Dataset Information

Tumor heterogeneity at protein level as an independent prognostic factor in endometrial cancer.

ABSTRACT: Intratumor heterogeneity implies heterogeneous protein function, facilitating tumor adaptation which results in therapeutic failure. We hypothesized that tumor heterogeneity at protein level may influence the course of the disease. As a single biopsy might not represent the full biologic complexity of the tumor, we have analyzed immunohistochemically four different cores obtained from each primary tumor within the cohort of 364 patients with endometrial cancer (EC). The following proteins were examined: estrogen receptor 1 (ESR1), progesterone receptor, epidermal growth factor receptor, v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, receptor tyrosine-protein kinase erbB-3, v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 4, phosphatidylinositol-4,5-bisphosphate 3-kinase, phosphorylated v-akt murine thymoma viral oncogene homolog 1, v-myc avian myelocytomatosis viral oncogene homolog, DNA topoisomerase II alpha 170 kDa (TOP2A), cyclin-dependent kinase inhibitor 2A (CDKN2A), tumor protein p53, RAD21 homolog, S. pombe, and runt-related transcription factor 1. Particularly strong correlation was found between TOP2A and CDKN2A heterogeneity and higher stage of the disease (P = .0002 and P = .0003, respectively). Most correlations with clinicopathologic data were observed for ESR1 heterogeneity that correlated with non-endometrioid carcinomas (P=.02), higher stage (P=.005), grade (P=.01), and the presence of metastases (P = .01). Thirty-nine (11.0%) patients were classified as "globally heterogeneous". Cumulative tumor heterogeneity strongly correlated with the presence of metastases, higher stage, and higher grade of the disease (all P b .05). It also carried negative prognostic value (P=.0008). We show that the degree of heterogeneity in EC might serve as a clinically valid molecular marker.

SUBMITTER: Supernat A

PROVIDER: S-EPMC4225657 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tumor heterogeneity at protein level as an independent prognostic factor in endometrial cancer.

Supernat Anna A Lapińska-Szumczyk Sylwia S Majewska Hanna H Gulczyński Jacek J Biernat Wojciech W Wydra Dariusz D Zaczek Anna J AJ

Translational oncology 20140719 5

Intratumor heterogeneity implies heterogeneous protein function, facilitating tumor adaptation which results in therapeutic failure. We hypothesized that tumor heterogeneity at protein level may influence the course of the disease. As a single biopsy might not represent the full biologic complexity of the tumor, we have analyzed immunohistochemically four different cores obtained from each primary tumor within the cohort of 364 patients with endometrial cancer (EC). The following proteins were e ...[more]

PMID: 25048628

Similar Datasets

Project description:Background:Uterine corpus endometrial carcinoma (UCEC) is a clinically heterogeneous disease, and this heterogeneity is associated with tumor development, clinical characteristics, and prognostic outcomes. Mutant-allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative measure to assess intra-tumor heterogeneity based on next-generation sequencing data. We aimed to explore the use of MATH as a measure for tumor heterogeneity and its prognostic role in UCEC patients. Methods:We calculated MATH scores from the available data of 560 UCEC patients from The Cancer Genome Atlas (TCGA) and investigated their correlations with clinical characteristics, genetic alterations, and overall survival. Predictive accuracy was quantified using the area under the receiver operating characteristic curve (AUC) and the index of concordance (C-index). Results:In total, 242 MATH scores were obtained from the UCEC cohort. MATH scores were significantly related to age, race, cancer type, clinical stage, histological grade, molecular type, targeted molecular therapy, and hormonal therapy. Furthermore, the genomic pattern on the basis of MATH scores showed that mutation rates of TP53 (tumor protein p53) and ARID1A (AT-rich interaction domain 1A) were independently associated with MATH scores. Correlation analysis revealed a significantly positive association of MATH scores with the fraction of somatic copy number alteration (SCNA). Importantly, a high MATH score was significantly associated with shorter overall survival [hazard ratio (HR), 2.342; 95% confidence interval (CI), 1.110-4.942]. Multivariate Cox regression combined with stratified analysis revealed that the MATH score is an independent prognostic factor in UCEC patients under 60 years old, and predictive quantification showed the MATH score had an AUC of 0.756 and a C-index of 0.845. Conclusions:Our results suggest that MATH, a practical and useful way to measure intra-tumor heterogeneity, may serve as a significant biomarker for the prognosis of patients with UCEC, enabling more accurate prediction of clinical outcomes.

Project description:BackgroundIn endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI-based radiomic tumor profiling may aid in preoperative risk-stratification and support clinical treatment decisions in EC.PurposeTo develop MRI-based whole-volume tumor radiomic signatures for prediction of aggressive EC disease.Study typeRetrospective.PopulationA total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30).Field strength/sequenceAxial oblique T1 -weighted gradient echo volumetric interpolated breath-hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.AssessmentPrimary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically-verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high-grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.Statistical testsLogistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT ) and validation (AUCV ) cohorts. Progression-free survival was assessed using the Kaplan-Meier and Cox proportional hazard model.ResultsThe whole-tumor radiomic signatures yielded AUCT /AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high-grade (E3) tumor. Single-slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUCT to the whole-tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUCT for E3 tumors (P < 0.05). All of the whole-tumor radiomic signatures significantly predicted poor progression-free survival with hazard ratios of 4.6-9.8 (P < 0.05 for all).Data conclusionMRI-based whole-tumor radiomic signatures yield medium-to-high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.Level of evidence4 TECHNICAL EFFICACY STAGE: 2.

Project description:BackgroundTo explore the correlation of tumor necrosis factor-α-induced protein 8-like protein 3 (TIPE3) expressions in colorectal cancer (CRC) with tumor-immune infiltration and patient prognosis.MethodsFormalin-fixed paraffin-embedded tumor samples from CRC patients (n = 110) were used in this study. Immunohistochemistry staining of TIPE3 and three prognostic immune biomarkers (CD8, CD20, and CD66b) was conducted in the tumor tissues and adjacent normal tissues. A Cox regression analysis of univariate and multivariate variables was performed to assess the correlation between TIPE3 and patient prognosis.ResultWe found that TIPE3 was mainly expressed in the cytoplasm, with a small amount in the nucleus. The expression of TIPE3 in tumor tissues is significantly higher than in adjacent normal tissues, and it is significantly correlated with the survival rate of patients in tumor tissues (p = 0.0038) and adjacent normal tissues (p<0.0001). Patients with a high TIPE3 expression had a lower survival rate, while patients with a low TIPE3 expression had a higher survival rate. Univariate regression analysis showed that the TIPE3 expression in tumor tissues (p = 0.007), the TIPE3 expression in adjacent normal tissues (p<0.001), the number of CD8+ T cells in tumor tissues (p = 0.020), the number of CD20+ B cells in tumor tissues (p = 0.023), the number of CD20+ B cells in adjacent normal tissues (p = 0.023), the number of CD66b+ neutrophils in tumor tissues (p = 0.005), the number of CD66b+ neutrophils in adjacent normal tissues (p<0.001), lymphatic metastasis (p = 0.010), TNM stage (p = 0.013), and tumor grade (p = 0.027) were significantly correlated with overall survival (OS). These prognostic factors were then subjected to multivariate regression analysis, and the results showed that the expression of TIPE3, the number of CD8+ T cells, and the number of CD66b+ neutrophils were prognostic factors affecting the OS rate of CRC patients.ConclusionWe found that the TIPE3 protein is upregulated in CRC cancer tissues and is correlated with survival rate.

Dataset Information

Tumor heterogeneity at protein level as an independent prognostic factor in endometrial cancer.

Publications

Tumor heterogeneity at protein level as an independent prognostic factor in endometrial cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets