A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.

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Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.748442 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xinshuang Yu ◽

Peng Dong ◽

Yu Yan ◽

Fengjun Liu ◽

Hui Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Messenger Rna ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Risk Scores ◽

Significant Difference ◽

Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P < 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P < 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P < 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

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Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.743938 ◽

2021 ◽

Vol 12 ◽

Author(s):

Qian Zhang ◽

Liping Lv ◽

Ping Ma ◽

Yangyang Zhang ◽

Jiang Deng ◽

...

Keyword(s):

High Risk ◽

Pancreatic Adenocarcinoma ◽

Small Molecule ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

M2 Macrophage ◽

Related Gene ◽

Significant Difference

BackgroundPancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.MethodsAutophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P<0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.ResultsRisk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p<0.001), Treg cells (r=-0.31, p<0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p<0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.ConclusionOur results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

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Prognostic Autophagy-Related Model Revealed by Integrating Single-Cell RNA Sequencing Data and Bulk Gene Profiles in Gastric Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.729485 ◽

2022 ◽

Vol 9 ◽

Author(s):

Tianying Tong ◽

Jie Zhang ◽

Xiaoqiang Zhu ◽

Pingping Hui ◽

Zhimin Wang ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Single Cell ◽

Rna Sequencing ◽

Immune Checkpoint ◽

Risk Model ◽

Risk Group ◽

High Risk Group ◽

Related Model ◽

Single Cell Rna Sequencing

Autophagy has been associated with tumor progression, prognosis, and treatment response. However, an autophagy-related model and their clinical significance have not yet been fully elucidated. In the present study, through the integrative analysis of bulk RNA sequencing and single-cell RNA sequencing, an autophagy-related risk model was identified. The model was capable of distinguishing the worse prognosis of patients with gastric cancer (GC), which was validated in TCGA and two independent Gene Expression Omnibus cohorts utilizing the survival analysis, and was also independent of other clinical covariates evaluated by multivariable Cox regression. The clinical value of this model was further assessed using a receiver operating characteristic (ROC) and nomogram analysis. Investigation of single-cell RNA sequencing uncovered that this model might act as an indicator of the dysfunctional characteristics of T cells in the high-risk group. Moreover, the high-risk group exhibited the lower expression of immune checkpoint markers (PDCD1 and CTLA4) than the low-risk group, which indicated the potential predictive power to the current immunotherapy response in patients with GC. In conclusion, this autophagy-associated risk model may be a useful tool for prognostic evaluation and will facilitate the potential application of this model as an indicator of the predictive immune checkpoint biomarkers.

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Risk-Adapted Postmastectomy Radiotherapy Decision Based on Prognostic Nomogram for pT1-2N1M0 Breast Cancer: A Multicenter Study

Frontiers in Oncology ◽

10.3389/fonc.2020.588859 ◽

2020 ◽

Vol 10 ◽

Author(s):

Ming Li ◽

Jinbo Yue ◽

Xiangbo Wan ◽

Bin Hua ◽

Qiuan Yang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Locoregional Recurrence ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Moderate Risk ◽

Primary Tumor Site ◽

Free Survival ◽

Significant Difference

PurposeThe aim of this study was to develop a widely accepted prognostic nomogram and establish a risk-adapted PMRT strategy based on locoregional recurrence for pT1-2N1M0 breast cancer.Methods and MaterialsA total of 3,033 patients with pT1-2N1M0 breast cancer treated at 6 participating institutions between 2000 and 2016 were retrospectively reviewed. A nomogram was developed to predicted locoregional recurrence-free survival (LRFS). A propensity score-matched (PSM) analyses was performed in risk-adapted model.ResultsWith the median follow-up of 65.0 months, the 5-year overall survival (OS), disease free survival (DFS) and LRFS were 93.0, 84.8, and 93.6%, respectively. There was no significant difference between patients who received PMRT or not for the entire group. A nomogram was developed and validated to estimate the probability of 5-year LRFS based on five independent factors including age, primary tumor site, positive lymph nodes number, pathological T stage, and molecular subtype that were selected by a multivariate analysis of patients who did not receive PMRT in the primary cohort. According to the total nomogram risk scores, the entire patients were classified into low- (40.0%), moderate- (42.4%), and high-risk group (17.6%). The 5-year outcomes were significantly different among these three groups (P<0.001). In low-risk group, patients who received PMRT or not both achieved a favorable OS, DFS, and LRFS. In moderate-risk group, no differences in OS, DFS, and LRFS were observed between PMRT and no PMRT patients. In high-risk group, compared with no PMRT, PMRT resulted in significantly different OS (86.8 vs 83.9%, P = 0.050), DFS (77.2 vs 70.9%, P = 0.049), and LRFS (90.8 vs. 81.6%, P = 0.003). After PSM adjustment, there were no significant differences in OS, DFS, and LRFS in low-risk and moderate-risk groups. However, in the high-risk group, PMRT still resulted in significantly better OS, DFS and improved LRFS.ConclusionsThe proposed nomogram provides an individualized risk estimate of LRFS in patients with pT1-2N1M0 breast cancer. Risk-adapted PMRT for high-risk patients is a viable effective strategy.

