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. 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.
Project description:Samples were prospectively collected during colonoscopic examination from 46 rectal cancer patients before starting preoperative chemoradiotherapy. The expression profiles were determined using Affymetrix Human Genome U133 Plus 2.0 arrays. Comparison between the sample groups allow to identify a set of discriminating genes that can be used for prediction of the response to radiotherapy in rectal cancer. Specimens from 46 rectal cancer patients who approved to receive preoperative chemoradiotherapy were studied. We prospectively collected biopsy specimens during colonoscopic examination from rectal cancer before starting preoperative chemoradiotherapy. Specimens from tumors were snap-frozen in liquid nitrogen and stored at -80 C until use. Paralleled tumor specimens were formalin fixed and paraffin embedded for histologic examination and other specimens were used for RNA extraction. RNA was extracted from tumor tissue using frozen samples. The patients provided written, informed consent to the collection of specimens, and the local Ethics Committee approved the study protocol. All patients received a total dose of 50.4 Gy of radiation, UFT(300-500mg/day) and LV (75mg/day) and underwent standardized curative resection, following an interval of 4 weeks after chemoradiotherapy.
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.
Project description:The identification of surrogate single nucleotide polymorphism (SNP) markers that can predict responses to preoperative chemoradiotherapy (CRT) in rectal cancer patients. Genome-wide association studies in clinical populations are theoretically capable of identifying markers that are capable of tumor regression after CRT. We used Affymetrix’s SNP Array 6.0 to detail genetic polymorphism of patient’s group showing differential responsiveness to preoperative CRT and profiled SNP biomarkers.
Project description:Samples were prospectively collected during colonoscopic examination from 46 rectal cancer patients before starting preoperative chemoradiotherapy. The expression profiles were determined using Affymetrix Human Genome U133 Plus 2.0 arrays. Comparison between the sample groups allow to identify a set of discriminating genes that can be used for prediction of the response to radiotherapy in rectal cancer.
Project description:Emerging evidence suggests that an increased density of pre-treatment CD8+ tumor-infiltrating lymphocytes (TILs) is associated with good response to chemoradiotherapy (CRT) in patients with locally advanced rectal cancer. However, the significance of T-cell complexity in the clinical setting remains unknown. High-throughput T-cell receptor (TCR) β sequencing was applied to quantify the TCR repertoire of pre-treatment biopsies from 67 patients with advanced rectal cancer receiving preoperative CRT. Changes in TCR repertoire before and after CRT were also analysed in 23 patients.
Project description:The identification of surrogate single nucleotide polymorphism (SNP) markers that can predict responses to preoperative chemoradiotherapy (CRT) in rectal cancer patients. Genome-wide association studies in clinical populations are theoretically capable of identifying markers that are capable of tumor regression after CRT. We used Affymetrix’s SNP Array 6.0 to detail genetic polymorphism of patient’s group showing differential responsiveness to preoperative CRT and profiled SNP biomarkers. The chemoradiosensitivity of tumor tissue from the initial cohort of 43 patients was assessed using clinical responses of tumor regression grade (TRG). TRG was clinically categorized as complete response (CR) as TRG 1, dominant response (ER or finally as DR) as TRG 1 and 2, and efficient response (RYN or finally as ER) as TRG 1, 2, and 3 (TRG grade from Mandard et al, 1994). Blood DNAs were prepared from each patients and hybridized to Affymetrix’s SNP Array 6.0. Genotypes were determined using the Affymetrix Genotyping Console software (version 2.1) based on the BRLMM-P algorithm. We used an ANOVA test to identify SNPs associated with quantitative TRG responses.
Project description:The identification of surrogate methylation markers that can predict responses to preoperative chemoradiotherapy (CRT) in rectal cancer patients. Genome-wide association studies in clinical populations are theoretically capable of identifying markers that are capable of tumor regression after CRT. We used Infinium® Methylation Assay to detail methylation status of patient’s group showing differential responsiveness to preoperative CRT and profiled SNP biomarkers. The chemoradiosensitivity of tumor tissue from the initial cohort of 45 patients was assessed using clinical responses of tumor regression grade (TRG). TRG was clinically categorized as complete response (CR) as TRG 1, dominant response (ER or finally as DR) as TRG 1 and 2, and efficient response (RYN or finally as ER) as TRG 1, 2, and 3 (TRG grade from Mandard et al, 1994).
Project description:Background: Neoadjuvant radiotherapy (neo-RT) is widely used in locally advanced rectal cancer (LARC) as a component of radical treatment. Despite the advantages of neo-RT, which typically improves outcomes in LARC patients, the lack of reliable biomarkers that predict response and monitor the efficacy of therapy, can result in the application of unnecessary aggressive therapy affecting patients’ quality of life. Hence, the search for molecular biomarkers for assessing the radio responsiveness of this cancer represents a relevant issue. Methods: Here, we combined proteomic and metabolomic approaches to identify molecular signatures, which could discriminate LARC tumors with good and poor responses to neo-RT. Results: The integration of data on differentially accumulated proteins and metabolites made it possible to identify disrupted metabolic pathways and signaling processes connected with response to irradiation, including ketone bodies synthesis and degradation, purine metabolism, energy metabolism, degradation of fatty acid, amino acid metabolism, and focal adhesion. Moreover, we proposed multi-component panels of proteins and metabolites which could serve as a solid base to develop biomarkers for monitoring and predicting the efficacy of preoperative RT in rectal cancer patients. Conclusions: We proved that an integrated multi-omic approach presents a valid look at the analysis of the global response to cancer treatment from the perspective of metabolomic reprogramming.