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  • Machine learning system offers new hope for diagnosis of rare genetic disorders

    Machine learning system offers new hope for diagnosis of rare genetic disorders

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    Diagnosing rare Mendelian disorders is a labor-intensive task, even for experienced geneticists. Investigators at Baylor College of Medicine are trying to make the process more efficient using artificial intelligence. The team developed a machine learning system called AI-MARRVEL (AIM) to help prioritize potentially causative variants for Mendelian disorders. The study is published today in NEJM AI. 

    Researchers from the Baylor Genetics clinical diagnostic laboratory noted that AIM’s module can contribute to predictions independent of clinical knowledge of the gene of interest, helping to advance the discovery of novel disease mechanisms. “The diagnostic rate for rare genetic disorders is only about 30%, and on average, it is six years from the time of symptom onset to diagnosis. There is an urgent need for new approaches to enhance the speed and accuracy of diagnosis,” said co-corresponding author Dr. Pengfei Liu, associate professor of molecular and human genetics and associate clinical director at Baylor Genetics.

    AIM is trained using a public database of known variants and genetic analysis called Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) previously developed by the Baylor team. The MARRVEL database includes more than 3.5 million variants from thousands of diagnosed cases. Researchers provide AIM with patients’ exome sequence data and symptoms, and AIM provides a ranking of the most likely gene candidates causing the rare disease. 

    Researchers compared AIM’s results to other algorithms used in recent benchmark papers. They tested the models using three data cohorts with established diagnoses from Baylor Genetics, the National Institutes of Health-funded Undiagnosed Diseases Network (UDN) and the Deciphering Developmental Disorders (DDD) project. AIM consistently ranked diagnosed genes as the No. 1 candidate in twice as many cases than all other benchmark methods using these real-world data sets. 

    We trained AIM to mimic the way humans make decisions, and the machine can do it much faster, more efficiently and at a lower cost. This method has effectively doubled the rate of accurate diagnosis.”


    Dr. Zhandong Liu, co-corresponding author, associate professor of pediatrics – neurology at Baylor and investigator at the Jan and Dan Duncan Neurological Research Institute (NRI) at Texas Children’s Hospital

    AIM also offers new hope for rare disease cases that have remained unsolved for years. Hundreds of novel disease-causing variants that may be key to solving these cold cases are reported every year; however, determining which cases warrant reanalysis is challenging because of the high volume of cases. The researchers tested AIM’s clinical exome reanalysis on a dataset of UDN and DDD cases and found that it was able to correctly identify 57% of diagnosable cases.

    “We can make the reanalysis process much more efficient by using AIM to identify a high-confidence set of potentially solvable cases and pushing those cases for manual review,” Zhandong Liu said. “We anticipate that this tool can recover an unprecedented number of cases that were not previously thought to be diagnosable.”

    Researchers also tested AIM’s potential for discovery of novel gene candidates that have not been linked to a disease. AIM correctly predicted two newly reported disease genes as top candidates in two UDN cases.

    “AIM is a major step forward in using AI to diagnose rare diseases. It narrows the differential genetic diagnoses down to a few genes and has the potential to guide the discovery of previously unknown disorders,” said co-corresponding author Dr. Hugo Bellen, Distinguished Service Professor in molecular and human genetics at Baylor and chair in neurogenetics at the Duncan NRI.

    “When combined with the deep expertise of our certified clinical lab directors, highly curated datasets and scalable automated technology, we are seeing the impact of augmented intelligence to provide comprehensive genetic insights at scale, even for the most vulnerable patient populations and complex conditions,” said senior author Dr. Fan Xia, associate professor of molecular and human genetics at Baylor and vice president of clinical genomics at Baylor Genetics. “By applying real-world training data from a Baylor Genetics cohort without any inclusion criteria, AIM has shown superior accuracy. Baylor Genetics is aiming to develop the next generation of diagnostic intelligence and bring this to clinical practice.”

