Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics

Peripheral Biomarkers of Anorexia Nervosa: A Meta-Analysis

The pathogenesis of anorexia nervosa (AN) has been hypothesized to involve several biological systems. However, reliable biomarkers for AN have yet to be established. This study was aimed to identify statistically significant and clinically meaningful peripheral biomarkers associated with AN. A systematic literature search was conducted to identify studies published in English from inception until 30 June 2022. We conducted two-level random-effects meta-analyses to examine the difference between AN and comparison groups across 52 distinct biomarkers and found that acylated ghrelin, adrenocorticotropic hormone (ACTH), carboxy-terminal collagen crosslinks (CTX), cholesterol, cortisol, des-acyl ghrelin, ghrelin, growth hormone (GH), obestatin, and soluble leptin receptor levels were significantly higher in cases of AN compared with those in non-AN controls. Conversely, C-reactive protein (CRP), CD3 positive, CD8, creatinine, estradiol, follicle-stimulating hormone (FSH), free thyroxine, free triiodothyronine, glucose, insulin, insulin-like growth factor 1 (IGF-1), leptin, luteinizing hormone, lymphocyte, and prolactin levels were significantly lower in AN compared with those in non-AN controls. Our findings indicate that peripheral biomarkers may be linked to the pathophysiology of AN, such as processes of adaptation to starvation. Scientific investigation into peripheral biomarkers may ultimately yield breakthroughs in personalized clinical care for AN.

Roles, underlying mechanisms and clinical significances of LINC01503 in human cancers

Long intergenic non-coding RNA 01503 (LINC01503) is a long non-coding RNA (lncRNA) located on human chromosome 9q34.11. There is compelling evidence indicating that LINC01503 is upregulated in multiple types of tumors and functions as a tumor stimulator. The upregulation of LINC01503 was significantly associated with the risk of 12 tumors and showed a strong correlation with clinicopathological characteristics and poor prognosis in 9 tumors. The expression of LINC01503 is regulated by transcription factors such as TP63, EGR1, c-MYC, GATA1 and AR. The downstream regulatory mechanisms of LINC01503 are complex and multifaceted. LINC01503, as a competing endogenous RNA (ceRNA), regulates gene expression by competitively inhibiting miRNA. LINC01503 may also regulate gene expression via interacting with biomolecules or recruiting chromatin-modifying complexes. In addition, LINC01503 can abnormally activate the ERK/MAPK, PI3K/AKT and Wnt/β-catenin signaling pathways to enhance tumor progression. Here, this review presents an overview of the latest research progress of LINC01503 in the field of oncology, summarizes its comprehensive network involved in multiple cancer molecular mechanisms, and explores its potential applications in cancer diagnosis, prognosis, and treatment.

Perspective to precision medicine in scleroderma

Systemic sclerosis (SSc) is a rare and heterogeneous disease with no relevant environmental trigger or significant responsible gene. It has been and will continue to be difficult to identify large enough patients to conduct classic population-based epidemiologic exposure/non-exposure studies with adequate power to ascertain environmental and genetic risk factors for these entities. The complexity of pathogenesis and heterogeneity are likely to require personalized/precision medicine for SSc. Since several potential drugs are currently available for specific patients if not whole SSc, classification of SSc seems to form the foundation for a better therapeutic strategy. To date, SSc has been classified based on the extent/severity of the affected area as well as some disease markers, including the autoantibody profile. However, such an analysis should also lead to improvements in the design of appropriately stratified clinical trials to determine the effects and prediction of targeted therapies. An approach based on drug response preclinically conducted using patients' own fibroblasts in vitro, can provide a precise disease marker/therapeutic selection for clinical practice. Because scleroderma dermal fibroblasts have a persistent hyper-productive phenotype occurring not only in person, but also in cell culture conditions. Thus, an accumulating approach based on disease markers ensures progression and de-escalation to re-establish a better life with a personally optimized drug environment after the onset of SSc.

Nrf2, A Target for Precision Oncology in Cancer Prognosis and Treatment

Activating nuclear factor-erythroid 2-related factor (Nrf2), a master regulator of redox homeostasis, has been shown to suppress initiation of carcinogenesis in normal cells. However, this transcription factor has recently been reported to promote proliferation of some transformed or cancerous cells. In tumor cells, Nrf2 is prone to mutations that result in stabilization and concurrent accumulation of its protein product. A hyperactivated mutant form of Nrf2 could support the cancer cells for enhanced proliferation, invasiveness, and resistance to chemotherapeutic agents and radiotherapy, which are associated with a poor clinical outcome. Hence understanding mutations in Nrf2 would have a significant impact on the prognosis and treatment of cancer in the era of precision medicine. This perspective would provide an insight into the genetic alterations in Nrf2 and suggest the application of small molecules, RNAi, and genome editing technologies, particularly CRISR-Cas9, in therapeutic intervention of cancer in the context of the involvement of Nrf2 mutations.

Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations

Identifying novel therapeutic agents is a fundamental challenge in contemporary drug development, especially in the context of complex diseases like cancer, neurodegenerative disorders, and metabolic syndromes. Here, we present a comprehensive computational study to identify potential inhibitors of SIRT1 (Sirtuin 1), a critical protein involved in various cellular processes and disease pathways. Leveraging the concept of drug repurposing, we employed a multifaceted approach that integrates molecular docking and molecular dynamics (MD) simulations to predict the binding affinities and dynamic behavior of a diverse set of FDA-approved drugs from DrugBank against the SIRT1. Initially, compounds were shortlisted based on their binding affinities and interaction analyses to identify safe and promising binding partners for SIRT1. Among these candidates, Doxercalciferol and Timiperone emerged as potential candidates, displaying notable affinity, efficiency, and specificity towards the binding pocket of SIRT1. Extensive evaluation revealed that these identified compounds boast a range of favorable biological properties and prefer binding to the active site of SIRT1. To delve deeper into the interactions, all-atom MD simulations were conducted for 500 nanoseconds (ns). These simulations assessed the conformational dynamics, stability, and interaction mechanism of the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes. The MD simulations illustrated that the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes maintain stability over a 500 ns trajectory. These insightful outcomes propose that Doxercalciferol and Timiperone hold promise as viable scaffolds for developing potential SIRT1 inhibitors, with implications for tackling complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.

Patient-derived tumor models: a suitable tool for preclinical studies on esophageal cancer

Esophageal cancer (EC) is the tenth most common cancer worldwide and has high morbidity and mortality. Its main subtypes include esophageal squamous cell carcinoma and esophageal adenocarcinoma, which are usually diagnosed during their advanced stages. The biological defects and inability of preclinical models to summarize completely the etiology of multiple factors, the complexity of the tumor microenvironment, and the genetic heterogeneity of tumors severely limit the clinical treatment of EC. Patient-derived models of EC not only retain the tissue structure, cell morphology, and differentiation characteristics of the original tumor, they also retain tumor heterogeneity. Therefore, compared with other preclinical models, they can better predict the efficacy of candidate drugs, explore novel biomarkers, combine with clinical trials, and effectively improve patient prognosis. This review discusses the methods and animals used to establish patient-derived models and genetically engineered mouse models, especially patient-derived xenograft models. It also discusses their advantages, applications, and limitations as preclinical experimental research tools to provide an important reference for the precise personalized treatment of EC and improve the prognosis of patients.

Cellular organelles as drug carriers for disease treatment

Organelles not only constitute the basic structure of the cell but also are important in maintaining the normal physiological activities of the cell. With the development of biomimetic nanoscience, researchers have developed technologies to use organelles as drug carriers for disease treatment. Compared with traditional drug carriers, organelle drug carriers have the advantages of good biocompatibility, high drug loading efficiency, and modifiability, and the surface biomarkers of organelles can also participate in intracellular signal transduction to enhance intracellular and intercellular communication, and assist in enhancing the therapeutic effect of drugs. Among different types of organelles, extracellular vesicles, lipid droplets, lysosomes, and mitochondria have been used as drug carriers. This review briefly reviews the biogenesis, isolation methods, and drug-loading methods of four types of organelles, and systematically summarizes the research progress in using organelles as drug-delivery systems for disease treatment. Finally, the challenges faced by organelle-based drug delivery systems are discussed. Although the organelle-based drug delivery systems still face challenges before they can achieve clinical translation, they offer a new direction and vision for the development of next-generation drug carriers.

Orthogonally Engineered Albumin with Attenuated Macrophage Phagocytosis for the Targeted Visualization and Phototherapy of Liver Cancer

The five-year survival rate of hepatocellular carcinoma (HCC) remains unsatisfactory. This reflects, in part, the paucity of effective methods that allow the target-specific diagnosis and therapy of HCC. Here, we report a strategy based on engineered human serum albumin (HSA) that permits the HCC-targeted delivery of diagnostic and therapeutic agents. Covalent cysteine conjugation combined with the exploitation of host-guest chemistry was used to effect the orthogonal functionalization of HSA with two functionally independent peptides. One of these peptides targets glypican-3 (GPC-3), an HCC-specific biomarker, while the second reduces macrophage phagocytosis through immune-checkpoint stimulation. This orthogonally engineered HSA proved effective for the GPC-3-targeted delivery of near-infrared fluorescent and phototherapeutic agents, thus permitting target-specific optical visualization and photodynamic ablation of HCC in vivo. This study thus offers new insights into specificity-enhanced fluorescence-guided surgery and phototherapy of HCC through the orthogonal engineering of biocompatible proteins.

Modulation of Reoviral Cytolysis (II): Cellular Stemness

Oncolytic viruses (OVs) are an emerging cancer therapeutic that are intended to act by selectively targeting and lysing cancerous cells and by stimulating anti-tumour immune responses, while leaving normal cells mainly unaffected. Reovirus is a well-studied OV that is undergoing advanced clinical trials and has received FDA approval in selected circumstances. However, the mechanisms governing reoviral selectivity are not well characterised despite many years of effort, including those in our accompanying paper where we characterize pathways that do not consistently modulate reoviral cytolysis. We have earlier shown that reovirus is capable of infecting and lysing both certain types of cancer cells and also cancer stem cells, and here we demonstrate its ability to also infect and kill healthy pluripotent stem cells (PSCs). This led us to hypothesize that pathways responsible for stemness may constitute a novel route for the modulation of reoviral tropism. We find that reovirus is capable of killing both murine and human embryonic and induced pluripotent stem cells. Differentiation of PSCs alters the cells' reoviral-permissive state to a resistant one. In a breast cancer cell line that was resistant to reoviral oncolysis, induction of pluripotency programming rendered the cells permissive to cytolysis. Bioinformatic analysis indicates that expression of the Yamanaka pluripotency factors may be associated with regulating reoviral selectivity. Mechanistic insights from these studies will be useful for the advancement of reoviral oncolytic therapy.

Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice

Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent years, a patient-derived xenograft (PDX) tumor model has been actively generated and applied, which preserves both cell-cell interactions and the microenvironment of tumors by directly transplanting cancer tissue from tumors into immunodeficient mice. In addition to this, the advent of alternative hosts, such as zebrafish hosts, or in vitro models (organoids and microfluidics), has also facilitated the advancement of cancer research. However, they still have a long way to go before they become reliable models. The development of immunodeficient mice has enabled PDX to become more mature and radiate new vitality. As one of the most reliable and standard preclinical models, the PDX model in immunodeficient mice (PDX-IM) exerts important effects in drug screening, biomarker development, personalized medicine, co-clinical trials, and immunotherapy. Here, we focus on the development procedures and application of PDX-IM in detail, summarize the implications that the evolution of immunodeficient mice has brought to PDX-IM, and cover the key issues in developing PDX-IM in preclinical studies.

Nanoproteomics deciphers the prognostic value of EGFR family proteins-based liquid biopsy

Monitoring tumor-associated protein status in serum can effectively track tumors and avoid time-consuming, costly, and invasive tissue biopsy. Epidermal growth factor receptor (EGFR) family proteins are often recommended in the clinical management of multiple solid tumors. However, the low-abundance of serum EGFR (sEGFR) family proteins hinders the depth-understanding of their function and tumor management. Herein, a nanoproteomics approach coupling with aptamer-modified MOFs (NMOFs-Apt) with mass spectrometry was developed for the enrichment and quantitative analysis of sEGFR family proteins. This nanoproteomics approach exhibited high sensitivity and specificity for sEGFR family protein quantification, with the limit of quantification as low as 1.00 nM. After detecting 626 patients' sEGFR family proteins with various malignant tumors, we concluded that the levels of serum proteins had a moderate concordance with tissue counterparts. Metastatic breast cancer patients with a high level of serum human epidermal growth factor receptor 2 (sHER2) and a low level of sEGFR had a poor prognosis, and patients with a sHER2 decrease of more than 20% had longer disease-free time after receiving chemotherapy. This nanoproteomics method provided a simple and effective approach for low-abundant serum protein detection and our results clarified the potential of sHER2 and sEGFR as cancer markers.

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Application of the Failure Mode and Effects Analysis (FMEA) to identify vulnerabilities and opportunities for improvement prior to implementing a computerized prescription order entry (CPOE) system in a university hospital oncology clinic

INTRODUCTION

Prior to implementing a new computerized prescription order entry (CPOE) application, the potential risks associated with this system were assessed and compared to those of paper-based prescriptions. The goal of this study is to identify the vulnerabilities of the CPOE process in order to adapt its design and prevent these potential risks.

METHODS AND MATERIALS

Failure mode and effects analysis (FMEA) was used as a prospective risk-management technique to evaluate the chemotherapy medication process in a university hospital oncology clinic. A multidisciplinary team assessed the process and compared the critical steps of a newly developed CPOE application versus paper-based prescriptions. The potential severity, occurrence and detectability were assessed prior to the implementation of the CPOE application in the clinical setting.

RESULTS

The FMEA led to the identification of 24 process steps that could theoretically be vulnerable, therefore called failure modes. These failure modes were grouped into four categories of potential risk factors: prescription writing, patient scheduling, treatment dispensing and patient follow-up. Criticality scores were calculated and compared for both strategies. Three failure modes were prioritized and led to modification of the CPOE design. Overall, the CPOE pathway showed a potential risk reduction of 51% compared to paper-based prescriptions.

CONCLUSION

FMEA was found to be a useful approach to identify potential risks in the chemotherapy medication process using either CPOE or paper-based prescriptions. The e-prescription mode was estimated to result in less risk than the traditional paper mode.

Sex difference in response to non-small cell lung cancer immunotherapy: an updated meta-analysis

BACKGROUND

Studying sex differences in the efficacy of immunotherapy may contribute to the practice of the precision medicine, especially in non-small cell lung cancer (NSCLC), a kind of cancer with sexual bimorphism.

METHODS

Published randomized controlled trials (RCTs), published by PubMed, Medline, Embase, and Scopus, before 15 June 2022, testing immunotherapy (CTLA-4 or PD-1/L1 inhibitor alone, combination or with chemotherapy) versus non-immunotherapy (receiving chemotherapy or placebo only) were included to assess different efficacy between males and females. The primary endpoint was overall survival (OS). This meta-analysis was registered with PROSPERO (CRD42022298439).

RESULTS

Sixteen RCTs, involving 10,155 patients with advanced NSCLC, was collected in this meta-analysis. The pooled HR comparing immunotherapy vs non-immunotherapy were 0.76 (95%CI 0.71-0.81) for males and 0.74 (95%CI 0.63-0.87) for females. The pooled HRs comparing immune-checkpoint inhibitors (ICIs) plus chemotherapy versus chemotherapy were 0.79 (95%CI 0.70-0.89) for males and 0.63 (95%CI 0.42-0.92) for females. The pooled HRs comparing ICIs versus chemotherapy were 0.74 (95%CI 0.67-0.81) for males and 0.83 (95%CI 0.73-0.95) for females. In squamous NSCLC, the pooled HRs comparing immunotherapy vs non-immunotherapy were 0.73 (95%CI 0.58-0.91) for males and 0.74 (95%CI 0.37-1.48) for females. In non-squamous NSCLC, the pooled HRs comparing immunotherapy versus non-immunotherapy were 0.62 (95%CI 0.71-0.94) for males and 0.59 (95%CI 0.39-0.89) for females.

