Validation of the Lower Risk MD Anderson Prognostic Scoring System for Patients with Myelodysplastic Syndromes

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3826-3826 ◽  
Author(s):  
Rami S. Komrokji ◽  
Maria Corrales-Yepez ◽  
Najla H Al Ali ◽  
Eric Padron ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Stratification ◽  
Lower Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Refractory Anemia ◽  
Kaplan Meier ◽  
Disease Behavior ◽  
Md Anderson

Abstract Abstract 3826 Background: The International Prognostic Scoring System (IPSS) is the most widely used clinical tool for risk stratification and tailoring treatment in myelodysplastic syndromes (MDS). Outcome of patients stratified as lower risk MDS by IPSS is variable, with a subset of patients experiencing inferior than expected outcomes. Identifying patients with higher risk disease behavior is indispensible for proper implantation of disease altering therapy. The Lower Risk MD Anderson Risk Model (LR-MDAS) is a recently proposed model provides prognostic refinement to identify such patients (Garcia-Manero et al, Leukemia 2008). To validate this model, we tested the new risk model in large external single institution cohort of patients. Methods: Data were collected retrospectively from the Moffitt Cancer Center (MCC) MDS database and chart review. The primary objective was to validate the new risk model when applied at time of initial presentation to MCC. The LR-MDAS was calculated as published using the sum of points generated from unfavorable (non-del(5q), non–diploid) cytogenetics, hemoglobin (hgb) <10g/dl, platelet count (plt) <50 k/uL or 50–200k/uL, bone marrow blast % >= 4, and age>= 60 years. Patients were divided into 3 prognostic categories. All analyses were conducted using SPSS version 15.0. (SPSS Inc, Chicago, IL). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between the groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 479 patients with low or int-1 risk IPSS were captured by MCC MDS database. The median age was 69 years, MDS subtypes were coded as Refractory anemia (RA) 113 (24%), refractory anemia with ring sideroblasts (RARS) 73 (15%), MDS with del(5q) 19 (4%), refractory cytopenia with multi-lineage dysplasia (RCMD) 109 (23%), refractory anemia with excess blasts (RAEB) 147 (31%), and MDS-unclassified (U) 18 (4%). IPSS risk groups were low risk in 145 (30%), and intermediate-1 (int-1) 334 (70%). Only 31 patients (7%) had a poor risk karyotype by IPSS. Red blood cell transfusion dependence was documented in 42% (n=202), 22% had elevated serum ferritin ≥ 1000 ng/ml, and 45% (n=217) received azanucleoside treatment. Based on the LR-MDAS, 52 patients (11%) were category 1, 188 (39%) category 2, 232 (48%) category 3, and 7 (2%) were unknown. The median OS from time of referral to MCC for all patients was 32 months (95% CI 27–37 mo), Age, IPSS risk group, serum ferritin, and RBC transfusion dependence were all significant prognostic factors in univariate analysis. The median OS for the corresponding categories was, 1 - not reached (NR), 2– 50 mo (95%CI 33–68 mo), and 3 – 22 mo (95%CI 16–27 mo), from time of MCC referral, respectively. (Figure-1) (P < 0.005). Among 142 patients classified as low risk by IPSS, 25 patients (18%) were category 1 LR-MDAS, 81 (57%) category 2, and 36 (25%) category 3 with corresponding median OS of, NR, 62 month, and 35 month respectively (p=0.002). Among 330 patients risk stratified as int-1 IPSS group, 27 patients where category 1 LR-MDAS, 107 category 2, and 196 category 3 where median OS was NR, 28 months and 20 months, respectively. (p< 0.001) (Figure-2). When we applied IPSS risk stratification among each category of LR-MDAS to assess if IPSS can further refine prognosis within LR-MDAS categories, only in patients classified as Category 2 LR-MDAS the median OS was different among low and int-1 risk IPSS (62 month versus 28 month). (p <0.005). The rate of AML transformation according to LR-MDAS was 4%, 12%, and 20% for category 1,2, and 3, respectively. (p<0.02). In Cox regression analysis higher risk LR-MDAS predicted inferior OS (Hazard ratio (HR) 1.8 (95%CI 1.4–2.3) (p <0.005) independent of IPSS risk group (HR 2 95%CI 1.4–2.8) (p <0.005). Conclusion: Our data validates the prognostic value of the proposed LR-MDAS risk model, demonstrating predictive power for overall survival and AML transformation among low/int-1 risk IPSS. The LR-MDAS is complementary to the IPSS, offering further discrimination to identifying those patients with aggressive disease behavior that merit disease altering therapy. The utility of the model as treatment decision tool should be studied prospectively. Disclosures: No relevant conflicts of interest to declare.