    Other authors of this work include Dongxue Mao, Chaozhong Liu, Linhua Wang, Rami AI-Ouran, Cole Deisseroth, Sasidhar Pasupuleti, Seon Young Kim, Lucian Li, Jill A.Rosenfeld, Linyan Meng, Lindsay C. Burrage, Michael Wangler, Shinya Yamamoto, Michael Santana, Victor Perez, Priyank Shukla, Christine Eng, Brendan Lee and Bo Yuan. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Al Hussein Technical University, Baylor Genetics and the Human Genome Sequencing Center at Baylor.

    This work was supported by the Chang Zuckerberg Initiative and the National Institute of Neurological Disorders and Stroke (3U2CNS132415).

    Source:

    Baylor College of Medicine

    Journal reference:

    Mao, D., et al. (2024) AI-MARRVEL — A Knowledge-Driven AI System for Diagnosing Mendelian Disorders. NEJM AI. doi.org/10.1056/AIoa2300009.

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  • New system personalizes chemotherapy doses for cancer patients

    New system personalizes chemotherapy doses for cancer patients

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    When cancer patients undergo chemotherapy, the dose of most drugs is calculated based on the patient’s body surface area. This is estimated by plugging the patient’s height and weight into an equation, dating to 1916, that was formulated from data on just nine patients.

    This simplistic dosing doesn’t take into account other factors and can lead to patients receiving either too much or too little of a drug. As a result, some patients likely experience avoidable toxicity or insufficient benefit from the chemotherapy they receive.

    To make chemotherapy dosing more accurate, MIT engineers have come up with an alternative approach that can enable the dose to be personalized to the patient. Their system measures how much drug is in the patient’s system, and these measurements are fed into a controller that can adjust the infusion rate accordingly.

    This approach could help to compensate for differences in drug pharmacokinetics caused by body composition, genetic makeup, chemotherapy-induced toxicity of the organs that metabolize the drugs, interactions with other medications being taken and foods consumed, and circadian fluctuations in the enzymes responsible for breaking down chemotherapy drugs, the researchers say.

    Recognizing the advances in our understanding of how drugs are metabolized, and applying engineering tools to facilitate personalized dosing, we believe, can help transform the safety and efficacy of many drugs.”


    Giovanni Traverso, associate professor of mechanical engineering at MIT, gastroenterologist at Brigham and Women’s Hospital, and  senior author of the study

    Louis DeRidder, an MIT graduate student, is the lead author of the paper, which appears today in the journal Med.

    Continuous monitoring

    In this study, the researchers focused on a drug called 5-fluorouracil, which is used to treat colorectal cancers, among others. The drug is typically infused over a 46-hour period, and the dosage is determined using a formula based on the patient’s height and weight, which gives the estimated body surface area.

    However, that approach doesn’t account for differences in body composition that can affect how the drug spreads through the body, or genetic variations that influence how it is metabolized. Those differences can lead to harmful side effects, if too much drug is present. If not enough drug is circulating, it may not kill the tumor as expected.

    “People with the same body surface area could have very different heights and weights, could have very different muscle masses or genetics, but as long as the height and the weight plugged into this equation give the same body surface area, their dose is identical,” says DeRidder, a PhD candidate in the Medical Engineering and Medical Physics program within the Harvard-MIT Program in Health Sciences and Technology.

    Another factor that can alter the amount of drug in the bloodstream at any given time is circadian fluctuations of an enzyme called dihydropyrimidine dehydrogenase (DPD), which breaks down 5-fluorouracil. DPD’s expression, like many other enzymes in the body, is regulated on a circadian rhythm. Thus, the degradation of 5-FU by DPD is not constant but changes according to the time of the day. These circadian rhythms can lead to tenfold fluctuations in the amount of 5-fluorouracil in a patient’s bloodstream over the course of an infusion.

    “Using body surface area to calculate a chemotherapy dose, we know that two people can have profoundly different toxicity from 5-fluorouracil chemotherapy. Looking at one patient, they can have cycles of treatment with minimal toxicity and then have a cycle with miserable toxicity. Something changed in how that patient metabolized chemo from one cycle to the next. Our antiquated dosing fails to capture that change, and patients suffer as a result,” says Douglas Rubinson, a clinical oncologist at Dana-Farber Cancer Institute and an author of the paper.

    One way to try to counteract the variability in chemotherapy pharmacokinetics is a strategy called therapeutic drug monitoring, in which the patient gives a blood sample at the end of one treatment cycle. After this sample is analyzed for the drug concentration, the dosage can be adjusted, if needed, at the beginning of the next cycle (usually two weeks later for 5-fluorouracil). This approach has been shown to result in better outcomes for patients, but it is not widely used for chemotherapies such as 5-fluorouracil.

    The MIT researchers wanted to develop a similar type of monitoring, but in a manner that is automated and enables real-time drug personalization, which could result in better outcomes for patients. In their “closed-loop” system, drug concentrations can be continually monitored, and that information is used to automatically adjust the infusion rate of the chemotherapy drug and keep the dose within the target range. Such a closed-loop system enables personalization of the drug dose in a manner that considers circadian rhythm changes in the levels of drug-metabolizing enzymes, as well as any changes in the patient’s pharmacokinetics since their last treatment, such as chemotherapy-induced toxicity of the organs that metabolize the drugs.

    The new system they designed, known as CLAUDIA (Closed-Loop AUtomated Drug Infusion regulAtor), makes use of commercially available equipment for each step. Blood samples are taken every five minutes and rapidly prepared for analysis. The concentration of 5-fluorouracil in the blood is measured and compared to the target range. The difference between the target and measured concentration is input to a control algorithm, which then adjusts the infusion rate if necessary, to keep the dose within the range of concentrations between which the drug is effective and nontoxic.

    “What we’ve developed is a system where you can constantly measure the concentration of drug and adjust the infusion rate accordingly, to keep the drug concentration within the therapeutic window,” DeRidder says.

    Rapid adjustment

    In tests in animals, the researchers found that using CLAUDIA, they could keep the amount of drug circulating in the body within the target range around 45 percent of the time. Drug levels in animals that received chemotherapy without CLAUDIA remained in the target range only 13 percent of the time, on average. In this study, the researchers did not do any tests of the effectiveness of the drug levels, but keeping the concentration within the target window is believed to lead to better outcomes and less toxicity.

    CLAUDIA was also able to keep the dose of 5-fluorouracil within the target range even when the researchers administered a drug that inhibits the DPD enzyme. In animals that received this inhibitor without continuous monitoring and adjustment, levels of 5-fluorouracil increased by up to eightfold.

    For this demonstration, the researchers manually performed each step of the process, using off-the-shelf equipment, but they now plan to work on automating each step so that the monitoring and dose adjustment can be done without any human intervention.

    To measure drug concentrations, the researchers used high-performance liquid chromatography mass spectroscopy (HPLC-MS), a technique that could be adapted to detect nearly any type of drug.

    “We foresee a future where we’re able to use CLAUDIA for any drug that has the right pharmacokinetic properties and is detectable with HPLC-MS, thereby enabling the personalization of dosing for many different drugs,” DeRidder says.

    The research was funded by the National Science Foundation Graduate Research Fellowship Program, a MathWorks Fellowship, MIT’s Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

    Other authors of the paper include Kyle A. Hare, Aaron Lopes, Josh Jenkins, Nina Fitzgerald, Emmeline MacPherson, Niora Fabian, Josh Morimoto, Jacqueline N. Chu, Ameya R. Kirtane, Wiam Madani, Keiko Ishida, Johannes L. P. Kuosmanen, Naomi Zecharias, Christopher M. Colangelo, Hen-Wei Huang, Makaya Chilekwa, Nikhil B. Lal, Shriya S. Srinivasan, Alison M Hayward, Brian M. Wolpin, David Trumper, Troy Quast, and Robert Langer.

