scholarly journals Multi-Omic Prediction of Overall Survival in Patients With Glioblastoma: Additive and Synergistic Value of Clinical Measures, Radiomics, and Genomics

2021 ◽  
Author(s):  
Anahita Fathi Kazerooni ◽  
Sanjay Saxena ◽  
Erik Toorens ◽  
Danni Tu ◽  
Vishnu Bashyam ◽  
...  
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
High Risk ◽  
Low Risk ◽  
Survival Prediction ◽  
Mgmt Methylation ◽  
Replication Cohort ◽  
Treatment Naïve ◽  
Integrated Diagnostics ◽  
Clinical Measures

Abstract Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OS<6 months) vs all, and low-risk (OS≥18 months) vs all. The trained patient stratification model was independently tested in the replication cohort (n=112) and a patient-wise survival prediction index (SPIradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.

scholarly journals Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3616
Author(s):  
Viet-Huan Le ◽  
Quang-Hien Kha ◽  
Truong Nguyen Khanh Hung ◽  
Nguyen Quoc Khanh Le
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Low Risk ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Training Set ◽  
Prediction Ability ◽  
Nsclc Patients ◽  
Overall Survival Prediction

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

scholarly journals Gene Signature Associated With Bromodomain Genes Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junwan Lu ◽  
Changrui Qian ◽  
Yongan Ji ◽  
Qiyu Bao ◽  
Bin Lu
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Epigenetic Regulation ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction

Bromodomain (BRD) proteins exhibit a variety of activities, such as histone modification, transcription factor recruitment, chromatin remodeling, and mediator or enhancer complex assembly, that affect transcription initiation and elongation. These proteins also participate in epigenetic regulation. Although specific epigenetic regulation plays an important role in the occurrence and development of cancer, the characteristics of the BRD family in renal clear cell carcinoma (KIRC) have not been determined. In this study, we investigated the expression of BRD family genes in KIRC at the transcriptome level and examined the relationship of the expression of these genes with patient overall survival. mRNA levels of tumor tissues and adjacent tissues were extracted from The Cancer Genome Atlas (TCGA) database. Seven BRD genes (KAT2A, KAT2B, SP140, BRD9, BRPF3, SMARCA2, and EP300) were searched by using LASSO Cox regression and the model with prognostic risk integration. The patients were divided into two groups: high risk and low risk. The combined analysis of these seven BRD genes showed a significant association with the high-risk groups and lower overall survival (OS). This analysis demonstrated that total survival could be predicted well in the low-risk group according to the time-dependent receiver operating characteristic (ROC) curve. The prognosis was determined to be consistent with that obtained using an independent dataset from TCGA. The relevant biological functions were identified using Gene Set Enrichment Analysis (GSEA). In summary, this study provides an optimized survival prediction model and promising data resources for further research investigating the role of the expression of BRD genes in KIRC.

scholarly journals A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chaocai Zhang ◽  
Minjie Wang ◽  
Fenghu Ji ◽  
Yizhong Peng ◽  
Bo Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Glucose Metabolism ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

scholarly journals A novel epigenetic signature for overall survival prediction in patients with breast cancer

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Xuanwen Bao ◽  
Natasa Anastasov ◽  
Yanfang Wang ◽  
Michael Rosemann
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Cox Model ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Breast Cancer Patients

Abstract Background Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer. Method DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases. Results The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts. Conclusion An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2020 ◽  
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

2020 ◽  
Author(s):  
Mo Chen ◽  
Tian-en Li ◽  
Pei-zhun Du ◽  
Junjie Pan ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Distant Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Risk Classification ◽  
Low Risk ◽  
Classification Model ◽  
Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

scholarly journals Risk stratification in pediatric low-grade glioma and glioneuronal tumor treated with radiation therapy: an integrated clinicopathologic and molecular analysis

2020 ◽  
Vol 22 (8) ◽  
pp. 1203-1213 ◽  
Author(s):  
Sahaja Acharya ◽  
Jo-Fen Liu ◽  
Ruth G Tatevossian ◽  
Jason Chiang ◽  
Ibrahim Qaddoumi ◽  
...  
Keyword(s):  
Overall Survival ◽  
Radiation Therapy ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Groups ◽  
Low Risk ◽  
Glioneuronal Tumor ◽  
Low Grade ◽  
Diffuse Astrocytoma ◽  
Prognostic Features

Abstract Background Management of unresectable pediatric low-grade glioma and glioneuronal tumor (LGG/LGGNT) is controversial. There are no validated prognostic features to guide use of radiation therapy (RT). Our study aimed to identify negative prognostic features in patients treated with RT using clinicopathologic and molecular data and validate these findings in an external dataset. Methods Children with non-metastatic, biopsy-proven unresectable LGG/LGGNT treated with RT at a single institution between 1997 and 2017 were identified. Recursive partitioning analysis (RPA) was used to stratify patients into low- and high-risk prognostic groups based on overall survival (OS). CNS9702 data were used for validation. Results One hundred and fifty patients met inclusion criteria. Median follow-up was 11.4 years. RPA yielded low- and high-risk groups with 10-year OS of 95.6% versus 76.4% (95% CI: 88.7%–98.4% vs 59.3%–87.1%, P = 0.003), respectively. These risk groups were validated using CNS9702 dataset (n = 48) (4-year OS: low-risk vs high-risk: 100% vs 64%, P &lt; 0.001). High-risk tumors included diffuse astrocytoma or location within thalamus/midbrain. Low-risk tumors included pilocytic astrocytoma/ganglioglioma located outside of the thalamus/midbrain. In the subgroup with known BRAF status (n = 49), risk stratification remained prognostic independently of BRAF alteration (V600E or fusion). Within the high-risk group, delayed RT, defined as RT after at least one line of chemotherapy, was associated with a further decrement in overall survival (P = 0.021). Conclusion A high-risk subgroup of patients, defined by diffuse astrocytoma histology or midbrain/thalamus tumor location, have suboptimal long-term survival and might benefit from timely use of RT. These results require validation.

