Practice Of Precision Medicine Research Articles

Product development process for a new healthcare service in the Industry 4.0 context: an action research approach

Precision Medicine (PM) is a personalised approach that uses genetic, environmental, and lifestyle information to prevent, diagnose, and treat disease more effectively. This study aims to explore how Industry 4.0 technologies can enable the development of a new healthcare product, specifically a new PM service. Action Research has been used to identify the needs and orient the design and development of a technological platform that enables PM to be applied in healthcare institutions. This methodology can also act as a guide for advancing research and making the service a regular clinical practice. This research furthers understanding of how I4.0 technologies enable the generation of specific capabilities and their use to create other innovative healthcare products, in general, and PM products, in particular. Study findings offer practical and governmental implications and can serve as the basis for digital platform design and development to facilitate PM practices in healthcare institutions.

Abstract PO1-17-06: Driving diversity in research participation with ShareForCures™, a patient-centered, nationwide breast cancer research registry to improve outcomes

Abstract Background The lack of diversity in research and small and siloed datasets have led to a lack of real-world evidence, limiting our ability to practice precision medicine and optimize treatments and care for all breast cancer patients. Susan G. Komen,® a breast cancer advocacy organization and the largest non-profit breast cancer funder outside the U.S. government, launched ShareForCures™ (SFC), a secure, patient-centered research registry. The registry aims to engage breast cancer survivors from diverse backgrounds as research partners to facilitate sharing of their data to drive innovative breast cancer research and improve outcomes. SFC is IRB-approved and open to any adult living in the U.S. and diagnosed with breast cancer. SFC is designed to be easy to join and accessible online. Participants provide survey information about their diagnosis, quality of life, lifestyle, family history, etc. Some of them will be asked to provide a saliva sample, and their medical records will be retrieved. Methods Patient advocates have driven every aspect of the development of SFC, from serving on its governance council to participating in focus groups and beta-testing the platform build. Most important to the success of the registry was to ensure we worked with community partners to conduct breast cancer survivor focus groups and to solicit views and expectations about patient-centered research engagement. Those focus groups were conducted in 2020 and 2021 in-person (N=4) and virtually (N=8) across the U.S., with three conducted in Spanish. The findings that emerged from the focus groups informed the establishment of SFC, from governance and technology implementation to platform features and processes. For feedback on pre-enrollment information, we invited 432 diverse breast cancer survivors to participate in an audience testing of two versions of a SFC landing page, a flyer, and a social media post. One version was designed to be more technical and the other more inspiring/motivational. Finally, we pilot tested SFC with 56 breast cancer survivors. Those who joined provided feedback through individual debriefing sessions about the onboarding process, consent, the questionnaire (“About You”) and the SFC platform. Results Four main themes emerged from the focus groups (N=64 participants): tailor communications to specific groups of participants, create an environment of trust and transparency, address concerns about data privacy and security, and share news and results. Audience testing on SFC materials revealed the majority liked positive personal messaging, although technical messaging highlighting the science, research, and data was also favorably reviewed. Participants ranked privacy and data security as priority concerns. Of the 56 breast cancer survivors invited to participate in the pilot testing of SFC, 31 fully onboarded, three started but did not complete onboarding, and six declined. To fully onboard, participants had to accept the informed consent and medical records release forms and complete the “About You” questionnaire. They indicated the process and online platform were easy, and the dashboard was an important feature for participants to stay engaged. They also confirmed focus group emphasis on clarity regarding SFC research goals and importance of sharing results. Conclusions All individuals who provided feedback expressed interest in sharing their experiences, inspiring others’ experiences, and accelerating research. They understood that this research may not benefit them directly but that it could benefit others. The results indicate that people are willing to participate in research if asked. Komen’s patient-centered approach of involving patients at all steps of developing SFC fosters trust, transparency and engagement that will enhance SFC’s impact. Citation Format: Jerome Jourquin, Stephanie McCoy, Barbara Segarra, Emily Marks, Carlita Proctor McIlwain, Brady Kazar, Melissa Troester, Kimberly Sabelko, Melissa Bondy. Driving diversity in research participation with ShareForCures™, a patient-centered, nationwide breast cancer research registry to improve outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-17-06.

Optimizing the Use of Next-Generation Sequencing Assays in Patients With Urothelial Carcinoma: Recommendations by the 2023 San Raffaele Retreat Panel

BackgroundThe application of precision medicine in clinical practice implies a thorough evaluation of actionable genomic alterations to streamline therapeutic decision making. Comprehensive genomic profiling of tumor via next-generation sequencing (NGS) represents a great opportunity but also several challenges. During the 2023 San Raffaele Retreat, we aimed to provide expert recommendations for the optimal use of NGS in urothelial carcinoma (UC). Materials and MethodsA modified Delphi method was utilized, involving a panel of 12 experts in UC from European and United States centers, including oncologists, urologists, pathologists, and translational scientists. An initial survey, conducted before the meeting, delivered 15 statements to the panel. A consensus was defined when ≥70% agreement was reached for each statement. Statements not meeting the consensus threshold were discussed during the meeting. ResultsNine of the 15 statements covering patient selection, cancer characteristics, and type of NGS assay, achieved a consensus during the survey. The remaining six statements addressing the optimal timing of NGS use, the ideal source of tumor biospecimen for NGS testing, and the subsequent need to evaluate the germline nature of certain genomic findings were discussed during the meeting, leading to unanimous agreement at the end of the conference. ConclusionThis consensus-building effort addressed multiple unanswered questions regarding the use of NGS in UC. The opinion of experts was in favor of broader use of NGS. In a setting where recommendations/guidelines may be limited, these insights may aid clinicians to provide informed counselling and raise the bar of precision and personalized therapy.

Updates on Genetic Hearing Loss: From Diagnosis to Targeted Therapies.

Sensorineural hearing loss (SNHL) is the most common sensory disorder, with a high Mendelian genetic contribution. Considering the genotypic and phenotypic heterogeneity of SNHL, the advent of next-generation sequencing technologies has revolutionized knowledge on its genomic architecture. Nonetheless, the conventional application of panel and exome sequencing in real-world practice is being challenged by the emerging need to explore the diagnostic capability of whole-genome sequencing, which enables the detection of both noncoding and structural variations. Small molecules and gene therapies represent good examples of how breakthroughs in genetic understanding can be translated into targeted therapies for SNHL. For example, targeted small molecules have been used to ameliorate autoinflammatory hearing loss caused by gain-of-function variants of NLRP3 and inner ear proteinopathy with OSBPL2 variants underlying dysfunctional autophagy. Strikingly, the successful outcomes of the first-in-human trial of OTOF gene therapy highlighted its potential in the treatment of various forms of genetic hearing loss. clustered regularly interspaced short palindromic repeats (CRISPR)-based technologies are currently being developed for site-specific genome editing to treat human genetic disorders. These advancements have led to an era of genotype- and mechanism-based precision medicine in SNHL practice.

Qualitative assessment of knowledge, attitude and practice of oncologists about precision medicine in cancer patients- study from Lahore, Pakistan.

Precision medicine (PM) is in great progressive stages in the West and allows healthcare practitioners (HCPs) to give treatment according to the patient's genetic findings, physiological and environmental characteristics. PM is a relatively new treatment approach in Pakistan Therefore, it is important to investigate the level of awareness, attitude, and challenges faced by oncology physicians while practicing PM for various therapies, especially cancer treatment. The present study aims to explore the level of awareness, attitude, and practice of PM in Pakistan along with the challenges faced by the oncologists for the treatment of cancer using the PM approach. Phenomenology-based qualitative approach was used. Face-to-face in-depth interviews were conducted using the purposive sampling approach among oncologists in Lahore, Pakistan. The data were analyzed using thematic content analysis to identify themes and sub-themes. Out of 14 physicians interviewed 11 were aware of PM. They were keen on training to hone their skills and agreed on providing PM. Oncologists believed PM was expensive and given to affluent patients only. Other impeding factors include cost, lack of knowledge, and drug unavailability. Despite basic knowledge and will to practice, resource and cost constraints were marked as significant barriers. Additional training programs and inclusion into the curriculum may help to pave the way to PM implementation in the future. In addition, health authorities and policymakers need to ensure a cheaper PM treatment can be made available for all cancer patients.

Abstract 4257: A novel 3D co-culture platform for patient-specific immune-oncology treatment

Abstract Lung cancer is the leading cause of global cancer-related mortality resulting in ~1.8 million deaths. annually. Over the past decade, the contribution of tumor microenvironment (TME) in lung cancer progression has been highly appreciated and provided significant improvements of survival outcomes for patients. That includes the complexity of the tumor immune microenvironment (TIME) playing a critical role in cancer progression. However, drug resistance usually arises and there is an urgent need for novel immune cell-based therapies. 3D tumor cell structures that recapitulate the architecture and functionality of the original tumor have revolutionized the understanding of disease mechanisms and testing potential therapeutics. However, there are certain drawbacks that need to be overcome. For example, these models mainly consist of only the epithelial compartment and lack signals from the microenvironment. Recent advances with engineering co-culture systems with immune cells at Champions Oncology help to emulate cell-cell and immune cell infiltration. We have successfully established a biobank of TumorGraft3D (CTG3Ds) derived from non-small cell lung cancer (NSCLC) patient-derived xenografts as well as their autologous and allogeneic immune cells (TILs, PBMCs) to establish a proprietary co-culture platform to investigate disease mechanisms and test potential therapeutics. In brief, surgical resections from patients were used to generate patient-derived xenografts organoids, and matched immune cells were expanded using standard rapid expansion protocol. The combination of a comprehensive staining system and high-content imaging as an endpoint allowed for 3D confocal analysis and deep evaluation of cancer-immune cellular interaction at the single cell level. Our pipeline of image analysis not only differentiates cancer-immune cell spatial distribution and cellular death but also allows for the evaluation of tumor infiltration and drug cytotoxicity. The characterization of the platform showed proliferation of the cancer cells and functional immune profiling by flow cytometry. The molecular profile of the organoids composing the platform showed a tight overlap with the original tumor highlighting the clinical relevance of the model. In addition, this novel co-culture platform has been tested with several NSCLC models to test the efficacy of various therapeutics such as immune-checkpoint inhibitors (ICIs) in reproducible, cost-effective, and patient-specific manner. In summary, Champion’s novel co-culture platform allows screening of novel immune targeting agents to determine impacts tumor cytotoxicity. Furthermore, our lung cancer tumorGraft3D platform serves as a powerful tool to facilitate the practice of precision medicine by investigating a variety of therapeutic modalities, alone or in combination, to identify patient-specific treatment strategies. Citation Format: Taylor Light, Samaneh Kamali, Mara Gilardi, Abhay Andar, Karin Abarca-Heidemann, Maria Mancini. A novel 3D co-culture platform for patient-specific immune-oncology treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4257.

Big Data Research on Severe Asthma.

The continuously increasing prevalence of severe asthma has imposed an increasing burden worldwide. Despite the emergence of novel therapeutic agents, management of severe asthma remains challenging. Insights garnered from big data may be helpful in the effort to determine the complex nature of severe asthma. In the field of asthma research, a vast amount of big data from various sources, including electronic health records, national claims data, and international cohorts, is now available. However, understanding of the strengths and limitations is required for proper utilization of specific datasets. Use of big data, along with advancements in artificial intelligence techniques, could potentially facilitate the practice of precision medicine in management of severe asthma.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust.

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

From diagnosis to treatment in genetic epilepsies: Implementation of precision medicine in real-world clinical practice

The implementation of whole exome sequencing (WES) has had a major impact on the diagnostic yield of genetic testing in individuals with epilepsy. The identification of a genetic etiology paves the way to precision medicine: an individualized treatment approach, based on the disease pathophysiology. The aim of this retrospective cohort study was to: (1) determine the diagnostic yield of WES in a heterogeneous cohort of individuals with epilepsy referred for genetic testing in a real-world clinical setting, (2) investigate the influence of epilepsy characteristics on the diagnostic yield, (3) determine the theoretical yield of treatment changes based on genetic diagnosis and (4) explore the barriers to implementation of precision medicine. WES was performed in 247 individuals with epilepsy, aged between 7 months and 68 years. In 34/247 (14 %) a (likely) pathogenic variant was identified. In 7/34 (21 %) of these individuals the variant was found using a HPO-based filtering. Diagnostic yield was highest for individuals with an early onset of epilepsy (39 %) or in those with a developmental and epileptic encephalopathy (34 %). Precision medicine was a theoretical possibility in 20/34 (59 %) of the individuals with a (likely) pathogenic variant but implemented in only 11/34 (32 %). The major barrier to implementation of precision treatment was the limited availability or reimbursement of a given drug. These results confirm the potential impact of genetic analysis on treatment choices, but also highlight the hurdles to the implementation of precision medicine. To optimize precision medicine in real-world practice, additional endeavors are needed: unifying definitions of precision medicine, establishment of publicly accessible databases that include data on the functional effect of gene variants, increasing availability and reimbursement of precision therapeutics, and broadening access to innovative clinical trials.

Identification of necroptosis-related lncRNAs for prognosis prediction and screening of potential drugs in patients with colorectal cancer

BACKGROUND Tumor recurrence and metastasis lead to a poor prognosis in colorectal cancer (CRC). Necroptosis is closely related to the tumor microenvironment (TME) and affects tumor recurrence and metastasis. We aimed to stratify CRC patients according to necroptosis-related long noncoding RNAs (lncRNAs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs. AIM To stratify CRC patients according to necroptosis-related lncRNAs (NRLs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs. METHODS LncRNA expression profiles were collected from The Cancer Genome Atlas. NRLs were identified by coexpression analysis. Cox regression analysis identified a NRL signature. Then, the value of this signature was comprehensively and multidimensionally evaluated, and its reliability for CRC prognosis prediction was assessed with clinical CRC data and compared with that of six other lncRNA signatures. Gene set enrichment analysis, TME analysis and half-maximal inhibitory concentration (IC50) prediction were also performed according to the risk score (RS) of the signature. RESULTS An 8-lncRNA signature significantly associated with overall survival (OS) was constructed, and its reliability was validated with clinical CRC data. Most of the areas under the receiver operating characteristic curves (AUCs) values for 1-, 3- and 5-year OS for this signature were higher than those for the other six lncRNA signatures. OS, disease-specific survival and the progression-free interval were all significantly poorer in the high-risk group. The RS of the signature showed good concordance with the predicted prognosis, with AUCs for 1-, 3- and 5-year OS of 0.79, 0.81 and 0.77, respectively. Additionally, the calibration plots for this signature combined with clinical factors showed that this combination could effectively improve the ability to predict OS. The RS was correlated with tumor stage, lymph node metastasis and distant metastasis. Most of the enriched Kyoto Encyclopedia of Genes and Genomes and Gene Ontology terms were tumor metastasis-related pathways in the high-risk group; these patients showed greater infiltration of immunosuppressive cells, such as cancer-associated fibroblasts, hematopoietic stem cells and M2 macrophages, but less infiltration of infiltrating antitumor effector immune cells, such as cluster of differentiation 8+ T cells and regulatory T cells (Tregs). We explored additional potential immune checkpoint genes and potential immunotherapeutic and chemotherapeutic drugs with relatively low IC50 values. CONCLUSION We identified an NRL signature with strong fidelity that could stably predict prognosis and might be an indicator of the TME of CRC. Furthermore, additional potential immunotherapeutic and chemotherapeutic drugs were explored.

Precision medicine in regulatory decision making: Biomarkers used for patient selection in European Public Assessment Reports from 2018 to 2020.

Biomarkers can guide precision medicine in clinical trials and practice. They can increase clinical trials' efficiency through selection of study populations more likely to benefit from treatment, thus increasing statistical power and reducing sample size requirements or study duration. We performed a narrative synthesis to explore biomarker utilization for patient selection to guide precision medicine trials in marketing authorization dossiers of centrally approved medicines in Europe between 2018 and 2020 and analyzed in-depth those that eventually included biomarkers in the medicines' indications. From 119 eligible products, 26 included a biomarker in the indication, of which most were oncology products (n = 15). Included biomarkers were often known from literature or from previously approved products in the European Union or the United States. Additionally, 52 dossiers mentioned one or more biomarkers for patient selection in their clinical efficacy and safety information. Although these were not always included in the medicines' indication, they were often implicitly embedded in condition definitions adopted from clinical guidelines or practice. In 15 out of the 26 medicines with a biomarker-guided indication, only biomarker-positive populations were included in the main clinical studies supporting the marketing authorization. These studies were mostly randomized controlled trials or single-arm trials; only two products were studied for multiple indications in an innovative basket trial. Definitions of biomarkers could be subject of debate and needed adaptation after post hoc analyses requested by the assessment committee in four cases, stressing the importance of thorough justification of these definitions to include the right population for an optimal benefit-risk balance, enabling precise medicine.

Sex-oriented perspectives in immunopharmacology

Several immunopharmacological agents are effective in the treatment of cancer and immune-mediated conditions, with a favorable impact on life expectancy and clinical outcomes for a large number of patients. Nevertheless, response variation and undesirable effects of these drugs represent major issues, and overall efficacy remains unpredictable. Males and females show a distinct difference in immune system responses, with females generally mounting stronger responses to a variety of stimuli. Therefore, exploring sex differences in the efficacy and safety of immunopharmacological agents would strengthen the practice of precision medicine. As a pharmacological target highlight, programmed cell death 1 ligand 1 (PD-L1) is the first functionally characterized ligand of the coinhibitory programmed death receptor 1 (PD-1). The PD-L1/PD-1 crosstalk plays an important role in the immune response and is relevant in cancer, infectious and autoimmune disease. Sex differences in the response to immune checkpoint inhibitors are well documented, with male patients responding better than female patients. Similarly, higher efficacy of and adherence to tumor necrosis factor inhibitors in chronic inflammatory conditions including rheumatoid arthritis and Crohn’s disease have been reported in male patients. The pharmacological basis of sex-specific responses to immune system modulating drugs is actively investigated in other settings such as stroke and type 1 diabetes. Advances in therapeutics targeting the endothelium could soon be wielded against autoimmunity and metabolic disorders. Based on the established sexual dimorphism in immune-related pathophysiology and disease presentation, sex-specific immunopharmacological protocols should be integrated into clinical guidelines.

Precision medicine in hemostasis: a review of prothrombin complex concentrates and the role of viscoelastic tests in tailoring therapy

This review explores the role of precision medicine in the management of bleeding disorders and anticoagulation therapy, with a focus on the use of visco-elastic tests such as Thromboelastography (TEG) and Rotational Thromboelastometry (ROTEM). These tests provide real-time, dynamic insight into a patient's coagulation status, guiding the choice between three-factor prothrombin complex concentrate (PCC3) and four-factor PCC (PCC4), as well as the use of activated four-factor PCC (FEIBA). The specific ROTEM tests, INTEM and EXTEM, further enhance our understanding of the intrinsic and extrinsic coagulation pathways. Moreover, the use of tranexamic acid (TXA) and fibrinogen, guided by these visco-elastic tests, has shown promise in trauma patients. TXA has been associated with survival benefit when administered immediately or within 3 hours of injury. Fibrinogen, a key factor in clot formation, can be monitored and supplemented as needed to optimize hemostasis. In conclusion, the practice of precision medicine, with the aid of TEG and ROTEM, offers the potential to enhance the safety and efficacy of PCC therapy, TXA administration, and fibrinogen supplementation. These tools are invaluable in tailoring therapy to the specific needs of each patient, potentially optimizing patient outcomes and minimizing the risk of adverse events.

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models.

Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting. Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk. Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted. Clinicaltrial.gov identifier is NCT02188355.

What rare cancers have in common. The making of lists of (very) rare cancers and the coordination of medical work.

This article aims to understand why medical actors recently published lists of rare and very rare cancers. It studies four lists of rare and very rare cancers based on interviews with the main actors on these lists and an analysis of medical articles in which these lists were published. It argues that these lists constitute boundary objects whose aim is to deal with the organizational challenges raised by precision medicine, which imply increasing the coordination work between various types of actors. Our work therefore allows a better understanding of the functioning of the recursive standardization process of a boundary object and, by analyzing how the category of rarity is built at the intersection of both professional and nosographic principles, shows the intertwining of the biomedical, organizational, and political aspects on which rests the practice of contemporary precision medicine.

Molecular biomarkers for facilitating genome‑directed precision medicine in gynecological cancer (Review).

Prominent recent advancements in cancer treatment include the development and clinical application of next-generation sequencing (NGS) technologies, alongside a diverse array of novel molecular targeting therapeutics. NGS has enabled the high-speed and low-cost sequencing of whole genomes in individual patients, which has opened the era of genome-based precision medicine. The development of numerous molecular targeting agents, including anti-VEGF antibodies, poly (ADP-ribose) polymerase inhibitors and immune checkpoint inhibitors, have all improved the efficacy of systemic cancer therapy. Accumulating bench and translational research evidence has led to identification of various cancer-related biomarker profiles. In particular, companion diagnostics have been developed for some of these biomarkers, which can be clinically applied and are now widely used for guiding cancer therapies. Selecting biomarkers accurately will improve therapeutic efficacy, avoid overtreatment, enable earlier diagnosis and reduce the cost of preventing and treating gynecological cancer. Therefore, biomarkers are fast becoming indispensable tools in the practice of genome-directed precision medicine. In the present review, the current evidence of cancer-related biomarkers in the field of gynecological oncology, their molecular interpretations and future perspectives are outlined. The aim of the present review is to provide potentially useful information for the formulation of clinical trials.

A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making

Screening potential drug–drug interactions, drug–gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making. The approach focuses on raising the performance of four potential interaction screenings in the prescribing process, including drug–drug interactions, drug–gene interactions, drug–herb interactions, drug–social lifestyle interactions, and two potential considerations for patients with liver or renal impairment. The work describes the design of a curated knowledge-based model called the knowledge model for potential interaction and consideration screening, the screening logic for both the detection module and inference module, and the personalized prescribing report. Three case studies have demonstrated the proof-of-concept and effectiveness of this approach. The proposed approach aims to reduce decision-making processes for healthcare professionals, reduce medication-related harm, and enhance treatment effectiveness. Additionally, the recommendation with a semantic network is suggested to assist in risk–benefit analysis when health professionals plan therapeutic interventions with new medicines that have insufficient evidence to establish explicit recommendations. This approach offers a promising solution to implementing precision medicine in clinical practice.

Identification of Necroptosis-related Molecular Subtypes and Construction of Necroptosis-related Gene Signature for Glioblastoma Multiforme.

Background Necroptosis is a highly regulated and genetically controlled process, and therefore, attention has been paid to the exact effects of this disorder on a variety of diseases, including cancer. An in-depth understanding of the key regulatory factors and molecular events that trigger necroptosis can not only identify patients at risk of cancer development but can also help to develop new treatment strategies. Aims This study aimed to increase understanding of the complex role of necroptosis in glioblastoma multiforme (GBM) and provide a new perspective and reference for accurate prediction of clinical outcomes and gene-targeted therapy in patients with GBM. The objective of this study was to analyze the gene expression profile of necroptosis regulatory factors in glioblastoma multiforme (GBM) and establish a necroptosis regulatory factor-based GBM classification and prognostic gene signature to recognize the multifaceted impact of necroptosis on GBM. Method The necroptosis score of the glioblastoma multiforme (GBM) sample in TCGA was calculated by ssGSEA, and the correlation between each gene and the necroptosis score was calculated. Based on necroptosis score-related genes, unsupervised consensus clustering was employed to classify patients. The prognosis, tumor microenvironment (TME), genomic changes, biological signal pathways and gene expression differences among clusters were analyzed. The gene signature of GBM was constructed by Cox and LASSO regression analysis of differentially expressed genes (DEGs). Result Based on 34 necroptosis score-related genes, GBM was divided into two clusters with different overall survival (OS) and TME. A necroptosis-related gene signature (NRGS) containing 8 genes was developed, which could stratify the risk of GBM in both the training set and verification set and had good prognostic value. NRGS and age were both independent prognostic indicators of GBM, and a nomogram developed by the integration of both of them showed a better predictive effect than traditional clinical features. Conclusion In this study, patients from public data sets were divided into two clusters and the unique TME and molecular characteristics of each cluster were described. Furthermore, an NRGS was constructed to effectively and independently predict the survival outcome of GBM, which provides some insights for the implementation of personalized precision medicine in clinical practice.