[Expectations and challenges for clinical application of cancer genome panels in acute myeloid leukemia].

The blood cancer field has played a pioneering role in advancing precision medicine, with milestones such as development of ABL1 inhibitors for chronic myeloid leukemia. The significance of gene mutation information in AML treatment has increased, evident in classifications and guidelines from organizations such as WHO and ELN. This article examines the anticipated roles of cancer genome panels (CGPs) in AML treatment from three perspectives: diagnosis, risk stratification, and treatment selection. Use of CGPs enables more accurate diagnosis and risk stratification. In treatment selection, CGPs not only complements but also substitutes existing companion diagnostics, and is expected to be a crucial information source for future drug adoption and investigation of tumor-agnostic therapies. However, various challenges remain to be addressed, including the purpose and timing of CGPs, the time required for the tests, and how to utilize expert panels.

Unlocking the Next Frontier in Precision Oncology: Addressing Drug-Tolerant Residual Disease.

SUMMARY: Drug-tolerant residual disease (DTRD) after the initial maximal response to a systemic therapy can serve as a tumor reservoir for the development of acquired drug resistance and represents a major clinical challenge across various cancers and types of therapies. To unlock the next frontier in precision oncology, we propose a fundamental paradigm shift in the treatment of metastatic cancers with a sharpened focus towards defining, monitoring, and therapeutically targeting the DTRD state.

Biomarkers for personalised prevention of chronic diseases: a common protocol for three rapid scoping reviews.

INTRODUCTION: Personalised prevention aims to delay or avoid disease occurrence, progression, and recurrence of disease through the adoption of targeted interventions that consider the individual biological, including genetic data, environmental and behavioural characteristics, as well as the socio-cultural context. This protocol summarises the main features of a rapid scoping review to show the research landscape on biomarkers or a combination of biomarkers that may help to better identify subgroups of individuals with different risks of developing specific diseases in which specific preventive strategies could have an impact on clinical outcomes. This review is part of the "Personalised Prevention Roadmap for the future HEalThcare" (PROPHET) project, which seeks to highlight the gaps in current personalised preventive approaches, in order to develop a Strategic Research and Innovation Agenda for the European Union. OBJECTIVE: To systematically map and review the evidence of biomarkers that are available or under development in cancer, cardiovascular and neurodegenerative diseases that are or can be used for personalised prevention in the general population, in clinical or public health settings. METHODS: Three rapid scoping reviews are being conducted in parallel (February-June 2023), based on a common framework with some adjustments to suit each specific condition (cancer, cardiovascular or neurodegenerative diseases). Medline and Embase will be searched to identify publications between 2020 and 2023. To shorten the time frames, 10% of the papers will undergo screening by two reviewers and only English-language papers will be considered. The following information will be extracted by two reviewers from all the publications selected for inclusion: source type, citation details, country, inclusion/exclusion criteria (population, concept, context, type of evidence source), study methods, and key findings relevant to the review question/s. The selection criteria and the extraction sheet will be pre-tested. Relevant biomarkers for risk prediction and stratification will be recorded. Results will be presented graphically using an evidence map. INCLUSION CRITERIA: Population: general adult populations or adults from specific pre-defined high-risk subgroups; concept: all studies focusing on molecular, cellular, physiological, or imaging biomarkers used for individualised primary or secondary prevention of the diseases of interest; context: clinical or public health settings. SYSTEMATIC REVIEW REGISTRATION: https://doi.org/10.17605/OSF.IO/7JRWD (OSF registration DOI).

Editorial: The RNA revolution and cancer.

RNA biology has revolutionized cancer understanding and treatment, especially in endocrine-related malignancies. This editorial highlights RNA's crucial role in cancer progression, emphasizing its influence on tumor heterogeneity and behavior. Processes like alternative splicing and noncoding RNA regulation shape cancer biology, with microRNAs, long noncoding RNAs, and circular RNAs orchestrating gene expression dynamics. Aberrant RNA signatures hold promise as diagnostic and prognostic biomarkers in endocrine-related cancers. Recent findings, such as aberrant PI3Kδ splice isoforms and epithelial-mesenchymal transition-related lncRNA signatures, unveil potential therapeutic targets for personalized treatments. Insights into m6A-associated lncRNA prognostic models and the function of lncRNA LINC00659 in gastric cancer represents ongoing research in this field. As understanding of RNA's role in cancer expands, personalized therapies offer transformative potential in managing endocrine-related malignancies. This signifies a significant stride towards precision oncology, fostering innovation for more effective cancer care.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Personalized bacteriophage therapy outcomes for 100 consecutive cases: a multicentre, multinational, retrospective observational study.

In contrast to the many reports of successful real-world cases of personalized bacteriophage therapy (BT), randomized controlled trials of non-personalized bacteriophage products have not produced the expected results. Here we present the outcomes of a retrospective observational analysis of the first 100 consecutive cases of personalized BT of difficult-to-treat infections facilitated by a Belgian consortium in 35 hospitals, 29 cities and 12 countries during the period from 1 January 2008 to 30 April 2022. We assessed how often personalized BT produced a positive clinical outcome (general efficacy) and performed a regression analysis to identify functional relationships. The most common indications were lower respiratory tract, skin and soft tissue, and bone infections, and involved combinations of 26 bacteriophages and 6 defined bacteriophage cocktails, individually selected and sometimes pre-adapted to target the causative bacterial pathogens. Clinical improvement and eradication of the targeted bacteria were reported for 77.2% and 61.3% of infections, respectively. In our dataset of 100 cases, eradication was 70% less probable when no concomitant antibiotics were used (odds ratio = 0.3; 95% confidence interval = 0.127-0.749). In vivo selection of bacteriophage resistance and in vitro bacteriophage-antibiotic synergy were documented in 43.8% (7/16 patients) and 90% (9/10) of evaluated patients, respectively. We observed a combination of antibiotic re-sensitization and reduced virulence in bacteriophage-resistant bacterial isolates that emerged during BT. Bacteriophage immune neutralization was observed in 38.5% (5/13) of screened patients. Fifteen adverse events were reported, including seven non-serious adverse drug reactions suspected to be linked to BT. While our analysis is limited by the uncontrolled nature of these data, it indicates that BT can be effective in combination with antibiotics and can inform the design of future controlled clinical trials. BT100 study, ClinicalTrials.gov registration: NCT05498363 .

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Personalized cancer T-cell therapy takes the stage, mirroring vaccine success.

Personalized T-cell therapy is emerging as a pivotal treatment of cancer care by tailoring cellular therapies to individual genetic and antigenic profiles, echoing the exciting success of personalized vaccines. We describe here the parallel evolution and analogies of cancer vaccines and T-cell therapies.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review.

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

[Progress of allergic rhinitis research based on transcriptome sequencing].

Traditional studies on allergic rhinitis（AR） have mainly adopted animal models and biomolecular approaches. In addition, the advent of transcriptome sequencing technology is promoting the development of AR at the genetic level. Recently, many scholars have focused on the role of common RNA in the pathogenesis of AR, suggesting that breakthroughs have been made in the field of AR bioinformatics analysis. This review aims to summarize the research advances in AR, the development of transcriptome sequencing technology, and the application of transcriptome sequencing in AR, in order to explore potential drug targets for AR treatment and provide new insights into precision medicine.

[Subphenotypes and precision medicine in acute respiratory distress syndrome].

The presence of significant complex heterogeneity among patients with acute respiratory distress syndrome (ARDS) is a major reason for the failure of drug treatments. Precision medicine seeks to elucidate the potential mechanisms of ARDS heterogeneity, define subtypes of ARDS patients with specific characteristics, and rapidly identify the patient groups most likely to benefit from targeted treatments, thereby maximizing treatment efficiency and minimizing adverse reactions. This review discusses on the current state of research on ARDS subtypes from multiple perspectives, including etiology, onset time, radiology, pathology, oxygenation index, respiratory mechanics, protein biomarkers, genetics, transcriptomics, and microbiomics, with the aim of deepening the understanding of the pathogenesis of ARDS and thereby guiding precision treatment of ARDS.

Machine learning in infectious diseases: potential applications and limitations.

Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.

Infectious diseases are a major challenge worldwideArtificial Intelligence (AI) combined algorithms have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseasesFuture directions include applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases.