[The pharmacogenetics as integral part of personalized medicine: problems and prospects].

The article considers issues of implementation into clinical practice the principles of 5P medicine in its part of individualization of therapeutic tactics considering genetic characteristics of patients. The analysis of studies concerning influence of allelic variations on metabolism, safety and tolerance of the most often prescribed medicinal preparations was implemented. The main assumptions of pharmacogenomics were considered. Despite broad perspective of applying obtained data in clinical practice, there are a number of unresolved problems related to accessibility of genetic testing to population, ambiguity of approaches to interpretation of obtaining results, ethical issues and legal regulation.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group.

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Concordance in Molecular Tumor Board Case Reviews in the ASCO TAPUR Study.

PURPOSE: With the advent of precision medicine, molecular tumor boards (MTBs) were established to interpret genomic results and guide decision making for targeted therapy in oncology patients. There are currently no universal guidelines for how MTBs should operate and thus variance can be seen depending on which MTB is reviewing the case. This study assesses the concordance of MTB recommendations when a participant case is reviewed by two different MTBs, establishes potential reasons for discordance, and advocates for the establishment of standard MTB operating guidelines. PATIENTS AND METHODS: Participants with advanced cancer, who had exhausted all standard treatment options were screened for the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. Cases were submitted for MTB review if the treatment proposal was outside the protocol genomic matching rules, or if multiple treatment options were identified. Of the 306 cases submitted for review by the TAPUR MTB from 2016 to 2018, 107 were randomly selected for secondary review by a different MTB group. Recommendations from the original review were not disclosed. Concordance between MTB group recommendations was assessed. Concordance was defined as agreement between MTB reviews on the genomic alteration and study drug match proposed by the clinical site. Thematic qualitative analysis was conducted for the discordant cases to assess reasons for discordance. RESULTS: Complete or partial concordance was observed in 79% of cases (95% CI, 70 to 86; one-sided P = .25). Most discordant analyses were due to disagreements on the strength of evidence regarding efficacy of the proposed treatment (32%). CONCLUSION: When presented with identical participant cases, different MTB review groups make the same or similar treatment recommendations approximately 80% of the time.

[Chinese expert consensus on the analytical validation of tumor comprehensive genomic profiling next generation sequencing testing (2024 edition)].

In hospital laboratories-developed testing is of great significance for the clinical testing products that has not been approved by the National Medical Product Administration and is urgently needed to meet clinical practice needs. With the development of cancer precision medicine in recent years, comprehensive genomic profiling (CGP) has become an important means and method for the detection of drug targets, precise molecular typing, and immunotherapy biomarkers in cancer patients. However, there is still a lack of unified understanding and consensus on clinical testing standards and application specifications for laboratory-developed testing in the hospitals. The Molecular Pathology Collaboration Group of the Cancer Experts Committee of the Chinese Anti-Cancer Association and the Molecular Pathology Group of the Pathology Branch of the Chinese Medical Association initiated the expert consensus on relevant specifications for analytical validation of CGP next-generation sequencing (NGS) testing in Chinese hospitals. Combined with domestic clinical practice, refer to domestic and foreign literatures, from the background of the laboratory-developed testing, analytical validation scenarios, evaluation indicators and variation ranges, sample types and quantities covered by analytical validation, clinical performance and drug efficacy determination, and site personnel for analytical validation, quality control, inter-laboratory quality evaluation and document management, etc. After the discussion by the expert group, 12 expert consensuses were formed to provide reference for the analytical validation and clinical application of tumor CGP NGS testing in Chinese hospitals, so as to promote the laboratory-developed testing applications in Chinese hospitals.

The 37TrillionCells initiative for improving global healthcare via cell-based interception and precision medicine: focus on neurodegenerative diseases.

One of the main burdens in the treatment of diseases is imputable to the delay between the appearance of molecular dysfunctions in the first affected disease cells and their presence in sufficient number for detection in specific tissues or organs. This delay obviously plays in favor of disease progression to an extent that makes efficient treatments difficult, as they arrive too late. The development of a novel medical strategy, termed cell-based interception and precision medicine, seeks to identify dysfunctional cells early, when tissue damages are not apparent and symptoms not yet present, and develop therapies to treat diseases early. Central to this strategy is the use of single-cell technologies that allow detection of molecular changes in cells at the time of phenotypical bifurcation from health to disease. In this article we describe a general procedure to support such an approach applied to neurodegenerative disorders. This procedure combines four components directed towards highly complementary objectives: 1) a high-performance single-cell proteomics (SCP) method (Detect), 2) the development of disease experimental cell models and predictive computational models of cell trajectories (Understand), 3) the discovery of specific targets and personalized therapies (Cure), and 4) the creation of a community of collaborating laboratories to accelerate the development of this novel medical paradigm (Collaborate). A global initiative named 37TrillionCells (37TC) was launched to advance the development of cell-based interception and precision medicine.

