pISSN 1738-6756   eISSN 2092-9900
Open Access, Peer-reviewed
    J Breast Cancer. 2023 Aug;26(4):363-377. English.
    Published online Jul 12, 2023.
    https://doi.org/10.4048/jbc.2023.26.e30
    © 2023 Korean Breast Cancer Society
    Original Article

    Concomitant PIK3CA and TP53 Mutations in Breast Cancer: An Analysis of Clinicopathologic and Mutational Features, Neoadjuvant Therapeutic Response, and Prognosis

    Xiao-Yi Lin,1,2,* Lijuan Guo,1,3,* Xin Lin,1,4,* Yulei Wang,1 and Guochun Zhang1
      • 1Department of Breast Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
      • 2Shantou University Medical College, Shantou, Guangdong, China.
      • 3School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
      • 4The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
    • Correspondence to Guochun Zhang. Department of Breast Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Yuexiu District, Guangzhou, 510080, Guangdong, China. Email: zhangguochun@gdph.org.cn

      *These authors contributed equally to this work.
    Received December 23, 2022; Revised March 05, 2023; Accepted May 23, 2023.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    Purpose

    PIK3CA and TP53 are the most prevalently mutated genes in breast cancer (BC). Previous studies have indicated an association between concomitant PIK3CA/TP53 mutations and shorter disease-free survival. As its clinical utility remains largely unknown, we aimed to analyze the prognostic and predictive roles of this co-mutation.

    Methods

    We retrospectively analyzed patients who were diagnosed with BC at Guangdong Provincial People’s Hospital (GDPH) who underwent next-generation sequencing. The correlation of concomitant PIK3CA/TP53 mutations with clinicopathological and mutational characteristics, and neoadjuvant systemic therapy (NST) responses was analyzed. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset was used to verify associations between concurrent mutations and survival outcomes.

    Results

    In the GDPH cohort, concomitant PIK3CA/TP53 mutations were associated with more aggressive phenotypes, including human epidermal growth factor receptor 2 positive status, hormone receptor negative status, high Ki-67 expression, high histological grade, advanced TNM stage, and additional genetic alterations. Co-mutations also portended a worse response to NST, especially taxane-containing regimens, when compared with the TP53 mutant alone (odds ratio, 3.767; 95% confidence interval, 1.205–13.087; p = 0.028). A significant association was observed between concomitant PIK3CA/TP53 mutations and poor survival outcomes in the METABRIC cohort.

    Conclusion

    Concomitant PIK3CA/TP53 mutations not only suggested unfavorable features and poor prognosis in BC but also conferred less benefit to NST than TP53 mutations alone.

    Keywords
    Breast Neoplasms; High-Throughput Nucleotide Sequencing; Mutation; Neoadjuvant Therapy; Survival

    INTRODUCTION

    Breast cancer (BC) is the most prevalent malignancy in women [1], with an estimated 2.26 million cases worldwide in 2020 [2]. BC survival has improved since the 1980s; however, the mortality rate decline has recently slowed [3]. Recent studies have suggested that survival improvement is hampered by the heterogeneity of BC, which may be attributed to its diverse genomic profiles [4, 5]. Therefore, numerous studies have explored the predictive markers of therapeutic effects and prognosis [6]. The response to neoadjuvant systemic therapies (NSTs) has enabled the assessment of drugs and long-term outcomes [7], thus serving as a valid surrogate endpoint.

    The PIK3CA mutation, present in 30%–50% of BCs [8, 9], has been proven to be a positive prognostic biomarker in hormone receptor (HR)-positive and triple-negative BC (TNBC) [10, 11], but is inversely related to the human epidermal growth factor receptor 2 (HER2)-positive subtype [12, 13]. According to the SOLAR-1 trial [14], the α-specific PI3K inhibitor, alpelisib, significantly prolonged the survival of patients with PIK3CA-mutated BC. The incidence of TP53 mutations ranges from 15% to 85% in different BC subtypes [6, 15], and has been recognized to be associated with poor survival outcomes [16, 17]. As the most frequent genomic alterations, both are associated with resistance to chemotherapy, anti-HER2 therapy, and endocrine therapy resistance [17, 18]. More specifically, PIK3CA mutations predicted less benefit in patients with BC treated with neoadjuvant HER2-targeted therapies [19, 20]. However, a higher tumor response after neoadjuvant chemotherapy was observed in TP53 mutants [21, 22]; however, this was controversial in TNBC [15]. More clinical trials, such as the p53 Breast Cancer Trial (NCT02965950) and SPEAR (NCT05022342), are underway to evaluate the effects of these mutations on BC treatment efficacy.

