scholarly journals DNA Repair-related Gene Signature In Predicting Prognosis Of Colorectal Cancer Patients

2021 ◽  
Author(s):  
Min-Yi Lv ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Du Cai ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Repair ◽  
High Risk ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Training Cohort ◽  
The Impact

Abstract Background: Increasing evidence has depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients.Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into training cohort (n=566) and 2 validation cohorts (n=624 and 578, respectively). LASSO-Cox model was applied to construct a prediction model.Results: Among 1,376 DRGs, a prognostic DRGS consisting of 11 distinct genes stratified patients into high and low -risk groups. In all cohorts, patients in the high -risk groups had significantly worse disease-free survival (DFS) compared with those in the low-risk groups (training cohort: hazard ratio (HR) = 2.40, 95% confidence interval (CI) = 1.67-3.44, P < 0.001; validation-1: HR = 2.20, 95% CI = 1.38-3.49, P < 0.001; validation-2 cohort: HR = 2.12, 95% CI = 1.40-3.21, P < 0.001). After adjusting for clinical features and molecular types, DRGS still remained as an independent prognostic marker in multivariable analysis (training cohort: HR = 1.80; 95% CI = 1.22-2.64, P = 0.0028; validation-1: HR = 1.85, 95% CI = 1.13-3.02, P = 0.015; validation-2 cohort: HR = 1.75, 95% CI = 1.15-2.65, P = 0.0085). Gene Set Enrichment Analysis (GSEA) showed significant dysregulated pathways in the high-risk involved in angiogenesis, KRAS signaling, epithelial mesenchymal transit (EMT) and myogenesis (P < 0.001).Conclusions: DNA repair-related gene signature is a favorable prognostic model for patients with CRC, and further studies are necessary to validate the exact biological mechanism.

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 Establishment and Validation of a Ferroptosis-Related Gene Signature to Predict Overall Survival in Lung Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Su Wang ◽  
Zhen Xie ◽  
Zenghong Wu
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Functional Enrichment ◽  
Related Gene ◽  
Prognostic Signature

Background: Lung adenocarcinoma (LUAD) is the most common and lethal subtype of lung cancer. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a target in cancer therapy. However, the prognostic value of ferroptosis-related genes (FRGs)x in LUAD remains to be explored.Methods: In this study, we used RNA sequencing data and relevant clinical data from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset to construct and validate a prognostic FRG signature for overall survival (OS) in LUAD patients and defined potential biomarkers for ferroptosis-related tumor therapy.Results: A total of 86 differentially expressed FRGs were identified from LUAD tumor tissues versus normal tissues, of which 15 FRGs were significantly associated with OS in the survival analysis. Through the LASSO Cox regression analysis, a prognostic signature including 11 FRGs was established to predict OS in the TCGA tumor cohort. Based on the median value of risk scores calculated according to the signature, patients were divided into high-risk and low-risk groups. Kaplan–Meier analysis indicated that the high-risk group had a poorer OS than the low-risk group. The area under the curve of this signature was 0.74 in the TCGA tumor set, showing good discrimination. In the GEO validation set, the prognostic signature also had good predictive performance. Functional enrichment analysis showed that some immune-associated gene sets were significantly differently enriched in two risk groups.Conclusion: Our study unearthed a novel ferroptosis-related gene signature for predicting the prognosis of LUAD, and the signature may provide useful prognostic biomarkers and potential treatment targets.

scholarly journals Identifying novel cell glycolysis related gene signature predictive of overall survival in bladder urothelial carcinoma

2020 ◽  
Author(s):  
Xin Zhao ◽  
Jia Li ◽  
Jiafeng Li ◽  
Wenjun Xiong ◽  
Rui Jiang
Keyword(s):  
Data Mining ◽  
Overall Survival ◽  
High Risk ◽  
Urothelial Carcinoma ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Bladder Urothelial Carcinoma