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Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.669931 ◽

2021 ◽

Vol 8 ◽

Author(s):

Lingling Guo ◽

Yu Jing

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Model ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

M2 Macrophage ◽

Hub Genes ◽

Cox Regression Analysis

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

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Oseltamivir, Lopinavir/ritonavir and Reduning May Improve Survival of COVID-19 Patients With High-risk

10.21203/rs.3.rs-42377/v1 ◽

2020 ◽

Author(s):

zhiyong zeng ◽

Chaohui Wu ◽

Zhenlv Lin ◽

Yong Ye ◽

Shaodan Feng ◽

...

Keyword(s):

High Risk ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Lasso Regression ◽

Kaplan Meier ◽

Significant Difference ◽

Reduning Injection ◽

Total Point

Abstract Background No therapeutics have demonstrated specific efficacy for patients with COVID-19. Methods We retrospectively evaluated 351 patients with COVID-19 admitted to the Third People's Hospital of Yichang from 9 January to 25 March, 2020.Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were employed to identify risk factors associated with progression, which were then incorporated into the nomogram. Survival of patients between high-risk and low-risk groups was compared by kaplan-Meier analysis. Moreover, we assessed the effects of existing common drugs on survival of patients with high-risk. Results Based on the LASSO, four variables (white blood cell, C-reactive protein, whether lymphocyte ≥ 0.8 × 109/L, and whether lactate dehydrogenase ≥ 400 U/L) were selected for construction of the nomogram. Patients in the total cohort were stratified into low-risk group (total point < 160) and high-risk group (total point ≥ 160). Kaplan-Meier analysis demonstrated that there was significant difference in survival of patients between high-risk and low-risk groups (8-week survival rate: 71.41% vs 100%, P < 0.0001). Among the common drugs, we found that patients with high-risk received oseltamivir, lopinavir/ritonavir or Reduning injection exhibited better survival. The combination of these three drugs showed the effect of improving survival, although single drug may have no effect in different grouping analysis. Conclusions The combination of oseltamivir, lopinavir/ritonavir and Reduning injection may improve survival of COVID-19 patients with high-risk identified by our simple-to-use nomogram.

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Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

10.21203/rs.3.rs-708494/v1 ◽

2021 ◽

Author(s):

Fang Wen ◽

Xiaoxue Chen ◽

Wenjie Huang ◽

Shuai Ruan ◽

Suping Gu ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Iron Metabolism ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

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Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

Disease Markers ◽

10.1155/2020/8841859 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Wenkai Han ◽

Xiaoyan Xu ◽

Kai Che ◽

Guofeng Ma ◽

Danxia Li ◽

...

Keyword(s):

High Risk ◽

Prognostic Factor ◽

Cell Carcinoma ◽

Risk Model ◽

Clear Cell ◽

Risk Group ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Low Risk

Background. Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC). Methods. We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed. Results. The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability. Conclusion. The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.

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Trans-bronchial lung cryobiopsy in patients at high-risk of complications

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01503-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Benjamin Bondue ◽

Pascal Schlossmacher ◽

Nathalie Allou ◽

Virgile Gazaille ◽

Olivier Taton ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Lung Biopsy ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Systolic Pulmonary Artery Pressure ◽

Mortality And Morbidity ◽

Number Of Patients ◽

Significant Difference

Abstract Background The surgical lung biopsy (SLB) is the recommended sampling technique when the pathological analysis of the lung is required in the work-up of an interstitial lung disease (ILD) but trans-bronchial lung cryobiopsy (TBLC) is increasingly recognized as an alternative approach. As TBLCs have lower mortality and morbidity risks than SLB, this study aimed to investigate the safety of TBLCs in patients at higher risk of complications and for whom SLB was not considered as an alternative. Method This prospective study was conducted in two hospitals in which TBLCs were performed in patients with body mass index (BMI) > 35, and/or older than 75 years, and/or with severely impaired lung function (FVC < 50% or DLCO < 30%), and/or systolic pulmonary artery pressure > 45 mmHg, and/or a clinically significant cardiac disease. Patients with any of these risk factors constituted the high-risk group. Clinical outcomes were compared with those obtained in patients without these risk factors (low-risk group). Results Ninety-six patients were included between April 2015 and April 2020, respectively 38 and 58 in the high-risk or the low-risk group. No statistically significant difference was observed between both groups in terms of severity and rate of bleeding, pneumothorax, or duration of hospital stay (p value ranging from 0.419 to 0.914). Conclusion This preliminary study on a limited number of patients suggests that TBLC appears safe in those in whom lung biopsy is at high-risk of complications according to their age, BMI, lung impairment, and cardiac comorbidities.

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Prognostic Value of Circulating Myeloblasts in Patients with Myelodysplastic Syndromes (MDS): A Proposal for Incorporation in the Revised International Prognostic Scoring System (R-IPSS)

Blood ◽

10.1182/blood.v124.21.4650.4650 ◽

2014 ◽

Vol 124 (21) ◽

pp. 4650-4650

Author(s):

Rami S Komrokji ◽

Eric Padron ◽

Najla H Al Ali ◽

Jeffrey E Lancet ◽

Jeff Hall ◽

...

