scholarly journals Peer Review #2 of "Establishment of a 12-gene expression signature to predict colon cancer prognosis (v0.1)"

Author(s):  
P Dunne
2005 ◽  
Vol 65 (20) ◽  
pp. 9200-9205 ◽  
Author(s):  
Craig P. Giacomini ◽  
Suet Yi Leung ◽  
Xin Chen ◽  
Siu Tsan Yuen ◽  
Young H. Kim ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Pačínková ◽  
Vlad Popovici

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.


Aging ◽  
2019 ◽  
Vol 11 (19) ◽  
pp. 8710-8727 ◽  
Author(s):  
Haojie Yang ◽  
Hua Liu ◽  
Hong-Cheng Lin ◽  
Dan Gan ◽  
Wei Jin ◽  
...  

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4942 ◽  
Author(s):  
Dalong Sun ◽  
Jing Chen ◽  
Longzi Liu ◽  
Guangxi Zhao ◽  
Pingping Dong ◽  
...  

A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets:GSE39582includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics;GSE17538is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rankp= 0.0034; overall survival (OS): KM Log Rankp= 0.0336) inGSE17538. For patients with proficient mismatch repair system (pMMR) inGSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rankp= 0.005; Relapse free survival (RFS): KM Log Rankp= 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher’s exact testp= 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients inGSE17538from good prognosis within stage II (Log Rankp = 0.01) and stage II & III (Log Rankp= 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rankp= 0.046; III & II, OS: KM Log Rankp= 0.041). Among stage II/III pMMR patients with lower 12-gene scores inGSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rankp= 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rankp= 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21117-21117
Author(s):  
D. Tobin ◽  
T. Lindahl ◽  
N. Hagen ◽  
K. Bårdsen ◽  
M. Jensen ◽  
...  

21117 Background: Existing methods to detect breast cancer (BC) in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. We recently demonstrated the potential use of gene expression profiling in peripheral blood cells (PBC) for early detection of BC (1) and repeated this with a larger study using the Agilent platform with an accuracy of 75± 7%. Objective: 2 studies are presented that investigate: i) whether effective normalization of experimental conditions can improve diagnostic accuracy, ii) whether a blood based signature developed for BC can discriminate other forms of cancer, and iii) whether an expression signature developed using stage 0 patients can be used to predict BC in stage I disease, and vice versa. Material and Methods: Study I enrolled 60 females with BC and 60 healthy females. Study II enrolled 20 females with early stage BC (10 stage 0 and 10 stage I), 20 healthy females, and 8 females with colon cancer. Gene expression analysis was conducted using the ABI HGSM v2.0 with 32,878 oligo probes. Expression data were analyzed by PLSR for model building and results validated using cross-validation and test set validation. Results: Effective normalization of the data led to improved diagnostic performance. The signature developed using 20 BC and 20 non-BC samples classified 7/8 colon cancer patients as non-BC. The signature developed using stage 0 vs non-BC detected cancer in stage I patients, and the signature developed for stage 1 detected cancer in stage 0 patients. Conclusion: A blood-based gene expression signature can be developed for early stage breast cancer, which is specific and able to distinguish between other forms of malignancy such as colon cancer. The gene expression pattern is systemically affected in early stage BC patients in which there is typically no direct contact of blood cells with cancer cells. References: 1. Sharma P et al. (2005) Breast Cancer Res. 7 (5): R 634–44 2. Aaroe J et al (2006), poster no:125, 97th AACR Annual Meeting, Washington DC, USA Some RT-PCR analyses were performed by Marion Hirt IMGM Laboratories, Martinsried, Germany. [Table: see text]


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