1. Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy
- Author
-
Emons, Georg, Auslander, Noam, Jo, Peter, Kitz, Julia, Azizian, Azadeh, Hu, Yue, Hess, Clemens F., Rödel, Claus, Sax, Ulrich, Salinas-Riester, Gabriela, Ströbel, Philipp, Kramer, Frank, Beissbarth, Tim, Ghadimi, Michael, Ruppin, Eytan, Ried, Thomas, Gaedcke, Jochen Werner Christian, Grade, Marian, Emons, Georg, Auslander, Noam, Jo, Peter, Kitz, Julia, Azizian, Azadeh, Hu, Yue, Hess, Clemens F., Rödel, Claus, Sax, Ulrich, Salinas-Riester, Gabriela, Ströbel, Philipp, Kramer, Frank, Beissbarth, Tim, Ghadimi, Michael, Ruppin, Eytan, Ried, Thomas, Gaedcke, Jochen Werner Christian, and Grade, Marian more...
- Abstract
Purpose: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging. Experimental design: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial. Results: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76). Conclusion: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy. Translational relevance: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles o more...
- Published
- 2022