Determination of Both TP53 Mutation Status and the Amount of p53 Protein Has Limited Diagnostic Importance for Patients with Ovarian Cancer

2022 ◽  
Vol 29 ◽  
Author(s):  
Sebastian M. Klein ◽  
Maria Bozko ◽  
Astrid Toennießen ◽  
Nisar P. Malek ◽  
Przemyslaw Bozko

Background: Ovarian cancer is one of the most aggressive types of gynecologic cancers. Many patients have a relapse within two years after diagnosis and subsequent therapy. Among different genetic changes generally believed to be important for the development of cancer, TP53 is the most common mutation in the case of ovarian tumors. Objective: Our work aims to compare the outcomes of different comparisons based on the overall survival of ovarian cancer patients, determination of TP53 status, and amount of p53 protein in tumor tissues. Methods: We analyzed and compared a collective of 436 ovarian patient’s data. Extracted data include TP53 mutation status, p53 protein level, and information on the overall survival. Values for p53 protein level in dependence of TP53 mutation status were compared using the Independent-Samples t-Test. Survival analyses were displayed by Kaplan-Meier plots, using the log-rank test to check for statistical significance. Results: We have not found any statistically significant correlations between determination of TP53 status, amount of p53 protein in tumor tissues, and overall survival of ovarian cancer patients. Conclusion: In ovarian tumors both determination of TP53 status as well as p53 protein amount has only limited diagnostic importance.

2019 ◽  
Vol 29 (2) ◽  
pp. 357-364 ◽  
Author(s):  
Jennifer Taylor Veneris ◽  
Lei Huang ◽  
Jane E Churpek ◽  
Suzanne D Conzen ◽  
Gini F Fleming

ObjectiveHigh glucocorticoid receptor (GR) protein expression is associated with decreased progression-free survival in ovarian cancer patients and decreased sensitivity to chemotherapy in preclinical models. Prior studies suggest wild type BRCA1 promotes GR activation. The objective of this study was to characterize the relationship of tumor GR gene expression to outcome in ovarian cancer, and to evaluate the relationship of GR expression with BRCA status.MethodsWhole exome and whole genome sequencing, gene expression, and clinical data were obtained for high-grade serous ovarian cancers in The Cancer Genome Atlas. Cases with pathogenic somatic or germline BRCA1 or BRCA2 mutations were identified and classified as BRCA mutated. High or low glucocorticoid receptor expression was defined as expression above or below median of the GR/nuclear receptor subfamily 3 C1 (NR3C1) gene level. Overall survival was estimated by the Kaplan-Meier method and compared by Cox regression analysis.ResultsCombined germline DNA sequencing and tumor microarray expression data were available for 222 high-grade serous ovarian cancer cases. Among these, 47 had a deleterious germline and/or somatic mutation in BRCA1 or BRCA2. In multivariate analysis, high glucocorticoid receptor gene expression was associated with decreased overall survival among ovarian cancer patients, independently of BRCA mutation status. No correlation of GR/NR3C1 gene expression with BRCA mutation status or BRCA1 or BRCA2 mRNA level was observed.ConclusionsIncreased GR gene expression is associated with decreased overall survival in ovarian cancer patients, independently of BRCA mutation status. High-grade serous ovarian cancers with high GR expression and wild type BRCA have a particularly poor outcome.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4121-4121 ◽  
Author(s):  
Nguyen Johnny ◽  
Kathy L McGraw ◽  
Rami S. Komrokji ◽  
David Sallman ◽  
Najla H. Al Ali ◽  
...  

