Personalized medicine: Clinical oncology on molecular view of treatment.

Cancer, the second leading global cause of death, impacts both physically and emotionally. Conventional treatments such as surgeries, chemotherapy, and radiotherapy have adverse effects, driving the need for more precise approaches. Precision medicine enables more targeted treatments. Genetic mapping, alongside other molecular biology approaches, identifies specific genes, contributing to accurate prognoses. The review addresses, in clinical use, a molecular perspective on treatment. Biomarkers like alpha-fetoprotein, beta-human chorionic gonadotropin, 5-hydroxyindoleacetic acid, programmed death-1, and cytotoxic T lymphocyte-associated protein 4 are explored, providing valuable information. Bioinformatics, with an emphasis on artificial intelligence, revolutionizes the analysis of biological data, offering more accurate diagnoses. Techniques like liquid biopsy are emphasized for early detection. Precision medicine guides therapeutic strategies based on the molecular characteristics of the tumor, as evidenced in the molecular subtypes of breast cancer. Classifications allow personalized treatments, highlighting the role of trastuzumab and endocrine therapies. Despite the benefits, challenges persist, including high costs, tumor heterogeneity, and ethical issues. Overcoming obstacles requires collaboration, ensuring that advances in molecular biology translate into accessible benefits for all.

Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges.

Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

Bioactivity and Bioavailability of Carotenoids Applied in Human Health: Technological Advances and Innovation.

This article presents a groundbreaking perspective on carotenoids, focusing on their innovative applications and transformative potential in human health and medicine. Research jointly delves deeper into the bioactivity and bioavailability of carotenoids, revealing therapeutic uses and technological advances that have the potential to revolutionize medical treatments. We explore pioneering therapeutic applications in which carotenoids are used to treat chronic diseases such as cancer, cardiovascular disease, and age-related macular degeneration, offering novel protective mechanisms and innovative therapeutic benefits. Our study also shows cutting-edge technological innovations in carotenoid extraction and bioavailability, including the development of supramolecular carriers and advanced nanotechnology, which dramatically improve the absorption and efficacy of these compounds. These technological advances not only ensure consistent quality but also tailor carotenoid therapies to each patient's health needs, paving the way for personalized medicine. By integrating the latest scientific discoveries and innovative techniques, this research provides a prospective perspective on the clinical applications of carotenoids, establishing a new benchmark for future studies in this field. Our findings underscore the importance of optimizing carotenoid extraction, administration, bioactivity, and bioavailability methods to develop more effective, targeted, and personalized treatments, thus offering visionary insight into their potential in modern medical practices.

Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.

3D printed benznidazole tablets based on an interpolyelectrolyte complex by melting solidification printing process (MESO-PP): An innovative strategy for personalized treatment of Chagas disease.

3D printing technology is revolutionizing pharmaceuticals, offering tailored solutions for solid dosage forms. This innovation is particularly significant for conditions like Chagas disease, which require weight-dependent treatments. In this work, a formulation of benznidazole (BNZ), the primary treatment for this infection, was developed to be utilized with the Melting Solidification Printing Process (MESO-PP) 3D printing technique. Considering the limited aqueous solubility of BNZ, an interpolyelectrolyte complex (IPEC), composed of chitosan and pectin, was integrated to improve its dissolution profile. The formulations, also called inks in this context, with and without IPEC were integrally characterized and compared. The printing process was studied, the release of BNZ from 3D-prints (3DP) was exhaustively analyzed and a physiologically based pharmacokinetic model (PKPB) was developed to forecast their pharmacokinetic performance. 3DP were successfully achieved loading 25, 50 and 100 mg of BNZ. The presence of the IPEC in the ink caused a decrease in the crystalline domain of BNZ and facilitated the printing process, reaching a print success rate of 83.3 %. Interestingly, 3DP-IPEC showed accelerated release dissolution profiles, releasing over 85 % of BNZ in 90 min, while 3DP took up to 48 h for doses above 25 mg. The PBPK model demonstrated that 3DP-IPEC tablets would present high bioavailability (0.92), higher than 3DP (0.36) and similar to the commercial product. This breakthrough holds immense potential for improving treatment outcomes for neglected diseases.

Lobectomy in patients with differentiated thyroid cancer: experience of a Chilean tertiary center.

PURPOSE: Thyroid lobectomy (TL) is an appropriate treatment for up to 4 cm intrathyroidal differentiated thyroid cancer (DTC). There is scarce data regarding TL outside first-world centers. Our aim is to report a cohort of patients with DTC treated with TL in Chile. METHODS: We included DTC patients treated with TL, followed for at least 6 months, characterized their clinicopathological features and classified their risk of recurrence and response to treatment. RESULTS: Eighty-two patients followed for a median of 2.3 years (0.5-7.0). Seventy-three (89%) patients had papillary, 8 (9.8%) follicular and 1 (1.2%) high-grade DTC. The risk of recurrence was low in 56 (68.3%) and intermediate in 26 (31.7%). Eight (9.8%) patients required early completion thyroidectomy and radioiodine. At last follow-up, 52 (70.3%) had excellent, 19 (25.7%) had indeterminate, and 1 (1.4%) had structural incomplete response. CONCLUSION: In a developing country, TL is an adequate option for appropriately selected DTC patients.

Phenotyping OSA: Conceptualization and Implications for Treatment with Mandibular Advancement Devices.

Obstructive Sleep Apnea (OSA) is a common medical disorder and the most impacting sleep disturbance. OSA derive from the narrowing of the upper airway during sleep, which result in recurrent episodes of ventilatory disturbances expressed by an increased airflow resistance (flow limitation and hypopneas) and often an absence of ventilation (apneas). The high heterogeneity in the clinical picture of OSA turns diagnostic and treatment challenging. In the last decade different phenotypes, referring to specific categories of patients that can be distinguished from others by features and related clinical meaningful attributes, were identified. Those phenotypes may predict clinically important outcomes as those deriving from MAD therapy.

MicroRNA as a promising molecular biomarker in the diagnosis of breast cancer.

Introduction: Breast cancer represents the most prevalent malignancy among women. Recent advancements in translational research have focused on the identification of novel biomarkers capable of providing valuable insights into patient outcomes. Furthermore, comprehensive investigations aimed at discovering novel miRNAs, unraveling their biological functions, and deciphering their target genes have significantly contributed to our understanding of the roles miRNAs play in tumorigenesis. Consequently, these investigations have facilitated the way for the development of miRNA-based approaches for breast cancer prognosis, diagnosis, and treatment. However, conducting a more extensive array of studies, particularly among diverse ethnic groups, is imperative to expand the scope of research and validate the significance of miRNAs. This study aimed to assess the expression patterns of circulating miRNAs in plasma as a prospective biomarker for breast cancer patients within a population primarily consisting of individuals from Black, Indigenous, and People of Color (BIPOC) communities. Methods: We evaluated 49 patients with breast cancer compared to 44 healthy women. Results and discussion: All miRNAs analyzed in the plasma of patients with breast cancer were downregulated. ROC curve analysis of miR-21 (AUC = 0.798, 95% CI: 0.682-0.914, p <0.0001), miR-1 (AUC = 0.742, 95% CI: 0.576-0.909, p = 0.004), miR-16 (AUC = 0.721, 95% CI: 0.581-0.861, p = 0.002) and miR-195 (AUC = 0.672, 95% CI: 0.553-0.792, p = 0.004) showed better diagnostic accuracy in discrimination of breast cancer patients in comparison with healthy women. miR-210, miR-21 showed the highest specificities values (97.3%, 94.1%, respectively). Following, miR-10b and miR-195 showed the highest sensitivity values (89.3%, and 77.8%, respectively). The panel with a combination of four miRNAs (miR-195 + miR-210 + miR-21 + miR-16) had an AUC of 0.898 (0.765-0.970), a sensitivity of 71.4%, and a specificity of 100.0%. Collectively, our results highlight the miRNA combination in panels drastically improves the results and showed high accuracy for the diagnosis of breast cancer displaying good sensitivity and specificity.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population.

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Mantle Cell Lymphoma Under the Scope of Personalized Medicine: Perspective and Directions.

Mantle cell lymphoma (MCL) is a rare, incurable non-Hodgkin's lymphoma characterized by naive B cells infiltrating the lymphoid follicle's mantle zone. A key feature of MCL is the cytogenetic abnormality t(11;14) (q13:q14), found in 95% of cases, leading to Cyclin D1 overexpression resulting in uncontrolled cell cycle progression and genetic instability. Occasionally, Cyclin D2 or D3 overexpression can substitute for Cyclin D1, causing similar effects. The transcription factor SOX11 is a hallmark of classical Cyclin D1-positive MCL and also in cases without the typical t(11;14) abnormality, making it an important diagnostic marker. MCL's development necessitates secondary genetic changes, including mutations in the ATM, TP53, and NOTCH1 genes, with the TP53 mutation being the only genetic biomarker with established clinical prognostic value. The Mantle Cell Lymphoma International Prognostic Index (MIPI) score, which considers age, performance status, serum LDH levels, and leukocyte count, stratifies patients into risk groups. Histologic variants of MCL, such as classic, blastoid, and pleomorphic, offer additional prognostic information. Recent research highlights new mutations potentially tied to specific populations among MCL patients, suggesting the benefit of personalized management for better predicting outcomes like progression-free survival. This approach could lead to more effective, risk-adapted treatment strategies. However, challenges remain in patient stratification and in developing new therapeutic targets for MCL. This review synthesizes current knowledge on genetic mutations in MCL and their impact on prognosis. It aims to explore the prognostic value of genetic markers related to population traits, emphasizing the importance of tailored molecular medicine in MCL.

Drug Repurposing and Personalized Treatment Strategies for Bipolar Disorder Using Transcriptomic.

OBJECTIVE: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD). METHODS: Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases. RESULTS: MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies. CONCLUSION: Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.

Drug repurposing and personalized treatment strategies for bipolar disorder using transcriptomics: an exploratory study

Objective: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify new bioactive compounds or Food and Drug Administration-approved drugs for the treatment of bipolar disorder (BD). Methods: Five transcriptomic datasets containing 165 blood samples from individuals with BD were selected from the Gene Expression Omnibus (GEO). The number of participants varied from six to 60, with a mean age between 35 and 48 years and a gender difference between them. Most of these patients were receiving pharmacological treatment. Master regulator analysis (MRA) and gene set enrichment analysis (GSEA) were performed to identify genes that were significantly different between patients with BD and healthy controls and their associations with mood states in patients with BD. In addition, molecules that could reverse the transcriptomic profiles of BD-altered regulons were identified from the Library of Network-Based Cellular Signatures Consortium (LINCS) and the Broad Institute Connectivity Map Drug Repurposing Database (cMap) databases. Results: MRA identified 59 candidate master regulators (MRs) that modulate regulatory units enriched with BD-altered genes. In contrast, GSEA identified 134 enriched genes and 982 regulons whose activation state was determined. Both analyses revealed genes exclusively associated with mania, depression, or euthymia, and some genes were shared among these three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastic agents, as promising candidates for the treatment of BD. However, experimental validation is essential to confirm these findings in further studies. Conclusion: Although our data are still preliminary, they provide some insights into the biological patterns of different mood states in patients with BD and their potential therapeutic targets. The strategy of transcriptomics plus bioinformatics offers a way to advance drug discovery and personalized medicine by using gene expression information.

Clinical Spectrum of USP8 Pathogenic Variants in Cushing's Disease.

Cushing's disease (CD) is a life-threatening condition with a challenging diagnostic process and scarce treatment options. CD is caused by usually benign adrenocorticotrophic hormone (ACTH)-secreting pituitary neuroendocrine tumors (PitNETs), known as corticotropinomas. These tumors are predominantly of sporadic origin, and usually derive from the monoclonal expansion of a mutated cell. Somatic activating variants located within a hotspot of the USP8 gene are present in 11-62% of corticotropinomas, making USP8 the most frequent genetic driver of corticotroph neoplasia. In contrast, other somatic defects such as those affecting the glucocorticoid receptor gene (NR3C1), the BRAF oncogene, the deubiquitinase-encoding gene USP48, and TP53 are infrequent. Moreover, patients with familial tumor syndromes, such as multiple endocrine neoplasia, familial isolated pituitary adenoma, and DICER1 rarely develop corticotropinomas. One of the main molecular alterations in USP8-driven tumors is an overactivation of the epidermal growth factor receptor (EGFR) signaling pathway, which induces ACTH production. Hotspot USP8 variants lead to persistent EGFR overexpression, thereby perpetuating the hyper-synthesis of ACTH. More importantly, they condition a characteristic transcriptomic signature that might be useful for the clinical prognosis of patients with CD. Nevertheless, the clinical phenotype associated with USP8 variants is less well defined. Hereby we discuss the current knowledge on the molecular pathogenesis and clinical picture associated with USP8 hotspot variants. We focus on the potential significance of the USP8 mutational status for the design of tailored clinical strategies in CD.

Classification of patients with metastatic colorectal cancer into consensus molecular subtypes into real-world: A pilot study.

