SOPHiA GENETICS’ Post

We're gearing up for the American Association for Cancer Research (AACR) Annual Meeting, which is just a couple of weeks away! Join us on April 8th to delve into groundbreaking research with three poster presentations by Smita Agrawal, Executive Director of ConcertAI. In the morning session on April 8th from 9:00 AM – 12:30 PM, we'll explore a retrospective analysis uncovering the response of STK11+ aNSCLC patients to ICIs, revealing promising avenues for enhancing treatment outcomes in this subgroup. Additionally, we'll delve into the distribution of KRAS mutations and their impact on patient outcomes in pancreatic cancer (PC) with insights from ConcertAI’s Genome360TM PC dataset. In the afternoon session from 1:30 PM – 5:00 PM, join us as we dive into the genomic landscape of HR+/HER2- mBC patients who relapsed or were refractory to AIs, identifying key biomarkers and high-frequency mutations associated with resistance. These findings pave the way for the development of innovative therapies to overcome AI resistance. Don't miss out on these impactful presentations! Learn more about these presentations: https://lnkd.in/eafmt7H9 https://lnkd.in/eMPQFD2a https://lnkd.in/eNPSu_r3 #AACR2024 #CancerResearch #PosterPresentations

What about this for innovation in early ovarian cancer detection? Researchers from the Georgia Institute of Technology have developed machine learning models to detect ovarian cancer using patient metabolic profiles. This approach addresses the issue of heterogeneity amongst ovarian cancer patients, which has obstructed early detection mechanisms due to limited common biomarkers. The models, which distinguished cancerous samples with a 93% accuracy, based on testing over 564 samples, could aid in boosting survival rates that currently sit at 31% for late-stage ovarian cancer, but can reach over 90% when diagnosed early. Professor John McDonald, lead researcher and Director of Integrated Cancer Research Center at Georgia Tech, emphasised the value of artificial intelligence in diagnosing individual targets, as only about 5% of patients benefit from current therapies due to patient differences. Their machine learning methodology can recognise patterns amongst the vast amount of uncharacterized metabolites – over 90% of metabolites in human blood – and correlate this with an individual’s likelihood of developing ovarian cancer. By doing so, patients identified with higher probabilities can be immediately screened and treated. #medicalinnovation #ovariancancer #HIPEC #CombatMedical

A new study led by investigators from Mass General Brigham and @bidmchealth has identified blood proteins that may predict liver cancer risk years before diagnosis. Liver cancer is the third leading cause of cancer globally and the second leading cause of cancer-related deaths. Early detection is crucial, especially for high-risk individuals like those with cirrhosis and hepatitis, but current tools are inadequate, expensive, invasive and limited to major hospitals. The research team utilized proteomics to develop an early detection model for liver cancer. Using the SomaScan Assay Kit, they measured 1,305 proteins in blood samples from health study participants. They identified 56 elevated proteins in liver cancer patients, selecting four for a predictive model. When tested, this model showed higher accuracy in predicting liver cancer than traditional risk factors. #cancer #medical_tourism #hospital #liver_cancer #chinesehospitals #Turkiyehospitals #Singapore

CAR therapies are powerful treatments against cancer. However, 30-40% of people relapse after treatment. The key may lie in the cancer we’re trying to fight.   Here, the authors showcase three key observations that can better prepare CAR-NK therapies to overcome cancer cells:   1 - Engineered Power: CAR-NK cells engineered with IL-15 show enhanced proliferation and effector functions, improving tumor control compared to standard CAR-NK and untransduced NK cells.   2 - Metabolic Competition: Over time, CD19/IL-15 NK cells lose metabolic fitness in the presence of highly metabolic tumor cells, as they struggle to compete for nutrients.   3 - Second Dose Advantage: Infusing a second dose of CD19-IL-15 NK when there is a low tumor burden increases the long-term survival of functional NK cells, potentially tipping the balance in favor of the immune system.   Kudos to the authors – Li Li, Vakul Mohanty, JINZHUANG DOU, Yuefan Huang, Ken Chen, and Katayoun Rezvani great work!   #celltherapy #celltherapyjournalclub #biotechnology #innovation

What about this for innovation in early ovarian cancer detection? Researchers from the Georgia Institute of Technology have developed machine learning models to detect ovarian cancer using patient metabolic profiles. This approach addresses the issue of heterogeneity amongst ovarian cancer patients, which has obstructed early detection mechanisms due to limited common biomarkers. The models, which distinguished cancerous samples with a 93% accuracy, based on testing over 564 samples, could aid in boosting survival rates that currently sit at 31% for late-stage ovarian cancer, but can reach over 90% when diagnosed early. Professor John McDonald, lead researcher and Director of Integrated Cancer Research Center at Georgia Tech, emphasised the value of artificial intelligence in diagnosing individual targets, as only about 5% of patients benefit from current therapies due to patient differences. Their machine learning methodology can recognise patterns amongst the vast amount of uncharacterized metabolites – over 90% of metabolites in human blood – and correlate this with an individual’s likelihood of developing ovarian cancer. By doing so, patients identified with higher probabilities can be immediately screened and treated. #medicalinnovation #ovariancancer #HIPEC #CombatMedical

