scholarly journals Prediction of high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques

2021 ◽  
Author(s):  
Sumeet Patiyal ◽  
Anjali Dhall ◽  
Gajendra P.S. Raghava
Keyword(s):  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Somatic Mutations ◽  
Prediction Models ◽  
Single Gene ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Prognostic Analysis ◽  
High Risk Cancer

Identification of somatic mutations with high precision is one of the major challenges in prediction of high-risk liver-cancer patients. In the past number of mutation calling techniques have been developed that include MuTect2, MuSE, Varscan2, and SomaticSniper. In this study an attempt has been made to benchmark potential of these techniques in predicting prognostic biomarkers for liver cancer. In this study, we extracted somatic mutations in liver-cancer patients using VCF and MAF files from the cancer genome atlas. In terms of size, the MAF files are 42 times smaller than VCF files and containing only high-quality somatic mutations. Secondly, machine learning based models have been developed for predicting high-risk cancer patients using mutations obtain from different techniques. The performance of different techniques and data files have been compared based on their potential to discriminate high and low risk liver-cancer patients. Further, univariate survival analysis revealed the prognostic role of highly mutated genes. Based on correlation analysis, we selected 80 genes negatively associated with the overall survival of the liver cancer patients. Single-gene based analysis showed that MuTect2 technique based MAF file has achieved maximum HRLAMC3 9.25 with p-value 1.78E-06. Finally, we developed various prediction models using selected genes for each technique, and the results indicate that MuTect2 technique based VCF files outperform all other methods with maximum AUROC of 0.72 and HR 4.50 (p-value 3.83E-15). Based on overall analysis, VCF file generated using MuTect2 technique performs better among other mutation calling techniques to explore the prognostic potential of mutations in liver cancer. We hope that our findings will provide a useful and comprehensive comparison of various mutation calling techniques for the prognostic analysis of cancer patients.

Venous Thromboembolism (VTE) Prevention with Semuloparin in Cancer Patients Initiating Chemotherapy: Benefit-Risk Assessment by VTE Risk in SAVE-ONCO

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 206-206 ◽  
Author(s):  
Daniel George ◽  
Giancarlo Agnelli ◽  
William Fisher ◽  
Ajay Kakkar ◽  
Michael R Lassen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Board Of Directors ◽  
Cancer Patients ◽  
Research Funding ◽  
P Value ◽  
Advisory Committees ◽  
Benefit Risk Assessment ◽  
Bayer Healthcare ◽  
High Risk Cancer

Abstract Abstract 206 Background: Cancer patients receiving chemotherapy are at increased risk for VTE. Recent oncology guidelines emphasize the need for randomized studies with VTE risk assessment in these patients (Streiff MB, et al. JNCCN. 2011;9:714–777). Semuloparin is a new ultra-low-molecular-weight heparin with high anti-factor Xa and minimal anti-factor IIa activities. The SAVE-ONCO study investigated semuloparin vs placebo for VTE prevention in cancer patients receiving chemotherapy. Methods: Patients with metastatic or locally advanced cancer of lung, pancreas, stomach, colon-rectum, bladder or ovary initiating a chemotherapy course, were randomized to once-daily subcutaneous semuloparin 20 mg or placebo until change of chemotherapy. The primary efficacy outcome was a composite of symptomatic deep-vein thrombosis, any non-fatal pulmonary embolism, or VTE-related death. The main safety outcome was clinically relevant bleeding (major and non major). Baseline VTE risk was assessed by a score specifically developed and validated in chemotherapy-treated cancer patients (Khorana AA, et al. Blood. 2008;111:4902–7). According to this predictive model a score of 2 was assigned to very high-risk cancer sites (pancreatic or gastric), a score of 1 was assigned to high-risk cancer sites (lung, ovarian, or bladder cancer) and 1 is added to the score for each of the following parameters: platelet count ≥350 × 109/L, hemoglobin <10 g/dL and/or use of erythropoietin-stimulating agents, leukocyte count >11 × 109/L, and body mass index ≥35 kg/m2. Results: Among the 3212 patients randomized, the majority had lung (36.6%) or colorectal (28.9%) cancer and approximately two-thirds had metastatic cancer. In total, 550 (17.4%) of patients enrolled were at high risk of VTE, 1998 (63.2%) were at moderate risk, and 614 (19.4%) were at low risk (VTE risk score of ≥ 3, 1–2, or 0 points, respectively). All risk groups were well balanced between the treatment groups. Median treatment duration was approximately 3.5 months. Overall, semuloparin significantly reduced VTE or VTE-related death by 64% (p<0.0001; Table) vs placebo. The treatment effect was consistent across various levels of VTE risk (interaction p-value=0.6048; Table). Clinically relevant bleeding occurred in 2.8% and 2.0% of the patients in the semuloparin and placebo groups, respectively (Table). The incidence of major bleeding was similar: 1.2% and 1.1% patients in the semuloparin and placebo groups, respectively (hazard ratio [HR] 1.05; 95% confidence interval [CI] 0.55–1.99). No increased incidence of clinically relevant bleeding was observed with semuloparin vs placebo across various levels of VTE risk (interaction p-value=0.9409; Table). Conclusions: In cancer patients receiving chemotherapy, thromboprophylaxis with semuloparin was consistently associated with a favorable benefit-risk profile across various levels of VTE risk, but greatest in moderate to high risk patients. Antithrombotic prophylaxis should be considered in patients with cancer receiving chemotherapy, particularly in those who are at moderate to high risk of VTE. Disclosures: George: Viamet: Consultancy, Research Funding; Sanofi: Consultancy, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau; Medivation: Consultancy; Janssen: Consultancy, Research Funding, Speakers Bureau; Ipsen: Consultancy, Research Funding; Genentech/Roche: Consultancy, Speakers Bureau; Dendreon: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy; Astellas: Consultancy; GSK: Research Funding, Speakers Bureau; BMS: Research Funding; Exelixis: Research Funding. Agnelli:GlaxoSmithKline: Honoraria; Boehringer Ingelheim: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; sanofi-aventis: Honoraria. Fisher:Boehringer Ingelheim: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; sanofi-aventis: Honoraria, Research Funding. Kakkar:Bayer HealthCare: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; sanofi-aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Eisai: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; ARYx Therapeutics: Consultancy; Canyon: Consultancy; GlaxoSmithKline: Honoraria. Lassen:Astellas Pharma Europe: Consultancy; Bayer HealthCare AG: Consultancy; Bristol-Myers Squibb: Consultancy; Boehringer Ingelheim: Consultancy; GlaxoSmithKline: Consultancy; Merck Serono: Consultancy; Pfizer: Consultancy; Protola Pharma: Consultancy; sanofi-aventis: Consultancy. Mismetti:sanofi-aventis: served as a member of Steering Committees. Mouret:Bayer HealthCare: Consultancy, Honoraria; sanofi-aventis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria. Lawson:Sanofi: Employment. Turpie:Astellas Pharma Europe: Consultancy; Bayer HealthCare AG: Consultancy; Portola Pharma: Consultancy; sanofi-aventis: Consultancy.

scholarly journals Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

2019 ◽  
Vol 28 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Bai Ji ◽  
Hongqiao Cai ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Rank Test ◽  
P Value ◽  
Rna Seq ◽  
Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

scholarly journals Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Zhao ◽  
Jiaxuan Zou ◽  
Ziwei Wang ◽  
Ge Li ◽  
Yi Lei
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Malignant Tumors ◽  
Single Gene ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Specificity And Sensitivity

Background. Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients’ prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods. In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. Results. We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients’ OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion. The signature consisting of four glycolysis-related genes could be used to forecast the GC patients’ prognosis.

scholarly journals A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

Epidemiology of cancer-associated venous thrombosis

Blood ◽  
2013 ◽  
Vol 122 (10) ◽  
pp. 1712-1723 ◽  
Author(s):  
Jasmijn F. Timp ◽  
Sigrid K. Braekkan ◽  
Henri H. Versteeg ◽  
Suzanne C. Cannegieter
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Venous Thrombosis ◽  
Cancer Patients ◽  
Prediction Models ◽  
Related Factors ◽  
Treatment Measures ◽  
Risk Patients ◽  
Cancer Types

Abstract Cancer-associated venous thrombosis is a common condition, although the reported incidence varies widely between studies depending on patient population, start and duration of follow-up, and the method of detecting and reporting thrombotic events. Furthermore, as cancer is a heterogeneous disease, the risk of venous thrombosis depends on cancer types and stages, treatment measures, and patient-related factors. In general, cancer patients with venous thrombosis do not fare well and have an increased mortality compared with cancer patients without. This may be explained by the more aggressive type of malignancies associated with this condition. It is hypothesized that thromboprophylaxis in cancer patients might improve prognosis and quality of life by preventing thrombotic events. However, anticoagulant treatment leads to increased bleeding, particularly in this patient group, so in case of proven benefit of thromboprophylaxis, only patients with a high risk of venous thrombosis should be considered. This review describes the literature on incidence of and risk factors for cancer-associated venous thrombosis, with the aim to provide a basis for identification of high-risk patients and for further development and refinement of prediction models. Furthermore, knowledge on risk factors for cancer-related venous thrombosis may enhance the understanding of the pathophysiology of thrombosis in these patients.

Gene Expression Profiling (GEP) of Purified Plasma Cells at Baseline and 48hr after-Dexamethasone (D) or Thalidomide (T) Improve Outcome Predicition of Baseline GEP Alone in Patients with Multiple Myeloma (MM) Treated with Total Therapy 2 (TT2).

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 502-502
Author(s):  
Bart Burington ◽  
John Shaughnessy ◽  
Bart Barlogie ◽  
Crowley John
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Prediction Models ◽  
Single Agent ◽  
Baseline Risk ◽  
P Value ◽  
P Values ◽  
Cytogenetic Abnormalities ◽  
Change Models ◽  
Hazard Ratios

Abstract The prognosis of patients with MM varies widely. High risk is best captured by cellular and molecular genetic features. Objective: to determine whether predictive power of baseline GEP and metaphase cytogenetic abnormalities (CA) could be improved by availability of GEP data obtained 48hr after single agent D or T, pre-therapy. A total of 668 patients were enrolled on TT2, 323 randomized to T and 345 without T (ASCO 05). When randomized to T/no T, Baseline and 48 hr GEP samples were obtained from 32/41 receiving a test dose of T/D vs 10/14 receiving full VAD+T/VAD regimen. A total of 97 baseline/early treatment GEP pairs were analyzed. Combined baseline expression and 48hr expression changes of 151 genes predicted EFS at a false discovery rate (FDR) of 10%. The table compares baseline EFS high-risk dysregulation to the direction of 48 hour changes, confering improved EFS. Decreases over 48 hours are associated with improved EFS in 74 of 78 genes (upregulated expression confers poor survival at baseline). In the remaining 4, perturbation in the direction of an additional increase may be a marker of early response. With EFS-associated genes, we trained 15 EFS prediction models using baseline expression and 15 prediction models using the change in expression between baseline and 48 hours. Training sets were random splits of 97 patients and baseline and change models separately predicted an EFS risk index in the remaining validation patients (standardized to a variance of 1). Risk indices were compared to an indicator of cytogenetic abnormalities (CA) among validation patients using multivariate proportional hazards analyses. The table shows median hazard ratios and p-values for competing predictors in 15 validation sets. Without cytogenetics, combined GEP baseline and change indices were significant predictors in all 15 validation sets (median combined P-value of 0.002). The table shows median performing GEP model of 15 in a multivariate analysis including cytogenetics for all 97 patients. 48 hour changes in gene expression in newly diagnosed myeloma patients can significantly predict EFS in validated prediction models, alone and in combination with baseline GEP. After adjustment for baseline and 48-hour GEP change indices, metaphase cytogenetics is no longer a significant predictor in independent patient samples. Baseline EFS risk and 48 hour changes associated with good outcome in 151 EFS-associated genes. Improved EFS Decrease (HR&gt;=1 Increase (HR &lt;1) Baseline High Risk Downregulated (HR&lt;1) 6 67 Upregulated (HR&gt;+ 1) 74 4 Median Hazard Ratios and P-values for Multivariate Models in 15 Validation Sets HR P # of p-values below .05 (of 15) GEP baseline Risk 2.1 0.037 10 EP 48 hr change risk 1.9 0.052 7 CA 1.6 0.310 0 Median validation set overall P-value 0.0003 Median GEP EFS baseline/48 hour EFS prediction model n=97 HR P GEP baseline Risk 2.0 0.004 GEP 48 hr change risk 2.6 0.001 CA 1.4 0.330