[Molecular pathological analysis through the ages]. / Molekularpathologische Untersuchungen im Wandel der Zeit.

BACKGROUND: Molecular pathological examinations of tumor samples encompass a wide range of diagnostic analyses. Especially in recent years, numerous new biomarkers have come to the forefront-the analysis of which is crucial for therapy decisions. OBJECTIVES: Within the field of molecular pathology, the demands of next generation sequencing (NGS)-based requirements have experienced massive growth in recent years. To meet this demand, methods are constantly being adapted and further developed. The following sections aim to illuminate how this trend arises and which analyses are gaining importance. METHODS: The article provides an overview of the essential nucleic acid-based analysis techniques in the field of massive parallel sequencing. Terms such as DNA- and RNA-based techniques, as well as the associated analysis methods, are described, particularly with regard to their use in routine molecular pathological diagnostics. RESULTS: The breadth of genomic sequencing has been steadily growing in recent years, particularly due to the increasing relevance of personalized medicine, along with the rising approvals of targeted therapeutics. This necessitates, among other things, the analysis of new biomarkers. The diagnostics as part of interdisciplinary molecular tumor boards (MTB) are now based on large gene panels (> 1 megabase). Furthermore, through the "Modellvorhaben Genomsequenzierung" § 64e, whole exome or whole genome sequencing has been made available for oncological patients. Given these developments, it is evident that future analyses will require the integration of additional omics fields, such as whole transcriptome analysis, epigenomics, and proteomics. CONCLUSION: The challenges of personalized medicine along with the necessity of simultaneously assessing numerous new biomarkers require the implementation and execution of new techniques in molecular pathology whose complexity is steadily increasing.

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities.

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

3D printing processes in precise drug delivery for personalized medicine.

With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Theranostics - a sure cure for cancer after 100 years?

Cancer has remained a formidable challenge in medicine and has claimed an enormous number of lives worldwide. Theranostics, combining diagnostic methods with personalized therapeutic approaches, shows huge potential to advance the battle against cancer. This review aims to provide an overview of theranostics in oncology: exploring its history, current advances, challenges, and prospects. We present the fundamental evolution of theranostics from radiotherapeutics, cellular therapeutics, and nanotherapeutics, showcasing critical milestones in the last decade. From the early concept of targeted drug delivery to the emergence of personalized medicine, theranostics has benefited from advances in imaging technologies, molecular biology, and nanomedicine. Furthermore, we emphasize pertinent illustrations showcasing that revolutionary strategies in cancer management enhance diagnostic accuracy and provide targeted therapies customized for individual patients, thereby facilitating the implementation of personalized medicine. Finally, we describe future perspectives on current challenges, emerging topics, and advances in the field.

Theranostics and artificial intelligence: new frontiers in personalized medicine.

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

Emerging therapies for childhood-onset movement disorders.

PURPOSE OF REVIEW: We highlight novel and emerging therapies in the treatment of childhood-onset movement disorders. We structured this review by therapeutic entity (small molecule drugs, RNA-targeted therapeutics, gene replacement therapy, and neuromodulation), recognizing that there are two main approaches to treatment: symptomatic (based on phenomenology) and molecular mechanism-based therapy or 'precision medicine' (which is disease-modifying). RECENT FINDINGS: We highlight reports of new small molecule drugs for Tourette syndrome, Friedreich's ataxia and Rett syndrome. We also discuss developments in gene therapy for aromatic l-amino acid decarboxylase deficiency and hereditary spastic paraplegia, as well as current work exploring optimization of deep brain stimulation and lesioning with focused ultrasound. SUMMARY: Childhood-onset movement disorders have traditionally been treated symptomatically based on phenomenology, but focus has recently shifted toward targeted molecular mechanism-based therapeutics. The development of precision therapies is driven by increasing capabilities for genetic testing and a better delineation of the underlying disease mechanisms. We highlight novel and exciting approaches to the treatment of genetic childhood-onset movement disorders while also discussing general challenges in therapy development for rare diseases. We provide a framework for molecular mechanism-based treatment approaches, a summary of specific treatments for various movement disorders, and a clinical trial readiness framework.

Diagnosis, management and therapeutic options for eosinophilic esophagitis.