Validation of the Newly Proposed MD Anderson Prognostic Risk Model for Patients with Myelodysplastic Syndromes

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 444-444
Author(s):  
Maria Corrales-Yepez ◽  
Jeffrey E. Lancet ◽  
Alan F. List ◽  
Mohamed A. Kharfan-Dabaja ◽  
Teresa Field ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myelodysplastic Syndromes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Refractory Anemia ◽  
Kaplan Meier ◽  
Md Anderson

Abstract Abstract 444 Background: The international prognostic scoring system (IPSS) is the most widely used clinical tool for risk stratification and tailoring treatment in myelodysplastic syndromes (MDS). Despite its utility, the IPSS has several limitations. The IPSS was developed using outcomes of untreated primary MDS patients at time of diagnosis, and does not account for patient age, performance, and degree of cytopenia. The recently reported MD Anderson risk model (MDAS) addresses many of the limitations of IPSS (Kantarjian et al, CANCER September 15, 2008 / Volume 113 / Number 6). We validated this new risk model in a large external single institution cohort of patients. Methods: Data were collected retrospectively from Moffitt Cancer Center (MCC) MDS database and chart review of patients with MDS. The primary objective was to validate the new risk model calculated at time of initial presentation MCC. The MDAS was calculated as published based on age, performance status, blast%, degree of thrombocytopenia, cytogenetics, white blood cell count, and prior history if transfusion. Patients were divided into four risk groups: low (0-4 points), int-1 (5-6 points), int-2 (7-8 points), and high risk (≥ 9 points). All analyses were conducted using SPSS version 15.0. (SPSS Inc, Chicago, IL). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between two groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 844 patients were captured by MCC MDS database. The median age was 69 years, MDS subtypes were coded as Refractory anemia (RA) 98 (12%), refractory anemia with ring sideroblasts (RARS) 76 (9%), MDS with del(5q) 20 (2.4%), refractory cytopenia with multi-lineage dysplasia (RCMD) 96 (11%), refractory anemia with excess blasts (RAEB) 255 (30%), therapy related MDS 22 (2.6%), and MDS-nos 275 (33%). IPSS risk groups were low risk in 158 (18.7%), intermediate-1 (int-1) 362 (42.9%), intermediate-2 (int-2) 168 (19.9%), high risk 45 (5.3%), and missing in 111 (13.2%). Based on the new risk model 169 patients (20%) were low risk, 250 (29.6%) int-1, 182 (21.6%) int-2, 135 (16%) high risk, and 94 (11.1%) were unknown. The median OS for all patients was 36 months (95% CI 31.5–40.5 mo). Age, IPSS risk group, serum ferritin, and RBC transfusion dependence were all significant prognostic factors in univariable analysis. The median OS was 92 mo (95%CI 68.1–115.9 mo), 49 mo (95%CI 40.4–57.6 mo), 26 mo (95%CI 21.2–30.8 mo), and 15 month (95%CI 11.8–42.1 mo) respectively for patients with low, int-1, int-2 and high risk patients according to MDAS. (Figure-1) (P < 0.005). In patients with low/int-1 IPSS risk group the median OS according to MDAS was 92 mo (95%CI 68.3–115.7 mo), 49 mo (95%CI 49.3–58.7 mo), 28 mo (95%CI 20.7–35.3), and 19 mo(95% CI 9.9–28.1 mo) respectively for patients with low, int-1, int-2, high risk MDAS (p<0.005). In patients with int-2/high IPSS risk categories only 4 patients were reclassified as low MDAS risk and the median OS for those patients was 10 month (95% CI 0–38 mo). The median OS was 49 mo (95%CI 23.5–74.5 mo), 23 mo (95%CI 19.4–26.6 mo), 14 mo (95% CI 11.5–16.5 mo). (p<0.005). For all the patients the rate of AML transformation according to MDAS was 5.9%, 16.8%, 36.3%, and 50.4% respectively for low, int-1, int-2, and high risk MDAS groups. (p <0.005). In Cox regression analysis, higher risk MDAS predicted inferior OS (Hazard ratio (HR) 1.54 (95%CI 1.35–1.75) (p <0.005) independent of IPSS risk group (HR 1.25 95%CI 1.1–1.45) (p =0.004). Conclusion: Our data validates the prognostic value of the MDAS risk model which was predictive for overall survival and AML transformation. The MDAS complements the IPSS particularly in low/int-1 risk group by identifying patients with higher risk disease behavior and inferior outcome. The utility of this model as a treatment decision tool should be studied prospectively. Disclosures: No relevant conflicts of interest to declare.

Obesity Is Poor Prognostic Factor in Lower Risk Myelodysplastic Syndrome

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 5018-5018
Author(s):  
Asmita Mishra ◽  
Dana E Rollison ◽  
Najla H Al Ali ◽  
Maria Corrales-Yepez ◽  
Pearlie K Epling-Burnette ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factor ◽  
Myelodysplastic Syndrome ◽  
Lower Risk ◽  
Cox Regression ◽  
Cancer Center ◽  
Kaplan Meier ◽  
Baseline Characteristics ◽  
Md Anderson ◽  
The Impact

Abstract Abstract 5018 Background: Obesity was associated with a more than 2-fold greater risk of myelodysplastic syndrome (MDS) in a recent epidemiological study (Ma et al, Am J Epidemiol. 2009 June 15; 169(12): 1492–1499). The impact of obesity on outcome of disease in patients with an established diagnosis of MDS has not been studied. We examined the prognostic value of obesity in a large cohort of lower risk MDS patients treated at the Moffitt Cancer Center (MCC). Methods: Data were collected retrospectively from the Moffitt Cancer Center (MCC) MDS database and individual charts reviewed. The primary objective was to evaluate the impact of obesity on overall survival (OS) in lower risk patients with MDS. Patients with low or intermediate-1 (int-1) risk disease by International Prognostic Scoring System (IPSS) were included. Obesity was defined as a body mass index (BMI) ≥ 30 (Standard definition) measured at time of referral to MDS program at MCC. Patients were divided into two groups according to BMI ≥ 30 or BMI < 30. All analyses were conducted using SPSS version 19.0. Chi square and independent t-test were used to compare baseline characteristics between the 2 groups for categorical and continuous variables, respectively. The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between two groups. Cox regression was used for multivariable analysis. Results: Between January 2001 and December 2009, 479 low/int-1 IPSS risk MDS patients were included. Among those, 132 (27.6%) had BMI ≥ 30 and 325 (67.8%) had BMI <30; BMI was missing in 22 patients (4.6%). The baseline characteristics between the two groups were comparable. No difference was noted in mean age, WHO subtype, karyotype, MD Anderson risk model group, red blood cell transfusion dependency (RBC-TD), or serum ferritin (Table-1). The median OS was 59 mo (95%CI 48–70) in patients with BMI <30 compared to 44 (95%CI 38–50) in patients with BMI ≥ 30. (p=0.03). There was no difference in rate of AML transformation according to BMI, 12.9% and 15.7% respectively for BMI ≥ 30 and BMI <30. (P=0.3). In Cox regression analysis obesity predicted inferior OS (Hazard ratio (HR) 1.7 (95%CI 1.15–2.4) (P=0.007) after adjustment for age, MD Anderson risk group, serum ferritin, RBC-TD, use of hypomethylating agents and tobacco use. Conclusion: Our data suggest that obesity is an independent adverse prognostic factor for OS in patients with lower risk MDS. Obesity may be associated with other comorbidities and metabolic dearrangements that contribute to the pathogenesis of the underlying disease. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Identification of a Six-Gene SLC Family Signature With Prognostic Value in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Jing Zhu ◽  
Yong Mou ◽  
Shenglan Ye ◽  
Hongling Hu ◽  
Rujuan Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Prognostic Signature ◽  
Kaplan Meier ◽  
Immune Cell Infiltrates

