scholarly journals Peer Review #2 of "Prognostic value of KRAS mutation status in colorectal cancer patients: a population-based competing risk analysis (v0.1)"

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9149
Author(s):  
Dongjun Dai ◽  
Yanmei Wang ◽  
Liyuan Zhu ◽  
Hongchuan Jin ◽  
Xian Wang

Background To use competing analyses to estimate the prognostic value of KRAS mutation status in colorectal cancer (CRC) patients and to build nomogram for CRC patients who had KRAS testing. Method The cohort was selected from the Surveillance, Epidemiology, and End Results database. Cumulative incidence function model and multivariate Fine-Gray regression for proportional hazards modeling of the subdistribution hazard (SH) model were used to estimate the prognosis. An SH model based nomogram was built after a variable selection process. The validation of the nomogram was conducted by discrimination and calibration with 1,000 bootstraps. Results We included 8,983 CRC patients who had KRAS testing. SH model found that KRAS mutant patients had worse CSS than KRAS wild type patients in overall cohort (HR = 1.10 (95% CI [1.04–1.17]), p < 0.05), and in subgroups that comprised stage III CRC (HR = 1.28 (95% CI [1.09–1.49]), p < 0.05) and stage IV CRC (HR = 1.14 (95% CI [1.06–1.23]), p < 0.05), left side colon cancer (HR = 1.28 (95% CI [1.15–1.42]), p < 0.05) and rectal cancer (HR = 1.23 (95% CI [1.07–1.43]), p < 0.05). We built the SH model based nomogram, which showed good accuracy by internal validation of discrimination and calibration. Calibration curves represented good agreement between the nomogram predicted CRC caused death and actual observed CRC caused death. The time dependent area under the curve of receiver operating characteristic curves (AUC) was over 0.75 for the nomogram. Conclusion This is the first population based competing risk study on the association between KRAS mutation status and the CRC prognosis. The mutation of KRAS indicated a poor prognosis of CRC patients. The current competing risk nomogram would help physicians to predict cancer specific death of CRC patients who had KRAS testing.


Neoplasma ◽  
2009 ◽  
Vol 56 (3) ◽  
pp. 275-278 ◽  
Author(s):  
K. Zavodna ◽  
M. Konecny ◽  
T. Krivulcik ◽  
S. Spanik ◽  
R. Behulova ◽  
...  

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e14164-e14164
Author(s):  
Dan Rhodes ◽  
Sean Eddy ◽  
Paul Williams ◽  
Mark Tomilo ◽  
Seth Sadis ◽  
...  

e14164 Background: While KRAS mutation predicts resistance to anti-EGFR therapy in colorectal cancer, not all KRAS wild-type patients benefit from such therapy, suggesting that complementary biomarkers capable of identifying additional non-responsive patients would have clinical utility. Methods: To search for such a biomarker, we studied the relationship of cetuximab response with twelve gene expression modules, derived from an unsupervised analysis of 20 independent microarray datasets comprising more than 2,000 colorectal cancer patients. Each module represents a set of highly co-expressed genes related to an important aspect of colorectal cancer variability. Two cetuximab-treated cohorts were studied. The first was a Phase II clinical trial (Khambata-Ford et al, J Clin Oncol, 2007) with accompanying microarray data from pre-treatment biopsies. The second was a single-institution study of cetuximab response from which formalin-fixed paraffin-embedded primary tumor specimens were available. Results: In the first study, module scores were computed by averaging co-expressed module genes in the microarray data. In the second study, module scores were generated from a qPCR gene expression module test, OncoScore Colon, which quantifies modules by averaging three representative module genes relative to housekeeping controls. Notably, in both studies, the mesenchymal module was significantly associated with cetuximab resistance, with module positive patients tending to progress on cetuximab within 10 weeks. Additionally, the status of this module was independent of KRAS mutation status—KRAS mutations occurred in both module-positive and -negative patients. Future clinical studies will continue to test the predictive capacity of the module in regards to cetuximab resistance and other mechanisms. Conclusions: In summary, this study demonstrates the value of a gene expression module-based qPCR panel for stratifying colorectal cancer patients for treatment response, and suggests that our approach may have immediate utility for cetuximab treatment response prediction.


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