Personalized medicine and cancer

Novel role of lncRNAs regulatory network in papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most common endocrine malignancy. The incidence of PTC has increased annually worldwide. Thus, PTC diagnosis and treatment attract more attention. Noncoding RNAs (lncRNAs) play crucial roles in PTC progression and act as prognostic biomarkers. Moreover, microRNAs (miRNAs) and epithelial-mesenchymal transition (EMT)-associated proteins have potential biomarkers for diagnosing and treating PTC. However, the correlation of lncRNAs with miRNAs and EMT-associated proteins needs further clarification. The present review highlights the recent advances of lncRNAs in PTC. We significantly summarized the two molecular regulatory mechanisms in PTC progress, including lncRNAs-miRNAs-protein signaling axes and lncRNAs-EMT pathways. This review will help our understanding of the association between lncRNAs and PTC and may assist us in evaluating the prognosis for PTC patients. Taken together, targeting the lncRNAs regulatory network has promising applications in diagnosing and treating PTC.

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

MOTIVATION

The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.

RESULTS

Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies

In recent years, many studies have focused their attention on the dog as a proper animal model for human cancer. In dogs, mammary tumors develop spontaneously, involving a complex interplay between tumor cells and the immune system and revealing several molecular and clinical similarities to human breast cancer. In this review, we summarized the major features of canine mammary tumor, risk factors, and the most important biomarkers used for diagnosis and treatment. Traditional therapy of mammary tumors in dogs includes surgery, which is the first choice, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these therapeutic strategies may not always be sufficient on their own; advancements in understanding cancer mechanisms and the development of innovative treatments offer hope for improved outcomes for oncologic patients. There is still a growing interest in the use of personalized medicine, which should play an irreplaceable role in the research not only in human cancer therapy, but also in veterinary oncology. Moreover, immunotherapy may represent a novel and promising therapeutic option in canine mammary cancers. The study of novel therapeutic approaches is essential for future research in both human and veterinary oncology.

Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

Adhesion, metastasis, and inhibition of cancer cells: a comprehensive review

This comprehensive review delves into cancer's complexity, focusing on adhesion, metastasis, and inhibition. It explores the pivotal role of these factors in disease progression and therapeutic strategies. This review covers cancer cell migration, invasion, and colonization of distant organs, emphasizing the significance of cell adhesion and the intricate metastasis process. Inhibition approaches targeting adhesion molecules, such as integrins and cadherins, are discussed. Overall, this review contributes significantly to advancing cancer research and developing targeted therapies, holding promise for improving patient outcomes worldwide. Exploring different inhibition strategies revealed promising therapeutic targets to alleviate adhesion and metastasis of cancer cells. The effectiveness of integrin-blocking antibodies, small molecule inhibitors targeting Focal adhesion kinase (FAK) and the Transforming Growth Factor β (TGF-β) pathway, and combination therapies underscores their potential to disrupt focal adhesions and control epithelial-mesenchymal transition processes. The identification of as FAK, Src, β-catenin and SMAD4 offers valuable starting points for further research and the development of targeted therapies. The complex interrelationships between adhesion and metastatic signaling networks will be relevant to the development of new treatment approaches.

Economic Evaluations of Imaging Biomarker-Driven Companion Diagnostics for Cancer: A Systematic Review

INTRODUCTION

There is a boom in imaging biomarker-driven companion and complementary diagnostics (CDx) for cancer, which brings opportunity for personalized medicine. Whether adoption of these technologies is likely to be cost-effective is a relevant question, and studies on this topic are emerging. Despite the growing number of economic evaluations, no review of the methods used, quality of reporting, and potential risk of bias has been done. We report a systematic review to identify, summarize, and critique the cost-effectiveness evidence for the use of biomarker-driven and imaging-based CDx to inform cancer treatments.

METHODS

The Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Systematic literature searches until 30 December 2022 were performed in PubMed, Web of Science, Medline, Embase, and Scopus for economic evaluations of imaging biomarker-based CDx for cancer. The inclusion and exclusion of studies were determined by pre-specified eligibility criteria informed by the 'Patient, Intervention, Comparison, Outcome' (PICO) framework. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) was used to assess the quality of reporting, and the Bias in Economic Evaluation (ECOBIAS) was used to examine the potential risk of bias of included studies.

RESULTS

A total of 12 papers were included, with eight model-based and four trial-based studies. Implementing biomarker-driven, imaging-based CDx was reported to be cost-effective, cost saving, or dominant (cost saving and more effective) in ten papers. Inconsistent methods were found in the studies, and the quality of reporting was lacking against the CHEERS reporting guideline. Several potential sources of 'risk of bias' were identified. These should be acknowledged and carefully considered by researchers planning future health economic evaluations.

CONCLUSION

Despite favorable results towards the implementation of imaging biomarker-based CDx for cancer, there is room for improvement regarding the quantity and quality of economic evaluations, and that is expected as the awareness of current study limitations increases and more clinical data become available in the future.

Predictors of community pharmacists' readiness to manage the effective and safe use of oral anticancer medicines in a developing setting

BACKGROUND

There is a paucity of studies about the readiness of community pharmacists to manage the safe and effective use of oral anticancer medicines (OAMs) in developing settings.

OBJECTIVES

Using the readiness component (knowledge and willingness) of the situational theory of leadership, the study assessed community pharmacists' readiness to manage the safe and effective use of OAMs in Qatar, and also identified its significant predictors.

METHODS

A cross-sectional assessment of 252 community pharmacists was conducted with a pre-tested 48-item questionnaire. Readiness was assessed with a 5-point Likert-type scale and the maximum obtainable score was 70. The mean was used as the cut-off point to categorize willingness and knowledge as either high (≥ mean) or low (< mean). Independent t-test, one-way analysis of variance and multiple linear regression was used for data analyses.

