Navigating the clinical landscape: Update on the diagnostic and prognostic biomarkers in multiple myeloma.

Multiple myeloma, a complex hematologic malignancy, has devastating consequences for patients, including dramatic bone loss, severe bone pain, and pathological fractures that markedly decrease the quality of life and impact the survival of affected patients. This necessitates a refined understanding of biomarkers for accurate diagnosis and prognosis of such severe malignancy. Therefore, this article comprehensively covers current research, elucidating the diverse spectrum of biomarkers employed in clinical settings. From traditional serum markers to advanced molecular profiling techniques, the review provides a thorough examination of their utility and limitations. Through this scoping review, emphasis is placed on the evolving landscape of personalized medicine, where biomarkers play a pivotal role in tailoring therapeutic strategies. The integration of genomic, proteomic, next generation sequencing and flow cytometric data further enriches the discussion, unravelling the molecular intricacies underlying disease progression. The updated criteria allow for the treatment of people who clearly would benefit from therapy and might live longer if treated before significant organ damage occurs. Navigating through the evolving diagnostic and prognostic paradigms in multiple myeloma, this article equips clinicians and researchers with crucial insights for optimizing patient care and advancing future therapeutic approaches.

Biomarker-Guided Tailored Therapy in Major Depression.

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Telepsychiatry in the Treatment of Major Depressive Disorders.

This chapter explores the transformative role of telepsychiatry in managing major depressive disorders (MDD). Traversing geographical barriers and reducing stigma, this innovative branch of telemedicine leverages digital platforms to deliver effective psychiatric care. We investigate the evolution of telepsychiatry, examining its diverse interventions such as videoconferencing-based psychotherapy, medication management, and mobile applications. While offering significant advantages like increased accessibility, cost-effectiveness, and improved patient engagement, challenges in telepsychiatry include technological barriers, privacy concerns, ethical and legal considerations, and digital literacy gaps. Looking forward, emerging technologies like virtual reality, artificial intelligence, and precision medicine hold immense potential to personalize and enhance treatment effectiveness. Recognizing its limitations and advocating for equitable access, this chapter underscores telepsychiatry's power to revolutionize MDD treatment, making quality mental healthcare a reality for all.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches.

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

Navigating the Intersection of Technology and Depression Precision Medicine.

This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.

The prognostic difference study on the individualized clopidogrel administration in Hmong and Dong patients based on the CYP2C19 gene polymorphism after percutaneous coronary intervention.

This study explored the distribution characteristics of CYP2C19 gene polymorphism among Hmong and Dong patients in the Qiandongnan region of Guizhou province after percutaneous coronary intervention (PCI). The aim was to assess the clinical impact of individualized clopidogrel administration based on CYP2C19 genotypes. A total of 208 patients were classified into ultra-fast, fast, intermediate, and slow metabolic groups. They were randomly assigned to clopidogrel individualized administration (IA) or conventional treatment (CA) groups. Patients were followed for 6 months to evaluate major adverse cardiovascular events (MACE) and adverse reactions. The CYP2C19 genotype distribution was in Hardy-Weinberg equilibrium, showing consistency in the population. While no significant ethnic differences were found in genotype and metabolic distribution, allele distribution varied, with Hmong patients exhibiting a higher proportion of CYP2C19*1 alleles than Dong patients. Following individualized administration, the IA group demonstrated lower incidences of non-fatal myocardial infarction and emergency revascularization compared to the CA group. Bleeding events were higher in the IA group, but the total MACE incidence was lower. No statistical difference in MACE and adverse drug reactions (ADR) was observed in the CA group across metabolic types, but MACE incidence was higher in intermediate and slow metabolic groups. In the IA group, no significant difference in MACE was noted among metabolic types, but ADR incidence varied significantly, particularly in dyspnea. The study highlighted significant CYP2C19 allele distribution differences between Hmong and Dong patients post-PCI in Qiandongnan. Patients with slow metabolic profiles demonstrated higher MACE incidence with conventional clopidogrel dosage, whereas CYP2C19-guided therapy reduced MACE without increasing bleeding risk. These findings supported clinical individualized clopidogrel administration in post-PCI patients in the Qiandongnan region, contributing to rational clopidogrel use.

