scholarly journals A Prognostic Model for Colon Cancer Patients Based on Eight Signature Autophagy Genes

2020 ◽  
Vol 8 ◽  
Author(s):  
Jiasheng Xu ◽  
Siqi Dai ◽  
Ying Yuan ◽  
Qian Xiao ◽  
Kefeng Ding
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Differentially Expressed Genes ◽  
Immune Cells ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Training Set ◽  
Autophagy Gene

ObjectiveTo screen key autophagy genes in colon cancer and construct an autophagy gene model to predict the prognosis of patients with colon cancer.MethodsThe colon cancer data from the TCGA were downloaded as the training set, data chip of GSE17536 as the validation set. The differential genes of the training set were obtained and were analyzed for enrichment and protein network. Acquire autophagy genes from Human Autophagy Database www.autophagy.lu/project.html. Autophagy genes in differentially expressed genes were extracted using R-packages limma. Using LASSO/Cox regression analysis combined with clinical information to construct the autophagy gene risk scoring model and divide the samples into high and low risk groups according to the risk value. The Nomogram assessment model was used to predict patient outcomes. CIBERSORT was used to calculate the infiltration of immune cells in the samples and study the relationship between high and low risk groups and immune checkpoints.ResultsNine hundred seventy-six differentially expressed genes were screened from training set, including five hundred sixty-eight up-regulated genes and four hundred eight down regulated genes. These differentially expressed genes were mainly involved: the regulation of membrane potential, neuroactive ligand-receptor interaction. We identified eight autophagy genes CTSD, ULK3, CDKN2A, NRG1, ATG4B, ULK1, DAPK1, and SERPINA1 as key prognostic genes and constructed the model after extracting the differential autophagy genes in the training set. Survival analysis showed significant differences in sample survival time after grouping according to the model. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with colon cancer in the 1, 3, 5 years. In the high-risk group, the infiltration degrees of nine types of immune cells are different and the samples can be well distinguished according to these nine types of immune cells. Immunological checkpoint correlation results showed that the expression levels of CTLA4, IDO1, LAG3, PDL1, and TIGIT increased in high-risk groups.ConclusionThe prognosis prediction model based on autophagy gene has a good evaluation effect on the prognosis of colon cancer patients. Eight key autophagy genes can be used as prognostic markers for colon cancer.

Integrated Transcriptomic Analysis of Necrosis-related Gene in Diffuse Gliomas

2019 ◽  
Vol 80 (04) ◽  
pp. 240-249
Author(s):  
Jiajia Wang ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Prognostic Index ◽  
Gene Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Low Grade

Glioblastoma multiforme (GBM), an aggressive brain tumor, is characterized histologically by the presence of a necrotic center surrounded by so-called pseudopalisading cells. Pseudopalisading necrosis has long been used as a prognostic feature. However, the underlying molecular mechanism regulating the progression of GBMs remains unclear. We hypothesized that the gene expression profiles of individual cancers, specifically necrosis-related genes, would provide objective information that would allow for the creation of a prognostic index. Gene expression profiles of necrotic and nonnecrotic areas were obtained from the Ivy Glioblastoma Atlas Project (IVY GAP) database to explore the differentially expressed genes.A robust signature of seven genes was identified as a predictor for glioblastoma and low-grade glioma (GBM/LGG) in patients from The Cancer Genome Atlas (TCGA) cohort. This set of genes was able to stratify GBM/LGG and GBM patients into high-risk and low-risk groups in the training set as well as the validation set. The TCGA, Repository for Molecular Brain Neoplasia Data (Rembrandt), and GSE16011 databases were then used to validate the expression level of these seven genes in GBMs and LGGs. Finally, the differentially expressed genes (DEGs) in the high-risk and low-risk groups were subjected to gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway, and gene set enrichment analyses, and they revealed that these DEGs were associated with immune and inflammatory responses. In conclusion, our study identified a novel seven-gene signature that may guide the prognostic prediction and development of therapeutic applications.

scholarly journals Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Ruoyi Shen ◽  
Anqi Wang ◽  
Jian Zhao ◽  
Jieqi Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Differentially Expressed Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: 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 aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: 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’s correlation analysis between 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 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 IRLs signature in predicting the one-, two-, and three-year survival rates were 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. Conclusions: 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 An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

2020 ◽  
Author(s):  
Bin Wu ◽  
Yi Yao ◽  
Yi Dong ◽  
Si Qi Yang ◽  
Deng Jing Zhou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Score ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Gene Set Enrichment ◽  
Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

scholarly journals A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dakui Luo ◽  
Zezhi Shan ◽  
Qi Liu ◽  
Sanjun Cai ◽  
Qingguo Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Metabolic Signature ◽  
Metabolic Genes ◽  
Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

Development and validation of a robust prognostic and predictive signature for colorectal cancer (CRC) patients

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 4036-4036
Author(s):  
A. M. Glas ◽  
P. Roepman ◽  
R. Salazar ◽  
G. Capella ◽  
V. Moreno ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Gene Signature ◽  
Low Risk ◽  
Stage Ii ◽  
Significant Difference ◽  
Independent Cohort

4036 Background: Between 25 and 35% of stage II CRC patients will experience a recurrence of their disease and may benefit from adjuvant chemotherapy. Official guidelines give suggestions but no clear recommendation for best risk stratification. Here we describe the development a robust signature that predicts disease relapse and can assist in treatment decisions. Methods: Fresh frozen tumor tissues from 180 patients with stage I, II and III colorectal cancer undergoing surgery were analyzed using high density Agilent 44K oligonucleotide arrays. Median FU was 70.2 months; 85% of patients did not receive adjuvant chemotherapy. Unsupervised hierarchical clustering based on full-genome gene expression measurement indicated the existence of 3 main colon molecular subclasses. Survival analysis of the 3 classes showed that subtype C (n= 27) had a poor outcome and subtype A (n= 48) good outcome. Only the intermediate group B (n=104) was used to develop a signature by using a cross validation procedure to score all genes for their association with 5-yr distant metastasis free survival (DMFS) and subsequently applied to all samples (n=180). The obtained gene signature was further validated on an independent cohort of 178 stage II + III colon samples. Results: A set of 38 prognosis related gene probes showed robust DMFS association in over 50% of all iterations in the Training Set of 180 samples. The gene signature was validated on an independent cohort of 178 samples from stage II + III colon cancer patients. The profile classified 61% of the validation samples as low-risk and 39% as high-risk. The low- and high-risk samples showed a significant difference in DMFS with a HR of 3.19 (P= 8.5e-4). Five-year DMFS rates were 89% (95%CI 83–95) for low-risk and 62% (95%CI 50–77) for high-risk samples. Moreover, the profile showed a significant performance within stage II (P=0.0058) and III (P=0.036) only samples. The performance of the profile was significant for both untreated (P=0.0082) and treated patients (P=0.016) suggesting that its power is independent of treatment benefits. Conclusions: ColoPrint is able to predict the prognosis of stage II and III colon cancer patients and facilitates the identification of patients who would benefit from adjuvant chemotherapy. [Table: see text]

Impact of the EndoPredict-clin score on risk stratification in ER-positive, HER2-negative breast cancer after considering clinical guidelines.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 542-542
Author(s):  
Martin Filipits ◽  
Peter Christian Dubsky ◽  
Margaretha Rudas ◽  
Jan C. Brase ◽  
Ralf Kronenwett ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Clinical Guidelines ◽  
Risk Groups ◽  
Low Risk ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Er Positive

542 Background: Many ER-positive, HER2-negative breast cancer patients are treated by adjuvant chemotherapy according to current clinical guidelines. We retrospectively assessed whether the combined gene expression/ clinicopathological EndoPredict-clin (EPclin) score improved the accuracy of risk classification in addition to considering clinical guidelines. Methods: Three clinical breast cancer guidelines (National Comprehensive Cancer Center Network (NCCN), German S3 and St. Gallen 2011), and the EPclin score - assessed by quantitative RT-PCR in formalin-fixed paraffin-embedded tissue - were used to assign risk groups in 1,702 ER-positive, HER2-negative breast cancer patients from two randomized phase III trials (Austrian Breast and Colorectal Cancer Study Group 6 and 8) treated with endocrine therapy only. Results: Although all analyzed clinical guidelines identified a low-risk group with improved metastasis-free survival, the overwhelming majority of all patients (81-94%) were classified as intermediate / high risk. In contrast to that, the EPclin classified only 37% of all patients as high risk and that stratification resulted in the best separation between low and high risk groups (p < 0.001, HR = 5.11 (3.48-7.51). Consequently, the majority of all patients deemed intermediate / high risk by the clinical guidelines was re-classified as low risk by the EPclin score. Kaplan Meier analyses demonstrated that the re-classified subgroups (47 to 57% of all patients) had an excellent 10-year metastasis-free survival of 95% comparable to the clinical assigned low-risk groups although encompassing a higher proportion of the trial patients. Conclusions: The EPclin score predicted distant recurrence more accurately than all three clinical guidelines and is especially useful to reclassify patients considered as intermediate / high risk by the guidelines. The data suggests that the EPclin score provides clinically useful prognostic information beyond common clinical guidelines and can be used to accurately identify the clinically relevant group of patients who are adequately and sufficiently treated with adjuvant endocrine therapy alone.

