Community cohesion looseness in gene networks reveals individualized drug targets and resistance.

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Unlocking Precision Medicine: Liquid Biopsy Advancements in Renal Cancer Detection and Monitoring.

Renal cell carcinoma (RCC) remains a formidable diagnostic challenge, especially in the context of small renal masses. The quest for non-invasive screening tools and biomarkers has steered research towards liquid biopsy, focusing on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). MiRNAs, small non-coding RNAs, exhibit notable dysregulation in RCC, offering promising avenues for diagnosis and prognosis. Studies underscore their potential across various biofluids, including plasma, serum, and urine, for RCC detection and subtype characterization. Encouraging miRNA signatures show correlations with overall survival, indicative of their future relevance in RCC management. Exosomes, with their diverse molecular cargo, including miRNAs, emerge as enticing biomarkers, while CTCs, emanating from primary tumors into the bloodstream, provide valuable insights into cancer progression. Despite these advancements, clinical translation necessitates further validation and standardization, encompassing larger-scale studies and robust evidence generation. Currently lacking approved diagnostic assays for renal cancer, the potential future applications of liquid biopsy in follow-up care, treatment selection, and outcome prediction in RCC patients are profound. This review aims to discuss and highlight recent advancements in liquid biopsy for RCC, exploring their strengths and weaknesses in the comprehensive management of this disease.

Personalized, Precision Medicine to Cure Alzheimer's Dementia: Approach #1.

The goal of the treatment for Alzheimer's dementia (AD) is the cure of dementia. A literature review revealed 18 major elements causing AD and 29 separate medications that address them. For any individual with AD, one is unlikely to discern which major causal elements produced dementia. Thus, for personalized, precision medicine, all causal elements must be treated so that each individual patient will have her or his causal elements addressed. Twenty-nine drugs cannot concomitantly be administered, so triple combinations of drugs taken from that list are suggested, and each triple combination can be administered sequentially, in any order. Ten combinations given over 13 weeks require 2.5 years, or if given over 26 weeks, they require 5.0 years. Such sequential treatment addresses all 18 elements and should cure dementia. In addition, any comorbid risk factors for AD whose first presence or worsening was within ±1 year of when AD first appeared should receive appropriate, standard treatment together with the sequential combinations. The article outlines a randomized clinical trial that is necessary to assess the safety and efficacy of the proposed treatments; it includes a triple-drug Rx for equipoise. Clinical trials should have durations of both 2.5 and 5.0 years unless the data safety monitoring board (DSMB) determines earlier success or futility since it is uncertain whether three or six months of treatment will be curative in humans, although studies in animals suggest that the briefer duration of treatment might be effective and restore defective neural tracts.

Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.

Diabetic kidney disease (DKD) is a chronic microvascular complication in patients with diabetes mellitus (DM) and the leading cause of end-stage kidney disease (ESKD). Although glomerulosclerosis, tubular injury and interstitial fibrosis are typical damages of DKD, the interplay of different processes (metabolic factors, oxidative stress, inflammatory pathway, fibrotic signaling, and hemodynamic mechanisms) appears to drive the onset and progression of DKD. A growing understanding of the pathogenetic mechanisms, and the development of new therapeutics, is opening the way for a new era of nephroprotection based on precision-medicine approaches. This review summarizes the therapeutic options linked to specific molecular mechanisms of DKD, including renin-angiotensin-aldosterone system blockers, SGLT2 inhibitors, mineralocorticoid receptor antagonists, glucagon-like peptide-1 receptor agonists, endothelin receptor antagonists, and aldosterone synthase inhibitors. In a new era of nephroprotection, these drugs, as pillars of personalized medicine, can improve renal outcomes and enhance the quality of life for individuals with DKD.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era.

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

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

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

Dual biomarkers-activatable hollow MnO2-Based theranostic nanoplatform for efficient breast cancer-specific multisite fluorescence imaging and synergistic therapy.

BACKGROUND: Theranostic nanoplatforms with integrated diagnostic imaging and multiple therapeutic functions play a vital role in precise diagnosis and efficient treatment for breast cancer, but unfortunately, these nanoplatforms are usually stuck in single-site imaging and single mode of treatment, causing unsatisfactory diagnostic and therapeutic efficiency. Herein, a dual biomarkers-activatable facile hollow mesoporous MnO2 (H-MnO2)-based theranostic nanoplatform, DNAzyme@H-MnO2-MUC1 aptamer (DHMM), was constructed for the simultaneous multi-site diagnosis and multiple treatment of breast cancer. RESULTS: The DHMM acted as an integrated diagnostic and therapeutic nanoplatform that realizes multi-site fluorescence imaging-guided high-efficient photothermal/chemodynamic/gene synergistic therapy (PTT/CDT/GT) for breast cancer. The H-MnO2 exhibits high loading capacity for Cy5-MUC1 aptamer (3.05 pmoL µg-1) and FAM-DNAzyme (3.37 pmoL µg-1), and excellent quenching for the probes. In the presence of MUC1 on the cell membrane and GSH in the cytoplasm, Cy5-MUC1 aptamer and FAM-DNAzyme was activated triggering dual-channel fluorescence imaging at different sites. Moreover, the self-supplied Mn2+ was further supplied as DNAzyme cofactors to catalytic cleavage intracellular EGR-1 mRNA for high-efficient GT and stimulated the Fenton-like reaction for CDT. The H-MnO2 also showcases a favorable photothermal performance with a photothermal conversion efficiency of 44.16%, which ultimately contributes to multi-site fluorescence imaging-guided synergistic treatment with an apoptosis rate of 71.82%. SIGNIFICANCE: This dual biomarker-activatable multiple therapeutic nanoplatform was realized multi-site fluorescence imaging-guided PTT/CDT/GT combination therapy for breast cancer with higher specificity and efficiency, which provides a promising theranostic nanoplatform for the precision and efficiency of breast cancer treatment.

New frontiers in salivary extracellular vesicles: transforming diagnostics, monitoring, and therapeutics in oral and systemic diseases.

Salivary extracellular vesicles (EVs) have emerged as key tools for non-invasive diagnostics, playing a crucial role in the early detection and monitoring of diseases. These EVs surpass whole saliva in biomarker detection due to their enhanced stability, which minimizes contamination and enzymatic degradation. The review comprehensively discusses methods for isolating, enriching, quantifying, and characterizing salivary EVs. It highlights their importance as biomarkers in oral diseases like periodontitis and oral cancer, and underscores their potential in monitoring systemic conditions. Furthermore, the review explores the therapeutic possibilities of salivary EVs, particularly in personalized medicine through engineered EVs for targeted drug delivery. The discussion also covers the current challenges and future prospects in the field, emphasizing the potential of salivary EVs in advancing clinical practice and disease management.

Tumor Organoids: The Era of Personalized Medicine.

The strategies of future medicine are aimed to modernize and integrate quality approaches including early molecular-genetic profiling, identification of new therapeutic targets and adapting design for clinical trials, personalized drug screening (PDS) to help predict and individualize patient treatment regimens. In the past decade, organoid models have emerged as an innovative in vitro platform with the potential to realize the concept of patient-centered medicine. Organoids are spatially restricted three-dimensional clusters of cells ex vivo that self-organize into complex functional structures through genetically programmed determination, which is crucial for reconstructing the architecture of the primary tissue and organs. Currently, there are several strategies to create three-dimensional (3D) tumor systems using (i) surgically resected patient tissue (PDTOs, patient-derived tumor organoids) or (ii) single tumor cells circulating in the patient's blood. Successful application of 3D tumor models obtained by co-culturing autologous tumor organoids (PDTOs) and peripheral blood lymphocytes have been demonstrated in a number of studies. Such models simulate a 3D tumor architecture in vivo and contain all cell types characteristic of this tissue, including immune system cells and stem cells. Components of the tumor microenvironment, such as fibroblasts and immune system cells, affect tumor growth and its drug resistance. In this review, we analyzed the evolution of tumor models from two-dimensional (2D) cell cultures and laboratory animals to 3D tissue-specific tumor organoids, their significance in identifying mechanisms of antitumor response and drug resistance, and use of these models in drug screening and development of precision methods in cancer treatment.

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

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

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

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

From Base Pairs to City Squares: Comprehensive Precision Oncology for the Future.

SUMMARY: An increased understanding of the role of the social determinants of health in cancer prevention, cancer care, and outcomes can lead to their integration into genetics and genomics as well as informing interventions and clinical trials, creating a comprehensive precision oncology framework.

The Cancer Spectrum Theory.

SUMMARY: Biological characteristics of tumors are heterogeneous, forming spectra in terms of several factors such as age at onset, anatomic spatial localization, tumor subtyping, and the degree of tumor aggressiveness (encompassing a neoplastic property spectrum). Instead of blindly using dichotomized approaches, the application of the multicategorical and continuous analysis approaches to detailed cancer spectrum data can contribute to a better understanding of the etiology of cancer, ultimately leading to effective prevention and precision oncology. We provide examples of cancer spectra and emphasize the importance of integrating the cancer spectrum theory into large-scale population cancer research.

Precision Endpoints for Contemporary Precision Oncology Trials.

SUMMARY: Traditional endpoints such as progression-free survival and overall survival do not fully capture the pharmacologic and pharmacodynamic effects of a therapeutic intervention. Incorporating mechanism-driven biomarkers and validated surrogate proximal endpoints can provide orthogonal readouts of anti-tumor activity and delineate the relative contribution of treatment components on an individual level, highlighting the limitation of solely relying on aggregated readouts from clinical trials to facilitate go/no-go decisions for precision therapies.

Emerging Theranostics for Prostate Cancer and a Model of Prostate-specific Membrane Antigen Therapy.

