The growing role of precision and personalized medicine for cancer treatment

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.

The interweaving relationship between extracellular vesicles and T cells in cancer

The interdependency between cancer cells and immune cells is an important link in understanding cancer pathogenesis. T cells are important immune cells that are able to either impede or promote tumor growth. Extracellular vesicles or EVs are membrane-encapsulated vesicles that are released by both cancer and immune cells that can act as communicators. Studies have shown that tumor-derived EVs can interact with immune cells, particularly T cells. Vice versa, T cells-derived EVs have also been shown to possess immunomodulatory roles. Therefore, the purpose of this mini-review is to understand the role of tumor-derived EVs and T-cells derived EVs on cancer immunosuppression especially the interweaving role of different types of EVs and how it affects tumor immunity. We also discuss the role of EVs in different types of T cells namely CD8⁺, CD4⁺ Th17 and Treg cells. More importantly, we include the limitations and future directions involving this type of research. This will further elucidate our understanding of the important functions of these tiny mediators.

Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing

Glioblastoma (GBM) is the most invasive and deadliest brain cancer in adults. Its inherent heterogeneity has been designated as the main cause of treatment failure. Thus, a deeper understanding of the diversity that shapes GBM pathobiology is of utmost importance. Single-cell RNA sequencing (scRNA-seq) technologies have begun to uncover the hidden composition of complex tumor ecosystems. Herein, a semi-systematic search of reference literature databases provided all existing publications using scRNA-seq for the investigation of human GBM. We compared and discussed findings from these works to build a more robust and unified knowledge base. All aspects ranging from inter-patient heterogeneity to intra-tumoral organization, cancer stem cell diversity, clonal mosaicism, and the tumor microenvironment (TME) are comprehensively covered in this report. Tumor composition not only differs across patients but also involves a great extent of heterogeneity within itself. Spatial and cellular heterogeneity can reveal tumor evolution dynamics. In addition, the discovery of distinct cell phenotypes might lead to the development of targeted treatment approaches. In conclusion, scRNA-seq expands our knowledge of GBM heterogeneity and helps to unravel putative therapeutic targets.

Immunophenotyping of an Unusual Mixed-Type Extraskeletal Osteosarcoma in a Dog

A 6-year-old female Maltese dog presented with a cervical mass without pain. The tumor was surrounded by a thick fibrous tissue and consisted of an osteoid matrix with osteoblasts and two distinct areas: a mesenchymal cell-rich lesion with numerous multinucleated giant cells and a chondroid matrix-rich lesion. The tumor cells exhibited heterogeneous protein expression, including a positive expression of vimentin, cytokeratin, RANKL, CRLR, SOX9, and collagen 2, and was diagnosed as extraskeletal osteosarcoma. Despite its malignancy, the dog showed no sign of recurrence or metastasis three months after the resection. Further analysis of the tumor cells revealed a high expression of proliferation- and metastasis-related biomarkers in the absence of angiogenesis-related biomarkers, suggesting that the lack of angiogenesis and the elevated tumor-associated fibrosis resulted in a hypoxic tumor microenvironment and prevented metastasis.

Epigenetic Reprogramming and Landscape of Transcriptomic Interactions: Impending Therapeutic Interference of Triple-Negative Breast Cancer in Molecular Medicine

The mechanisms governing the development and progression of cancers are believed to be the consequence of hereditary deformities and epigenetic modifications. Accordingly, epigenetics has become an incredible and progressively explored field of research to discover better prevention and therapy for neoplasia, especially triple-negative breast cancer (TNBC). It represents 15-20% of all invasive breast cancers and will, in general, have bellicose histological highlights and poor clinical outcomes. In the early phases of triple-negative breast carcinogenesis, epigenetic deregulation modifies chromatin structure and influences the plasticity of cells. It up-keeps the oncogenic reprogramming of malignant progenitor cells with the acquisition of unrestrained selfrenewal capacities. Genomic impulsiveness in TNBC prompts mutations, copy number variations, as well as genetic rearrangements, while epigenetic remodeling includes an amendment by DNA methylation, histone modification, and noncoding RNAs of gene expression profiles. It is currently evident that epigenetic mechanisms assume a significant part in the pathogenesis, maintenance, and therapeutic resistance of TNBC. Although TNBC is a heterogeneous malaise that is perplexing to describe and treat, the ongoing explosion of genetic and epigenetic research will help to expand these endeavors. Latest developments in transcriptome analysis have reformed our understanding of human diseases, including TNBC at the molecular medicine level. It is appealing to envision transcriptomic biomarkers to comprehend tumor behavior more readily regarding its cellular microenvironment. Understanding these essential biomarkers and molecular changes will propel our capability to treat TNBC adequately. This review will depict the different aspects of epigenetics and the landscape of transcriptomics in triple-negative breast carcinogenesis and their impending application for diagnosis, prognosis, and treatment decision with the view of molecular medicine.

Hollongdione arylidene derivatives induce antiproliferative activity against melanoma and breast cancer through pro-apoptotic and antiangiogenic mechanisms

The use of natural compounds as starting point for semisynthetic derivatives has already been proven as a valuable source of active anticancer agents. Hollongdione (4,4,8,14-tetramethyl-18-norpregnan-3,20-dion), obtained by few steps from dammarane type triterpenoid dipterocarpol, was chemically modified at C2 and C21 carbon atoms by the Claisen-Schmidt aldol condensation to give a series of arylidene derivatives. The anticancer activity of the obtained compounds was assessed on NCI-60 cancer cell panel, revealing strong antiproliferative effects against a large variety of cancer cells. 2,21-Bis-[3-pyridinyl]-methylidenohollongdione 9 emerged as the most active derivative as indicated by its GI₅₀ values in the micromolar range which, combined with its high selectivity index values, indicated its suitability for deeper biological investigation. The mechanisms involved in compound 9 antiproliferative activity, were investigated through in vitro (DAPI staining) and ex vivo (CAM assay) tests, which exhibited its apoptotic and antiangiogenic activities. In addition, compound 9 showed an overall inhibition of mitochondrial respiration. rtPCR analysis identified the more intimate activity at pro-survival/pro-apoptotic gene level. Collectively, the hollongdione derivative stand as a promising therapeutic option against melanoma and breast cancer provided that future in vivo analysis will certify its clinical efficacy.

Biobanking: How Oncology Nurses Can Contribute to Its Use

Biobanking is one of the most valuable tools in precision medicine. The ability of scientists to sequence tumors, blood, and normal tissue obtained from biorepositories has defined efficacious tumor targeting and a much better understanding of cancer pathology pathways. However, these biorepositories require a great deal of effort to establish and maintain. Oncology nurses are key in helping to bank tissue during routine procedures as well as complex surgeries. Nurses can obtain informed consent from patients and coordinate the banking of samples in a timely manner to ensure sample quality. Oncology nurses play an important role in informing patients of their biobanking options and connecting patients with the appropriate team for their biobanking needs.

Piperine analogs arrest c-myc gene leading to downregulation of transcription for targeting cancer

G-quadruplex (G4) structures are considered a promising therapeutic target in cancer. Since Ayurveda, Piperine has been known for its medicinal properties. Piperine shows anticancer properties by stabilizing the G4 motif present upstream of the c-myc gene. This gene belongs to a group of proto-oncogenes, and its aberrant transcription drives tumorigenesis. The transcriptional regulation of the c-myc gene is an interesting approach for anticancer drug design. The present study employed a chemical similarity approach to identify Piperine similar compounds and analyzed their interaction with cancer-associated G-quadruplex motifs. Among all Piperine analogs, PIP-2 exhibited strong selectivity, specificity, and affinity towards c-myc G4 DNA as elaborated through biophysical studies such as fluorescence emission, isothermal calorimetry, and circular dichroism. Moreover, our biophysical observations are supported by molecular dynamics analysis and cellular-based studies. Our study showed that PIP-2 showed higher toxicity against the A549 lung cancer cell line but lower toxicity towards normal HEK 293 cells, indicating increased efficacy of the drug at the cellular level. Biological evaluation assays such as TFP reporter assay, quantitative real-time PCR (qRT- PCR), and western blotting suggest that the Piperine analog-2 (PIP-2) stabilizes the G-quadruplex motif located at the promoter site of c-myc oncogene and downregulates its expression. In conclusion, Piperine analog PIP-2 may be used as anticancer therapeutics as it affects the c-myc oncogene expression via G-quadruplex mediated mechanism.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers

Simple Summary

Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers.

Abstract

To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers.

Cutaneous iontophoresis of vasoactive medications in patients with scleroderma-associated pulmonary arterial hypertension

BACKGROUND

It remains unknown whether the cutaneous microvascular responses are different between patients with scleroderma-associated pulmonary arterial hypertension (SSc-PAH) and SSc without pulmonary hypertension (PH).

METHODS

We included 59 patients with SSc between March 2013 and September 2019. We divided patients into 4 groups: (a) no PH by right heart catheterization (RHC) (n = 8), (b) no PH by noninvasive screening tests (n = 16), (c) treatment naïve PAH (n = 16), and (d) PAH under treatment (n = 19). Microvascular studies using laser Doppler flowmetry (LDF) were done immediately after RHC or at the time of an outpatient clinic visit (group b).

RESULTS

The median (IQR) age was 59 (54-68) years, and 90% were females. The responses to local thermal stimulation and postocclusive reactive hyperemia, acetylcholine, and sodium nitroprusside iontophoresis were similar among groups. The microvascular response to treprostinil was more pronounced in SSc patients without PH by screening tests (% change: 340 (214-781)) compared with SSc-PAH (naïve + treatment) (Perfusion Units (PU) % change: 153 (94-255) % [p = .01]). The response to A-350619 (a soluble guanylate cyclase (sGC) activator) was significantly higher in patients with SSc without PH by screening tests (PU % change: 168 (46-1,296)) than those with SSc-PAH (PU % change: 22 (15-57) % [p = .006]). The % change in PU with A350619 was directly associated with cardiac index and stroke volume index (R: 0.36, p = .03 and 0.39, p = .02, respectively).

CONCLUSIONS

Patients with SSc-PAH have a lower cutaneous microvascular response to a prostacyclin analog treprostinil and the sGC activator A-350619 when compared with patients with SSc and no evidence of PH on screening tests, presumably due to a peripheral reduction in prostacyclin receptor expression and nitric oxide bioavailability.

High-Throughput immunogenetics for precision medicine in cancer

Cancer is characterized by an extremely complex biological background, which hinders personalized therapeutic interventions. Precision medicine promises to overcome this obstacle through integrating information from different 'subsystems', including the host, the external environment, the tumor itself and the tumor micro-environment. Immunogenetics is an essential tool that allows dissecting both lymphoid cancer ontogeny at both a cell-intrinsic and a cell-extrinsic level, i.e. through characterizing micro-environmental interactions, with a view to precision medicine. This is particularly thanks to the introduction of powerful, high-throughput approaches i.e. next generation sequencing, which allow the comprehensive characterization of immune repertoires. Indeed, NGS immunogenetic analysis (Immune-seq) has emerged as key to both understanding cancer pathogenesis and improving the accuracy of clinical decision making in oncology. Immune-seq has applications in lymphoid malignancies, assisting in the diagnosis e.g. through differentiating from reactive conditions, as well as in disease monitoring through accurate assessment of minimal residual disease. Moreover, Immune-seq facilitates the study of T cell receptor clonal dynamics in critical clinical contexts, including transplantation as well as innovative immunotherapy for solid cancers. The clinical utility of Immune-seq represents the focus of the present contribution, where we highlight what can be achieved but also what must be addressed in order to maximally realize the promise of Immune-seq in precision medicine in cancer.

Pathways to Acceptance in Participants of Advanced Cancer Online Support Groups

Background and Objectives: Individuals with cancer, especially advanced cancer, are faced with numerous difficulties associated with the disease, including an earlier death than expected. Those who are able to confront and accept the hardships associated with the disease in a way that aligns with their beliefs benefit from more positive psychological outcomes compared to those who are aware of their diagnosis but are unable to accept it. To date, there is limited research exploring factors contributing to illness and death acceptance in the context of advanced cancer in group therapy settings. Materials and Methods: The current study used a Directed Content Analysis approach on transcripts of online advanced cancer support groups to investigate if and how Yalom’s existential factors played a role in the emergence of acceptance. Results: The online support group platform, combined with the help of facilitators, offered supportive environments for individuals seeking help with cancer-related distress by helping patients move towards acceptance. Some participants had already begun the process of accepting their diagnosis before joining the group, others developed acceptance during the group process, while a few continued to be distressed. Our analysis revealed the emergence of four themes related to illness acceptance: (1) Facilitator-Initiated Discussion, including sub-themes of Mindfulness, Relaxation and Imagery, Changing Ways of Thinking, and Spirituality; (2) Personal attitudes, including sub-themes of Optimism and Letting Go of Control; (3) Supportive Environment, including the sub-themes of Providing Support to Others and Receiving Support from Others; and (4) Existential Experience, which included sub-themes of Living with the Diagnosis for an Extended Amount of Time, Legacy and Death Preparations, and Appreciating life. Conclusions: With a paradigm shift to online delivery of psychological services, recognizing factors that contribute to acceptance when dealing with advanced cancer may help inform clinical practices. Future studies should explore patient acceptance longitudinally to inform whether it emerges progressively, which has been suggested by Kübler-Ross.

Targeting the Transcriptome Through Globally Acting Components

Transcription is a step in gene expression that defines the identity of cells and its dysregulation is associated with diseases. With advancing technologies revealing molecular underpinnings of the cell with ever-higher precision, our ability to view the transcriptomes may have surpassed our knowledge of the principles behind their organization. The human RNA polymerase II (Pol II) machinery comprises thousands of components that, in conjunction with epigenetic and other mechanisms, drive specialized programs of development, differentiation, and responses to the environment. Parts of these programs are repurposed in oncogenic transformation. Targeting of cancers is commonly done by inhibiting general or broadly acting components of the cellular machinery. The critical unanswered question is how globally acting or general factors exert cell type specific effects on transcription. One solution, which is discussed here, may be among the events that take place at genes during early Pol II transcription elongation. This essay turns the spotlight on the well-known phenomenon of promoter-proximal Pol II pausing as a step that separates signals that establish pausing genome-wide from those that release the paused Pol II into the gene. Concepts generated in this rapidly developing field will enhance our understanding of basic principles behind transcriptome organization and hopefully translate into better therapies at the bedside.

Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program

Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients' cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.

Metabolomic Approaches to Investigate the Effect of Metformin: An Overview

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

Copy number alteration of the interferon gene cluster in cancer: Individual patient data meta-analysis prospects to personalized immunotherapy

Interferon (IFN) therapy has been the standard of care for a variety of cancers for decades due to the pleiotropic actions of IFNs against malignancies. However, little is known about the role of copy number alteration (CNA) of the IFN gene cluster, located at the 9p21.3, in cancer. This large individual patient data meta-analysis using 9937 patients obtained from cBioportal indicates that CNA of the IFN gene cluster is prevalent among 24 cancer types. Two statistical approaches showed that notably deletion of this cluster is significantly associated with increased mortality in many cancer types particularly uterus (OR = 2.71), kidney (OR = 2.26), and brain (OR = 2.08) cancers. The Cancer Genome Atlas PanCancer analysis also showed that CNA of the IFN gene cluster is significantly associated with decreased overall survival. For instance, the overall survival of patients with brain glioma reduced from 93m (diploidy) to 24m (with the CNA of the IFN gene). In conclusion, the CNA of the IFN gene cluster is associated with increased mortality and decreased overall survival in cancer. Thus, in the prospect of immunotherapy, CNA of IFN gene may be a useful biomarker to predict the prognosis of patients and also as a potential companion diagnostic test to prescribe IFN α/β therapy.

