Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics

Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

How did we get there? AI applications to biological networks and sequences

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein-protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field.

Precision Targeting in Metastatic Prostate Cancer: Molecular Insights to Therapeutic Frontiers

Metastatic prostate cancer (mPCa) remains a significant cause of cancer-related mortality in men. Advances in molecular profiling have demonstrated that the androgen receptor (AR) axis, DNA damage repair pathways, and the PI3K/AKT/mTOR pathway are critical drivers of disease progression and therapeutic resistance. Despite the established benefits of hormone therapy, chemotherapy, and bone-targeting agents, mPCa commonly becomes treatment-resistant. Recent breakthroughs have highlighted the importance of identifying actionable genetic alterations, such as BRCA2 or ATM defects, that render tumors sensitive to poly-ADP ribose polymerase (PARP) inhibitors. Parallel efforts have refined imaging-particularly prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography-to detect and localize metastatic lesions with high sensitivity, thereby guiding patient selection for PSMA-targeted radioligand therapies. Multi-omics innovations, including liquid biopsy technologies, enable the real-time tracking of emergent AR splice variants or reversion mutations, supporting adaptive therapy paradigms. Nonetheless, the complexity of mPCa necessitates combination strategies, such as pairing AR inhibition with PI3K/AKT blockade or PARP inhibitors, to inhibit tumor plasticity. Immuno-oncological approaches remain challenging for unselected patients; however, subsets with mismatch repair deficiency or neuroendocrine phenotypes may benefit from immune checkpoint blockade or targeted epigenetic interventions. We present these pivotal advances, and discuss how biomarker-guided integrative treatments can improve mPCa management.

Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

Adoptive immune cell therapy for colorectal cancer

Colorectal cancer (CRC) is a major cause of cancer-related morbidity and mortality worldwide, with limited options for patients at advanced stages. Immunotherapy, particularly immune cell-based therapies, has gained significant attention as an innovative approach for targeting CRC. This review summarizes the progress in various immune cell therapies, including DC vaccine, CAR/TCR-T cells, CAR-NK cells et al, each engineered to recognize and attack cancer cells expressing specific antigens. CAR-T cell therapy, which has been successful in hematologic cancers, faces challenges in CRC due to the solid tumor microenvironment, which limits cell infiltration and persistence. CAR-NK cells, CAR-M and CAR-γδ T cells, however, offer alternative strategies due to their unique properties, such as the ability to target tumor cells without prior sensitization and a lower risk of inducing severe cytokine release syndrome. Recent advances in lentiviral transduction have enabled effective expression of CARs on NK and γδ T cells, providing promising preclinical results in CRC models. This review explores the mechanisms, tumor targets, preclinical studies, and early-phase clinical trials of these therapies, addressing key challenges such as enhancing specificity to tumor antigens and overcoming the immunosuppressive tumor microenvironment. The potential of combination therapies, including immune checkpoint inhibitors and cytokine therapy, is also discussed some as a means to improve the effectiveness of immune cell-based treatments for CRC. Continued research is essential to translate these promising approaches into clinical settings, offering new hope for CRC patients.

Unlocking the potential of CRISPR-Cas9 for cystic fibrosis: A systematic literature review

CRISPR-Cas9 technology has revolutionized genetic engineering, offering precise and efficient genome editing capabilities. This review explores the application of CRISPR-Cas9 for cystic fibrosis (CF), particularly targeting mutations in the CFTR gene. CF is a multiorgan disease primarily affecting the lungs, gastrointestinal system (e.g., CF-related diabetes (CFRD), CF-associated liver disease (CFLD)), bones (CF-bone disease), and the reproductive system. CF, a genetic disorder characterized by defective ion transport leading to thick mucus accumulation, is often caused by mutations like ΔF508 in the CFTR gene. This review employs a systematic methodology, incorporating an extensive literature search across multiple academic databases, including PubMed, Web of Science, and ScienceDirect, to identify 40 high-quality studies focused on CRISPR-Cas9 applications for CFTR gene editing. The data collection process involved predefined inclusion criteria targeting experimental approaches, gene-editing outcomes, delivery methods, and verification techniques. Data analysis synthesized findings on editing efficiency, off-target effects, and delivery system optimization to present a comprehensive overview of the field. The review highlights the historical development of CRISPR-Cas9, its mechanism, and its transformative role in genetic engineering and medicine. A detailed examination of CRISPR-Cas9's application in CFTR gene correction emphasizes the potential for therapeutic interventions while addressing challenges such as off-target effects, delivery efficiency, and ethical considerations. Future directions include optimizing delivery systems, integrating advanced editing tools like prime and base editing, and expanding personalized medicine approaches to improve treatment outcomes. By systematically analyzing the current landscape, this review provides a foundation for advancing CRISPR-Cas9 technologies for cystic fibrosis treatment and related disorders.

AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly in developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise in cancer cases. Despite the potential to mitigate the adverse effects of cancer by early detection and the implementation of cancer prevention methods, several nations have limited screening facilities. In oncology, the use of artificial intelligence (AI) represents a transformative advancement in cancer diagnosis, prognosis, and treatment. The use of AI in biomarker discovery improves precision medicine by uncovering biomarker signatures that are essential for early detection and treatment of diseases within vast and diverse datasets. Deep learning and machine learning diagnostics are two examples of AI technologies that are changing the way biomarkers are made by finding patterns in large datasets and making new technologies that make it possible to deliver accurate and effective therapies. Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. The advancement of biomarker discovery methodologies with AI seeks to transform cancer by improving patient survival rates through enhanced early diagnosis and targeted therapy. This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. Data from reputable scientific databases such as PubMed, Scopus, and ScienceDirect were utilized.

Learn from artificial intelligence: the pursuit of objectivity

Humans continuously face threats from emerging novel pathogens and antimicrobial resistant bacteria or fungi, which requires urgently and efficient solutions. Alternatively, microbes also produce compounds or chemicals highly valuable to humans of which require continuous refinement and improvement of yields. Artificial intelligence (AI) is a promising tool to search for solutions combatting against diseases and facilitating productivity underpinned by robust research providing accurate information. However, the extent of AI credibility is yet to be fully understood. In terms of human bias, AI could arguably act as a means of ensuring scientific objectivity to increase accuracy and precision, however, whether this is possible or not has not been fully discussed. Human bias and error can be introduced at any step of the research process, including conducting experiments and data processing, through to influencing clinical applications. Despite AI's contribution to advancing knowledge, the question remains, is AI able to achieve objectivity in microbiological research? Here, the benefits, drawbacks, and responsibilities of AI utilization in microbiological research and clinical applications were discussed.

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma

AIMS

Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics analysis to establish and validate a prognosis and treatment vulnerability signature (PTVS) capable of effectively predicting patient prognosis and drug responsiveness.

MATERIALS AND METHODS

To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data. Afterwards, we applied bootstrapping in univariate Cox regression and the Boruta algorithm to pinpoint clinically relevant genes. Based on this, we developed a robust PTVS using seven machine learning algorithms.

KEY FINDINGS

Our analysis revealed two distinct ccRCC subtypes with differential prognostic implications, notably identifying subtype 2 with poorer outcomes. Patients in the low PTVS group exhibited superior prognosis statistics and an augmented sensitivity to immunotherapy, features consistent with a 'hot tumor' phenotype. Conversely, individuals within the high PTVS group exhibited diminished prognosis statistic and restricted advantages from immunotherapy. Importantly, the PTVS holds future potential as a notable biomarker for guiding personalized treatment strategies, with four prospective targets (CTSK, XDH, PKMYT1, and EGLN2) indicating therapeutic promise in patients scoring high on PTVS.

SIGNIFICANCE

The integrative analysis of multi-omics data profoundly enhances the molecular stratification of ccRCC, underscoring far-reaching impact of such comprehensive profiling on its therapeutic strategies.

Progress and prospects of Parkinson's disease with depression research: A global bibliometric analysis based on CiteSpace

BACKGROUND

Parkinson's disease (PD) is a common neurodegenerative disorder marked by motor impairments such as stiffness, involuntary shaking, and slowed movement. In addition, PD patients frequently experience nonmotor symptoms, especially depression. This study uses a mixed-methods scientometric analysis to review global research trends and advancements in PD and depression. This analysis is vital for clinicians, researchers, and policymakers, identifying knowledge gaps and directing future research efforts.

METHODS

We conducted a comprehensive literature review on PD and depression using the Web of Science database from 2004 to 2023, facilitated by CiteSpace 6.1.R6. Our analysis examined collaborations among authors, institutions, countries, and keywords, incorporating insights from RCTs and qualitative studies. We calculated effect sizes and confidence intervals with precision. Ethical approval was not required as the study used publicly available data without personal information.

RESULTS

Our analysis included 3048 research papers and 915 reviews, involving 17,927 authors and 12,466 institutions. The United States and the University of Toronto led in publications. Studies revealed significant effect sizes with narrow confidence intervals, particularly on the prevalence and impact of depression in PD patients. High-frequency keywords included "Parkinson's disease," "depression," "quality of life," "non-motor symptom," and "dementia." Visual mapping identified critical research nodes and future directions.

CONCLUSION

Over the past 2 decades, research on the PD-depression link has accelerated. Our analysis highlights prevailing trends and critical areas, providing evidence-based recommendations for therapeutic strategies. This study offers valuable insights for clinicians and researchers, emphasizing future research priorities to improve patient outcomes.

Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR

The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI-driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines.

Appraisal of CRISPR Technology as an Innovative Screening to Therapeutic Toolkit for Genetic Disorders

The high frequency of genetic diseases compels the development of refined diagnostic and therapeutic systems. CRISPR is a precise genome editing tool that offers detection of genetic mutation with high sensitivity, specificity and flexibility for point-of-care testing in low resource environment. Advancements in CRISPR ushered new hope for the detection of genetic diseases. This review aims to explore the recent advances in CRISPR for the detection and treatment of genetic disorders. It delves into the advances like next-generation CRISPR diagnostics like nano-biosensors, digitalized CRISPR, and omics-integrated CRISPR technologies to enhance the detection limits and to facilitate the "lab-on-chip" technologies. Additionally, therapeutic potential of CRISPR technologies is reviewed to evaluate the implementation potential of CRISPR technologies for the treatment of hematological diseases, (sickle cell anemia and β-thalassemia), HIV, cancer, cardiovascular diseases, and neurological disorders, etc. Emerging CRISPR therapeutic approaches such as base/epigenetic editing and stem cells for the development of foreseen CRIPSR drugs are explored for the development of point-of-care testing. A combination of predictive models of artificial intelligence and machine learning with growing knowledge of genetic disorders has also been discussed to understand their role in acceleration of genetic detection. Ethical consideration are briefly discussed towards to end of review. This review provides the comprehensive insights into advances in the CRISPR diagnostics/therapeutics which are believed to pave the way for reliable, effective, and low-cost genetic testing.

Precision oncology: Using cancer genomics for targeted therapy advancements

Cancer genomics plays a crucial role in oncology by enhancing our understanding of how genes drive cancer and facilitating the development of improved treatments. This field meticulously examines various cancers' genetic makeup through various methodologies, leading to groundbreaking discoveries. Innovative tools such as rapid gene sequencing, single-cell studies, spatial gene mapping, epigenetic analysis, liquid biopsies, and computational modeling have significantly progressed the field. These techniques uncover genetic alterations, tumor heterogeneity, and the evolutionary dynamics of cancers. Genetic abnormalities and molecular markers that initiate and propagate distinct cancer types are classified according to tumor type. The integration of precision medicine with cancer genomics emphasizes the significance of utilizing genetic data in treatment decision-making, enabling personalized care and enhancing patient outcomes. Critical topics in cancer genomics encompass tumor diversity, alterations in non-coding DNA, epigenetic modifications, cancer-specific proteins, metabolic changes, and the impact of inherited genes on cancer risk.

Sustained efficacy of CRISPR-Cas13b gene therapy for FSHD is challenged by immune response to Cas13b

Facioscapulohumeral muscular dystrophy (FSHD) is a potentially devastating muscle disease caused by de-repression of the toxic DUX4 gene in skeletal muscle. FSHD patients may benefit from DUX4 inhibition therapies, and although several experimental strategies to reduce DUX4 levels in skeletal muscle are being developed, no approved disease modifying therapies currently exist. We developed a CRISPR-Cas13b system that cleaves DUX4 mRNA and reduces DUX4 protein level, protects cells from DUX4-mediated death, and reduces FSHD-associated biomarkers in vitro . In vivo delivery of the CRISPR-Cas13b system with adeno-associated viral vectors reduced acute damage caused by high DUX4 levels in a mouse model of severe FSHD. However, protection was not sustained over time, with decreases in Cas13b and guide RNA levels between 8 weeks and 6 months after injection. In addition, wild-type mice injected with AAV6.Cas13b showed muscle inflammation with infiltrates containing Cas13b-responsive CD8+ cytotoxic T cells. Our RNA-seq data confirmed that several immune response pathways were significantly increased in human FSHD myoblasts transfected with Cas13b. Overall, our findings suggest that CRISPR-Cas13b is highly effective for DUX4 silencing but successful implementation of CRISPR/Cas13-based gene therapies may require strategies to mitigate immune responses.

Optimizing CAR-T cell therapy for solid tumors: current challenges and potential strategies

Chimeric antigen receptor (CAR)-T cell therapy demonstrates substantial efficacy in various hematological malignancies. However, its application in solid tumors is still limited. Clinical studies report suboptimal outcomes such as reduced cytotoxicity of CAR-T cells and tumor evasion, underscoring the need to address the challenges of sliding cytotoxicity in CAR-T cells. Despite improvements from fourth and next-generation CAR-T cells, new challenges include systemic toxicity from continuously secreted proteins, low productivity, and elevated costs. Recent research targets genetic modifications to boost killing potential, metabolic interventions to hinder tumor progression, and diverse combination strategies to enhance CAR-T cell therapy. Efforts to reduce the duration and cost of CAR-T cell therapy include developing allogenic and in-vivo approaches, promising significant future advancements. Concurrently, innovative technologies and platforms enhance the potential of CAR-T cell therapy to overcome limitations in treating solid tumors. This review explores strategies to optimize CAR-T cell therapies for solid tumors, focusing on enhancing cytotoxicity and overcoming application restrictions. We summarize recent advances in T cell subset selection, CAR-T structural modifications, infiltration enhancement, genetic and metabolic interventions, production optimization, and the integration of novel technologies, presenting therapeutic approaches that could improve CAR-T cell therapy's efficacy and applicability in solid tumors.

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy

This narrative review examines the promising potential of integrating artificial intelligence (AI) with CRISPR-Cas9 genome editing to advance CAR T-cell therapy. AI algorithms offer unparalleled precision in identifying genetic targets, essential for enhancing the therapeutic efficacy of CAR T-cell treatments. This precision is critical for eliminating negative regulatory elements that undermine therapy effectiveness. Additionally, AI streamlines the manufacturing process, significantly reducing costs and increasing accessibility, thereby encouraging further research and development investment. A key benefit of AI integration is improved safety; by predicting and minimizing off-target effects, AI enhances the specificity of CRISPR-Cas9 edits, contributing to safer CAR T-cell therapy. This advancement is crucial for patient safety and broader clinical adoption. The convergence of AI and CRISPR-Cas9 has transformative potential, poised to revolutionize personalized immunotherapy. These innovations could expand the application of CAR T-cell therapy beyond hematologic malignancies to various solid tumors and other non-hematologic conditions, heralding a new era in cancer treatment that substantially improves patient outcomes.

Current progress in CRISPR-Cas systems for cancer

Cancer has been a primary contributor to morbidity and mortality worldwide. With an increasing trend of incidence and prevalence of cancer, progress has also been made in its treatment, starting from radiation and chemotherapy to immunotherapy and gene therapy. CRISPR-Cas technique, a promising gene editing tool, has been employed in cancer research for novel treatment regimens, identification of therapeutic targets, and unraveling the genetic mechanisms behind oncogenesis. CRISPR-based genome editing helped in identifying the roles of specific genetic factors linked to treatment resistance, metastasis, and cancer development. CRISPR allows the discovery of genes and treatment options through specifically interrupting tumor activators or activating tumor suppressor genes in cancer cells. Advancements in CRISPR technology, especially the use of immune cells like chimeric antigen receptor (CAR) T cells, has the potential to revolutionize personalized cancer treatment by precisely targeting and killing cancer cells. Furthermore, reactivating tumor suppressor genes makes cancer cells more susceptible to chemotherapy or immunotherapy. CRISPR-mediated genome editing can, hence, help to overcome resistance to traditional cancer treatments. The current manuscript covers that how is the CRISPR technology propelling revolutionary development in the field of cancer research, providing advance perspectives on the molecular causes of the disease and creating new lines for the development of more precise and potent cancer therapies.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