    Extensive studies on PIK3CA/TP53 mutations and their discrepant therapeutic effects have aroused interest. Kotoula et al. [23] reported that concurrent PIK3CA and TP53 mutations were more common in relapsed metastatic BC. Dual mutations also imply worse disease-free survival in patients receiving adjuvant and neoadjuvant chemotherapy [24, 25], probably because of the increased cancerous phenotypes observed in in vitro studies [26].

    Despite the association between concomitant PIK3CA/TP53 mutations and unfavorable outcomes, the clinical value of these mutations remains largely unknown. Hence, we aimed to analyze the clinicopathologic and mutational characteristics, as well as the neoadjuvant therapeutic response, in patients with BC with co-occurring PIK3CA/TP53 mutations. The correlations with survival outcomes were further verified using a larger database.

    METHODS

    Patients and variables

    Patients with BC confirmed histopathologically at Guangdong Provincial People’s Hospital (GDPH) were selected. Patients with treatment-naïve clinicopathological information and available DNA sequencing data were retrospectively included in our study. The TNM stage was determined according to the American Joint Committee on Cancer Tumor Stage Code. HR and HER2 statuses, as defined by the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines [27, 28], were assessed using immunohistochemistry (IHC) and fluorescence in situ hybridization. Other variables included age at diagnosis, menopausal status, histological type and grade, Ki-67 expression [29], and IHC-based surrogate molecular subtypes (Luminal A, Luminal B, HER2-enriched and basal-like). This study was approved by the Ethics Committee of GDPH (Approval No: KY-Q-2022-059-01). Written informed consent was obtained from all the patients.

    Next-generation sequencing and sequence data analysis

    DNA was extracted from archival formalin-fixed, paraffin-embedded BC samples obtained during biopsy or surgery, using a QIAamp kit (Qiagen, Hilden, Germany). The DNA concentration was measured using a Qubit 2.0 fluorimeter with a dsDNA high-sensitivity assay kit (Life Technologies, Carlsbad, USA). As described in our previous study [30], a commercial panel composed of 520 cancer-related genes was used to perform target capture using an Illumina NextSeq 500 instrument (Illumina, Inc., Hayward, USA) at Burning Rock Biotech, Guangzhou, China. Burrows-Wheeler Aligner v.0.7.10 mapped the sequence data to the reference human genome (hg19). GATK 3.2, MuTect, and VarScan were used to perform local alignment optimization, variant calling, and annotation. Single nucleotide variants were assessed using at least eight supporting reads. Single nucleotide polymorphism (SNP) was determined using the ExAC, 1000 Genomes, dbSNP, and ESP6500SI-V2 database. ANNOVAR and SnpEff v3.6 annotated variants without a population frequency of > 0.1%. The copy number variation was determined according to the depth of coverage data of the capture intervals. Coverage data were corrected accordingly. The coverage of the different samples was normalized to comparable scales. Copy number was calculated as the ratio of the depth of coverage in the samples to the average coverage of an adequate number of samples. Copy number variation was determined when the coverage data were significantly different from those of the reference control.

    Based on the DNA sequence data of PIK3CA/TP53 in the GDPH cohort, patients were classified into four groups: PIK3CA/TP53 co-mutant, PIK3CA mutant (TP53 wild-type with PIK3CA mutation), TP53 mutant (PIK3CA wild-type with TP53 mutation) and PIK3CA/TP53 wild-type. We then analyzed the mutation types and sites of PIK3CA and TP53 in the different groups. The mutation frequencies of the 20 most commonly altered genes in each group were compared.