🔬 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 𝐋𝐮𝐧𝐠 𝐂𝐚𝐧𝐜𝐞𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡! 🔬 🌟 Exciting Progress in FFPE Block Lung Cancer Studies 🌟 In the relentless fight against lung cancer, science is making remarkable strides! 🙌🏼 FFPE (Formalin-Fixed Paraffin-Embedded) blocks are becoming a game-changer in understanding and treating this devastating disease. 🚀 🧬 FFPE blocks preserve tissue samples for future analysis, helping researchers delve into the intricate details of lung cancer at a molecular level. 🧪 This means more precise diagnoses, tailored treatments, and improved patient outcomes. 💪🏽 📊 The data gathered from FFPE blocks is unlocking the mysteries of lung cancer's genetic makeup, paving the way for personalized therapies that target specific mutations. 🎯 🔍 Together, we're advancing towards a world where lung cancer is not just treatable, but ultimately preventable. 💡 Let's continue to support research, raise awareness Visit here👉 https://shorturl.at/fDO67 #FightLungCancer! 💙💪🏼 #FFPEBlockResearch #LungCancerAwareness #HopeForACure 🌼

A new qualitative study by recent PhD graduate Dr. Salma Shickh and Dr. Yvonne Bombard examined patients' views on the usefulness of receiving cancer results from genomic sequencing (GS). The study found that patients’ perceptions of GS results hinged on whether they triggered clinical management. For example, when patients received results that led to them being enrolled into high-risk breast cancer surveillance programs, they found the results to be very “useful.” However, when patients received results from GS that did not lead to clinical action, they found the results “concerning,” which led to harms such as hypervigilance and worry. These findings highlight the need to develop practices to support patients with cancer undergoing GS, the study concludes. Learn more: https://lnkd.in/g9FqYTGp #UnityHealthResearch

Dear Linkedin family, I am happy to share that I will be hosting a special edition titled "DNA Damage, Repair and Mutagenesis: Targeting Cancer's Achilles Heel" for the journal "Frontiers in Oncology" and will be joined by experts from the DNA Damage and Repair field, namely, Dr. Nimrat Chatterjee from UVM, and Dr Arvind Panday from Mayo Clinic as co-editors. We are currently accepting submissions for original research articles, short communications, perspectives and review articles on topics pertaining and not limited to - 1. Novel findings in the field of DNA damage, repair, and mutagenesis. 2. Mechanism of action of DNA damage, repair, and mutagenesis. 3. Genome instability and tumorigenesis 4. Principles of synthetic lethality for cancer treatment. 5. Development and identification of novel drug inhibitors that target these pathways. 6. DDR (DNA damage and repair) targets and associated clinical studies. If you have an interesting story to share, feel free to reach out to me or any of the other co-editors. Our goal will be to bring to you a special collection of articles that will help re-define our understanding about DDR and its role in cancer and how to leverage this vulnerability into an anti-cancer opportunity. #dnarepair #cancer #cancerresearch #cancertherapy #cancerimmunotherapy #cancertreatment Here is the link for more details - https://lnkd.in/eF8Qircv

Artificial Intelligence model for ovarian cancer detection -> The study, conducted across three Chinese hospitals, collected data from 98 laboratory tests and clinical features to develop an integrated AI-based prediction model for ovarian cancer diagnosis. -> The model employed a Prediction Modelling framework that involved constructing and ranking 176 base classification models, each consisting of a feature selection method and a machine-learning classifier. -> The developed model demonstrated superior performance, with higher Area Under the Curve (AUC) and sensitivity, when compared to traditional biomarkers CA125 and HE4, particularly in the identification of early-stage ovarian cancer patients. -> In conclusion, the developed model, utilizing routine laboratory tests exhibits high performance, offering potential cost-effective and accessible diagnostic tool for ovarian cancer Read more: https://lnkd.in/d7PfTrdy The Lancet digital health #cancer #artificialintelligence #diagnosis

🔬🩸 Breaking New Ground in Cancer Detection! Researchers have made a groundbreaking leap with a new blood test that accurately detects liver cancer, even in its early stages. This isn't just another blood test – it's a game-changer using fragmentomics technology to analyze DNA fragments in the blood. Liver cancer is the world’s third leading cause of cancer death. Early detection is crucial, with survival rates over 70% if caught early, but less than 20% when diagnosed late. Traditional tests have limitations, but this new approach, led by Amy Kim, Ph.D., of Johns Hopkins University, is set to change the game. The test’s power lies in machine learning, distinguishing those with and without liver cancer with remarkable accuracy. Despite needing larger-scale validation, this innovation brings hope to those at high risk, like those with cirrhosis or chronic hepatitis B. 💡 Liquid biopsies and fragmentomics are leading us to a future where detecting cancer could be as simple as a routine blood test. Imagine the lives that could be saved with such early detection methods! Source: https://lnkd.in/d3NU6vSZ #CancerResearch #LiverCancerAwareness #MedicalInnovation #HealthTech #JohnsHopkinsResearch #FightCancer #earlydetectionsaveslives #bioinformatics #cancerbiology #oncology