At #ASCO24 earlier this year, our collaborators presented impactful data highlighting NeXT Personal®, including two podium presentations! If you want to learn more about these data in breast cancer (in collaboration with The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust) and immunotherapy monitoring (in collaboration with Vall d'Hebron Institute of Oncology (VHIO)), check out our webinar with Endpoints News. In the 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. Watch the recording to learn more about the data presented at ASCO: https://bit.ly/4aUaRD7. #PrecisionOncology #Liquidbiopsy #BreastCancer #ctDNA #ImmunoTherapy
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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 no detectable 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
New ASCO data: Early detection with WGS-based ctDNA tracking up to 1800 variants - Endpoints Webinars
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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
UC San Diego Scientists Pioneer Machine Learning to Work on Predicting Cancer's Chemotherapy Resistance
tomorrow.bio
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Co-Director, Center for Applied Proteomics and Molecular Medicine University Professor, George Mason University
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.
Interested in the ovarian cancer tumor tissue microenvironment? Check out our use of laser microdissection and deep proteogenomics of the ovarian cancer tissue microenvironment that revealed new clinically relevant insights...at cohort scale. More to come from the Applied Proteogenomic Organizational Learning and Outcomes (APOLLO) research network! Videobyte: https://lnkd.in/dYf8TfnF Article: https://lnkd.in/d76BwGm8 #ovariancancer #proteogenomics #multiomics #cancermoonshot #APOLLO #APOLLO2 #massspectrometry #teamscience #lasermicrodissection #Inovahealthsystem #USUHS #HJF
Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities - npj Precision Oncology
nature.com
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🔔 Dr. Davey Daniel discusses real-world evidence and precision medicine with AJMC - The American Journal of Managed Care. ⚖ "In every point in technology, there's a tipping point, a point where we understand this is the new standard of care. I think that we're on the cusp; we've long understood that NGS [next-generation sequencing] testing, particularly with multiple targets now approved across multiple tumor types, that NGS is a standard of care in advanced cancer. 🥁 I think we're at a tipping point in understanding MRD [minimal residual disease] testing and I think that is where real-world evidence may help me understand better over time which patients benefit from that information, but I do think further testing and prospective trials are going to be necessary." 👀 See the entire interview. https://lnkd.in/eSzVkg2f
Dr Davey Daniel Navigates Precision Medicine With Real-World Evidence
ajmc.com
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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.
Machine Learning Can Spot Tumor-Reactive TCRs, Speed Immunotherapies
genengnews.com
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#liquidbiopsy #cancerresearch #oncology https://lnkd.in/gaHbCekQ Abstract Over the past 10 years, circulating tumor cells (CTC) and circulating tumor DNA (ctDNA) have received enormous attention as new #biomarkers and subjects of translational research. Although both biomarkers are already used in numerous clinical trials, their clinical utility is still under investigation with promising first results. Clinical applications include early #cancer detection, improved cancer staging, early detection of relapse, real-time monitoring of therapeutic efficacy, and detection of therapeutic targets and resistance mechanisms. Here, we propose a conceptual framework of CTC and ctDNA assays and point out current challenges of CTC and ctDNA research, which might structure this dynamic field of translational cancer research.
Liquid Biopsy: From Discovery to Clinical Application
aacrjournals.org
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👨🔬 Io9 AI-Driven Digital Pathology Test Shows Potential to Inform Breast, Ovarian Cancer Therapy After a recent validation study in the Journal of Clinical Oncology, io9, Inc. is now seeking funding and a pharma partner to advance its deep learning-based classifier DeepHRD into a companion diagnostic for identifying patients eligible for PARP inhibitors. Developed on Io9's OncoGaze AI platform, DeepHRD aims to provide a quicker and more cost-effective solution compared to NGS-based tests, offering a potential alternative for cancer patients. In their pursuit, Io9 plans to pair DeepHRD with a PARP inhibitor initially approved for ovarian cancer treatment. This move aligns with the FDA's approvals of companion diagnostics like Myriad Genetics' MyChoice CDx and Foundation Medicine's FoundationOne CDx, which evaluate HRD in ovarian cancer patients. By leveraging AI technology, Io9 and researchers from the University of California, San Diego aim to expedite treatment initiation and reduce costs, especially for patients with limited access to NGS testing. The current landscape highlights the time and cost inefficiencies associated with traditional genomic testing, emphasizing the need for innovative solutions like DeepHRD. With potential benefits in turnaround time and accessibility, Io9's development could revolutionize the diagnostic process for cancer patients worldwide. #AI #CancerResearch #PrecisionMedicine
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🧬 AI Tool Using Single-Cell Data Has Promise for Optimally Matching Cancer Drugs to Patients 🔬 Precision oncology has made significant strides, with many tests now available to match patients to targeted treatments based on genetic changes. However, predicting treatment responses still poses challenges. A promising new approach involves analyzing a tumor’s RNA in addition to its DNA. 🔸 Exciting Development: A recent study introduces PERCEPTION (PERsonalized single-Cell Expression-based Planning for Treatments In ONcology), an AI-driven tool developed by an #NIH-led team. This tool aims to fine-tune predictions of a patient’s treatment responses from bulk RNA data by focusing on single cells. 🔸 Key Findings: 🔷 PERCEPTION predicts the success of targeted treatments against cell lines with an accuracy reflected by an AUC score of about 0.8. 🔷 The model successfully predicted treatment responses in clinical trial data for patients with multiple myeloma and breast cancer. 🔷 It was able to predict the development of resistance to treatment in non-small cell lung cancer patients. 🔸 Impact: Single-cell RNA sequencing data could revolutionize how doctors match patients to optimal cancer treatments. As data becomes more widely available, the accuracy of this technique is expected to improve. 🔗 Read the full article: https://lnkd.in/gaVjMkNd 🔗 For more details on the study, check out the abstract on PubMed: https://lnkd.in/gB4iwgVS #HealthcareInnovation #PrecisionOncology #CancerResearch #HealthTech #AIinHealthcare The National Institutes of Health ♻️ Repost this if you found it interesting. Share with anyone interested in the latest advancements in AI or cancer treatment! ♻️ 🌐 Swenson Pharmacy Advisors (SPA) Website: https://lnkd.in/gWFbVAjz
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Nordic Diagnostics Head, Oncology at AstraZeneca I AI I Precision Medicine Oncology I Biomarker I Targeted Therapy I Medical Affairs
On the way to participate in the Association For Molecular Pathology meeting, Europe 2023. Looking forward to learning more about novel clinical cancer biomarkers and cutting-edge technologies that are being used for biomarker discovery, biomarker testing and cancer patient identification. Also, engaging with and learning from clinical experts and researchers contributing to precision oncology and diagnostics with the aim to make positive impact on cancer patient outcomes. If anyone in the network is also participating the meeting, let's meet and share insights. #biomarkers #precisiononcology #oncology #diagnostic https://lnkd.in/d5DvpCqn
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Chinese Medical Journal Review highlights novel pathogenic mechanisms and therapeutic potentials in cancer treatment targeting internal N6-methyladenosine and N7-methylguanine
Chinese Medical Journal Review highlights novel pathogenic mechanisms and
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