Prognostic Clinical and Biologic Features for Overall Survival after Relapse in Childhood Medulloblastoma.
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ABSTRACT:
SUBMITTER: Huybrechts S
PROVIDER: S-EPMC7795432 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Prognostic Clinical and Biologic Features for Overall Survival after Relapse in Childhood Medulloblastoma.
Huybrechts Sophie S Le Teuff Gwénaël G Tauziède-Espariat Arnault A Rossoni Caroline C Chivet Anaïs A Indersie Émilie É Varlet Pascale P Puget Stéphanie S Abbas Rachid R Ayrault Olivier O Guerrini-Rousseau Léa L Grill Jacques J Valteau-Couanet Dominique D Dufour Christelle C
Cancers 20201227 1
Given the very poor prognosis for children with recurrent medulloblastoma, we aimed to identify prognostic factors for survival post-relapse in children with childhood medulloblastoma. We retrospectively collected clinico-biological data at diagnosis and main clinical characteristics at relapse of children newly diagnosed with a medulloblastoma between 2007 and 2017 at Gustave Roussy and Necker Hospital. At a median follow-up of 6.6 years (range, 0.4-12.3 years), relapse occurred in 48 out 155 p ...[more]
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Project description:PurposeDespite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis.Patients and methodsA Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression-derived features representing disease subtype-independent (model B) and disease subtype-dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78).ResultsOn an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse.ConclusionThe proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification.
| S-EPMC3082982 | biostudies-literature
Project description:BackgroundAlthough chemotherapy is used routinely in pediatric medulloblastoma (MB) patients, its benefit for adult MB is unclear. We evaluated the survival impact of adjuvant chemotherapy in adult MB.MethodsUsing the National Cancer Data Base, we identified patients aged 18 years and older who were diagnosed with MB in 2004-2012 and underwent surgical resection and adjuvant craniospinal irradiation (CSI). Patients were divided into those who received adjuvant CSI and chemotherapy (CRT) or CSI alone (RT). Predictors of CRT compared with RT were evaluated with univariable and multivariable logistic regression. Survival analysis was limited to patients receiving CSI doses between 23 and 36 Gy. Overall survival (OS) was evaluated using the Kaplan-Meier estimator, log-rank test, multivariable Cox proportional hazards modeling, and propensity score matching.ResultsOf the 751 patients included, 520 (69.2%) received CRT, and 231 (30.8%) received RT. With median follow-up of 5.0 years, estimated 5-year OS was superior in patients receiving CRT versus RT (86.1% vs 71.6%, P < .0001). On multivariable analysis, after controlling for risk factors, CRT was associated with superior OS compared with RT (HR: 0.53; 95%CI: 0.32-0.88, P = .01). On planned subgroup analyses, the 5 year OS of patients receiving CRT versus RT was improved for M0 patients (P < .0001), for patients receiving 36 Gy CSI (P = .0007), and for M0 patients receiving 36 Gy CSI (P = .0008).ConclusionsThis national database analysis demonstrates that combined postoperative chemotherapy and radiotherapy are associated with superior survival for adult MB compared with radiotherapy alone, even for M0 patients who receive high-dose CSI.
| S-EPMC5464064 | biostudies-literature
Project description:BackgroundMedulloblastoma is a rare diagnosis among adolescents and young adults (AYA). Though prognostic factors and treatment are well characterized among children with medulloblastoma, equivalent data for AYA are sparse. We conducted a systematic review to identify predictors of survival among AYA with medulloblastoma.MethodsWe searched for primary studies of AYA (age 15-39 at diagnosis) with medulloblastoma in high-income countries within OVID MEDLINE, EMBASE, and EBM Reviews-Cochrane library databases from inception to August 2020. Patient demographics, primary outcomes, and univariate and multivariable data on all prognostic factors were collected from included studies. Prognosticators were characterized as patient, disease, or treatment-related.ResultsWe identified 18 articles. 5-year overall survival ranged between 40% and 89%, while disease-free survival ranged from 49% to 89%. Study quality was low as assessed by the Quality in Prognostic factor Studies tool. Though meta-analyses were not possible due heterogeneity, narrative summaries suggested that lower disease burden, superior postoperative functional status, and higher doses and larger fields of radiation were associated with improved survival. Reported chemotherapy regimens were heterogeneous in timing, agents, and relationship with radiation, precluding meaningful comparisons. Only one study included molecular subgroups for analysis, with the majority (76.5%) of tumors classified as Sonic Hedgehog (SHH).ConclusionsPrognostication and treatment of AYA medulloblastoma is limited by a dearth of primary evidence and lack of specificity for patients aged 15-39. Dedicated prospective trials to delineate the benefit of various chemotherapy and radiation regimens are required in this population to identify prognosticators and ideal treatment regimens.
