The growing role of precision and personalized medicine for cancer treatment

A 3D, Compartmental Tumor-Stromal Microenvironment Model of Patient-Derived Bone Metastasis

Bone is a frequent site of tumor metastasis. The bone-tumor microenvironment is heterogeneous and complex in nature. Such complexity is compounded by relations between metastatic and bone cells influencing their sensitivity/resistance to chemotherapeutics. Standard chemotherapeutics may not show efficacy for every patient, and new therapeutics are slow to emerge, owing to the limitations of existing 2D/3D models. We previously developed a 3D interface model for personalized therapeutic screening, consisting of an electrospun poly lactic acid mesh activated with plasma species and seeded with stromal cells. Tumor cells embedded in an alginate-gelatin hydrogel are overlaid to create a physiologic 3D interface. Here, we applied our 3D model as a migration assay tool to verify the migratory behavior of different patient-derived bone metastasized cells. We assessed the impact of two different chemotherapeutics, Doxorubicin and Cisplatin, on migration of patient cells and their immortalized cell line counterparts. We observed different migratory behaviors and cellular metabolic activities blocked with both Doxorubicin and Cisplatin treatment; however, higher efficiency or lower IC50 was observed with Doxorubicin. Gene expression analysis of MDA-MB231 that migrated through our 3D hybrid model verified epithelial-mesenchymal transition through increased expression of mesenchymal markers involved in the metastasis process. Our findings indicate that we can model tumor migration in vivo, in line with different cell characteristics and it may be a suitable drug screening tool for personalized medicine approaches in metastatic cancer treatment.

Gov➔Value: How to combine reported quality experiences and patient-reported outcome measures. First results on vulvar cancer patients in an Italian Research Hospital

Introduction

Vulvar cancer (VC) accounts for <1% of cancers affecting the female gender. Clinical Pathways (CP) and Clinical Outcomes Monitoring are useful for providing high-quality care to these patients. However, it is essential to integrate them with the patient's perspective according to Value-Based Healthcare paradigms. Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are tools for assessing outcomes and experiences with health care from the patient's perspective. The aim of this paper is to collect and synthesize PROMs and main stakeholders' experience on the VC CP, according to a value-based approach.

Materials and methods

To select the most appropriate instrument, a review was conducted on the main databases and official websites of specific institutions and organizations. In the second phase, a 2-round Delphi survey was conducted to assess the Reported Experience Measures (REMs) tool. Questions were evaluated according to four criteria (general relevance, evidence-based, measurability, actionability) and included if strong agreement was reached. A Principal Component Analysis (PCA) was executed. Cronbach's alpha and McDonald's omega were computed. Fisher's exact test and Wilcoxon rank sum test were used to compare ratings between groups. Descriptive statistics were performed for both PROMs and REMs instruments.

Results

For PROMs assessment, EORTC QLQ-C30 questionnaire was selected and administered to 28 patients. Global Health Status/Quality of Life and Functional Scales Scores were high or very high, while symptoms scale reported low or medium scores. The final REMs consists of 22 questions for professionals and 16 for patients and caregivers. It was administered to 22 patients, 11 caregivers, 5 physicians, 2 nurses and 1 clinical senior manager. PCA identified 4 components. Scale reliability was acceptable (α = 0.75 95% CI: 0.61-0.85; ω = 0.69; 95% CI: 0.54, 0.82). A statistically significant difference between the patient/caregiver group and the professionals was found for items 8 (follow-up), 10 (perceived quality), 12 (safety), and 16 (climate) (p = 0.02; p = 0.03; p < 0.001; p < 0.001, respectively).

Discussion

PROMs could provide new ways of intercepting patients' needs and feedback, thus acting on them. The proposed REMs tool would allow to detect information not available elsewhere, which, through Audit and feedback strategies, could lead to enhancement of healthcare experience, according to a value-based approach.

Associations of Physical Activity and Handgrip Strength with Health-Related Quality of Life in Older Korean Cancer Survivors

Background: Uncertainty exists regarding the associations between physical activity (PA), relative handgrip strength (RHGS), and health-related quality of life (HRQoL) among elderly Korean cancer survivors. This study investigated the moderating effect of RHGS on the relationship between PA and HRQoL in 308 cancer survivors aged ≥65 years using the data from the Korean National Health and Nutrition Examination Survey in 2014−2019. Methods: HRQoL was evaluated with the EuroQol-5-dimension instrument. PA and handgrip strength were assessed with the Global Physical Activity Questionnaire and a hand dynamometer, respectively. Age, sex, body mass index, income, marital status, educational background, heavy alcohol consumption, smoking status, multimorbidity, and hemoglobin were included as covariates. Results: Bivariate logistic regression showed that insufficient PA and no PA were associated with higher odds of a low HRQoL (odds ratio, OR = 2.6, 95% confidence interval, CI = 1.3~5.1, p = 0.005; OR = 2.4, 95% CI = 1.1~5.0, p = 0.024, respectively), compared with sufficient PA (OR = 1). Weak RHGS was associated with high odds of a low HRQoL (OR = 2.6, 95%= 1.530~4.6, p < 0.001) compared with normal RHGS (OR = 1). Particularly, RHGS (β = −0.0573, 95% CI = −0.1033~−0.0112) had a significant moderating effect on the relationship between PA and HRQoL even after adjustments for all the covariates. The negative influence of physical inactivity on HRQoL was observed in cancer survivors with weak RHGS but not in cancer survivors with normal RHGS. Conclusions: The current findings suggest that maintaining or promoting muscular strength through regular exercise is critical for the HRQoL of elderly Korean cancer survivors.

Looking into the black box of "Medical Innovation": rising health expenditures by illness type

There is agreement among health economists that on the whole medical innovation causes health care expenditures (HCE) to rise. This paper analyzes for which diagnoses HCE per patient have grown significantly faster than average HCE. We distinguish decedents (patients in their last 4 years of life) from survivors and use a unique dataset comprising detailed HCE of all members of a regional health insurance fund in Upper Austria for the period 2005-2018. Our results indicate that among decedents in particular, the expenditures for treatment of neoplasms have exceeded the general trend in HCE. This confirms that medical innovation for this group of diseases has been particularly strong over the last 15 years. For survivors, we find a noticeable growth in cases and cost per case for pregnancies and childbirth, and also for treatment of mental and behavioral disorders. We discuss whether these findings contradict the widespread interpretation of cost-increasing innovations as "medical progress" and offer some policy recommendations.

Mechanisms of cancer metastasis

Metastatic cancer is almost always terminal, and more than 90% of cancer deaths result from metastatic disease. Combating cancer metastasis and post-therapeutic recurrence successfully requires understanding each step of metastatic progression. This review describes the current state of knowledge of the etiology and mechanism of cancer progression from primary tumor growth to the formation of new tumors in other parts of the body. Open questions, avenues for future research, and therapeutic approaches with the potential to prevent or inhibit metastasis through personalization to each patient's mutation and/or immune profile are also highlighted.

TissueGrinder, a novel technology for rapid generation of patient-derived single cell suspensions from solid tumors by mechanical tissue dissociation

Introduction

Recent advances hold promise of making personalized medicine a step closer to implementation in clinical settings. However, traditional sample preparation methods are not robust and reproducible. In this study, the TissueGrinder, a novel mechanical semi-automated benchtop device, which can isolate cells from tissue in a very fast and enzyme-free way is tested for cell isolation from surgically resected tumor tissues.

Methods

Thirty-three surgically resected tumor tissues from various but mainly pancreatic, liver or colorectal origins were processed by both novel TissueGrinder and explant method. An optimized processing program for tumors from pancreatic, liver or colorectal cancer was developed. The viability and morphological characteristics of the isolated cells were evaluated microscopically. Expression of pancreatic cancer markers was evaluated in cells isolated from pancreatic tumors. Finally, the effect of mechanical stress on the cells was evaluated by assessing apoptosis markers via western blotting.

Results

TissueGinder was more efficient in isolating cells from tumor tissue with a success rate of 75% when compared to explant method 45% in terms of cell outgrowth six weeks after processing. Cells isolated with TissueGinder had a higher abundance and were more heterogeneous in composition as compared to explant method. Mechanical processing of the cells with TissueGrinder does not lead to apoptosis but causes slight stress to the cells.

Discussion

Our results show that TissueGrinder can process solid tumor tissues more rapidly and efficiently and with higher success rate compared to the conventionally used explant method. The results of the study suggest that the TissueGrinder might be a suitable method for obtaining cells, which is important for its application in individualized therapy. Due to the great variance in different tumor entities and the associated individual tissue characteristics, a further development of the dissociation protocol for other types of tumors and normal tissue will be targeted.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.

Results

ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.

Conclusion

Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Biosensing chips for cancer diagnosis and treatment: a new wave towards clinical innovation

Recent technological advances in nanoscience and material designing have led to the development of point-of-care devices for biomolecule sensing and cancer diagnosis. In situ and portable sensing devices for bedside, diagnosis can effectively improve the patient's clinical outcomes and reduce the mortality rate. Detection of exosomal RNAs by immuno-biochip with increased sensitivity and specificity to diagnose cancer has raised the understanding of the tumor microenvironment and many other technology-based biosensing devices hold great promise for clinical innovations to conquer the unbeatable fort of cancer metastasis. Electrochemical biosensors are the most sensitive category of biomolecule detection sensors with significantly low concentrations down to the atomic level. In this sense, this review addresses the recent advances in cancer detection and diagnosis by developing significant biological sensing devices that are believed to have better sensing potential than existing facilities.

The Use of Medicinal Plant-Derived Metallic Nanoparticles in Theranostics

In the quest to effectively diagnose and treat the diseases that afflict mankind, the development of a tool capable of simultaneous detection and treatment would provide a significant cornerstone for the survival and control of these diseases. Theranostics denotes a portmanteau of therapeutics and diagnostics which simultaneously detect and treat ailments. Research advances have initiated the advent of theranostics in modern medicine. Overall, theranostics are drug delivery systems with molecular or targeted imaging agents integrated into their structure. The application of theranostics is rising exponentially due to the urgent need for treatments that can be utilized for diagnostic imaging as an aid in precision and personalised medicine. Subsequently, the emergence of nanobiotechnology and the green synthesis of metallic nanoparticles (MNPs) has provided one such avenue for nanoscale development and research. Of interest is the drastic rise in the use of medicinal plants in the synthesis of MNPs which have been reported to be potentially effective in the diagnosis and treatment of diseases. At present, medicinal plant-derived MNPs have been cited to have broad pharmacological applications and have been studied for their potential use in the treatment and management of cancer, malaria, microbial and cardiovascular diseases. The subject of this article regards the role of medicinal plants in the synthesis of MNPs and the potential role of MNPs in the field of theranostics.

Recent advances in gene therapy: genetic bullets to the root of the problem

Genetics and molecular genetic techniques have changed many perspectives and paradigms in medicine. Using genetic methods, many diseases have been cured or alleviated. Gene therapy, in its simplest definition, is application of genetic materials and related techniques to treat various human diseases. Evaluation of the trends in the field of medicine and therapeutics clarifies that gene therapy has attracted a lot of attention due to its powerful potential to treat a number of diseases. There are various genetic materials that can be used in gene therapy such as DNA, single- and double-stranded RNA, siRNA and shRNA. The main gene editing techniques used for in vitro and in vivo gene modification are ZNF, TALEN and CRISPR-Cas9. The latter has increased hopes for more precise and efficient gene targeting as it requires two separate recognition sites which makes it more specific and can also cause rapid and sufficient cleavage within the target sequence. There must be carriers for delivering genes to the target tissue. The most commonly used carriers for this purpose are viral vectors such as adenoviruses, adeno-associated viruses and lentiviruses. Non-viral vectors consist of bacterial vectors, liposomes, dendrimers and nanoparticles.

Still Searching for Understanding: The Importance of Diverse Research Designs, Methods, and Perspectives

BACKGROUND

Evidence-based medicine and evidence hierarchies have been widely adopted and have strongly influenced decision making across many fields, including clinical aphasiology. However, questions remain about the creation, usefulness, and validity of current evidence hierarchies.

AIMS

This article builds on ideas about scientific approaches and evidence originally shared by Elman (1995, 1998, 2006). This article reviews the history of evidence hierarchies and argues that improving the diversity of research designs, methods, and perspectives will improve understanding of the numerous and complex variables associated with aphasia intervention. Researchers and clinicians are encouraged to synthesize diverse types of scientific evidence. It is hoped that this article will stimulate thought and foster discussion in order to encourage high-caliber research of all types.

MAIN CONTRIBUTION

Concepts from a wide variety of fields including philosophy of science, research design and methodology, and precision medicine are brought together in an attempt to focus research on the scientific understanding of aphasia treatment effects.

CONCLUSION

It is hoped that by incorporating diverse research designs, methods, and perspectives, clinical aphasiologists will become better able to provide effective, personalized treatments, ensuring that each person with aphasia is able to improve their communication ability and quality of life.

Selectively Targeting Breast Cancer Stem Cells by 8-Quinolinol and Niclosamide

Cancer maintenance, metastatic dissemination and drug resistance are sustained by cancer stem cells (CSCs). Triple negative breast cancer (TNBC) is the breast cancer subtype with the highest number of CSCs and the poorest prognosis. Here, we aimed to identify potential drugs targeting CSCs to be further employed in combination with standard chemotherapy in TNBC treatment. The anti-CSC efficacy of up to 17 small drugs was tested in TNBC cell lines using cell viability assays on differentiated cancer cells and CSCs. Then, the effect of 2 selected drugs (8-quinolinol -8Q- and niclosamide -NCS-) in the cancer stemness features were evaluated using mammosphere growth, cell invasion, migration and anchorage-independent growth assays. Changes in the expression of stemness genes after 8Q or NCS treatment were also evaluated. Moreover, the potential synergism of 8Q and NCS with PTX on CSC proliferation and stemness-related signaling pathways was evaluated using TNBC cell lines, CSC-reporter sublines, and CSC-enriched mammospheres. Finally, the efficacy of NCS in combination with PTX was analyzed in vivo using an orthotopic mouse model of MDA-MB-231 cells. Among all tested drug candidates, 8Q and NCS showed remarkable specific anti-CSC activity in terms of CSC viability, migration, invasion and anchorage independent growth reduction in vitro. Moreover, specific 8Q/PTX and NCS/PTX ratios at which both drugs displayed a synergistic effect in different TNBC cell lines were identified. The sole use of PTX increased the relative presence of CSCs in TNBC cells, whereas the combination of 8Q and NCS counteracted this pro-CSC activity of PTX while significantly reducing cell viability. In vivo, the combination of NCS with PTX reduced tumor growth and limited the dissemination of the disease by reducing circulating tumor cells and the incidence of lung metastasis. The combination of 8Q and NCS with PTX at established ratios inhibits both the proliferation of differentiated cancer cells and the viability of CSCs, paving the way for more efficacious TNBC treatments.

Raman Metabolomics of Candida auris Clades: Profiling and Barcode Identification

This study targets on-site/real-time taxonomic identification and metabolic profiling of seven different Candida auris clades/subclades by means of Raman spectroscopy and imaging. Representative Raman spectra from different Candida auris samples were systematically deconvoluted by means of a customized machine-learning algorithm linked to a Raman database in order to decode structural differences at the molecular scale. Raman analyses of metabolites revealed clear differences in cell walls and membrane structure among clades/subclades. Such differences are key in maintaining the integrity and physical strength of the cell walls in the dynamic response to external stress and drugs. It was found that Candida cells use the glucan structure of the extracellular matrix, the degree of α-chitin crystallinity, and the concentration of hydrogen bonds between its antiparallel chains to tailor cell walls’ flexibility. Besides being an effective ploy in survivorship by providing stiff shields in the α–1,3–glucan polymorph, the α–1,3–glycosidic linkages are also water-insoluble, thus forming a rigid and hydrophobic scaffold surrounded by a matrix of pliable and hydrated β–glucans. Raman analysis revealed a variety of strategies by different clades to balance stiffness, hydrophobicity, and impermeability in their cell walls. The selected strategies lead to differences in resistance toward specific environmental stresses of cationic/osmotic, oxidative, and nitrosative origins. A statistical validation based on principal component analysis was found only partially capable of distinguishing among Raman spectra of clades and subclades. Raman barcoding based on an algorithm converting spectrally deconvoluted Raman sub-bands into barcodes allowed for circumventing any speciation deficiency. Empowered by barcoding bioinformatics, Raman analyses, which are fast and require no sample preparation, allow on-site speciation and real-time selection of appropriate treatments.

Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma

Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.

omicsGAT: Graph Attention Network for Cancer Subtype Analyses

The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (GAT) model to integrate graph-based learning with an attention mechanism for RNA-seq data analysis. The multi-head attention mechanism in omicsGAT can more effectively secure information of a particular sample by assigning different attention coefficients to its neighbors. Comprehensive experiments on The Cancer Genome Atlas (TCGA) breast cancer and bladder cancer bulk RNA-seq data and two single-cell RNA-seq datasets validate that (1) the proposed model can effectively integrate neighborhood information of a sample and learn an embedding vector to improve disease phenotype prediction, cancer patient stratification, and cell clustering of the sample and (2) the attention matrix generated from the multi-head attention coefficients provides more useful information compared to the sample correlation-based adjacency matrix. From the results, we can conclude that some neighbors play a more important role than others in cancer subtype analyses of a particular sample based on the attention coefficient.

Towards Better Pharmaceutical Provision in Europe-Who Decides the Future?

Significant progress has been achieved in human health in the European Union in recent years. New medicines, vaccines, and treatments have been developed to tackle some of the leading causes of disease and life-threatening illnesses. It is clear that investment in research and development (R&D) for innovative medicines and treatments is essential for making progress in preventing and treating diseases. Ahead of the legislative process, which should begin by the end of 2022, discussions focus on how Europe can best promote the huge potential benefits of new science and technology within the regulatory framework. The challenges in European healthcare were spelled out by the panellists at the roundtable organised by European Alliance for Personalised Medicine (EAPM). Outcomes from panellists' discussions have been summarized and re-arranged in this paper under five headings: innovation, unmet medical need, access, security of supply, adapting to progress, and efficiency. Some of the conclusions that emerged from the panel are a call for a better overall holistic vision of the future of pharmaceuticals and health in Europe and a collaborative effort among all stakeholders, seeing the delivery of medicines as part of a broader picture of healthcare.

Nanotechnology Advances in the Detection and Treatment of Cancer: An Overview

Over the last few years, progress has been made across the nanomedicine landscape, in particular, the invention of contemporary nanostructures for cancer diagnosis and overcoming complexities in the clinical treatment of cancerous tissues. Thanks to their small diameter and large surface-to-volume proportions, nanomaterials have special physicochemical properties that empower them to bind, absorb and transport high-efficiency substances, such as small molecular drugs, DNA, proteins, RNAs, and probes. They also have excellent durability, high carrier potential, the ability to integrate both hydrophobic and hydrophilic compounds, and compatibility with various transport routes, making them especially appealing over a wide range of oncology fields. This is also due to their configurable scale, structure, and surface properties. This review paper discusses how nanostructures can function as therapeutic vectors to enhance the therapeutic value of molecules; how nanomaterials can be used as medicinal products in gene therapy, photodynamics, and thermal treatment; and finally, the application of nanomaterials in the form of molecular imaging agents to diagnose and map tumor growth.

The expression pattern of Immune checkpoints after chemo/radiotherapy in the tumor microenvironment

As a disease with the highest disease-associated burden worldwide, cancer has been the main subject of a considerable proportion of medical research in recent years, intending to find more effective therapeutic approaches with fewer side effects. Combining conventional methods with newer biologically based treatments such as immunotherapy can be a promising approach to treating different tumors. The concept of "cancer immunoediting" that occurs in the field of the tumor microenvironment (TME) is the aspect of cancer therapy that has not been at the center of attention. One group of the role players of the so-called immunoediting process are the immune checkpoint molecules that exert either co-stimulatory or co-inhibitory effects in the anti-tumor immunity of the host. It involves alterations in a wide variety of immunologic pathways. Recent studies have proven that conventional cancer therapies, such as chemotherapy, radiotherapy, or a combination of them, i.e., chemoradiotherapy, alter the "immune compartment" of the TME. The mentioned changes encompass a wide range of variations, including the changes in the density and immunologic type of the tumor-infiltrating lymphocytes (TILs) and the alterations in the expression patterns of the different immune checkpoints. These rearrangements can have either anti-tumor immunity empowering or immune attenuating sequels. Thus, recognizing the consequences of various chemo(radio)therapeutic regimens in the TME seems to be of great significance in the evolution of therapeutic approaches. Therefore, the present review intends to summarize how chemo(radio)therapy affects the TME and specifically some of the most important, well-known immune checkpoints' expressions according to the recent studies in this field.