A Preclinical Pipeline for Translational Precision Medicine-Experiences from a Transdisciplinary Brain Tumor Stem Cell Project

Efficient transdisciplinary cooperation promotes the rapid discovery and clinical application of new technologies, especially in the competitive sector of oncology. In this review, written from a clinical-scientist point of view, we used glioblastoma—the most common and most aggressive primary brain tumor as a model disease with a largely unmet clinical need, despite decades of intensive research—to promote transdisciplinary medicine. Glioblastoma stem-like cells (GSCs), a special tumoral cell population analogue to healthy stem cells, are considered largely responsible for the progression of the disease and the mediation of therapy resistance. The presented work followed the concept of translational science, which generates the theoretical backbones of translational research projects, and aimed to close the preclinical gap between basic research and clinical application. Thus, this generated an integrated translational precision medicine pipeline model based on recent theoretical and experimental publications, which supports the accelerated discovery and development of new paths in the treatment of GSCs. The work may be of interest to the general field of precision medicine beyond the field of neuro-oncology such as in Cancer Neuroscience.

Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

Advances of Tumorigenesis, Diagnosis at Early Stage, and Cellular Immunotherapy in Gastrointestinal Malignancies

Globally, in 2018, 4.8 million new patients have a diagnosis of gastrointestinal (GI) cancers, while 3.4 million people died of such disorders. GI malignancies are tightly relevant to 26% of the world-wide cancer incidence and occupies 35% of all cancer-associated deaths. In this article, we principally investigated molecular and cellular mechanisms of tumorigenesis in five major GI cancers occurring at esophagus, stomach, liver, pancreas, and colorectal region that illustrate high morbidity in Eastern and Western countries. Moreover, through this investigation, we not only emphasize importance of the tumor microenvironment in development and treatment of malignant tumors but also identify significance of M2PK, miRNAs, ctDNAs, circRNAs, and CTCs in early detection of GI cancers, as well as systematically evaluate contribution of personalized precision medicine including cellular immunotherapy, new antigen and vaccine therapy, and oncolytic virotherapy in treatment of GI cancers.

Precision Medicine: An Emerging Paradigm for Improved Diagnosis and Safe Therapy in Pediatric Oncology

Cancer is a lethal disease that kills a great number of people each year. Standard treatments such as chemotherapy or radiation are only effective in a small percentage of individuals due to illness variability. Tumors can be caused by a variety of genetic factors and express a variety of proteins depending on the individual. Because of developments in high-throughput technology, there has been a flood of large-scale biological data produced in recent decades. As a result, the focus of medical research has evolved. It was a once-in-a-lifetime chance for translational research to explore molecular alterations across the entire genome. In this setting, precision medicine was developed, and the possibility of better diagnostic and treatment tools became a reality. This is especially true in the case of cancer, which is becoming more prevalent around the world. The goal of this study is to look at precision medicine technology and its applications to cancer, with a focus on children. The inherent diversity of cancer lends itself to the rapidly expanding field of precision and personalized medicine.

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

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.

Interactions Networks for Primary Heart Sarcomas

Personalized medicine incorporates genetic information into medical practice so as to optimize the management of chronic diseases. In rare diseases, such as heart cancer (incidence 0.0017-0.33%), this may be elusive. Ninety-five percent of the cases are due to secondary involvementwith the neoplasm originating in the lungs, breasts, kidney, blood, or skin. The clinical manifestations of heart tumors (benign or malignant) include heart failure, hypertension, and cardiac arrhythmias of varying severity, frequently resulting in blood vessel emboli, including strokes. This study aims to explain the pathophysiology and contribute to a P4 medicine model for use by cardiologists, pathologists, and oncologists. We created six gene/protein heart-related and tumor-related targets high-confidence interactomes, which unfold the main pathways that may lead to cardiac diseases (heart failure, hypertension, coronary artery disease, arrhythmias), i.e., the sympathetic nervous system, the renin-angiotensin-aldosterone axis and the endothelin pathway, and excludes others, such as the K oxidase or cytochrome P450 pathways. We concluded that heart cancer patients could be affected by beta-adrenergic blockers, ACE inhibitors, QT-prolonging antiarrhythmic drugs, antibiotics, and antipsychotics. Interactomes may elucidate unknown pathways, adding to patient/survivor wellness during/after chemo- and/or radio-therapy.

The Role of Raman Spectroscopy Within Quantitative Metabolomics

Ninety-four years have passed since the discovery of the Raman effect, and there are currently more than 25 different types of Raman-based techniques. The past two decades have witnessed the blossoming of Raman spectroscopy as a powerful physicochemical technique with broad applications within the life sciences. In this review, we critique the use of Raman spectroscopy as a tool for quantitative metabolomics. We overview recent developments of Raman spectroscopy for identification and quantification of disease biomarkers in liquid biopsies, with a focus on the recent advances within surface-enhanced Raman scattering-based methods. Ultimately, we discuss the applications of imaging modalities based on Raman scattering as label-free methods to study the abundance and distribution of biomolecules in cells and tissues, including mammalian, algal, and bacterial cells.