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63-0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96-14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83-21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.

Generation of Novel Tumour-Selective SEA Superantigen-Based Peptides with Improved Safety and Efficacy for Precision Cancer Immunotherapy.

Bacterial superantigens are T-cell-stimulatory protein molecules which produce massive cytokines and cause human diseases. Due to their ability to activate up to 20% of resting T-cells, they have effectively killed T-cell-dependent tumours in vivo. However, the intrinsic toxicity of whole SAg molecules highlights the urgent need to develop more effective and safer SAg-based immunotherapy. With its unique approach, our study is a significant step towards developing safer tumour-targeted superantigen peptides (TTSP). We identified the T-cell activation function regions on the SEA superantigen and produced variants with minimal lethality, ensuring a safer approach to cancer treatment. This involved the creation of twenty 50-amino-acid-long overlapping peptides covering the full-length SEA superantigen (P1-P20). We then screened these peptides for T-cell activation, successfully isolating two peptides (P5 and P15) with significant T-cell activation. These selected peptides were used to design and synthesise tumour-targeted superantigen peptides, which were linked to a cancer-specific third loop (L3) of transforming growth factor-α (TGF-α), TGFαL3 from either a C' or N' terminal with an eight-amino-acid flexible linker in between. We also produced several P15 variants by changing single amino acids or by amino acid deletions. The novel molecules were then investigated for cytokine production and tumour-targeted killing. The findings from our previous study and the current work open up new avenues for peptide-based immunotherapy, particularly when combined with other immunotherapy techniques, thereby ensuring effective and safer cancer treatment.

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

MOTIVATION: Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model them in vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patients in silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process. RESULTS: We present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drug-sensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients. AVAILABILITY AND IMPLEMENTATION: https://github.com/UEFBiomedicalInformaticsLab/MODAE.

Federated unsupervised random forest for privacy-preserving patient stratification.

MOTIVATION: In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. RESULTS: We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. AVAILABILITY AND IMPLEMENTATION: The proposed methods are available as an R-package (https://github.com/pievos101/uRF).

Receptor Pharmacogenomics: Deciphering Genetic Influence on Drug Response.

The paradigm "one drug fits all" or "one dose fits all" will soon be challenged by pharmacogenetics research and application. Drug response-efficacy or safety-depends on interindividual variability. The current clinical practice does not include genetic screening as a routine procedure and does not account for genetic variation. Patients with the same illness receive the same treatment, yielding different responses. Integrating pharmacogenomics in therapy would provide critical information about how a patient will respond to a certain drug. Worldwide, great efforts are being made to achieve a personalized therapy-based approach. Nevertheless, a global harmonized guideline is still needed. Plasma membrane proteins, like receptor tyrosine kinase (RTK) and G protein-coupled receptors (GPCRs), are ubiquitously expressed, being involved in a diverse array of physiopathological processes. Over 30% of drugs approved by the FDA target GPCRs, reflecting the importance of assessing the genetic variability among individuals who are treated with these drugs. Pharmacogenomics of transmembrane protein receptors is a dynamic field with profound implications for precision medicine. Understanding genetic variations in these receptors provides a framework for optimizing drug therapies, minimizing adverse reactions, and advancing the paradigm of personalized healthcare.

The emerging role of AI in enhancing intratumoral immunotherapy care.

The emergence of immunotherapy (IO), and more recently intratumoral IO presents a novel approach to cancer treatment which can enhance immune responses while allowing combination therapy and reducing systemic adverse events. These techniques are intended to change the therapeutic paradigm of oncology care, and means that traditional assessment methods are inadequate, underlining the importance of adopting innovative approaches. Artificial intelligence (AI) with machine learning algorithms and radiomics are promising approaches, offering new insights into patient care by analyzing complex imaging data to identify biomarkers to refine diagnosis, guide interventions, predict treatment responses, and adapt therapeutic strategies. In this editorial, we explore how integrating these technologies could revolutionize personalized oncology. We discuss their potential to enhance the survival and quality of life of patients treated with intratumoral IO by improving treatment effectiveness and minimizing side effects, potentially reshaping practice guidelines. We also identify areas for future research and review clinical trials to confirm the efficacy of these promising approaches.

SNMMI Clinical Trials Network Research Series for Technologists: An Introduction to Conducting Theranostic Clinical Trials.

This article is intended to introduce nuclear medicine technologists (NMTs) to the nuances of radiopharmaceutical therapy clinical trials. Here, we outline the potential roles and responsibilities of the NMT in clinical trials and provide context on different aspects of radionuclide therapy. The regulatory process involving investigational therapeutic radiopharmaceuticals is seldom taught to NMT students, nor is it included in the entry-level nuclear medicine certification examinations. Often, NMTs must spend significant time preparing for therapeutic clinical trials on their own, using multiple academic sources, seeking advice from various health care professionals, and reviewing numerous trial-specific manuals to recognize the detailed requirements. The emergence of theranostics has spurred an increase in the development of therapeutic radiopharmaceuticals. Investigators with a robust nuclear medicine background are required to help develop successful therapeutic clinical trials, and well-informed NMTs are crucial to the success of such trials. This article follows a series of previous publications from the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network research series for technologists and is intended to guide the investigational radiopharmaceutical landscape.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research.

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

A Novel Microbial Dysbiosis Index and Intestinal Microbiota-Associated Markers as Tools of Precision Medicine in Inflammatory Bowel Disease Paediatric Patients.

Recent evidence indicates that the gut microbiota (GM) has a significant impact on the inflammatory bowel disease (IBD) progression. Our aim was to investigate the GM profiles, the Microbial Dysbiosis Index (MDI) and the intestinal microbiota-associated markers in relation to IBD clinical characteristics and disease state. We performed 16S rRNA metataxonomy on both stools and ileal biopsies, metabolic dysbiosis tests on urine and intestinal permeability and mucosal immunity activation tests on the stools of 35 IBD paediatric patients. On the GM profile, we assigned the MDI to each patient. In the statistical analyses, the MDI was correlated with clinical parameters and intestinal microbial-associated markers. In IBD patients with high MDI, Gemellaceae and Enterobacteriaceae were increased in stools, and Fusobacterium, Haemophilus and Veillonella were increased in ileal biopsies. Ruminococcaceae and WAL_1855D were enriched in active disease condition; the last one was also positively correlated to MDI. Furthermore, the MDI results correlated with PUCAI and Matts scores in ulcerative colitis patients (UC). Finally, in our patients, we detected metabolic dysbiosis, intestinal permeability and mucosal immunity activation. In conclusion, the MDI showed a strong association with both severity and activity of IBD and a positive correlation with clinical scores, especially in UC. Thus, this evidence could be a useful tool for the diagnosis and prognosis of IBD.

Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review.

BACKGROUND: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. OBJECTIVE: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. METHODS: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. RESULTS: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. CONCLUSIONS: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58705.

Precision Medicine in Bariatric Procedures.

Obesity is a multi-factorial disease that is influenced by genetic, epigenetic, and environmental factors. Precision medicine is a practice wherein prevention and treatment strategies take individual variability into account. It involves using a variety of factors including deep phenotyping using clinical, physiologic, and behavioral characteristics, 'omics assays (eg, genomics, epigenomics, transcriptomics, and microbiomics among others), and environmental factors to devise practices that are individualized to subsets of patients. Personalizing the therapeutic modality to the individual can lead to enhanced effectiveness and tolerability. The authors review advances in precision medicine made in the field of bariatrics and discuss future avenues and challenges.

3D Sapiotyping: A Three Dimensional Definition.

Precision medicine and personalized care have been at centre-stage in diabetology, and rightfully so. Various means of classification and clustering have been proposed to help identify clinical features, causative factors and 'curative' strategies for people living with diabetes. Sapiotype describes "the various attitudes that person with diabetes may have towards their disease, their doctor or health care providers, a specific diagnostic procedure, drug, delivery device, and the health care system at large". The sapiotypic spectrum is a wide one, which encapsulates sapiotypic fluidity, or variability in attitudes, as well. In this communication, we further expand the sapiotypic spectrum by creating a 3- dimensional model.