Differential Gene Expression Associated with Chronic Myeloid Leukemia (CML) Progression Predicts Relapse and Survival Prior to Allogeneic Transplantation In Chronic Phase CML Patients

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3507-3507 ◽  
Author(s):  
Vivian G. Oehler ◽  
KaYee Yeung ◽  
Ailin Zhang ◽  
Theodore A. Gooley ◽  
Jerald P. Radich

Abstract Abstract 3507 Disease phase, transplant donor type, donor recipient match, age, and interval from diagnosis to transplantation (the EBMT risk score) are recognized variables that affect transplant outcomes for chronic myeloid leukemia (CML), but do not entirely account for the heterogeneity in outcomes. We have previously applied a probabilistic method to a large CML microarray gene expression dataset, and found a 6-gene signature of disease phase that discriminated between early and late chronic phase (CP). The combined expression of all 6 genes could be represented as a probability score where values closer to 0 are more similar to CP and values closer to 1 are more similar to blast crisis (BC). Moreover, in 17 accelerated phase (AP) CML patients, the 6-gene probability score was associated with outcomes after transplantation. We thus hypothesized that genes predictive of CML progression could be used to predict outcomes after transplantation regardless of disease phase. We derived 6 additional models (i.e. gene sets) from our CML microarray data, each consisting of 6–10 genes (total of 35 genes), that are also highly predictive of CML progression. These gene sets were derived using a novel network-driven approach aimed to identify genes that are functionally related to genes in pathways that are known to be associated with CML. We then examined expression of the genes in these models using quantitative PCR in bone marrow samples from 213 patients (176 CP, 23 AP, and 14 BC remission patients) prior to myeloablative allogeneic transplantation. GUSB was used as an endogenous control to correct for RNA integrity. Transplants occurred between 1993 and 2007 and a majority of patients did not receive prior tyrosine kinase inhibitor therapy. For CP patients, gene expression for all genes and models was independent of white blood cell and blast count. Among 176 CP CML patients, 45 patients died and 24 patients relapsed by last contact, leading to 1-year and 5-year estimates of overall survival of 85% and 78%, respectively, and 1-year and 5-year estimates of relapse of 7% and 12%, respectively. In CP patients we found not only that the expression of the original six-gene model (NOB1, DDX47, CD101, LTB4R, SCARB1, SLC25A3) was associated with a trend towards increased relapse, but that another model (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) was statistically significantly associated with an increased risk of relapse. In CP patients we found that an increase of 0.2 in the 6-gene probability score correlated with an increase in relapse of 46% (HR=1.46 (1.06-2.02, p=.02)) after adjustment for EBMT risk score (Figure 1a). Lastly, we also found that, individually, several of our progression-associated genes were statistically significantly associated with overall survival (G6PD and CAMK1D (Figure 1b)), relapse (RAC2 and ADRBK1), and non-relapse mortality (G6PD, CIQBP, and CAMK1D). In conclusion, these data suggest that gene expression prior to therapy is associated with treatment outcomes even after considering the contribution from known risk factors. These data provide evidence that a molecular signature associated with disease progression when detected in CP patients drives outcomes after transplantation. Given that all treatment outcomes are dependent on phase, it is possible that the expression of these genes prior to tyrosine kinase inhibitor therapy may also predict response. Figure 1. After adjustment for EBMT risk score, the probability of expression (Prob Score) of a 6-gene signature (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) correlates with relapse (Figure 1a) and CAMK1D expression correlates with overall survival (Figure 1b) after allogeneic transplantation in CP CML patients. Figure 1. After adjustment for EBMT risk score, the probability of expression (Prob Score) of a 6-gene signature (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) correlates with relapse (Figure 1a) and CAMK1D expression correlates with overall survival (Figure 1b) after allogeneic transplantation in CP CML patients. Disclosures: Oehler: Pfizer: Research Funding. Radich:Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria.

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18033-e18033
Author(s):  
Jun Chen ◽  
Bei Zhang

