scholarly journals Prognosis-Predictive Signature and Nomogram Based on Autophagy-Related Long Non-coding RNAs for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yu Jia ◽  
Yan Chen ◽  
Jiansheng Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Predictive Ability ◽  
Low Risk ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Risk Patients ◽  
Non Coding Rnas

Autophagy plays a vital role in hepatocellular carcinoma (HCC) pathogenesis. Long non-coding RNAs (lncRNAs) are considered regulators of autophagy, and the aim of the present study was to investigate the prognostic value of autophagy-related lncRNA (ARlncRNA) and develop a new prognostic signature to predict the 1-year and 3-year overall survival (OS) of HCC patients. Transcriptome and clinical survival information of HCC patients was obtained from The Cancer Genome Atlas database. A set of ARlncRNAs was identified by co-expression analysis, from which seven ARlncRNAs (AC005229.4, AL365203.2, AL117336.3, AC099850.3, ELFN1-AS1, LUCAT1, and AL031985.3) were selected for use as a predictive signature. Risk scores were derived for each patient, who were then divided into high-risk and low-risk groups according to the median risk value. The OS of high-risk patients was significantly lower than that of low-risk patients (P < 0.0001). The 1- and 3-year time-dependent ROC curves were used to evaluate the predictive ability of the risk score (AUC = 0.785 of 1 year, 0.710 of 3 years), and its predictive ability was found to be better than TNM stage. Moreover, the risk score was significantly, linearly related to pathological grade and TNM stage (P < 0.05). Overall, a novel nomogram to predict the 1-year and 3-year OS of HCC patients was developed, which shows good reliability and accuracy, for use in improved treatment decision-making.

Abstract 15329: The CADILLAC Risk Score Outperforms the TIMI Risk Score in Accurately Identifying Patients at Low Risk for In-hospital Mortality and Adverse Cardiovascular Events Following ST-Elevation Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Peter Malamas ◽  
Sumeet Lall ◽  
Brent Dembo ◽  
Ninad Zaman ◽  
Brandon Peterson ◽  
...  
Keyword(s):  
Logistic Regression ◽  
High Risk ◽  
Risk Score ◽  
Odds Ratio ◽  
Event Rate ◽  
Low Risk ◽  
Risk Scores ◽  
Circulatory Support ◽  
Risk Patients ◽  
Event Rates

The CADILLAC and TIMI risk scores have been used to predict events following revascularization following STEMI. Utilizing these scores may help to identify patients at low risk for mortality and major adverse cardiac events (MACE) following STEMI. We aimed to compare the prognostic accuracy of CADILLAC and TIMI risk scores for identifying patients with low risk for mortality and MACE. Retrospective review at a tertiary medical center of patients presenting with STEMI from 2014-2018, excluding those who presented with cardiogenic shock, cardiac arrest, or need for mechanical circulatory support. MACE was defined as sustained ventricular arrhythmias, ACS, CHF, and stroke. Low risk patient (CADILLAC 0-2, TIMI 0-1) outcomes were compared to high risk (CADILLAC>2, TIMI>1) using chi square, ROC, and logistic regression analyses. The study includes 341 patients. In hospital CADILLAC score for low risk patients had significantly lower event rate compared to high risk (4.7% vs. 11.7%, odds ratio = 0.37, 95% CI 0.16-0.85, p= 0.028). In hospital TIMI score comparing low vs. high risk patients showed no difference (8.3% vs. 7.2%, odds ratio= 1.18, 95% CI 0.39 - 3.59, p= 0.48). ROC curve predicting event rate showed CADILLAC (C=0.66, odds ratio 1.18; 95% CI 1.04 - 1.33; p=0.0064) vs. TIMI (C= 0.57, odds ratio 1.20; 95% CI 0.98 - 1.46; p=0.071). Evaluating 30 day and 1 year follow up, CADILLAC better predicted event rates vs. TIMI (Figure 1). Logistic regression for CADILLAC at 1 year shows significant association with event rates independent of parameters comprising either risk score (odds ratio 1.29; 95% CI 1.07 - 1.57; p=0.011) while TIMI does not (odds ratio 1.21; 95% CI 0.93 - 1.56; p=0.16). In conclusion, patients defined as low risk by CADILLAC following STEMI have significantly reduced in-hospital event rates. Utilizing this scoring system may help to guide feasibility of early hospital discharge. CADILLAC outperforms TIMI in its prognostic ability both in-hospital and follow ups.

