Dataset Information

A novel immune prognostic index for stratification of high-risk patients with early breast cancer.

ABSTRACT: The prognostic value of current multigene assays for breast cancer is limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer. Despite the prognostic significance of immune response-related genes in breast cancer, immune gene signatures have not been incorporated into most multigene assays. Here, using public gene expression microarray datasets, we classified breast cancer patients into three risk groups according to clinical risk and proliferation risk. We then developed the immune prognostic index based on expression of five immune response-related genes (TRAT1, IL2RB, CTLA4, IGHM and IL21R) and lymph node status to predict the risk of recurrence in the clinical and proliferation high-risk (CPH) group. The 10-year probability of disease-free survival (DFS) or distant metastasis-free survival (DMFS) of patients classified as high risk according to the immune prognostic index was significantly lower than those of patients classified as intermediate or low risk. Multivariate analysis revealed that the index is an independent prognostic factor for DFS or DMFS. Moreover, the C-index revealed that it is superior to clinicopathological variables for predicting prognosis. Its prognostic significance was also validated in independent datasets. The immune prognostic index identified low-risk patients among patients classified as CPH, regardless of the molecular subtype of breast cancer, and may overcome the limitations of current multigene assays.

SUBMITTER: Lee H

PROVIDER: S-EPMC7794340 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel immune prognostic index for stratification of high-risk patients with early breast cancer.

Lee Hannah H Kwon Mi Jeong MJ Koo Beom-Mo BM Park Hee Geon HG Han Jinil J Shin Young Kee YK

Scientific reports 20210108 1

The prognostic value of current multigene assays for breast cancer is limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer. Despite the prognostic significance of immune response-related genes in breast cancer, immune gene signatures have not been incorporated into most multigene assays. Here, using public gene expression microarray datasets, we classified breast cancer patients into three risk groups according to clinical risk and prolifera ...[more]

PMID: 33420250

Similar Datasets

Project description:Each of the pathological stages (I-IIIa) of surgically resected non-small-cell lung cancer has hidden biological heterogeneity, manifested as heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers with which to assess individual patient risk is an unmet medical need. Here, we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI, and SLC2A1) to stratify early-stage lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged according to the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8th edition, 2018). A test cohort (n = 239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression with the use of stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of 5-year outcome for disease-free survival (p < 0.001) and overall survival (p < 0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n = 114, p = 0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging, with a highly significant improvement of the likelihood ratio. We subsequently developed a combined PI including both the molecular and the pathological data that improved the risk stratification in both cohorts (p ≤ 0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with a high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even when the new IASLC 8th edition staging criteria are applied. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Project description:BackgroundThe individual risk of recurrence in hormone receptor-positive primary breast cancer patients determines whether adjuvant endocrine therapy should be combined with chemotherapy. Clinicopathological parameters and molecular tests such as EndoPredict(®) (EPclin) can support decision making in patients with estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative cancer.ObjectiveUsing a life-long Markov state transition model, we determined the health economic impact and incremental cost effectiveness of EPclin-based risk stratification in combination with clinical guidelines [German-S3, National Comprehensive Cancer Center Network (NCCN), and St. Gallen] to decide on chemotherapy use.MethodsInformation on overall and metastasis-free survival came from Austrian Breast & Colorectal Cancer Study Group clinical trials 6/8 (n = 1,619) and published literature. Effectiveness was assessed as quality-adjusted life-years (QALYs). Costs (2010) were assessed from a German third-party payer perspective.ResultsLifetime costs per patient ranged from <euro>28,268 (St.Gallen and EPclin) to <euro>33,756 (NCCN). Due to an imperfect prognostic value and differences in chemotherapy use, strategies achieved between 13.165 QALYs (NCCN) and 13.173 QALYs (EPclin alone) per patient. Using German-S3 as reference, three strategies showed dominant results (St. Gallen and EPclin, German-S3 and EPclin, EPclin alone). Compared to German-S3, the addition of EPclin saved <euro>3,388 and gained 0.002 QALYs per patient. Combining guidelines with EPclin remained preferable in sensitivity analysis.ConclusionOur study suggests that molecular markers can be sensibly combined with clinical guidelines to determine the risk profile of adjuvant breast cancer patients. Compared with the current German best practice (German-S3), combinations of EPclin with the St. Gallen, German-S3 or NCCN guideline and EPclin alone were dominant from the perspective of the German healthcare system.

Project description:ObjectiveThis study aims at investigating the potential prognostic significance of the breast immune prognostic index (BIPI) in breast cancer patients who received neoadjuvant chemotherapy (NACT).MethodsThe optimal cutoff value was calculated through the receiver operating characteristic curve (ROC). The correlations between BIPI and clinicopathologic characteristics were determined by the chi-square test or Fisher's exact test. The Kaplan-Meier method was used to estimate the survival probability, and the log-rank test was used to analyze the differences in the survival probability among patients. The univariate and multivariate Cox proportional hazard regression model was used to screen the independent prognostic factors. A prognostic nomogram for disease-free survival (DFS) and overall survival (OS) was built on the basis of the multivariate analyses. Furthermore, the calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram.ResultsAll enrolled patients were split into three subgroups based on the BIPI score. The mean DFS and OS of the BIPI score 0 group and BIPI score 1 group were significantly longer than those of the BIPI score 2 group (42.02 vs. 38.61 vs. 26.01 months, 77.61 vs. 71.83 vs. 53.15 months; p < 0.05). Univariate and multivariate analyses indicated that BIPI was an independent prognostic factor for patients' DFS and OS (DFS, hazard ratio (HR): 6.720, 95% confidence interval (CI): 1.629-27.717; OS, HR: 8.006, 95% CI: 1.638-39.119). A nomogram with a C-index of 0.873 (95% CI: 0.779-0.966) and 0.801 (95% CI: 0.702-0.901) had a favorable performance for predicting DFS and OS survival rates for clinical use by combining immune scores with other clinical features. The calibration curves at 1-, 3-, and 5-year survival suggested a good consistency between the predicted and actual DFS and OS probability. The DCA demonstrated that the constructed nomogram had better clinical predictive usefulness than only BIPI in predictive clinical applications of 5-year DFS and OS prognostic assessments.ConclusionsThe patients with low BIPI score have better prognoses and longer DFS and OS. Furthermore, the BIPI-based nomogram may serve as a convenient prognostic tool for breast cancer and help in clinical decision-making.

Dataset Information

A novel immune prognostic index for stratification of high-risk patients with early breast cancer.

Publications

A novel immune prognostic index for stratification of high-risk patients with early breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets