Lipid Nanovesicle Platforms for Hepatocellular Carcinoma Precision Medicine Therapeutics: Progress and Perspectives

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality globally. HCC is highly heterogenous with diverse etiologies leading to different driver mutations potentiating unique tumor immune microenvironments. Current therapeutic options, including immune checkpoint inhibitors and combinations, have achieved limited objective response rates for the majority of patients. Thus, a precision medicine approach is needed to tailor specific treatment options for molecular subsets of HCC patients. Lipid nanovesicle platforms, either liposome- (synthetic) or extracellular vesicle (natural)-derived present are improved drug delivery vehicles which may be modified to contain specific cargos for targeting specific tumor sites, with a natural affinity for liver with limited toxicity. This mini-review provides updates on the applications of novel lipid nanovesicle-based therapeutics for HCC precision medicine and the challenges associated with translating this therapeutic subclass from preclinical models to the clinic.

The clinical and genetic landscape of developmental and epileptic encephalopathies in Egyptian children

Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of epilepsies characterized by early-onset, refractory seizures associated with developmental regression or impairment, with a heterogeneous genetic landscape including genes implicated in various pathways and mechanisms. We retrospectively studied the clinical and genetic data of patients with genetic DEE who presented at two tertiary centers in Egypt over a 10-year period. Exome sequencing was used for genetic testing. We report 74 patients from 63 unrelated Egyptian families, with a high rate of consanguinity (58%). The most common seizure type was generalized tonic-clonic (58%) and multiple seizure types were common (55%). The most common epilepsy syndrome was early infantile DEE (50%). All patients showed variable degrees of developmental impairment. Microcephaly, hypotonia, ophthalmological involvement and neuroimaging abnormalities were common. Eighteen novel variants were identified and the phenotypes of five DEE genes were expanded with novel phenotype-genotype associations. Obtaining a genetic diagnosis had implications on epilepsy management in 17 patients with variants in 12 genes. In this study, we expand the phenotype and genotype spectrum of DEE in a large single ethnic cohort of patients. Reaching a genetic diagnosis guided precision management of epilepsy in a significant proportion of patients.

Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope?

Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

A systematic review of allergen cross-reactivity: Translating basic concepts into clinical relevance

Access to the molecular culprits of allergic reactions allows for the leveraging of molecular allergology as a new precision medicine approach-one built on interdisciplinary, basic, and clinical knowledge. Molecular allergology relies on the use of allergen molecules as in vitro tools for the diagnosis and management of allergic patients. It complements the conventional approach based on skin and in vitro allergen extract testing. Major applications of molecular allergology comprise accurate identification of the offending allergen thanks to discrimination between genuine sensitization and allergen cross-reactivity, evaluation of potential severity, patient-tailored choice of the adequate allergen immunotherapy, and prediction of its expected efficacy and safety. Allergen cross-reactivity, defined as the recognition of 2 or more allergen molecules by antibodies or T cells of the same specificity, frequently interferes with allergen extract testing. At the mechanistic level, allergen cross-reactivity depends on the allergen, the host's immune response, and the context of their interaction. The multiplicity of allergen molecules and families adds further difficulty. Understanding allergen cross-reactivity at the immunologic level and translating it into a daily tool for the management of allergic patients is further complicated by the ever-increasing number of characterized allergenic molecules, the lack of dedicated resources, and the need for a personalized, patient-centered approach. Conversely, knowledge sharing paves the way for improved clinical use, innovative diagnostic tools, and further interdisciplinary research. Here, we aimed to provide a comprehensive and unbiased state-of-the art systematic review on allergen cross-reactivity. To optimize learning, we enhanced the review with basic, translational, and clinical definitions, clinical vignettes, and an overview of online allergen databases.

Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).

Transcriptome analysis of primary adult B-cell lineage acute lymphoblastic leukemia identifies pathogenic variants and gene fusions, and predicts subtypes for in depth molecular diagnosis

BACKGROUND

B-cell acute lymphoblastic leukemia (B-ALL) is classified into subgroups based on known driver oncogenes and molecular lesions, including translocations and recurrent mutations. However, the current diagnostic tests do not identify subtypes or oncogenic lesions for all B-ALL samples, creating a heterogeneous B-ALL group of unknown subtypes.

METHODS

We sorted primary adult B-ALL cells and performed transcriptome analysis by bulk RNA sequencing (RNA-seq).

RESULTS

Transcriptomic analysis of an adult B-ALL cohort allowed the classification of four patient samples with subtypes that were not previously revealed by standard gene panels. The leukemia of two patients were of the DUX4 subtype and two were CRLF2+ Ph-like B-ALL. Furthermore, single nucleotide variant analysis detected the oncogenic NRAS-G12D, KRAS-G12D, and KRAS-G13D mutations in three of the patient samples, presenting targetable mutations. Additional oncogenic variants and gene fusions were uncovered, as well as multiple variants in the PDE4DIP gene across five of the patient samples.

CONCLUSION

We demonstrate that RNA-seq is an effective tool for precision medicine in B-ALL by providing comprehensive molecular profiling of leukemia cells, identifying subtype and oncogenic lesions, and stratifying patients for appropriate therapy.

Development and External Validation of Models to Predict Persistent Hypoxemic Respiratory Failure for Clinical Trial Enrichment

OBJECTIVES

Improving the efficiency of clinical trials in acute hypoxemic respiratory failure (HRF) depends on enrichment strategies that minimize enrollment of patients who quickly resolve with existing care and focus on patients at high risk for persistent HRF. We aimed to develop parsimonious models predicting risk of persistent HRF using routine data from ICU admission and select research immune biomarkers.

DESIGN

Prospective cohorts for derivation ( n = 630) and external validation ( n = 511).

SETTING

Medical and surgical ICUs at two U.S. medical centers.

PATIENTS

Adults with acute HRF defined as new invasive mechanical ventilation (IMV) and hypoxemia on the first calendar day after ICU admission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We evaluated discrimination, calibration, and practical utility of models predicting persistent HRF risk (defined as ongoing IMV and hypoxemia on the third calendar day after admission): 1) a clinical model with least absolute shrinkage and selection operator (LASSO) selecting Pa o2 /F io2 , vasopressors, mean arterial pressure, bicarbonate, and acute respiratory distress syndrome as predictors; 2) a model adding interleukin-6 (IL-6) to clinical predictors; and 3) a comparator model with Pa o2 /F io2 alone, representing an existing strategy for enrichment. Forty-nine percent and 69% of patients had persistent HRF in derivation and validation sets, respectively. In validation, both LASSO (area under the receiver operating characteristic curve, 0.68; 95% CI, 0.64-0.73) and LASSO + IL-6 (0.71; 95% CI, 0.66-0.76) models had better discrimination than Pa o2 /F io2 (0.64; 95% CI, 0.59-0.69). Both models underestimated risk in lower risk deciles, but exhibited better calibration at relevant risk thresholds. Evaluating practical utility, both LASSO and LASSO + IL-6 models exhibited greater net benefit in decision curve analysis, and greater sample size savings in enrichment analysis, compared with Pa o2 /F io2 . The added utility of LASSO + IL-6 model over LASSO was modest.

CONCLUSIONS

Parsimonious, interpretable models that predict persistent HRF may improve enrichment of trials testing HRF-targeted therapies and warrant future validation.

MicroRNA in cardiometabolic health and disease: The perspectives of sex, gender and personalised medicine

BACKGROUND

Personalized medicine represents a novel and integrative approach that focuses on an individual's genetics and epigenetics, precision medicine, lifestyle and exposures as key players of health status and disease phenotypes.

METHODS

In this narrative review, we aim to carefully discuss the current knowledge on gender disparities in cardiometabolic diseases, and we consider the sex- specific expression of miRNAs and their role as promising tool in precision medicine.

RESULTS

Personalised medicine overcomes the restricted care of patient based on a binomial sex approach, by enriching itself with a holistic and dynamic gender integration. Recognized as a major worldwide health emergency, cardiometabolic disorders continue to rise, impacting on health systems and requiring more effective and targeted strategies. Several sex and gender drivers might affect the onset and progression of cardiometabolic disorders in males and females at multiple levels. In this respect, distinct contribution of genetic and epigenetic mechanisms, molecular and physiological pathways, sex hormones, visceral fat and subcutaneous fat and lifestyle lead to differences in disease burden and outcomes in males and females.

CONCLUSIONS

Sex and gender play a pivotal role in precision medicine because the influence the physiology of each individual and the way they interact with environment from intrauterine life.

Effectiveness of Clopidogrel vs Alternative P2Y12 Inhibitors Based on the ABCD-GENE Score

BACKGROUND

An ABCD-GENE (age, body mass index, chronic kidney disease, diabetes, and CYP2C19 genetic variants) score ≥10 predicts reduced clopidogrel effectiveness, but its association with response to alternative therapy remains unclear.

OBJECTIVES

The aim of this study was to evaluate the association between ABCD-GENE score and the effectiveness of clopidogrel vs alternative P2Y12 inhibitor (prasugrel or ticagrelor) therapy after percutaneous coronary intervention (PCI).

METHODS

A total of 4,335 patients who underwent PCI, CYP2C19 genotyping, and P2Y12 inhibitor treatment were included. The primary outcome was major atherothrombotic events (MAE) within 1 year after PCI. Cox regression was performed to assess event risk in clopidogrel-treated (reference) vs alternatively treated patients, with stabilized inverse probability weights derived from exposure propensity scores after stratifying by ABCD-GENE score and further by CYP2C19 loss-of-function (LOF) genotype.

RESULTS

Among patients with scores <10 (n = 3,200), MAE was not different with alternative therapy vs clopidogrel (weighted HR: 0.89; 95% CI: 0.65-1.22; P = 0.475). The risk for MAE also did not significantly differ by treatment among patients with scores ≥10 (n = 1,135; weighted HR: 0.75; 95% CI: 0.51-1.11; P = 0.155). Among CYP2C19 LOF allele carriers, MAE risk appeared lower with alternative therapy in both the group with scores <10 (weighted HR: 0.50; 95% CI: 0.25-1.01; P = 0.052) and the group with scores ≥10 (weighted HR: 0.48; 95% CI: 0.29-0.80; P = 0.004), while there was no difference in the group with scores <10 and no LOF alleles (weighted HR: 1.03; 95% CI: 0.70-1.51; P = 0.885).

CONCLUSIONS

These data support the use of alternative therapy over clopidogrel in CYP2C19 LOF allele carriers after PCI, regardless of ABCD-GENE score, while clopidogrel is as effective as alternative therapy in non-LOF patients with scores <10.

Efficacy of bupropion and varenicline genetic markers in choosing pharmacological treatment for smoking cessation, and implications for combining drugs: A randomized controlled trial - GENTSMOKING

INTRODUCTION

Smoking cessation is the best strategy for reducing tobacco-related morbimortality. The goal of this randomized controlled trial was to test whether using the genetically favorable markers to choose a smoking cessation drug treatment (precision medicine) was superior to using the most effective drug (varenicline) in terms of abstinence rates. Additionally, combination therapy was tested when monotherapy failed.

METHODS

This partially blind, single-center study randomized (1:1) 361 participants into two major groups. In the genetic group (n=184), CYP2B6 rs2279343 (genotype AA) participants started treatment with bupropion, and CHRNA4 rs1044396 (genotype CT or TT) participants started treatment with varenicline; when genetic favorable to both, participants started treatment with bupropion, and when favorable to neither, on both drugs. In the control group (n=177), participants started treatment with varenicline, regardless of genetic markers. Drug treatment lasted 12 weeks. Efficacy endpoints were abstinence rates at Weeks 4, and Weeks 8-12, biochemically validated by carbon monoxide in exhaled air. Participants who did not achieve complete abstinence at Week 4, regardless of group, were given the choice to receive combination therapy.

RESULTS

Abstinence rates were 42.9% (95% CI: 36-64) in the control group versus 30.4% (95% CI: 23-37) in the genetic group at Week 4 (p=0.01); and 74% (95% CI: 67-80) versus 52% (95% CI: 49-64) at Week 12 (p<0.001), respectively. The strategy of combining drugs after Week 4 increased abstinence rates in both groups and the significant difference between genetic and control groups was maintained.

CONCLUSIONS

Results show that using these selected genetic markers was inferior to starting treatment with varenicline (control group), which is currently the most effective smoking cessation drug; moreover, the addition of bupropion in cases of varenicline monotherapy failure improves the efficacy rate until the end of treatment.

CLINICAL TRIAL IDENTIFIER

NCT03362099.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

Validation of the RSClin risk calculator in the National Cancer Data Base

BACKGROUND

Guidelines recommend the use of genomic assays such as OncotypeDx to aid in decisions regarding the use of chemotherapy for hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. The RSClin prognostic tool integrates OncotypeDx and clinicopathologic features to predict distant recurrence and chemotherapy benefit, but further validation is needed before broad clinical adoption.

METHODS

This study included patients from the National Cancer Data Base (NCDB) who were diagnosed with stage I-III HR+/HER2- breast cancer from 2010 to 2020 and received adjuvant endocrine therapy with or without chemotherapy. RSClin-predicted chemotherapy benefit was stratified into low (<3% reduction in distant recurrence), intermediate (3%-5%), and high (>5%). Cox models were used to model mortality adjusted for age, comorbidity index, insurance, and race/ethnicity.

RESULTS

A total of 285,441 patients were identified for inclusion from the NCDB, with an average age of 60 years and a median follow-up of 58 months. Chemotherapy was associated with improved overall survival only for those predicted to have intermediate (adjusted hazard ratio [aHR], 0.68; 95% confidence interval [CI], 0.60-0.79) and high benefit per RSClin (aHR, 0.66; 95% CI, 0.61-0.72). Consistent benefit was seen in the subset with a low OncotypeDx score (<26) and intermediate (aHR, 0.66; 95% CI, 0.53-0.82) or high (aHR, 0.71; 95% CI, 0.58-0.86) RSClin-predicted benefit. No survival benefit with chemotherapy was seen in patients with a high OncotypeDx score (≥26) and low benefit per RSClin (aHR, 1.70; 95% CI, 0.41-6.99).

CONCLUSIONS

RSClin may identify high-risk patients who benefit from treatment intensification more accurately than OncotypeDx, and further prospective study is needed.

Improving predictive accuracy in primary biliary cholangitis: A new genetic risk score

BACKGROUND AND AIMS

Genetic variants influence primary biliary cholangitis (PBC) risk. We established and tested an accurate polygenic risk score (PRS) using these variants.

METHODS

Data from two Italian cohorts (OldIT 444 cases, 901 controls; NewIT 255 cases, 579 controls) were analysed. The latest international genome-wide meta-analysis provided effect size estimates. The PRS, together with human leukocyte antigen (HLA) status and sex, was included in an integrated risk model.

RESULTS

Starting from 46 non-HLA genes, 22 variants were selected. PBC patients in the OldIT cohort showed a higher risk score than controls: -.014 (interquartile range, IQR, -.023, .005) versus -.022 (IQR -.030, -.013) (p < 2.2 × 10-16). For genetic-based prediction, the area under the curve (AUC) was .72; adding sex increased the AUC to .82. Validation in the NewIT cohort confirmed the model's accuracy (.71 without sex, .81 with sex). Individuals in the top group, representing the highest 25%, had a PBC risk approximately 14 times higher than that of the reference group (lowest 25%; p < 10-6).

CONCLUSION

The combination of sex and a novel PRS accurately discriminated between PBC cases and controls. The model identified a subset of individuals at increased risk of PBC who might benefit from tailored monitoring.

Harnessing gastrointestinal organoids for cancer therapy

Gastrointestinal organoids have emerged as a model system that authentically recapitulates the in vivo situation. Despite biomedical and technical challenges, self-assembled 3D structures derived from pluripotent stem cells or healthy and diseased tissues have proved to be invaluable tools for cancer drug discovery, disease modeling, and studying infection with carcinogenic pathogens.

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

Genomic multidisciplinary teams: A model for navigating genetic mainstreaming and precision medicine

AIM

Recent rapid advances in genomics are revolutionising patient diagnosis and management of genetic conditions. However, this has led to many challenges in service provision, education and upskilling requirements for non-genetics health-care professionals and remuneration for genomic testing. In Australia, Medicare funding with a Paediatric genomic testing item for patients with intellectual disability or syndromic features has attempted to address this latter issue. The Sydney Children's Hospitals Network - Westmead (SCHN-W) Clinical Genetics Department established Paediatric and Neurology genomic multidisciplinary team (MDT) meetings to address the Medicare-specified requirement for discussion with clinical genetics, and increasing genomic testing advice requests.

METHODS

This SCHN-W genomic MDT was evaluated with two implementation science frameworks - the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and GMIR - Genomic Medicine Integrative Research frameworks. Data from June 2020 to July 2022 were synthesised and evaluated, as well as process mapping of the MDT service.

RESULTS

A total of 205 patients were discussed in 34 MDT meetings, facilitating 148 genomic tests, of which 73 were Medicare eligible. This was equivalent to 26% of SCHN-W genetics outpatient activity, and 13% of all Medicare-funded paediatric genomic testing in NSW. 39% of patients received a genetic diagnosis.

CONCLUSION

The genomic MDT facilitated increased genomic testing at a tertiary paediatric centre and is an effective model for mainstreaming and facilitating precision medicine. However, significant implementation issues were identified including cost and sustainability, as well as the high level of resourcing that will be required to scale up this approach to other areas of medicine.

A Clinical Trial of High Dose Growth Hormone in a Patient with a Dominant Negative Growth Hormone Receptor Mutation

CONTEXT

Rare patients with short stature and growth hormone (GH) resistance have dominant-negative variants in the GH receptor. We describe a patient with GH resistance due to elevated levels of GH binding protein and demonstrate the potential for a precision medicine intervention.

OBJECTIVE

To determine whether high dose GH can overcome GH resistance in this specific patient resulting in normal IGF-1 levels and improved growth rates.

DESIGN

Single patient trial of ascending doses of GH followed by dose stable phase; total 12 months of treatment.

PATIENT

Patient has a heterozygous variant in GH receptor resulting in elevated levels of GH binding protein manifesting as GH resistance and severe short stature.

INTERVENTIONS

Daily subcutaneous GH starting at 50 micrograms/kg/day and escalating to 250 micrograms/kg/day until goal IGF-1 achieved. Subject continued 250 micrograms/kg/day for a total treatment duration of 12 months.

OUTCOME MEASURES

The primary outcome measure was the dose of GH required to achieve an IGF-1 level above the mid-point of the normal range. Secondary endpoints included height velocity and the change in height SDS during the 1st year of treatment.

RESULTS

A dose of GH of 250 micrograms/kg/day achieved the target IGF-1 level. The patient's annualized height velocity was 8.7 cm/year, an increase of 3.4 cm/year from baseline, resulting in a 0.81 SD gain in height.

CONCLUSIONS

A precision medicine approach of extremely high dose GH was able to overcome GH resistance in a patient with a dominant-negative variant in the GH receptor resulting in elevated GH binding protein levels.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels

Artificial intelligence or machine learning (AI/ML) based systems can be used to help personalize prescribing decisions for individual patients. These AI/ML clinical decision support systems may provide either specific or more open-ended recommendations for the most appropriate medications to prescribe. These systems must fundamentally relate to the label of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. The label for a medicine may evolve as new information on safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. Therefore, any AI/ML recommendation system would need to reference these label updates. However, the speed and consistency which these updates are made may influence the safety of prescribing decisions, since change control procedures and revalidation of algorithms may slow down any changes. This is especially important if changes need to be made quickly to protect patients. These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within this conversation. Furthermore, the guiding role that regulators have in regulating the development and use of these AI/ML clinical decision support systems is highlighted.

Revolutionizing Cancer Treatment: The Promising Horizon of Zein Nanosystems

Various nanomaterials have recently become fascinating tools in cancer diagnostic applications because of their multifunctional and inherent molecular characteristics that support efficient diagnosis and image-guided therapy. Zein nanoparticles are a protein derived from maize. It belongs to the class of prolamins possessing a spherical structure with conformational properties similar to those of conventional globular proteins like ribonuclease and insulin. Zein nanoparticles have gained massive interest over the past couple of years owing to their natural hydrophilicity, ease of functionalization, biodegradability, and biocompatibility, thereby improving oral bioavailability, nanoparticle targeting, and prolonged drug administration. Thus, zein nanoparticles are becoming a promising candidate for precision cancer drug delivery. This review highlights the clinical significance of applying zein nanosystems for cancer theragnostic─moreover, the role of zein nanosystems for cancer drug delivery, anticancer agents, and gene therapy. Finally, the difficulties and potential uses of these NPs in cancer treatment and detection are discussed. This review will pave the way for researchers to develop theranostic strategies for precision medicine utilizing zein nanosystems.

Cognitive functioning in untreated glioma patients: The limited predictive value of clinical variables

BACKGROUND

Previous research identified many clinical variables that are significantly related to cognitive functioning before surgery. It is not clear whether such variables enable accurate prediction for individual patients' cognitive functioning because statistical significance does not guarantee predictive value. Previous studies did not test how well cognitive functioning can be predicted for (yet) untested patients. Furthermore, previous research is limited in that only linear or rank-based methods with small numbers of variables were used.

METHODS

We used various machine learning models to predict preoperative cognitive functioning for 340 patients with glioma across 18 outcome measures. Predictions were made using a comprehensive set of clinical variables as identified from the literature. Model performances and optimized hyperparameters were interpreted. Moreover, Shapley additive explanations were calculated to determine variable importance and explore interaction effects.

RESULTS

Best-performing models generally demonstrated above-random performance. Performance, however, was unreliable for 14 out of 18 outcome measures with predictions worse than baseline models for a substantial number of train-test splits. Best-performing models were relatively simple and used most variables for prediction while not relying strongly on any variable.

CONCLUSIONS

Preoperative cognitive functioning could not be reliably predicted across cognitive tests using the comprehensive set of clinical variables included in the current study. Our results show that a holistic view of an individual patient likely is necessary to explain differences in cognitive functioning. Moreover, they emphasize the need to collect larger cross-center and multimodal data sets.

An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data

We describe an approach for combining and analyzing high-dimensional genomic and low-dimensional phenotypic data. The approach leverages a scheme of weights applied to the variables instead of observations and, hence, permits incorporation of the information provided by the low dimensional data source. It can also be incorporated into commonly used downstream techniques, such as random forest or penalized regression. Finally, the simulated lupus studies involving genetic and clinical data are used to illustrate the overall idea and show that the proposed enriched penalized method can select significant genetic variables while keeping several important clinical variables in the final model.

Multi-omics analysis of adamantinomatous craniopharyngiomas reveals distinct molecular subgroups with prognostic and treatment response significance

BACKGROUND

Adamantinomatous craniopharyngioma (ACP) is the commonest pediatric sellar tumor. No effective drug is available and interpatient heterogeneity is prominent. This study aimed to identify distinct molecular subgroups of ACP based on the multi-omics profiles, imaging findings, and histological features, in order to predict the response to anti-inflammatory treatment and immunotherapies.

METHODS

Totally 142 Chinese cases diagnosed with craniopharyngiomas were profiled, including 119 ACPs and 23 papillary craniopharyngiomas. Whole-exome sequencing (151 tumors, including recurrent ones), RNA sequencing (84 tumors), and DNA methylome profiling (95 tumors) were performed. Consensus clustering and non-negative matrix factorization were used for subgrouping, and Cox regression were utilized for prognostic evaluation, respectively.

RESULTS

Three distinct molecular subgroups were identified: WNT, ImA, and ImB. The WNT subgroup showed higher Wnt/β-catenin pathway activity, with a greater number of epithelial cells and more predominantly solid tumors. The ImA and ImB subgroups had activated inflammatory and interferon response pathways, with enhanced immune cell infiltration and more predominantly cystic tumors. Mitogen-activated protein kinases (MEK/MAPK) signaling was activated only in ImA samples, while IL-6 and epithelial-mesenchymal transition biomarkers were highly expressed in the ImB group, mostly consisting of children. The degree of astrogliosis was significantly elevated in the ImA group, with severe finger-like protrusions at the invasive front of the tumor. The molecular subgrouping was an independent prognostic factor, with the WNT group having longer event-free survival than ImB (Cox, P = 0.04). ImA/ImB cases were more likely to respond to immune checkpoint blockade (ICB) therapy than the WNT group ( P <0.01). In the preliminary screening of subtyping markers, CD38 was significantly downregulated in WNT compared with ImA and ImB ( P = 0.01).

CONCLUSIONS

ACP comprises three molecular subtypes with distinct imaging and histological features. The prognosis of the WNT type is better than that of the ImB group, which is more likely to benefit from the ICB treatment.

Clinical efficacy of tumor organoid-guided cancer therapy for locally advanced unresectable or metastatic breast cancer

Patient-derived organoids (PDOs) may facilitate treatment selection. This retrospective cohort study evaluated the feasibility and clinical benefit of using PDOs to guide personalized treatment in metastatic breast cancer (MBC). Patients diagnosed with MBC were recruited between January 2019 and August 2022. PDOs were established and the efficacy of customized drug panels was determined by measuring cell mortality after drug exposure. Patients receiving organoid-guided treatment (OGT) were matched 1:2 by nearest neighbor propensity scores with patients receiving treatment of physician's choice (TPC). The primary outcome was progression-free survival. Secondary outcomes included objective response rate and disease control rate. Targeted gene sequencing and pathway enrichment analysis were performed. Forty-six PDOs (46 of 51, 90.2%) were generated from 45 MBC patients. PDO drug screening showed an accuracy of 78.4% (95% CI 64.9%-91.9%) in predicting clinical responses. Thirty-six OGT patients were matched to 69 TPC patients. OGT was associated with prolonged median progression-free survival (11.0 months vs. 5.0 months; hazard ratio 0.53 [95% CI 0.33-0.85]; p = .01) and improved disease control (88.9% vs. 63.8%; odd ratio 4.26 [1.44-18.62]) compared with TPC. The objective response rate of both groups was similar. Pathway enrichment analysis in hormone receptor-positive, human epidermal growth factor receptor 2-negative patients demonstrated differentially modulated pathways implicated in DNA repair and transcriptional regulation in those with reduced response to capecitabine/gemcitabine, and pathways associated with cell cycle regulation in those with reduced response to palbociclib. Our study shows that PDO-based functional precision medicine is a feasible and effective strategy for MBC treatment optimization and customization.

Genetics of cystogenesis in base-edited human organoids reveal therapeutic strategies for polycystic kidney disease

In polycystic kidney disease (PKD), microscopic tubules expand into macroscopic cysts. Among the world's most common genetic disorders, PKD is inherited via heterozygous loss-of-function mutations but is theorized to require additional loss of function. To test this, we establish human pluripotent stem cells in allelic series representing four common nonsense mutations, using CRISPR base editing. When differentiated into kidney organoids, homozygous mutants spontaneously form cysts, whereas heterozygous mutants (original or base corrected) express no phenotype. Using these, we identify eukaryotic ribosomal selective glycosides (ERSGs) as PKD therapeutics enabling ribosomal readthrough of these same nonsense mutations. Two different ERSGs not only prevent cyst initiation but also limit growth of pre-formed cysts by partially restoring polycystin expression. Furthermore, glycosides accumulate in cyst epithelia in organoids and mice. Our findings define the human polycystin threshold as a surmountable drug target for pharmacological or gene therapy interventions, with relevance for understanding disease mechanisms and future clinical trials.

Simulated cost-effectiveness of a novel precision-guided dosing strategy in adult patients with Crohn's disease initiating infliximab maintenance therapy

BACKGROUND

Patients with Crohn's disease (CD) who lose response to biologics experience reduced quality of life (QoL) and costly hospitalizations. Precision-guided dosing (PGD) provides a comprehensive pharmacokinetic (PK) profile that allows for biologic dosing to be personalized. We analyzed the cost-effectiveness of infliximab (IFX) PGD relative to two other dose intensification strategies (DIS).

METHODS

We developed a hybrid (Markov and decision tree) model of patients with CD who had a clinical response to IFX induction. The analysis had a US payer perspective, a base case time horizon of 5 years, and a 4-week cycle length. There were three IFX dosing comparators: PGD; dose intensification based on symptoms, inflammatory markers, and trough IFX concentration (DIS1); and dose intensification based on symptoms alone (DIS2). Patients that failed IFX initiated ustekinumab, followed by vedolizumab, and conventional therapy. Transition probabilities for IFX were estimated from real-world clinical PK data and interventional clinical trial patient-level data. All other transition probabilities were derived from published randomized clinical trials and cost-effectiveness analyses. Utility values were sourced from previous health technology assessments. Direct costs included biologic acquisition and infusion, surgeries and procedures, conventional therapy, and lab testing. The primary outcomes were incremental cost-effectiveness ratios (ICERs). The robustness of results was assessed via one-way sensitivity, scenario, and probabilistic sensitivity analyses (PSA).

RESULTS

PGD was the cost-effective IFX dosing strategy with an ICER of 122,932 $ per quality-adjusted life year (QALY) relative to DIS1 and dominating DIS2. PGD had the lowest percentage (1.1%) of patients requiring a new biologic through 5 years (8.9% and 74.4% for DIS1 and DIS2, respectively). One-way sensitivity analysis demonstrated that the cost-effectiveness of PGD was most sensitive to the time between IFX doses. PSA demonstrated that joint parameter uncertainty had moderate impact on some results.

CONCLUSIONS

PGD provides clinical and QoL benefits by maintaining remission and avoiding IFX failure; it is the most cost-effective under conservative assumptions.

A Somatic BRCA2-Mutated Pancreatic Adenocarcinoma With Sustained Exceptional Response to Modified FOLFIRINOX

Homologous recombination repair (HRR) pathway deficiency opens multiple therapeutic avenues within pancreatic cancer. Patients with HRR deficiency-associated gene mutations such as BRCA1, BRCA2, and PALB2 are more susceptible to platinum-based chemotherapies and in those with somatic BRCA mutations, PARP inhibitor therapy prolongs progression-free survival. The case discussed herein illustrates the therapeutic opportunities offered through the identification of HRR deficiency in pancreatic cancer, as well as the challenges associated with treatment and prevention of central nervous system metastases in long-term survivors of pancreatic cancer.

Retinal Imaging Findings in Inherited Retinal Diseases

Inherited retinal diseases (IRDs) represent one of the major causes of progressive and irreversible vision loss in the working-age population. Over the last few decades, advances in retinal imaging have allowed for an improvement in the phenotypic characterization of this group of diseases and have facilitated phenotype-to-genotype correlation studies. As a result, the number of clinical trials targeting IRDs has steadily increased, and commensurate to this, the need for novel reproducible outcome measures and endpoints has grown. This review aims to summarize and describe the clinical presentation, characteristic imaging findings, and imaging endpoint measures that are being used in clinical research on IRDs. For the purpose of this review, IRDs have been divided into four categories: (1) panretinal pigmentary retinopathies affecting rods or cones; (2) macular dystrophies; (3) stationary conditions; (4) hereditary vitreoretinopathies.

The potential role of precision medicine to alleviate racial disparities in prostate, bladder and renal urological cancer care

Background

Racial disparities in oncological outcomes resulting from differences in social determinants of health (SDOH) and tumour biology are well described in prostate cancer (PCa) but similar inequities exist in bladder (BCa) and renal cancers (RCCs). Precision medicine (PM) aims to provide personalized treatment based on individual patient characteristics and has the potential to reduce these inequities in GU cancers.

Objective

This article aims to review the current evidence outlining racial disparities in GU cancers and explore studies demonstrating improved oncological outcomes when PM is applied to racially diverse patient populations.

Evidence acquisition

Evidence was obtained from Pubmed and Web of Science using keywords prostate, bladder and renal cancer, racial disparity and precision medicine. Because limited studies were found, preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were not applied but rather related articles were studied to explore existing debates, identify the current status and speculate on future applications.

Results

Evidence suggests addressing SDOH for PCa can reverse racial inequities in oncological outcomes but differences in incidence remain. Similar disparities in BCa and RCC are seen, and it would be reasonable to suggest achieving parity in SDOH for all races would do the same. Research applying a PM approach to different ethnicities is lacking although in African Americans (AAs) with metastatic castrate-resistant prostate cancer (mCRPCa) better outcomes have been shown with androgen receptor inhibitors, radium-223 and sipuleucel. Exploiting the abscopal effect with targeted radiation therapy (RT) and immunotherapy has promise but requires further study, as does defining actionable mutations in specific patient groups to tailor treatments as appropriate.

Conclusion

For all GU cancers, the historical underrepresentation of ethnic minorities in clinical trials still exists and there is an urgent need for recruitment strategies to address this. PM is a promising development with the potential to reduce inequities in GU cancers, however, both improved understanding of race-specific tumour biology, and enhanced recruitment of minority populations into clinical trials are required. Without this, the benefits of PM will be limited.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists

INTRODUCTION

The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes.

AREAS COVERED

We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed.

EXPERT OPINION

The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.

Xenograft and organoid models in developing precision medicine for gastric cancer (Review)

Gastric cancer (GC), a highly heterogeneous disease, has diverse histological and molecular subtypes. For precision medicine, well‑characterized models encompassing the full spectrum of subtypes are necessary. Patient‑derived tumor xenografts and organoids serve as important preclinical models in GC research. The main advantage of these models is the retention of phenotypic and genotypic heterogeneity present in parental tumor tissues. Utilizing diverse sequencing techniques and preclinical models for GC research facilitates accuracy in predicting personalized clinical responses to anti‑cancer treatments. The present review summarizes the latest advances of these two preclinical models in GC treatment and drug response assessment.

Imagenetics for Precision Medicine in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common heart muscle disorder of nonischemic etiology associated with heart failure development and the risk of malignant ventricular arrhythmias and sudden cardiac death. A tailored approach to risk stratification and prevention of sudden cardiac death is required in genetic DCM given its variable presentation and phenotypic severity. Currently, advances in cardiogenetics have shed light on disease mechanisms, the complex genetic architecture of DCM, polygenic contributors to disease susceptibility and the role of environmental triggers. Parallel advances in imaging have also enhanced disease recognition and the identification of the wide spectrum of phenotypes falling under the DCM umbrella. Genotype-phenotype associations have been also established for specific subtypes of DCM, such as DSP (desmoplakin) or FLNC (filamin-C) cardiomyopathy but overall, they remain elusive and not readily identifiable. Also, despite the accumulated knowledge on disease mechanisms, certain aspects remain still unclear, such as which patients with DCM are at risk for disease progression or remission after treatment. Imagenetics, that is, the combination of imaging and genetics, is expected to further advance research in the field and contribute to precision medicine in DCM management and treatment. In the present article, we review the existing literature in the field, summarize the established knowledge and emerging data on the value of genetics and imaging in establishing genotype-phenotype associations in DCM and in clinical decision making for DCM patients.

Recent advances in patient-derived tumor organoids for reconstructing TME of head and neck cancer

BACKGROUND

The differences between existing preclinical models and the tumor microenvironment in vivo are one of the significant challenges hindering cancer therapy development. Patient-derived tumor organoids (PDTO) can highly retain tumor heterogeneity. Thus, it provides a more reliable platform for research in tumor biology, new drug screening, and precision medicine.

METHODS

We conducted a systematic review to summarise the characteristics of the existing preclinical models, the advantages of patient-derived tumor organoids in reconstructing the tumor microenvironment, and the latest research progress. Moreover, this study deciphers organoid culture technology in the clinical precision treatment of head and neck cancer to achieve better transformation. Studies were identified through a comprehensive search of Ovid MEDLINE (Wolters Kluwer), PubMed (National Library of Medicine), web of Science (Thomson Reuters) and, Scopus (Elsevier) databases, without publication date or language restrictions.

RESULTS

In tumor development, the interaction between cellular and non-cellular components in the tumor microenvironment (TME) has a crucial role. Co-culture, Air-liquid interface culture, microfluidics, and decellularized matrix have depicted great potential in reconstructing the tumor microenvironment and simulating tumor genesis, development, and metastasis.

CONCLUSION

An accurate determination of stromal cells, immune cells, and extracellular matrix can be achieved by reconstructing the head and neck cancer tumor microenvironment using the PDTO model. Moreover, the interaction between head and neck cancer cells can also play an essential role in implementing the individualized precision treatment of head and neck cancer.

Precision Hypertension

Hypertension affects >1 billion people worldwide. Complications of hypertension include stroke, renal failure, cardiac hypertrophy, myocardial infarction, and cardiac failure. Despite the development of various antihypertensive drugs, the number of people with uncontrolled hypertension continues to rise. While the lack of compliance associated with frequent side effects to medication is a contributory issue, there has been a failure to consider the diverse nature of hypertensive populations. Instead, we propose that hypertension can only be truly managed by precision. A precision medicine approach would consider each patient's unique factors. In this review, we discuss the progress toward precision medicine for hypertension with more predictiveness and individualization of treatment. We will highlight the advances in data science, omics (genomics, metabolomics, proteomics, etc), artificial intelligence, gene therapy, and gene editing and their application to precision hypertension.

A successful linkage of a named patient products of sublingual immunotherapy-dispensing registry to French healthcare insurance database (SNDS): methodological constitution of the EfficAPSI cohort

BACKGROUND

The only causal treatment for allergic rhinitis (AR) is allergen immunotherapy (AIT) including personalized liquid sublingual AIT (SLIT). We present the methodology for establishing the EfficAPSI cohort to further evaluate the real-life effectiveness and use of SLIT liquid.

RESEARCH DESIGN AND METHODS

The EfficAPSI cohort was constituted by deterministic linkage of Stallergenes Greer dispensing and nationwide French healthcare insurance system (SNDS) databases. Data from 2006 to 2018 were extracted. All patients who initiated Stallergenes Greer SLIT liquid between 2010 and 2013 were considered as exposed and those dispensed with AR symptomatic treatment only as control. To limit the impact of confounding, the models will be weighted using the inverse probability of treatment weighting (IPTW).

RESULTS

A total of 445,574 patients were included; median age was 38 years; 59.1% were female. Exposed patients (n = 112,492) were significantly younger, more frequently males, and less likely to have comorbidities than controls (n = 333,082). After IPTW, patients' characteristics from both groups were similar.

CONCLUSIONS

To date, the EfficAPSI cohort has the largest number of person-years of follow-up in the field of AIT. The completeness of the data allows to evaluate SLIT liquid effectiveness with rigorous methodology, leading to important insights on personalized medicine in real-life.

