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Predicting multi-class responses to preoperative chemoradiotherapy in rectal patients

ABSTRACT: The treatment strategy of rectal cancer has substantially changed in recent decades. Historically postoperative chemoradiotherapy (CRT) was considered to be the first-line therapy for stage II and III rectal cancers. However, the preoperative CRT is now considered to be the optimal therapy regimen for locally advanced rectal ancer due to its improved local control, reduced toxicity, and increased rate of sphincter preservation. Our study established a clinically practical multi-class prediction model by adopting a novel strategy that applies two separate prediction models (MI and TO predictor) sequentially to a patient to predict the response. For promising clinical practice, we validated our model in a published dataset, which is completely independent dataset from ours. This study suggests a clinically plausible prediction model that correctly infers the preoperative CRT response of patients with high accuracy based on 163 gene signatures we identified. Total RNAs were isolated from primary rectal tumor tissues of 69 patients who underwent chemoradiation therapy (CRT). These patients are classified into four different CRT responses: minimal response (MI), moderate response (MO), near total response (NT) and total response (TO). All the RNAs were subjected to microarray analysis using Affymetrix GenChip arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Jungsoo Gim

PROVIDER: E-GEOD-46862 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Predicting multi-class responses to preoperative chemoradiotherapy in rectal patients

Project description:The treatment strategy of rectal cancer has substantially changed in recent decades. Historically postoperative chemoradiotherapy (CRT) was considered to be the first-line therapy for stage II and III rectal cancers. However, the preoperative CRT is now considered to be the optimal therapy regimen for locally advanced rectal ancer due to its improved local control, reduced toxicity, and increased rate of sphincter preservation. Our study established a clinically practical multi-class prediction model by adopting a novel strategy that applies two separate prediction models (MI and TO predictor) sequentially to a patient to predict the response. For promising clinical practice, we validated our model in a published dataset, which is completely independent dataset from ours. This study suggests a clinically plausible prediction model that correctly infers the preoperative CRT response of patients with high accuracy based on 163 gene signatures we identified.

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