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Worldwide, lung cancer is the most common cause of cancer-related deaths. Molecular targeted therapies and immunotherapies for non-small-cell lung cancer (NSCLC) have improved outcomes markedly over the past two decades. However, the vast majority of advanced NSCLCs become resistant to current treatments and eventually progress. In this Perspective, we discuss some of the recent breakthrough therapies developed for NSCLC, focusing on immunotherapies and targeted therapies. We highlight our current understanding of mechanisms of resistance and the importance of incorporating genomic analyses into clinical studies to decipher these further. We underscore the future role of neoadjuvant and maintenance combination therapy approaches to potentially cure early disease. A major challenge to successful development of rational combination therapies will be the application of robust predictive biomarkers for clear-cut patient stratification, and we provide our views on clinical research areas that could influence how NSCLC will be managed over the coming decade. This Perspective discusses recent developments in NSCLC immunotherapy and targeted therapy, and highlights the key challenges and future directions for NSCLC management.

Tumor protein p53 ( TP53 ) is the most frequently mutated gene in cancer 1 , 2 . In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease 3 , 4 , rapid transformation to acute myeloid leukemia (AML) 5 , resistance to conventional therapies 6 – 8 and dismal outcomes 9 . Consistent with the tumor-suppressive role of TP53 , patients harbor both mono- and biallelic mutations 10 . However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53 -mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R) 11 . Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response. Clinical sequencing across a large prospective cohort of patients with myelodysplasic syndrome uncovers distinct associations between the mono- and biallelic states of TP53 and clinical presentation

Animal studies show aging varies between individuals as well as between organs within an individual 1 – 4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects. Blood plasma protein data was combined with machine learning models for a simple method to determine differences in organ-specific aging; the study provides a basis for the prediction of diseases and aging effects using plasma proteomics.

The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and red cell distribution width (RDW) are emerging biomarkers to predict outcomes in general ward patients. However, their role in the prognostication of critically ill patients with pneumonia is unclear. A total of 216 adult patients were enrolled over 2 years. They were classified into viral and bacterial pneumonia groups, as represented by influenza A virus and Streptococcus pneumoniae, respectively. Demographics, outcomes, and laboratory parameters were analysed. The prognostic power of blood parameters was determined by the respective area under the receiver operating characteristic curve (AUROC). Performance was compared using the APACHE IV score. Discriminant ability in differentiating viral and bacterial aetiologies was examined. Viral and bacterial pneumonia were identified in 111 and 105 patients, respectively. In predicting hospital mortality, the APACHE IV score was the best prognostic score compared with all blood parameters studied (AUC 0.769, 95% CI 0.705–0.833). In classification tree analysis, the most significant predictor of hospital mortality was the APACHE IV score (adjusted P = 0.000, χ 2 = 35.591). Mechanical ventilation was associated with higher hospital mortality in patients with low APACHE IV scores ≤ 70 (adjusted P = 0.014, χ 2 = 5.999). In patients with high APACHE IV scores > 90, age > 78 (adjusted P = 0.007, χ 2 = 11.221) and thrombocytopaenia (platelet count ≤ 128, adjusted P = 0.004, χ 2 = 12.316) were predictive of higher hospital mortality. The APACHE IV score is superior to all blood parameters studied in predicting hospital mortality. The single inflammatory marker with comparable prognostic performance to the APACHE IV score is platelet count at 48 h. However, there is no ideal biomarker for differentiating between viral and bacterial pneumonia.

In recent years, machine learning techniques have been frequently applied to uncovering neuropsychiatric biomarkers with the aim of accurately diagnosing neuropsychiatric diseases and predicting treatment prognosis. However, many studies did not perform cross validation (CV) when using machine learning techniques, or others performed CV in an incorrect manner, leading to significantly biased results due to overfitting problem. The aim of this study is to investigate the impact of CV on the prediction performance of neuropsychiatric biomarkers, in particular, for feature selection performed with high-dimensional features. To this end, we evaluated prediction performances using both simulation data and actual electroencephalography (EEG) data. The overall prediction accuracies of the feature selection method performed outside of CV were considerably higher than those of the feature selection method performed within CV for both the simulation and actual EEG data. The differences between the prediction accuracies of the two feature selection approaches can be thought of as the amount of overfitting due to selection bias. Our results indicate the importance of correctly using CV to avoid biased results of prediction performance of neuropsychiatric biomarkers.

The continuous improvement in cancer care over the past decade has led to a gradual decrease in cancer-related deaths. This is largely attributed to improved treatment and disease management strategies. Early detection of recurrence using blood-based biomarkers such as circulating tumour DNA (ctDNA) is being increasingly used in clinical practice. Emerging real-world data shows the utility of ctDNA in detecting molecular residual disease and in treatment-response monitoring, helping clinicians to optimize treatment and surveillance strategies. Many studies have indicated ctDNA to be a sensitive and specific biomarker for recurrence. However, most of these studies are largely observational or anecdotal in nature, and peer-reviewed data regarding the use of ctDNA are mainly indication-specific. Here we provide general recommendations on the clinical utility of ctDNA and how to interpret ctDNA analysis in different treatment settings, especially in patients with solid tumours. Specifically, we provide an understanding around the implications, strengths and limitations of this novel biomarker and how to best apply the results in clinical practice. This Perspective reviews the utility and interpretation of circulating tumour DNA for the detection of residual and recurrent cancers and provides recommendations regarding its clinical application for a variety of solid tumours.

A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models’ potential to improve ocular pathology diagnosis and clinical decision-making.

Key Points Alterations in microRNAs (miRNAs) are involved in the pathogenesis of various types of human cancers, and because of the stability of tumour-derived cell-free microRNAs, these show potential as novel biomarkers Cell-free miRNAs can be detected not only in plasma and serum, but also in other body fluids, such as urine and saliva, and serve as a non-invasive diagnostic tool miRNAs also have an important role in chemo-resistance of cancer cells, and could be useful predictors of therapeutic response Methods of detection, such as microarray analysis and deep-sequencing, enable a comprehensive profiling of cell-free miRNAs (including various isoforms) from low amounts of RNA samples The discovery of miRNAs in body fluids has opened up the possibility of using them as non-invasive biomarkers in cancer detection and as predictors of therapy response in cancer treatment. This Review article considers the latest developments in the use of circulating microRNAs as prognostic and predictive biomarkers and discusses their utility in personalized medicine. Efficient patient management relies on early diagnosis of disease and monitoring of treatment. In this regard, much effort has been made to find informative, blood-based biomarkers for patients with cancer. Owing to their attributes—which are specifically modulated by the tumour—circulating cell-free microRNAs found in the peripheral blood of patients with cancer may provide insights into the biology of the tumour and the effects of therapeutic interventions. Moreover, the role of microRNAs in the regulation of different cellular processes points to their clinical utility as blood-based biomarkers and future therapeutic targets. MicroRNAs are optimal biomarkers owing to high stability under storage and handling conditions and their presence in blood, urine and other body fluids. In particular, detection of levels of microRNAs in blood plasma and serum has the potential for an earlier cancer diagnosis and to predict prognosis and response to therapy. This Review article considers the latest developments in the use of circulating microRNAs as prognostic and predictive biomarkers and discusses their utility in personalized medicine.

Various substances in the blood plasma serve as prognostic indicators of the progression of COVID-19. Consequently, multi-omics studies, such as proteomic and metabolomics, are ongoing to identify accurate biomarkers. Cytokines and chemokines, which are crucial components of immune and inflammatory responses, play pivotal roles in the transition from mild to severe illness. To determine the relationship between plasma cytokines and the progression of COVID-19, we used four study cohorts to perform a systematic study of cytokine levels in patients with different disease stages. We observed differential cytokine expression between patients with persistent-mild disease and patients with mild-to-severe transformation. For instance, IL-4 and IL-17 levels significantly increased in patients with mild-to-severe transformation, indicating differences within the mild disease group. Subsequently, we analysed the changes in cytokine and chemokine expression in the plasma of patients undergoing two opposing processes: the transition from mild to severe illness and the transition from severe to mild illness. We identified several factors, such as reduced expression of IL-16 and IL-18 during the severe phase of the disease and up-regulated expression of IL-10, IP-10, and SCGF-β during the same period, indicative of the deterioration or improvement of patients’ conditions. These factors obtained from fine-tuned research cohorts could provide auxiliary indications for changes in the condition of COVID-19 patients.

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.