scholarly journals Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiwen Wu ◽  
Tian Lan ◽  
Muqi Li ◽  
Junfeng Liu ◽  
Xukun Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Validation Cohort ◽  
Risk Group ◽  
Cancer Genome ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Cohort

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients.Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. A high-risk group and low-risk group were separated for further study depending on their median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort.Results: A total of 548 differential mRNAs were identified from HCC samples (n = 374) and normal controls (n = 50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender.Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.

scholarly journals Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Pingfei Tang ◽  
Weiming Qu ◽  
Taoli Wang ◽  
Minji Liu ◽  
Dajun Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Targeted Therapy ◽  
High Risk ◽  
Clinical Outcomes ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Testing Dataset ◽  
Non Coding Rnas

Abstract Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

scholarly journals NOL12 Acts as an Oncogenic Biomarker and Predicts the Efficacy of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jianfeng Huang ◽  
Weibiao Kang ◽  
Shubo Pan ◽  
Changjun Yu ◽  
Zhigang Jie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Clinicopathological Parameters ◽  
Migration And Invasion

Abstract Background: Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis worldwide. However, the pathogenesis of HCC remains poorly understood. Methods: Through data mining and analyses of The Cancer Genome Atlas (TCGA) datasets, the NOL12 expression in HCC was determined and the associations between its expression and patient survival and clinicopathological parameters were evaluated. The pro-tumorigenic roles of NOL12 on HCC in vitro were further verified by loss-of-function assay. The correlation between NOL12 expression and tumor-infiltrating immune cells (TICs) was analyzed by CIBERSORTx method. In addition, the risk signature based on 8 NOL12-related genes was established to accurately evaluate the prognosis of patients with HCC and to further predict the efficacy of immune checkpoint inhibitors (ICIs) in HCCResults: We found that NOL12 was significantly overexpressed in independent HCC datasets from TCGA database. High expression of NOL12 is associated with worse reduced overall survival (OS), high pathological grade, node metastasis and advanced clinical stage in patients with HCC. Moreover, NOL12 knockdown significantly inhibited cell proliferation, migration and invasion. CIBERSORTx analysis revealed that twelve types of TICs are correlated with NOL12 expression. The risk signature based on 8 NOL12-related genes is an independent prognostic factor for patients with HCC. The OS rate of patients in the low-risk score group was better than that in the high-risk score group. In addition, the total tumor mutation burden (TMB) in the high-risk score group increased significantly, and the risk scores could be used as an alternative indicator of ICI response. Conclusions: Our findings indicated that NOL12 might be involved in the progression of HCC and can be used as a potential therapeutic target. Moreover, the NOL12-related risk signature may have predictive relevance with regard to ICI therapy.

scholarly journals Assessing the clinical utility of genetic risk scores for targeted cancer screening

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Carly A. Conran ◽  
Zhuqing Shi ◽  
William Kyle Resurreccion ◽  
Rong Na ◽  
Brian T. Helfand ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Primary Care ◽  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Clinical Utility ◽  
Low Risk ◽  
Risk Scores ◽  
Average Risk ◽  
Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

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 Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

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.

