Background Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. MethodsIn this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

The rapid recognition of DNA double-strand breaks (DSBs) by the MRE11/RAD50/NBS1 (MRN) complex is critical for the initiation of DNA damage response and DSB end resection. Here, we show that MRN complex interacting protein (MRNIP) forms liquid-like condensates to promote homologous recombination-mediated DSB repair. The intrinsically disordered region is essential for MRNIP condensate formation. Mechanically, the MRN complex is compartmentalized and concentrated into MRNIP condensates in the nucleus. After DSB formation, MRNIP condensates move to the damaged DNA rapidly to accelerate the binding of DSB by the concentrated MRN complex, therefore inducing the autophosphorylation of ATM and subsequent activation of DNA damage response signaling. Meanwhile, MRNIP condensates-enhanced MRN complex loading further promotes DSB end resection. In addition, data from xenograft models and clinical samples confirm a correlation between MRNIP and radioresistance. Together, these results reveal an important role of MRNIP phase separation in DSB response and the MRN complex-mediated DSB end resection.

Patients with locally advanced rectal cancer (LARC) who achieve a pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) typically have a good prognosis. An early and accurate prediction of the treatment response, i.e., whether a patient achieves pCR, could significantly help doctors make tailored plans for LARC patients. This study proposes a pipeline of pCR prediction using a combination of deep learning and radiomics analysis. Taking into consideration missing pre-nCRT magnetic resonance imaging (MRI), as well as aiming to improve the efficiency for clinical application, the pipeline only included a post-nCRT T2-weighted (T2-w) MRI. Unlike other studies that attempted to carefully find the region of interest (ROI) using a pre-nCRT MRI as a reference, we placed the ROI on a “suspicious region”, which is a continuous area that has a high possibility to contain a tumor or fibrosis as assessed by radiologists. A deep segmentation network, termed the two-stage rectum-aware U-Net (tsraU-Net), is designed to segment the ROI to substitute for a time-consuming manual delineation. This is followed by a radiomics analysis model based on the ROI to extract the hidden information and predict the pCR status. The data from a total of 275 patients were collected from two hospitals and partitioned into four datasets: Seg-T (N = 88) for training the tsraUNet, Rad-T (N = 107) for building the radiomics model, In-V (N = 46) for internal validation, and Ex-V (N = 34) for external validation. The proposed method achieved an area under the curve (AUC) of 0.829 (95% confidence interval [CI]: 0.821, 0.837) on In-V and 0.815 (95% CI, 0.801, 0.830) on Ex-V. The performance of the method was considerable and stable in two validation sets, indicating that the well-designed pipeline has the potential to be used in real clinical procedures.

Background For patients with locally advanced rectal cancer (LARC), it is unclear whether neoadjuvant chemoradiotherapy-induced pathologic complete response (pCR) individuals would further benefit from adjuvant chemotherapy (ACT). Methods The pCR individuals who received different ACT cycles were paired by propensity score matching. Overall survival (OS), disease-free survival (DFS), local recurrence-free survival (LRFS), and distant metastasis-free survival (DMFS) were calculated by Kaplan–Meier and log-rank test. Results In total, 1041 pCR individuals were identified from 5567 LARC cases. Specifically, 303 pCR cases had no ACT treatment, and 738 pCR patients received fluoropyrimidine-based ACT (median, 4 cycles) treatment. After 1:3 propensity score matching, 297 cases without ACT treatment were matched to 712 cases who received ACT treatment. Kaplan–Meier analysis showed that pCR individuals treated with or without ACT had the similar 3-year outcome (OS, DFS, LRFS and DMFS) (all P > 0.05). Moreover, the pCR patients received different ACT cycle(s) (0 vs. 1–4 cycles, 0 vs. ≥5 cycles) had comparable 3-year OS, DFS, LRFS and DMFS (all P > 0.05). In stratified analysis, ACT treatment did not improve 3-year survival (OS, DFS, LRFS and DMFS) for the baseline high-risk (cT3–4/cN1–2) subgroup patients (all P > 0.05). Conclusion ACT, which did not improve survival, is unnecessary to neoadjuvant treatment-induced pCR LARC patients. Trial registration 2019ZSLYEC-136 (24-6-2019).

Abstract. The present study aims to investigate the effects and underlying mechanisms of miRNA-145 (miR-145) in rat models of chronic constriction injury (CCI). Rats were randomly divided into control, sham, CCI, agomiRNA (agomiR)-normal control (NC) and agomiR-145 groups (n=25 in each group); in addition, 30 rats with CCI were divided into small hairpin (sh)RNA-NC and shRNA-ras responsive element binding protein 1 (RREB1) groups. Paw withdrawal thermal latency (PWTL) and paw withdrawal mechanical threshold (PWMT) were detected. Reverse transcription-quantitative polymerase chain reaction was used to detect miR-145 expression levels, and western blotting was performed to measure RREB1 and phosphorylated-protein kinase B (p-AKT) expression levels. In addition, a dual luciferase reporter assay was conducted to identify the target gene of miR-145. PWMT and PWTL were decreased in CCI rats and this decrease was alleviated by miR-145 injection. At 1, 3, 5 and 7 days after CCI, miR-145 expression level in the spinal cord tissue of rats in the CCI group was significantly decreased compared with 1 day before CCI (P<0.05). Compared with the CCI group, miR-145 expression level in the agomiR-145 group was significantly higher (P<0.05). In addition, expression levels of RREB1 and p-AKT were significantly increased in the CCI group and significantly decreased in the agomiR-145 group (P<0.05). Furthermore, knockdown of RREB1 expression by shRNA-RREB1 significantly increased values of PWMT and PWTL, decreased expression levels of RREB1 and p-AKT, and increased miR-145 expression levels (P<0.05). Further investigation demonstrated that miR-145 can bind with RREB1 mRNA. In conclusion, miR-145 may be involved in the development of CCI through regulating the expression of RREB1.

Dear Editor, Carcinoembryonic antigen (CEA) is regarded as an important tumor marker for colorectal cancer (CRC). 1,2 The preoperative and postoperative serum CEAs are both associated with the CRC outcome. [3][4][5] However, the dynamic serum CEA changes after surgery is ignored, and the trajectory of perioperative serum CEA has not been well characterized. The link of it with CRC outcome is unknown.We used a latent class growth mixed model to distinguish potential CEA dynamic changing trajectories of CRC patients from preoperative to 36 months after surgery using a retrospective, multicenter longitudinal cohort. Then we examined the association of these trajectories with CRC outcome. 6 A detailed description about the methods can be found in the Supplementary Information.The number of participants assessed for eligibility and the reasons for exclusion appear in Figure 1A. A total of 2160 patients (1295 [60.0%] male; median [interquartile range, IQR], [49-65] years) with a median follow-up time of 43 months (IQR: 32-60 months) were included. The characteristics of cases are outlined in Table 1. In the analysis, 17 836 individual CEA measurements were investigated. The median number of CEA measurements was 8 (range, 4-21) (Figure 1B).We identified three distinct trajectory groups of perioperative CEA, labeled as low-stable (n = 1680, 77.8%), early-rising (n = 291, 13.5%), and later-rising (n = 189, 8.7%) (Figure 2A). In the low-stable group, the CEA remained within normal range (0-5.0 ng/mL) from preoperative to 36 months after surgery. In the early-rising group, CEA declined rapidly from elevated preoperative level (>5.0 ng/mL) toward the normal range within 3 months of surgery, increased rapidly to elevated level (>5.0 ng/mL) (9-20 months after surgery), and decreased toward the normal range (21-36 months after surgery). In the later-rising group, CEA declined rapidly from ele-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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