Validation of a genomic classifier (ColoPrint) for predicting outcome in the T3-MSS subgroup of stage II colon cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3510-3510 ◽  
Author(s):  
Ramon Salazar ◽  
Josep Tabernero ◽  
Victor Moreno ◽  
Ulrich Nitsche ◽  
Thomas Bachleitner-Hofmann ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Cancer Patients ◽  
Lower Risk ◽  
Low Risk ◽  
Stage Ii ◽  
Disease Relapse ◽  
Risk Of Disease

3510 Background: Adjuvant therapy for stage II patients is recommended for patients with high risk features, especially with T4 tumors. Adjuvant therapy is not indicated for patients with MSI-H status who are considered of being at low risk of disease relapse. However, this leaves the majority of patients with an undetermined risk. ColoPrint is an 18-gene expression classifier that identifies early-stage colon cancer patients at higher risk of disease relapse. Methods: ColoPrint was developed using whole genome expression data and was validated in public datasets (n=322) and independent patient cohorts from 5 European hospitals. Tissue specimen, clinical parameters, MSI-status and follow-up data (median follow-up 70 months) for patients were available and the ColoPrint index was determined using validated diagnostic arrays. Uni-and multivariate analysis was performed on the pooled stage II patient set (n=320) and the subset of patients who were T3/ MSS (n=227). Results: In the analysis of all stage II patients, ColoPrint classified two-third of stage II patients as being at lower risk. The 3-year Relapse-Free-Survial (RFS) RFS was 91% for Low Risk and 74% for patients at higher risk with a HR of 2.9 (p=0.001). Clinicopathological parameters from the ASCO recommendations (T4, perforation, <12 LN assessed, and/ or high grade) or NCCN guidelines (ASCO factors plus angio-lymphatic invasion) did not predict a differential outcome for high risk patients (p< 0.20). In the subgroup of patients with T3 and MSS phenotype, ColoPrint classified 61% of patients at lower risk with a 3-year RFS of 91% (86-96%) and 39% of patients at higher risk with a 3-year RFS of 73% (63-83%) (p=0.002). No clinical parameter was significantly prognostic in this subgroup. Conclusions: ColoPrint combined with established clinicopathological factors and MSI, significantly improves prognostic accuracy, thereby facilitating the identification of patients at higher risk who might be considered for additional treatment.

Genomic classifiers (ColoPrint/MSI-Print) predict outcome and chemotherapy benefit in stage II and III colon cancer patients.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 378-378 ◽  
Author(s):  
Scott Kopetz ◽  
Zhi-Qin Jiang ◽  
Michael J. Overman ◽  
Christa Dreezen ◽  
Sun Tian ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Additional Treatment ◽  
Low Risk ◽  
Stage Ii ◽  
High Risk Patients ◽  
Risk Patients

378 Background: Although the benefit of chemotherapy in stage II and III colon cancer patients is significant, many patients might not need adjuvant chemotherapy because they have a good prognosis even without additional treatment. ColoPrint is a gene expression classifier that distinguish patients with low or high risk of disease relapse. It was developed using whole genome expression data and has been validated in public datasets, independent European patient cohorts and technical studies (Salazar 2011 JCO, Maak 2012 Ann Surg). Methods: In this study, the commercial ColoPrint test was validated in stage II (n=96) and III patients (n=95) treated at the MD Anderson Cancer Center from 2003 to 2009. Frozen tissue specimen, clinical parameters, MSI-status and follow-up data (median follow-up 64 months) were available. The 64-gene MSI-signature developed to identify patients with deficient mismatch repair system (Tian 2012 J Path) was evaluated for its accuracy to identify MSI patients and also for prognosis. Results: In this cohort, ColoPrint classified 56% of stage II and III patients as being at low risk. The 3-year Relapse-Free-Survival (RFS) was 90.6% for Low Risk and 78.4% for High Risk patients with a HR of 2.33 (p=0.025). In uni-and multivariate analysis ColoPrint and stage were the only significant factors to predict outcome. The MSI-signature classified 47 patients (24.6%) as MSI-H and most MSI-H patients were ColoPrint low risk (81%). Patients who were ColoPrint low risk and MSI-H by signature had the best outcome with a 3-year RFS of 95% while patients with ColoPrint high risk had a worse outcome independently of the MSI-status. Low risk ColoPrint patients had a good outcome independent of stage or chemotherapy treatment (90.1% 3-year RFS for treated patients, 91.4% for untreated patients) while ColoPrint high risk patients treated with adjuvant chemotherapy had 3-year RFS of 84%, compared to 70.1% 3-year RFS in untreated patients (p=0.026). Conclusions: The combination of ColoPrint and MSI-Print improves the prognostic accuracy in stage II and stage III patients and may help the identification of patients at higher risk who are more likely to benefit from additional treatment

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