CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant.

Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference (CRISPRi) screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system. Among the enhancers uncovered, we focused on a long-range enhancer ∼664 kb from the ONECUT1 promoter, as coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hPSCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. This enhancer contains a confidently fine-mapped type 2 diabetes (T2D) associated variant (rs528350911) which disrupts a GATA motif. Introduction of the risk variant into hPSCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a slight reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.

Natural small-molecules reverse Xeroderma Pigmentosum Complementation Group C (XPC) deficient-mediated drug-resistance in renal cell carcinoma.

BACKGROUND: Renal cancer is insensitive to radiotherapy or most chemotherapies. While the loss of the XPC gene was correlated with drug resistance in colon cancer, the expression of XPC and its role in the drug resistance of renal cancer have not yet been elucidated. With the fact that natural small-molecules have been adopted in combinational therapy with classical chemotherapeutic agents to increase the drug sensitivity and reduce adverse effects, the use of herbal compounds to tackle drug-resistance in renal cancer is advocated. PURPOSE: To correlate the role of XPC gene deficiency to drug-resistance in renal cancer, and to identify natural small-molecules that can reverse drug-resistance in renal cancer via up-regulation of XPC. METHODS: IHC was adopted to analyze the XPC expression in human tumor and adjacent tissues. Clinical data extracted from The Cancer Genome Atlas (TCGA) database were further analysed to determine the relationship between XPC gene expression and tumor staging of renal cancer. Two types of XPC-KD renal cancer cell models were established to investigate the drug-resistant phenotype and screen XPC gene enhancers from 134 natural small-molecules derived from herbal plants. Furthermore, the identified XPC enhancers were verified in single or in combination with FDA-approved chemotherapy drugs for reversing drug-resistance in renal cancer using MTT cytotoxicity assay. Drug resistance gene profiling, ROS detection assay, immunocytochemistry and cell live-dead imaging assay were adopted to characterize the XPC-related drug resistant mechanism. RESULTS: XPC gene expression was significantly reduced in renal cancer tissue compared with its adjacent tissue. Clinical analysis of TCGA database also identified the downregulated level of XPC gene in renal tumor tissue of stage IV patients with cancer metastasis, which was also correlated with their lower survival rate. 6 natural small-molecules derived from herbal plants including tectorigenin, pinostilbene, d-pinitol, polygalasaponin F, atractylenolide III and astragaloside II significantly enhanced XPC expression in two renal cancer cell types. Combinational treatment of the identified natural compound with the treatment of FDA-approved drug, further confirmed the up-regulation of XPC gene expression can sensitize the two types of XPC-KD drug-resistant renal cancer cells towards the FDA-approved drugs. Mechanistic study confirmed that GSTP1/ROS axis was activated in drug resistant XPC-KD renal cancer cells. CONCLUSION: XPC gene deficiency was identified in patient renal tumor samples, and knockdown of the XPC gene was correlated with a drug-resistant phenotype in renal cancer cells via activation of the GSTP1/ROS axis. The 6 identified natural small molecules were confirmed to have drug sensitizing effects via upregulation of the XPC gene. Therefore, the identified active natural small molecules may work as an adjuvant therapy for circumventing the drug-resistant phenotype in renal cancer via enhancement of XPC expression.

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING: US National Institutes of Health.

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.

BACKGROUND: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE: Retrospective. POPULATION: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. ASSESSMENT: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. STATISTICAL TESTS: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. RESULTS: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively. DATA CONCLUSION: AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.