. No. 790-4493; Roche/Ventana, ready to use). Additional HER2 silver \u003cem\u003ein situ\u003c/em\u003e hybridization (SISH), when necessary, was conducted using the VENTANA HER2 Dual ISH DNA Probe Cocktail. Classification of histological types for cases was based on the 5th edition of the WHO classification of breast tumors\u003csup\u003e34\u003c/sup\u003e. The histological type, histological grade, and tumor stage, as per the 8th edition of the American Joint Committee on Cancer (AJCC) TNM classification, were retrieved from their pathology reports.\u003c/p\u003e\n\u003ch2\u003eImmunohistochemistry\u003c/h2\u003e\n\u003cp\u003eTissue acquisition to fixation time was minimized to within 2 hours. Samples were then fixed in 10% neutral buffered formalin (NBF) for 6\u0026nbsp;\u0026ndash;\u0026nbsp;72 hours and sliced at 5 mm intervals. Sections of 4-\u0026micro;m-thick were obtained from paraffin-embedded blocks, deparaffinized in xylene, and rehydrated through a graded series of ethanol. P53 IHC was performed for 113 TNBC cases with DO7 antibody (cat. No. 800-2912; Roche Diagnostics, IN, USA; ready to use) and a Leica Bond III system (Leica Biosystems, Inc., Buffalo Grove, IL, USA) following the manufacturer\u0026rsquo;s protocol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of four experienced pathologists reviewed p53 IHC whole slides while blinded to tumor\u0026apos;s \u003cem\u003eTP53\u003c/em\u003e mutation status. The p53 IHC staining pattern was manually assessed based on nuclear expression in tumor cells. The proportion of expression across all tumor cells was manually quantified in 5% increments for each case, ranging from 0 to 100%. Three other pathologists reviewed assessments. Cases with discordant measures were discussed until a consensus was reached.\u0026nbsp;To define a cutoff for p53 IHC overexpression, a p53 expression rate-dependent receiver operating characteristic (ROC) curve was analyzed using \u003cem\u003eTP53\u003c/em\u003e mutation status as the endpoint to optimize the Youden index.\u003c/p\u003e\n\u003ch2\u003eWhole exome sequencing\u003c/h2\u003e\n\u003cp\u003eWhole exome sequencing was conducted for formalin-fixed, paraffin-embedded tissue blocks of a representative normal and tumor slide for the 113 TNBC cases. Paired-end sequences were generated with a NovaSeq6000 (Illumina, San Diego, CA, USA) platform following the manufacturer\u0026apos;s protocols. The sequencing quality was checked using FastQC. Sequence reads were aligned to human genome assembly hg19 (GRCh37) using Burrows-Wheeler Aligner (BWA MEM, http://bio-bwa.sourceforge.net/), specifically the BWA-MEM algorithm\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaired normal and tumor sequence reads were aligned and processed together to identify somatic \u003cem\u003eTP53\u003c/em\u003e mutations. MuTect2 was used to detect somatic mutations. Mutations were filtered using the filtration tool of the Genome Analysis Toolkit (GATK). Functional annotation of filtered mutations was performed with SnpEff. Interpretation was performed with dbSNP and SNPs from the 1000 Genome Project. Further annotation with databases including ESP6500, ClinVar, dbNSFP, and American College of Medical Genetics and Genomics (ACMG) information was performed using an in-house program and SnpEff. VCF files of somatic mutations were processed to MAF files using the vcf2maf program. The \u0026lsquo;maftools\u0026rsquo; R package was used to filter out synonymous mutations.\u003c/p\u003e\n\u003ch2\u003eCopy number and loss of heterozygosity analysis\u003c/h2\u003e\n\u003cp\u003eCopy number analysis of \u003cem\u003eTP53\u003c/em\u003e was performed using GISTIC 2.0 (Genomic Identification of Significant Targets in Cancer, RRID:SCR_000151) applied to segmentation files obtained from Sequenza analysis\u003csup\u003e36,37\u003c/sup\u003e. The analysis was undertaken to pinpoint significant loci of somatic CNVs. The analysis was configured with a focal length cutoff of 0.5 to focus solely on focal alterations. A confidence level of 0.9 was set to ensure high reliability of findings, along with a \u003cem\u003eq\u003c/em\u003e-value threshold of 0.05 to distinguish between significant and non-significant results. Additionally, the X chromosome was excluded to eliminate variations related to sex chromosomes. The algorithm calculated G-scores for each region, taking into account the amplitude of copy number changes and their frequency across the sample set. Subsequently, it categorized these changes into homozygous deletions, heterozygous deletions, low-level gains, and amplifications.\u003c/p\u003e\n\u003cp\u003eTo characterize the LOH at the \u003cem\u003eTP53\u003c/em\u003e locus, the GenomicRanges package in R was employed to process segmented CNV data from whole-exome sequencing. We annotated segmented data for traceability and determined LOH status for each segment by assessing the absence of heterozygosity. This binary information was then integrated into a dataset. Specifically, segments overlapping with the \u003cem\u003eTP53\u003c/em\u003e gene on chromosome 17, indicating LOH at the \u003cem\u003eTP53\u003c/em\u003e locus, were identified. These data were compiled into a \u003cem\u003eTP53\u003c/em\u003e-specific LOH dataset for further analysis. If LOH occurred without a change in copy number in \u003cem\u003eTP53\u003c/em\u003e, it was designated as copy neutral LOH.\u003c/p\u003e\n\u003ch2\u003eDatabase for functional classification of TP53 mutation\u003c/h2\u003e\n\u003cp\u003eInformation on functional properties of \u003cem\u003eTP53\u003c/em\u003e mutations was compiled in the NCI \u003cem\u003eTP53\u003c/em\u003e database (R20 version; https://tp53.isb-cgc.org)\u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;Using this database, we systematically explored and analyzed functional properties of mutant p53. In this dataset, data were extracted from publications that reported functional assessment of mutant p53 proteins in human or yeast cells covering various aspects, such as transcriptional activities on p53-response elements, DNE on activities of wild-type p53, the ability to transactivate promoters not induced by wild-type p53, and the capability to promote cell growth and confer tumorigenicity. This assessment was conducted either by transfection and overexpression of mutant proteins or by evaluating endogenous mutants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on functional outcomes in the experimental database, \u003cem\u003eTP53\u003c/em\u003e mutations in