Latest news with #TCGA
Associated Press
29-05-2025
- Health
- Associated Press
Chinese Neurosurgical Journal Report Identifies FAM111B as Key Molecular Driver of Glioma Progression
Study shows FAM111B overexpression enhances glioma malignancy via PI3K/AKT pathway, suggesting a novel treatment target BEIJING, CHINA, May 29, 2025 / / -- Gliomas are among the deadliest brain tumors, with limited treatment options and poor survival rates. Scientists from China identified FAM111B, a DNA-repair-associated protein, as a key driver of glioma progression. The study shows that FAM111B overexpression enhances tumor growth and aggressiveness by activating the PI3K/AKT pathway. This is the first research to link FAM111B to gliomas, offering a promising new biomarker and therapeutic target for this intractable disease. Gliomas are the most prevalent and aggressive form of primary brain tumors in adults, with dismal survival rates despite surgery, radiation, and chemotherapy. Scientists continue to search for molecular drivers that could serve as new therapeutic targets. Now, researchers led by Dr. Quan Du from Zhejiang Chinese Medical University and Westlake University in China have identified a promising candidate: a protein known as FAM111B. 'Our findings revealed that FAM111B affected glioma malignancy by modulating the PI3K/AKT pathway,' highlights lead researcher Dr. Du. 'This presents a new potential avenue for therapeutic intervention in the treatment of glioma.' The study, published on May 19 2025, in the Chinese Neurosurgical Journal, is the first to examine the role of FAM111B in gliomas. Prior research had linked FAM111B to cell cycle regulation, DNA repair, and fibrosis-related diseases. However, its function in brain cancer was previously unknown. Using genomic databases including The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), the research team found that FAM111B expression is significantly elevated in glioma tissues compared to healthy brain tissue. Moreover, higher expression levels correlated with older patient age, more advanced tumor grade, and poorer clinical outcomes—including reduced overall survival and disease-free survival. The authors confirmed these findings experimentally. Glioma cell lines and tumor samples showed significantly higher levels of FAM111B protein compared to normal tissues. When FAM111B was overexpressed in glioma cells, their proliferation, invasion, and migration dramatically increased. Conversely, knocking down FAM111B suppressed these malignant traits. Further, in vivo experiments using mice confirmed FAM111B's role in promoting tumor growth. Mice injected with glioma cells overexpressing FAM111B developed significantly larger and heavier tumors than controls. To uncover the molecular mechanism behind these effects, the team conducted pathway enrichment analysis. Results pointed strongly to the PI3K/AKT signaling cascade—a pathway long associated with tumor growth and resistance to therapy. Further tests showed that FAM111B overexpression increased phosphorylation of PI3K and AKT, while silencing the protein had the opposite effect. 'FAM111B regulates glioma cell malignant features via the PI3K/AKT pathway,' the Dr. Du wrote. 'These results support the hypothesis that FAM111B influences the malignant features of glioma cells primarily through the PI3K/AKT pathway.'Treatment with a PI3K inhibitor reversed the aggressive behavior caused by FAM111B overexpression, strongly suggesting a direct regulatory role. This not only strengthens the case for FAM111B as a key driver of glioma but also highlights it as a promising therapeutic target. The study's strength lies in its comprehensive approach, combining bioinformatics, cell culture, animal modeling, and molecular assays. However, the authors acknowledge the study's limitations, particularly the small patient sample size and the need for broader validation across multiple research centers. Nonetheless, the implications are significant. Identifying FAM111B as an independent prognostic marker and a key modulator of a known cancer pathway adds a valuable tool to the glioma research arsenal. While therapies targeting the PI3K/AKT pathway already exist, this research may pave the way for more precise, FAM111B-guided interventions. 'FAM111B has emerged not only as a critical biomarker for the development of glioma,' Dr Du concludes, 'but also as a promising novel target for therapeutic intervention.' As researchers work to solve the complex puzzle of brain cancer, FAM111B may soon take center stage. *** Reference Title of original paper: The role of FAM111B in the malignant progression and molecular regulation of human glioma through the PI3K/Akt pathway Journal: Chinese Neurosurgical Journal DOI: Yi Lu Chinese Neurosurgical Journal +86 10 5997 8478 [email protected] Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. 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Business Wire
13-05-2025
- Business
- Business Wire
Pluto Bio Raises $3.6M to Expand AI-Powered Multi-Omics Analysis Platform for Pharma
DENVER--(BUSINESS WIRE)-- Pluto Bio, the leading, AI-powered platform for computational biology, today announced it has raised $3.6 million in new funding to accelerate growth and scale adoption of its enterprise-grade platform among global pharma companies. The round includes participation from new investor Kickstart, alongside existing investor Silverton Partners and existing angel investors. Pluto is fueling its mission to make high-dimensional biological data accessible to scientists discovering new therapies for unmet needs. Pluto's platform serves as the industry's most powerful "canvas for computational biology" – a secure, collaborative environment where scientists at therapeutics companies explore large, high-dimensional datasets, run auto-scaling bioinformatics pipelines, and generate publication-ready visualizations tailored to their specific scientific questions without writing code. As large language models (LLMs) reshape drug discovery and development, Pluto is at the forefront of empowering biology and translational medicine teams to harness AI in a manner that complements the vital insights from human scientists. 'AI is transforming how we interrogate biology,' said Dalton Wright, General Partner at Kickstart. 'What impressed us about Pluto is how it puts the power of LLMs into the hands of domain expert scientists – biologists, translational researchers, and discovery teams – by giving them an instantly intuitive interface for expressing scientific questions faster and in a way that increases scientific rigor and reproducibility.' Pluto enables scientists to run proprietary analyses on both public datasets – such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Gene Expression Omnibus (GEO) – and internal, raw data using industry-validated, auto-scaling pipelines built for scale, security, and reproducibility. Scientists use Pluto for critical steps in early-mid drug discovery, such as target discovery, mechanism of action (MoA) studies, translational medicine, and biomarkers/precision medicine research. The platform supports the wide range of assays used for investigating targets and biomarkers, including genomics, transcriptomics, epigenetics, and proteomics. The platform is trusted by mid-market and enterprise clients across North America and Europe, with users spanning discovery biology, translational research, and computational teams. As a collaborative layer over drug discovery data infrastructure, Pluto integrates seamlessly into existing scientific workflows and supports data governance needs for regulated environments. 'With this funding, we're fueling our mission to make high-dimensional biological data accessible to the people who deeply understand the underlying biology and unmet therapeutic needs,' said Dr. Rani Powers, Founder and CEO of Pluto. 'Teams using Pluto are already demonstrating that innovation in drug discovery accelerates when scientists can directly generate insights from complex data. Our platform was built to make that vision real, whether for a team of ten scientists or an enterprise organization with hundreds.' The new capital will support continued development of Pluto's AI agents and copilots, expansion of integrations with other R&D tools, enable build-out of commercial roles, and accelerate deployment across therapeutic areas and modalities. About Pluto Pluto Bio is the leading AI-powered data management, analysis, and visualization platform for multi-omics. Designed for pharma, Pluto enables scientists to analyze, visualize, and collaborate on large-scale biological data without writing code. From target discovery to translational medicine, Pluto empowers R&D and translational science teams to turn complex datasets into insights faster and with greater confidence. Learn more at
Yahoo
13-05-2025
- Business
- Yahoo
Pluto Bio Raises $3.6M to Expand AI-Powered Multi-Omics Analysis Platform for Pharma
Funding led by new investor Kickstart, with continued support from Silverton Partners and other investors DENVER, May 13, 2025--(BUSINESS WIRE)--Pluto Bio, the leading, AI-powered platform for computational biology, today announced it has raised $3.6 million in new funding to accelerate growth and scale adoption of its enterprise-grade platform among global pharma companies. The round includes participation from new investor Kickstart, alongside existing investor Silverton Partners and existing angel investors. Pluto's platform serves as the industry's most powerful "canvas for computational biology" – a secure, collaborative environment where scientists at therapeutics companies explore large, high-dimensional datasets, run auto-scaling bioinformatics pipelines, and generate publication-ready visualizations tailored to their specific scientific questions without writing code. As large language models (LLMs) reshape drug discovery and development, Pluto is at the forefront of empowering biology and translational medicine teams to harness AI in a manner that complements the vital insights from human scientists. "AI is transforming how we interrogate biology," said Dalton Wright, General Partner at Kickstart. "What impressed us about Pluto is how it puts the power of LLMs into the hands of domain expert scientists – biologists, translational researchers, and discovery teams – by giving them an instantly intuitive interface for expressing scientific questions faster and in a way that increases scientific rigor and reproducibility." Pluto enables scientists to run proprietary analyses on both public datasets – such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Gene Expression Omnibus (GEO) – and internal, raw data using industry-validated, auto-scaling pipelines built for scale, security, and reproducibility. Scientists use Pluto for critical steps in early-mid drug discovery, such as target discovery, mechanism of action (MoA) studies, translational medicine, and biomarkers/precision medicine research. The platform supports the wide range of assays used for investigating targets and biomarkers, including genomics, transcriptomics, epigenetics, and proteomics. The platform is trusted by mid-market and enterprise clients across North America and Europe, with users spanning discovery biology, translational research, and computational teams. As a collaborative layer over drug discovery data infrastructure, Pluto integrates seamlessly into existing scientific workflows and supports data governance needs for regulated environments. "With this funding, we're fueling our mission to make high-dimensional biological data accessible to the people who deeply understand the underlying biology and unmet therapeutic needs," said Dr. Rani Powers, Founder and CEO of Pluto. "Teams using Pluto are already demonstrating that innovation in drug discovery accelerates when scientists can directly generate insights from complex data. Our platform was built to make that vision real, whether for a team of ten scientists or an enterprise organization with hundreds." The new capital will support continued development of Pluto's AI agents and copilots, expansion of integrations with other R&D tools, enable build-out of commercial roles, and accelerate deployment across therapeutic areas and modalities. About Pluto Pluto Bio is the leading AI-powered data management, analysis, and visualization platform for multi-omics. Designed for pharma, Pluto enables scientists to analyze, visualize, and collaborate on large-scale biological data without writing code. From target discovery to translational medicine, Pluto empowers R&D and translational science teams to turn complex datasets into insights faster and with greater confidence. Learn more at View source version on Contacts Media Contactpress@
