Incorporation of prognostic and predictive factors into glioma clinical trials.
Ontology highlight
ABSTRACT:
SUBMITTER: Johnson DR
PROVIDER: S-EPMC3869572 | biostudies-other | 2013 Feb
REPOSITORIES: biostudies-other
Publications
Incorporation of prognostic and predictive factors into glioma clinical trials.
Johnson Derek R DR Galanis Evanthia E
Current oncology reports 20130201 1
Treatment of brain tumors is increasingly informed by biomarkers that predict patient prognosis and response to therapy. While this progress represents a great opportunity for the field of neuro-oncology, it also presents significant challenges. Biomarkers are not straightforward to identify, and previously used clinical trial paradigms are poorly suited to the task of identifying treatments effective only in selected subsets of patients. Unless investigators adapt new tools and procedures that ...[more]
Similar Datasets
Project description:It would be helpful to have factors that could identify patients who will, or will not, benefit from treatment with specific therapies. Ideally, these should be molecular-based factors. When results with molecular-based factors are disappointing, physicians often use clinical characteristics to make treatment decisions. Several characteristics have been suggested to predict sensitivity to epidermal growth factor receptor inhibitors in patients with non-small lung cancer, including gender, histology, smoking history. This report demonstrates that gender and histology are actually prognostic, rather than predictive factors. Before biomarkers or clinical characteristics are included in guidelines for selecting patients for specific treatments, it is imperative that the prognostic effects of these factors are distinguished from their ability to predict a differential clinical benefit from the specific treatment.
| S-EPMC5543832 | biostudies-other
Project description:Background: Previous data, mostly from clinical trials, reported that HER2-low status is associated with low pathological complete response (pCR), and favourable prognosis. Since these findings suggest the existence of an additional breast cancer subtype, we questioned if the predictive/prognostic value of HER2-low was also relevant in the real world. Methods: Data from non-metastatic breast cancer patients treated with neoadjuvant chemotherapy and surgery (2009-2020) were retrieved from our institutional prospectively-maintained registry. Univariable and multivariable logistic models were implemented to study the association between pCR and baseline HER2 status. Univariable analysis of disease-free survival (DFS) was performed through Kaplan-Meier survival curves and log-rank tests. Results: Starting from a total of 790 consecutive cases, we identified 444 newly-diagnosed breast cancer patients featuring HER2 immunohistochemistry (IHC) 0 (HER2-0, n = 109), and 1 + or IHC 2+/in situ hybridization negative (HER2-low, n = 335) receiving anthracycline and taxane-based regimens in 88.9% of cases. Most of the patients were diagnosed with stage II (67.3%) and there was no difference of disease presentation according to HER2-status. pCR was attained by 71 (16.0%) patients and was significantly associated with increased DFS (p = 0.031). Compared to HER2-0, HER2-low cases were more likely hormone receptor-positive (81.2% vs. 43.1%, p < 0.001), well-differentiated (47.5% vs. 26.6%, p = 0.001), less proliferative (21.5% vs. 8.3%, p = 0.001) and less responsive to treatment (pCR 11.6% vs. 29.4%, p < 0.0001). There was no difference in DFS according to HER2 status, though hormone-receptor (HR) negative/HER2-low cases tended to have a worse prognosis compared to HR-negative/HER2-0. By pCR achievement, 3-years DFS was 87.5.% (75.1-100%) vs. 71.6% (65.9-77.8%) (p = 0.161) in HER2-low and 89.1% (75.8-100%) vs. 72.1% (59.7-87.0%) (p = 0.092) in HER2-0. Conclusion: Our real-world data show that HER2-low breast cancer patients represent roughly a half of the cases treated with neoadjuvant therapy, and have poor treatment response. In absence of pCR, HER2-low breast cancer patients have a dismal prognosis, especially when primary tumor hormone receptor status is negative. Studies are therefore needed to define the biology of these tumors for new therapeutic targets and to incorporate HER2-targeting agents in early-stage treatment.
| S-EPMC9549400 | biostudies-literature
Project description:Pancreatic cancer is one of the leading causes of cancer death worldwide. Its high mortality rate has remained unchanged for years. Radiotherapy and surgery are considered standard treatments in early and locally advanced stages. Chemotherapy is the only option for metastatic patients. Two treatment regimens, i. e. the association of 5-fluorouracil- irinotecan-oxaliplatin (FOLFIRINOX) and the association of nab-paclitaxel with gemcitabine, have been shown to improve outcomes for metastatic pancreatic adenocarcinoma patients. However, there are not standardized predictive biomarkers able to identify patients who benefit most from treatments. CA19-9 is the most studied prognostic biomarker, its predictive role remains unclear. Other clinical, histological and molecular biomarkers are emerging in prognostic and predictive settings. The aim of this review is to provide an overview of prognostic and predictive markers used in clinical practice and to explore the most promising fields of research in terms of treatment selection and tailored therapy in pancreatic cancer.