Abstract Background: Bladder urothelial carcinoma (BLCA) is the most common pathological type of bladder cancer and featured by a high risk for relapse and metastasis. Although many biomarkers have been developed by data mining and experimental studies to predict the prognosis of BLCA, a single-gene biomarker usually has poor specificity and sensitivity, leading to unsatisfactory prediction. Therefore, novel gene signatures are needed to more accurately predict the prognosis of BLCA.Methods: Data mining was performed for expression profile analysis of 433 mRNA expression data from the TCGA BLCA patients (n=412). Gene Set Enrichment Analysis (GSEA) was used to interpret the glycolysis-related gene sets. Gene signature related to the prognosis of BLCA was identified by univariate and multivariate Cox regression. A risk score was computed based on three genes by linear regression model and its relation with overall survival was investigated by Kaplan-Meier analysis.Results: Three genes (CHPF, AK3, NUP188) were found to be significantly correlated to the overall survival of BLCA patients. Based on the signature composed of these three genes, 412 BLCA patients were divided into high-risk and low-risk groups. The survival time of the high-risk group was significantly shorter than that of the low-risk group, indicating a worse prognosis.Conclusion: A signature composed of three glycolysis-related genes was developed as biomarkers to predict the prognosis of BLCA and to provide a meaningful reference for the clinical treatment of BLCA.

scholarly journals Prognostic Signatures Based on Thirteen Immune-Related Genes in Colorectal Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Xiao-Bo Ma ◽  
Yuan-Yuan Xu ◽  
Meng-Xuan Zhu ◽  
Lu Wang
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Immune Related Genes

BackgroundThe immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC.Materials and MethodsCRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group.ResultsBy analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups.ConclusionsThe IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.

scholarly journals CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario

2019 ◽  
Author(s):  
Satish Sankaran ◽  
Jyoti Bajpai Dikshit ◽  
Chandra Prakash SV ◽  
SE Mallikarjuna ◽  
SP Somashekhar ◽  
...  
Keyword(s):  
High Risk ◽  
Early Stage ◽  
Risk Groups ◽  
Treatment Decisions ◽  
Low Risk ◽  
Not Given ◽  
Breast Cancer Patients ◽  
Number Of Patients ◽  
Risk Of Cancer ◽  
The Impact

AbstractCanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis–free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.

Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15565-e15565
Author(s):  
Qiqi Zhu ◽  
Du Cai ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Training Cohort ◽  
Gene Pairs ◽  
Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

The national impact of the COVID-19 pandemic on U.S. prostate cancer community care.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 5061-5061
Author(s):  
Matthew R. Cooperberg ◽  
Paul Brendel ◽  
Daniel J. Lee ◽  
Rahul Doraiswami ◽  
Hariesh Rajasekar ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Language Processing ◽  
Care Delivery ◽  
Specific Antigen ◽  
Risk Groups ◽  
Low Risk ◽  
T Stage ◽  
Risk Stratum ◽  
The Impact

5061 Background: We used data from a specialty-wide, community-based urology registry to determine trends in outpatient prostate cancer (PCa) care during the COVID-19 pandemic. Methods: 3,165 (̃ 25%) of US urology providers, representing 48 states and territories, participate in the American Urological Association Quality (AQUA) Registry, which collects data via automated extraction from electronic health record systems. We analyzed trends in PCa care delivery from 156 practices contributing data in 2019 and 2020. Risk stratification was based on prostate-specific antigen (PSA) at diagnosis, biopsy Gleason, and clinical T-stage, and we used a natural language processing algorithm to determine Gleason and T-stage from unstructured clinical notes. The primary outcome was mean weekly visit volume by PCa patients per practice (visits defined as all MD and mid-level visits, telehealth and face-to-face), and we compared each week in 2020 through week 44 (November 1) to the corresponding week in 2019. Results: There were 267,691 PCa patients in AQUA who received care between 2019 and 2020. From mid-March to early November, 2020 (week 10 – week 44) the magnitude of the decline and recovery varied by risk stratum, with the steepest drops for low-risk PCa (Table). For 2020, overall mean visits per day (averaged weekly) were similar to 2019 for the first 9 weeks (̃25). Visits declined to week 14 (18.19; a 31% drop from 2019), recovered to 2019 levels by week 23, and declined steadily to 11.89 (a 58% drop from 2019) as of week 44, the cut off of this analysis. Conclusions: Access to care for men with PCa was sharply curtailed by the COVID-19 pandemic, and while the impact was less for men with high-risk disease compared to those with low-risk disease, visits even for high-risk individuals were down nearly one-third and continued to fall through November. This study provides real-world evidence on the magnitude of decline in PCa care across risk groups. The impact of this decline on cancer outcomes should be followed closely.[Table: see text]

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

scholarly journals An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

2020 ◽  
Author(s):  
Bin Wu ◽  
Yi Yao ◽  
Yi Dong ◽  
Si Qi Yang ◽  
Deng Jing Zhou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Score ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Gene Set Enrichment ◽  
Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

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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