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Join us on Tuesday, June 18, from 1:00 pm - 2:00 pm EDT for a webinar highlighting new data in breast cancer and immunotherapy monitoring presented at ASCO 2024. In a breast cancer study with The Royal Marsden, NeXT Personal® found that 100% of patients with undetectable residual disease in repeat testing did not have cancer recurrence. ctDNA detection correctly identified disease recurrence with a 15-month median lead time over imaging detection in all 14 patients that relapsed. Learn more about this study and other data from ASCO, including: - Immunotherapy treatment monitoring, pan-cancer with prognostic and predictive results - Therapy monitoring in patients with gastrointestinal (GI) cancers Some of our key partners and collaborators include The Royal Marsden NHS Foundation Trust, The Institute of Cancer Research, Dana-Farber Cancer Institute, Duke Cancer Institute , and Vall d'Hebron Institute of Oncology (VHIO). Register today: https://bit.ly/4aUaRD7 #PrecisionOncology #Liquidbiopsy #BreastCancer #GastrointestinalCancer #ctDNA #ASCO24

Precision medicine for cancer is rapidly evolving to integrate a multi-omics approach to understand cancer progression. How do high-throughput genomics technology provide actionable insights to prioritize research and therapies? Check out this ESMO review. https://lnkd.in/g2Et4zDx #oncology #cancerresearch #genomics #multiomics

Researchers at UC San Diego have achieved a significant advancement in oncology by employing machine learning technology to predict chemotherapy resistance in cancer patients. This innovative approach enables medical professionals to foresee which individuals may not benefit from chemotherapy, thus facilitating the creation of customized treatment regimens that cater to the unique requirements of each patient. The utilization of machine learning in this context not only minimizes the potential for unnecessary patient discomfort but also enhances the likelihood of successful therapeutic outcomes. The implications of this research are far-reaching, suggesting that machine learning could transform the healthcare industry by improving the precision and efficacy of treatments for a broad spectrum of conditions. Read the full article: https://ow.ly/bPcq50QxRWU

New open-source method to improve decoding of single-cell data: Researchers have developed a new open-source computational method, dubbed Spectra, which improves the analysis of single-cell transcriptomic data. By guiding data analysis in a unique way, Spectra can offer new insights into the complex interplay between cells — like the interactions between cancer cells and immune cells, which are critical to improving immunotherapy treatments. #ScienceDaily #Technology

Artificial intelligence is poised to revolutionize cancer management through applications in medical imaging, genomic analysis, and precision treatment selection. the_biotechtrailbalzer (Momena) recently published a blog exploring the transformative role AI is playing in the realm of oncology, from accelerating diagnostic workflows to facilitating a shift towards personalized, data-driven care paradigms. The blog details how AI algorithms excel at detecting subtle patterns across vast medical imaging datasets, opening doors for earlier detection. It also outlines how machine learning is empowering precision medicine approaches through insights into cancer subtypes and biomarker correlations with targeted therapies. While AI shows tremendous ability to enhance outcomes, the blog discusses important considerations around ethical deployment, data representative, algorithm accountability and ongoing human oversight that must accompany integration into clinical practice. If responsibly advanced, artificial intelligence technology holds tremendous promise to catalyze new paradigms advancing both cancer research and patient care. Read the full blog here: [https://lnkd.in/da7JG4yA] and learn how artificial intelligence is transforming oncology for the 21st century and beyond. Comments and discussion welcome.

Tom- very exciting to collaborate with you on this important study and the APOLLO program. There are very provocative results from the work IMO: -        most notably the HRD work and the BMI-1 finding you uncovered- a potential new therapeutic target for HRP tumors (and potential synergy with PARPi) and for patients with overall worse prognosis! -       that LMD enrichment of tumor epithelium may improve the coverage of neoepitopes that suggests that this workflow may better support personalized T-cell immunotherapy workflows -        the verification that historical expression-based tumor types are largely reflective of tumor purity and that multiple prognostically relevant proteins are actually stromal in origin.: MES subtype are really just tumors that have a lot of stroma but at the molecular level they are DIF… -       That bulk-tumor based analysis missed important biology and targetable candidates: these bulk tumor-based inputs have underpinned many well-known studies and beg the question: what did they miss? A lot perhaps….as they say “you don’t know what you don’t know” I am glad the field is finally starting to recognize the value  and really necessity of upfront cellular enrichment prior to these comprehensive molecular profiling studies. 

Artificial intelligence (A.I.) and machine learning (ML) are designed and trained to aid oncology doctors and health professionals in treating patients with cancer. These systems are extremely valuable, as they can make the diagnosis and treatment process faster and more accurate. Read it here. https://lnkd.in/dcfPZKjQ Experience our refreshed website at https://betoparedes.com/ and stay connected to our latest endeavors. Explore the new look and stay informed about our initiatives! #OncologyAdvancements #FutureOfCancerCare #MedicalBreakthroughs #PrecisionMedicine #GenomicResearch #Nanomedicine #NextGenCancerTherapies #BetoParedesFamilyofCompanies

Webinar: Capillary electrophoresis in Cancer research today - Utility of fragment analysis in the era of NGS-based translational research and clinical molecular diagnostics. Join this webinar to learn how Capillary Electrophoresis (CE) complements sequencing technologies, such as Next generation sequencing, that are being used by cancer researchers today. Additionally, Dr Somak Roy, of Cincinnati Children’s Hospital, will describe why he has chosen capillary electrophoresis for his clinical testing research. Learning Objectives: - Understand the use of Sanger sequencing and fragment analysis through capillary electrophoresis relative to other sequencing technologies, such as next generation sequencing (NGS). - Workflow overview, key features and benefits of fragment analysis. - Understand the utility of fragment analysis in translational research oncology molecular diagnostics. - Benefits of fragment analysis for detection of large complex Indel variants plus orthogonal confirmation. Register now: http://spr.ly/6049udKQW #Capillary_Electrophoresis #NGS #Webinar #ThermoFisher #Cancer_Research #clinical #molecular_diagnostics #Fragment_Analysis #Learning #Healthcare #Register_Now #GSG #Clinical_Testing

Machine Learning Can Spot Tumor-Reactive TCRs, Speed Immunotherapies According to scientists at the DKFZ German Cancer Research Center and the Universitätsmedizin Mannheim, predicTCR, , a machine learning classifier, can identify individual tumor-reactive tumor-infiltrating lymphocyte (TILs) in an antigen-agnostic manner based on single-TIL RNA sequencing.

🤖 AI —— Canacer Predictor 👀 Cancer of unknown primary (CUP) is a kind of cancer that cannot be traced back to its original location, accounting for 3-5% of all cancers. There are no established targeted therapeutics for CUP, resulting in generally poor outcomes. OncoNPC is a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors from three universities spanning 22 cancer types. Oncology NGS-based primary cancer-type classifier (OncoNPC) earned a weighted F1 score of 0.942 for high confidence predictions (≥0.9) on held-out tumor samples, accounting for 65.2% of all held-out samples. View more: https://lnkd.in/gBXkt3NP #ai #cancer #cancerresearch