Advancing Precision Medicine Research Articles

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry.

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

MicroRNAs and their therapeutic strategy in phase Iand phase IIclinical trials.

miRNAs play a crucial therapeutic role in diseases such as cancer, diabetes and viral infections, with around 1900 identified in the human genome. Some have progressed to clinical trials, and miRNA mimics and miRNA inhibitors are pivotal therapeutic molecules undergoing evaluation. The review delves into various miRNA-associated clinical trials, emphasizing their precision in targeting specific genes, modulating disease pathways and diagnostic potential. This underscores the importance of miRNA therapy, foreseeing innovations in precision medicine techniques for diverse diseases. The future envisions improved delivery systems addressing challenges like immunogenicity and digestion, while a comprehensive miRNA-based omics database could guide the development of tailored antisense miRNAs, further advancing precision medicine strategies.

DNA methylation modulates epigenetic regulation in colorectal cancer diagnosis, prognosis and precision medicine.

Colorectal cancer (CRC) is a multifaceted disease influenced by the interplay of genetic and environmental factors. The clinical heterogeneity of CRC cannot be attributed exclusively to genetic diversity and environmental exposures, and epigenetic markers, especially DNA methylation, play a critical role as key molecular markers of cancer. This review compiles a comprehensive body of evidence underscoring the significant involvement of DNA methylation modifications in the pathogenesis of CRC. Moreover, this review explores the potential utility of DNA methylation in cancer diagnosis, prognostics, assessment of disease activity, and prediction of drug responses. Recognizing the impact of DNA methylation will enhance the ability to identify distinct CRC subtypes, paving the way for personalized treatment strategies and advancing precision medicine in the management of CRC.

Access, autonomy, and affordability: ethical and human rights issues surrounding multigene panel testing for cancer in Japan and Switzerland.

Introduction: Advancements in precision medicine and genomics have led to prospects in a wide range of clinical fields, including oncology. In particular, developments in next-generation sequencing multigene panel tests have led to the possibility of tailoring treatment to the specific genomic markers of a patient's cancer. However, findings from current literature suggest that the path to implementation and uptake of genomic medicine is not without uncertainties and challenges. Methods: To better understand the current challenges to the implementation of genomic medicine services, we investigated the current state of patient access to genomic medicine in Japan and Switzerland. In this investigation, we focused on equal access, patient autonomy, and healthcare affordability. Results: Results have shown that although multigene panel testing is in principle covered by health insurance in both countries, barriers exist in terms of where the tests are available, comprehensive information for patients, and the affordability of not only the test itself but the overall process from diagnosis to treatment. Discussion: These results suggest a need to continue examining a more diverse range of clinical landscapes for genomic medicine to reveal more nuanced understandings of barriers to implementation and thus better identify best practices for overcoming them.

A framework towards digital twins for type 2 diabetes.

A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes. Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships. Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.

Transcriptomic-Based Microenvironment Classification Reveals Precision Medicine Strategies for Pancreatic Ductal Adenocarcinoma

Background & AimsThe complex tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) has hindered the development of reliable predictive biomarkers for targeted therapy and immunomodulatory strategies. A comprehensive characterization of the TME is necessary to advance precision therapeutics in PDAC. MethodsA transcriptomic profiling platform for TME classification based on functional gene signatures was applied to 14 publicly available PDAC datasets (n = 1657) and validated in a clinically annotated independent cohort of patients with PDAC (n = 79). Four distinct subtypes were identified using unsupervised clustering and assessed to evaluate predictive and prognostic utility. ResultsTME classification using transcriptomic profiling identified 4 biologically distinct subtypes based on their TME immune composition: immune enriched (IE); immune enriched, fibrotic (IE/F); fibrotic (F); and immune depleted (D). The IE and IE/F subtypes demonstrated a more favorable prognosis and potential for response to immunotherapy compared with the F and D subtypes. Most lung metastases and liver metastases were subtypes IE and D, respectively, indicating the role of clonal phenotype and immune milieu in developing personalized therapeutic strategies. In addition, distinct TMEs with potential therapeutic implications were identified in treatment-naive primary tumors compared with tumors that underwent neoadjuvant therapy. ConclusionsThis novel approach defines a distinct subgroup of PADC patients that may benefit from immunotherapeutic strategies based on their TME subtype and provides a framework to select patients for prospective clinical trials investigating precision immunotherapy in PDAC. Further, the predictive utility and real-world clinical applicability espoused by this transcriptomic-based TME classification approach will accelerate the advancement of precision medicine in PDAC.

Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies.

Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.

Multi-Omics Reveals the Role of Osteopontin/Secreted Phosphoprotein 1 in Regulating Ovarian Aging.

Secreted phosphoprotein 1 (SPP1), also known as osteopontin (OPN), is located on chromosome 4q22.1. This multifunctional secreted acidic glycoprotein is expressed intracellularly and extracellularly in various tissues, where it interacts with regulatory proteins and pro-inflammatory immune chemokines, contributing to the pathogenesis of multiple diseases. Nevertheless, the intricate genetic connections between SPP1 and ovarian aging remain largely unexplored. This study aims to bridge this knowledge gap by delving into ovarian aging and its associations with SPP1 using multi-omics data analysis. Our findings indicate that SPP1 is a potential gene related to ovarian aging. To comprehend the role of SPP1, we conducted spatial transcriptomic analyses on young and aged female mouse ovaries, revealing a significant decline in SPP1 expression in the aging group compared to the young group. Similarly, a significantly low level of SPP1 was found in the 73-year-old sample. Additionally, in-depth single-cell RNA-sequencing analysis identified associations between SPP1 and ITGAV, ITGB1, CD44, MMP3, and FN1. Notably, co-expression analysis highlighted a strong correlation between SPP1 and ITGB1. In summary, this study pioneers the identification of SPP1 as a gene implicated in ovarian aging. Further research into the role of SPP1 has the potential to advance precision medicine and improve treatment strategies for ovarian aging-related conditions.

OM2Seq: learning retrieval embeddings for optical genome mapping.

Genomics-based diagnostic methods that are quick, precise, and economical are essential for the advancement of precision medicine, with applications spanning the diagnosis of infectious diseases, cancer, and rare diseases. One technology that holds potential in this field is optical genome mapping (OGM), which is capable of detecting structural variations, epigenomic profiling, and microbial species identification. It is based on imaging of linearized DNA molecules that are stained with fluorescent labels, that are then aligned to a reference genome. However, the computational methods currently available for OGM fall short in terms of accuracy and computational speed. This work introduces OM2Seq, a new approach for the rapid and accurate mapping of DNA fragment images to a reference genome. Based on a Transformer-encoder architecture, OM2Seq is trained on acquired OGM data to efficiently encode DNA fragment images and reference genome segments to a common embedding space, which can be indexed and efficiently queried using a vector database. We show that OM2Seq significantly outperforms the baseline methods in both computational speed (by 2 orders of magnitude) and accuracy. https://github.com/yevgenin/om2seq.

Uncovering dark matter in cancer by identifying epigenetic drivers

The complex relationship between chromatin accessibility, transcriptional regulation, and cancer transitions presents a daunting puzzle. Terekhanova et al. created a pan-cancer epigenetic and transcriptomic atlas at single-cell resolution, yielding important insights into the underlying chromatin architecture of cancer transitions and novel discoveries with the potential to advance precision medicine.

Magnetic Graphene Oxide Nanocomposites for Selective miRNA Separation and Recovery.

In this study, we developed magnetic graphene oxide composites by chemically attaching Fe3O4 nanoparticles to graphene oxide nanosheets. Characterization techniques, including Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and transmission electron microscopy (TEM), confirmed the successful synthesis of Fe3O4@GO composites with desirable properties. The resulting composites exhibited superparamagnetic behavior, solubility, and compatibility for efficient miRNA separation. Using miR-29a as a model, we demonstrated the effective binding of miR-29a to the magnetic graphene oxide (GO) composites at an optimal concentration of 1.5 mg/mL, followed by a simple separation using magnetic forces. Additionally, the addition of 5.0 M urea enhanced the miRNA recovery. These findings highlight the potential use of our magnetic graphene oxide composites for the efficient separation and recovery of miR-29a, suggesting their broad applicability in various miRNA-based studies. Further exploration can focus on investigating endogenous miRNAs with aberrant expression patterns, contributing to the advancements in precision medicine.

Chinese Society of Clinical Oncology (CSCO) Breast Cancer guidelines 2024.

Developing guidelines for the diagnosis and treatment of common cancers in China based on the evidence-based practice, the availability of diagnosis and treatment products, and the up-to-date advances in precision medicine is one of the basic tasks of the Chinese Society of Clinical Oncology Breast Cancer (CSCO BC) Committee. Protocols with high evidence level and good availability are used as the Level I recommendations; protocols with relatively high evidence level but slightly lower expert consensus or with poor availability are used as the Level II recommendations; and protocols that are clinically applicable but with low evidence level are regarded as the Level III recommendations. Based on the findings of clinical research at home and abroad and the opinions of CSCO BC experts, the CSCO BC guidelines determine the levels of recommendations for clinical application. For human epidermal growth factor receptor 2 (HER2)-positive breast cancer, a combination of trastuzumab and pertuzumab regimen were recommended as Level I recommendation for neoadjuvant and first line metastatic breast cancer. Pyrotinib is also recommended as Level I recommendation in first line and second line therapy according to the latest studies conducted in China. Antibody drug conjugates was also recommended for patients with trastuzumab progression. For triple negative breast cancer, immunotherapy in early and metastatic breast cancer was highlighted and listed as new chapters in this version of guideline. For hormone receptor (HR)-positive breast cancer, cyclin dependent kinase 4/6 (CDK4/6) was recommended in different stages, especially in adjuvant therapy. There was also a new chapter for HER2-low breast cancer stratified by HR status. We firmly believe that evidence-based, availability-concerned, and consensus-based guidelines will be more feasible for clinical practice in China and in other countries with similar situations.

Real-world treatments and clinical outcomes in unfit AML patients receiving first-line treatment or best supportive care in Italy (CURRENT study)

Real-world data on treatment patterns and outcomes of patients with acute myeloid leukemia unfit for intensive chemotherapy are lacking before the advent of precision medicine in this setting.Herein, we present the Italian sub-analysis of the CURRENT study in AML patients unfit for first line intensive chemotherapy, evaluating patients’ outcomes between 2015 and 2018.Among 74 evaluable patients, 62 received systemic treatments (most used therapy was hypomethylating agents), while 12 best supportive care.Key results include both efficacy and safety data, as well as HCRU and treatment patterns. In first-line therapy cohort median OS was 13.4 months vs. 2.7 months for BSC.

Cancer: A review

Cancer remains one of the most formidable challenges in modern medicine, posing significant health burdens globally. In recent years, extensive research efforts have aimed at understanding the intricate mechanisms underlying cancer development, progression, and treatment. This review provides a comprehensive overview of the latest advancements in cancer research across various domains. The elucidation of genetic and molecular alterations driving oncogenesis has revolutionized our understanding of cancer biology. Key discoveries in genomics, transcriptomics, and proteomics have unveiled the heterogeneous nature of tumors, paving the way for personalized treatment approaches. Moreover, advancements in high-throughput sequencing technologies have facilitated the identification of novel cancer biomarkers with diagnostic, prognostic, and therapeutic implications. The tumor microenvironment (TME) has emerged as a critical determinant of cancer progression and therapy response. Research focusing on the dynamic interactions between cancer cells, immune cells, and stromal components within the TME has led to the development of immunotherapeutic strategies, including immune checkpoint inhibitors and adoptive cell therapies, which have demonstrated remarkable efficacy in various cancer types. In addition to targeted therapies and immunotherapies, the advent of precision medicine has transformed cancer treatment paradigms. Molecular profiling of tumors enables clinicians to match patients with specific targeted therapies, optimizing therapeutic outcomes while minimizing adverse effects. Furthermore, the integration of artificial intelligence and machine learning algorithms in cancer research has facilitated the prediction of treatment responses and identification of novel therapeutic targets.

How to incorporate new agents into precise medicine for cholangiocarcinoma?

Cholangiocarcinoma, a rare and aggressive form of cancer originating from the bile ducts in the liver, poses a significant challenge for treatment. However, the emergence of precision medicine has brought newfound hope for more effective therapies. Several precision medicine approaches have demonstrated promise in the treatment of cholangiocarcinoma. One such approach is targeted therapy, which involves utilizing drugs that specifically target the genetic mutations or alterations present in the tumor cells. In the case of cholangiocarcinoma, mutations in the IDH1 and IDH2 genes are frequently observed. Immunotherapy is another precision medicine approach being explored for the treatment of cholangiocarcinoma. Immune checkpoint inhibitors like pembrolizumab and nivolumab can be used to bolster the body's immune response against cancer cells. While the response to immunotherapy can vary among individuals, studies have shown promising results, particularly in patients with high levels of tumor-infiltrating lymphocytes or microsatellite instability. Moreover, molecular profiling of cholangiocarcinoma tumors can play a crucial role in identifying potential targets for precision medicine. Through advanced next-generation sequencing techniques, specific gene alterations or dysregulations in pathways can be identified, potentially guiding treatment decisions. This personalized approach enables tailored treatment plans based on the unique genetic characteristics of each patient's tumor. In conclusion, the advent of precision medicine has opened up new avenues for the treatment of cholangiocarcinoma. Targeted therapy and immunotherapy have exhibited promising results, and further molecular profiling is expected to uncover additional therapeutic options. Such advancements represent a significant step forward in the quest to enhance outcomes for individuals affected by cholangiocarcinoma.

Oleate alters the immune response in non-small cell lung adenocarcinoma through regulation of HMGB1 release.

