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Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.

ABSTRACT: Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1-2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control.

SUBMITTER: Liu Z

PROVIDER: S-EPMC7452897 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.

Liu Zhenyu Z Meng Xiaochun X Zhang Hongmei H Li Zhenhui Z Liu Jiangang J Sun Kai K Meng Yankai Y Dai Weixing W Xie Peiyi P Ding Yingying Y Wang Meiyun M Cai Guoxiang G Tian Jie J

Nature communications 20200827 1

Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates ...[more]

PMID: 32855399

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Project description:BackgroundLARC patients commonly receive adjuvant therapy, however, hidden micrometastases still limit the improvement of OS. This study aims to investigate the impact of VASN in rectal cancer with pulmonary metastasis and understand the underlying molecular mechanisms to guide adjuvant chemotherapy selection.MethodsSequencing data from rectal cancer patients with pulmonary metastasis from Sun Yat-sen University Cancer Center (SYSUCC) and publicly available data were meticulously analyzed. The functional role of VASN in pulmonary metastasis was validated in vivo and in vitro. Coimmunoprecipitation (co-IP), immunofluorescence, and rescue experiments were conducted to unravel potential molecular mechanisms of VASN. Moreover, VASN expression levels in tumor samples were examined and analyzed for their correlations with pulmonary metastasis status, tumor stage, adjuvant chemotherapy benefit, and survival outcome.ResultsOur study revealed a significant association between high VASN expression and pulmonary metastasis in LARC patients. Experiments in vitro and in vivo demonstrated that VASN could promote the cell proliferation, metastasis, and drug resistance of colorectal cancer. Mechanistically, VASN interacts with the NOTCH1 protein, leading to concurrent activation of the NOTCH and MAPK pathways. Clinically, pulmonary metastasis and advanced tumor stage were observed in 90% of VASN-positive patients and 53.5% of VASN-high patients, respectively, and VASN-high patients had a lower five-year survival rate than VASN-low patients (26.7% vs. 83.7%). Moreover, the Cox analysis and OS analysis indicated that VASN was an independent prognostic factor for OS (HR = 7.4, P value < 0.001) and a predictor of adjuvant therapy efficacy in rectal cancer.ConclusionsOur study highlights the role of VASN in decreasing drug sensitivity and activating the NOTCH and MAPK pathways, which leads to tumorigenesis and pulmonary metastasis. Both experimental and clinical data support that rectal cancer patients with VASN overexpression detected in biopsies have a higher risk of pulmonary metastasis and adjuvant chemotherapy resistance.

Project description:Background Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential. Method The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer. Result The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer. Conclusion The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation

Project description:PurposeRadiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC).Material and methods200 NSCLC patients of histological adenocarcinoma were included in this study. The investigated lung tissues were tumor rim, defined to be 3mm of tumor and parenchymal tissue on either side of the tumor border and the exterior region extended from 3 to 9mm outside of the tumor. Fifteen stable radiomic features were extracted and evaluated from each of these regions on pre-treatment CT images. For comparison, features from expert-delineated tumor contours were similarly prepared. The patient cohort was separated into training and validation datasets for prognostic power evaluation. Both univariable and multivariable analyses were performed for each region using concordance index (CI).ResultsUnivariable analysis reveals that six out of fifteen tumor rim features were significantly prognostic of DM (p-value < 0.05), as were ten features from the visible tumor, and only one of the exterior features was. Multivariablely, a rim radiomic signature achieved the highest prognostic performance in the independent validation sub-cohort (CI = 0.64, p-value = 2.4×10-5) significantly over a multivariable clinical model (CI = 0.53), a visible tumor radiomics model (CI = 0.59), or an exterior tissue model (CI = 0.55). Furthermore, patient stratification by the combined rim signature and clinical predictor led to a significant improvement on the clinical predictor alone and also outperformed stratification using the combined tumor signature and clinical predictor.ConclusionsWe identified peritumoral rim radiomic features significantly associated with DM. This study demonstrated that peritumoral imaging characteristics may provide additional valuable information over the visible tumor features for patient risk stratification due to cancer metastasis.

Project description:Introduction: Neoadjuvant chemoradiotherapy (nCRT) was the foundation treatment for locally advanced rectal cancer (LARC). The nCRT can improve the efficacy of immunotherapy on account of its in-situ vaccine effect. Objective: The aim is to identify stable and reliable transcriptome signatures to predict the efficacy of immune checkpoint blockade (ICB) in patients with LARC. Methods: Immunophenotyping was established by using xCell immune cell infiltration abundance and consistent clustering in GSE39582 and verified in several data sets. The effects of immunophenotyping, follicular regulatory T cells, tumor associated fibroblasts (CAF) and tertiary lymphoid structures (TLS) signatures on the efficacy of ICB were analyzed by using IMvigor210, GSE91061 and an independent Daping Hospital (DPH) cohort. Results: There are four stable and repeatable immune subtypes in rectal cancer, among which C1 is low immune infiltration type, C2 is high interstitial infiltration type, C3 is high immune infiltration type and C4 is ion channel type. C2 is mainly characterized by high infiltration CAF, C3 is characterized by high infiltration of cytotoxic T lymphocytes, high expression of PD-L1 and TLS. In rectal cancer patients receiving nCRT, immunophenotyping was not significantly associated with pathological remission rate, but immunophenotyping was an independent prognostic factor of RFS. In IMvigor210 patients treatment with atezolizumab, the pathological remission rates of C1, C2, C3 and C4 were 23.86%, 10.94%, 33.33% and 23.08% respectively (χ2 = 8.981, P = 0.029), which were 11.76%, 50.00%, 42.86% and 0.0% respectively in the GSE91061 patients treatment with nivolumab (Fisher's exact probability, P = 0.018). Both follicular regulatory T cells and CAF showed a further impact on the ICB therapeutic efficacy of C2 and C3 subtype. In addition, both GSE91404 and DPH cohorts showed that nCRT treatment induced a significant increase in the expression of TNFRSF9 and abundance of macrophages in C3 subtype. Conclusion: Our data suggest that there are four immune types of rectal cancer, which are related to the prognosis of patients. Among them, C3 and some C2 subtypes represents the patients who may benefit from ICB after nCRT treatment.

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Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.

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Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.

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