CONCLUSION

Compared to chemotherapy, immunotherapy can improve the prognosis of patients with advanced NSCLC. Meanwhile, there are sex differences in the efficacy of immunotherapy.KEY MESSAGECompared to chemotherapy, immunotherapy can improve the prognosis of patients with advanced NSCLC.The most interesting thing in this study is that immunotherapy showed significant sex differences in the treatment of squamous NSCLC.

Activatable Dual Cancer-Related RNA Imaging and Combined Gene-Chemotherapy through the Target-Induced Intracellular Disassembly of Functionalized DNA Tetrahedron

The desire for a cancer theranostic system with simultaneously accurate diagnosis and efficient therapy is undeniably interminable. Heretofore, theranostic systems with simple components were designed for cancer theranostics but with confined accuracy of diagnosis and side effects of administered drugs. Here, we report an activatable theranostic system for simultaneously imaging dual cancer-related RNAs, mRNA Bcl-2 and piRNA-36026, and combined gene-chemotherapy through the target-induced intracellular disassembly of DNA tetrahedron. Briefly, five customized oligonucleotides are used to assemble the functionalized DNA tetrahedron. The relevant functional nucleic acids, including the antisequence of mRNA Bcl-2, the antisequence of piRNA-36026, and aptamer AS1411, are designed in the customized oligonucleotides with the signal reporters Cy3 and Cy5. Doxorubicin (DOX) is loaded in the functionalized DNA tetrahedron by inlaying between cytosine and guanine to form the activatable cancer theranostic system. The activatable cancer theranostic system is able to recognize MCF-7 cells by aptamer AS1411 and then enter the cells. In the presence of targets, the antisequences in the activatable cancer theranostic system hybridize with intracellular mRNA Bcl-2 and piRNA-36026, leading to the fluorescence signal recovery of Cy3 and Cy5 and the downregulation of two targets in the cytoplasm as well as the consequent apoptosis of MCF-7 cells in the form of gene therapy. Interestingly, as the antisequences are designed in the assembly strands, the hybridization between targets and the antisequences results in the disassembly of the activatable cancer theranostic system and the release of DOX as well as sequential chemotherapy. Advantageously, the activatable cancer theranostic system can achieve imaging of dual cancer-related RNAs with an imaging time window as long as 15 h and exhibit an obvious therapeutic effect in vivo. Therefore, this work is in furtherance of exploration for activatable cancer theranostic systems with high accuracy and efficiency and sheds new light on the development of precision medicine.

A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction

To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.

A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy

Background

Vitreous is an accessible, information-rich biofluid that has recently been studied as a source of retinal disease-related proteins and pathways. However, the number of samples required to confidently identify perturbed pathways remains unknown. In order to confidently identify these pathways, power analysis must be performed to determine the number of samples required, and sample preparation and analysis must be rigorously defined.

Methods

Control (n = 27) and proliferative diabetic retinopathy (n = 23) vitreous samples were treated as biologically distinct individuals or pooled together and aliquoted into technical replicates. Quantitative mass spectrometry with tandem mass tag labeling was used to identify proteins in individual or pooled control samples to determine technical and biological variability. To determine effect size and perform power analysis, control and proliferative diabetic retinopathy samples were analyzed across four 10-plexes. Pooled samples were used to normalize the data across plexes and generate a single data matrix for downstream analysis.

Results

The total number of unique proteins identified was 1152 in experiment 1, 989 of which were measured in all samples. In experiment 2, 1191 proteins were identified, 727 of which were measured across all samples in all plexes. Data are available via ProteomeXchange with identifier PXD025986. Spearman correlations of protein abundance estimations revealed minimal technical (0.99–1.00) and biological (0.94–0.98) variability. Each plex contained two unique pooled samples: one for normalizing across each 10-plex, and one to internally validate the normalization algorithm. Spearman correlation of the validation pool following normalization was 0.86–0.90. Principal component analysis revealed stratification of samples by disease and not by plex. Subsequent differential expression and pathway analyses demonstrated significant activation of metabolic pathways and inhibition of neuroprotective pathways in proliferative diabetic retinopathy samples relative to controls.

Conclusions

This study demonstrates a feasible, rigorous, and scalable method that can be applied to future proteomic studies of vitreous and identifies previously unrecognized metabolic pathways that advance understanding of diabetic retinopathy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09328-8.

Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia’s Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm–matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems’ willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.

Neuropeptide Y: a potential theranostic biomarker for diabetic peripheral neuropathy in patients with type-2 diabetes

Background:

Diabetic peripheral neuropathy (DPN), the most common microvascular complication of type-2 diabetes mellitus (T2DM), results in nontraumatic lower-limb amputations. When DPN is not detected early, disease progression is irreversible. Thus, biomarkers for diagnosing DPN are needed.

Methods:

We analyzed three data sets of T2DM DPN: two for mouse models (GSE70852 and GSE34889) and one for a human model (GSE24290). We found common differentially expressed genes (DEGs) in the two mouse data sets and validated them in the human data set. To identify the phenotypic function of the DEGs, we overexpressed them in zebrafish embryos. Clinical information and serum samples of T2DM patients with and without DPN were obtained from the Korea Biobank Network. To assess the plausibility of DEGs as biomarkers of DPN, we performed an enzyme-linked immunosorbent assay.

Results:

Among the DEGs, only NPY and SLPI were validated in the human data set. As npy is conserved in zebrafish, its mRNA was injected into zebrafish embryos, and it was observed that the branches of the central nervous system became thicker and the number of dendritic branches increased. Baseline characteristics between T2DM patients with and without DPN did not differ, except for the sex ratio. The mean serum NPY level was higher in T2DM patients with DPN than in those without DPN (p = 0.0328), whereas serum SLPI levels did not differ (p = 0.9651).

Conclusion:

In the pathogenesis of DPN, NPY may play a protective role in the peripheral nervous system and may be useful as a biomarker for detecting T2DM DPN.

Patient-derived xenograft models for the study of benign human neoplasms

Preclinical models are a core feature of translational research, and patient-derived xenograft (PDX) models have increasingly been used with such purpose. PDX involves the transplantation of fresh human tumor samples into immunodeficient mice to overcome immunologic rejection. It is a valuable tool for basic as well as preclinical research, contributing to the establishment of models to characterize the neoplasms to drug screening and to allow the identification of therapeutic targets. The use of these models is justified because they retain the histological and genomic features of the primary tumor. PDX models are well described for malignant neoplasms, for which the advantages are clear and include the development of drug treatments. The establishment of malignant tumors PDX is undeniably important from a medical perspective. However, few studies have used such models for benign neoplasms. The use of PDX for benign neoplasm studies can help to clarify the pathobiology of these diseases, as well as invasion and malignant transformation mechanisms, which from a biological perspective is equally important to the study of malignant tumors. Therefore, the aim of this study is to review the current methodology for PDX model generation and to cover its main applications, focusing on benign neoplasms.

The Identification and Development of a Novel Oncolytic Virus: Alphavirus M1

Oncolytic virotherapy represents an ideal therapeutic platform for cancer in which natural or engineered viruses selectively replicate in and destroy tumor cells, whereas sparing normal cells. Oncolytic virotherapy is considered as a key contributor in modern immunotherapy. However, several challenges remain with regard to exploiting the potential of oncolytic viruses, such as the lack of biomarkers for precise treatment, the difficulty of systemic administration because of pre-existing neutralizing antibodies to popular oncolytic viral vectors in human serum and the lack of mature lyophilization technology for convenient transport and preservation of viral preparations. The M1 strain, which was isolated on Hainan Island of China in the 1960s, is a member of the alphavirus genus Togaviridae family. It was identified as a novel oncolytic virus in 2014. During the development of M1 virus, many challenges have been overcome: several biomarkers have been identified for precise treatment; systematic administration of M1 is suitable and feasible because of the extremely low percentage of pre-existing neutralizing antibodies in the general population, and a lyophilized powder that maintains high biological stability has been developed. This review provides an encyclopedia of studies supporting M1 as an oncolytic virus, including the biological characteristics, tumor selectivity and its mechanism, tumor killing mechanism, combination therapy, and nonclinical pharmacokinetics of M1 virus. The future development direction of oncolytic virus M1 is also discussed at the end of the review.

Precise medication for tumor patients in the context of mental stress

Cancer is the leading cause of disease-related death worldwide due to its late diagnosis and poor outcomes. Precision medicine plays an important role in the treatment of tumors. As found for many types of tumors, mental stress plays a vital role in the promotion and progression of tumors. In this paper, we briefly introduce the manifestation and effects of mental symptoms in tumor patients. We next specifically discuss the multiple roles of precision medicine in the tumor therapy. Finally, we also highlight the precision medicine strategy for psychiatric symptoms in tumor patients, which promises to enhance the efficacy of tumor therapy.

Accelerating protein biomarker discovery and translation from proteomics research for clinical utility

Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.

Identification and Validation of Novel Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma

Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction.

Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan–Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression.

Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC.

Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

Divining responder populations from survival data

BACKGROUND

Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers.

MATERIALS AND METHODS

We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses.

RESULTS

Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data.

CONCLUSIONS

Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.

Cross-sectional analysis of circulating tumor DNA in primary colorectal cancer at surgery and during post-surgery follow-up by liquid biopsy

BACKGROUND

Liquid biopsy (LB) in early-stage, non-metastatic colorectal cancer (CRC) must be sensitive enough to detect extremely low circulating tumor DNA (ctDNA) levels. This challenge has been seldom and non-systematically investigated.

METHODS

Next generation sequencing (NGS) and digital PCR (dPCR) were combined to test tumor DNAs (tDNAs) and paired ctDNAs collected at surgery from 39 patients, 12 of whom were also monitored during the immediate post-surgery follow up. Patients treated for metastatic disease (n = 14) were included as controls.