Interactions between noncoding RNAs as epigenetic regulatory mechanisms in cardiovascular diseases

Cardiovascular diseases (CVDs) represent the foremost cause of mortality in the United States and worldwide. It is estimated that CVDs account for approximately 17.8 million deaths each year. Despite the advances made in understanding cellular mechanisms and gene mutations governing the pathophysiology of CVDs, they remain a significant cause of mortality and morbidity. A major segment of mammalian genomes encodes for genes that are not further translated into proteins. The roles of the majority of such noncoding ribonucleic acids (RNAs) have been puzzling for a long time. However, it is becoming increasingly clear that noncoding RNAs (ncRNAs) are dynamically expressed in different cell types and have a comprehensive selection of regulatory roles at almost every step involved in DNAs, RNAs and proteins. Indeed, ncRNAs regulate gene expression through epigenetic interactions, through direct binding to target sequences, or by acting as competing endogenous RNAs. The profusion of ncRNAs in the cardiovascular system suggests that they may modulate complex regulatory networks that govern cardiac physiology and pathology. In this review, we summarize various functions of ncRNAs and highlight the recent literature on interactions between ncRNAs with an emphasis on cardiovascular disease regulation. Furthermore, as the broad-spectrum of ncRNAs potentially establishes new avenues for therapeutic development targeting CVDs, we discuss the innovative prospects of ncRNAs as therapeutic targets for CVDs.

S1P signaling, its interactions and cross-talks with other partners and therapeutic importance in colorectal cancer

Sphingosine-1-Phosphate (S1P) plays an important role in normal physiology, inflammation, initiation and progression of cancer. Deregulation of S1P signaling causes aberrant proliferation, affects survival, leads to angiogenesis and metastasis. Sphingolipid rheostat is crucial for cellular homeostasis. Discrepancy in sphingolipid metabolism is linked to cancer and drug insensitivity. Owing to these diverse functions and being a potent mediator of tumor growth, S1P signaling might be a suitable candidate for anti-tumor therapy or combination therapy. In this review, with a focus on colorectal cancer we have summarized the interacting partners of S1P signaling pathway, its therapeutic approaches along with the contribution of S1P signaling to various cancer hallmarks.

Application of untargeted volatile profiling and data driven approaches in wine flavoromics research

Traditional flavor chemistry research usually makes use of targeted approaches by focusing on the detection and quantification of key flavor active metabolites that are present in food and beverages. In the last decade, flavoromics has emerged as an alternative to targeted methods where non-targeted and data driven approaches have been used to determine as many metabolites as possible with the aim to establish relationships among the chemical composition of foods and their sensory properties. Flavoromics has been successfully applied in wine research to gain more insights into the impact of a wide range of flavor active metabolites on wine quality. In this review, we aim to provide an overview of the applications of flavoromics approaches in wine research based on existing literature mainly by focusing on untargeted volatile profiling of wines and how this can be used as a powerful tool to generate novel insights. We highlight the fact that untargeted volatile profiling used in flavoromics approaches ultimately can assist the wine industry to produce different wine styles and to market existing wines appropriately based on consumer preference. In addition to summarizing the main steps involved in untargeted volatile profiling, we also provide an outlook about future perspectives and challenges of wine flavoromics research.

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent high-throughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine.

A systematic review of systematic reviews and pooled meta-analysis on pharmacological interventions to improve cancer-related fatigue

BACKGROUND

Evidence regarding the pharmacological interventions to manage cancer-related fatigue (CRF) is currently synthesized in several systematic reviews, portraying a fragmented literature synthesis. Thus, we aimed to critically appraise the available systematic reviews on pharmacological intervention for improving CRF in adult cancer patients.

METHODS

Three databases were systematically searched from January 2010 to July 2020. The pooled meta-analyses' effect sizes (standardized mean difference, SMD) were quantitatively pooled using a random-effects model. Chi-squared (Q) and I-square statistics (I²) tested the heterogeneity.

RESULTS

The SMD of the effect of psychostimulants on CRF was -0.20 (95% CI: -0.32, 0.08; p < 0.0001), along with significant higher improvement of fatigue (SMD=-0.69; 95% CI=-1.29, -0,09, p < 0.0001) after methylphenidate administration. No statistical differences were found in the occurrences of adverse events between methylphenidate and placebo.

CONCLUSIONS

This study corroborated that psychostimulant therapy may be moderately effective in reducing CRF. Scarce evidence on the short- and long-term adverse events.

PROSPERO

CRD42020181879 (registration date: 26/07/2020).

A bibliometric review of oncolytic virus research as a novel approach for cancer therapy

Background

In recent years, oncolytic viruses (OVs) have drawn attention as a novel therapy to various types of cancers, both in clinical and preclinical cancer studies all around the world. Consequently, researchers have been actively working on enhancing cancer therapy since the early twentieth century. This study presents a systematic review of the literature on OVs, discusses underlying research clusters and, presents future directions of OVs research.

Methods

A total of 1626 published articles related to OVs as cancer therapy were obtained from the Web of Science (WoS) database published between January 2000 and March 2020. Various aspects of OVs research, including the countries/territories, institutions, journals, authors, citations, research areas, and content analysis to find trending and emerging topics, were analysed using the bibliometrix package in the R-software.

Results

In terms of the number of publications, the USA based researchers were the most productive (n = 611) followed by Chinese (n = 197), and Canadian (n = 153) researchers. The Molecular Therapy journal ranked first both in terms of the number of publications (n = 133) and local citations (n = 1384). The most prominent institution was Mayo Clinic from the USA (n = 117) followed by the University of Ottawa from Canada (n = 72), and the University of Helsinki from Finland (n = 63). The most impactful author was Bell J.C with the highest number of articles (n = 67) and total local citations (n = 885). The most impactful article was published in the Cell journal. In addition, the latest OVs research mainly builds on four research clusters.