Given the importance of solute carrier (SLC) proteins in maintaining cellular metabolic homeostasis and that their dysregulation contributes to cancer progression, here we constructed a robust SLC family signature for lung adenocarcinoma (LUAD) patient stratification. Transcriptomic profiles and relevant clinical information of LUAD patients were downloaded from the TCGA and GEO databases. SLC family genes differentially expressed between LUAD tissues and adjacent normal tissues were identified using limma in R. Of these, prognosis-related SLC family genes were further screened out and used to construct a novel SLC family-based signature in the training cohort. The accuracy of the prognostic signature was assessed in the testing cohort, the entire cohort, and the external GSE72094 cohort. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. We found that seventy percent of SLC family genes (279/397) were differentially expressed between LUAC tissues and adjacent normal. Twenty-six genes with p-values &lt; 0.05 in univariate Cox regression analysis and Kaplan-Meier survival analysis were regarded as prognosis-related SLC family genes, six of which were used to construct a prognostic signature for patient classification into high- and low-risk groups. Kaplan-Meier survival analysis in all internal and external cohorts revealed a better overall survival for patients in the low-risk group than those in the high-risk group. Univariate and multivariate Cox regression analyses indicated that the derived risk score was an independent prognostic factor for LUAD patients. Moreover, a nomogram based on the six-gene signature and clinicopathological factors was developed for clinical application. High-risk patients had lower stromal, immune, and ESTIMATE scores and higher tumor purities than those in the low-risk group. The proportions of infiltrating naive CD4 T cells, activated memory CD4 T cells, M0 macrophages, resting dendritic cells, resting mast cells, activated mast cells, and eosinophils were significantly different between the high- and low-risk prognostic groups. In all, the six-gene SLC family signature is of satisfactory accuracy and generalizability for predicting overall survival in patients with LUAD. Furthermore, this prognostics signature is related to tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

scholarly journals Construction and Validation of a Prognostic Risk Model for Triple Negative Breast Cancer Based on Autophagy-Related Genes

2021 ◽  
Author(s):  
Cheng Yan ◽  
Qingling Liu ◽  
Ruoling Jia
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Autophagy plays an important role in triple negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.Methods: A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from the TCGA database. Differential gene expression of ARGs (DE-ARGs) between normal samples and TNBC samples was determined by the EdgeR software package. Then, univariate Cox, Lasso, and multivariate Cox regression analyses were performed. According to the Lasso regression results based on univariate Cox, we identified a prognostic signature for overall-survival (OS), which was further validated by using GEO cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of a ARG in our model for TNBC cell survival and metastasis.Results: There are 43 differentially expressed ARGs (DE-ARGs) were identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3 by Lasso regression analysis was established based on univariate Cox regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and ROC curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as independent predictor. Then a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells. Conclusion: An autophagy-related prognosis model in TNBCs was constructed using ARGs signature containing CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3. It could serve as an independent prognostic biomarker in TNBC.

scholarly journals Prognostic Significance of Severe Thrombocytopenia in Overall Survival of Patients with Myelodysplastic Syndromes Treated with Azacytidine. a Multicenter Study By the Hellenic MDS Study Group

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1822-1822
Author(s):  
Athanasios Galanopoulos ◽  
Evdoxia Kamouza ◽  
Christos K. Kontos ◽  
Argiris Symeonidis ◽  
Vassiliki Pappa ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
High Risk ◽  
Myelodysplastic Syndromes ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
High Risk Group ◽  
Severe Thrombocytopenia ◽  
Kaplan Meier