Keyword(s):

High Risk ◽

Gene Mutation ◽

Risk Model ◽

Risk Group ◽

Gene Mutations ◽

High Risk Group ◽

Low Risk ◽

Medical Laboratory ◽

Intermediate Risk ◽

Prognostic Variable

Abstract Introduction The World Health organization (WHO) MDS classification proposes presence of 2-4% peripheral myeloblasts (PB) as criteria for Refractory anemia with excess blasts I (RAEB-I) and 5-19% PB for RAEB-II classification, while 1% PB persistently is recognized as MDS unclassified. The most widely used clinical prognostic models such as IPSS, R-IPSS, and MD Anderson risk model (MDAS) have not incorporated PB as a prognostic variable. We evaluated the prognostic value of PB in a large MDS cohort and provide a proposal to incorporate presence of PB in R-IPSS. Methods The MDS database at Moffitt Cancer Center (MCC) was used to identify MDS patients (pts). Pts were classified into two groups based on presence of PB (1% or more) at time of diagnosis as PB-MDS group and those without PB called BM-MDS. Kaplan-Meier curves were used to depict survivals, and the log-rank test was used to compare median overall survival (OS). We collaborated with Genoptix Medical Laboratory to assess the correlation between presence of PB and gene mutations identified by next generation sequencing (21 myeloid gene panel) in a cohort of de-identified MDS pts. Results There were 1905 pts included from MCC MDS database among whom 260 pts (14%) had evidence of PB near time of diagnosis. PB-MDS patients were younger, more likely to have trilineage cytopenia, complex karyotype, transfusion dependent and more in therapy related MDS. Among PB-MDS pts 175 (67%) received hypomethylating agent (HMA) compared to 977 (59%) in the BM-MDS group, p 0.017 According to IPSS risk stratification PB-MDS pts were more likely to be classified as higher risk (HR-MDS) 157 (63%) compared to 445 (28%) in the BM-MDS group (p<0.005). Pts with PB-MDS were also HR-MDS by MDAS (p <0.005). The rate of AML transformation was 46% (n=120) compared to 25% (n=418) in PB-MDS and BM-MDS group respectively, p <0.005. Median overall survival (OS) was 46 month (95% CI 42 to 49.6) in the absence of PB compared to 17.5 mo (95% CI 14.9-20) with PB (p < 0.005). Impact on OS was greater in IPSS lower risk MDS patients where median OS was 34 mo in PB-MDS compared to 60 mo in BM-MDS patients (p < 0.005), while in IPSS HR- MDS the median OS was 16.5 in PB-MDS compared to 18 month BM-MDS (p 0.018) We then examined prognostic discrimination of PB among each R-IPSS category. Median OS for very low risk R-IPSS was 104 mo in absence of PB compared to 37 mo in BM-MDS (p 0.032). Among low risk patients the median OS was 69 mo in absence of PB compared to 40 mo (p 0.07). In the intermediate risk group, median OS was 40 mo in absence of PB compared to 23 mo with PB (p 0.001), whereas median OS in the high risk group was 24 mo without PB compared to 20 mo with PB (p 0.11). Finally, in the very high risk R-IPSS the median OS was 15 mo compared to 13 mo respectively (p 0.44). The presence of PB upgraded pts with very low or low risk R-IPSS to intermediate risk. The outcome of intermediate risk group with PB was similar to the high risk group. In Cox regression analysis the presence of PB was an independent prognostic covariate for OS after adjusting for R-IPSS and age, HR 1.5 (95% CI 1.3-1.8). Among HR-MDS pts treated with HMA (n=470) presence of PB was independent prognostic variable for OS. (HR 1.3, p 0.027) We next created a R-IPSS+PB risk model where one additional point was awarded for presence of PB and pts categorized into risk groups based on the same lump score suggested by R-IPSS for each risk category. We applied this score for 245 pts with PB where R-IPSS score was known (Table-1). Sixty three pts (26%) were upstaged to high or very high risk group and most pts were upstaged to next risk group. Among 51 pts in Genoptix Medical Laboratory database with known PB, the rate of at least single gene mutation identification in pts with PB-MDS was 100%, (4 out of 4 with PB) compared to 81% in those without PB (38 out of 47 without PB, 12 of those were single SF3B1 gene mutation in ring sideroblasts MDS subtype). The gene mutations in PB-MDS included U2AF1 gene mutation in 2 pts, SRSF2, TET-2, ASXL-1, and RUNX-1. Two pts had two gene mutations. Conclusions Presence of PB in MDS is an adverse independent prognostic variable that refines prognostic discrimination in Low to Intermediate risk R-IPSS groups. Accounting for presence of PB particularly the intermediate risk group, prioritizes disease altering therapeutic strategies. TableRisk groupR-IPSSR-IPSS+PBNOverall survival (mo)NOverall survival (mo)Very low91low2440854intermediate51231238High63204330Very high991318115 Disclosures Hall: Genoptix Medical Laboratory: Employment. Kwok:Genoptix, Inc., a Novartis company: Employment, Equity Ownership.

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