Ping Lan

Studies of the human immune system have been limited by the lack of an appropriate in vivo model. For this reason, efforts have been made to develop murine models with a functional human immune system. We report here that cotransplantation of human fetal thymus/liver tissues and CD34(+) hematopoietic stem/progenitor cells led to the development of sustained human hematopoiesis and a functional human immune system in immunodeficient NOD/SCID mice. The humanized mice showed systemic repopulation with a comprehensive array of human lymphohematopoietic cells, including T cells, B cells, and dendritic cells, and the formation of secondary lymphoid organs. Furthermore, these mice produce high levels of human IgM and IgG antibodies and mediate strong immune responses in vivo as demonstrated by skin xenograft rejection. Thus, the humanized NOD/SCID mice described in this paper provide a powerful model system to study human immune function.

Metastasis is a well-known poor prognostic factor in cancer. However, the mechanisms how long non-coding RNAs (lncRNAs) regulate metastasis in colorectal cancer (CRC) remain largely unknown. Besides, tumor-associated macrophages (TAMs) play an important role in tumor progression, yet the contribution of lncRNA-mediated crosstalk between TAMs and CRC cells to tumor progression is not well understood. In this study, we report that lncRNA RPPH1 was significantly upregulated in CRC tissues, and the RPPH1 overexpression was associated with advanced TNM stages and poor prognosis. RPPH1 was found to promote CRC metastasis in vitro and in vivo. Mechanistically, RPPH1 induced epithelial-mesenchymal transition (EMT) of CRC cells via interacting with β-III tubulin (TUBB3) to prevent its ubiquitination. Furthermore, CRC cell-derived exosomes transported RPPH1 into macrophages which mediate macrophage M2 polarization, thereby in turn promoting metastasis and proliferation of CRC cells. In addition, exosomal RPPH1 levels in blood plasma turned out to be higher in treatment-naive CRC patients but lower after tumor resection. Compared to CEA and CA199, exosomal RPPH1 in CRC plasma displayed a better diagnostic value (AUC = 0.86). Collectively, RPPH1 serves as a potential therapeutic and diagnostic target in CRC.

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. METHODS: In 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). FINDINGS: Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001). INTERPRETATION: RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer. FUNDING: National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.