Genomic predictor of complete response after chemoradiotherapy in rectal cancer.
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ABSTRACT:
SUBMITTER: Noh GT
PROVIDER: S-EPMC5233494 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:With the increasing use of preoperative treatment rather than upfront surgery, it has become evident that the response of rectal carcinoma to standard chemoradiotherapy (CRT) shows a great variety that includes histopathologiocally confirmed complete tumor regression in 10-30% of cases. Adaptive strategies to avoid radical surgery, either by local excision or non-operative management, have been proposed in these highly responsive tumors. A growing number of prospective clinical trials and experiences from large databases, such as the European Registration of Cancer Care (EURECCA) watch-and-wait database, or the recent Oncological Outcome after Clinical Complete Response in Patients with Rectal Cancer (OnCoRe) project, will provide more information on its safety and efficacy, and help to select appropriate patients. Future studies will have to establish appropriate inclusion criteria and optimize CRT regimens in order to maximize the number of patients achieving complete response. Standardized re-staging procedures have to be investigated to improve the prediction of a sustained complete response, and long-term close follow-up with thorough documentation of failure patterns and salvage therapies will have to prove the oncological safety of this approach.
| S-EPMC6754042 | biostudies-literature
Project description:Background and purposePatients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables' ability to predict cCR in patients pre chemoradiotherapy.Materials and methodsUsing the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced.ResultsUsing Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69-0.83) in the training set and 0.68 (95% CI 0.57-0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66-0.80) and 0.62 (95% CI 0.51-0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61-0.75) and 0.66 (95% CI 0.56-0.77) in the training and validation sets.ConclusionThe predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.
| S-EPMC9253904 | biostudies-literature
Project description:ObjectiveTo evaluate the predictive value of the apparent diffusion coefficient (ADC) for pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer.MethodsA total of 265 patients with rectal adenocarcinoma, whole Diffusion-Weighted MRI (DWI-MRI) images, clinically stage II to III (cT3-4 and/or cN+) and treated with NCRT followed by TME were screened. Fifty patients with pCR and another 50 patients without pCR with similar clinical charcacters and treatment regimens were selected for statistical analysis. All the patients' pre-CRT and post-CRT average ADC values were calculated from the coefficient maps created by DWI-MRI and recorded independently. The difference in the ADC values between the pCR and non-pCR was analyzed by the Mann-Whitney U test. The cut-off ADC value of the receiver operating characteristic (ROC) curve with pCR was then established.ResultsThe mean pre- and post-ADC values in all patients, and in pCR patients and non-pCR patients were 0.879±0.06 and 1.383±0.11, 0.859±0.04 and 1.440±0.10, 0.899±0.07 and 1.325±0.09 (×10(-3) mm(2)/s), respectively. The difference between the pre- and post-ADC values in all patients, pCR patients, and non-pCR patients were considered to be statistically significant. The pre-ADC value was significantly lower in the pCR patients than in the non-pCR patients (p = 0.003), whereas the post-ADC values were significantly higher in the pCR patients than in the non-pCR patients. The percentage increase of the ADC value (?ADC%) in the pCR and non-pCR patients were 68% and 48% respectively (p<0.001). The ROC curves of the cut-off value of the pre-CRT patient ADC value was 0.866×10(-3) mm(2)/s. The AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.670 (95% CI 0.563-0.777), 0.600, 0.640, 60%, 60%, and 60%, respectively. The cut-off value of ?ADC% was 58%. The corresponding AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.856 (95% CI 0.783-0.930), 0.800, 0.760, 76.9%, 79.2%, and 78%, respectively.ConclusionsDWI-MRI technology can be efficient for predicting pCR for LARC after NCRT. Although the mean pre-CRT ADC value and the ?ADC% are moderate predictors for pCR, the latter would be more accurate.
| S-EPMC4839695 | biostudies-literature
Project description:Therapeutic strategies for patients with locally advanced rectal cancer (LARC) who are achieving a pathological complete response (pCR) after neoadjuvant radio-chemotherapy (neoCRT) are being increasingly investigated. Recent trials challenge the current standard therapy of total mesorectal excision (TME). For some patients, the treatment strategy of "watch-and-wait" seems a preferable procedure. The key factor in determining individual treatment strategies following neoCRT is the precise evaluation of the tumor response. Contrast-enhanced computer tomography (ceCT) has proven its ability to discriminate benign and malign lesions in multiple cancers. In this study, we retrospectively analyzed the ceCT based density of LARC in 30 patients, undergoing neoCRT followed by TME. We compared the tumors´ pre- and post-neoCRT density and correlated the results to the amount of residual vital tumor cells in the resected tissue. Overall, the density decreased after neoCRT, with the highest decrease in patients achieving pCR. Densitometry demonstrated a specificity of 88% and sensitivity of 68% in predicting pCR. Thus, we claim that ceCT based densitometry is a useful tool in identifying patients with LARC who may benefit from a "watch-and-wait" strategy and suggest further prospective studies.
| S-EPMC8202275 | biostudies-literature
Project description:Standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant chemoradiotherapy (NACRT), followed by surgical resection. However, >70% of patients do not achieve a complete pathological response and have higher rates of relapse and death. There are no validated pre- or on-treatment factors that predict response to NACRT besides tumour stage and size. We characterised the response of 33 LARC patients to NACRT, collected tumour samples from patients prior to, during and after NACRT, and performed whole exome, transcriptome and high-depth targeted sequencing. The pre-treatment LARC genome was not predictive of response to NACRT. However, in line with the increasing recognition of microbial influence in cancer, RNA analysis of pre-treatment tumours suggested a greater abundance of Fusobacteria in intermediate and poor responders. In addition, we investigated tumour heterogeneity and evolution in response to NACRT. While matched pre-treatment, on-treatment and post-treatment tumours revealed minimal genome evolution overall, we identified cases in which microsatellite instability developed or was selected for during NACRT. Recent research has suggested a role for adaptive mutability to targeted therapy in colorectal cancer cells. We provide preliminary evidence of selection for mismatch repair deficiency in response to NACRT. Furthermore, pre-NACRT genomic landscapes do not predict treatment response but pre-NACRT microbiome characteristics may be informative.
| S-EPMC7408989 | biostudies-literature
Project description: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 relevanceForgoing 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".
| S-EPMC9381580 | biostudies-literature
Project description:Objective This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images. Background nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty. Methods 842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning. Results The proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively). Conclusions The DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.
| S-EPMC9214314 | biostudies-literature
Project description:BackgroundNeoadjuvant chemoradiotherapy is the treatment of choice in advanced rectal cancer, even though there are many patients who will not benefit from it. There are still no effective methods for predicting which patients will respond or not. The present study aimed to define the genomic profile of rectal tumors and to identify alterations that are predictive of response in order to optimize therapeutic strategies.MethodsForty-eight candidates for neoadjuvant chemoradiotherapy were recruited and their pretherapy biopsies analyzed by array Comparative Genomic Hybridization (aCGH). Pathologic response was evaluated by tumor regression grade.ResultsBoth Hidden Markov Model and Smoothing approaches identified similar alterations, with a prevalence of DNA gains. Non responsive patients had a different alteration profile from responsive ones, with a higher number of genome changes mainly located on 2q21, 3q29, 7p22-21, 7q21, 7q36, 8q23-24, 10p14-13, 13q12, 13q31-34, 16p13, 17p13-12 and 18q23 chromosomal regions.ConclusionsThis exploratory study suggests that an in depth characterization of chromosomal alterations by aCGH would provide useful predictive information on response to neoadjuvant chemoradiotherapy and could help to optimize therapy in rectal cancer patients.The data discussed in this study are available on the NCBI Gene Expression Omnibus [GEO: GSE25885].
| S-EPMC3236016 | biostudies-literature
Project description:BackgroundFoxp3+ regulatory T cells (Tregs) play a vital role in preventing autoimmunity, but also suppress antitumour immune responses. Tumour infiltration by Tregs has strong prognostic significance in colorectal cancer, and accumulating evidence suggests that chemotherapy and radiotherapy efficacy has an immune-mediated component. Whether Tregs play an inhibitory role in chemoradiotherapy (CRT) response in rectal cancer remains unknown.MethodsFoxp3+, CD3+, CD4+, CD8+ and IL-17+ cell density in post-CRT surgical samples from 128 patients with rectal cancer was assessed by immunohistochemistry. The relationship between T-cell subset densities and clinical outcome (tumour regression and survival) was evaluated.ResultsStromal Foxp3+ cell density was strongly associated with tumour regression grade (P=0.0006). A low stromal Foxp3+ cell density was observed in 84% of patients who had a pathologic complete response (pCR) compared with 41% of patients who did not (OR: 7.56, P=0.0005; OR: 5.27, P=0.006 after adjustment for presurgery clinical factors). Low stromal Foxp3+ cell density was also associated with improved recurrence-free survival (HR: 0.46, P=0.03), although not independent of tumour regression grade.ConclusionsRegulatory T cells in the tumour microenvironment may inhibit response to neoadjuvant CRT and may represent a therapeutic target in rectal cancer.
| S-EPMC4702002 | biostudies-literature
Project description:Background: Patients with rectal cancer who achieve pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) may have a better prognosis and may be eligible for non-operative management. The aim of this research was to identify variables for predicting pCR in rectal cancer patients after nCRT and to define clinical risk factors for poor outcome after pCR to nCRT and radical resection in rectal cancer patients. Methods: A retrospective review was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2013. Non-metastatic rectal cancer patients who received radical resection after neoadjuvant chemoradiotherapy were included in this study. Multivariate analysis of the association between clinicopathological characteristics and pCR was performed, and a logistic regression model was used to identify independent predictors for pCR. A nomogram based on the multivariate logistics regression was built with decision curve analyses to evaluate the clinical usefulness. Results: A total of 6,555 patients were included in this study. The proportion of patients with pCR was 20.5% (n = 1,342). The nomogram based on multivariate logistic regression analysis showed that clinical T4 and N2 stages were the most significant independent clinical predictors for not achieving pCR, followed by mucinous adenocarcinoma and positive pre-treatment serum CEA results. The 3-year overall survival rate was 92.4% for those with pCR and 88.2% for those without pCR. Among all the pCR patients, mucinous adenocarcinoma patients had the worst survival, with a 3-year overall survival rate of 67.5%, whereas patients with common adenocarcinoma had an overall survival rate of 93.8% (P < 0.001). Univariate and multivariate analyses showed that histology and clinical N2 stage were independent risk factors. Conclusion: Mucinous adenocarcinoma, positive pre-treatment serum CEA results, and clinical T4 and N2 stages may impart difficulty for patients to achieve pCR. Mucinous adenocarcinoma and clinical N2 stage might be indicative of a prognostically unfavorable biological tumor profile with a greater propensity for local or distant recurrence and decreased survival.
| S-EPMC6585388 | biostudies-literature