scholarly journals Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhicheng Zhuang ◽  
Huajun Cai ◽  
Hexin Lin ◽  
Bingjie Guan ◽  
Yong Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Tumor Purity ◽  
Kaplan Meier

Background. Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear. Methods. Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan–Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry. Results. A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan–Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group. Conclusion. The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.

The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2970-2970 ◽  
Author(s):  
Martin van Vliet ◽  
Joske Ubels ◽  
Leonie de Best ◽  
Erik van Beers ◽  
Pieter Sonneveld
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Standard Risk

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

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

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.

scholarly journals Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Cell Analysis ◽  
Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

scholarly journals A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Zijin Xiang ◽  
Xueru Chen ◽  
Qiaoli Lv ◽  
Xiangdong Peng
Keyword(s):  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Low Grade ◽  
Prognostic Signature ◽  
The Relationship ◽  
Genome Atlas

BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.

scholarly journals A Prognostic Ferroptosis-Related lncRNAs Signature Associated With Immune Landscape and Radiotherapy Response in Glioma

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianglin Zheng ◽  
Zijie Zhou ◽  
Yue Qiu ◽  
Minjie Wang ◽  
Hao Yu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Clinical Information ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Lasso Regression ◽  
Who Grade

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

scholarly journals MO048MULTICENTRE PROSPECTIVE VALIDATION OF THE URINARY PEPTIDOME-BASED CLASSIFIER CKD273 AS A PREDICTOR OF RENAL FUNCTION DECLINE IN SUBJECTS WITH TYPE 2 DIABETES

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Morten Lindhardt ◽  
Nete Tofte ◽  
Gemma Currie ◽  
Marie Frimodt-Moeller ◽  
Heiko Von der Leyen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Renal Function ◽  
High Risk ◽  
Risk Group ◽  
Cox Model ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Renal Function Decline

Abstract Background and Aims In the PRIORITY study, it was recently demonstrated that the urinary peptidome-based classifier CKD273 was associated with increased risk for progression to microalbuminuria. As a prespecified secondary outcome, we aim to evaluate the classifier CKD273 as a determinant of relative reductions in eGFR (CKD-EPI) of 30% and 40% from baseline, at one timepoint without requirements of confirmation. Method The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) is the first prospective observational study to evaluate the early detection of diabetic kidney disease in subjects with type 2 diabetes (T2D) and normoalbuminuria using the CKD273 classifier. Setting 1775 subjects from 15 European sites with a mean follow-up time of 2.6 years (minimum of 7 days and a maximum of 4.3 years). Patients Subjects with T2D, normoalbuminuria and estimated glomerular filtration rate (eGFR) ≥ 45 ml/min/1.73m2. Participants were stratified into high- or low-risk groups based on their CKD273 score in a urine sample at screening (high-risk defined as score &gt; 0.154). Results In total, 12 % (n = 216) of the subjects had a high-risk proteomic pattern. Mean (SD) baseline eGFR was 88 (15) ml/min/1.73m2 in the low-risk group and 81 (17) ml/min/1.73m2 in the high-risk group (p &lt; 0.01). Baseline median (interquartile range) urinary albumin to creatinine ratio (UACR) was 5 (3-8) mg/g and 7 (4-12) mg/g in the low-risk and high-risk groups, respectively (p &lt; 0.01). A 30 % reduction in eGFR from baseline was seen in 42 (19.4 %) subjects in the high-risk group as compared to 62 (3.9 %) in the low-risk group (p &lt; 0.0001). In an unadjusted Cox-model the hazard ratio (HR) for the high-risk group was 5.7, 95 % confidence interval (CI) (3.9 to 8.5; p&lt;0.0001). After adjustment for baseline eGFR and UACR, the HR was 5.2, 95 % CI (3.4 to 7.8; p&lt;0.0001). A 40 % reduction in eGFR was seen in 15 (6.9 %) subjects in the high-risk group whereas 22 (1.4 %) in the low-risk group developed this endpoint (p&lt;0.0001). In an unadjusted Cox-model the HR for the high-risk group was 5.0, 95 % CI (2.6 to 9.6; p&lt;0.0001). After adjustment for baseline eGFR and UACR, the HR was 4.8, 95 % CI (2.4 to 9.7; p&lt;0.0001). Conclusion In normoalbuminuric subjects with T2D, the urinary proteomic classifier CKD273 predicts renal function decline of 30 % and 40 %, independent of baseline eGFR and albuminuria.