RESULTS

NGS and dPCR concordantly (100% agreement) called at least one single nucleotide variant (SNV) in 34 tDNAs, estimated differences in allelic frequencies being negligible (±1.4%). However, despite dPCR testing, SNVs were only detectable in 15/34 (44.1%) ctDNAs from patients at surgery, as opposed to 14/14 (100%) metastatic patients. This was likely due to striking differences (average 10 times, up to 500) in ctDNA levels between groups. NGS revealed blood-only SNVs, suggesting spatial heterogeneity since pre-surgery disease stages, and raising the combined NGS/dPCR sensitivity to 58.8%. ctDNA levels at surgery correlated with neither tumor size, stage, grade, or nodal status, nor with variant abundance in paired tDNA. LB sensitivity reached 63.6% when ctDNA was combined with CEA. Finally, persistence and absence of ctDNA on the first conventional (month 3) post-surgery follow-up were associated with fast relapse and a disease-free status in 3 and 7 patients, respectively.

CONCLUSIONS

A simple clinical NGS/dPCR/CEA combination effectively addresses the LB challenge in a fraction of non-metastatic CRC patients.

Cancer Stem Cells: Devil or Savior-Looking behind the Scenes of Immunotherapy Failure

Although the introduction of immunotherapy has tremendously improved the prognosis of patients with metastatic cancers of different histological origins, some tumors fail to respond or develop resistance. Broadening the clinical efficacy of currently available immunotherapy strategies requires an improved understanding of the biological mechanisms underlying cancer immune escape. Globally, tumor cells evade immune attack using two main strategies: avoiding recognition by immune cells and instigating an immunosuppressive tumor microenvironment. Emerging data suggest that the clinical efficacy of chemotherapy or molecularly targeted therapy is related to the ability of these therapies to target cancer stem cells (CSCs). However, little is known about the role of CSCs in mediating tumor resistance to immunotherapy. Due to their immunomodulating features and plasticity, CSCs can be especially proficient at evading immune surveillance, thus potentially representing the most prominent malignant cell component implicated in primary or acquired resistance to immunotherapy. The identification of immunomodulatory properties of CSCs that include mechanisms that regulate their interactions with immune cells, such as bidirectional release of particular cytokines/chemokines, fusion of CSCs with fusogenic stromal cells, and cell-to-cell communication exerted by extracellular vesicles, may significantly improve the efficacy of current immunotherapy strategies. The purpose of this review is to discuss the current scientific evidence linking CSC biological, immunological, and epigenetic features to tumor resistance to immunotherapy.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.

OBJECTIVE

This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.

METHODS

The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities.

RESULTS

The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.

CONCLUSIONS

The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

Generation and application of patient-derived xenograft models in pancreatic cancer research

OBJECTIVE

Pancreatic ductal adenocarcinoma cancer (PDAC) is one of the leading causes of cancer-related death worldwide. Hence, the development of effective anti-PDAC therapies is urgently required. Patient-derived xenograft (PDX) models are useful models for developing anti-cancer therapies and screening drugs for precision medicine. This review aimed to provide an updated summary of using PDX models in PDAC.

DATA SOURCES

The author retrieved information from the PubMed database up to June 2019 using various combinations of search terms, including PDAC, pancreatic carcinoma, pancreatic cancer, patient-derived xenografts or PDX, and patient-derived tumor xenografts or PDTX.

STUDY SELECTION

Original articles and review articles relevant to the review's theme were selected.

RESULTS

PDX models are better than cell line-derived xenograft and other models. PDX models consistently demonstrate retained tumor morphology and genetic stability, are beneficial in cancer research, could enhance drug discovery and oncologic mechanism development of PDAC, allow an improved understanding of human cancer cell biology, and help guide personalized treatment.

CONCLUSIONS

In this review, we outline the status and application of PDX models in both basic and pre-clinical pancreatic cancer researches. PDX model is one of the most appropriate pre-clinical tools that can improve the prognosis of patients with pancreatic cancer in the future.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Entrectinib resistance mechanisms in ROS1-rearranged non-small cell lung cancer

Entrectinib is a pan-tyrosine-kinase inhibitor that targets oncogenic rearrangements in NTRK, ROS1 and ALK. The combined results of two clinical trials demonstrated the efficacy of entrectinib in ROS1-rearranged NSCLC. Because the development of drug resistance is inevitable, it would be helpful to determine the mechanisms of entrectinib resistance in a ROS1-rearranged tumor model so that future therapeutic strategies can be developed. Here, we characterized the molecular basis of resistance in entrectinib-resistant ROS1-rearranged HCC78 cells (HCC78ER cells). These cells were analyzed by next-generation sequencing and genetic profiling, which revealed the acquisition of KRAS G12C and the amplification of KRAS and FGF3. However, there were no secondary mutations in the ROS1 kinase domain. We also found that sustained ERK activation was involved in entrectinib resistance, and that combined treatment with selumetinib resensitized HCC78ER cells to entrectinib in cell viability and colony formation assays. Our data suggest that activation of the RAS signaling pathway can cause entrectinib resistance in ROS1-rearranged NSCLC, and is unlikely to be overcome by sequential single agent ROS1-targeting strategies against such tumors. Instead, co-targeting ROS1 and MEK may be an effective strategy for overcoming entrectinib resistance in ROS1-rearranged NSCLC.