    NST and pathological complete response (pCR)

    In the GDPH cohort, patients treated with NSTs were selected. All patients received standard-of-care treatment based on institutional guidelines. According to the age, stage, and IHC-based molecular subtypes, NST mainly involved chemotherapy and HER2-targeted therapy. The majority of patients with HER2-positive BC received docetaxel/carboplatin/trastuzumab, and the majority of patients with HER2-negative BC received docetaxel/epirubicin/cyclophosphamide. The pCR was defined as the absence of invasive tumor cells in the breast and axilla (ypT0/is ypN0). To analyze the effect of co-occurring PIK3CA/TP53 mutations on NST benefit, univariate and multivariate logistic regression models were fitted to pCR and included PIK3CA/TP53 mutation status, as well as the aforementioned clinicopathological variables. Moreover, treatment effects were examined separately for subgroups treated with taxane-containing regimens and HER2-targeted therapy.

    Drug sensitivity estimation

    Gene expression and somatic mutation data from The Cancer Genome Atlas (TCGA) were downloaded from UCSC Xena [31] for subsequent bioinformatics analysis. RNA sequence data were obtained by alignment with STAR and mRNA expression workflow using HT-Seq-Count. Variants were detected using VarScan 2 [32]. To verify treatment efficacy, the “oncoPredict” R package was used to predict the therapeutic responses to specific drugs [33]. Ridge regression models were built based on the transcriptomic cell line data and logarithmically transformed drug half maximal inhibitory concentration (IC50) information from the Cancer Genome Project (CGP) [34]. Finally, the estimated therapeutic responses were obtained for patients with BC from TCGA database.

    Survival analysis in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort

    Follow-up data, as well as somatic mutation profiles and clinicopathological parameters in the METABRIC data set, were acquired from UCSC Xena platform [31, 35]. A total of 1979 BC cases with complete information were included in the METABRIC cohort. Overall survival (OS) was defined as the time from diagnosis to death from any disease. Relapse-free survival (RFS) was defined as the time from diagnosis to relapse or death of any cause. To compare OS and RFS in the different mutation groups, we conducted Kaplan–Meier analysis and log-rank tests. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate whether co-mutation was an independent prognostic factor.

    Statistical analysis

    The distribution of categorical parameters was compared using the χ2 or Fisher’s exact test. Univariate and multivariate logistic regression models were used to analyze risk factors for pCR. A t-test was performed to compare the estimated IC50 values between the different groups. Survival analyses included Kaplan–Meier estimators, log-rank tests, and Cox regression. R 4.1.2 software was applied for statistical analysis. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant.

    RESULTS

    Baseline characteristics in the GDPH cohort

    Among the 587 patients with BC in the GDPH cohort, concomitant PIK3CA and TP53 mutations were identified in 18.91% (111/587) of cases. The proportions of patients with PIK3CA and TP53 mutations were 25.38% and 28.62%, respectively. A total of 159 (27.09%) patients did not harbor PIK3CA or TP53 mutations. The baseline characteristics are summarized in Table 1. Patients with PIK3CA/TP53 co-mutated BC were more likely to be diagnosed at an older age (84.68% vs. 70.83%, p = 0.012) and with a postmenopausal status (51.35% vs. 38.69%, p = 0.0496) than those with only the TP53 mutant. HER2-positive malignancies accounted for more than half of the co-mutated BC cases. HER2 positive status was significantly associated with concomitant PIK3CA/TP53 mutations (co-mutant vs. PIK3CA mutant, 53.15% vs. 10.74%, p < 0.001; co-mutant vs. TP53 mutant, 53.15% vs. 40.48%, p = 0.026; co-mutant vs. PIK3CA/TP53 wild-type, 53.15% vs. 20.13%, p < 0.001). An HR-negative status, high Ki-67 expression, and a high histological grade were more frequent in patients with co-occurring mutations. However, they were no significant differences between patients with co-mutants and those with TP53 mutants alone. Patients with BC with co-mutations tended to be diagnosed at TNM stage III compared to those with only the PIK3CA mutant (29.73% vs. 14.09%, p = 0.003). Moreover, the distribution of IHC-based molecular subtypes in the co-mutant group was closer to that of the TP53 mutant group, whereas luminal B (HER2+) (31.53% vs. 24.40%) and HER2-enriched (21.62% vs. 16.07%) subtypes were more frequent, and basal-like BC (8.11% vs. 26.79%) was less common. Although the PIK3CA mutation is known to be enriched in luminal subgroups, lower proportions of luminal A (4.50%) and luminal B (HER2-) (28.83%) subtypes were observed in the co-mutant BC than in the PIK3CA mutant group (Lum A, 36.24%; Lum B [HER2-], 42.28%) or wild-type BC group (Lum A, 22.01%; Lum B [HER2-], 47.08%).