scholarly journals Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianfeng Zheng ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu ◽  
Jinyi Tong
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

The Glasgow whipple risk score to predict pancreas-specific complications after pancreaticoduodenectomy.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 394-394
Author(s):  
Lavanniya Kumar Palani Velu ◽  
Vishnuvardhan Chandrabalan ◽  
Ross Carter ◽  
Colin McKay ◽  
Donald McMillan ◽  
...  
Keyword(s):  
High Risk ◽  
Serum Amylase ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prolonged Stay ◽  
Increased Risk ◽  
Clinically Significant ◽  
Serum Crp

394 Background: Pancreas-specific complications (PSC), comprising postoperative pancreatic fistula, post-pancreatectomy haemorrhage, and intra-abdominal collections, are drivers of morbidity following pancreaticoduodenectomy (PD). Intra-operatively derived pancreatic gland texture is a major determinant of postoperative PSC. We have previously demonstrated that a postoperative day 0 (PoD0) serum amylase ≥ 130 IU/L is an objective surrogate of pancreatic texture, and is associated with PSC. We sought to refine the PSC risk prediction model by including serial measurements of serum C-reactive protein (CRP). Methods: 230 consecutive patients undergoing PD between 2008 and 2014 were included in the study. Routine serum investigations, including amylase and CRP were performed from the pre-operative day. Receiver operating characteristic (ROC) curve analysis was used to identify a threshold value of serum CRP associated with clinically significant PSC. Results: 95 (41.3%) patients experienced a clinically significant PSC. ROC analysis identified post-operative day 2 (PoD2) serum CRP of 180 mg/L as the optimal threshold (P=0.005) associated with clinically significant PSC, a prolonged stay in critical care (P =0.032), and a relaparotomy (P = 0.045). Patients with a PoD0 serum amylase ≥ 130 IU/L who then developed a PoD2 serum CRP ≥ 180 mg/L had a higher incidence of postoperative complications. Patients were categorised into high, intermediate and low risk groups based on PoD0 serum amylase and PoD2 serum CRP. Patients in the high risk group (PoD0 serum amylase ≥ 130 IU/L and PoD2 serum CRP ≥ 180 mg/l) had significantly higher incidence of PSC, a return to theatre, prolonged lengths stay (all P≤ 0.05) and a four-fold increase in perioperative mortality compared patients in the intermediate and low risk groups (7 deaths in the high risk group versus 2 and nil in the intermediate and low risk groups respectively). Conclusions: A high risk profile, defined as PoD0 serum amylase ≥ 130 IU/L and PoD2 serum CRP ≥ 180 mg/l, should raise the clinician’s awareness of the increased risk of clinically significant PSC and a complicated postoperative course following pancreaticoduodenectomy.

scholarly journals Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers of Drug Resistance in CLL Patients Treated with Ibrutinib

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 186-186 ◽  
Author(s):  
Inhye E. Ahn ◽  
Xin Tian ◽  
Maher Albitar ◽  
Sarah E. M. Herman ◽  
Erika M. Cook ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Models ◽  
Discovery Cohort ◽  
Treatment Naïve ◽  
Equity Ownership