scholarly journals The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Youyin Tang ◽  
Tao Zhang ◽  
Xianghong Zhou ◽  
Yunuo Zhao ◽  
Hanyue Xu ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Intrahepatic Cholangiocarcinoma ◽  
Prognostic Value ◽  
Survival Rates ◽  
Low Risk ◽  
Liver Carcinoma ◽  
Incidence And Mortality ◽  
Significant Difference ◽  
Prognostic Prediction

Abstract Background Intrahepatic cholangiocarcinoma is an aggressive liver carcinoma with increasing incidence and mortality. A good auxiliary prognostic prediction tool is desperately needed for the development of treatment strategies. The purpose of this study was to explore the prognostic value of the radiomics nomogram based on enhanced CT in intrahepatic cholangiocarcinoma. Methods In this retrospective study, 101 patients with pathological confirmation of intrahepatic cholangiocarcinoma were recruited. A radiomics nomogram was developed by radiomics score and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by a nomogram. Model performance and clinical usefulness were assessed by calibration curve, ROC curve, and survival curve. Results A total of 101patients (mean age, 58.2 years old; range 36–79 years old) were included in the study. The 1-year, 3-year, and 5-year overall survival rates were 49.5%, 26.6%, and 14.4%, respectively, with a median survival time of 12.2 months in the whole set. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found three independent prognostic factors. The radiomics nomogram showed a significant prognosis value with overall survival. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole set (30.4% vs. 56.4% and 13.0% vs. 30.6%, respectively, p = 0.018). Conclusions This radiomics nomogram has potential application value in the preoperative prognostic prediction of intrahepatic cholangiocarcinoma and may facilitate in clinical decision-making.

Prognostic Indices in Older DLBCL Patients Receiving R-CHOP: An Analysis of the U.S. Intergroup Study (E4494, CALGB 9793).

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 813-813
Author(s):  
R.H. Advani ◽  
H. Chen ◽  
T.M. Habermann ◽  
V.A. Morrison ◽  
E. Weller ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Univariate Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Risk Category ◽  
Free Survival ◽  
Prognostic Tool ◽  
The Impact ◽  
The U.S

Abstract Background: We reported that addition of rituximab (R) to chemotherapy significantly improves outcome in DLBCL patients (pt) &gt;60 years (JCO24:3121–27, 2006). Although the IPI is a robust clinical prognostic tool in DLBCL, Sehn et al (ASH 2005: abstract 492) reported that a revised (R) IPI more accurately predicted outcome in pt treated with rituximab-chemotherapy. Methods: We evaluated outcomes of the Intergroup study with respect to the standard IPI, R-IPI, age-adjusted (aa) IPI for evaluable pt treated with R-CHOP alone or with maintenance rituximab. We further assessed a modified IPI (mIPI) using age ≥ 70 y as a cutoff rather than age 60 y. Results: The 267 pt in this analysis were followed for a median of 4 y. Pt characteristics were: age &gt; 70 (48%) (median=69), male 52%, stage III/IV 75%, &gt;1 EN site 30%, LDH elevated 60%, PS ≥2 15%. On univariate analysis all of these characteristics were significant for 3 y failure-free survival (FFS) and overall survival (OS). The IPI provided additional discrimination of risk compared to the R-IPI with significant differences in FFS and OS for 3 vs 4–5 factors. The aa-IPI defined relatively few pt as low or high risk. The impact of age was studied using a cut-off of 70 years in a modified IPI, yielding 4 risk groups as shown below. Conclusions: For pt ≥ 60 treated with rituximab-chemotherapy the distinction between 3 vs 4,5 factors in the IPI was significant.The IPI also provided additional discrimination of risk compared to the R-IPI. In this older group of pt, use of an age cutoff ≥70 y placed more patients in the low risk category. It is of interest to apply the mIPI in other datasets with DLBCL pt &gt;60 y. Group # Factors # Pt % 3y FFS* % 3y OS* *All risk groups significantly different; logrank p &lt; 0.001 **95 % CI: FFS (0.46,0.66), OS (0.58,0.78) ***95 % CI: FFS (0.21,0.45), OS (0.31,0.55) L: Low, LI: Low Intermediate, HI: High Intermediate, H; High IPI L 0–1 12 78 83 LI 2 28 70 80 HI 3 33 56** 68** H 4–5 37 33*** 43*** R-IPI Very Good 0 0 - - Good 1–2 40 72 81 Poor 3–5 60 46 57 aa-IPI L 0 12 78 83 LI 1 35 68 78 HI 2 44 47 59 H 3 9 31 35 mIPI (age ≥ 70) L 0–1 27 77 86 LI 2 28 62 74 HI 3 29 47 58 H 4–5 16 28 36

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