Yuma Wada1, Mitsuo Shimada2, Yuji Morine2, Tetsuya Ikemoto2, Yu Saito2, Hideo Baba3, Masaki Mori4, Ajay Goel5. 1. Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope Comprehensive Cancer Center, Duarte, CA, USA; Department of Surgery, Tokushima University, Tokushima, Japan; Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX, USA. 2. Department of Surgery, Tokushima University, Tokushima, Japan. 3. Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan. 4. Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 5. Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope Comprehensive Cancer Center, Duarte, CA, USA; Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX, USA. Electronic address: ajgoel@coh.org.
Abstract
BACKGROUND: Cancer recurrence is an important predictor of survival outcomes in patients with colorectal cancer-associated liver metastasis (CRLM), who undergo radical hepatectomy. Therefore, identification of patients with the greatest risk of recurrence is critical for developing a precision oncology strategy that might include frequent surveillance (in low-risk patients) or a more aggressive treatment approach (in high-risk patients). We performed genome-wide expression profiling, to identify and develop a transcriptomic signature for predicting recurrence in patients with CRLM. METHODS: We analysed a total of 383 patients with CRLM, including 63 patients from a publicly available data set (the NCBI's Gene Expression Omnibus with accession number GSE81423). and 320 patients from whom surgical specimens were collected for independent training (n = 169) and validation (n = 151) of identified biomarkers. Using Cox's proportional hazard regression analysis, we evaluated the clinical significance of the identified gene signature by comparing its performance with several key clinical factors. RESULTS: We identified a six-gene panel that robustly categorised patients with recurrence in the discovery (area under the curve (AUC) = 0.90). We showed that the panel was a significant predictor of recurrence in the clinical training (AUC = 0.83) and validation cohorts (AUC = 0.81). By combining our panel with key clinical factors, we established a risk-stratification model that emerged as an independent predictor of recurrence (AUC = 0.85; univariate: hazard ratio (HR) = 4.34, 95% confidence interval (CI) = 2.71-6.93, P < 0.001; multivariate: HR = 3.40, 95% CI = 1.76-6.56, P < 0.001). The stratification model revealed recurrence prediction in 89% of high-risk group and non-recurrence in 62% of low-risk group. CONCLUSIONS: We established a novel transcriptomic signature that robustly predicts recurrence, which has significant implications for the management of patients with CRLM.
BACKGROUND: Cancer recurrence is an important predictor of survival outcomes in patients with colorectal cancer-associated liver metastasis (CRLM), who undergo radical hepatectomy. Therefore, identification of patients with the greatest risk of recurrence is critical for developing a precision oncology strategy that might include frequent surveillance (in low-risk patients) or a more aggressive treatment approach (in high-risk patients). We performed genome-wide expression profiling, to identify and develop a transcriptomic signature for predicting recurrence in patients with CRLM. METHODS: We analysed a total of 383 patients with CRLM, including 63 patients from a publicly available data set (the NCBI's Gene Expression Omnibus with accession number GSE81423). and 320 patients from whom surgical specimens were collected for independent training (n = 169) and validation (n = 151) of identified biomarkers. Using Cox's proportional hazard regression analysis, we evaluated the clinical significance of the identified gene signature by comparing its performance with several key clinical factors. RESULTS: We identified a six-gene panel that robustly categorised patients with recurrence in the discovery (area under the curve (AUC) = 0.90). We showed that the panel was a significant predictor of recurrence in the clinical training (AUC = 0.83) and validation cohorts (AUC = 0.81). By combining our panel with key clinical factors, we established a risk-stratification model that emerged as an independent predictor of recurrence (AUC = 0.85; univariate: hazard ratio (HR) = 4.34, 95% confidence interval (CI) = 2.71-6.93, P < 0.001; multivariate: HR = 3.40, 95% CI = 1.76-6.56, P < 0.001). The stratification model revealed recurrence prediction in 89% of high-risk group and non-recurrence in 62% of low-risk group. CONCLUSIONS: We established a novel transcriptomic signature that robustly predicts recurrence, which has significant implications for the management of patients with CRLM.
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Authors: Luai R Zarour; Sudarshan Anand; Kevin G Billingsley; William H Bisson; Andrea Cercek; Michael F Clarke; Lisa M Coussens; Charles E Gast; Cristina B Geltzeiler; Lissi Hansen; Katherine A Kelley; Charles D Lopez; Shushan R Rana; Rebecca Ruhl; V Liana Tsikitis; Gina M Vaccaro; Melissa H Wong; Skye C Mayo Journal: Cell Mol Gastroenterol Hepatol Date: 2017-01-20