Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy

Sun, Yi; Zhang, Xiang; Jin, Chuandi; Yue, Kaile; Sheng, Dashuang; Zhang, Tao; Dou, Xuelin; Liu, Jing; Jing, Hongbiao; Zhang, Lei; Yue, Jinbo

doi:10.1186/s12967-023-04054-1

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Preoperative neoadjuvant chemoradiotherapy was an important part of rectal cancer treatment ( 28 ), and many previous studies had reported prediction models for the response of rectal cancer to preoperative neoadjuvant therapy ( 18 , 29 , 30 ). However, cases of progression after preoperative neoadjuvant therapy could not be ignored.…”

Section: Disscussionmentioning

confidence: 99%

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Hu,

Sheng,

et al. 2024

Front. Oncol.

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BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.

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Section: Disscussionmentioning

confidence: 99%

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Hu,

Sheng,

et al. 2024

Front. Oncol.

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Novel radiation and targeted therapy combinations for improving rectal cancer outcomes

Pennel,

Dutton,

Melissourgou-Syka

et al. 2024

Expert Rev. Mol. Med.

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Neoadjuvant radiotherapy (RT) is commonly used as standard treatment for rectal cancer. However, response rates are variable and survival outcomes remain poor, highlighting the need to develop new therapeutic strategies. Research is focused on identifying novel methods for sensitising rectal tumours to RT to enhance responses and improve patient outcomes. This can be achieved through harnessing tumour promoting effects of radiation or preventing development of radio-resistance in cancer cells. Many of the approaches being investigated involve targeting the recently published new dimensions of cancer hallmarks. This review article will discuss key radiation and targeted therapy combination strategies being investigated in the rectal cancer setting, with a focus on exploitation of mechanisms which target the hallmarks of cancer.

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Methylglyoxal from gut microbes boosts radiosensitivity and radioimmunotherapy in rectal cancer by triggering endoplasmic reticulum stress and cGAS-STING activation

Zhou,

Wang,

Lin

et al. 2023

J Immunother Cancer

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BackgroundPreoperative radiation therapy (preRT) is a fundamental aspect of neoadjuvant treatment for rectal cancer (RC), but the response to this treatment remains unsatisfactory. The combination of radiation therapy (RT) and immunotherapy (iRT) presents a promising approach to cancer treatment, though the underlying mechanisms are not yet fully understood. The gut microbiota may influence the response to RT and immunotherapy. Therefore, we aimed to identify the metabolism of gut microbiota to reverse radioresistance and enhance the efficacy of iRT.MethodsFecal and serum samples were prospectively collected from patients with locally advanced rectal cancer (LARC) who had undergone pre-RT treatment. Candidate gut microbiome-derived metabolites linked with radiosensitization were screened using 16s rRNA gene sequencing and ultrahigh-performance liquid chromatography-mass coupled with mass spectrometry. In vitro and in vivo studies were conducted to assess the radiosensitizing effects of the metabolites including the syngeneic CT26 tumor model and HCT116 xenograft tumor model, transcriptomics and immunofluorescence. The CT26 abscopal effect modeling was employed to evaluate the combined effects of metabolites on iRT.ResultsWe initially discovered the gut microbiota-associated metabolite, methylglyoxal (MG), which accurately predicts the response to preRT (Area Under Curve (AUC) value of 0.856) among patients with LARC. Subsequently, we observed that MG amplifies the RT response in RC by stimulating intracellular reactive oxygen species (ROS) and reducing hypoxia in the tumor in vitro and in vivo. Additionally, our study demonstrated that MG amplifies the RT-induced activation of the cyclic guanosine monophosphate AMP synthase-stimulator of interferon genes pathway by elevating DNA double-strand breaks. Moreover, it facilitates immunogenic cell death generated by ROS-mediated endoplasmic reticulum stress, consequently leading to an increase in CD8+T and natural killer cells infiltrated in the tumor immune microenvironment. Lastly, we discovered that the combination of anti-programmed cell death protein 1 (anti-PD1) therapy produced long-lasting complete responses in all irradiated tumor sites and half of the non-irradiated ones.ConclusionsOur research indicates that MG shows promise as a radiosensitizer and immunomodulator for RC. Furthermore, we propose that combining MG with iRT has great potential for clinical practice.

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Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy

Cited by 5 publications

References 41 publications

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Novel radiation and targeted therapy combinations for improving rectal cancer outcomes

Methylglyoxal from gut microbes boosts radiosensitivity and radioimmunotherapy in rectal cancer by triggering endoplasmic reticulum stress and cGAS-STING activation

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