Zebrafish Is a Powerful Tool for Precision Medicine Approaches to Neurological Disorders

Personalized medicine is currently one of the most promising tools which give hope to patients with no suitable or no available treatment. Patient-specific approaches are particularly needed for common diseases with a broad phenotypic spectrum as well as for rare and yet-undiagnosed disorders. In both cases, there is a need to understand the underlying mechanisms and how to counteract them. Even though, during recent years, we have been observing the blossom of novel therapeutic techniques, there is still a gap to fill between bench and bedside in a patient-specific fashion. In particular, the complexity of genotype-to-phenotype correlations in the context of neurological disorders has dampened the development of successful disease-modifying therapeutics. Animal modeling of human diseases is instrumental in the development of therapies. Currently, zebrafish has emerged as a powerful and convenient model organism for modeling and investigating various neurological disorders. This model has been broadly described as a valuable tool for understanding developmental processes and disease mechanisms, behavioral studies, toxicity, and drug screening. The translatability of findings obtained from zebrafish studies and the broad prospect of human disease modeling paves the way for developing tailored therapeutic strategies. In this review, we will discuss the predictive power of zebrafish in the discovery of novel, precise therapeutic approaches in neurosciences. We will shed light on the advantages and abilities of this in vivo model to develop tailored medicinal strategies. We will also investigate the newest accomplishments and current challenges in the field and future perspectives.

Stem cell membrane, stem cell-derived exosomes and hybrid stem cell camouflaged nanoparticles: A promising biomimetic nanoplatforms for cancer theranostics

Nanomedicine research has advanced dramatically in recent decades. Nonetheless, traditional nanomedicine faces significant obstacles such as the low concentration of the drug at target sites and accelerated removal of the drug from blood circulation. Various techniques of nanotechnology, including cell membrane coating, have been developed to address these challenges and to improve targeted distribution and redcue cell membrane-mediated immunogenicity. Recently, stem cell (SC) membranes, owing to their immunosuppressive and regenerative properties, have grabbed attention as attractive therapeutic carriers for targeting specific tissues or organs. Bioengineering strategies that combine synthetic nanoparticles (NPs) with SC membranes, because of their homing potential and tumor tropism, have recently received a lot of publicity. Several laboratory experiments and clinical trials have indicated that the benefits of SC-based technologies are mostly related to the effects of SC-derived exosomes (SC-Exos). Exosomes are known as nano-sized extracellular vehicles (EVs) that deliver particular bioactive molecules for cell-to-cell communication. In this regard, SC-derived exosome membranes have recently been employed to improve the therapeutic capability of engineered drug delivery vehicles. Most recently, for further enhancing NPs' functionality, a new coating approach has been offered that combines membranes from two separate cells. These hybrid membrane delivery vehicles have paved the way for the development of biocompatible, high-efficiency, biomimetic NPs with varying hybrid capabilities that can overcome the drawbacks of present NP-based treatment techniques. This review explores stem cell membranes, SC-Exos, and hybrid SC-camouflaged NPs preparation methods and their importance in cancer therapy.

Biofabrication approaches and regulatory framework of metastatic tumor-on-a-chip models for precision oncology

The complexity of the tumor microenvironment (TME) together with the development of the metastatic process are the main reasons for the failure of conventional anticancer treatment. In recent years, there is an increasing need to advance toward advanced in vitro models of cancer mimicking TME and simulating metastasis to understand the associated mechanisms that are still unknown, and to be able to develop personalized therapy. In this review, the commonly used alternatives and latest advances in biofabrication of tumor‐on‐chips, which allow the generation of the most sophisticated and optimized models for recapitulating the tumor process, are presented. In addition, the advances that have allowed these new models in the area of metastasis, cancer stem cells, and angiogenesis are summarized, as well as the recent integration of multiorgan‐on‐a‐chip systems to recapitulate natural metastasis and pharmacological screening against it. We also analyze, for the first time in the literature, the normative and regulatory framework in which these models could potentially be found, as well as the requirements and processes that must be fulfilled to be commercially implemented as in vitro study model. Moreover, we are focused on the possible regulatory pathways for their clinical application in precision medicine and decision making through the generation of personalized models with patient samples. In conclusion, this review highlights the synergistic combination of three‐dimensional bioprinting systems with the novel tumor/metastasis/multiorgan‐on‐a‐chip systems to generate models for both basic research and clinical applications to have devices useful for personalized oncology.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Nanomedicines: A Focus on Nanomaterials as Drug Delivery System with Current Trends and Future Advancement

The rapid advancement of nanomedicine presents novel alternatives that have the potential to transform health care. Targeted drug delivery as well as the synthesis of nanocarriers is a growing discipline that has been intensively researched to reduce the complexity of present medicines in a variety of diseases and to develop new treatment and diagnostic techniques. There are several designed nanomaterials used as a delivery system such as liposomes, micelles, dendrimers, polymers, carbon-based materials, and many other substances, which deliver the drug moiety directly into its targeted body area reducing toxic effect of conventional drug delivery, thus reducing the amount of drug required for therapeutic efficacy and offering many more advantages. Currently, these are used in many applications, including cancer treatment, imaging contrast agents, and biomarker detection and so on. This review provides a comprehensive update in the field of targeted nano-based drug delivery systems, by conducting a thorough examination of the drug synthesis, types, targets, and application of nanomedicines in improving the therapeutic efficiency.

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.

Association of human gut microbiota with rare diseases: A close peep through

The human body harbors approximately 1014 cells belonging to a diverse group of microorganisms. Bacteria outnumbers protozoa, fungi and viruses inhabiting our gastrointestinal tract (GIT), commonly referred to as the "human gut microbiome". Dysbiosis occurs when the balanced relationship between the host and the gut microbiota is disrupted, altering the usual microbial population there. This increases the susceptibility of the host to pathogens, and chances of its morbidity. It is due to the fact that the gut microbiome plays an important role in human health; it influences the progression of conditions varying from colorectal cancer to GIT disorders linked with the nervous system, autoimmunity, metabolism and inheritance. A rare disease is a lethal and persistent condition affecting 2-3 people per 5,000 populaces. This review article intends to discuss such rare neurological, autoimmune, cardio-metabolic and genetic disorders of man, focusing on the fundamental mechanism that links them with their gut microbiome. Ten rare diseases, including Pediatric Crohn's disease (PCD), Lichen planus (LP), Hypophosphatasia (HPP), Discitis, Cogan's syndrome, Chancroid disease, Sennetsu fever, Acute cholecystitis (AC), Grave's disease (GD) and Tropical sprue (TS) stands to highlight as key examples, along with personalized therapeutics meant for them. This medicinal approach addresses the individual's genetic and genomic pathography, and tackles the illness with specific and effective treatments.

Precision Medicine Testing and Disparities in Health Care for Individuals With Non-Small Cell Lung Cancer: A Narrative Review

PROBLEM IDENTIFICATION

Precision medicine initiatives provide opportunities for optimal targeted therapy in individuals with non-small cell lung cancer. However, there are barriers to these initiatives that reflect social determinants of health.

LITERATURE SEARCH

MEDLINE®, CINAHL®, PsycINFO®, Embase®, and Google ScholarTM databases were searched for articles published in English in the United States from 2016 to 2020.

DATA EVALUATION

Data that were collected included individual demographic information, specific diagnosis, status of targeted genomic testing, and receipt of targeted therapy. All studies were retrospective and involved database review of insurance claims or medical records.

SYNTHESIS

Individuals with non-small cell lung cancer received less genetic testing and targeted therapy if they were of a lower socioeconomic status, had public health insurance or no health insurance, were Black, or lived in rural communities.

IMPLICATIONS FOR NURSING

Social determinants of health affect health equity, including in precision medicine initiatives for individuals with lung cancer. Gaining an understanding of this impact is the first step in mitigating inequities.

Hamizul MHA, Khairul Bariah AAN, Wan Kamarul Zaman WS, Nordin F. Regenerative Medicine Therapy in Malaysia: An Update

Regenerative medicine is a field in medicine that relates to the ability to correct congenital anomalies and to repair or replace tissues and organs that have been destroyed by age, disease, or trauma. To date, promising preclinical and clinical data supported the possibility of using regenerative medicine to treat both chronic diseases and acute insults, as well as maladies affecting a wide range of organ systems and contexts, such as dermal wounds, cardiovascular diseases and traumas, cancer treatments, and more. One of the regenerative medicine therapies that have been used widely is stem cells. Stem cells, especially mesenchymal and hematopoietic stem cells, play an important role in treating chronic diseases, such as leukemia, bone marrow, autoimmune disease, and urinary problems. Despite considerable advancements in stem cell biology, their applications are limited by ethical concerns about embryonic stem cells, tumor development, and rejection. Nevertheless, many of these constraints, are being overcome, which could lead to significant advancements in disease management. This review discusses the current developments and advancements of regenerative medicine therapy (RMT) advancements in Malaysia compared to other Asian countries. The limitations in the application of RMT are also highlighted.

Fusion Genes in Prostate Cancer: A Comparison in Men of African and European Descent

Simple Summary

Men of African origin have a 2–3 times greater chance of developing prostate cancer than those of European origin, and of patients that are diagnosed with the disease, men of African descent are 2 times more likely to die compared to white men. Men of African origin are still greatly underrepresented in genetic studies and clinical trials. This, unfortunately, means that new discoveries in cancer treatment are missing key information on the group with a greater chance of mortality. A fusion gene is a hybrid gene formed from two previously independent genes. Fusion genes have been found to be common in all main types of human cancer. The objective of this study was to increase our knowledge of fusion genes in prostate cancer using computational approaches and to compare fusion genes between men of African and European origin. This identified novel gene fusions unique to men of African origin and suggested that this group has a greater number of fusion genes.

Abstract

Prostate cancer is one of the most prevalent cancers worldwide, particularly affecting men living a western lifestyle and of African descent, suggesting risk factors that are genetic, environmental, and socioeconomic in nature. In the USA, African American (AA) men are disproportionately affected, on average suffering from a higher grade of the disease and at a younger age compared to men of European descent (EA). Fusion genes are chimeric products formed by the merging of two separate genes occurring as a result of chromosomal structural changes, for example, inversion or trans/cis-splicing of neighboring genes. They are known drivers of cancer and have been identified in 20% of cancers. Improvements in genomics technologies such as RNA-sequencing coupled with better algorithms for prediction of fusion genes has added to our knowledge of specific gene fusions in cancers. At present AA are underrepresented in genomic studies of prostate cancer. The primary goal of this study was to examine molecular differences in predicted fusion genes in a cohort of AA and EA men in the context of prostate cancer using computational approaches. RNA was purified from prostate tissue specimens obtained at surgery from subjects enrolled in the study. Fusion gene predictions were performed using four different fusion gene detection programs. This identified novel putative gene fusions unique to AA and suggested that the fusion gene burden was higher in AA compared to EA men.

Religion and Spirituality in Oncology: An Exploratory Study of the Communication Experiences of Clinicians in Poland

Communication with patients regarding oncology-related aspects is a challenging experience and requires a high level of skill from the interlocutors. The aim of this study was to verify the influence of religion/spirituality in oncological settings from the health professionals' perspectives in Poland. It assessed the role of religion/spirituality in patient-clinician communication, death or stress self-management, empathy, and breaking bad news skills. Data collection was carried out through a standardized self-administered questionnaire with varying scales. The study cohort consisted of 60 medical practitioners specializing in oncological radiotherapy treatments. It was observed that strategies used for coping with patients' death, stress reduction, empathy, communication with patients and/or their relatives, or breaking bad news skills, may be gender-specific or may depend on the length of time employed, as well as experience in a cancer-related work environment. This study shows that spirituality and religiousness can support clinicians in managing challenging or negative emotions related to their work in cancer settings. Religiousness and spirituality can also serve as a potential therapeutic strategies for those exposed to patient suffering and death.

An integrative prognostic and immune analysis of PTPRD in cancer

PTPRD plays an indispensable role in the occurrence of multiple tumors. However, pan-cancer analysis is unavailable. The purpose of this research was to preliminarily study its prognostic landscape across various tumors and investigate its relationship with immunotherapy. We exhibited the expression profile, survival analysis, and genomic alterations of PTPRD based on the TIMER, GEPIA, UALCAN, PrognoScan and cBioPortal database. The frequency of PTPRD mutation and its correlation with response to immunotherapy were evaluated using the cBioPortal database. The relationship between PTPRD and immune-cell infiltration was analyzed by the TIMER and TISIDB databases. A protein interaction network was constructed by the STRING database. GO and KEGG enrichment analysis was executed by the Metascape database. A correlation between PTPRD expression and prognosis was found in various cancers. Aberrant PTPRD expression was closely related to immune infiltration. In non-small cell lung cancer and melanoma, patients with PTPRD mutations had better overall survival with immune checkpoint inhibitors, and these patients had higher TMB scores. PTPRD mutation was involved in numerous biological processes, including immunological signaling pathways. A PTPRD protein interaction network was constructed, and genes that interacted with PTPRD were identified. Functional enrichment analysis demonstrated that a variety of GO biological processes and KEGG pathways associated with PTPRD were involved in the therapeutic mechanisms. These results revealed that PTPRD might function as a biomarker for prognosis and immune infiltration in cancers, throwing new light on cancer therapeutics.

CRISPR based therapeutics: a new paradigm in cancer precision medicine

Background

Clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated protein (Cas) systems are the latest addition to the plethora of gene-editing tools. These systems have been repurposed from their natural counterparts by means of both guide RNA and Cas nuclease engineering. These RNA-guided systems offer greater programmability and multiplexing capacity than previous generation gene editing tools based on zinc finger nucleases and transcription activator like effector nucleases. CRISPR-Cas systems show great promise for individualization of cancer precision medicine.

Main body

The biology of Cas nucleases and dead Cas based systems relevant for in vivo gene therapy applications has been discussed. The CRISPR knockout, CRISPR activation and CRISPR interference based genetic screens which offer opportunity to assess functions of thousands of genes in massively parallel assays have been also highlighted. Single and combinatorial gene knockout screens lead to identification of drug targets and synthetic lethal genetic interactions across different cancer phenotypes. There are different viral and non-viral (nanoformulation based) modalities that can carry CRISPR-Cas components to different target organs in vivo.

Conclusion

The latest developments in the field in terms of optimization of performance of the CRISPR-Cas elements should fuel greater application of the latter in the realm of precision medicine. Lastly, how the already available knowledge can help in furtherance of use of CRISPR based tools in personalized medicine has been discussed.

Tracking the Evolution of Metastasis with Self-Functionalized 3D Nanoprobes

Despite recent advances in cancer treatment, metastasis is the cause of mortality in 90% of cancer cases. It has now been well-established that dissemination of cancer cells to distant sites occurs very early during tumorigenesis, resulting in the minimal effect of surgical or chemotherapeutic treatments after the detection of metastasis. The underlying reason for this challenge is mostly due to the limited understanding of molecular mechanisms of the metastasis cascade, particularly related to metastatic traits. Therefore, there is an urgent need to investigate this currently invisible evolution of metastasis. The tracking of metastasis evolution has not been addressed yet. Here, we introduce, for the first time, a synchronous approach to unveil the molecular mechanisms of the metastasis cascade. As cancer stem cells (CSCs) demonstrate cancer initiation, drug resistance, metastasis, and tumor relapse and can exist in a quasi-intermediate epithelial-mesenchymal transition state, the tumor-initiating events during a CSCs metamorphosis were monitored with single-cell sensitivity. Because of the invasive and resistive properties of the metastable intermediate CSCs, investigation of the molecular profiles of the quasi-intermediate CSCs was necessary for the detection of metastasis dissemination. For this purpose, the ultrasensitive technique of surface-enhanced Raman scattering (SERS) was adopted. Titanium-based, biocompatible three-dimensional (3D) nanoprobes that were synthesized for multiphoton ionization achieved a substantial SERS enhancement of ∼80-fold due to the oxygen vacancy-enriched composition of the nanoprobes. The 3D interconnected complex nanoarchitecture of the nanoprobes enabled us to entrap the nonadherent CSCs of three metastatic cancer cell lines (triple negative breast adenocarcinoma (MDAMB231), human Caucasian colon adenocarcinoma (COLO 205), and cervical adenocarcinoma (HeLa)─all very aggressive forms of cancer). The nanoprobes not only promoted the CSC proliferation to successfully attain the quasi-intermediate states but also monitored its reprogramming into a cancer cell state. The nanoprobes substantially amplified weak intracellular Raman signals to capture the molecular events during a CSC transformation. The detection of cancer was achieved with 100% accuracy. We experimentally demonstrated that the molecular signatures of CSC reprogramming are cancer-type specific. This observation enabled us to identify the origin of metastasis with 100% accuracy, providing more clarity on the relatively unknown quasi-intermediate states. This first demonstration of CSC-based tracking of metastasis evolution has the potential to provide an insightful perspective of tumorigenesis that could be useful in cancer diagnosis and prognosis as well as in the monitoring of therapeutic interventions.

Drug Metabolism for the Identification of Clinical Biomarkers in Breast Cancer

Breast cancer is classified into four major molecular subtypes, and is considered a heterogenous disease. The risk profiles and treatment of breast cancer differ according to these subtypes. Early detection dramatically improves the prospects of successful treatment, resulting in a reduction in overall mortality rates. However, almost 30% of women primarily diagnosed with the early-stage disease will eventually develop metastasis or resistance to chemotherapies. Immunotherapies are among the most promising cancer treatment options; however, long-term clinical benefit has only been observed in a small subset of responding patients. The current strategies for diagnosis and treatment rely heavily on histopathological examination and molecular diagnosis, disregarding the tumor microenvironment and microbiome involving cancer cells. In this review, we aim to praise the use of pharmacogenomics and pharmacomicrobiomics as a strategy to identify potential biomarkers for guiding and monitoring therapy in real-time. The finding of these biomarkers can be performed by studying the metabolism of drugs, more specifically, immunometabolism, and its relationship with the microbiome, without neglecting the information provided by genetics. A larger understanding of cancer biology has the potential to improve patient care, enable clinical decisions, and deliver personalized medicine.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

3D Tissue-Engineered Vascular Drug Screening Platforms: Promise and Considerations

Despite the efforts devoted to drug discovery and development, the number of new drug approvals have been decreasing. Specifically, cardiovascular developments have been showing amongst the lowest levels of approvals. In addition, concerns over the adverse effects of drugs to the cardiovascular system have been increasing and resulting in failure at the preclinical level as well as withdrawal of drugs post-marketing. Besides factors such as the increased cost of clinical trials and increases in the requirements and the complexity of the regulatory processes, there is also a gap between the currently existing pre-clinical screening methods and the clinical studies in humans. This gap is mainly caused by the lack of complexity in the currently used 2D cell culture-based screening systems, which do not accurately reflect human physiological conditions. Cell-based drug screening is widely accepted and extensively used and can provide an initial indication of the drugs' therapeutic efficacy and potential cytotoxicity. However, in vitro cell-based evaluation could in many instances provide contradictory findings to the in vivo testing in animal models and clinical trials. This drawback is related to the failure of these 2D cell culture systems to recapitulate the human physiological microenvironment in which the cells reside. In the body, cells reside within a complex physiological setting, where they interact with and respond to neighboring cells, extracellular matrix, mechanical stress, blood shear stress, and many other factors. These factors in sum affect the cellular response and the specific pathways that regulate variable vital functions such as proliferation, apoptosis, and differentiation. Although pre-clinical in vivo animal models provide this level of complexity, cross species differences can also cause contradictory results from that seen when the drug enters clinical trials. Thus, there is a need to better mimic human physiological conditions in pre-clinical studies to improve the efficiency of drug screening. A novel approach is to develop 3D tissue engineered miniaturized constructs in vitro that are based on human cells. In this review, we discuss the factors that should be considered to produce a successful vascular construct that is derived from human cells and is both reliable and reproducible.

A liquid chromatography tandem mass spectrometry method for a semiquantitative screening of cellular acyl-CoA

This study describes LC-ESI-MS/MS method that covers the analysis of various cellular acyl-CoA in a single injection. The method is based on a quick extraction step eliminating LLE/SPE clean up. Method performance characteristics were determined after spiking acyl-CoA standards in different concentrations into a surrogate matrix. The extensive matrix effect for most acyl-CoA except for palmitoyl-CoA was compensated by using isotopically labeled internal standard and matrix-matched calibration. As a result of the high matrix effect, the accuracy for palmitoyl-CoA at the low concentration deviated from the target range of ±20%. The developed method was applied to identify twenty-one cellular acyl-CoA in SK-HEP-1 cells and screening for alterations in acyl-CoA levels post Mito Q antioxidant intervention.

Assessment of patient's knowledge, attitude, and beliefs about cancer: An institute-based study

INTRODUCTION

Knowledge about cancer, treatment, and likely treatment outcomes is critically important for decision-making regarding adherence to the treatment. Hence, it seems imperative to find out the attitude and belief of cancer patients to determine the efficacy of cancer diagnostic and treatment available along with assessing the efficacy of prevention. The purpose of this study was to determine the level of knowledge, belief, and attitude of cancer patients who came to our facility.

MATERIALS AND METHODS

The present study was a descriptive cross-sectional study which was conducted at All India Institute of Medical Sciences, Rishikesh, from July 2020 to December 2020. One hundred fifty-six newly diagnosed cancer patients were recruited. The participants were provided with a validated 44 key questionnaire divided into four different categories including the sociodemographic profile, knowledge, belief, and attitude subheading. Mean, frequency, percentage, and Chi-square test were calculated to do data analysis using SPSS version 23.0.

RESULTS

Out of the total of 156 newly diagnosed cancer patients, only 130 patients provided responses so the overall response rate was 84.3%. Most of cancer patients had moderately adequate knowledge (79 [60.5%]), poor belief (111 [86%]), and neutral attitude (69 [54%]) regarding cancer. The Chi-square test revealed a link between educational level and cancer knowledge (P = 0.01). Males showed a more positive attitude than females, and this difference was statistically significant (P = 0.02).

CONCLUSIONS

Cancer patients need to be made more aware of the red flag signs of cancer and improvise their attitude toward cancer. Cancer care programs should not only involve cancer patients but also involve their caregivers because the understanding of family members is of utmost importance to fight this disease.

Treatment Access, Health Economics, and the Wave of a Magic Wand

New drugs are expensive, in part due to excessive drug development costs. Governments are trying to reduce drug prices. This can delay access to effective agents. A country’s access to new drugs correlates with prices they agree to pay. After Health Canada approves a drug, the Canadian Agency for Drug and Technologies in Health (CADTH) assesses it. CADTH’s approval is usually contingent on it costing ≤CAD 50,000 per quality adjusted life year (QALY) gained. This value (unchanged from the 1970s) is inappropriately low. An inflation-adjusted CAD 50,000 1975 QALY should translate into a CAD 250,000 2021 QALY. CADTH’s target also does not consider that drug development costs have risen much faster than inflation or that new precision therapies may only be used in small populations. In a separate process, proposals from the Patented Medicines Price Review Board (PMPRB) would decrease initial Canadian drug prices by 20%, but prices would fall further as sales increased, with ultimate price reductions of up to 80%. PMPRB claims its proposal would not reduce drug access, but multiple analyses strongly suggest otherwise. Government price controls target the symptom (high prices), not the disease. They translate into shortages without solving the problem. CADTH and PMPRB approaches both threaten access to effective drugs.

Patient-specific Boolean models of signalling networks guide personalised treatments

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics

Background

Type 2 diabetes mellitus (T2DM) is caused by a combination of environmental, genetic, and epigenetic factors including, fasting blood glucose (FBG), genetic variant rs841853, and cg19693031 methylation. We evaluated the interaction between rs841853 and cg19693031 on the FBG levels of non-diabetic Taiwanese adults.

Methods

We used Taiwan Biobank (TWB) data collected between 2008 and 2016. The TWB data source contains information on basic demographics, personal lifestyles, medical history, methylation, and genotype. The study participants included 1300 people with DNA methylation data. The association of cg19693031 methylation (stratified into quartiles) with rs841853 and FBG was determined using multiple linear regression analysis. The beta-coefficients (β) and p-values were estimated.

Results

The mean ± standard deviation (SD) of FBG in rs841853-CC individuals (92.07 ± 7.78) did not differ significantly from that in the CA + AA individuals (91.62 ± 7.14). However, the cg19693031 methylation levels were significantly different in the two groups (0.7716 ± 0.05 in CC individuals and 0.7631 ± 0.05 in CA + AA individuals (p = 0.002). The cg19693031 methylation levels according to quartiles were β < 0.738592 (< Q1), 0.738592 ≤ 0.769992 (Q1–Q2), 0.769992 ≤ 0.800918 (Q2–Q3), and β ≥ 0.800918 (≥ Q3). FBG increased with decreasing cg19693031 methylation levels in a dose–response manner (p_trend = 0.005). The β-coefficient was − 0.0236 (p = 0.965) for Q2–Q3, 1.0317 (p = 0.058) for Q1–Q2, and 1.3336 (p = 0.019 for < Q1 compared to the reference quartile (≥ Q3). The genetic variant rs841853 was not significantly associated with FBG. However, its interaction with cg19693031 methylation was significant (p-value = 0.036). Based on stratification by rs841853 genotypes, only the CC group retained the inverse and dose–response association between FBG and cg19693031 methylation. The β (p-value) was 0.8082 (0.255) for Q2–Q3, 1.6930 (0.022) for Q1–Q2, and 2.2190 (0.004) for < Q1 compared to the reference quartile (≥ Q3). The p_trend was 0.002.

Conclusion

Summarily, methylation at cg19693031 was inversely associated with fasting blood glucose in a dose-dependent manner. The inverse association was more prominent in rs841853-CC individuals, suggesting that rs841853 could modulate the association between cg19693031 methylation and FBG. Our results suggest that genetic variants may be involved in epigenetic mechanisms associated with FBG, a hallmark of diabetes. Therefore, integrating genetic and epigenetic data may provide more insight into the early-onset of diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03269-y.

Comprehensive gene cluster analysis of head and neck squamous cell carcinoma TCGA RNA-seq data defines B cell immunity-related genes as a robust survival predictor

BACKGROUND

The authors aimed to define novel gene expression signatures that are associated with patients' survival with head and neck squamous cell carcinoma (HNSCC).

METHODS

TCGA RNA-seq data were used for gene expression clusters extraction from 499 tumor samples by the "EPIG" method. Tumor samples were then partitioned into lower and higher than median level groups for survival relevant analysis by Kaplan-Meier estimator.

RESULTS

We found that two gene clusters (_1, _2) are favorably, while two (_3, _4) are unfavorably, associated with patients' survival with HNSCC. Notably, most genes on the top lists of cluster_2 are associated with B cells. A gene expression signature with combined genes from cluster_2 and _4 was further determined to be associated with HNSCC survival rate.

CONCLUSION

Our work strongly supported a favorable role of B cells in patients' survival with HNSCC and identified a novel coexpressed gene signature as prognostic biomarker for patients' survival with HNSCC estimation.

Utility of a Recombinant HSV-1 Vaccine Vector for Personalized Cancer Vaccines

Current approaches to cancer immunotherapy include immune checkpoint inhibitors, cancer vaccines, and adoptive cellular therapy. These therapies have produced significant clinical success for specific cancers, but their efficacy has been limited. Oncolytic virotherapy (OVT) has emerged as a promising immunotherapy for a variety of cancers. Furthermore, the unique characteristics of OVs make them a good choice for delivering tumor peptides/antigens to induce enhanced tumor-specific immune responses. The first oncolytic virus (OV) approved for human use is the attenuated herpes simplex virus type 1 (HSV-1), Talimogene laherparepvec (T-VEC) which has been FDA approved for the treatment of melanoma in humans. In this study, we engineered the recombinant oncolytic HSV-1 (oHSV) VC2-OVA expressing a fragment of ovalbumin (OVA) as a fusion protein with VP26 virion capsid protein. We tested the ability of VC2-OVA to act as a vector capable of stimulating strong, specific antitumor immunity in a syngeneic murine melanoma model. Therapeutic vaccination with VC2-OVA led to a significant reduction in colonization of tumor cells in the lungs of mice intravenously challenged B16cOVA cells. In addition, VC2-OVA induced a potent prophylactic antitumor response and extended survival of mice that were intradermally engrafted with B16cOVA tumors compared with mice immunized with control virus.

Comprehensive Molecular Landscape of Cetuximab Resistance in Head and Neck Cancer Cell Lines

Cetuximab is the sole anti-EGFR monoclonal antibody that is FDA approved to treat head and neck squamous cell carcinoma (HNSCC). However, no predictive biomarkers of cetuximab response are known for HNSCC. Herein, we address the molecular mechanisms underlying cetuximab resistance in an in vitro model. We established a cetuximab resistant model (FaDu), using increased cetuximab concentrations for more than eight months. The resistance and parental cells were evaluated for cell viability and functional assays. Protein expression was analyzed by Western blot and human cell surface panel by lyoplate. The mutational profile and copy number alterations (CNA) were analyzed using whole-exome sequencing (WES) and the NanoString platform. FaDu resistant clones exhibited at least two-fold higher IC50 compared to the parental cell line. WES showed relevant mutations in several cancer-related genes, and the comparative mRNA expression analysis showed 36 differentially expressed genes associated with EGFR tyrosine kinase inhibitors resistance, RAS, MAPK, and mTOR signaling. Importantly, we observed that overexpression of KRAS, RhoA, and CD44 was associated with cetuximab resistance. Protein analysis revealed EGFR phosphorylation inhibition and mTOR increase in resistant cells. Moreover, the resistant cell line demonstrated an aggressive phenotype with a significant increase in adhesion, the number of colonies, and migration rates. Overall, we identified several molecular alterations in the cetuximab resistant cell line that may constitute novel biomarkers of cetuximab response such as mTOR and RhoA overexpression. These findings indicate new strategies to overcome anti-EGFR resistance in HNSCC.

Applying FHIR Genomics for Research - From Sequencing to Database

The availability of next-generation sequencing (NGS) technologies and their continually declining costs have resulted in the accumulation of large genomic data sets. NGS results have traditionally been delivered in PDF format, and in some cases, structured data, e.g., XML or JSON formats, are also made available, but there is a lack of uniformity around the profiling of external vendor testing platforms. Atrium Health Wake Forest Baptist and TriNetX have harmonized and mapped genomic data to FHIR Genomic standards and imported it into the TriNetX database through a data pipeline. This process is translatable to other sequencing platforms and to other institutions. The addition of genotypic data to the TriNetX database to the reservoir of phenotypic data will promote enhanced industry trial recruitment, (ii) comprehensive intra-institutional genomic benchmarking/quality improvement, and eventually (iii) sweeping inter-institutional genomic research and treatment paradigm shifts.

Multi-omics data integration by generative adversarial network

MOTIVATION

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals.

RESULTS

Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.

AVAILABILITY AND IMPLEMENTATION

Source code is available at: https://github.com/CompbioLabUCF/omicsGAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Building a Personalized Medicine Infrastructure for Gynecological Oncology Patients in a High-Volume Hospital

Gynecological cancers require complex intervention since patients have specific needs to be addressed. Centralization to high-volume centers improves the oncological outcomes of patients with gynecological cancers. Research in gynecological oncology is increasing thanks to modern technologies, from the comprehensive molecular characterization of tumors and individual pathophenotypes. Ongoing studies are focusing on personalizing therapies by integrating information across genomics, proteomics, and metabolomics with the genetic makeup and immune system of the patient. Hence, several challenges must be faced to provide holistic benefit to the patient. Personalized approaches should also recognize the unmet needs of each patient to successfully deliver the promise of personalized care, in a multidisciplinary effort. This may provide the greatest opportunity to improve patients' outcomes. Starting from a narrative review on gynecological oncology patients' needs, this article focuses on the experience of building a research and care infrastructure for personalized patient management.

Relationship Between Quality of Life Indicators and Cardiac Status Indicators in Chemotherapy Patients

Aim

With the aim of improving personalized treatment of patients on chemotherapy, the objective of the study was to assess the degree of association between selected Quality of life (QoL) indicators and both clinical and imaging cardiac status indicators when detecting deterioration in QoL of these patients.

Methods

In a cohort clinical study in Hamburg, from August 2017 through October 2020, 59 cancer patients, aged 18-80 years, were evaluated before chemotherapy, and at several follow-ups, using EQ-5D and SF-36 QoL questionnaires, fast strain-encoded (fast-SENC) cardiac magnetic resonance (CMR), conventional CMR, and echocardiography, and further received a clinical and biomarker examination. Data was analyzed using survival analyses. A decline of more than 5% in each observed QoL metric value was defined as the observed event. Patient were separated into groups according to the presentation of cardiotoxicity as per its clinical definition, the establishment of the indication for cardioprotective therapy initiation, and by a worsening in the value of each observed imaging metric by more than 5% in the previous follow-up compared to the corresponding pre-chemotherapy baseline value.

Results

Among clinical cardiac status indicators, the indication for cardioprotective therapy showed statistically good association with QoL scores (EQ-5D p=0.028; SF-36 physical component p=0.016; SF-36 mental component p=0.012). In terms of imaging metrics, the MyoHealth segmental myocardial strain score was the only one demonstrating consistently good QoL score association (EQ-5D p=0.005; SF-36 physical component p=0.056; SF-36 mental component p=0.002).

Conclusions

Established fast-SENC CMR scores are capable of highlighting patients with reduced QoL, who require more frequent/optimal management.

Therapeutic opportunities in cancer therapy: targeting the p53-MDM2/MDMX interactions

Ubiquitination is a key enzymatic post-translational modification that influences p53 stability and function. p53 protein regulates the expression of MDM2 (mouse double-minute 2 protein) E3 ligase and MDMX (double-minute 4 protein), through proteasome-based degradation. Exploration of targeting the ubiquitination pathway offers a potentially promising strategy for precision therapy in a variety of cancers. The p53-MDM2-MDMX pathway provides multiple molecular targets for small molecule screening as potential therapies for wild-type p53. As a result of its effect on molecular carcinogenesis, a personalized therapeutic approach based on the wild-type and mutant p53 protein is desirable. We highlighted the implications of p53 mutations in cancer, p53 ubiquitination mechanistic details, targeting p53-MDM2/MDMX interactions, significant discoveries related to MDM2 inhibitor drug development, MDM2 and MDMX dual target inhibitors, and clinical trials with p53-MDM2/MDMX-targeted drugs. We also investigated potential therapeutic repurposing of selective estrogen receptor modulators (SERMs) in targeting p53-MDM2/MDMX interactions. Molecular docking studies of SERMs were performed utilizing the solved structures of the p53/MDM2/MDMX proteins. These studies identified ormeloxifene as a potential dual inhibitor of p53/MDM2/MDMX interaction, suggesting that repurposing SERMs for dual targeting of p53/MDM2 and p53/MDMX interactions is an attractive strategy for targeting wild-type p53 tumors and warrants further preclinical research.