RESULTS

The mean SD score for the readiness of community pharmacists was 39.3 ± 11.2 (min = 11, max = 70). Only a minority of the respondents expressed an excellent understanding of the chemotherapy cycles (19.6%; 45/230), and familiarity with targeted anticancer therapy (14.3%; 33/230), side effects (22.2%; 51/230), and dosing of OAMs (14.4%; 33/230). Employment status, number of OAMs prescriptions dispensed per month and adequacy of the contents related to OAMs in undergraduate training were the significant predictors of readiness (R2 = 0.558, (F (11, 209) = 3.559, p = 0.0001).

CONCLUSIONS

Community pharmacists' readiness appear inadequate especially regarding its dosing, side effects, handling, and disposal of OAMs. These inadequacies probably underline community pharmacists' low familiarity and comfortability with dispensing and educating patients on the effective and safe use of OAMs.

Selection and Optimization of a Bioink Based on PANC-1- Plasma/Alginate/Methylcellulose for Pancreatic Tumour Modelling

3D bioprinting involves using bioinks that combine biological and synthetic materials. The selection of the most appropriate cell-material combination for a specific application is complex, and there is a lack of consensus on the optimal conditions required. Plasma-loaded alginate and alginate/methylcellulose (Alg/MC) inks were chosen to study their viscoelastic behaviour, degree of recovery, gelation kinetics, and cell survival after printing. Selected inks showed a shear thinning behavior from shear rates as low as 0.2 s-1, and the ink composed of 3% w/v SA and 9% w/v MC was the only one showing a successful stacking and 96% recovery capacity. A 0.5 × 106 PANC-1 cell-laden bioink was extruded with an Inkredible 3D printer (Cellink) through a D = 410 μm tip conical nozzle into 6-well culture plates. Cylindrical constructs were printed and crosslinked with CaCl2. Bioinks suffered a 1.845 Pa maximum pressure at the tip that was not deleterious for cellular viability. Cell aggregates can be appreciated for the cut total length observed in confocal microscopy, indicating a good proliferation rate at different heights of the construct, and suggesting the viability of the selected bioink PANC-1/P-Alg3/MC9 for building up three-dimensional bioprinted pancreatic tumor constructs.

Advancements and Applications of Liquid Biopsies in Oncology: A Narrative Review

According to the World Health Organization (WHO), nearly 10 million people died from cancer worldwide in 2020, making it the leading cause of mortality. Liquid biopsies, which provide non-invasive and real-time monitoring of tumor dynamics, have evolved into innovative diagnostic techniques in the field of oncology. Liquid biopsies offer important insights into tumor heterogeneity, treatment response, minimum residual disease identification, and personalized treatment of cancer through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles, and microRNAs. They offer several advantages over traditional tissue biopsies, such as being less invasive, more convenient, more representative of tumor heterogeneity and dynamics, and more informative for guiding personalized treatment decisions. Liquid biopsies are being utilized increasingly in clinical oncology, particularly for patients with metastatic disease who require ongoing monitoring and treatment modification. In this narrative review article, we review the latest developments of liquid biopsy technologies, their applications and limitations, and their potential to transform diagnosis, prognosis, and management of cancer patients.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Analyses of Genes Critical to Tumor Survival Reveal Potential 'Supertargets': Focus on Transcription

The identification of mechanisms that underlie the biology of individual tumors is aimed at the development of personalized treatment strategies. Herein, we performed a comprehensive search of genes (termed Supertargets) vital for tumors of particular tissue origin. In so doing, we used the DepMap database portal that encompasses a broad panel of cell lines with individual genes knocked out by CRISPR/Cas9 technology. For each of the 27 tumor types, we revealed the top five genes whose deletion was lethal in the particular case, indicating both known and unknown Supertargets. Most importantly, the majority of Supertargets (41%) were represented by DNA-binding transcription factors. RNAseq data analysis demonstrated that a subset of Supertargets was deregulated in clinical tumor samples but not in the respective non-malignant tissues. These results point to transcriptional mechanisms as key regulators of cell survival in specific tumors. Targeted inactivation of these factors emerges as a straightforward approach to optimize therapeutic regimens.

Interleukins (Cytokines) as Biomarkers in Colorectal Cancer: Progression, Detection, and Monitoring

Cancer is the primary cause of death in economically developed countries and the second leading cause in developing countries. Colorectal cancer (CRC) is the third most common cause of cancer-related deaths worldwide. Risk factors for CRC include obesity, a diet low in fruits and vegetables, physical inactivity, and smoking. CRC has a poor prognosis, and there is a critical need for new diagnostic and prognostic biomarkers to reduce related deaths. Recently, studies have focused more on molecular testing to guide targeted treatments for CRC patients. The most crucial feature of activated immune cells is the production and release of growth factors and cytokines that modulate the inflammatory conditions in tumor tissues. The cytokine network is valuable for the prognosis and pathogenesis of colorectal cancer as they can aid in the cost-effective and non-invasive detection of cancer. A large number of interleukins (IL) released by the immune system at various stages of CRC can act as "biomarkers". They play diverse functions in colorectal cancer, and include IL-4, IL-6, IL-8, IL-11, IL-17A, IL-22, IL-23, IL-33, TNF, TGF-β, and vascular endothelial growth factor (VEGF), which are pro-tumorigenic genes. However, there are an inadequate number of studies in this area considering its correlation with cytokine profiles that are clinically useful in diagnosing cancer. A better understanding of cytokine levels to establish diagnostic pathways entails an understanding of cytokine interactions and the regulation of their various biochemical signaling pathways in healthy individuals. This review provides a comprehensive summary of some interleukins as immunological biomarkers of CRC.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.

Emerging technologies in personalized medicine

A variety of technologies are emerging to help clinicians provide patient-specific diagnosis and therapies. This special edition of the Molecular Aspects of Medicine is a collection of mini reviews covering a broad range of topics, from systems to model patient variants and discover therapies (Microphysiological systems with patient derived tissue and CRISPR-humanized animal models), to new modalities in diagnostics and therapeutics (Extracellular Vesicles, RNA therapeutics, microbiome and molecular dynamics).

E3 Ubiquitin Ligase TRIP12 Controls Exit from Mitosis via Positive Regulation of MCL-1 in Response to Taxol

Chemotherapy resistance is a major hurdle in cancer treatment. Taxol-based chemotherapy is widely used in the treatment of cancers including breast, ovarian, and pancreatic cancer. Loss of function of the tumor suppressor F-box WD-40 domain containing 7 (FBW7) mutations leads to the accumulation of its substrate MCL-1 which is associated with Taxol resistance in human cancers. We recently showed that E3 ubiquitin ligase TRIP12 is a negative regulator of FBW7 protein. In this study, we find that Taxol-induced mitotic block in cancer cells is partly controlled by TRIP12 via its positive regulation of MCL-1 protein. Genetic inhibition of TRIP12 accelerates MCL-1 protein degradation in mitosis. Notably, introducing double-point mutations in lysines 404/412 of FBW7 to arginine which makes it resistant to proteasomal degradation, leads to the sharp reduction of MCL-1 protein levels and sensitizes cancer cells to Taxol-induced cell death. Finally, TRIP12 deletion leads to enhanced mitotic arrest and cell death in an FBW7 and MCL-1 dependent manner in multiple cell lines including colorectal and ovarian cancer but not in breast cancer. Thus, the TRIP12/FBW7/MCL-1 axis may provide a therapeutic target to overcome Taxol-associated chemotherapy resistance in cancer.

Targeted Therapy and Personalized Medicine

Targeted therapy and personalized medicine are novel emerging disciplines of cancer research intended for treatment and prevention. One of the most significant advancements in modern oncology is the shift from an organ-centric strategy to a personalized strategy guided by deep molecular analysis. This shift in view, which focuses on the tumour's precise molecular changes, has paved the way for individualized treatment. Researchers and clinicians are using targeted therapies to select the best treatment available based on the molecular characterization of malignant cancer. In the treatment of a cancer, personalized medicine entails the use of genetic, immunological, and proteomic profiling to provide therapeutic alternatives as well as prognostic information about cancer. In this book, targeted therapies and personalized medicine have been covered for specific malignancies, including latest FDA-approved targeted therapies and it also sheds light on effective anti-cancer regimens and drug resistance. This will help to enhance our ability to conduct individualized health planning, make early diagnoses, and choose optimal medications for each cancer patient with predictable side effects and outcomes in a quickly evolving era. Various applications and tools' capacity have been improved for early diagnosis of cancer and the growing number of clinical trials that choose specific molecular targets reflects this predicament. Nevertheless, there are several limitations that must need to be addressed. Hence, in this chapter, we will discuss recent advancements, challenges, and opportunities in personalized medicine for various cancers, with a specific emphasis on target therapies in diagnostics and therapeutics.

A global analysis of the value of precision medicine in oncology - The case of non-small cell lung cancer

Objectives

Biomarker testing is indispensable for the implementation of precision medicine (PM) in oncology. The aim of this study was to assess the value of biomarker testing from a holistic perspective based on the example of advanced non-small cell lung cancer (aNSCLC).

Materials and methods

A partitioned survival model was populated with data from pivotal clinical trials of first-line treatments in aNSCLC. Three testing scenarios were considered; "no biomarker testing" encompassing chemotherapy treatment, "sequential testing" for EGFR and ALK encompassing treatment with targeted- or chemotherapy, and "multigene testing" covering EGFR, ALK, ROS1, BRAF, NTRK, MET, RET and encompassing treatment with targeted- or immuno(chemo)therapy. Analyses of health outcomes and costs were run for nine countries (Australia, Brazil, China, Germany, Japan, Poland, South Africa, Turkey, United States). A 1-year and 5-year time horizon was applied. Information on test accuracy was combined with country-specific information on epidemiology and unit costs.

Results

Compared to the no-testing scenario, survival improved and treatment-related adverse events decreased with increased testing. Five-year survival increased from 2% to 5-7% and to 13-19% with sequential testing and multigene testing, respectively. The highest survival gains were observed in East Asia due to a higher local prevalence of targetable mutations. Overall costs increased with increased testing in all countries. Although costs for testing and medicines increased, costs for treatment of adverse events and end-of-life care decreased throughout all years. Non-health care costs (sick leave and disability pension payments) decreased during the first year but increased over a 5-year horizon.

Conclusion

The broad use of biomarker testing and PM in aNSCLC leads to more efficient treatment assignment and improves health outcomes for patients globally, in particular prolonged progression-free disease phase and overall survival. These health gains require investment in biomarker testing and medicines. While costs for testing and medicines would initially increase, cost decreases for other medical services and non-health care costs may partly offset the cost increases.

Exosomal microRNAs in cancer: Potential biomarkers and immunotherapeutic targets for immune checkpoint molecules

Exosomes are small extracellular vesicles with a lipid bilayer structure secreted from different cell types which can be found in various body fluids including blood, pleural fluid, saliva and urine. They carry different biomolecules including proteins, metabolites, and amino acids such as microRNAs which are small non-coding RNAs that regulate gene expression and promote cell-to-cell communication. One main function of the exosomal miRNAs (exomiRs) is their role in cancer pathogenesis. Alternation in exomiRs expression could indicate disease progression and can regulate cancer growth and facilitate drug response/resistance. It can also influence the tumour microenvironment by controlling important signaling that regulating immune checkpoint molecules leading to activation of T cell anti-tumour immunity. Therefore, they can be used as potential novel cancer biomarkers and innovative immunotherapeutic agents. This review highlights the use of exomiRs as potential reliable biomarkers for cancer diagnosis, treatment response and metastasis. Finally, discuses their potential as immunotherapeutic agents to regulate immune checkpoint molecules and promote T cell anti-tumour immunity.

Three-Dimensional Spheroids for Cancer Research

In vitro cell culture is one of the most widely used tools used today for increasing our understanding of various things such as protein production, mechanisms of drug action, tissue engineering, and overall cellular biology. For the past decades, however, cancer researchers have relied heavily on conventional two-dimensional (2D) monolayer culture techniques to test a variety of aspects of cancer research ranging from the cytotoxic effects of antitumor drugs to the toxicity of diagnostic dyes and contact tracers. However, many promising cancer therapies have either weak or no efficacy in real-life conditions, therefore delaying or stopping altogether their translating to the clinic. This is, in part, due to the reductionist 2D cultures used to test these materials, which lack appropriate cell-cell contacts, have altered signaling, do not represent the natural tumor microenvironment, and have different drug responses, due to their reduced malignant phenotype when compared to real in vivo tumors. With the most recent advances, cancer research has moved into 3D biological investigation. Three-dimensional (3D) cultures of cancer cells not only recapitulate the in vivo environment better than their 2D counterparts, but they have, in recent years, emerged as a relatively low-cost and scientifically accurate methodology for studying cancer. In this chapter, we highlight the importance of 3D culture, specifically 3D spheroid culture, reviewing some key methodologies for forming 3D spheroids, discussing the experimental tools that can be used in conjunction with 3D spheroids and finally their applications in cancer research.

Constructing a Cancer Patient-Specific Network Based on Second-Order Partial Correlations of Gene Expression and DNA Methylation

Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.

Acoustic-Based Theranostic Probes Activated by Tumor Microenvironment for Accurate Tumor Diagnosis and Assisted Tumor Therapy

Acoustic-based imaging techniques, including ultrasonography and photoacoustic imaging, are powerful noninvasive approaches for tumor imaging owing to sound transmission facilitation, deep tissue penetration, and high spatiotemporal resolution. Usually, imaging modes were classified into "always-on" mode and "activatable" mode. Conventional "always-on" acoustic-based probes often have difficulty distinguishing lesion regions of interest from surrounding healthy tissues due to poor target-to-background signal ratios. As compared, activatable probes have attracted attention with improved sensitivity, which can boost or amplify imaging signals only in response to specific biomolecular recognition or interactions. The tumor microenvironment (TME) exhibits abnormal physiological conditions that can be used to identify tumor sections from normal tissues. Various types of organic dyes and biomaterials can react with TME, leading to obvious changes in their optical properties. The TME also affects the self-assembly or aggregation state of nanoparticles, which can be used to design activatable imaging probes. Moreover, acoustic-based imaging probes and therapeutic agents can be coencapsulated into one nanocarrier to develop nanotheranostic probes, achieving tumor imaging and cooperative therapy. Satisfactorily, ultrasound waves not only accelerate the release of encapsulated therapeutic agents but also activate therapeutic agents to exert or enhance their therapeutic performance. Meanwhile, various photoacoustic probes can convert photon energy into heat under irradiation, achieving photoacoustic imaging and cooperative photothermal therapy. In this review, we focus on the recently developed TME-triggered ultrasound and photoacoustic theranostic probes for precise tumor imaging and targeted tumor therapy.

Examining interprofessional collaboration in oncogenetic service delivery models for hereditary cancers: a scoping review protocol

INTRODUCTION

In a context of limited genetic specialists, collaborative models have been proposed to ensure timely access to high quality oncogenetic services for individuals with inherited cancer susceptibility. Yet, extensive variability in the terminology used and lack of a clear understanding of how interprofessional collaboration is operationalised and evaluated currently constrains the development of a robust evidence base on the value of different approaches used to optimise access to these services. To fill in this knowledge gap, this scoping review aims to systematically unpack the nature and extent of collaboration proposed by these interventions, and synthesise the evidence available on their implementation, effectiveness and economic impact.

METHODS AND ANALYSIS

Following the Joanna Briggs Institute guidelines for scoping reviews, a comprehensive literature search will be conducted to identify peer-reviewed and grey literature on collaborative models used for adult patients with, or at increased risk of, hereditary breast, ovarian, colorectal and prostate cancers. An initial search was developed for Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane and Web of Science on 13 June 2022 and will be complemented by searches in Google and relevant websites. Documents describing either the theory of change, planning, implementation and/or evaluation of these interventions will be considered for inclusion. Results will be summarised descriptively and used to compare relevant model characteristics and synthesise evidence available on their implementation, effectiveness and economic impact. This process is expected to guide the development of a definition and typology of collaborative models in oncogenetics that could help strengthen the knowledge base on these interventions. Moreover, because we will be mapping the existing evidence on collaborative models in oncogenetics, the proposed review will help us identify areas where additional research might be needed.

ETHICS AND DISSEMINATION

This research does not require ethics approval. Results from this review will be disseminated through peer-reviewed articles and conferences.

A Comprehensive Review of Electrospun Fibers, 3D-Printed Scaffolds, and Hydrogels for Cancer Therapies

Anticancer therapies and regenerative medicine are being developed to destroy tumor cells, as well as remodel, replace, and support injured organs and tissues. Nowadays, a suitable three-dimensional structure of the scaffold and the type of cells used are crucial for creating bio-inspired organs and tissues. The materials used in medicine are made of non-degradable and degradable biomaterials and can serve as drug carriers. Developing flexible and properly targeted drug carrier systems is crucial for tissue engineering, regenerative medicine, and novel cancer treatment strategies. This review is focused on presenting innovative biomaterials, i.e., electrospun nanofibers, 3D-printed scaffolds, and hydrogels as a novel approach for anticancer treatments which are still under development and awaiting thorough optimization.

Fabrication of Superparamagnetic Bimetallic Magnesium Nanoferrite Using Green Polyol: Characterization and Anticancer Analysis in Vitro on Lung Cancer Cell Line A549

Magnetic bimetallic nanoparticles find many industrial and clinical applications in the field of water treatment, antibacterial and anticancer activities. Therefore, the current article reports green synthesis using oleo-polyol as a surface modifier and synthesis agent for bimetallic magnetic magnesium ferrite nanoparticles. The role of hydroxyl functionality of castor oil (a natural polyol) on the enhancement of structural, morphological, magnetic, and particle size properties has also been discussed. These properties were characterized using FTIR, XRD spectroscopy, SEM, AFM, and TEM microscopy, Brunauer-Emmett-Teller (BET), and vibrating sample magnetometer (VSM) techniques. The effect of calcination temperatures (600-900 °C) on particle size (23-40 nm to 500-600 nm), crystallite sizes (73.15-292.67 nm), and saturation magnetization (20.87, 23.07, 32.39, and 33.13 emu g^-1) was analyzed. The influence of calcined temperatures on the anticancer activity of these nanoparticles has also been investigated in vitro using lung cancer cells (A549). Their biocompatibility, cytotoxicity, flow cytometry, and statistical analysis against lung cancer cells (A549) have been discussed. The green synthesis of magnesium nanoferrite particles using natural polyol and their application as anticancer agents against lung cancer cells (A549) have not been reported previously. They have exhibited far superior IC₅₀ values and anticancer activity as compared to other reported metal oxides and magnesium oxide nanoparticles.

Knowledge, attitudes and practices of Egyptian healthcare professionals toward therapeutic drug monitoring service as a principal component of personalized medicine

Aim: To assess pharmacists' and physicians' knowledge, attitudes and practices toward therapeutic drug monitoring (TDM) service at the Children's Cancer Hospital Egypt 57357. Materials & methods: This was a single-site cross-sectional study where all practicing pharmacists and physicians were eligible to participate. Results: A statistically significant difference in the knowledge scores between pharmacists and physicians (p = 0.022) was found. In general, attitudes toward TDM among pharmacists and physicians were positive. Regarding practices, pharmacists were more likely than physicians to agree or strongly agree that they have studied some scientific references on TDM (p = 0.034), but more physicians recommend the TDM service (p = 0.046). Conclusion: A multidisciplinary educational program in Egypt for TDM for both medicine and pharmacy staff will improve interprofessional collaboration in the clinical setting, leading to better personalized medication management.

Assessing and Evaluating the Scope and Constraints of Idylla Molecular Assays by Using Different Source Materials in Routine Diagnostic Settings

For cancer treatment, diagnostics concerning tumor type and determination of molecular markers in short TAT is critical. The fully automated, real-time PCR-based molecular diagnostic Idylla assays are well established in many laboratories for qualitative detection, short TAT and routine screening of clinically relevant oncogenic mutations. According to the manufacturer, all IVD assays are recommended for use only with FFPE tissue samples of 5–10 µM dissections with at least 10% tumor content. In this study, we tested the performance and accuracy of the IVD assays along with the gene fusion assay (RUO) with different tissue/source materials like isolated DNA/RNA, cryomaterial, etc. The study also included testing archival FFPE tissue sections dating back from 20 years and a performance check for different pan-cancer samples individually. All the assays tested with FFPE sections and gDNA/RNA input showed above 96% accuracy and sensitivity, individually with 100% specificity. The Idylla assays also performed exceptionally well on the archival FFPE tissues, and the use of assays for other solid tumors was also remarkable. The performance test and accuracy of Idylla assays showed high efficiency with certain limitations. For the use of Idylla assays, both qualitative and quantitative applicability of different tumor source materials could produce efficient results in different diagnostic settings within a short TAT.

A case report of the sustained and rapid response of bevacizumab in a TP53-positive breast cancer and liver metastatic patient through personalized medicine

HER2-positive metastatic breast cancer is much less frequent than other subgroups of breast cancer. Treatment options for this cancer are mostly limited to systemic chemotherapy, which leads to moderate improvements. Targeted therapy against malignant breast cancer requires the identification of reliable biomarkers for personalized medicine to obtain the maximum benefit of this therapy. Any mutations in the TP53 signaling pathway can be considered as a significant causative factor of breast cancer, for which the identification of target genes plays an important role in selecting the appropriate treatment. The use of personalized gene expression profiling could be valuable to find the direct target of the treatment in this case. The present study assessed the genetic profile of an HER2-positive metastatic breast cancer patient (with a liver metastasis) and figured out a complete and sustained response to bevacizumab. According to the results of next-generation sequencing (NGS) analysis, the patient’s genetic profile showed an increased expression of p4EBP1 and PTEN and the activation of the mTOR signaling pathway with a mutation in the TP53 gene. Based on the common treatment of similar profiling, we administrated bevacizumab/Taxol/Gemzar chemotherapy up to six courses. Accordingly, as the response to treatment was revealed by reducing the volume of the liver metastasis from 4 to 1.4 cm, metastasectomy was performed as a complementary treatment. Hence, personalized gene expression profiling not only is useful for targeted therapy but also could be recommended to avoid prescription of non-responsive drugs.

Acute myelogenous leukemia – current recommendations and approaches in molecular-genetic assessment

Acute myelogenous leukemia is a multi-step hematological malignancy, affecting function, growth, proliferation and cell cycle of myeloid precursors. Overall assessment of patients with the disease requires among everything else, a comprehensive investigation of the genetic basis through various methods such as cytogenetic and molecular-genetic ones. This clarification provides diagnostic refinement and carries prognostic and predictive value in respect of essential therapeutic choices.

With this review of the literature, we focus on summarizing the latest recommendations and preferred genetic methods, as well as on emphasizing on their general benefits and limitations. Since none of these methods is actually totipotent, we also aim to shed light over the often-difficult choice of appropriate genetic analyses.

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

Cancer driver drug interaction explorer

Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.

Immune-Proteome Profiling in Classical Hodgkin Lymphoma Tumor Diagnostic Tissue

In classical Hodgkin Lymphoma (cHL), immunoediting via protein signaling is key to evading tumor surveillance. We aimed to identify immune-related proteins that distinguish diagnostic cHL tissues (=diagnostic tumor lysates, n = 27) from control tissues (reactive lymph node lysates, n = 30). Further, we correlated our findings with the proteome plasma profile between cHL patients (n = 26) and healthy controls (n = 27). We used the proximity extension assay (PEA) with the OlinkTM multiplex Immuno-Oncology panel, consisting of 92 proteins. Univariate, multivariate-adjusted analysis and Benjamini–Hochberg’s false discovery testing (=Padj) were performed to detect significant discrepancies. Proteins distinguishing cHL cases from controls were more numerous in plasma (30 proteins) than tissue (17 proteins), all Padj < 0.05. Eight of the identified proteins in cHL tissue (PD-L1, IL-6, CCL17, CCL3, IL-13, MMP12, TNFRS4, and LAG3) were elevated in both cHL tissues and cHL plasma compared with control samples. Six proteins distinguishing cHL tissues from controls tissues were significantly correlated to PD-L1 expression in cHL tissue (IL-6, MCP-2, CCL3, CCL4, GZMB, and IFN-gamma, all p ≤0.05). In conclusion, this study introduces a distinguishing proteomic profile in cHL tissue and potential immune-related markers of pathophysiological relevance.

An Overview on the Role of miR-451 in Lung Cancer: Diagnosis, Therapy, and Prognosis

MicroRNAs (miRNAs) are highly conserved non-coding RNAs involved in many physiological processes such as cell proliferation, inhibition, development of apoptosis, differentiation, suppresses tumorigenicity, and regulating cell growth. The description of the alterations of miRNA expression patterns in cancers will be helpful to recognize biomarkers for early detection and possible therapeutic intervention in the treatment of cancers. Recent studies have shown that miR-451 is broadly dysregulated in lung cancer and is a crucial agent in lung tumor progression. This review summarizes recent advances of the potential role of miR-451 in lung cancer diagnosis, prognosis, and treatment and provides an insight into the potential use of miR-451 for the development of advanced therapeutic methods in lung cancer.

Bacteriophage manipulation of the microbiome associated with tumour microenvironments-can this improve cancer therapeutic response?

Some cancer treatment failures have been attributed to the tumour microbiota, with implications that microbiota manipulation may improve treatment efficacy. While antibiotics have been used to control bacterial growth, their dysbiotic effects on the microbiome, failure to penetrate biofilms and decreased efficacy due to increasing antimicrobial resistance by bacteria, suggest alternatives are needed. Bacteriophages may provide a precise means for targeting oncobacteria whose relative abundance is increased in tumour tissue microbiomes. Fusobacterium, Streptococcus, Peptostreptococcus, Prevotella, Parvimonas, and Treponema species are prevalent in tumour tissue microbiomes of some cancers. They may promote cancer growth by dampening immunity, stimulating release of proinflammatory cytokines, and directly interacting with cancer cells to stimulate proliferation. Lytic bacteriophages against some of these oncobacteria have been isolated and characterised. The search continues for others. The possibility exists for their testing as adjuncts to complement existing therapies. In this review, we highlight the role of oncobacteria, specifically those whose relative abundance in the intra-tumour microbiome is increased, and discuss the potential for bacteriophages against these micro-organisms to augment existing cancer therapies. The capacity for bacteriophages to modulate immunity and kill specific bacteria makes them suitable candidates to manipulate the tumour microbiome and negate the effects of these oncobacteria.

Mass spectrometry based high-throughput bioanalysis of low molecular weight compounds: are we ready to support personalized medicine?

Liquid chromatography coupled to mass spectrometry (LC-MS) is the gold standard in bioanalysis for the development of quantitative assays to support drug development or therapeutic drug monitoring. High-throughput and low-cost gene sequencing have enabled a paradigm shift from one treatment fits all to personalized medicine (PM). However, gene monitoring provides only partial information about the health state. The full picture requires the combination of gene monitoring with the screening of exogenous compounds, metabolites, lipids, and proteins. This critical review discusses how mass spectrometry–based technologies and approaches including separation sciences, ambient ionization, and ion mobility are/could be used to support high-throughput bioanalysis of endogenous end exogenous low molecular weight compounds. It includes also various biological sample types (from blood to expired air), and various sample preparation techniques.

Zebrafish Patient-Derived Xenografts Identify Chemo-Response in Pancreatic Ductal Adenocarcinoma Patients

Simple Summary

Treating the PDAC (pancreatic ductal adenocarcinoma) zPDXs (zebrafish patient-derived xenografts) with chemotherapy regimens commonly used, we performed a co-clinical trial testing the predictiveness of the model. We found that zPDX may predict patient outcomes, classifying them into responders (R) and non-responders (NR), reporting a statistically significant higher cancer recurrence rate at 1 year after surgery in the NR group: 66.7 versus 14.3%. Our zPDX model seems to be a promising tool for the stratification of PDAC patients. This is a crucial starting point for future study involving more patients to obtain a method to really personalize the oncological treatment of PDAC patients.

Abstract

It is increasingly evident the necessity of new predictive tools for the treatment of pancreatic ductal adenocarcinoma in a personalized manner. We present a co-clinical trial testing the predictiveness of zPDX (zebrafish patient-derived xenograft) for assessing if patients could benefit from a therapeutic strategy (ClinicalTrials.gov: XenoZ, NCT03668418). zPDX are generated xenografting tumor tissues in zebrafish embryos. zPDX were exposed to chemotherapy regimens commonly used. We considered a zPDX a responder (R) when a decrease ≥50% in the relative tumor area was reported; otherwise, we considered them a non-responder (NR). Patients were classified as Responder if their own zPDX was classified as an R for the chemotherapy scheme she/he received an adjuvant treatment; otherwise, we considered them a Non-Responder. We compared the cancer recurrence rate at 1 year after surgery and the disease-free survival (DFS) of patients of both groups. We reported a statistically significant higher recurrence rate in the Non-Responder group: 66.7% vs. 14.3% (p = 0.036), anticipating relapse/no relapse within 1 year after surgery in 12/16 patients. The mean DFS was longer in the R-group than the NR-group, even if not statistically significant: 19.2 months vs. 12.7 months, (p = 0.123). The proposed strategy could potentially improve preclinical evaluation of treatment modalities and may enable prospective therapeutic selection in everyday clinical practice.

HIV-Associated Cancer Biomarkers: A Requirement for Early Diagnosis

Globally, HIV/AIDS and cancer are increasingly public health problems and continue to exist as comorbidities. The sub-Saharan African region has the largest number of HIV infections. Malignancies previously associated with HIV/AIDS, also known as the AIDS-defining cancers (ADCs) have been documented to decrease, while the non-AIDS defining cancer (NADCs) are on the rise. On the other hand, cancer is a highly heterogeneous disease and precision oncology as the most effective cancer therapy is gaining attraction. Among HIV-infected individuals, the increased risk for developing cancer is due to the immune system of the patient being suppressed, frequent coinfection with oncogenic viruses and an increase in risky behavior such as poor lifestyle. The core of personalised medicine for cancer depends on the discovery and the development of biomarkers. Biomarkers are specific and highly sensitive markers that reveal information that aid in leading to the diagnosis, prognosis and therapy of the disease. This review focuses mainly on the risk assessment, diagnostic, prognostic and therapeutic role of various cancer biomarkers in HIV-positive patients. A careful selection of sensitive and specific HIV-associated cancer biomarkers is required to identify patients at most risk of tumour development, thus improving the diagnosis and prognosis of the disease.

Of Screening, Stratification, and Scores

Technological innovations including risk-stratification algorithms and large databases of longitudinal population health data and genetic data are allowing us to develop a deeper understanding how individual behaviors, characteristics, and genetics are related to health risk. The clinical implementation of risk-stratified screening programmes that utilise risk scores to allocate patients into tiers of health risk is foreseeable in the future. Legal and ethical challenges associated with risk-stratified cancer care must, however, be addressed. Obtaining access to the rich health data that are required to perform risk-stratification, ensuring equitable access to risk-stratified care, ensuring that algorithms that perform risk-scoring are representative of human genetic diversity, and determining the appropriate follow-up to be provided to stratification participants to alert them to changes in their risk score are among the principal ethical and legal challenges. Accounting for the great burden that regulatory requirements could impose on access to risk-scoring technologies is another critical consideration.

The Impact of Foundation Medicine Testing on Cancer Patients: A Single Academic Centre Experience

BACKGROUND

The use of Next-Generation Sequencing (NGS) has recently allowed significant improvements in cancer treatment. Foundation Medicine® (FM) provides a genomic profiling test based on NGS for a variety of cancers. However, it is unclear if the Foundation Medicine test would result in a better outcome than the standard on-site molecular testing. In this retrospective chart review, we identified the FM cases from an academic Canadian hospital and determined whether these test results improved treatment options for those patients.

MATERIALS AND METHODS

A retrospective analysis was performed on patients with solid tumors who had FM testing between May 1, 2014 and May 1, 2018. Clinical factors and outcomes were measured using descriptive statistics using Microsoft Excel® Software.

RESULTS

Out of 66 FM tests, eight patients (= 12%) had a direct change in therapy based on the FM tests. Identified were 285 oncogenic mutations (median 1, range 0-31); where TP53 (n = 31, 10.9%), CDKN2A (n = 19, 6.7%), KRAS (n = 16, 5.6%) and APC (n = 9, 3.2%) were the most common FM mutations identified.

CONCLUSION

A small proportion of FM reports identified actionable mutations and led to direct treatment change. FM testing is expensive and a few of the identified mutations are now part of routine on-site testing. NGS testing is likely to become more widespread, but this research suggests that its true clinical impact may be restricted to a minority of patients.

Precision Medicine in Oncology: A Review of Multi-Tumor Actionable Molecular Targets with an Emphasis on Non-Small Cell Lung Cancer

Precision medicine is essential for the modern care of a patient with cancer. Comprehensive molecular profiling of the tumor itself is necessary to determine the presence or absence of certain targetable abnormalities or biomarkers. In particular, lung cancer is a disease for which targetable genomic alterations will soon guide therapy in the majority of cases. In this comprehensive review of solid tumor-based biomarkers, we describe the genomic alterations for which targeted agents have been approved by the United States Food and Drug Administration (FDA). While focusing on alterations leading to approvals in a tumor-agnostic fashion (MSI-h, TMB-h, NTRK) and on those alterations with approvals in multiple malignancies (BRAF, ERBB2, RET, BRCA, PD-L1), we also describe several biomarkers or indications that are likely to lead to an approved drug in the near future (e.g., KRAS G12C, PD-L1 amplification, HER2 overexpression in colon cancer, HER2 mutations in lung cancer). Finally, we detail the current landscape of additional actionable alterations (EGFR, ALK, ROS1, MET) in lung cancer, a biomarker-rich malignancy that has greatly benefitted from the precision oncology revolution.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

INTRODUCTION

Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.

METHODS

PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.

RESULTS

From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.

CONCLUSION

This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

Nanoparticles with PDT and PTT synergistic properties working with dual NIR-light source simultaneously

A non-toxic nano system using a cleverly designed dual light can be an important treatment strategy in cancer therapy.

Adipose MSCs Suppress MCF7 and MDA-MB-231 Breast Cancer Metastasis and EMT Pathways Leading to Dormancy via Exosomal-miRNAs Following Co-Culture Interaction

Globally, breast cancer is the most frequently diagnosed cancer in women, and it remains a substantial clinical challenge due to cancer relapse. The presence of a subpopulation of dormant breast cancer cells that survived chemotherapy and metastasized to distant organs may contribute to relapse. Tumor microenvironment (TME) plays a significant role as a niche in inducing cancer cells into dormancy as well as involves in the reversible epithelial-to-mesenchymal transition (EMT) into aggressive phenotype responsible for cancer-related mortality in patients. Mesenchymal stem cells (MSCs) are known to migrate to TME and interact with cancer cells via secretion of exosome- containing biomolecules, microRNA. Understanding of interaction between MSCs and cancer cells via exosomal miRNAs is important in determining the therapeutic role of MSC in treating breast cancer cells and relapse. In this study, exosomes were harvested from a medium of indirect co-culture of MCF7-luminal and MDA-MB-231-basal breast cancer cells (BCCs) subtypes with adipose MSCs. The interaction resulted in different exosomal miRNAs profiles that modulate essential signaling pathways and cell cycle arrest into dormancy via inhibition of metastasis and epithelial-to-mesenchymal transition (EMT). Overall, breast cancer cells displayed a change towards a more dormant-epithelial phenotype associated with lower rates of metastasis and higher chemoresistance. The study highlights the crucial roles of adipose MSCs in inducing dormancy and identifying miRNAs-dormancy related markers that could be used to identify the metastatic pattern, predict relapses in cancer patients and to be potential candidate targets for new targeted therapy.

Photoacoustic imaging biomarkers for monitoring biophysical changes during nanobubble-mediated radiation treatment

The development of novel anticancer therapies warrants the parallel development of biomarkers that can quantify their effectiveness. Photoacoustic imaging has the potential to measure changes in tumor vasculature during treatment. Establishing the accuracy of imaging biomarkers requires direct comparisons with gold histological standards. In this work, we explore whether a new class of submicron, vascular disrupting, ultrasonically stimulated nanobubbles enhance radiation therapy. In vivo experiments were conducted on mice bearing prostate cancer tumors. Combined nanobubble plus radiation treatments were compared against conventional microbubbles and radiation alone (single 8 Gy fraction). Acoustic resolution photoacoustic imaging was used to monitor the effects of the treatments 2- and 24-hs post-administration. Histological examination provided metrics of tumor vascularity and tumoral cell death, both of which were compared to photoacoustic-derived biomarkers. Photoacoustic metrics of oxygen saturation reveal a 20 % decrease in oxygenation within 24 h post-treatment. The spectral slope metric could separate the response of the nanobubble treatments from the microbubble counterparts. This study shows that histopathological assessment correlated well with photoacoustic biomarkers of treatment response.

Promising Applications of Tumor Spheroids and Organoids for Personalized Medicine

One of the promising directions in personalized medicine is the use of three-dimensional (3D) tumor models such as spheroids and organoids. Spheroids and organoids are three-dimensional cultures of tumor cells that can be obtained from patient tissue and, using high-throughput personalized medicine methods, provide a suitable therapy for that patient. These 3D models can be obtained from most types of tumors, which provides opportunities for the creation of biobanks with appropriate patient materials that can be used to screen drugs and facilitate the development of therapeutic agents. It should be noted that the use of spheroids and organoids would expand the understanding of tumor biology and its microenvironment, help develop new in vitro platforms for drug testing and create new therapeutic strategies. In this review, we discuss 3D tumor spheroid and organoid models, their advantages and disadvantages, and evaluate their promising use in personalized medicine.

Metabolic Signaling Cascades Prompted by Glutaminolysis in Cancer

Simple Summary

Within the last few years, accumulating evidences suggest the involvement of altered metabolisms in human diseases including cancer. Metabolism is defined as the sum of biochemical processes in living organisms that produce and consume energy. Tumor growth requires restructuring of cellular metabolism to meet the increasing demand for building blocks to support the ever-increasing cancer cell numbers. The principle of perturbed metabolism in tumors is known for 50–60 years, it regains greater appreciation within the last few years with the realization that there is interdependency between metabolism and all aspects of cellular function including regulation and control of cell growth. Tumor cells do not need stimulation signals from the surrounding environment to promote cell proliferation; in some cases, the tumor cells can generate their own growth signals. In order to support the continuous tumor cell growth even under stressful conditions, a change in metabolism is necessary to fulfill the continuous demand for energy and building blocks. A better understanding of the relationship between tumor environment and altered cell metabolisms will provide valuable insights to design innovative approaches to limit the supply of energy and macromolecules for the treatment of cancer including melanoma.

Abstract

Aberrant glutamatergic signaling has been implicated in altered metabolic activity and the demand to synthesize biomass in several types of cancer including melanoma. In the last decade, there has been a significant contribution to our understanding of metabolic pathways. An increasing number of studies are now emphasizing the importance of glutamate functioning as a signaling molecule and a building block for cancer progression. To that end, our group has previously illustrated the role of glutamatergic signaling mediated by metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous development of metastatic melanoma in vivo. Glutamate, the natural ligand of GRM1, is one of the most abundant amino acids in humans and the predominant excitatory neurotransmitter in the central nervous system. Elevated levels of glutaminolytic mitochondrial tricarboxylic acid (TCA) cycle intermediates, especially glutamate, have been reported in numerous cancer cells. Herein, we highlight and critically review metabolic bottlenecks that are prevalent during tumor evolution along with therapeutic implications of limiting glutamate bioavailability in tumors.