    Source:

    Massachusetts Institute of Technology

    Journal reference:

    DeRidder, L. B., et al. (2024) Closed-loop automated drug infusion regulator: A clinically translatable, closed-loop drug delivery system for personalized drug dosing. Med. doi.org/10.1016/j.medj.2024.03.020.

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  • Opportunities and limitations of using a large language model to respond to patient messages

    Opportunities and limitations of using a large language model to respond to patient messages

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    A new study by investigators from Mass General Brigham demonstrates that large language models (LLMs), a type of generative AI, may help reduce physician workload and improve patient education when used to draft replies to patient messages. The study also found limitations to LLMs that may affect patient safety, suggesting that vigilant oversight of LLM-generated communications is essential for safe usage. Findings, published in Lancet Digital Health, emphasize the need for a measured approach to LLM implementation.

    Rising administrative and documentation responsibilities have contributed to increases in physician burnout. To help streamline and automate physician workflows, electronic health record (EHR) vendors have adopted generative AI algorithms to aid clinicians in drafting messages to patients; however, the efficiency, safety and clinical impact of their use had been unknown.

    Generative AI has the potential to provide a ‘best of both worlds’ scenario of reducing burden on the clinician and better educating the patient in the process. However, based on our team’s experience working with LLMs, we have concerns about the potential risks associated with integrating LLMs into messaging systems. With LLM-integration into EHRs becoming increasingly common, our goal in this study was to identify relevant benefits and shortcomings.”


    Danielle Bitterman, MD, corresponding author, faculty member in the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham and physician in the Department of Radiation Oncology at Brigham and Women’s Hospital

    For the study, the researchers used OpenAI’s GPT-4, a foundational LLM, to generate 100 scenarios about patients with cancer and an accompanying patient question. No questions from actual patients were used for the study. Six radiation oncologists manually responded to the queries; then, GPT-4 generated responses to the questions. Finally, the same radiation oncologists were provided with the LLM-generated responses for review and editing. The radiation oncologists did not know whether GPT-4 or a human had written the responses, and in 31% of cases, believed that an LLM-generated response had been written by a human.

    On average, physician-drafted responses were shorter than the LLM-generated responses. GPT-4 tended to include more educational background for patients but was less directive in its instructions. The physicians reported that LLM-assistance improved their perceived efficiency and deemed the LLM-generated responses to be safe in 82.1 percent of cases and acceptable to send to a patient without any further editing in 58.3 percent of cases. The researchers also identified some shortcomings: If left unedited, 7.1 percent of LLM-generated responses could pose a risk to the patient and 0.6 percent of responses could pose a risk of death, most often because GPT-4’s response failed to urgently instruct the patient to seek immediate medical care.

    Notably, LLM-generated/physician-edited responses were more similar in length and content to LLM-generated responses versus the manual responses. In many cases, physicians retained LLM-generated educational content, suggesting that they perceived it to be valuable. While this may promote patient education, the researchers emphasize that overreliance on LLMs may also pose risks, given their demonstrated shortcomings.

    The emergence of AI tools in health has the potential to positively reshape the continuum of care and it is imperative to balance their innovative potential with a commitment to safety and quality. Mass General Brigham is leading the way in responsible use of AI, conducting rigorous research on new and emerging technologies to inform the incorporation of AI into care delivery, workforce support and administrative processes. Mass General Brigham is currently leading a pilot integrating generative AI into the electronic health record to draft replies to patient portal messages, testing the technology in a set of ambulatory practices across the health system. 

    Going forward, the study’s authors are investigating how patients perceive LLM-based communications and how patients’ racial and demographic characteristics influence LLM-generated responses, based on known algorithmic biases in LLMs.

    “Keeping a human in the loop is an essential safety step when it comes to using AI in medicine, but it isn’t a single solution,” Bitterman said. “As providers rely more on LLMs, we could miss errors that could lead to patient harm. This study demonstrates the need for systems to monitor the quality of LLMs, training for clinicians to appropriately supervise LLM output, more AI literacy for both patients and clinicians, and on a fundamental level, a better understanding of how to address the errors that LLMs make.”

