TY  - JOUR
A1  - Emons, Georg
A1  - Auslander, Noam
A1  - Jo, Peter
A1  - Kitz, Julia
A1  - Azizian, Azadeh
A1  - Hu, Yue
A1  - Hess, Clemens F.
A1  - Rödel, Claus
A1  - Sax, Ulrich
A1  - Salinas-Riester, Gabriela
A1  - Ströbel, Philipp
A1  - Kramer, Frank
A1  - Beissbarth, Tim
A1  - Ghadimi, Michael
A1  - Ruppin, Eytan
A1  - Ried, Thomas
A1  - Gaedcke, Jochen Werner Christian
A1  - Grade, Marian
T1  - Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy
T2  - British journal of cancer
N2  - 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 of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.
KW  - Genetics research
KW  - Predictive markers
KW  - Radiotherapy
KW  - Surgical oncology
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/63259
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-632598
SN  - 1532-1827
N1  - Gene-expression data were deposited to Gene Expression Omnibus (GSE87211).
N1  - This work was supported by the Deutsche Forschungsgemeinschaft (Klinische Forschergruppe 179) and the Intramural Research Program of the National Institutes of Health, National Cancer Institute. Open Access funding enabled and organized by Projekt DEAL.
VL  - 127
IS  - 4
SP  - 766
EP  - 775
PB  - Nature Publ. Group
CY  - Edinburgh
ER  -