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Abstract G-quadruplex (G4) is a noncanonical DNA secondary structure known to induce DNA damage and regulate the expression of immune-related genes. We aim to exploit the G4 folding as a treatment strategy to trigger anti-tumor immune response. In this study, we observe that the abundant genomic G4 in epithelial cells coexists with increased infiltration of CD8+ T cells in colorectal cancer tissue. Furthermore, our data substantiate the inhibitory effect of the G4 ligand TMPyP4 on cancer progression while concurrently stimulating anti-tumor immunity. Mechanistically, TMPyP4 impedes cancer cell proliferation and induces G2/M cell cycle arrest. Additionally, in vivo experiments demonstrate that TMPyP4 enhances the anti-tumor immune response by triggering DNA damage and activating the cGAS-STING pathway, which fosters CD8+ T cell activation and dendritic cell maturation. Importantly, the combined treatment of TMPyP4 and anti-PD1 exhibits a synergistic therapeutic effect on colorectal cancer. In summary, our findings underscore the potential of the G4 ligand TMPyP4 as a dual strategy to target colorectal cancer: inhibiting cancer progression and augmenting anti-tumor immunity through the activation of cGAS-STING pathway.

Abstract Background The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. Methods We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). Results The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. Conclusions Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Abstract Background The incidence of colorectal cancer is increasing among young adults and more rectal cancers are reported. This study aimed to identify the clinical features specific for early‐onset rectal cancer and provide insights on cancer management. Methods Early‐onset (

Abstract Background N6‐methyladenosine (m6A) modification is an emerging epigenetic regulatory mechanism in tumourigenesis. Considering that AlkB homolog 5 (ALKBH5) is a well‐described m6A demethylase in previous enzyme assays, we aimed to investigate the role of m6A methylation alteration conferred by disturbed ALKBH5 in colorectal cancer (CRC) development. Methods Expression of ALKBH5 and its correlation with clinicopathological characteristics of CRC were evaluated using the prospectively maintained institutional database. The molecular role and underlying mechanism of ALKBH5 in CRC were explored using in vitro and in vivo experiments with methylated RNA immunoprecipitation sequencing (MeRIP‐seq), RNA‐seq, MeRIP‐qPCR, RIP‐qPCR and luciferase reporter assays. Results ALKBH5 expression was significantly upregulated in CRC tissues compared to the paired adjacent normal tissues, and higher expression of ALKBH5 was independently associated with worse overall survival in CRC patients. Functionally, ALKBH5 promoted the proliferative, migrative and invasive abilities of CRC cells in vitro and enhanced subcutaneous tumour growth in vivo. Mechanistically, RAB5A was identified as the downstream target of ALKBH5 in CRC development, and ALKBH5 posttranscriptionally activated RAB5A by m6A demethylation, which impeded the YTHDF2‐mediated degradation of RAB5A mRNA. In addition, we demonstrated that dysregulation of the ALKBH5‐RAB5A axis could affect the tumourigenicity of CRC. Conclusions ALKBH5 facilitates the progression of CRC by augmenting the expression of RAB5A via an m6A‐YTHDF2‐dependent manner. Our findings suggested that ALKBH5‐RAB5A axis might serve as valuable biomarkers and effective therapeutic targets for CRC.

Abstract Objective We conducted this multicenter cohort study to evaluate the current tumor‐node‐metastasis staging system and treatment modality by analyzing the survival outcomes of patient groups with stage III and IV colon cancer. Patients and Methods Stage III and IV colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database (SEER cohort) and prospectively maintained Sun Yat‐sen University (SYSU) cohort were included in this study. Kaplan‐Meier method was used to estimate the cumulative rate of overall survival (OS) between patient groups, and the inverse probability weighting method was used to calculated age and sex‐adjusted survival curves. The Cox regression model was used to identify the risk factors for OS. Results A total of 17,911 and 1135 stage III–IV cases were included in the SEER and SYSU cohorts, respectively. Among them, 1448 and 124 resectable stage IV cases underwent curative‐intent treatment in the SEER and SYSU cohorts, respectively. The T4N2b group showed a significantly worse survival outcome compared with the M1a subset receiving curative‐intent treatment (HR, 1.46; p

Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treatment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg achieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained DeepInteg could be readily applied in clinic to predict pathological complete response after neoadjuvant therapy in rectal cancer patients.

Abstract Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.