Abstract Background: TP53 mutations have been reported in 5-10% of patients with myelodysplastic syndromes (MDS) with a higher frequency in those who harbor del(5q) and complex cytogenetics. Next generation sequencing (NGS) data in large clinical cohorts has revealed TP53 mutation is a strong independent prognostic factor. Limited studies have shown an association of p53 protein overexpression with TP53 mutation as well as other clinical characteristics, such as blast count, cytogenetics, and mutant TP53 variant allele frequency (VAF). The study aims to validate the association of p53 overexpression by immunohistochemical (IHC) staining with TP53 mutation, and to explore its relationship to clinical outcome and mutation burden in MDS. Methods: We retrospectively analyzed MDS patients with or without transformation to acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) diagnosed between 7/2013 and 5/2015. Only those cases with available bone marrow (BM) biopsies with good quality (>1.0 cm in length) corresponding to the NGS testing date were included. Five normal BMs were used as controls. IHC was performed according to our institutional standard protocol and modified in accordance with the manufacturer. Nuclear expression of p53 was assessed semi-quantitatively using an IHC score calculated by multiplying IHC stain intensity with percent positivity in hematopoietic cells. Paired t-test was used for numerical parameters. Hazard ratios were generated using the standard cox proportional harzard model. Survival was analyzed by the Kaplan-Meier method and overall survival (OS) was defined as the duration between date of diagnosis and date of death. Results: Of 201 patients with myeloid malignancies, 29 (14%) harbored clinically significant mutations by NGS testing. Twenty two of these (9 AML-MRC,13 MDS) had available BM biopsies. An additional 32 patients (27 MDS, 5 AML) with wild type (WT) TP53 were included. Clinicopathologic differences of these groups are summarized in Figure 1. IHC analysis showed significantly (p=1.21x10-6) elevated nuclear p53 expression in TP53 mutated patients (mean score, 1.11± 0.164) compared to WT (0.037 ±0.014). A higher p53 IHC score in mutated patients correlated with a complex karyotype (n=14) compared to those without (n=6) (1.235 ± 0.206, and 0.580 ± 0.257, respectively, p=0.054). A significant positive correlation between IHC score and cytogenetic risk group according to revised international prognostic scoring system ( R-IPSS) existed in our cohort (p<0.001). Importantly, p53 overexpression directly correlated with TP53 VAF within the MDS group (r=.855, p<0 .001). We found no correlation between p53 IHC score and blast count in mutated TP53 patients (r=0.355, p=0.110), but a significant correlation in those with WT TP53 (r=0.527, p=0.002), which merits further investigation. The proportion of del(17p) or del(5q) was greater in the TP53 mutated patients vs. WT patients [40% and 90% in mutated TP53 vs. 4% and 12% in WT, respectively). Median overall survival (OS) was 86 months (95%CI, 7-166) and median follow-up duration was 27 months (95% CI 7-46). The sensitivity and specificity of using a p53 IHC score in predicting TP53 mutation status using a cutoff of 1.00 was 59.1% and 100%, respectively. The sensitivity and specificity of using a p53 IHC score in predicting TP53 mutation status using a cutoff of 0.500 was 77.3% and 100%, respectively. Median OS was shorter in mutated patients [16 months (95% CI, 8-23)] compared to WT (not reached) (p=0.001). OS hazard ratio (HR) of mutant TP53 was 4.6 (95% CI, 1.8-12) (p= 0.002). We also found significant differences in OS by IHC score. Median OS was not reached in patients with an IHC score <1 compared to >1.0 [16 month (95% CI, 8-23)] (p= 0.007). Similarly, with a p53 IHC score cutoff of 0.5, median OS was again not reached in those patients with <0.5 IHC score but was 16 months (95% CI, 8-25) in those >0.5 (p=0.015). HR for IHC score > 0.5 was 3 (95% CI, 1.2-8), p=0.02. HR for IHC score >1.0 was 3.5 [(95% CI, 1.3-9), p=0.01]. Conclusion: p53 overexpression in MDS patients correlates with mutated TP53 and mutation burden. A higher IHC score was associated with poorer clinical features and outcomes. A larger cohort is warranted to define its diagnostic or prognostic utility. Figure 1. Clinicopathologic Characteristics in Our MDS/AML-MRC Cohort Figure 1. Clinicopathologic Characteristics in Our MDS/AML-MRC Cohort Disclosures Komrokji: Celgene: Consultancy, Research Funding; Pharmacylics: Speakers Bureau; Incyte: Consultancy; Novartis: Research Funding, Speakers Bureau. Padron:Incyte: Research Funding; Novartis: Speakers Bureau. Lancet:Celgene: Consultancy, Research Funding; Amgen: Consultancy; Pfizer: Consultancy; Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Seattle Genetics: Consultancy. List:Celgene Corporation: Honoraria, Research Funding.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3567
Author(s):  
Beata Szymanska ◽  
Zenon Lukaszewski ◽  
Beata Zelazowska-Rutkowska ◽  
Kinga Hermanowicz-Szamatowicz ◽  
Ewa Gorodkiewicz