Conclusion

The domain of OVs research has increased at a rapid rate from 2000 to 2020. Based on the synthesis of reviewed studies, adenovirus, herpes simplex virus, reovirus, and Newcastle disease virus have shown potent anti-cancer activity. Developed countries such as the USA, Canada, the UK, and Finland were the most productive, hence, contributed most to this field. Further collaboration will help improve the clinical research translation of this therapy and bring benefits to cancer patients worldwide.

Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer

The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.

Chemotherapeutic drugs: Cell death- and resistance-related signaling pathways. Are they really as smart as the tumor cells?

Chemotherapeutic drugs kill cancer cells or control their progression all over the patient's body, while radiation- and surgery-based treatments perform in a particular site. Based on their mechanisms of action, they are classified into different groups, including alkylating substrates, antimetabolite agents, anti-tumor antibiotics, inhibitors of topoisomerase I and II, mitotic inhibitors, and finally, corticosteroids. Although chemotherapeutic drugs have brought about more life expectancy, two major and severe complications during chemotherapy are chemoresistance and tumor relapse. Therefore, we aimed to review the underlying intracellular signaling pathways involved in cell death and resistance in different chemotherapeutic drug families to clarify the shortcomings in the conventional single chemotherapy applications. Moreover, we have summarized the current combination chemotherapy applications, including numerous combined-, and encapsulated-combined-chemotherapeutic drugs. We further discussed the possibilities and applications of precision medicine, machine learning, next-generation sequencing (NGS), and whole-exome sequencing (WES) in promoting cancer immunotherapies. Finally, some of the recent clinical trials concerning the application of immunotherapies and combination chemotherapies were included as well, in order to provide a practical perspective toward the future of therapies in cancer cases.

Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray

Detection and monitoring of single cancer cells (SCCs), such as circulating tumor cells (CTCs), would be of aid in an efficient early detection of cancer, a tailored (personalized) therapy, and in a fast bedside assessment of treatment efficacy. Nevertheless, currently available techniques, which mostly rely on the isolation of SCCs based on their physical or biological properties, suffer from low sensitivity, complicated technical procedures, low cost-effectiveness, and being unsuitable for continuous monitoring. We report here on the design and use of an artificially intelligent nanoarray based on a heterogeneous set of chemisensitive nanostructured films for the detection of SCCs using volatile organic compounds emanating in the air trapped above blood samples containing SCCs. For demonstration purposes, we have focused on samples containing A549 lung cancer cells (hereafter, SCC^A549). The nanoarray developed to detect SCC^A549 has >90% accuracy, >85% sensitivity, and >95% specificity. Detection works irrespective of the medium and/or the environment. These results were validated by complementary mass spectrometry. The ability to continuously record, store, and preprocess the signals increases the chances that this nanotechnology might also be useful in the early detection of cancer cells in the blood and continuous monitoring of their possible progression.

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy

Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.

Precision Oncology Beyond Genomics: The Future Is Here-It Is Just Not Evenly Distributed

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient's tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient's treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Neoantigen: A New Breakthrough in Tumor Immunotherapy

Cancer immunotherapy works by stimulating and strengthening the body’s anti-tumor immune response to eliminate cancer cells. Over the past few decades, immunotherapy has shown remarkable efficacy in the treatment of cancer, particularly the success of immune checkpoint blockade targeting CTLA-4, PD-1 and PDL1, which has led to a breakthrough in tumor immunotherapy. Tumor neoantigens, a new approach to tumor immunotherapy, include antigens produced by tumor viruses integrated into the genome and antigens produced by mutant proteins, which are abundantly expressed only in tumor cells and have strong immunogenicity and tumor heterogeneity. A growing number of studies have highlighted the relationship between neoantigens and T cells’ recognition of cancer cells. Vaccines developed against neoantigens are now being used in clinical trials in various solid tumors. In this review, we summarized the latest advances in the classification of immunotherapy and the process of classification, identification and synthesis of tumor-specific neoantigens, as well as their role in current cancer immunotherapy. Finally, the application prospects and existing problems of neoantigens were discussed.

Ultrasound-Responsive Nanocarriers in Cancer Treatment: A Review

The safe and effective delivery of anticancer agents to diseased tissues is one of the significant challenges in cancer therapy. Conventional anticancer agents are generally cytotoxins with poor pharmacokinetics and bioavailability. Nanocarriers are nanosized particles designed for the selectivity of anticancer drugs and gene transport to tumors. They are small enough to extravasate into solid tumors, where they slowly release their therapeutic load by passive leakage or biodegradation. Using smart nanocarriers, the rate of release of the entrapped therapeutic(s) can be increased, and greater exposure of the tumor cells to the therapeutics can be achieved when the nanocarriers are exposed to certain internally (enzymes, pH, and temperature) or externally (light, magnetic field, and ultrasound) applied stimuli that trigger the release of their load in a safe and controlled manner, spatially and temporally. This review gives a comprehensive overview of recent research findings on the different types of stimuli-responsive nanocarriers and their application in cancer treatment with a particular focus on ultrasound.

