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Tumour heterogeneity and a long-standing paucity of effective therapies other than chemotherapy have contributed to triple-negative breast cancer (TNBC) being the subtype with the least favourable outcomes. In the past few years, advances in omics technologies have shed light on the relevance of the TNBC microenvironment heterogeneity, unveiling a close dynamic relationship with cancer cell features. An improved understanding of tumour–immune system co-evolution supports the need to adopt a more comprehensive view of TNBC as an ecosystem that encompasses the intrinsic and extrinsic features of cancer cells. This new appreciation of the biology of TNBC has already led to the development of novel targeted agents, including PARP inhibitors, antibody–drug conjugates and immune-checkpoint inhibitors, which are revolutionizing the therapeutic landscape and providing new opportunities both for patients with early-stage TNBC and for those with advanced-stage disease. The current therapeutic scenario is only the tip of the iceberg, as hundreds of new compounds and combinations are in development. The translation of these experimental therapies into clinical benefit is a welcome and ongoing challenge. In this Review, we describe the current and upcoming therapeutic landscape of TNBC and discuss how an integrated view of the TNBC ecosystem can define different levels of risk and provide improved opportunities for tailoring treatment.In the past few years, advances in omics technologies have led to a better understanding of the heterogeneity of triple-negative breast cancers (TNBCs) and their microenvironment, supporting a view of this breast cancer subtype as an ecosystem that encompasses the intrinsic and extrinsic features of cancer cells. The authors of this Review describe the current and upcoming therapeutic landscape of TNBC and discuss how an integrated view of the TNBC ecosystem can provide improved opportunities for tailoring treatment.

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.Prognostication of outcome across multiple cancers and prediction of response to various treatment modalities are among the next generation of challenges that artificial intelligence (AI) tools can solve using radiology images. The authors of this Perspective describe the evolution of AI-based approaches in oncology imaging and address the path to their adoption as decision-support tools in the clinic.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754–0.846 and integrated Brier scores of 0.153–0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1–5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals. A deep learning algorithm shows promising performance in predicting progression to diabetic retinopathy in patients, up to 5 years in advance, potentially providing support for medical treatment decisions and indications for personalized screening frequency in a real-world cohort.

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic. Causal machine learning methods could be used to predict treatment outcomes for subgroups and even individual patients; this Perspective outlines the potential benefits and limitations of the approach, offering practical guidance for appropriate clinical use.

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation. The authors report a systematic analyses of blood biomarkers for metabolism against the whole spectrum of diseases in 100,000 individuals and reveals a prominent role of numerous metabolic biomarkers as risk markers beyond heart disease and diabetes.

Over the past decade, various liquid biopsy techniques have emerged as viable alternatives to the analysis of traditional tissue biopsy samples. Such surrogate ‘biopsies’ offer numerous advantages, including the relative ease of obtaining serial samples and overcoming the issues of interpreting one or more small tissue samples that might not reflect the entire tumour burden. To date, the majority of research in the area of liquid biopsies has focused on blood-based biomarkers, predominantly using plasma-derived circulating tumour DNA (ctDNA). However, ctDNA can also be obtained from various non-blood sources and these might offer unique advantages over plasma ctDNA. In this Review, we discuss advances in the analysis of ctDNA from non-blood sources, focusing on urine, cerebrospinal fluid, and pleural or peritoneal fluid, but also consider other sources of ctDNA. We discuss how these alternative sources can have a distinct yet complementary role to that of blood ctDNA analysis and consider various technical aspects of non-blood ctDNA assay development. We also reflect on the settings in which non-blood ctDNA can offer distinct advantages over plasma ctDNA and explore some of the challenges associated with translating these alternative assays from academia into clinical use.Advances in circulating tumour DNA (ctDNA) detection and analysis are beginning to be implemented in clinical practice. Nonetheless, much of this development has thus far focused on plasma ctDNA. Theoretically, all bodily fluids, including urine, cerebrospinal fluid, saliva, pleural fluid and others, can also contain measurable ctDNA and can provide several advantages over the reliance on plasma ctDNA. In this Review, Tivey et al. describe the potential roles of ctDNA obtained from non-plasma sources in optimizing the outcomes of patients with cancer.

The transcriptional upregulation of oncogenes is a driving force behind the progression of many tumours. However, until a decade ago, the concept of ‘switching off’ these oncogenic pathways represented a formidable challenge. Research has revealed that members of the bromo- and extra-terminal domain (BET) motif family are key activators of oncogenic networks in a spectrum of cancers; their function depends on their recruitment to chromatin through two bromodomains (BD1 and BD2). The advent of potent inhibitors of BET proteins (BETi), which target either one or both bromodomains, represents an important step towards the goal of suppressing oncogenic networks within tumours. Here, we discuss the biology of BET proteins, advances in BETi design and highlight potential biomarkers predicting their activity. We also outline the logic of incorporating BETi into combination therapies to enhance its efficacy. We suggest that understanding mechanisms of activity, defining predictive biomarkers and identifying potent synergies represents a roadmap for clinical success using BETi.

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour–immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.Immune-checkpoint inhibitors (ICIs) and other immunotherapies have revolutionized the treatment of patients with cancer. Nonetheless, most patients do not derive durable benefit, indicating a need for biomarkers to guide treatment selection. In this Review, the authors describe the role of antigen presentation in response to ICIs and other immunotherapies, with a focus on the role of molecular and/or genomic alterations affecting antigen presentation.

An unresolved question for the understanding of Alzheimer’s disease (AD) pathophysiology is why a significant percentage of amyloid-β (Aβ)-positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, clinical deterioration. In vitro evidence suggests that reactive astrocytes unleash Aβ effects in pathological tau phosphorylation. Here, in a biomarker study across three cohorts ( n  = 1,016), we tested whether astrocyte reactivity modulates the association of Aβ with tau phosphorylation in CU individuals. We found that Aβ was associated with increased plasma phosphorylated tau only in individuals positive for astrocyte reactivity (Ast + ). Cross-sectional and longitudinal tau–positron emission tomography analyses revealed an AD-like pattern of tau tangle accumulation as a function of Aβ only in CU Ast + individuals. Our findings suggest astrocyte reactivity as an important upstream event linking Aβ with initial tau pathology, which may have implications for the biological definition of preclinical AD and for selecting CU individuals for clinical trials. Cross-sectional and longitudinal analyses of tau pathology in preclinical Alzheimer’s disease reveal that tau tangles accumulate as a function of amyloid-β burden only in individuals positive for an astrocyte reactivity biomarker.

The UK Biobank combines detailed phenotyping and genotyping with tracking of long-term health outcomes in a large cohort. This study describes the recently launched brain-imaging component that will ultimately scan 100,000 individuals. Results from the first 5,000 subjects are reported, including thousands of associations, population modes and hypothesis-driven results. Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.