Abstract Introduction: We previously reported a prognostic scoring system in CLL using pre-treatment factors in patients treated with ibrutinib [Ahn et al, 2016 ASH Annual Meeting]. Here we present long-term follow-up results and validation of the prognostic models in a large independent cohort of patients. We also determine the incidence of resistance-conferring mutations in BTK and PLCG2 genes in different clinical risk groups. Methods and Patients: The discovery cohort comprised 84 CLL patients on a phase II study with either TP53 aberration (deletion 17p or TP53 mutation) or age ≥65 years (NCT01500733). The validation cohort comprised 607 patients pooled from four phase II and III studies for ibrutinib in treatment-naïve or relapsed/refractory CLL (NCT01105247; NCT01578707; NCT01722487; NCT01744691). All patients received single-agent ibrutinib 420mg once daily. We used Cox regression models to identify independent predictors of PFS, Kaplan-Meier method to estimate probabilities of PFS, log-rank test to compare PFS, and Cochran-Armitage trend test to compare the incidence of mutation among subgroups. We used R version 3.5.0 or SAS® version 9.3 for statistical analyses. For biomarker correlation, we tested cellular DNA or cell-free DNA collected from patients in the discovery cohort with the targeted sequencing of BTK and PLCG2 genes. Result: At a median follow-up of 5.2 years, 28 (33.3%) of 84 patients in the discovery cohort progressed or died. 52 (61.9%) patients had treatment-naïve CLL. Independent factors of PFS on univariate analysis were; TP53 aberration, prior treatment, and β-2 microglobulin (B2M) >4mg/L (P<0.05 for all tests). Unmutated IGHV and advanced Rai stage (III/IV) showed a trend toward inferior outcome without reaching statistical significance. Because higher levels of B2M were associated with relapsed/refractory CLL, we performed two multivariate Cox regression models to assess B2M and prior treatment status separately. Risk groups were determined by the presence of TP53 aberration, advanced Rai stage, and B2M >4mg/L for Model 1, and TP53 aberration, advanced Rai stage, and relapsed/refractory CLL for Model 2 (Table 1). The high-risk group had all three adverse risk factors; the intermediate-risk group had two risk factors; and the low-risk group, none or one. The median PFS of the high-risk group was 38.9 months for Model 1 and 38.4 months for Model 2, and was significantly shorter than those of intermediate and low-risk groups. In the validation cohort, 254 (41.8%) of 607 patients progressed or died at a median follow-up of 4.2 years. 167 (27.5%) patients had treatment-naïve CLL. Both models showed statistically significant differences in PFS by risk groups (Table 1). For the high-risk group, 4-year PFS was 30.2% in Model 1 and 30.5% in Model 2, which were inferior to those of intermediate (53.4 and 52.4%) and low-risk groups (68.7 and 73.7%). Model 1 classified 20% of patients and Model 2 classified 28% of patients to the high-risk group. BTK and PLCG2 mutations are common genetic drivers of ibrutinib resistance in CLL. To determine whether the incidence of these mutations correlates with prognostic risk groups, we performed targeted sequencing of BTK and PLCG2 of samples collected from patients in the discovery cohort. We used cell-free DNA for patients who received long-term ibrutinib (≥3 years) and had low circulating tumor burden, and cellular DNA, for samples collected within 3 years on ibrutinib or at progression. Of 84 patients, 69 (82.1%) were tested at least once, and 37 (44.0%) were tested at least twice. The frequency of testing was similar across the risk groups by two models (P>0.05). The cumulative incidences of mutations at 5 years in the low-, intermediate-, and high-risk groups were: 21.4%, 44.8% and 50%, respectively, by Model 1 (P=0.02); and 22.6%, 41.4% and 66.7%, respectively, by Model 2 (P=0.01). Conclusion: We developed and validated prognostic models to predict the risk of disease progression or death in CLL patients treated with ibrutinib. Risk groups classified by three commonly available pre-treatment factors showed statistically significant differences in PFS. The clinically-defined high-risk disease was linked to higher propensity to develop clonal evolution with BTK and/or PLCG2 mutations, which heralded ibrutinib resistance. Disclosures Albitar: Neogenomics Laboratories: Employment. Ma:Neogenomics Laboratories: Employment. Ipe:Pharmacyclics, an AbbVie Company: Employment, Other: Travel; AbbVie: Equity Ownership. Tsao:Pharmacyclics LLC, an AbbVie Company: Employment. Cheng:Pharmacyclics LLC, an AbbVie Company: Employment. Dean:CTI BioPharma Corp.: Employment, Equity Ownership; Pharmacyclics LLC, an AbbVie Company: Employment, Equity Ownership. Wiestner:Pharmacyclics LLC, an AbbVie Company: Research Funding.

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