Relationship between stromal regulatory T cells and the response to neoadjuvant chemotherapy for locally advanced rectal cancer

Surgical stress induced tumor immune suppressive environment

Despite significant advances in cancer treatment over the decades, surgical resection remains a prominent management approach for solid neoplasms. Unfortunately, accumulating evidence suggests that surgical stress caused by tumor resection may potentially trigger postoperative metastatic niche formation. Surgical stress not only activates the sympathetic-adrenomedullary axis and hypothalamic-pituitary-adrenocortical axis but also induces hypoxia and hypercoagulable state. These adverse factors can negatively impact the immune system by downregulating immune effector cells and upregulating immune suppressor cells, which contribute to the colonization and progression of postoperative tumor metastatic niche. This review summarizes the effects of surgical stress on four types of immune effector cells (neutrophils, macrophages, natural killer cells and cytotoxic T lymphocytes) and two types of immunosuppressive cells (regulatory T cells and myeloid-derived suppressor cells), and discusses the immune mechanisms of postoperative tumor relapse and progression. Additionally, relevant therapeutic strategies to minimize the pro-tumorigenic effects of surgical stress are elucidated.

Digitally assessed lymphocyte infiltration in rectal cancer biopsies is associated with pathological response to neoadjuvant therapy

A frequently used treatment strategy in locally advanced rectal cancer (RC) is neoadjuvant therapy followed by surgery. Patients treated with neoadjuvant therapy achieve varying pathological response, and currently, predicting the degree of response is challenging. This study examined the association between digitally assessed histopathological features in the diagnostic biopsies and pathological response to neoadjuvant therapy, aiming to find potential predictive biomarkers. 50 patients with RC treated with neoadjuvant chemotherapy and/or radiotherapy followed by surgery were included. Deep learning-based digital algorithms were used to assess the epithelium tumor area percentage (ETP) based on H&E-stained slides, and to quantify the density of CD3+ and CD8+ lymphocytes, as well as the CD8+/CD3+ lymphocyte percentage, based on immunohistochemically stained slides, from the diagnostic tumor biopsies. Pathological response was assessed according to the Mandard method. A good pathological response was defined as tumor regression grade (TRG) 1-2, and a complete pathological response was defined as Mandard TRG 1. Associations between the ETP and lymphocyte densities in the diagnostic biopsies and the pathological response were examined. The density of CD8+ lymphocytes, and the CD8+/CD3+ lymphocyte percentage, were associated with both good and complete response to neoadjuvant therapy, while the density of CD3+ lymphocytes was associated with complete response. The ETP did not correlate with response to neoadjuvant therapy. It is well-known that infiltration of lymphocytes in colorectal cancer is a prognostic biomarker. However, assessment of CD8+ and CD3+ lymphocytes in the diagnostic tumor biopsies of patients with RC may also be useful in predicting response to neoadjuvant therapy.

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

Background

We 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.

Methods

Data 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.

Results

Eventually, 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.

Conclusions

We 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.