    Table 1
    Baseline characteristics and PIK3CA/TP53 mutation in the Guangdong Provincial People’s Hospital cohort

    Mutational features

    The distributions of PIK3CA/TP53 mutations in a linear protein and its domains are shown in Figure 1. PIK3CA mutations differed between patients with concomitant PIK3CA/TP53 mutations and those with only PIK3CA mutations, although H1047R (co-mutant, n = 56, 47.86% vs. PIK3CA mutant, n = 70, 39.33%; p = 0.423) and E545K (co-mutant, n = 20, 17.09% vs. PIK3CA mutant, n = 19, 10.67%; p = 0.225) were the most common hotspots in both groups (Figure 1A). In the co-mutation group, E542K (n = 8, 6.84%) and H1047L (n = 6, 5.13%) were detected. However, in the PIK3CA mutant group, N345K (n = 17, 9.55%) accounted for a larger proportion of mutations than E542K (n = 15, 8.42%) (Figure 1B). For the co-mutant and only TP53 mutant groups, the distributions of TP53 mutations were also different. In the co-mutant group, the nonsense mutation in Q192 was the most common (n = 6, 5.22%) (Figure 1C), whereas R273C occurred most frequently in the TP53 mutant group (Figure 1D).

    Figure 1
    Lollipop diagrams of the mutation frequency, type and location in Guangdong Provincial People’s Hospital cohort.
    Frequency and type were denoted by length of the lollipop and different colored circles. Colored boxes represent different domains. (A) PIK3CA mutation distribution in PIK3CA/TP53 co-mutants. (B) PIK3CA mutation distribution in only PIK3CA mutants. (C) TP53 mutation distribution in PIK3CA/TP53 co-mutants. (D) TP53 mutation distribution in only TP53 mutants.

    PIK3CA H1047R remained the most frequent hotspot in all BC subtypes, and E545K was the second most common hotspot, with the exception of the HER2+ subtype. Regarding TP53, the most common hotspots were R273C and R248Q in luminal B (HER2-), Q192 in luminal B (HER2+) and HER2+, and R213 in TNBC. However, each hotspot only appeared once in the luminal A subtype.

    Compared with PIK3CA/TP53 wild-type BC (Figure 2A), BC with co-mutations displayed more mutations in ERBB2 (p < 0.001), CDK12 (p < 0.001), NF1 (p = 0.002), FGFR2 (p = 0.013), PREX2 (p = 0.041), POLE (p = 0.038), KAT6A (p = 0.007); and less mutations in GATA3 (p < 0.001), AKT1 (p < 0.001), PTEN (p = 0.034). It also had more mutations in ERBB2 (p < 0.001), CDK12 (p < 0.001), NF1 (p = 0.001), SPOP (p = 0.025), FGFR2 (p = 0.006), NBN (p = 0.003), POLE (p = 0.003), KAT6A (p = 0.025); and less mutations in GATA3 (p < 0.001), CBFB (p = 0.006) than PIK3CA mutant BC (Figure 2B). In addition, it harbored more mutations in ERBB2 (p = 0.047), NF1 (p = 0.030), MAP3K1 (p = 0.031), and POLE (p = 0.013); and fewer mutations in KRAS (p = 0.031) than TP53 mutant BC (Figure 2C).

    Figure 2
    Gene mutation frequency in breast cancer from Guangdong Provincial People’s Hospital cohort.
    (A) Comparison between PIK3CA/TP53 wild-type and PIK3CA/TP53 co-mutant. (B) Comparison between PIK3CA mutant and PIK3CA/TP53 co-mutant. (C) Comparison between TP53 mutant and PIK3CA/TP53 co-mutant.

    WT = wild-type.

    The asterisk (*) indicates statistically significant differences between groups based on χ2 or Fisher’s exact test.

    The therapeutic effect and its association with PIK3CA/TP53 co-mutation

    Among the patients treated with NST, 80 of 94 patients (85.11%) received taxane-containing chemotherapy and 40 (42.55%) received HER2-targeted therapies. Patients with PIK3CA/TP53 co-mutated BC had a pCR rate of 28.00% compared to 51.35%, 7.69%, and 15.79% for TP53 mutation, PIK3CA mutation, and PIK3CA/TP53 wild-type BC patients, respectively.

    As shown in Figure 3A,TP53 mutation in BC was independently associated with better pCR than concomitant PIK3CA/TP53 mutations (odds ratio [OR], 3.767; 95% confidence interval [CI], 1.205–13.087; p = 0.028). No significant interaction was observed between co-mutant and PIK3CA mutant or PIK3CA/TP53 wild-type BC. Multivariate logistic regression analysis also indicated that HER2-negative status conferred poorer pCR (OR, 0.239; 95% CI, 0.081–0.660; p = 0.007). Other variables, including age, grade, stage, HR status, and Ki-67 status did not correlate with pCR. In the subgroup receiving taxane-containing NST (Figure 3B), these results were consistent with the finding that patients with BC with only TP53 mutations were more likely to achieve pCR than those with PIK3CA/TP53 co-mutations (OR, 4.396; 95% CI, 1.293–16.905; p = 0.023). However, for patients with BC treated with anti-HER2 neoadjuvant therapy (Figure 3C), the distinction was slightly below the level of significance (TP53 mutant vs. co-mutant; OR, 4.000; 95% CI, 0.976–18.487; p = 0.062).

    Figure 3
    Univariate and multivariate logistic regression analysis of pathological complete response for patients with breast cancer with different PIK3CA/TP53 mutation statuses.
    (A) Comparison of pCR in patients with BC receiving neoadjuvant systemic therapy. (B) Comparison of pCR in patients with BC receiving taxane-containing chemotherapy. (C) Comparison of pCR in patients with BC receiving HER2-targeted therapy.

    pCR = pathological complete response; BC = breast cancer; HER2 = human epidermal growth factor receptor 2; OR = odds ratio; CI = confidence interval; WT = wild-type.

    To confirm the different therapeutic responses in patients with with concomitant PIK3CA/TP53 mutations, drug sensitivity was estimated (Figure 4A-C). Consistent with these results, the co-mutation was correlated with lower sensitivity to docetaxel, doxorubicin, and cisplatin when compared with the TP53 mutant alone (p < 0.001). Better drug responses were observed in the co-mutant group than in the PIK3CA mutant and PIK3CA/TP53 wild-type groups, although no differences were observed in the NST effect.

    Figure 4
    The estimated drug sensitivity for different PIK3CA/TP53 mutation groups.
    (A) The predicted IC50 for docetaxel. (B) The predicted IC50 for doxorubicin. (C) The predicted IC50 for cisplatin.

    IC50 = half maximal inhibitory concentration; WT = wild-type.