Disentangling the Structure-Activity Relationships of Naphthalene Diimides as Anticancer G-Quadruplex-Targeting Drugs

In the context of developing efficient anticancer therapies aimed at eradicating any sort of tumors, G-quadruplexes represent excellent targets. Small molecules able to interact with G-quadruplexes can interfere with cell pathways specific of tumors and common to all cancers. Naphthalene diimides (NDIs) are among the most promising, putative anticancer G-quadruplex-targeting drugs, due to their ability to simultaneously target multiple G-quadruplexes and their strong, selective in vitro and in vivo anticancer activity. Here, all the available biophysical, biological, and structural data concerning NDIs targeting G-quadruplexes were systematically analyzed. Structure–activity correlations were obtained by analyzing biophysical data of their interactions with G-quadruplex targets and control duplex structures, in parallel to biological data concerning the antiproliferative activity of NDIs on cancer and normal cells. In addition, NDI binding modes to G-quadruplexes were discussed in consideration of the structures and properties of NDIs by in-depth analysis of the available structural models of G-quadruplex/NDI complexes.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas

Soft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. Treatment options remain limited in advanced STS with high rates of recurrence following resection of localised disease. Prognostication in clinical practice relies predominantly on histological grading systems as well as sarcoma nomograms. Rapid developments in gene expression profiling technologies presented opportunities for applications in sarcoma. Molecular profiling of sarcomas has improved our understanding of the cancer biology of these rare cancers and identified potential novel therapeutic targets. In particular, transcriptomic signatures could play a role in risk classification in sarcoma to aid prognostication. Unlike other solid and haematological malignancies, transcriptomic signatures have not yet reached routine clinical use in sarcomas. Herein, we evaluate early developments in gene expression profiling in sarcomas that laid the foundations for transcriptomic signature development. We discuss the development and clinical evaluation of key transcriptomic biomarker signatures in sarcomas, including Complexity INdex in SARComas (CINSARC), Genomic Grade Index, and hypoxia-associated signatures. Prospective validation of these transcriptomic signatures is required, and prospective trials are in progress to evaluate reliability for clinical application. We anticipate that integration of these gene expression signatures alongside existing prognosticators and other Omics methodologies, including proteomics and DNA methylation analysis, could improve the identification of 'high-risk' patients who would benefit from more aggressive or selective treatment strategies. Moving forward, the incorporation of these transcriptomic prognostication signatures in clinical practice will undoubtedly advance precision medicine in the routine clinical management of sarcoma patients.

Evaluating the anticancer properties and real-time electrochemical extracellular bio-speciation of bis (1,10-phenanthroline) silver (I) acetate monohydrate in the presence of A549 lung cancer cells

According to the American Cancer Society report (2019-2021), the majority (63%) of stage III non-small cell lung cancer (NSCLC) patients are prescribed with chemo and/or radiation therapies, with 5-year relative survival rates of just 19%. Thus, directed drug development, toward personalised cancer treatment, is widely recognised as a necessary strategy in drug discovery research. However, broad generalisations on the modes of action of bioinorganic compounds are not conducive to tailored drug design, hence, fundamental mechanistic research is essential in realising personalised healthcare. In this work, anticancer properties of bis (1,10-phenanthroline) silver (I) acetate monohydrate (Ag-Phen), toward A549 lung cancer cells are presented. Biological assays were carried out to evaluate the effect of Ag-Phen on cell viability, reactive oxygen species generation and mitochondrial membrane potentials. In tandem with the biological assays, electrochemistry was employed to determine the real-time concentrations of intact Ag-Phen and dissociated Ag⁺ in the extracellular medium using platinum microelectrodes, as a function of cellular exposure time. Observations from the assays conducted include, Ag-Phen induced cytotoxicity (IC₅₀ 4.5 μM at 72 h) and 2-fold ROS generation, and a 50% decrease in mitochondrial membrane potentials with respect to equivalent concentrations of Ag⁺ and 1,10-phenanthroline. Bio-speciation studies, conducted electrochemically at platinum microelectrodes, revealed almost 50% of the Ag-Phen had dissociated after 2 h. Significant reductions in concentrations of dissociated Ag⁺ (from 67.7 μM to 6.7 μM), and the Ag-Phen complex (from 50.2 μM to 11.7 μM) between 4 and 24 h from the extracellular medium, indicate cellular uptake of both. This novel method facilitates the real-time identification and quantification of electroactive species, both the intact Ag-Phen and Ag⁺, in the presence of A549 cells.

Application of Radiosensitizers in Cancer Radiotherapy

Radiotherapy (RT) is a cancer treatment that uses high doses of radiation to kill cancer cells and shrink tumors. Although great success has been achieved on radiotherapy, there is still an intractable challenge to enhance radiation damage to tumor tissue and reduce side effects to healthy tissue. Radiosensitizers are chemicals or pharmaceutical agents that can enhance the killing effect on tumor cells by accelerating DNA damage and producing free radicals indirectly. In most cases, radiosensitizers have less effect on normal tissues. In recent years, several strategies have been exploited to develop radiosensitizers that are highly effective and have low toxicity. In this review, we first summarized the applications of radiosensitizers including small molecules, macromolecules, and nanomaterials, especially those that have been used in clinical trials. Second, the development states of radiosensitizers and the possible mechanisms to improve radiosensitizers sensibility are reviewed. Third, the challenges and prospects for clinical translation of radiosensitizers in oncotherapy are presented.

GUCY2C as a biomarker to target precision therapies for patients with colorectal cancer

Introduction

Colorectal cancer (CRC) is one of the most-deadly malignancies worldwide. Current therapeutic regimens for CRC patients are relatively generic, based primarily on disease type and stage, with little variation. As the field of molecular oncology advances, so too must therapeutic management of CRC. Understanding molecular heterogeneity has led to a new-found promotion for precision therapy in CRC; underlining the diversity of molecularly targeted therapies based on individual tumor characteristics.