BACKGROUND: Colorectal cancer is a complex disease with high mortality rates. Over time, the treatment of metastatic colorectal cancer (mCRC) has gradually improved due to the development of modern chemotherapy and targeted therapy regimens. However, due to the inherent heterogeneity of this condition, identifying reliable predictive biomarkers for targeted therapies remains challenging. A recent promising classification system-the consensus molecular subtype (CMS) system-offers the potential to categorize mCRC patients based on their unique biological and molecular characteristics. Four distinct CMS categories have been defined: immune (CMS1), canonical (CMS2), metabolic (CMS3), and mesenchymal (CMS4). Nevertheless, there is currently no standardized protocol for accurately classifying patients into CMS categories. To address this challenge, reverse transcription polymerase chain reaction (RT-qPCR) and next-generation genomic sequencing (NGS) techniques may hold promise for precisely classifying mCRC patients into their CMSs. AIM: To investigate if mCRC patients can be classified into CMS categories using a standardized molecular biology workflow. METHODS: This observational study was conducted at the University of Chile Clinical Hospital and included patients with unresectable mCRC who were undergoing systemic treatment with chemotherapy and/or targeted therapy. Molecular biology techniques were employed to analyse primary tumour samples from these patients. RT-qPCR was utilized to assess the expression of genes associated with fibrosis (TGF-ß and ß-catenin) and cell growth pathways (c-MYC). NGS using a 25-gene panel (TumorSec) was performed to identify specific genomic mutations. The patients were then classified into one of the four CMS categories according to the clinical consensus of a Tumour Board. Informed consent was obtained from all the patients prior to their participation in this study. All techniques were conducted at University of Chile. RESULTS: Twenty-six patients were studied with the techniques and then evaluated by the Tumour Board to determine the specific CMS. Among them, 23% (n = 6), 19% (n = 5), 31% (n = 8), and 19% (n = 5) were classified as CMS1, CMS2, CMS3, and CMS4, respectively. Additionally, 8% of patients (n = 2) could not be classified into any of the four CMS categories. The median overall survival of the total sample was 28 mo, and for CMS1, CMS2, CMS3 and CMS4 it was 11, 20, 30 and 45 mo respectively, with no statistically significant differences between groups. CONCLUSION: A molecular biology workflow and clinical consensus analysis can be used to accurately classify mCRC patients. This classification process, which divides patients into the four CMS categories, holds significant potential for improving research strategies and targeted therapies tailored to the specific characteristics of mCRC.

Proteolytic signaling in cancer.

INTRODUCTION: Cancer is a disease of (altered) biological pathways, often driven by somatic mutations and with several implications. Therefore, the identification of potential markers of disease is challenging. Given the large amount of biological data generated with omics approaches, oncology has experienced significant contributions. Proteomics mapping of protein fragments, derived from proteolytic processing events during oncogenesis, may shed light on (i) the role of active proteases and (ii) the functional implications of processed substrates in biological signaling circuits. Both outcomes have the potential for predicting diagnosis/prognosis in diseases like cancer. Therefore, understanding proteolytic processing events and their downstream implications may contribute to advances in the understanding of tumor biology and targeted therapies in precision medicine. AREAS COVERED: Proteolytic events associated with some hallmarks of cancer (cell migration and proliferation, angiogenesis, metastasis, as well as extracellular matrix degradation) will be discussed. Moreover, biomarker discovery and the use of proteomics approaches to uncover proteolytic signaling events will also be covered. EXPERT OPINION: Proteolytic processing is an irreversible protein post-translational modification and the deconvolution of biological data resulting from the study of proteolytic signaling events may be used in both patient diagnosis/prognosis and targeted therapies in cancer.

Corrigendum: Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.

[This corrects the article DOI: 10.3389/fgene.2023.1209416.].

Update in Immunotherapy for Advanced Non-Small Cell Lung Cancer: Optimizing Treatment Sequencing and Identifying the Best Choices.

The introduction of immunotherapy has brought about a paradigm shift in the management of advanced non-small cell lung cancer (NSCLC). It has not only significantly improved the prognosis of patients but has also become a cornerstone of treatment, particularly in those without oncogenic driver mutations. Immune checkpoint inhibitors (ICIs) play a crucial role in the treatment of lung cancer and can be classified into two main groups: Anti-cytotoxic T lymphocyte antigen-4 (Anti-CTLA-4) and anti-T-cell receptor programmed cell death-1 or its ligand (Anti-PD-1 and Anti-PD-L1). Certainly, the landscape of approved first line immunotherapeutic approaches has expanded to encompass monotherapy, immunotherapy-exclusive protocols, and combinations with chemotherapy. The complexity of decision-making in this realm arises due to the absence of direct prospective comparisons. However, a thorough analysis of the long-term efficacy and safety data derived from pivotal clinical trials can offer valuable insights into optimizing treatment for different patient subsets. Moreover, ongoing research is investigating emerging biomarkers and innovative therapeutic strategies that could potentially refine the current treatment approach even further. In this comprehensive review, our aim is to highlight the latest advances in immunotherapy for advanced NSCLC, including the mechanisms of action, efficacy, safety profiles, and clinical significance of ICI.

Challenges and opportunities in building a health economic framework for personalized medicine in oncology.

Personalized medicine has allowed for knowledge at an individual level for several diseases and this has led to improvements in prevention and treatment of various types of neoplasms. Despite the greater availability of tests, the costs of genomic testing and targeted therapies are still high for most patients, especially in low- and middle-income countries. Although value frameworks and health technology assessment are fundamental to allow decision-making by policymakers, there are several concerns in terms of personalized medicine pharmacoeconomics. A global effort may improve these tools in order to allow access to personalized medicine for an increasing number of patients with cancer.