Human epididymis protein 4 (HE4) is an ovarian cancer marker. Various cut-off values of the marker in blood are recommended, depending on the method used for its determination. An alternative biosensor for HE4 determination in blood plasma has been developed. It consists of rabbit polyclonal antibody against HE4, covalently attached to a gold chip via cysteamine linker. The biosensor is used with the non-fluidic array SPRi technique. The linear range of the analytical signal response was found to be 2–120 pM, and the biosensor can be used for the determination of the HE4 marker in the plasma of both healthy subjects and ovarian cancer patients after suitable dilution with a PBS buffer. Precision (6–10%) and recovery (101.8–103.5%) were found to be acceptable, and the LOD was equal to 2 pM. The biosensor was validated by the parallel determination of a series of plasma samples from ovarian cancer patients using the Elecsys HE4 test and the developed biosensor, with a good agreement of the results (a Pearson coefficient of 0.989). An example of the diagnostic application of the developed biosensor is given—the influence of ovarian tumor resection on the level of HE4 in blood serum.


2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii32-iii32
Author(s):  
H Noor ◽  
R Rapkins ◽  
K McDonald

Abstract BACKGROUND Tumour Protein 53 (TP53) is a tumour suppressor gene that is mutated in at least 50% of human malignancies. The prevalence of TP53 mutation is much higher in astrocytomas with reports of up to 75% TP53 mutant cases. Rare cases of TP53 mutation also exist in oligodendroglial tumours (10–13%). P53 pathway is therefore an important factor in low-grade glioma tumorigenesis. Although the prognostic impact of TP53 mutations has been studied previously, no concrete concordance were reached between the studies. In this study, we investigated the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. MATERIAL AND METHODS A cohort of 65 matched primary and recurrent fresh frozen tumours were sequenced to identify hotspot exons of TP53 mutation. Exons 1 to 10 were sequenced and pathogenic mutations were mostly predominant between Exons 4 and 8. The cohort was further expanded with 78 low grade glioma fresh frozen tissues and hotspot exons were sequenced. Selecting only the primary tumour from 65 matched tumours, a total of 50 Astrocytoma cases and 51 oligodendroglioma cases were analysed for prognostic effects of TP53. Only pathogenic TP53 mutations confirmed through COSMIC and NCBI databases were included in the over survival and progression-free survival analysis. RESULTS 62% (31/50) of astrocytomas and 16% (8/51) of oligodendrogliomas harboured pathogenic TP53 mutations. Pathogenic hotspot mutations in codon 273 (c.817 C>T and c.818 G>A) was prevalent in astrocytoma with 58% (18/31) of tumours with these mutations. TP53 mutation status was maintained between primary and recurrent tumours in 93% of cases. In astrocytoma, overall survival of TP53 mutant patients was longer compared to TP53 wild-type patients (p<0.01) but was not significant after adjusting for age, gender, grade and IDH1 mutation status. In contrast, astrocytoma patients with specific TP53 mutation in codon 273 showed significantly better survival compared to other TP53 mutant and TP53 wild-type patients combined (p<0.01) in our multivariate analysis. Time to first recurrence (progression-free survival) of TP53 mutant patients was significantly longer than TP53 wild-type patients (p<0.01) after adjustments were made, while TP53 mutation in codon 273 was not prognostic for progression-free survival. In oligodendroglioma patients, TP53 mutations did not significantly affect overall survival and progression-free survival. CONCLUSION In agreement with others, TP53 mutation is more prevalent in Astrocytoma and mutations in codon 273 are significantly associated with longer survival.


eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Gilles Gadea ◽  
Nikola Arsic ◽  
Kenneth Fernandes ◽  
Alexandra Diot ◽  
Sébastien M Joruiz ◽  
...  