Genotyping of Circulating Tumor DNA Reveals the Clinically Actionable Mutation Landscape of Advanced Colorectal Cancer

Circulating tumor DNA (ctDNA) enables genomic profiling of colorectal cancer. We investigated therapeutic targets by performing ctDNA panel-captured sequencing of 152 blood samples from advanced stage patients, from which somatic mutations and potentially actionable targets were evaluated. An additional 11 matched tissue samples were retrospectively obtained to verify target validity. The mutation frequencies of 1,127 collective genetic variants identified in our study strongly correlated with those of multiple public databases (Pearson R ² = 0.92, P < 0.0001). The clonal fraction of driver genes was 90.3%, which was significantly higher than that of potential passenger genes (58.12%). Totally, 90 drug-sensitive genes from 56 patients (36.84%) were identified, including recurring targets PIK3CA, FBXW7, EGFR, BRAF, and NRAS Various resistance mechanisms of anti-EGFR antibodies were revealed via ctDNA profiling, with 29 patients individually exhibiting multiple mechanisms, suggesting considerable resistance heterogeneity in our study population. Of the matched tissue/blood pairs, 88.14% of tissue-derived mutations were detected in ctDNA, and 88.9% of actionable targets were validated. The mutational landscape of ctDNA was highly consistent with tissue databases, and ctDNA profiling showed favorable concordance with tumor tissues in our matched analysis. Thus, comprehensive ctDNA genotyping is a promising noninvasive alternative to biopsy-derived analysis for determining targeted therapy in advanced colorectal cancer.

ADRB2-Targeting Therapies for Prostate Cancer

There is accumulating evidence that β-2 adrenergic receptor (ADRB2) signaling contributes to the progression and therapy resistance of prostate cancer, whereas availability of clinically tested β-blocker propranolol makes this pathway especially attractive as potential therapeutic target. Yet even in tumors with active ADRB2 signaling propranolol may be ineffective. Inhibition of apoptosis is one of the major mechanisms by which activation of ADRB2 contributes to prostate cancer pathophysiology. The signaling network that controls apoptosis in prostate tumors is highly redundant, with several signaling pathways targeting a few critical apoptosis regulatory molecules. Therefore, a comprehensive analysis of ADRB2 signaling in the context of other signaling mechanisms is necessary to identify patients who will benefit from propranolol therapy. This review discusses how information on the antiapoptotic mechanisms activated by ADRB2 can guide clinical trials of ADRB2 antagonist propranolol as potential life-extending therapy for prostate cancer. To select patients for clinical trials of propranolol three classes of biomarkers are proposed. First, biomarkers of ADRB2/cAMP-dependent protein kinase (PKA) pathway activation; second, biomarkers that inform about activation of other signaling pathways unrelated to ADRB2; third, apoptosis regulatory molecules controlled by ADRB2 signaling and other survival signaling pathways.

Modeling Human Digestive Diseases With CRISPR-Cas9-Modified Organoids

Insights into the stem cell niche have allowed researchers to cultivate adult tissue stem cells as organoids that display structural and phenotypic features of healthy and diseased epithelial tissues. Organoids derived from patients' tissues are used as models of disease and to test drugs. CRISPR-Cas9 technology can be used to genetically engineer organoids for studies of monogenic diseases and cancer. We review the derivation of organoids from human gastrointestinal tissues and how CRISPR-Cas9 technology can be used to study these organoids. We discuss burgeoning technologies that are broadening our understanding of diseases of the digestive system.

Perspectives in Radiomics for Personalized Medicine and Theranostics

Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.

Less Exploited GPCRs in Precision Medicine: Targets for Molecular Imaging and Theranostics

Precision medicine relies on individually tailored therapeutic intervention taking into account individual variability. It is strongly dependent on the availability of target-specific drugs and/or imaging agents that recognize molecular targets and patient-specific disease mechanisms. The most sensitive molecular imaging modalities, Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET), rely on the interaction between an imaging radioprobe and a target. Moreover, the use of target-specific molecular tools for both diagnostics and therapy, theranostic agents, represent an established methodology in nuclear medicine that is assuming an increasingly important role in precision medicine. The design of innovative imaging and/or theranostic agents is key for further accomplishments in the field. G-protein-coupled receptors (GPCRs), apart from being highly relevant drug targets, have also been largely exploited as molecular targets for non-invasive imaging and/or systemic radiotherapy of various diseases. Herein, we will discuss recent efforts towards the development of innovative imaging and/or theranostic agents targeting selected emergent GPCRs, namely the Frizzled receptor (FZD), Ghrelin receptor (GHSR-1a), G protein-coupled estrogen receptor (GPER), and Sphingosine-1-phosphate receptor (S1PR). The pharmacological and clinical relevance will be highlighted, giving particular attention to the studies on the synthesis and characterization of targeted molecular imaging agents, biological evaluation, and potential clinical applications in oncology and non-oncology diseases. Whenever relevant, supporting computational studies will be also discussed.

De Novo Design of Phototheranostic Sensitizers Based on Structure-Inherent Targeting for Enhanced Cancer Ablation

Structure-inherent targeting (SIT) agents are of particular importance for clinical precision medicine; however, there still exists a great lack of SIT phototheranostics for simultaneous cancer diagnosis and targeted photodynamic therapy (PDT). Herein, for the first time, we propose a "one-for-all" strategy by using the Förster resonance energy transfer (FRET) mechanism to construct such omnipotent SIT phototheranostics. Of note, this novel tactic can not only endow conventional sensitizers with highly effective native tumor-targeting potency but also simultaneously improve their photosensitization activities, resulting in dramatically boosted therapeutic index. After intravenous injection of the prepared SIT theranostic, the neoplastic sites are distinctly "lighted up" and distinguished from neighboring tissues, showing a near-infrared signal-to-background ratio value as high as 12.5. More importantly, benefiting from the FRET effect, markedly amplified light-harvesting ability and ¹O₂ production are demonstrated. Better still, other favorable features are also simultaneously achieved, including specific mitochondria anchoring, augmented cellular uptake (>13-fold), as well as ideal biocompatibility, all of which allow orders-of-magnitude promotion in anticancer efficiency both in vitro and in vivo. We believe this one-for-all SIT platform will provide a new idea for future cancer precision therapy.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.