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Project description:Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.
| S-EPMC10252343 | biostudies-literature
Project description:Background:Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment. Materials and Methods:Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively. Results:A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit. Conclusion:Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.
| S-EPMC7495025 | biostudies-literature
Project description:Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and random survival forest-variable hunting (RSF-VH)), and multivariate Cox regression analysis, we established two metastasis-related prognostic models, including the 47-mRNA-based model based on the Lasso method and the 21-mRNA-based model based on the RSF-VH method. In terms of the results of the receiver operating characteristic (ROC) curve analyses, we selected the 47-mRNA metastasis-associated model with the higher area under the curve (AUC). The 47-mRNA-based prognostic model could classify MB patients into two subgroups with different prognoses. The ROC analyses also suggested that the 47-mRNA metastasis-associated model may have a better predictive ability than MB subgroup. Multivariable Cox regression analysis demonstrated that the 47-mRNA-based model was independent of other clinical characteristics. In addition, a nomogram comprising the 47-mRNA-based model was built. The results of ROC analyses suggested that the nomogram had good discrimination ability. Our 47-mRNA metastasis-related prognostic model and nomogram might be an efficient and valuable tool for overall survival (OS) prediction and provide information for individualized treatment decisions in patients with MB.
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Project description:This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein-Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high-risk and low-risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan-Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C-index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low-risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients.
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Project description:Introduction: With a gap in a full understanding of the mechanisms by which survival is extended for patients with cancer who are treated with novel biologic and targeted agents, there is the risk that discordant progression-free and overall survival outcomes are observed due to poor clinical trial design or biases in the interpretation of data. This study was designed to examine the role of study quality and design on the outcomes observed with biologic and targeted agents. Methods: A review of studies in clinicaltrials.gov supplemented with a literature review in OVID Medline was conducted to identify all randomized trials of a biologic/targeted agent versus a non-biologic/targeted comparator in oncology that report both median overall and progression-free survival outcomes. Details of the study, design, population, drugs, and outcomes were extracted. Study quality was evaluated using the PEDro scale. Data were summarized using SPSS 22.0.0.0. Results: A total of 192 unique studies of 206 pairwise comparisons between a biologic/targeted and comparator were identified. The average absolute magnitude of post-progression survival (difference between OS and PFS) was 9.7 months for biologic/targeted therapy and 9.8 for the comparator. A total of 64 comparisons (31.1%) showed an increase in OS and decrease in PFS, or vice versa, and 25 (12.1%) showed a magnitude of more than 4 months difference between the delta of OS and delta of PFS between the biologic/targeted and comparator arms. Average study quality was high overall (7.7/10), and was comparable for studies with directional differences (7.2/10) as well as for those with the greatest magnitude in post-progression survival (7.4/10). Conclusion: This review and analysis specifically examined small PFS benefit with large OS benefit as well as small OS benefit with large PFS benefit, including differences in direction of PFS and OS outcomes. No evidence was identified that these are the result of poor study design, but may rather be due to the mechanism of action, specific disease, and population under study. Further work is needed to understand the mechanism of action of novel biologic/targeted agents to better understand their interaction with the tumor microenvironment.