    Concurrent PIK3CA/TP53 mutation and survival outcomes

    Because co-mutations were associated with poor prognostic features and therapeutic responses, we further assessed their effect on clinical outcomes in the METABRIC cohort. As shown in the Kaplan–Meier curves (Figure 5A), patients with concomitant PIK3CA/TP53 mutations had the worst OS (co-mutant vs. PIK3CA mutant; hazard ratio, 1.667; 95% CI, 1.334–2.084; p < 0.001 vs. TP53 mutant; hazard ratio, 1.391; 95% CI, 1.119–1.728; p = 0.0029 vs. PIK3CA/TP53 wild-type; hazard ratio, 1.653; 95% CI, 1.326–2.061; p < 0.001). The median OS time of patients with PIK3CA/TP53 co-mutant BC (90 months) was remarkably shorter than that of those with PIK3CA mutant (163.7 months), TP53 mutant (140.767 months), and PIK3CA/TP53 wild-type (169.233 months) BC. As shown in Figure 5B, shortened RFS was also associated with the dual PIK3CA/TP53 mutation. After adjusting for other potential variables, the PIK3CA/TP53 co-mutation was shown to be an independent risk factor for OS (vs. PIK3CA/TP53 wild-type; hazard ratio, 1.594; 95% CI, 1.306–1.946; p < 0.001). The results of the Cox proportional hazards regression analysis are shown in Table 2.

    Figure 5
    Kaplan–Meier survival analysis for patients with breast cancer by different PIK3CA/TP53 mutations statuses.
    (A) OS curves and (B) RFS curves.

    OS = overall survival; RFS = relapse-free survival.

    Table 2
    Cox proportional hazards regression analysis for overall survival in the Molecular Taxonomy of Breast Cancer International Consortium cohort

    DISCUSSION

    PIK3CA and TP53, two most commonly mutated genes in BC, have been extensively investigated with regard to prognosis and drug resistance [10, 11, 12, 13, 17]. It is vital to investigate concomitant PIK3CA/TP53 mutations in BC that may act as prognostic signatures. The mechanisms underlying for co-occurring mutations could potentially be developed as novel therapeutic targets.

    In this study, we first analyzed baseline features in relation to PIK3CA/TP53 mutations and measured the association between co-mutations and neoadjuvant therapeutic effects by pCR. Significant correlations were found between PIK3CA/TP53 co-mutations and unfavorable clinicopathological characteristics. Although the PIK3CA mutation was associated with HR positivity [36], we found that concomitant PIK3CA/TP53 mutations were associated with HR-negative and high-grade tumors. This is consistent with the results of a previous study that was confined to residual cancer tissues after neoadjuvant chemotherapy [24]. Moreover, the higher proportion of HER2-positive tumors in co-mutated BC observed in this study did not emerge in the existing research. The IHC-based subtype distribution was similar to that of TP53-mutant BC. In co-mutated BC, luminal B (HER2+) was the most common subtype, followed by luminal B (HER2-) and HER2-enriched BC. Additionally, co-mutated BC showed significantly more mutations in ERBB2, CDK12, NF1, FGFR2, PREX2, POLE, KAT6A, SPOP, NBN, and MAP3K1.

    Regarding treatment efficacy, the co-mutation rendered patients with BC less sensitive to neoadjuvant therapy than those with only TP53-mutated BC. This was most prominent in the patients who received taxane-containing chemotherapy. This finding was further confirmed by drug sensitivity estimation based on CGP for docetaxel, doxorubicin, and cisplatin. In other words, co-existing PIK3CA alteration in TP53-mutant decreased therapeutic response. Previous evidence supporting this result was provided by the observation that PIK3CA-mutated BC was less sensitive to chemotherapy, endocrine therapy, or anti-HER2 therapy [19, 37, 38]. In contrast, TP53 mutations predicted better responses to NST, including docetaxel [22, 39]. Apoptosis and mitotic catastrophe, but not growth arrest or senescence, are induced in TP53-mutant mammary tumors [40]. However, TP53 mutations co-occurring in PIK3CA-mutated BC did not enhance sensitivity to chemotherapy. Therefore, we hypothesized that PIK3CA mutation predominantly determines the therapeutic efficacy of NST in PIK3CA/TP53 co-mutated BC. PIK3CA mutations contribute to a good response to the α-selective and β-sparing PI3K inhibitors [41], which could potentially solve the problem of drug resistance.