Poor-Risk Acute Leukemia Patients with An EBMT Low-Risk Score and An 8/8 Matched Unrelated Donor Show Excellent Survival After Hematopoietic Stem Cell Transplantation.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1196-1196
Author(s):  
Tom Lodewyck ◽  
Machteld Oudshoorn ◽  
Bronno van der Holt ◽  
Eefke Petersen ◽  
Eric Spierings ◽  
...  
Keyword(s):  
High Resolution ◽  
Acute Leukemia ◽  
Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Unrelated Donor ◽  
Hematopoietic Stem ◽  
Poor Risk ◽  
Risk Patients ◽  
The Impact

Abstract Abstract 1196 Poster Board I-218 Introduction: Allogeneic hematopoietic stem cell transplantation (alloSCT) from volunteer unrelated donors (URD) may be associated with a higher non-relapse mortality (NRM) and worse outcome as compared to alloSCT using HLA-identical sibling donors. However, many parameters next to donor type define NRM. The impact on outcome of allele-matching for HLA-A, -B, -C and -DRB1 between donor and recipient has clearly been demonstrated. The prognostic impact of the EBMT risk score, that takes into account age, stage of disease, time from diagnosis to transplantation, donor type and donor-recipient gender combination, has recently been validated in a variety of hematological malignancies including acute leukemia and myelodysplastic syndrome (MDS). We evaluated the relative prognostic value of high-resolution HLA matching and the EBMT risk score for patients with poor-risk acute leukemia and MDS who received an URD transplant. Patients and methods: Between 1987 and 2006, 327 patients (≥16y) with poor-risk acute leukemia and MDS underwent URD alloSCT in the Netherlands. Patients were in 1st complete remission (CR1, n=129), 2nd CR (CR2, n=91), beyond CR2 or not in remission (n=107). The leukemia-risk was considered to be poor if patients had adverse cytogenetics or were not in CR1. The majority of the grafts was T-cell depleted (94%). High-resolution typing of HLA-A, -B, -C, and -DRB1 alleles was available for analysis in 270 donor-recipient pairs and had in part been performed retrospectively. Results: We evaluated the impact of high-resolution matching for HLA-A, -B, -C and -DRB1 on progression free survival (PFS) and overall survival (OS). Patients who were fully matched (8/8) with their donors (n=170) hadsignificantly superior PFS (40+/-4% vs 26+/-5%, hazard ratio (HR)=0.68; 95%CI 0.50–0.92, p=0.01) and OS (39+/-4% vs 29+/-5%, HR=0.70; 95%CI 0.51-0.96, p=0.03), compared to patients with mismatched (≤7/8) donors (n=100). Superior OS in the 8/8 group appeared to be due to a lower NRM (24+/-4% vs 39+/-5%, HR=0.54; 95%CI 0.35-0.85, p=0.008), while the relapse mortality rate was identical in both groups (37+/-4% vs 32+/-5%). Patients with EBMT risk scores of 1-2 (n=71), 3 (n=77), 4 (n=76) and 5-7 (n=103) had a predicted 5 year OS of 52%, 41% (HR=1.57; 95%CI 0.98-2.52), 29% (HR=2.07; 95%CI 1.32-3.26) and 19% (HR=2.69; 95%CI 1.76-4.11), respectively (p<0.001). Relapse mortality rate and NRM increased with increasing EBMT risk score. As shown in the table, the impact of allele-matching on OS was most evident in the EBMT low-risk group. EBMT low-risk (1-2) patients with 8/8 donors showed excellent 5 year OS compared to EBMT low-risk patients with ≤7/8 donors (73+/-8% vs 35+/-12%). The favorable impact of a fully matched donor was absent in patients with higher EBMT risk scores. Conclusions: Both the EBMT risk score and the degree of allele-matching independently predicted outcome after URD alloSCT. The predictive value of allele-matching was especially evident in EBMT low-risk patients, while patients with the highest EBMT risk scores (>4) had a dismal outcome, despite allele-matching. These results emphasize the importance of incorporating age, disease stage, donor-recipient gender combination and time interval from diagnosis to transplantation (EBMT risk score parameters) as well as high-resolution HLA-typing in the risk assessment prior to URD alloSCT. As excellent OS was noted in well matched EBMT low-risk patients, our data underscore the importance of an immediate search for an unrelated donor in poor-risk leukemia patients in CR1 below the age of 40, who should then receive their alloSCT as early consolidation therapy following induction chemotherapy. Disclosures: No relevant conflicts of interest to declare.

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