Metabolic profiling of aortic stenosis and hypertrophic cardiomyopathy identifies mechanistic contrasts in substrate utilization

Aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated. We undertook an invasive (aortic root, coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison with non-LVH controls to investigate cardiac fuel selection and metabolic remodeling. These patients were assessed under different physiological states (at rest, during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts. We identified a highly discriminant metabolomic signature in severe AS in all samples, regardless of sampling site, characterized by striking accumulation of long-chain acylcarnitines, intermediates of fatty acid transport across the inner mitochondrial membrane, and validated this in a separate cohort. Mechanistically, we identify a downregulation in the PPAR-α transcriptional network, including expression of genes regulating fatty acid oxidation (FAO). In silico modeling of β-oxidation demonstrated that flux could be inhibited by both the accumulation of fatty acids as a substrate for mitochondria and the accumulation of medium-chain carnitines which induce competitive inhibition of the acyl-CoA dehydrogenases. We present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate a progressive impairment of β-oxidation from HCM to AS, particularly for FAO of long-chain fatty acids, and that the PPAR-α signaling network may be a specific metabolic therapeutic target in AS.

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.

Cuproptosis-related molecular classification and gene signature of hepatocellular carcinoma and experimental verification

Background

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor overall prognosis. Cuproptosis, a recently proposed mode of copper-dependent cell death, plays a critical role in the malignant progression of various tumors; however, the expression and prognostic value of cuproptosis-related regulatory genes in HCC remain unclear.

Methods

Genomic, genetic, and expression profiles of ten key cuproptosis-related regulatory genes were analyzed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset and protein expression data from the Human Protein Atlas (HPA) database. Unsupervised clustering of HCC patients based on these ten key cuproptosis-related regulatory genes was used to identify different HCC subtypes and analyze the differences in clinical and immune characteristics among subtypes. Subsequently, univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox analyses were used to establish a cuproptosis-related prognostic signature, and the accuracy of prognostic signature prediction was internally validated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curve in TCGA training and testing cohorts. The prognostic signature was externally validated using TCGA-LIHC entire cohort and International Cancer Genome Consortium Liver Cancer (ICGC-LIRI) cohorts. Finally, the expression landscape of cuproptosis-related regulatory genes in prognostic signature was explored by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting and immunohistochemistry (IHC) experiments.

Results

Ten cuproptosis-related genes were differentially expressed in normal and HCC tissues. Unsupervised clustering identified two subtypes and HCC patients with these two subtypes had different clinical prognoses and immune characteristics, as well as different degrees of response to immunotherapy. Lipoyltransferase 1 (LIPT1), dihydrolipoamide s-acetyltransferase (DLAT), and cyclin dependent kinase inhibitor 2A (CDKN2A) were selected to construct a prognostic signature, which significantly distinguished HCC patients with different survival periods in the TCGA training and testing cohorts and was well validated in both the TCGA-LIHC entire cohort and ICGC-LIRI cohort. The risk score of the prognostic signature was confirmed to be an independent prognostic factor, and nomograms were generated to effectively predict the probability of HCC patient survival. The qRT-PCR, western blotting and IHC results also revealed a significant imbalance in the expression of these cuproptosis-related genes in HCC.

Conclusions

The classification and prognostic signature based on cuproptosis-related regulatory genes helps to explain the heterogeneity of HCC, which may contribute to the individualized treatment of patients with the disease.

Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm

Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his or her profiles. A standard grid search algorithm can be used to estimate the optimal linear PDR that maximizes the area under the receiver operating characteristics curve (AUC) among all the linear PDRs, but it is time-consuming especially when the number of variables is large. Alternatively, we developed an efficient grid rotation algorithm that provides a nearly suboptimal solution and studied its variation to find the optimal solution. We implemented the cross-validated forward variable selection method to find a subset of useful variables while avoid overfitting. Extensive simulations show that our proposed method reduces bias and variance. Analysis of a gastric cancer biomarker study and censored survival outcome data illustrates the practical utility of our proposed method. The proposed method is implemented in the open-source R package persDx.

Optimizing dynamic predictions from joint models using super learning

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Statistical inference on qualitative differences in the magnitude of an effect

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub-population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network-based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Co-design of a paediatric oncology medicines database (ProCure) to support complex care provision for children with a hard-to-treat cancer

Objectives

Paediatric oncologists often encounter challenges when seeking compassionate access to off-label therapies for their patients. This study employed implementation science and co-design techniques to develop the ProCure medicines database, with the goal of streamlining the application process and addressing identified barriers in paediatric oncology.

Methods

This study utilised an exploratory qualitative research design. Seventeen healthcare providers, including oncologists, nurse consultants, and allied health professionals, participated in semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) and a visual process map aid. Deductive qualitative data analysis, according to the CFIR constructs, identified key barriers and facilitators. Collaborative design sessions engaged multidisciplinary teams to develop the ProCure beta version.

Results

Barriers to off-label therapy access included resource-intensive applications, time sensitive decision-making, and complex pharmaceutical information. Facilitators included Drug Access Navigators, Molecular Tumour Boards, and a multi-disciplinary approach. ProCure addressed end-user needs by centralising medicines information. Additional features suggested by healthcare providers included blood-brain-barrier penetrability data and successful application examples.

Conclusion

ProCure represents a promising solution to the challenges paediatric oncologists face in accessing off-label therapies. By centralising information, it simplifies the application process, aids decision-making, and promotes a collaborative approach to patient care. The potential of the database to stream and enhance off-label therapy access underscores its relevance in improving paediatric oncology practise. Further research and implementation efforts are warranted to assess ProCure's real-world impact and refine its features based on user feedback.