Background: COVID-19 has spread rapidly around the world, affecting a large percentage of the population. When lifting certain mandatory measures for an economic restart, robust surveillance must be established and implemented, with nucleic acid detection for SARS-CoV-2 as an essential component. Methods: We tried to develop a one-tube detection platform based on RT-RPA (Reverse Transcription and Recombinase Polymerase Isothermal Amplification) and DNA Endonuclease-Targeted CRISPR Trans Reporter (DETECTR) technology, termed OR-DETECTR, to detect SARS-CoV-2. We designed RT-RPA primers of the RdRp and N genes following the SARS-CoV-2 gene sequence. We optimized reaction components so that the detection process could be carried out in one tube. Specificity was demonstrated by detecting nucleic acid samples from pseudoviruses from seven human coronaviruses and Influenza A (H1N1). Clinical samples were used to validate the platform and all results were compared to rRT-PCR. RNA standards and pseudoviruses diluted by different gradients were used to demonstrate the detection limit. Additionally, we have developed a lateral flow assay based on OR-DETECTR for detecting COVID-19. Results: The OR-DETECTR detection process can be completed in one tube, which takes approximately 50 min. This method can specifically detect SARS-CoV-2 from seven human coronaviruses and Influenza A (H1N1), with a low detection limit of 2.5 copies/µl input (RNA standard) and 1 copy/µl input (pseudovirus). Results of six samples from SARS-CoV-2 patients, eight samples from patients with fever but no SARS-CoV-2 infection, and one mixed sample from 40 negative controls showed that OR-DETECTR is 100% consistent with rRT-PCR. The lateral flow assay based on OR-DETECTR can be used for the detection of COVID-19, and the detection limit is 2.5 copies/µl input. Conclusions: The OR-DETECTR platform for the detection of COVID-19 is rapid, accurate, tube closed, easy-to-operate, and free of large instruments.

Lung cancer is an extremely heterogeneous disease, and its treatment remains one of the most challenging tasks in medicine. Few existing laboratory lung cancer models can faithfully recapitulate the diversity of the disease and predict therapy response. Here, we establish 12 patient-derived organoids from the most common lung cancer subtype, lung adenocarcinoma (LADC). Extensive gene and histopathology profiling show that the tumor organoids retain the histological architectures, genomic landscapes, and gene expression profiles of their parental tumors. Patient-derived lung cancer organoids are amenable for biomarker identification and high-throughput drug screening in vitro. This study should enable the generation of patient-derived lung cancer organoid lines, which can be used to further the understanding of lung cancer pathophysiology and to assess drug response in personalized medicine.

Background: Kidney cancer is one of the most common solid tumors. The advancement of human kidney cancer research and treatment has been hindered by a lack of research models that faithfully recapitulate the diversity of the disease.Methods: We established an effective three‐dimensional culture system for generating kidney cancer organoids from clinical renal cell carcinoma samples. Renal cell carcinoma (RCC) organoids were characterized by H&E staining, immunofluorescence, whole‐exome sequencing, RNA sequencing and single‐cell RNA sequencing. The use of RCC organoids in personalized cancer therapy was assessed by testing their responses to treatment drugs and chimeric antigen receptor T cells.Results: Using this organoid culture system, 33 kidney cancer organoid lines from common kidney cancer subtypes, including clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (chRCC), were generated. RCC organoids preserved the histological architectures, mutational landscapes, and transcriptional profile of the parental tumor tissues. Single‐cell RNA‐sequencing revealed inter‐ and intra‐tumoral heterogeneity in RCC organoids. RCC organoids allowed for in vitro drug screening and provided a tool for assessing the efficacy of chimeric antigen receptor T cells.Conclusions: Patient‐derived RCC organoids are valuable pre‐clinical models for academic research and personalized medicine.

Based on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.

Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions. We present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson correlation coefficient between predicted and measured binding affinities by up to 23 percentage points. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide–MHC class I interactions. ACME is available as an open source software at https://github.com/HYsxe/ACME . Supplementary data are available at Bioinformatics online.