e18033 Background: Genomic expression profiles have enabled the classification of head and neck squamous cell carcinoma (HNSCC) into molecular sub-types and provide prognostic information, which have implications for the personalized treatment of HNSCC beyond clinical and pathological features. Methods: Gene-expression profiling was identified in TCGA- HNSCC (n = 492) and validated with the Gene Expression Ominibus (GEO) dataset(n = 270) for which RNA sequencing data and clinical covariates were available. A single-sample gene set enrichment analysis (ssGSEA) algorithm were used to quantified the levels of various hallmarks of cancer. And LASSO Cox regression model was used to screen robust prognostic biomarkers to identify the best set of survival-associated gene signatures in HNSCC. Statistical analyses were performed using R version 3.4.4. Results: We identified unfolded protein response as the primary risk factor for survival(cox coefficient = 17.4 [8.4-26.3], P < 0.001)among various hallmarks of cancer in TCGA- HNSCC. And unfolded protein response ssGESA scores were significantly elevated in patients who died during follow up (P = 0.009). Kaplan-Meier analysis showed that patients with low ssGSEA scores of unfolded protein response exhibited better OS (HR = 0.69, P = 0.008). And we established an unfolded protein response-related gene signature based on lasso cox. We then apply the unfolded protein response -related gene signature to classify patients into the high risk group and the low risk group with the cutoff of 0.18. Adjusted for stage,age,gender, our signature was an independent risk factor for overall survival in TCGA cohorts (HR = 0.39 [0.28-0.53],P = < 0.001). In external independent cohorts, similar results were observed. In the validation cohort GEO65858, the patients with high unfolded protein response score showed longer survival (HR = 0.62 [0.38-1.0], P = 0.049). And adjusted for stage,age,HPV state, the multivariate cox regression analysis showed that unfolded protein response-related gene signature exhibited an independent risk prediction for overall survival in 270 patients with HNSCC (HR = 0.57 [0.35-0.94], P = 0.026). Conclusions: By analyzing the gene-expression data with bioinformation approach, we developed and validated a risk prediction model with unfolded protein response -related expression scores in HNSCC, which have the potential to identify patients who could have better overall survival.


2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.


Neurosurgery ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 202-210 ◽  
Author(s):  
William C Chen ◽  
Harish N Vasudevan ◽  
Abrar Choudhury ◽  
Melike Pekmezci ◽  
Calixto-Hope G Lucas ◽  
...  

Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P &lt; .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P &lt; .0001), and overall survival (OS, P &lt; .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.


2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Michal Lotem ◽  
Sharon Merims ◽  
Stephen Frank ◽  
Tamar Hamburger ◽  
Aviram Nissan ◽  
...  

Background. There is not yet an agreed adjuvant treatment for melanoma patients with American Joint Committee on Cancer stages III B and C. We report administration of an autologous melanoma vaccine to prevent disease recurrence.Patients and Methods. 126 patients received eight doses of irradiated autologous melanoma cells conjugated to dinitrophenyl and mixed with BCG. Delayed type hypersensitivity (DTH) response to unmodified melanoma cells was determined on the vaccine days 5 and 8. Gene expression analysis was performed on 35 tumors from patients with good or poor survival.Results. Median overall survival was 88 months with a 5-year survival of 54%. Patients attaining a strong DTH response had a significantly better (p=0.0001) 5-year overall survival of 75% compared with 44% in patients without a strong response. Gene expression array linked a 50-gene signature to prognosis, including a cluster of four cancer testis antigens: CTAG2 (NY-ESO-2), MAGEA1, SSX1, and SSX4. Thirty-five patients, who received an autologous vaccine, followed by ipilimumab for progressive disease, had a significantly improved 3-year survival of 46% compared with 19% in nonvaccinated patients treated with ipilimumab alone (p=0.007).Conclusion. Improved survival in patients attaining a strong DTH and increased response rate with subsequent ipilimumab suggests that the autologous vaccine confers protective immunity.


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 10583-10583
Author(s):  
N. Van Zandwijk

10583 Background: Current staging methods are imprecise for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We have developed a 28-gene signature that is closely associated with recurrence-free and overall survival. Methods: We used whole-genome gene expression microarrays to analyze frozen-tumor samples from 174 patients (pT1&2, N0&1, MO), who had undergone complete surgical resection in 5 European institutions. Randomly generated numbers were used to assign 2/3 of the samples to an algorithm training group with the remaining 1/3 set aside for independent validation. Cox proportional hazards models were used to evaluate the association between the level of expression and patient survival. We used risk scores and nearest centroid analysis to develop a gene-expression model for the prediction of treatment outcome. Leave-one-out cross validation was used to prevent model over-training. Results: 28 genes that correlated with survival were identified by analyzing microarray data and risk scores. Based on the expression of these genes, patients in training and validation groups were classified as either high (48%) or low (52%) risk. Analysis of predicted risk groups revealed significantly different survival distributions for patients in both the training set (p<0.001) and independent validation set (p=0.006). Genes in our prognostic signature encode for several membrane-bound proteins with previously demonstrated involvement in cell cycle regulation and cell proliferation processes. Conclusions: Our 28-gene signature is closely associated with time to recurrence and overall survival of completely-resected NSCLC patients. [Table: see text]


2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.


2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.


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