our cases were categorized into either LOF, DNE, or GOF. Specifically, we considered LOF when the mutant protein resulted in the loss of functional properties of wild-type p53. DNE was considered when mutant proteins inhibited the wild-type protein in transactivation or cell growth assays. GOF encompassed functional properties exhibited by the mutant protein but not by the wild-type counterpart. In the current study,\u0026nbsp;a mutant p53 was classified as having GOF when it met at least one of the following categories: 1) tumorigenic property (in nude mice) in transfected cells; 2) interference with p73 activity; 3) transactivation of genes repressed by wild-type p53; 4) resistance to a cytotoxic drug; 5) increase growth rate; 6) cooperation with an oncogene for the transformation of rat embryonic fibroblast or mouse embryonic fibroblast cells; and 7) alteration of mutant p53 stability and activity by impairing regulators, including HSC70 and MDM2\u003csup\u003e21,39\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe Youden Index was utilized to determine the optimal cutoff value for p53 overexpression, defined as sensitivity + specificity \u0026minus; 1. The performance of the p53 expression cutoff point in predicting \u003cem\u003eTP53\u003c/em\u003e mutation was assessed using AUC, sensitivity, specificity, PPV, negative predictive value, and accuracy. The relationship between p53 IHC pattern and \u003cem\u003eTP53\u003c/em\u003e mutation type was assessed using the Chi-square test or Fisher\u0026apos;s exact test, as appropriate. Results were considered significant if the \u003cem\u003ep-\u003c/em\u003evalue was equal to or less than 0.05. All statistical analyses were conducted using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was conducted using R version 4.3.1. The code used in the study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Research Fund of Seoul St.Mary\u0026rsquo;s Hospital, The Catholic University of Korea (M.K., No. ZC22TISI0880) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (J.K., No. 2021R1I1A1A01043754)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K., A.L., and J.K. designed the study. M.K. and M.L. collected patient materials, while B.C., W.P., and J.L. provided the patient data necessary for this collection. M.K., M.L., A.L., and J.K. reviewed the slides and manually quantified the immunoexpression. B.C., W.P., and J.L. analyzed and interpreted the patient data. J.K. provided the sequencing data. M.K and M.L performed the statistical analyses and interpreted the data. M.K. wrote the manuscript under the supervision of A.L. and J.K. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ede Ruijter, T. C., Veeck, J., de Hoon, J. P., van Engeland, M. \u0026amp; Tjan-Heijnen, V. C. Characteristics of triple-negative breast cancer. \u003cem\u003eJ Cancer Res Clin Oncol\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 183-192 (2011).\u003c/li\u003e\n\u003cli\u003eDuffy, M. J., Synnott, N. C., O\u0026apos;Grady, S. \u0026amp; Crown, J. Targeting p53 for the treatment of cancer. \u003cem\u003eSemin Cancer Biol\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 58-67 (2022).\u003c/li\u003e\n\u003cli\u003eMitri, Z. 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C.\u003cem\u003e et al.\u003c/em\u003e The TP53 Database: transition from the International Agency for Research on Cancer to the US National Cancer Institute. \u003cem\u003eCell Death Differ\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1071-1073 (2022).\u003c/li\u003e\n\u003cli\u003ePetitjean, A.\u003cem\u003e et al.\u003c/em\u003e Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database. \u003cem\u003eHum Mutat\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 622-629 (2007).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Correlation between p53 immunohistochemical staining pattern and\u003cem\u003e\u0026nbsp;TP53\u003c/em\u003e mutational status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP53 pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP53 mutation status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIn-frame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFrameshift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNonsense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSplicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverexpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003csup\u003ea\u003c/sup\u003e (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWild-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e The total number is based on the count of 60 mutant variants in 58 cases, including two cases with double mutations: R280T + G245D, and S127Lfs*42 + splicing mutation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. P53 immunostaining patterns and mutation type prediction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP53 Overexpression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Missense/In-frame)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP53 Null pattern\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Nonsense /Frameshift/Splicing)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003eSensitivity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e100% (93 \u0026ndash; 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e85% (70 \u0026ndash; 94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003eSpecificity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e85% (74 \u0026ndash; 92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e92% (83 \u0026ndash; 97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003ePositive predictive value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e83% (74 \u0026ndash; 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e85% (72 \u0026ndash; 93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003eNegative predictive value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e100% (94 \u0026ndash; 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e92% (84 \u0026ndash; 96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.27814569536424%\" valign=\"top\"\u003e\n \u003cp\u003eAccuracy (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.125827814569536%\" valign=\"top\"\u003e\n \u003cp\u003e91% (85 \u0026ndash; 96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.59602649006622%\" valign=\"top\"\u003e\n \u003cp\u003e89% (82 \u0026ndash; 94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Subclonal p53 immunostaining patterns and \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003emutations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"903\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003eCase no.