Molecular therapies and precision medicine for hepatocellular carcinoma

The global burden of hepatocellular carcinoma (HCC) is increasing and might soon surpass an annual incidence of 1 million cases. Genomic studies have established the landscape of molecular alterations in HCC; however, the most common mutations are not actionable, and only ~25% of tumours harbour potentially targetable drivers. Despite the fact that surveillance programmes lead to early diagnosis in 40-50% of patients, at a point when potentially curative treatments are applicable, almost half of all patients with HCC ultimately receive systemic therapies. Sorafenib was the first systemic therapy approved for patients with advanced-stage HCC, after a landmark study revealed an improvement in median overall survival from 8 to 11 months. New drugs - lenvatinib in the frontline and regorafenib, cabozantinib, and ramucirumab in the second line - have also been demonstrated to improve clinical outcomes, although the median overall survival remains ~1 year; thus, therapeutic breakthroughs are still needed. Immune-checkpoint inhibitors are now being incorporated into the HCC treatment armamentarium and combinations of molecularly targeted therapies with immunotherapies are emerging as tools to boost the immune response. Research on biomarkers of a response or primary resistance to immunotherapies is also advancing. Herein, we summarize the molecular targets and therapies for the management of HCC and discuss the advancements expected in the near future, including biomarker-driven treatments and immunotherapies.

Patient-Derived Xenograft Models for Endometrial Cancer Research

Endometrial cancer (EC) is the most common malignancy of the genital tract among women in developed countries. Recently, a molecular classification of EC has been performed providing a system that, in conjunction with histological observations, reliably improves EC classification and enhances patient management. Patient-derived xenograft models (PDX) represent nowadays a promising tool for translational research, since they closely resemble patient tumour features and retain molecular and histological features. In EC, PDX models have already been used, mainly as an individualized approach to evaluate the efficacy of novel therapies and to identify treatment-response biomarkers; however, their uses in more global or holistic approaches are still missing. As a collaborative effort within the ENITEC network, here we describe one of the most extensive EC PDX cohorts developed from primary tumour and metastasis covering all EC subtypes. Our models are histologically and molecularly characterized and represent an excellent reservoir of EC tumour samples for translational research. This review compiles the information on current methods of EC PDX generation and their utility and provides new perspectives for the exploitation of these valuable tools in order to increase the success ratio for translating results to clinical practice.

Breast cancer intratumour heterogeneity: current status and clinical implications

Breast cancer (BC) is a heterogeneous disease that varies in presentation, morphological features, behaviour, and response to therapy. High-throughput molecular profiling studies have revolutionised our understanding of BC heterogeneity, and have demonstrated that molecular profiles of tumours are variable not only between tumours, but also within individual tumours. Current evidence indicates that spatial and temporal intratumour heterogeneity of BC exists at levels beyond what are commonly expected. Intratumour heterogeneity poses critical challenges in the diagnosis, prediction of behaviour and management of BC. For instance, heterogeneous expression of oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 can be seen not only in primary tumours between different regions, but also between primary tumours and their corresponding metastatic/recurrent lesions. The demonstration of molecularly distinct subclones within individual tumours may explain, at least in part, the mechanisms controlling the variable behaviour of BC, and may change our approach to BC sampling and treatment. In this review, BC intratumour heterogeneity is highlighted, with a special emphasis on the current knowledge pertaining to the relationship between intratumour heterogeneity and BC pathogenesis, evolution, and progression, with consideration of its impact on disease diagnosis, management, and the emergence of novel therapeutic targets. The key role of high-throughput molecular and imaging techniques is also addressed.

Complexity of Delivering Precision Medicine: Opportunities and Challenges

Precision medicine has emerged as a tool to match patients with the appropriate treatment based on the precise molecular features of an individual patient's tumor. Although examples of targeted therapies exist resulting in dramatic improvements in patient outcomes, comprehensive genomic profiling of tumors has also demonstrated the incredible complexity of molecular alterations in tissue and blood. These sequencing methods provide opportunities to study the landscape of tumors at baseline and serially in response to treatment. These tools also serve as important biomarkers to detect resistance to treatment and determine higher likelihood of responding to particular treatments, such as immune checkpoint blockade. Federally funded and publicly available data repositories have emerged as mechanisms for data sharing. In addition, novel clinical trials are emerging to develop new ways of incorporating molecular matched therapy into clinical trials. Various challenges to delivery of precision oncology include understanding the complexity of advanced tumors based on evolving "omics" and treatment resistance. For physicians, determining when and how to incorporate genetic and molecular tools into clinic in a cost-effective manner is critical. Finally, we discuss the importance of well-designed prospective clinical trials, biomarkers such as liquid biopsies, the use of multidisciplinary tumor boards, and data sharing as evidence-based medicine tools to optimally study and deliver precision oncology to our patients.