ArticleBritish journal of cancer2022
Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy.
Article in British journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Watch-and-Wait strategy for locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a comprehensive review.Frontiers in oncology · 2026Pooled it
- Cytotoxic immune activity reflects a coordinated immune microenvironment and is associated with response to total neoadjuvant therapy in rectal cancer.Journal of gastroenterology · 2026Article
- Genomic, transcriptomic, and molecular predictors of response to neoadjuvant therapy in locally advanced rectal cancer: a narrative review.Medical oncology (Northwood, London, England) · 2026Review
- Targeting metabolic dependencies to reverse chemoradiotherapy resistance in colorectal cancer.Journal of experimental & clinical cancer research : CR · 2026Article
- IFI44L modulates chemoradiotherapy sensitivity and CD8Frontiers in oncology · 2026Article
- Pan-ERBB Inhibitors Synergize With KRAS Inhibitors in Rectal Cancer.United European gastroenterology journal · 2025Article
- Apoptosis-inducing factor (AIF) at the crossroad of cell survival and cell death: implications for cancer and mitochondrial diseases.Cell communication and signaling : CCS · 2025Review
- Future direction of total neoadjuvant therapy for locally advanced rectal cancer.Nature reviews. Gastroenterology & hepatology · 2024Review
- Review
- Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer.Heliyon · 2023Article
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Authors and funding
18 authors.
Funding
Abstract
purposePreoperative (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
designWe 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.
resultsA 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).
conclusionThe 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".
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