Overall survival in patients with lung cancer using a web-application-guided follow-up compared to standard modalities: Results of phase III randomized trial.

2016 ◽  
Vol 34 (18_suppl) ◽  
pp. LBA9006-LBA9006 ◽  
Author(s):  
Fabrice Denis ◽  
Claire Lethrosne ◽  
Nicolas Pourel ◽  
Olivier Molinier ◽  
Yoann Pointreau ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Supportive Care ◽  
Cancer Patients ◽  
Web Application ◽  
Phase Iii ◽  
P Value ◽  
Lung Cancer Patients

LBA9006 Background: We developed a web-application for an early detection of symptomatic relapse, complications and early supportive care in high-risk lung cancer patients between visits. A dynamical analysis of the weekly self-reported symptoms automatically triggered physician visit. Methods: We performed a national multi-institutional phase 3 prospective randomized study to compare web-application follow-up (experimental arm) for which patient’s self-scored symptoms that were weekly sent (between planned visits) to the oncologist and a clinical routine assessment with a CT-scan (every 3-6 months or at investigator’s discretion - standard arm). High risk lung cancer patients without progression and with a 0-2 performance status (PS) after an initial treatment were included. Maintenance chemotherapy or TKI therapy were allowed. In the experimental arm, an email alert was sent to the oncologist when some predefined clinical criteria were fulfilled: an imaging was then quickly prescribed. Early supportive cares were provided if adequate. The primary endpoint was to detect an improvement of 12% in 9 months survival in favor of the experimental arm (α = 5%, β = 20%, unilateral test). The boundary for declaring superiority with respect to overall survival at the pre-planned interim analysis was a p-value of less than 0.006. The PS at relapse, the quality of life (QOL) and cost-effectiveness were also investigated. Results: 121 patients were included in the intent-to-test survival analysis (90% were stage III/IV, median age: 65 y): 60 (61) in the experimental (standard) arms with equivalent baseline characteristics. Median follow-up was 9 months. Median overall survival in months was 19 (11.8), p=0.0014 (n  =  121; HR  =  0.33; 95 % CI, 0.16-0.67) and the PS at the first relapse was 0-1 for 81.5% (35.3%) of the patients (p<0.001) in the experimental (standard) arm. Conclusions: This trial shows a significant survival improvement using Web-application-guided follow-up that allowed better PS at relapse, earlier supportive care and reduction of routine imaging. QOL and cost analysis results will be presented during the meeting. Clinical trial information: NCT02361099.

Immune Biomarkers of BRONJ in High-Risk Cancer Patients

2012 ◽  
Vol 70 (9) ◽  
pp. e37
Author(s):  
A. Leung ◽  
P. Lee ◽  
A. Kiss ◽  
S. Choyee ◽  
J. Uyanne ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Immune Biomarkers ◽  
High Risk Cancer
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