| S-EPMC7075465 | biostudies-literature
Project description:PurposeTo comprehensively investigate prognostic factors, including clinical and molecular factors and treatment modalities, in adult glioma patients with leptomeningeal metastases (LM).MethodsTotal 226 patients with LM (from 2001 to 2021 among 1495 grade 2 to 4 glioma patients, 88.5% of LM patients being IDH-wildtype) with complete information on IDH mutation, 1p/19q codeletion, and MGMT promoter methylation status were enrolled. Predictors of overall survival (OS) of entire patients were determined by time-dependent Cox analysis, including clinical, molecular, and treatment data. Subgroup analyses were performed for patients with LM at initial diagnosis and LM diagnosed at recurrence (herein, initial and recurrent LM). Identical analyses were performed in IDH-wildtype glioblastoma patients.ResultsMedian OS was 17.0 (IQR 9.7-67.1) months, with shorter median OS in initial LM than recurrent LM patients (12.2 vs 20.6 months, P < 0.001). In entire patients, chemotherapy and antiangiogenic therapy were predictors of longer OS, while male sex and initial LM were predictors of shorter OS. In initial LM, higher KPS, chemotherapy, and antiangiogenic therapy were predictors of longer OS, while male sex was a predictor of shorter OS. In recurrent LM, chemotherapy and longer interval between initial glioma and LM diagnoses were predictors of longer OS, while male sex was a predictor of shorter OS. A similar trend was observed in IDH-wildtype glioblastoma.ConclusionActive chemotherapy and antiangiogenic therapy demonstrated survival benefit in glioma patients with LM. There is consistent female survival advantage, whereas longer interval between initial glioma diagnosis and LM development suggests longer OS in recurrent LM.
| S-EPMC10050057 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is the most common type of glioma, with a median survival of 14.6 months post-diagnosis. Understanding the molecular profile of such tumors allowed the development of specific targeted therapies toward GBM, with a major role attributed to tyrosine kinase receptor inhibitors and immune checkpoint inhibitors. Targeted therapeutics are drugs that work by specific binding to GBM-specific or overexpressed markers on the tumor cellular surface and therefore contain a recognition moiety linked to a cytotoxic agent, which produces an antiproliferative effect. In this review, we have summarized the available information on the targeted therapeutics used in clinical trials of GBM and summarized current obstacles and advances in targeted therapy concerning specific targets present in GBM tumor cells, outlined efficacy endpoints for major classes of investigational drugs, and discussed promising strategies towards an increase in drug efficacy in GBM.
| S-EPMC10820492 | biostudies-literature
Project description:Despite the vast amounts of information gathered about gliomas, the overall survival of glioma patients has not improved in the last four decades. This could partially be due to an apparent failure to include basic concepts of glioma biology into clinical trials. Specifically, attempts to overcome the limitations of the blood brain barrier (BBB) and the chemoresistance of glioma stem cells (GSCs) were seldom included (a phenomenon known as the translational gap, TG) in a study involving 29 Phase I/II clinical trials (P2CT) published in 2011. The aim of this study was to re-evaluate this finding with a new series of 100 ongoing, but still unpublished, P2CT in order to determine if there is a TG reduction. As indicators, we evaluated in each P2CT the number of drugs tested, concomitant radiotherapy, and the ability of drugs to pass the BBB and to target GSCs. Compared to clinical trials published in 2011, we found that while in OCT there is an increase in the number of P2CT using two drugs (from 24.1% to 44.9%), and an increase in the number of drugs able to pass the BBB (7.14% versus 64.29%) and target GSCs (0% versus 16.3%), there was a decrease in the number of P2CT using concomitant radiotherapy (34.5% versus 18.37%). Overall our results suggest that there is only a modest improvement regarding reducing the TG because the vast majority of ongoing P2CT are still not including well known concepts of glioma biology important for a successful treatment.
| S-EPMC5733731 | biostudies-literature
Project description:PurposePrognostic factor analyses have proven useful in predicting outcome in patients with newly diagnosed malignant glioma. Similar analyses in patients with recurrent glioma could affect the design and conduct of clinical trials substantially.Patients and methodsBetween 1995 and 2002, 333 adults with recurrent gliomas were enrolled onto 10 phase I or II trials of systemic or local therapy. The studies had similar inclusion criteria and were conducted within the New Approaches to Brain Tumor Therapy CNS Consortium. Ninety-three percent of the patients have died. Cox proportional hazards (PH) regression and recursive partitioning analysis (RPA) were performed to identify prognostic factors.ResultsFactors associated with an increased risk of death were increased age, lower Karnofsky performance score (KPS), initial and on-study histologies of glioblastoma multiforme (GBM), corticosteroid use, shorter time from original diagnosis to recurrence, and tumor outside frontal lobe. The final PH model included initial histology of GBM (relative risk [RR] = 2.01), 10-year increase in age (RR = 1.23), KPS less than 80 (RR = 1.54), and corticosteroid use (RR = 1.49). RPA resulted in seven classes. Median survival time was poorest in non-GBM patients with KPS less than 80 or GBM patients, age 50 years, corticosteroid use (4.4 months; 95% CI, 3.6 to 5.4 months); median survival was best in patients with initial histology other than GBM with KPS 80 and tumor confined to the frontal lobe (25.7 months; 95% CI, 18.7 to 52.5), and was 7.0 months (95% CI, 6.2 to 8.0 months) for all patients.ConclusionInitial histology, age, KPS, and corticosteroid use are prognostic for survival in recurrent glioma patients. To allow comparisons across phase II trials, enrollment criteria may need to be restricted.
| S-EPMC4118746 | biostudies-literature
Project description:PurposeGlioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.MethodsThe expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.ResultsFCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.ConclusionsThis study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.
| S-EPMC7881595 | biostudies-literature
Project description:BackgroundGlioma is a common brain tumour that is associated with poor prognosis. Immunotherapy has shown significant potential in the treatment of gliomas. Herein, we proposed a new prognostic risk model based on immune- and mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEGs) to enhance the accuracy of prognostic assessment in patients with glioma.MethodsData from samples from 671 glioma patients and 5 normal controls with available follow-up data and prognostic outcomes were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. All data were downloaded on 13 November 2023. IR&MEMRDEGs were screened from the GeneCards website and published literature. Prognostic prediction models were constructed and analysed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, Kaplan-Meier (KM) curve, and receiver operating characteristic (ROC) curve analyses. Single-sample gene set enrichment analysis (ssGSEA) was further performed to ascertain the percentage of immune cell infiltration in the glioma specimens.ResultsBioinformatics analysis of the GEO and TCGA databases identified eleven MEMRDEGs with dysregulated expression in gliomas: EIF4EBP1, TP53, IDH1, PRKCZ, CD200, GPI, PGM2, PKLR, AK2, ATP4A, and ALDH3B1. Further analysis identified EIF4EBP1, TP53, IDH1, PRKCZ, CD200, GPI, PGM2, AK2, and ALDH3B1 as separate predictive factors for glioma, among which PGM2 and AK2 exhibited superior accuracy [area under the ROC curve (AUC) >0.9], while EIF4EBP1, TP53, IDH1, PRKCZ, GPI, and ALDH3B1 demonstrated slightly lower accuracy (0.7< AUC <0.9), and CD200 displayed poor accuracy (0.5< AUC <0.7). Among these genes, the levels of AK2, ALDH3B1, EIF4EBP1, GPI, IDH1, PGM2, and TP53 were significantly higher in the high-risk group (HRG) compared with the low-risk group (LRG) (P<0.001), indicating a negative association with patient prognosis. In contrast, CD200 and PRKCZ were significantly downregulated in the HRG compared to the LRG (P<0.05), indicating a potential correlation with patient outcomes. Subsequently, prognostic models were constructed based on IR&MEMRDEG and MEMRDEGs to anticipate the outcomes of glioma patients, while the predictive efficacy of the model was validated via KM and ROC curve analysis. The results revealed that EIF4EBP1, TP53, IDH1, PRKCZ, GPI, PGM2, ALDH3B1, and AK2 had superior accuracy in predicting glioma prognosis. The ssGSEA results showed that only IDH1 was negatively linked to the amount of immune cell infiltration in the LRG, while displaying a positive connection in the HRG (r value>0), indicating that the expression levels of IDH1 may have a distinct influence on the tumour immune microenvironment.ConclusionsThe present study confirmed the significant predictive value of IDH1 for glioma prognosis, which may guide immunotherapy for glioma treatment.
| S-EPMC11833365 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is the most common and lethal primary malignant brain tumor. The traditional treatments for GBM, including surgery, radiation, and chemotherapy, only modestly improve patient survival. Therefore, immunotherapy has emerged as a novel therapeutic modality. Immunotherapeutic strategies exploit the immune system's ability to recognize and mount a specific response against tumor cells, but not normal cells. Current immunotherapeutic approaches for glioma can be divided into 3 categories: immune priming (active immunotherapy), immunomodulation (passive immunotherapy), and adoptive immunotherapy. Immune priming sensitizes the patient's immune cells to tumor antigens using various vaccination protocols. In the case of immunomodulation, strategies are aimed at reducing suppressive cytokines in the tumor microenvironment or using immune molecules to specifically target tumor cells. Adoptive immunotherapy involves harvesting the patient's immune cells, followed by ex vivo activation and expansion before reinfusion. This article provides an overview of the interactions between the central nervous system and the immune system, and discusses the challenges facing current immunotherapeutic strategies.
| S-EPMC2786905 | biostudies-literature