Abstract INTRODUCTION: The hypomethylating agents 5-azacitidine (5-AZA) and decitabine are recently considered the most preferable treatment option for patients with intermediate-2 and high-risk myelodysplastic syndromes (MDS), by International Prognostic Scoring System (IPSS). 5-AZA responders experience improved survival both in clinical trials (AZA 001) and in the real-life setting. Thrombocytopenia is a common event in MDS, during the course of the disease; recently, severe thrombocytopenia (≤30,000 platelets/μL) has been suggested as an important factor regarding the survival of MDS patients. In the present study, we examined the potential prognostic significance of severe thrombocytopenia, in intermediate-2- and high-risk MDS patients, being treated with 5-AZA, during the first 3 years of treatment. METHODS: This retrospective study included 850 higher-risk patients (intermediate-2- and high-risk), registered in the the Hellenic MDS Registry, treated with 5-AZA from 2010 to 2018 and were followed up for a time period up to 3 years. Complete patient data were available for 225 patients. Biostatistical analysis performed in this study included Kaplan-Meier survival analysis and Cox regression. The level of statistical significance was set at a probability value of less than 0.050 (P<0.050). RESULTS: The current study included 225 patients (159 male and 66 women) with intermediate-2- or high-risk MDS treated with 5-AZA, with a median age of 74 years (range: 47 - 89). WHO diagnosis included 1 (0.4%) case of RCUD, 8 (3.6%) cases of RCMD, 3 (1.3%) cases of RCMD-RS, 43 (19.1%) cases of RAEB-1, and 170 (75.6%) cases of RAEB-2. According to IPSS, 174 (77.3%) patients were classified in the intermediate-2 risk group and 51 (22.7%) patients in the high-risk group. In addition, according to IPSS-R, 24 (10.7%) patients were categorized in the intermediate risk group, 106 (47.1%) patients in the high-risk group, and 95 (42.2%) patients in the very-high risk group. All patients were evaluated regarding response to 5-AZA treatment. The initial response at 6 months was: complete remission (CR) in 40 (18.4%) patients, partial remission (PR) in 24 (11.1%) patients, hematological improvement (HI) in 35 (16.1%) patients; therefore, the initial overall response rate (CR, PR, and HI) was 45.6%. Stable disease (SD) was achieved by 56 (25.8%) MDS patients, while 62 (28.5%) patients showed progression of disease (PD) or treatment failure. Severe thrombocytopenia was not predictive of response, as shown using logistic regression analysis. However, severe thrombocytopenia predicted poor overall survival (OS) in the first 3 years of treatment with 5-AZA, as shown by the Kaplan-Meier analysis (Figure 1; P=0.016). Regarding AML-free survival, a strong trend was observed for thy unfavorable prognostic role of this severe cytopenia (P=0.096). Univariate Cox regression analysis for OS revealed a statistically significant hazard ratio (HR) of 1.6 for MDS patients with severe thrombocytopenia (HR=1.6, 95% CI=1.08, P=0.019). CONCLUSIONS: Our study showed that severe thrombocytopenia (≤ 30,000 platelets/μL) in intermediate-2- and high-risk MDS patients, treated with 5-AZA, predicts lower OS rates during the first 3 years of treatment. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Dan Li ◽  
LingLing Zhuang ◽  
Jian Zhang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Risk Group ◽  
High Risk Group ◽  
Mesenchymal Transition ◽  
Kaplan Meier

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and associates with a worse prognosis. Thus, we aimed to construct an EMT-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We built an EMT-related lncRNA risk signature in the training set by using Cox regression and LASSO regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results: 12 EMT-related lncRNAs were obtained for constructing the prognosis model in HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse survival than low-risk group. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the validation group. The nomogram was built and could accurately predict survival of HCC patients. GSEA results showed that in high-risk group cancer-related pathways were enriched, and exhibited more cell division activity suggested by Gene Ontology (GO) analysis.Conclusions: We established a novel EMT-related prognostic risk signature including 12 lncRNAs and constructed a nomogram to predict the prognosis in HCC patients, which may improve prognostic predictive accuracy for HCC patients and guide the individualized treatment methods for the patients with HCC.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

Combination of quantitative features from H&E biopsies and CT scans predicts response to chemotherapy and overall survival in small cell lung cancer (SCLC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8572-8572
Author(s):  
Cristian Barrera ◽  
Mohammadhadi Khorrami ◽  
Prantesh Jain ◽  
Pingfu Fu ◽  
Kate Butler ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Model ◽  
Risk Score ◽  
Risk Group ◽  
Image Features ◽  
Small Cell ◽  
Ct Scans ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
Response To Chemotherapy