Areas covered

We review current approaches for the treatment of CRC and discuss the potential of precision therapy in advanced CRC. We highlight the utility of the intestinal protein guanylyl cyclase C (GUCY2C), as a multi-purpose biomarker and unique therapeutic target in CRC. Here, we summarize current GUCY2C-targeted approaches for treatment of CRC.

Expert opinion

The GUCY2C biomarker has multi-faceted utility in medicine. Developmental investment of GUCY2C as a diagnostic and therapeutic biomarker offers a variety of options taking the molecular characteristics of cancer into account. From GUCY2C-targeted therapies, namely cancer vaccines, CAR-T cells, and monoclonal antibodies, to GUCY2C agonists for chemoprevention in those who are at high risk for developing colorectal cancer, the utility of this protein provides many avenues for exploration with significance in the field of precision medicine.

Factors Associated With Health-Related Quality of Life Among Cancer Survivors in the United States

Background

With increasing prevalence of cancer survivors in the United States, health-related quality of life (HRQOL) has become a major priority. We describe HRQOL in a nationally representative sample of cancer survivors and examine associations with key sociodemographic, clinical, and lifestyle characteristics.

Methods

Cancer survivors, defined as individuals ever diagnosed with cancer (N = 877), were identified from the 2016 Medical Expenditure Panel Survey-Experiences with Cancer Survivorship Supplement, a nationally representative survey. Physical and mental health domains of HRQOL were measured by the Global Physical Health (GPH) and Global Mental Health (GMH) subscales of the Patient-Reported Outcomes Measurement Information System Global-10. Multivariable linear regression was used to examine associations of sociodemographic, clinical, and lifestyle factors with GPH and GMH scores. All statistical tests were 2-sided.

Results

Cancer survivors' mean GPH (49.28, SD = 8.79) and mean GMH (51.67, SD = 8.38) were similar to general population means (50, SD = 10). Higher family income was associated with better GPH and GMH scores, whereas a greater number of comorbidities and lower physical activity were statistically significantly associated with worse GPH and GMH. Survivors last treated 5 years ago and longer had better GPH than those treated during the past year, and current smokers had worse GMH than nonsmokers (all β > 3 and all P < .001).

Conclusions

Cancer survivors in the United States have generally good HRQOL, with similar physical and mental health scores to the general US population. However, comorbidities, poor health behaviors, and recent treatment may be risk factors for worse HRQOL. Multimorbidity management and healthy behavior promotion may play a key role in maximizing HRQOL for cancer survivors.

Diet as a Potential Moderator for Genome Stability and Immune Response in Pediatric Leukemia

Simple Summary

Pediatric acute lymphoblastic leukemia (ALL) is the most prevalent cancer affecting children in developed societies. Here, we review the role of diet in control of the incidence and progression of childhood ALL. Prenatally, ALL risk is associated with higher birthweights of newborns, suggesting that ALL begins to evolve in-utero. Indeed, maternal diet influences the fetal genome and immune development. Postnatally, breastfeeding associates with decreased risk of ALL development. Finally, for the ALL-affected child, certain dietary regimens that impact the hormonal environment may impede disease progression. Improved understanding of the dietary regulation of hormones and immunity may inform better approaches to predict, protect, and ultimately save children afflicted with pediatric leukemia.

Abstract

Pediatric leukemias are the most prevalent cancers affecting children in developed societies, with childhood acute lymphoblastic leukemia (ALL) being the most common subtype. As diet is a likely modulator of many diseases, this review focuses on the potential for diet to influence the incidence and progression of childhood ALL. In particular, the potential effect of diets on genome stability and immunity during the prenatal and postnatal stages of early childhood development are discussed. Maternal diet plays an integral role in shaping the bodily composition of the newborn, and thus may influence fetal genome stability and immune system development. Indeed, higher birth weights of newborns are associated with increased risk of ALL, which suggests in-utero biology may shape the evolution of preleukemic clones. Postnatally, the ingestion of maternal breastmilk both nourishes the infant, and provides essential components that strengthen and educate the developing immune system. Consistently, breast-feeding associates with decreased risk of ALL development. For children already suffering from ALL, certain dietary regimens have been proposed. These regimens, which have been validated in both animals and humans, alter the internal hormonal environment. Thus, hormonal regulation by diet may shape childhood metabolism and immunity in a manner that is detrimental to the evolution or expansion of preleukemic and leukemic ALL clones.

Exploring the Potential of Drug Response Assays for Precision Medicine in Ovarian Cancer

One of the major challenges in the treatment of cancer are differential responses of patients to existing standard of care anti-cancer drugs. These differential responses may, in part, be due to a diverse range of genomic, epigenomic, proteomic, and metabolic alterations among individuals suffering from the same type of cancer. Precision medicine is an emerging approach in cancer therapeutics that takes into account specific molecular alterations, environmental factors as well as lifestyle of individual patients. This approach allows clinicians and researchers to select or predict treatments that would most likely benefit the patient based on their individual tumor characteristics. One class of precision medicine tools are predictive, in vitro drug-response assays designed to test the sensitivity of patient tumor cells to existing or novel therapies. These assays have the potential to rapidly identify the most effective treatments for cancer patients and thus hold great promise in the field of precision medicine. In this review, we have highlighted several drug-response assays developed in ovarian cancer and discussed the current challenges and future prospects of these assays in the clinical management of this disease.