    In addition, the worse prognostic role of concomitant PIK3CA/TP53 mutations was confirmed in the METABRIC cohort, which included the largest number of BC cases. There have been many studies on single TP53 or PIK3CA mutations [42, 43], but there is little evidence regarding the targeted treatment of TP53/PIK3CA co-mutated BC. Confirming that concomitant PIK3CA/TP53 mutations are unfavorable prognostic markers, our study could play an important role in guiding decision-making and improving clinical efficacy.

    In 2017, Croessmann et al. [26] suggested a synergistic interaction between PIK3CA and TP53 mutations, which were responsible for the defects in the cell cycle. However, the exact underlying mechanisms have not been fully elucidated. PIK3CA mutations can lead to the activation of p53-mediated growth suppression, implying the role of p53 as a brake during the PI3K pathway in cancer pathogenesis [44]. Conversely, the PI3K pathway is a crucial mediator of p53 activation. PI3K, whose catalytic subunit is encoded by PIK3CA, catalyzes the phosphorylation of a second messenger that recruits and activates downstream AKT [45]. AKT activation synergized with TP53 ablation to promote cell growth and proliferation [46]. PTEN is a catalytic antagonist of PI3K and is associated with oncogenic cellular transformation [45]. Loss of TP53 can inactivate PTEN transcription, thus inhibiting growth arrest [47, 48]. In addition, TP53 mutants lose or retain very few weak functions during cancer suppression [49]. As a result, the TP53 mutation not only disables wild-type TP53-mediated transactivation or repression, but also assists in AKT activation and PTEN inactivation. Nevertheless, Adams et al. [50] observed that the PIK3CA H1047R mutation and TP53 loss-of-function mutation cooperate in BC initiation rather than in apoptosis or proliferation.

    Although our study provides a comprehensive analysis of concomitant PIK3CA/TP53 mutations, there are some limitations. First, discrepancies in clinicopathological and mutational characteristics, as well as therapeutic responses, were obtained using a relatively small sample size from a single center. Second, therapeutic in vitro drug sensitivity experiments are needed to verify our results. Moreover, the molecular mechanism of concomitant mutations should be investigated in future studies.

    Concomitant PIK3CA/TP53 mutations indicated poor clinicopathological and mutational characteristics and poor prognosis in patients with BC. In addition, they were shown to be correlated with worse therapeutic responses than the TP53 mutant alone. New therapeutic strategies should be developed to improve undesirable outcomes in patients with co-mutant BC.

    Notes

    Funding:This study was funded by the GDPH Supporting Fund for National Natural Science Foundation of China Program (KY012021151, 8217100986, 8207101000), National Natural Science Foundation of China (82273169), Guangzhou Municipal Science and Technology Project (201804010430), Natural Science Foundation of Guangdong Province (2018A030313292), Guangdong Basic and Applied Basic Research Foundation (A2020025), and Administration of Traditional Chinese Medicine of Guangdong Province, China (20202003). The funding bodies had no role in the design of the study; collection, analysis, and interpretation of data; writing of the manuscript; or decision to publish the paper.

    Conflict of Interest:The authors declare that they have no competing interests.

    Author Contributions:

    • Conceptualization: Zhang GC.

    • Data curation: Lin XY.

    • Formal analysis: Lin XY.

    • Funding acquisition: Wang Y, Zhang G.

    • Investigation: Guo L, Wang Y.

    • Methodology: Lin XY, Guo L, Wang Y.

    • Project administration: Wang Y.

    • Resources: Lin XY.

    • Software: Lin X.

    • Supervision: Zhang G.

    • Validation: Lin X.

    • Visualization: Lin XY.

    • Writing - original draft: Lin XY, Guo L, Lin X.

    • Writing - review & editing: Wang Y, Zhang G.

    ACKNOWLEDGMENTS

    The authors acknowledge all patients and their families for their participation in the study. The authors also acknowledged the efforts of TCGA, CGP and METABRIC program.

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    MeSH Terms
    Breast Neoplasms*
    Breast*
    Classification
    Cohort Studies
    Dataset
    Disease-Free Survival
    ErbB Receptors
    High-Throughput Nucleotide Sequencing
    Humans
    Neoadjuvant Therapy
    Phenotype
    Prognosis*
    Retrospective Studies
    Metrics
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