8572 Background: Small Cell Lung Cancer (SCLC) is an aggressive malignancy with a rapid growth, and Chemotherapy remains mainstay of treatment. Identifying therapeutic targets in SCLC presents a challenge, partially due to a lack of accurate and consistently predictive biomarkers. In this study we sought to evaluate the utility of a combination of computer-extracted radiographic and pathology features from pretreatment baseline CT and H&E biopsy images to predict sensitivity to platinum-based chemotherapy and overall survival (OS) in SCLC. Methods: Seventy-eight patients with extensive and limited-stage SCLC who received platinum-doublet chemotherapy were selected. Objective response to chemotherapy (RECIST criteria) and overall survival (OS) as clinical endpoints were available for 51 and 78 patients respectively. The patients were divided randomly into two sets (Training (Sd), Validation (Sv)) with a constraint (equal number of responders and nonresponders in Sd)—Sd comprised twenty-one patients with SCLC. Sv included thirty patients. CT scans and digitized Hematoxylin Eosin-stained (H&E) biopsy images were acquired for each patient. A set of CT derived (46%) and tissue derived (53%) image features were captured. These included shape and textural patterns of the tumoral and peritumoral regions from CT scans and of tumor regions on H&E images. A random forest feature selection and linear regression model were used to identify the most predictive CT and H&E derived image features associated with chemotherapy response from Sd. A Cox proportional hazard regression model was used with these features to compute a risk score for each patients in Sd. Patients in Sv were stratified into high and low-risk groups based on the median risk score. Kaplan-Meier survival analysis was used to assess the prognostic ability of the risk score on Sv. Results: The risk score comprised nine CT (intra and peri-tumoral texture) and six H&E derived (cancer cell texture and shape) features. A linear regression model in conjunction with these 15 features was significantly associated with chemo-sensitivity in Sv (AUC = 0.76, PRC = 0.81). A multivariable model with these 15 features was significantly associated with OS in Sv (HR = 2.5, 95% CI: 1.3-4.9, P = 0.0043). Kaplan-Meier survival analysis revealed a significantly reduced OS in the high-risk group compared to the low-risk group. Conclusions: A combined CT and H&E tissue derived image signature model predicted response to chemotherapy and improved OS in SCLC patients. Image features from baseline CT scans and H&E tissue slide images may help in better risk stratification of SCLC patients. Additional independent validation of these quantitative image-based biomarkers is warranted.

Survival Outcomes for Induction vs Adjuvant Chemotherapy in Squamous Cell Carcinoma of the Maxillary Sinus

2018 ◽  
Vol 160 (4) ◽  
pp. 658-663 ◽  
Author(s):  
Phoebe Kuo ◽  
Sina J. Torabi ◽  
Dennis Kraus ◽  
Benjamin L. Judson
Keyword(s):  
Overall Survival ◽  
Adjuvant Chemotherapy ◽  
Maxillary Sinus ◽  
Induction Chemotherapy ◽  
Squamous Cell ◽  
Cox Regression ◽  
Rank Test ◽  
Controlled Clinical Trial ◽  
Log Rank Test ◽  
Kaplan Meier

Objective In advanced maxillary sinus cancers treated with surgery and radiotherapy, poor local control rates and the potential for organ preservation have prompted interest in the use of systemic therapy. Our objective was to present outcomes for induction compared to adjuvant chemotherapy in the maxillary sinus. Study Design Secondary database analysis. Setting National Cancer Database (NCDB). Subjects and Methods In total, 218 cases of squamous cell maxillary sinus cancer treated with surgery, radiation, and chemotherapy between 2004 and 2012 were identified from the NCDB and stratified into induction chemotherapy and adjuvant chemotherapy cohorts. Univariate Kaplan-Meier analyses were compared by log-rank test, and multivariate Cox regression was performed to evaluate overall survival when adjusting for other prognostic factors. Propensity score matching was also used for further comparison. Results Twenty-three patients received induction chemotherapy (10.6%) and 195 adjuvant chemotherapy (89.4%). The log-rank test comparing induction to adjuvant chemotherapy was not significant ( P = .076). In multivariate Cox regression when adjusting for age, sex, race, comorbidity, grade, insurance, and T/N stage, there was a significant mortality hazard ratio of 2.305 for adjuvant relative to induction chemotherapy (confidence interval, 1.076-4.937; P = .032). Conclusion Induction chemotherapy was associated with improved overall survival in comparison to adjuvant chemotherapy in a relatively small cohort of patients (in whom treatment choice cannot be characterized), suggesting that this question warrants further investigation in a controlled clinical trial before any recommendations are made.

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