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eHistologic type and grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eP53 immunostaining pattern\u003c/p\u003e\n \u003cp\u003eand proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003eTP53 mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eMutation type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eHotspot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003eVAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eNucleotide change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003eAmino acid change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression (30%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand wild-type (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eDBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003ec.711G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003ep.Met237Ile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression with cytoplasmic \u0026nbsp;staining (20%) and wild-type (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eFrameshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eNLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003ec.902del\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003ep.Pro301GlnfsTer44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression (15%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand wild-type (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eDBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003ec.524G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003ep.Arg175His\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eNull pattern (90%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand wild-type (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eFrameshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003ec.1028del\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003ep.Glu343GlyfsTer2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression (40%), null pattern (30%), and wild-type (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eNonsense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003ePRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003ec.281C\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003ep.Ser94Ter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eIBC-NST, G3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression (˂ 5%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand wild-type (\u0026gt; 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.199115044247788%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.402654867256636%\" valign=\"top\"\u003e\n \u003cp\u003eAPO, G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.004424778761063%\" valign=\"top\"\u003e\n \u003cp\u003eOverexpression (˂ 5%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand wild-type (\u0026gt; 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.300884955752212%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3053097345132745%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.20353982300885%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.606194690265486%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.8584070796460175%\" valign=\"top\"\u003e\n \u003cp\u003eLOH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIBC-NST, invasive breast carcinoma of no special type; G, grade; APO, carcinoma with apocrine differentiation; VAF, variant allele frequency; LOH, loss of heterozygosity\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"p53 immunohistochemistry, p53 expression, mutant p53, gain-of-function, TP53 mutation, triple-negative breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-4645054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4645054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Immunohistochemical (IHC) staining pattern of p53 has been recognized as a potential surrogate marker for TP53 mutations across diverse cancer types. Nevertheless, interpretative criteria for p53 IHC expression as a predictive tool for TP53 mutation in triple-negative breast cancer (TNBC) have not been established yet. A total of 113 TNBC cases were analyzed for p53 IHC pattern and somatic TP53 mutation using whole exome sequencing. Functional properties of TP53 mutations were determined using the National Cancer Institute (NCI) TP53 database. P53 IHC staining patterns fell into three distinct categories: nuclear overexpression (n = 58), absence of staining indicating null pattern (n = 40), and wild-type expression (n = 15). The cutoff for predictive p53 nuclear overexpression was determined to be 80%. This threshold correlated strongly with TP53 mutations. Notably, nuclear overexpression had a positive predictive value (PPV) of 83% for missense or in-frame mutations, while the null pattern showed a PPV of 85% for detecting nonsense, frameshift, or splicing mutations. Furthermore, p53 overexpression was significantly linked to missense mutations within the DNA-binding domain (DBD) exhibiting gain-of-functon(GOF) or dominant-negative effect (DNE). Cases exhibiting both nuclear overexpression and cytoplasmic expression (n = 5) were correlated with nonsense or frameshift mutations affecting the DBD, NLS, or splice sites. Subclonal p53 staining patterns observed in 7 cases were found to be associated with TP53 mutations. Our study proposes newly defined criteria for interpreting p53 immunostaining patterns in TNBC, potentially allowing for the prediction of TP53 mutation types and their functional implications.","manuscriptTitle":"Correlating p53 immunostaining patterns with somatic TP53 mutation and functional properties of mutant p53 in triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 18:53:55","doi":"10.21203/rs.3.rs-4645054/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f7d9fbc-8e3d-4cb8-8436-f5b4a1c14a7c","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34472296,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":34472297,"name":"Biological sciences/Cancer/Breast cancer"},{"id":34472298,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2024-07-19T18:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 18:53:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4645054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4645054","identity":"rs-4645054","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}