Precision oncology revolution: CRISPR-Cas9 and PROTAC technologies unleashed

Cancer continues to present a substantial global health challenge, with its incidence and mortality rates persistently reflecting its significant impact. The emergence of precision oncology has provided a breakthrough in targeting oncogenic drivers previously deemed "undruggable" by conventional therapeutics and by limiting off-target cytotoxicity. Two groundbreaking technologies that have revolutionized the field of precision oncology are primarily CRISPR-Cas9 gene editing and more recently PROTAC (PROteolysis TArgeting Chimeras) targeted protein degradation technology. CRISPR-Cas9, in particular, has gained widespread recognition and acclaim due to its remarkable ability to modify DNA sequences precisely. Rather than editing the genetic code, PROTACs harness the ubiquitin proteasome degradation machinery to degrade proteins of interest selectively. Even though CRISPR-Cas9 and PROTAC technologies operate on different principles, they share a common goal of advancing precision oncology whereby both approaches have demonstrated remarkable potential in preclinical and promising data in clinical trials. CRISPR-Cas9 has demonstrated its clinical potential in this field due to its ability to modify genes directly and indirectly in a precise, efficient, reversible, adaptable, and tissue-specific manner, and its potential as a diagnostic tool. On the other hand, the ability to administer in low doses orally, broad targeting, tissue specificity, and controllability have reinforced the clinical potential of PROTAC. Thus, in the field of precision oncology, gene editing using CRISPR technology has revolutionized targeted interventions, while the emergence of PROTACs has further expanded the therapeutic landscape by enabling selective protein degradation. Rather than viewing them as mutually exclusive or competing methods in the field of precision oncology, their use is context-dependent (i.e., based on the molecular mechanisms of the disease) and they potentially could be used synergistically complementing the strengths of CRISPR and vice versa. Herein, we review the current status of CRISPR and PROTAC designs and their implications in the field of precision oncology in terms of clinical potential, clinical trial data, limitations, and compare their implications in precision clinical oncology.

Personalized Medicine in Pancreatic Cancer: The Promise of Biomarkers and Molecular Targeting with Dr. Michael J. Pishvaian

Pancreatic cancer, with its alarming rising incidence, is predicted to become the second deadliest type of solid tumor by 2040, highlighting the urgent need for improved diagnostic and treatment strategies. Despite medical advancements, the five-year survival rate for pancreatic cancer remains about 14%, dropping further when metastasized. This review explores the promise of biomarkers for early detection, personalized treatment, and disease monitoring. Molecular classification of pancreatic cancer into subtypes based on genetic mutations, gene expression, and protein markers guides treatment decisions, potentially improving outcomes. A plethora of clinical trials investigating different strategies are currently ongoing. Targeted therapies, among which those against CLAUDIN 18.2 and inhibitors of Claudin 18.1, have shown promise. Next-generation sequencing (NGS) has emerged as a powerful tool for the comprehensive genomic analysis of pancreatic tumors, revealing unique genetic alterations that drive cancer progression. This allows oncologists to tailor therapies to target specific molecular abnormalities. However, challenges remain, including limited awareness and uptake of biomarker-guided therapies. Continued research into the molecular mechanisms of pancreatic cancer is essential for developing more effective treatments and improving patient survival rates.

Epigenetics in diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM) is a critical complication that poses a significant threat to the health of patients with diabetes. The intricate pathological mechanisms of DCM cause diastolic dysfunction, followed by impaired systolic function in the late stages. Accumulating researches have revealed the association between DCM and various epigenetic regulatory mechanisms, including DNA methylation, histone modifications, non-coding RNAs, and other epigenetic molecules. Recently, a profound understanding of epigenetics in the pathophysiology of DCM has been broadened owing to advanced high-throughput technologies, which assist in developing potential therapeutic strategies. In this review, we briefly introduce the epigenetics regulation and update the relevant progress in DCM. We propose the role of epigenetic factors and non-coding RNAs (ncRNAs) as potential biomarkers and drugs in DCM diagnosis and treatment, providing a new perspective and understanding of epigenomics in DCM.

Clinical Insights into Structure, Regulation, and Targeting of ABL Kinases in Human Leukemia

Chronic myeloid leukemia is a multistep, multi-lineage myeloproliferative disease that originates from a translocation event between chromosome 9 and chromosome 22 within the hematopoietic stem cell compartment. The resultant fusion protein BCR::ABL1 is a constitutively active tyrosine kinase that can phosphorylate multiple downstream signaling molecules to promote cellular survival and inhibit apoptosis. Currently, tyrosine kinase inhibitors (TKIs), which impair ABL1 kinase activity by preventing ATP entry, are widely used as a successful therapeutic in CML treatment. However, disease relapses and the emergence of resistant clones have become a critical issue for CML therapeutics. Two main reasons behind the persisting obstacles to treatment are the acquired mutations in the ABL1 kinase domain and the presence of quiescent CML leukemia stem cells (LSCs) in the bone marrow, both of which can confer resistance to TKI therapy. In this article, we systemically review the structural and molecular properties of the critical domains of BCR::ABL1 and how understanding the essential role of BCR::ABL1 kinase activity has provided a solid foundation for the successful development of molecularly targeted therapy in CML. Comparison of responses and resistance to multiple BCR::ABL1 TKIs in clinical studies and current combination treatment strategies are also extensively discussed in this article.

Detection of Parasites in the Field: The Ever-Innovating CRISPR/Cas12a

The rapid and accurate identification of parasites is crucial for prompt therapeutic intervention in parasitosis and effective epidemiological surveillance. For accurate and effective clinical diagnosis, it is imperative to develop a nucleic-acid-based diagnostic tool that combines the sensitivity and specificity of nucleic acid amplification tests (NAATs) with the speed, cost-effectiveness, and convenience of isothermal amplification methods. A new nucleic acid detection method, utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) nuclease, holds promise in point-of-care testing (POCT). CRISPR/Cas12a is presently employed for the detection of Plasmodium falciparum, Toxoplasma gondii, Schistosoma haematobium, and other parasites in blood, urine, or feces. Compared to traditional assays, the CRISPR assay has demonstrated notable advantages, including comparable sensitivity and specificity, simple observation of reaction results, easy and stable transportation conditions, and low equipment dependence. However, a common issue arises as both amplification and cis-cleavage compete in one-pot assays, leading to an extended reaction time. The use of suboptimal crRNA, light-activated crRNA, and spatial separation can potentially weaken or entirely eliminate the competition between amplification and cis-cleavage. This could lead to enhanced sensitivity and reduced reaction times in one-pot assays. Nevertheless, higher costs and complex pre-test genome extraction have hindered the popularization of CRISPR/Cas12a in POCT.

Cas-based bacterial detection: recent advances and perspectives

Persistent bacterial infections pose a formidable threat to global health, contributing to widespread challenges in areas such as food safety, medical hygiene, and animal husbandry. Addressing this peril demands the urgent implementation of swift and highly sensitive detection methodologies suitable for point-of-care testing and large-scale screening. These methodologies play a pivotal role in the identification of pathogenic bacteria, discerning drug-resistant strains, and managing and treating diseases. Fortunately, new technology, the CRISPR/Cas system, has emerged. The clustered regularly interspaced short joint repeats (CRISPR) system, which is part of bacterial adaptive immunity, has already played a huge role in the field of gene editing. It has been employed as a diagnostic tool for virus detection, featuring high sensitivity, specificity, and single-nucleotide resolution. When applied to bacterial detection, it also surpasses expectations. In this review, we summarise recent advances in the detection of bacteria such as Mycobacterium tuberculosis (MTB), methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli (E. coli), Salmonella and Acinetobacter baumannii (A. baumannii) using the CRISPR/Cas system. We emphasize the significance and benefits of this methodology, showcasing the capability of diverse effector proteins to swiftly and precisely recognize bacterial pathogens. Furthermore, the CRISPR/Cas system exhibits promise in the identification of antibiotic-resistant strains. Nevertheless, this technology is not without challenges that need to be resolved. For example, CRISPR/Cas systems must overcome natural off-target effects and require high-quality nucleic acid samples to improve sensitivity and specificity. In addition, limited applicability due to the protospacer adjacent motif (PAM) needs to be addressed to increase its versatility. Despite the challenges, we are optimistic about the future of bacterial detection using CRISPR/Cas. We have already highlighted its potential in medical microbiology. As research progresses, this technology will revolutionize the detection of bacterial infections.

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review

The advent of targeted therapies has led to tremendous improvements in treatment options and their outcomes in the field of oncology. Yet, many cancers outsmart precision drugs by developing on-target or off-target resistance mechanisms. Gaining the ability to resist treatment is the rule rather than the exception in tumors, and it remains a major healthcare challenge to achieve long-lasting remission in most cancer patients. Here, we discuss emerging strategies that take advantage of innovative high-throughput screening technologies to anticipate on- and off-target resistance mechanisms before they occur in treated cancer patients. We divide the methods into non-systematic approaches, such as random mutagenesis or long-term drug treatment, and systematic approaches, relying on the clustered regularly interspaced short palindromic repeats (CRISPR) system, saturated mutagenesis, or computational methods. All these new developments, especially genome-wide CRISPR-based screening platforms, have significantly accelerated the processes for identification of the mechanisms responsible for cancer drug resistance and opened up new avenues for future treatments.

CRISPR-Based Therapies: Revolutionizing Drug Development and Precision Medicine

With the discovery of CRISPR-Cas9, drug development and precision medicine have undergone a major change. This review article looks at the new ways that CRISPR-based therapies are being used and how they are changing the way medicine is done. CRISPR technology's ability to precisely and flexibly edit genes has opened up new ways to find, validate, and develop drug targets. Also, it has made way for personalized gene therapies, precise gene editing, and advanced screening techniques, all of which hold great promise for treating a wide range of diseases. In this article, we look at the latest research and clinical trials that show how CRISPR could be used to treat genetic diseases, cancer, infectious diseases, and other hard-to-treat conditions. However, ethical issues and problems with regulations are also discussed in relation to CRISPR-based therapies, which shows how important it is to use them safely and responsibly. As CRISPR continues to change how drugs are made and used, this review shines a light on the amazing things that have been done and what the future might hold in this rapidly changing field.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.

Applications of CRISPR Technology to Breast Cancer and Triple Negative Breast Cancer Research

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology has transformed oncology research in many ways. Breast cancer is the most prevalent malignancy globally and triple negative breast cancer (TNBC) is one of the most aggressive subtypes with numerous challenges still to be faced. In this work, we have explained what CRISPR consists of and listed its applications in breast cancer while focusing on TNBC research. These are disease modelling, the search for novel genes involved in tumour progression, sensitivity to drugs and immunotherapy response, tumour fitness, diagnosis, and treatment. Additionally, we have listed the current delivery methods employed for the delivery of CRISPR systems in vivo. Lastly, we have highlighted the limitations that CRISPR technology is subject to and the future directions that we envisage. Overall, we have provided a round summary of the aspects concerning CRISPR in breast cancer/TNBC research.

Engineered adult stem cells: Current clinical trials status of disease treatment

Regenerative medicine is an interdisciplinary field involving the process of replacing and regenerating cells/tissues or organs by integrating medicine, science, and engineering principles to enhance the intrinsic regenerative capacity of the host. Recently, engineered adult stem cells have gained attention for their potential use in regenerative medicine by reducing inflammation and modulating the immune system. This chapter introduces adult stem cell engineering and chimeric antigen receptor T cells (CAR T) gene therapy and summarises current engineered stem cell- and extracellular vesicles (EVs)-focused clinical trial studies that provide the basis for the proposal of a personalised medicine approach to diseases diagnosis and treatment.

Biodrug Delivery Systems: Do mRNA Lipid Nanoparticles Come of Age?

As an appealing alternative to treat and prevent diseases ranging from cancer to COVID-19, mRNA has demonstrated significant clinical effects. Nanotechnology facilitates the successful implementation of the systemic delivery of mRNA for safe human consumption. In this manuscript, we provide an overview of current mRNA therapeutic applications and discuss key biological barriers to delivery and recent advances in the development of nonviral systems. The relevant challenges that LNPs face in achieving cost-effective and widespread clinical implementation when delivering mRNA are likewise discussed.