| S-EPMC6636299 | biostudies-literature
Project description:BackgroundMetaplastic breast cancer (MBC) is a rare breast tumor and the prognostic factors for survival in patients still remain controversial. This study aims to develop and validate a nomogram to predict the overall survival (OS) of patients with MBC.MethodsWe searched the Surveillance, Epidemiology, and End Results (SEER) database for data about patients including metaplastic breast cancer and infiltrating ductal carcinoma (IDC) from 2010 to 2018. The survival outcomes of patients between MBC and IDC were analyzed and compared with the Kaplan-Meier (KM) method. MBC patients were randomly allocated to the training set and validation I set by a ratio of eight to two. Meanwhile, the performance of this model was validated again by the validation II set, which consisted of MBC patients from the Union Hospital of Fujian Medical University between 2010 and 2018. The independent prognostic factors were selected by univariate and multivariate Cox regression analyses. The nomogram was constructed to predict individual survival outcomes for MBC patients. The discriminative power, calibration, and clinical effectiveness of the nomogram were evaluated by the concordance index (C-index), the receiver operating characteristic (ROC) curve, and the decision curve analysis (DCA).ResultsMBC had a significantly higher T stage (T2 and above accounting for 75.1% vs 39.9%), fewer infiltrated lymph nodes (N0 accounted for 76.2% vs 67.7%), a lower proportion of ER (22.2% vs 81.2%), PR (13.6% vs 71.4%), and HER-2(6.7% vs 17.7%) positive, radiotherapy(51.6% vs 58.0%) but more chemotherapy(67.5% vs 44.7%), and a higher rate of mastectomy(53.2% vs 36.8%), which was discovered when comparing the clinical baseline data between MBC and IDC. Age at diagnosis, T, N, and M stage, as well as surgery and radiation treatment, were all significant independent prognostic factors for overall survival (OS). In the validation I cohort, the nomogram's C-index (0.769 95% CI 0.710 -0.828) was indicated to be considerably higher than the standard AJCC model's (0.700 95% CI 0.644 -0.756). Nomogram's great predictive capability capacity further was supported by the comparatively high C-index of the validation II sets (0.728 95%CI 0.588-0.869).ConclusionsMetaplastic breast cancer is more aggressive, with a worse clinical prognosis than IDC. This nomogram is recommended for patients with MBC, both American and Chinese, which can help clinicians make more accurate individualized survival analyses.
| S-EPMC10018193 | biostudies-literature
Project description:BackgroundAcute myeloid leukemia (AML) is one of the most common hematological malignancies and accounts for about 20% of childhood leukemias. Currently, immunotherapy is one of the recommended treatment schemes for recurrent AML patients to improve their survival rates. Nonetheless, low remission and high mortality rates are observed in recurrent AML and challenge the prognosis of AML patients. To address this problem, we aimed to establish and verify a reliable prognostic risk model using immune-related genes to improve the prognostic evaluation and recommendation for personalized treatment of AML.MethodsTranscriptome data and clinical data were acquired from the TARGET database while immune genes were sourced from InnateDB and ImmPort Shared databases. The mRNA expression profile matrix of immune genes from 62 normal samples and 1408 AML cases was extracted from the transcriptome data and subjected to differential expression (DE) analysis. The entire cohort of DE immune genes was randomly divided into the test group and training group. The prognostic model associated with immune genes was constructed using the training group. The test group and entire cohort were employed for model validation. Lastly, we analyzed the potential clinical application of the model and its association with immune cell infiltration.ResultsIn total, 751 DE immune genes were differentially regulated, including 552 upregulated and 199 downregulated. Based on these DE genes, we developed and validated a prognostic risk model composed of seven immune genes, GDF1, TPM2, IL1R1, PSMD4, IL5RA, DHCR24, and IL12RB2. This model is able to predict the 5-year survival rate more accurately compared with age, gender, and risk stratification. Further analysis showed that CD8+ T-cell contents and neutrophil infiltration decreased but macrophage infiltration increased as the risk score increased.ConclusionsA seven-immune gene model of AML was developed and validated. We propose this model as an independent prognostic variable able to estimate the 5-year survival rate. In addition, the model can also reflect the immune microenvironment of AML patients.
| S-EPMC9744619 | biostudies-literature