There is increasing demand worldwide to develop diagnostic and therapeutic (theranostic) markers for prostate cancer. One target of interest is prostate-specific membrane antigen (PSMA), a protein which is overexpressed in prostate cancer cells. Over the past decade, a growing body of literature has demonstrated that radiolabeled ligands that target PSMA show favorable clinical response and survival outcomes in patients with advanced prostate cancer. This focused review provides background to the development of PSMA as a target, an overview of key studies informing our current approach to radioligand-based imaging and therapy for prostate cancer, and a model for real-world implementation of PSMA theranostics based on an Australian experience.

Hybridized quantum dot, silica, and gold nanoparticles for targeted chemo-radiotherapy in colorectal cancer theranostics.

Multimodal nanoparticles, utilizing quantum dots (QDs), mesoporous silica nanoparticles (MSNs), and gold nanoparticles (Au NPs), offer substantial potential as a smart and targeted drug delivery system for simultaneous cancer therapy and imaging. This method entails coating magnetic GZCIS/ZnS QDs with mesoporous silica, loading epirubicin into the pores, capping with Au NPs, PEGylation, and conjugating with epithelial cell adhesion molecule (EpCAM) aptamers to actively target colorectal cancer (CRC) cells. This study showcases the hybrid QD@MSN-EPI-Au-PEG-Apt nanocarriers (size ~65 nm) with comprehensive characterizations post-synthesis. In vitro studies demonstrate the selective cytotoxicity of these targeted nanocarriers towards HT-29 cells compared to CHO cells, leading to a significant reduction in HT-29 cell survival when combined with irradiation. Targeted delivery of nanocarriers in vivo is validated by enhanced anti-tumor effects with reduced side effects following chemo-radiotherapy, along with imaging in a CRC mouse model. This approach holds promise for improved CRC theranostics.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.

BACKGROUND: Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program. METHODS: We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model's predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics. RESULTS: The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases. CONCLUSION: Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities.

Rapid technological advancements have created opportunities for new solutions in various industries, including healthcare. One exciting new direction in this field of innovation is the combination of skin-based technologies and augmented reality (AR). These dermatological devices allow for the continuous and non-invasive measurement of vital signs and biomarkers, enabling the real-time diagnosis of anomalies, which have applications in telemedicine, oncology, dermatology, and early diagnostics. Despite its many potential benefits, there is a substantial information vacuum regarding using flexible photonics in conjunction with augmented reality for medical purposes. This review explores the current state of dermal augmented reality and flexible optics in skin-conforming sensing platforms by examining the obstacles faced thus far, including technical hurdles, demanding clinical validation standards, and problems with user acceptance. Our main areas of interest are skills, chiroptical properties, and health platform applications, such as optogenetic pixels, spectroscopic imagers, and optical biosensors. My skin-enhanced spherical dichroism and powerful spherically polarized light enable thorough physical inspection with these augmented reality devices: diabetic tracking, skin cancer diagnosis, and cardiovascular illness: preventative medicine, namely blood pressure screening. We demonstrate how to accomplish early prevention using case studies and emergency detection. Finally, it addresses real-world obstacles that hinder fully realizing these materials' extraordinary potential in advancing proactive and preventative personalized medicine, including technical constraints, clinical validation gaps, and barriers to widespread adoption.

[Population characterization of mutations for sickle cell anemia and its treatment: One step towards personalized medicine for the disease]. / Caracterización poblacional de mutaciones relevantes para la Anemia Falciforme y su tratamiento: Un paso hacia la personalización de la enfermedad.

Sickle cell anemia (SCA) is the most common genetic disease worldwide. There are countries with massive public health programs for early detection of this condition. In the literature, several specific haplotypes or single-base polymorphic variants (SNPs) have been associated with the SCA prognosis. OBJECTIVE: To demonstrate the significant correlation of SNPs relevant to the diagnosis and prognosis of SCA among different ethnic groups. METHODOLOGY: we analyzed population frequencies and correlations of several SNPs related to the prognosis of SCA (i.e., baseline fetal hemoglobin levels), response to hydroxyurea treatment, and response to other drugs used in the SCA treatment, collected from validated genomic databases among different ethnic groups. RESULTS: The calculation of the Hardy-Weinberg equilibrium and the logistic regression was successful in classifying the ethnic groups as African (0 = 0.78, 1 = 0.89), and with a lower efficiency as American (AMR) (0 = 0.88, 1 = 0.00), East Asian (EAS) (0 = 0.80, 1 = 0.00), European (EUR) (0 = 0.79, 1 = 0.00), and South Asian (SAS) (0 = 0.80, 1 = 0.00). CONCLUSIONS: The results extend those from previous reports and show that the profile of most of the SNPs studied presented statistically significant distributions among general ethnic groups, pointing to the need to carry out massive early screening of relevant SNPs for SCA in patients diagnosed with this disease. It is concluded that the application of a broad mutation detection program will lead to a more personalized and efficient response in the treatment of SCA.