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Genome sequencing is providing physicians with more data about the causes of cancer and changing the way some forms of the disease are treated.

Genome sequencing is providing physicians with more data about the causes of cancer and changing the way some forms of the disease are treated.

Cancer traps, artificial intelligence and other highlights from clinical trials and laboratory studies.

Cancer traps, artificial intelligence and other highlights from clinical trials and laboratory studies.

A study of cancer-associated mutations in normal endometrial glands of the uterus has now been performed using whole-genome sequencing. The analysis sheds light on the early changes that lead to invasive disease.

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.

Chronic inflammation is accompanied by recurring cycles of tissue destruction and repair and is associated with an increased risk of cancer(1-3). However, how such cycles affect the clonal composition of tissues, particularly in terms of cancer development, remains unknown. Here we show that in patients with ulcerative colitis, the inflamed intestine undergoes widespread remodelling by pervasive clones, many of which are positively selected by acquiring mutations that commonly involve the NFKBIZ, TRAF3IP2, ZC3H12A, PIGR and HNRNPF genes and are implicated in the downregulation of IL-17 and other pro-inflammatory signals. Mutational profiles vary substantially between colitis-associated cancer and non-dysplastic tissues in ulcerative colitis, which indicates that there are distinct mechanisms of positive selection in both tissues. In particular, mutations in NFKBIZ are highly prevalent in the epithelium of patients with ulcerative colitis but rarely found in both sporadic and colitis-associated cancer, indicating that NFKBIZ-mutant cells are selected against during colorectal carcinogenesis. In further support of this negative selection, we found that tumour formation was significantly attenuated in Nfkbiz-mutant mice and cell competition was compromised by disruption of NFKBIZ in human colorectal cancer cells. Our results highlight common and discrete mechanisms of clonal selection in inflammatory tissues, which reveal unexpected cancer vulnerabilities that could potentially be exploited for therapeutics in colorectal cancer.