资源环境科技发展态势分析平台

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

Acute myeloid leukaemia (AML) is a heterogeneous disease characterized by transcriptional dysregulation that results in a block in differentiation and increased malignant self-renewal. Various epigenetic therapies aimed at reversing these hallmarks of AML have progressed into clinical trials, but most show only modest efficacy owing to an inability to effectively eradicate leukaemia stem cells (LSCs)(1). Here, to specifically identify novel dependencies in LSCs, we screened a bespoke library of small hairpin RNAs that target chromatin regulators in a unique ex vivo mouse model of LSCs. We identify the MYST acetyltransferase HBO1 (also known as KAT7 or MYST2) and several known members of the HBO1 protein complex as critical regulators of LSC maintenance. Using CRISPR domain screening and quantitative mass spectrometry, we identified the histone acetyltransferase domain of HBO1 as being essential in the acetylation of histone H3 at K14. H3 acetylated at K14 (H3K14ac) facilitates the processivity of RNA polymerase II to maintain the high expression of key genes (including Hoxa9 and Hoxa10) that help to sustain the functional properties of LSCs. To leverage this dependency therapeutically, we developed a highly potent small-molecule inhibitor of HBO1 and demonstrate its mode of activity as a competitive analogue of acetyl-CoA. Inhibition of HBO1 phenocopied our genetic data and showed efficacy in a broad range of human cell lines and primary AML cells from patients. These biological, structural and chemical insights into a therapeutic target in AML will enable the clinical translation of these findings.

A programme of structure-assisted drug design and high-throughput screening identifies six compounds that inhibit the main protease of SARS-CoV-2, demonstrating the ability of this strategy to isolate drug leads with clinical potential.

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019-2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)(1-4). Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (M-pro) of SARS-CoV-2: M-pro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2(5,6). We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of M-pro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds-including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds-as inhibitors of M-pro. Six of these compounds inhibited M-pro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 mu M. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available.