Background: Cancer cell evasion of the immune response is critical to cancer development and metastases. Clinicians' ability to kickstart the immune system to target these rogue cells is an ever-growing area of research and medicine. This study delved into the relationship between lipid metabolism, High Mobility Group Box 1 protein (HMGB1)-a pro-inflammatory damage-associated molecular pattern protein-and immune regulation within non-small cell lung adenocarcinoma (NSCLC). Method: To address this question, we used a combination of proteomics, molecular biology, and bioinformatic techniques to investigate the relationship between fatty acids and immune signals within NSCLC. Results: We found that the expression of stearoyl CoA desaturase 1 (SCD1) was decreased in NSCLC tumors compared to normal tissues. This emphasized the critical role of lipid metabolism in tumor progression. Interestingly, monounsaturated fatty acid (MUFA) availability affected the expression of programmed death ligand-1 (PD-L1), a pivotal immune checkpoint target in lung cancer cells and immune cells, as well as HMGB1, suggesting a novel approach to modulating the immune response. This study uncovered a complex interplay between SCD1, PD-L1, and HMGB1, influencing the immunological sensitivity of tumors. Conclusion: Our work underscores the critical importance of understanding the intricate relationships between lipid metabolism and immune modulation to develop more effective NSCLC treatments and personalized therapies. As we continue to explore these connections, we hope to contribute significantly to the ever-evolving field of cancer research, improving patient outcomes and advancing precision medicine in NSCLC.

Functional neuroimaging biomarkers of anhedonia response to escitalopram plus adjunct aripiprazole treatment for major depressive disorder.

Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine. To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram. Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole. Anhedonia severity significantly improved after treatment with adjunct aripiprazole.There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus. Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

Genetic profiling in radiotherapy: a comprehensive review.

This comprehensive review explores the pivotal role of radiotherapy in cancer treatment, emphasizing the diverse applications of genetic profiling. The review highlights genetic markers for predicting radiation toxicity, enabling personalized treatment planning. It delves into the impact of genetic profiling on radiotherapy strategies across various cancer types, discussing research findings related to treatment response, prognosis, and therapeutic resistance. The integration of genetic profiling is shown to transform cancer treatment paradigms, offering insights into personalized radiotherapy regimens and guiding decisions in cases where standard protocols may fall short. Ultimately, the review underscores the potential of genetic profiling to enhance patient outcomes and advance precision medicine in oncology.

Advancing precision medicine through the integration of clinical cardiovascular genetics - An Asian perspective

PurposeThe integration of cardiovascular genetic (CVG) testing into clinical practice is gaining recognition, but its implementation in the Asian setting has not been widely reported. We present our experience developing a clinical CVG service and analyse its impact on patient care at our centre. MethodsIn 2020, National Heart Centre Singapore (NHCS) collaborated with SingHealth Duke-NUS Genomic Medicine Centre (SDGMC), to establish a comprehensive clinical CVG service. We retrospectively gathered details regarding referral indication and clinical utility of genetic counselling and testing. ResultsOver a period of two and a half years, 113 patients, aged 17 to 94 years old, were seen by the CVG team. The cohort included 79 males and 34 females: 82 of Chinese ancestry, 11 Indian, 7 Malay, and 13 from other ancestries. The most common reason for referral was for cardiomyopathy, followed by aortopathy. After clinical evaluation, 98 patients were offered genetic testing, of which 63 (64%) patients proceeded with genetic testing (diagnostic testing n=51, predictive testing n=10, familial segregation analysis n=2). Eleven patients were referred for continuation of care. Overall, CVG testing added value to the care of 44 patients by clarifying clinical diagnosis, ruling out inherited cardiac disorders, aiding variant of uncertain significance (VUS) resolution, and/or facilitating cascade testing. ConclusionOur pilot initiative has provided insights into the practical value, obstacles, and opportunities for developing a clinical CVG service. Establishment of our clinical CVG service not only enhanced patient care, but also demonstrated its scalability through collaborative partnerships with domain experts.

Expectations and challenges for clinical application of cancer genome panels in acute myeloid leukemia

The blood cancer field has played a pioneering role in advancing precision medicine, with milestones such as development of ABL1 inhibitors for chronic myeloid leukemia. The significance of gene mutation information in AML treatment has increased, evident in classifications and guidelines from organizations such as WHO and ELN. This article examines the anticipated roles of cancer genome panels (CGPs) in AML treatment from three perspectives: diagnosis, risk stratification, and treatment selection. Use of CGPs enables more accurate diagnosis and risk stratification. In treatment selection, CGPs not only complements but also substitutes existing companion diagnostics, and is expected to be a crucial information source for future drug adoption and investigation of tumor-agnostic therapies. However, various challenges remain to be addressed, including the purpose and timing of CGPs, the time required for the tests, and how to utilize expert panels.