TP53 is conventionally thought to prevent cancer formation and progression to metastasis, while mutant TP53 has transforming activities. However, in the clinic, TP53 mutation status does not accurately predict cancer progression. Here we report, based on clinical analysis corroborated with experimental data, that the p53 isoform Δ133p53β promotes cancer cell invasion, regardless of TP53 mutation status. Δ133p53β increases risk of cancer recurrence and death in breast cancer patients. Furthermore Δ133p53β is critical to define invasiveness in a panel of breast and colon cell lines, expressing WT or mutant TP53. Endogenous mutant Δ133p53β depletion prevents invasiveness without affecting mutant full-length p53 protein expression. Mechanistically WT and mutant Δ133p53β induces EMT. Our findings provide explanations to 2 long-lasting and important clinical conundrums: how WT TP53 can promote cancer cell invasion and reciprocally why mutant TP53 gene does not systematically induce cancer progression.


2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.


2019 ◽  
Vol 29 (5) ◽  
pp. 904-909
Author(s):  
Brooke A Schlappe ◽  
Qin C Zhou ◽  
Roisin O'Cearbhaill ◽  
Alexia Iasonos ◽  
Robert A Soslow ◽  
...  

ObjectiveWe described progression-free survival and overall survival in patients with primary mucinous ovarian cancer receiving adjuvant gynecologic versus gastrointestinal chemotherapy regimens.MethodsWe identified all primary mucinous ovarian cancer patients receiving adjuvant gynecologic or gastrointestinal chemotherapy regimens at a single institution from 1994 to 2016. Gynecologic pathologists using strict pathologic/clinical criteria determined diagnosis. Adjuvant therapy was coded as gynecologic or gastrointestinal based on standard agents and schedules. Clinical/pathologic/treatment characteristics were recorded. Wilcoxon rank-sum test was used for continuous variables, and Fisher’s exact test for categorical variables. Progression-free and overall survival were calculated using the Kaplan-Meier method, applying landmark analysis.ResultsOf 62 patients identified, 21 received adjuvant chemotherapy: 12 gynecologic, 9 gastrointestinal. Median age (in years) at diagnosis: 58 (range 25–68) gynecologic cohort, 38 (range 32–68) gastrointestinal cohort (p=0.13). Median body mass index at first post-operative visit: 25 kg/m2(range 18–31) gynecologic cohort, 23 kg/m2(range 18–31) gastrointestinal cohort (p=0.23). History of smoking: 6/12 (50%) gynecologic cohort, 3/9 (33%) gastrointestinal cohort (p=0.66). Stage distribution in gynecologic and gastrointestinal cohorts, respectively: stage I: 9/12 (75%) and 3/9 (33%); stage II: 2/12 (17%) and 1/9 (11%); stage III: 1/12 (8%) and 5/9 (56%) (p=0.06). Grade distribution in gynecologic and gastrointestinal cohorts, respectively: grade 1: 8/12 (67%) and 1/9 (13%); grade 2/3: 4/12 (33%) and 7/9 (88%) (p=0.03). Three-year progression-free survival: 90.9% (95% CI 50.8% to 98.7 %) gynecologic, 53.3% (95% CI 17.7% to 79.6%) gastrointestinal. Three-year overall survival: 90.9% (95% CI 50.8% to 98.7%) gynecologic, 76.2% (95% CI 33.2% to 93.5%) gastrointestinal.ConclusionOngoing international collaborative research may further define associations between chemotherapy regimens and survival.


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