HKFP
23-04-2025
- Health
- HKFP
University of Hong Kong medical school develops world's first AI model for thyroid cancer diagnosis
The University of Hong Kong's medical school (HKUMed) has developed the world's first AI model for diagnosing thyroid cancer, showing over 90 per cent accuracy and slashing clinicians' pre-consultation time by 50 per cent. The new AI model, trained to analyse clinical documents, can classify the stage and risk category of thyroid cancer, HKUMed announced on Wednesday. The medical school said its model is more efficient than the traditional manual integration of clinical information conducted through the systems of the American Joint Committee on Cancer (AJCC) and the American Thyroid Association (ATA). Researchers trained the AI model with pathology reports of 50 thyroid cancer patients from The Cancer Genome Atlas Programme (TCGA) using four offline open-source large language models, including Google's Gemma and Meta's Llama. The team then checked the results against pathology reports from 289 TCGA patients, as well as 35 pseudo-cases created by endocrine surgeons. The accuracy exceeded 90 per cent in classifying cancer stages and risk categories, HKUMed said. The AI assistant provides high accuracy in extracting and analysing information from complicated pathology reports, operation records, and clinical notes, said Matrix Fung, chief of endocrine surgery at HKUMed and one of the project's two leading researchers. The model can also be 'readily integrated' into the public and private healthcare sectors, as well as local and overseas research institutes, Fung said. '[O]ur AI model also dramatically reduces doctors' preparation time by almost half compared to human interpretation,' he said, adding that 'doctors will have more time to counsel with their patients.' Professor Joseph Wu, another HKUMed academic leading the research, pointed out the model's offline capability as a major advantage, allowing doctors to use it without having to share or upload patients' information online, thus protecting patient privacy. HKUMed said the next step would be reviewing the performance of the AI assistant with a large amount of real-world patient data. The model can be deployed in real clinical settings and hospitals once the results are validated, it said.
Yahoo
28-03-2025
- Health
- Yahoo
Institute scientists are optimizing data for more precise cancer diagnosis, treatment
Mar. 27—David Guinovart, PhD, and Eric Rahrmann, PhD, assistant professors at The Hormel Institute, University of Minnesota, are the recipients of a two-year, $100,000 Data Science Initiative (DSI) Seed Grant from the University of Minnesota. Their funded project, MOOBI: Multi-Omics Optimization-Based Integration for Enhanced Cancer Research Datasets, aims to tackle key challenges in integrating multi-omics data for more precise breast cancer diagnosis and therapeutic targets. Multi-omics is a biological analysis approach that makes use of multiple types of datasets. In this project, Guinovart and team are leveraging data made publicly available from The Cancer Genome Atlas (TCGA). They aim to develop a new, integrated dataset with the ultimate goal of enhancing breast cancer subtype classifications and identifying biomarkers for diagnosis and therapeutic targets. This means patients could have a higher likelihood of being matched with the right treatment options for the right cancer as early as possible. With his background in applied mathematics, Guinovart sought Rahrmann's expertise in cancer biology and metastasis for this interdisciplinary collaboration. "The way we see this collaboration is an integrative process: we develop an idea, Eric will offer his ideas, and we will adjust as needed. It keeps the model not only accurate, but also fresh," Guinovart said. "In this particular case, we are trying to add more information to this already available data, developing a model that can respond to real-life problems more effectively. I think this could be used for other opportunities, but we will also have a clean dataset that has been validated at another level. We will also be able to share this ready-to-use data to fit other models, ideas, or research." With Guinovart an applied mathematician and Rahrmann an expert in developmental biology, cancer biology and metastasis, this endeavor is a collaboration that helps keep the ideas considered and models developed accurate and fresh. "Too often, we only look at the tip of the iceberg and ignore the rest of the data. Essentially, we're going back with these new, innovative approaches to revisit old questions and ultimately identify new biomarkers and therapeutic targets," Rahrmann said. The project also holds potential for broader applications in the future. Rahrmann said that the data may be helpful in identifying transition phases of cancer toward hybrid cancer types at critical times of disease progression. The TCGA has a treasure trove of data — 2.5 petabytes, in fact, or 2.5 million gigabytes — that has been gathered over decades of research. With so much information at hand, projects like this can find new connections and applications that have yet to be discovered. Once the research team has its refined, well-structured data, they will feed that data to machine learning models that use multiple parameters for optimized classification to minimize false positives in cancer diagnosis as much as possible. Post-Doctoral Associate Mohammed Qaraad, PhD, and Senior Scientist Kayum Alam, PhD, are also contributing to the project.
