BUFFALO, NY - December 11, 2024 – A #news feature on the #research paper “Next-generation cell-penetrating antibodies for tumor targeting and RAD51 inhibition” by Rackear et al. was #published in Oncotarget's Volume 15 on November 22, 2024, titled “Advancements in cell-penetrating monoclonal antibody treatment." This new publication by Sai Pallavi Pradeep and Raman Bahal from the Department of Pharmaceutical Sciences at the University of Connecticut highlights significant advancements in monoclonal antibody (mAb) therapies. The focus is on the 3E10 antibody, originally derived from autoimmune mouse studies in systemic lupus erythematosus. Unlike traditional mAbs, which struggle to reach intracellular targets, this cell-penetrating antibody targets cancer cells by addressing a major limitation of current therapies. By targeting RAD51, a key intracellular protein involved in DNA repair, the 3E10 antibody shows great promise for cancer treatment, particularly in cancers with defective DNA repair pathways. mAbs have already changed the landscape of cancer therapy, offering treatments that are more targeted and have fewer side effects compared to chemotherapy. However, current therapies are limited since mAbs only target proteins on the surface of cancer cells. This research pushes the boundaries by demonstrating how 3E10 antibodies can penetrate cells and access their internal molecules. This unique capability expands the potential of mAb therapies and targeted cancer treatments. Different humanized versions of the 3E10 antibody were created and carefully tested. Some versions were particularly effective at blocking RAD51, while others showed promise for carrying other therapeutic molecules like genetic material into the cancer cells. This flexibility means that 3E10 could be used to treat different cancer types and deliver various therapeutic molecules directly into tumor cells. This progress offers exciting new possibilities for treating cancer tumors that are resistant to conventional therapies. In conclusion, the 3E10 antibody’s dual function—targeting DNA repair pathways and delivering therapeutic molecules—positions it as a transformative tool in cancer research and targeted cancer treatments. DOI - https://doi.org/10.18632/oncotarget.28674 Correspondence to - Raman Bahal - raman.bahal@uconn.edu Video short - https://www.youtube.com/watch?v=3uMdPvThFHA Sign up for free Altmetric alerts about this article: https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28674 Subscribe for free publication alerts from Oncotarget: https://www.oncotarget.com/subscribe/ Keywords - cancer, monoclonal anti-bodies, cell penetration, nucleic acid delivery, 3E10 About Oncotarget Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science). To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957

BUFFALO, NY - December 9, 2024 – A new #research paper was #published in Oncotarget's Volume 15 on November 22, 2024, entitled “Computed tomography-based radiomics and body composition model for predicting hepatic decompensation." Mayo Clinic researchers Yashbir Singh, John E. Eaton, Sudhakar K. Venkatesh, and Bradley J. Erickson have developed an innovative AI tool to predict hepatic decompensation in individuals with primary sclerosing cholangitis (PSC). PSC is a chronic disease that damages the bile ducts and can lead to liver failure. Hepatic decompensation marks a critical stage of advanced liver disease, and clinicians have long faced challenges in predicting who is at risk. The Mayo Clinic's new AI tool addresses this gap by combining body fat and muscle composition data with insights extracted from computed tomography (CT) scans using computational radiomics. By analyzing these tissues, the AI model identifies patterns linked to an increased risk of liver failure. The study involved 80 PSC patients, including 30 with hepatic decompensation, 30 without, and 20 patients in an external validation set. The AI model achieved impressive results, correctly identifying at-risk patients with 97% accuracy. By recognizing these risks early, clinicians may be able to intervene sooner and improve patient outcomes. While the study focused on PSC, the team emphasized the broader implications of their work. “It may hold promise for the detection of other PSC-related complications, such as cholangiocarcinoma, as well as applications in more prevalent chronic liver diseases like non-alcoholic fatty liver disease (NAFLD).” This non-invasive, data-driven approach offers a powerful way to assess health risks and provide more tailored treatments. Despite the promising findings, the researchers acknowledge the limitations of the study, which include a limited sample size and a single-center design. “However, further research is necessary to validate our findings on a large-scale, independent dataset, ensuring the robustness and generalizability of the model.” In conclusion, this study shows how detailed information from CT scans can help clinicians predict severe liver problems in patients with PSC. By identifying hidden patterns in the images, they can better understand risks and create personalized treatment plans. This approach could improve care for PSC and other long-term liver diseases. DOI - https://doi.org/10.18632/oncotarget.28673 Correspondence to - Bradley J. Erickson - bje@mayo.edu Video short - https://www.youtube.com/watch?v=QCekNtYni4w Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28673 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, radiomics, body composition, machine learning, primary sclerosing cholangitis, computer tomography About Oncotarget Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science). To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh MEDIA@IMPACTJOURNALS.COM

Dive into the world of small cell lung cancer, one of the most aggressive forms of lung cancer. Recent advancements in precision medicine reveal critical biomarkers like DLL3 and TTF1 that could transform treatment strategies. These groundbreaking findings promise enhanced diagnostic accuracy and better patient outcomes, addressing the urgent need for new therapies. With less than 5% survival beyond five years, the research highlights the race against time for improved interventions.

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Discover how mesenchymal stem cells (MSCs) could revolutionize cancer immunotherapy while presenting intriguing challenges. These remarkable cells have the ability to locate tumors and deliver treatments directly, potentially enhancing outcomes and minimizing side effects. Yet, they may unintentionally promote tumor growth under certain conditions. The discussion also highlights the importance of personalized MSC therapies for safer and more effective treatments. Clinical trials are stepping into the future of cancer care, exploring the transformative potential of MSCs.

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Dive into the fascinating world of small cell lung carcinoma as researchers explore the role of DLL3 and its connections to the Notch receptor. Discover how the expressions of ASC1 and TTF-1 could serve as predictive markers for tumor outcomes. The conversation delves into the implications of these findings for future precision medicine approaches. Plus, gain insights into the molecular mechanisms behind SCLC and the exciting potential for improved treatment strategies.

Discover how topological data analysis (TDA) is revolutionizing radiology by tackling bias in AI diagnostic tools. Researchers highlight TDA's ability to reveal complex patterns in medical images, such as the unique shapes of blood vessels. This innovative technique promises to enhance the fairness and accuracy of AI systems, leading to more reliable medical diagnoses. Tune in for insights on making medical imaging smarter and more equitable!