PURPOSE OF REVIEW: Eosinophilic esophagitis is a chronic and commonly evolving condition leading to relevant and potentially irreversible burden in terms of tissue damage and related functional impairment, thus significantly impacting on quality of life. The aim of the present review is to summarize the recent advances in terms of diagnostic work-up and pharmacological and nonpharmacological management of the disease, under the broader perspective of type 2 inflammation. RECENT FINDINGS: Two major novelties have prompted an innovative approach to EoE. In terms of diagnosis, it has been proposed to dissect the disease heterogeneity in three endotypes, independent from tissue eosinophil number: EoEe1, characterized by normal appearing oesophagus; EoEe2, associated with type 2 inflammation and steroid-refractoriness; EoEe3, whose features include adult onset, a more fibro-stenotic aspect and loss of epithelial gene expression. Concerning treatment, two recently licensed drugs for EoE, oro-dispersible budesonide and dupilumab represent the first treatment options specifically developed for EoE and addressing EoE-related peculiar pathobiological features. SUMMARY: In the era of precision medicine, managing EoE according to a phenotype-driven approach might be helpful in defining the best treatment options in the different disease forms or stages. In addition, exploring the coexistence or the previous occurrence of other type 2 conditions may suggest the opportunity to specifically target type 2 inflammation through biologic therapy. The complex EoE pathobiology combining inflammatory and functional features, both at organ and systemic level, requires a multidimensional approach relying on the strict integration of gastroenterologists and allergist-immunologists.

Temporal trends and regional variability in BRAF and KRAS genetic testing in Denmark (2010-2022): Implications for precision medicine.

OBJECTIVE: This study aims to evaluate the developments in the testing of Kirsten Rat Sarcoma viral oncogene homolog (KRAS) and v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations across different cancer types and regions in Denmark from 2010 to 2022. STUDY DESIGN AND SETTING: Using comprehensive data from the Danish health registries, we linked molecular test results from the Danish Pathology Registry with cancer diagnoses from the Danish National Patient Registry between 2010 and 2022. We assessed the frequency and distribution of KRAS and BRAF mutations across all cancer types, years of testing, and the five Danish regions. RESULTS: The study included records of KRAS testing for 30 671 patients and BRAF testing for 30 860 patients. Most KRAS testing was performed in colorectal (78%) and lung cancer (18%), and BRAF testing in malignant melanoma (13%), colorectal cancer (67%), and lung cancer (12%). Testing rates and documentation mutational subtypes increased over time. Reporting of wildtype results varied between lung and colorectal cancer, with underreporting in lung cancer. Regional variations in testing and reporting were observed. CONCLUSION: Our study highlights substantial progress in KRAS and BRAF testing in Denmark from 2010 to 2022, evidenced by increased and more specific reporting of mutational test results, thereby improving the precision of cancer diagnosis and treatment. However, persistent regional variations and limited testing for cancer types beyond melanoma, colorectal, and lung cancer highlight the necessity for a nationwide assessment of the optimal testing approach.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs.

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Ex vivo therapeutic screening of metastatic cSCC: A review of methodological considerations for clinical implementation.

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignancy worldwide, with most deaths caused by locally advanced and metastatic disease. Treatment of resectable metastases is typically limited to invasive surgery with adjuvant radiotherapy; however, many patients fail to respond and there is minimal data to predict response or propose effective alternatives. Precision medicine could improve this, though genomic biomarkers remain elusive in the high mutational background and genomic complexity of cSCC. A phenotypic approach to precision medicine using patient-derived ex vivo tumour models is gaining favour for its capacity to directly assess biological responses to therapeutics as a functional, predictive biomarker. However, the use of ex vivo models for guiding therapeutic selection has yet to be employed for metastatic cSCC. This review will therefore evaluate the existing experimental models of metastatic cSCC and discuss how ex vivo methods could overcome the shortcomings of these existing models. Disease-specific considerations for a prospective methodological pipeline will also be discussed in the context of precision medicine.

[The revolution of AI in drug development]. / L'intelligence artificielle, une révolution dans le développement des médicaments.

Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Title: L'intelligence artificielle, une révolution dans le développement des médicaments. Abstract: L'intelligence artificielle (IA) et l'apprentissage automatique produisent des modèles prédictifs qui aident à la prise de décisions dans le processus de découverte de nouveaux médicaments. Cette modélisation par ordinateur permet de représenter l'hétérogénéité d'une maladie, d'identifier des cibles thérapeutiques, de concevoir et optimiser des candidats-médicaments et d'évaluer ces médicaments sur des patients virtuels, ou des jumeaux numériques. En facilitant à la fois une connaissance détaillée des caractéristiques des patients et en prédisant les propriétés de multiples médicaments possibles, l'IA permet l'émergence d'une médecine de précision « computationnelle ¼ offrant des traitements parfaitement adaptés aux spécificités des patients.