    Source:

    Journal reference:

    Chen, S., et al. (2024) The effect of using a large language model to respond to patient messages. The Lancet Digital Health. doi.org/10.1016/S2589-7500(24)00060-8.

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  • Research reveals potential target for enfortumab vedotin therapy in urothelial carcinoma

    Research reveals potential target for enfortumab vedotin therapy in urothelial carcinoma

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    Under the leadership of PD Dr. Niklas Klümper, Assistant Physician at the Department of Urology at the University Hospital Bonn (UKB) and Clinician Scientist of the BMBF-funded ACCENT program and PD Dr. Markus Eckstein, senior physician at the Institute of Pathology at the Uniklinikum Erlangen of the Friedrich-Alexander-University Erlangen-Nürnberg (FAU), an interdisciplinary research team has published new findings that indicate which patients with advanced urothelial carcinoma could benefit in particular from the new targeted therapy with the antibody-drug conjugate enfortumab vedotin. The study, published yesterday in the prestigious Journal of Clinical Oncology, identifies NECTIN4 amplification as a promising genomic biomarker for predicting treatment response to enfortumab vedotin. These findings could represent a significant advance in the improved treatment of this cancer.

    As an alternative to chemotherapies used to treat aggressive advanced and metastatic urothelial carcinoma, a new class of drugs, known as antibody-drug conjugates, has recently become available. Enfortumab vedotin (EV) is the first drug in this new class to be approved by the EMA at for the treatment of patients and patients with metastatic urothelial carcinoma. Given the extremely promising results of the EV-302 trial, which showed a near doubling of survival with the combination therapy of EV and pembrolizumab, an immune checkpoint inhibitor, compared to conventional platinum-based chemotherapy in untreated patients with metastatic urothelial carcinoma, it is expected that the use of EV will increase significantly in the future.

    Modern targeted oncology therapy

    Antibody-drug conjugates consist of an antibody directed against a target structure on tumor cells and combined with a highly toxic chemotherapeutic agent. This combines the selectivity of targeted antibody therapy with the cytotoxic potential of conventional chemotherapy, which represents an innovative and new oncological therapeutic approach.

    Research for more targeted therapy: precision oncology

    Long-term efficacy of the new drug EV has so far been shown in an uncharacterized group of patients with metastatic urothelial carcinoma. The research team led by PD Dr. Niklas Klümper (UKB) and PD Dr. Markus Eckstein (Uniklinikum Erlangen) therefore wanted to analyze in more detail which patients benefit effectively from EV therapy in order to be able to use it in a more targeted manner – and conversely to identify patients who benefit less or not from EV, as they could possibly be treated more effectively with other therapies.

    Nectin-4, the target structure of EV, is localized on chromosome 1q23.3. This gene segment is increased in about 20-25 percent of urothelial carcinomas, which is referred to as amplification. The new study aimed to investigate NECTIN4 amplifications as a potential genomic biomarker to predict treatment response to the drug EV in patients with advanced urothelial carcinoma.

    “We have successfully developed and applied a simple FISH test (fluorescence in situ hybridization) that is specific for NECTIN4. This test proved to be a reliable method for identifying NECTIN4 amplification. Our studies showed that the presence of NECTIN4 amplification is a robust biomarker for response to EV therapy. In fact, over 90 percent of patients with NECTIN4 amplification showed tumor response to EV therapy, compared to about 30 percent of patients without this amplification”, says PD Dr. Markus Eckstein. These new findings can help to better select patients for this promising therapy in the future. “NECTIN4 amplification is a promising biomarker for predicting treatment response to EV. Excitingly, NECTIN4 amplification is also common in other solid tumors besides urothelial carcinoma, e.g. lung and breast cancer. The consideration of NECTIN4 amplification could therefore also be an exciting option for other tumor types in order to select patients for anti-NECTIN4-directed therapy in a more targeted manner. Further studies on this topic are needed, but our work could be the starting signal for the establishment of new targeted treatment strategies “, says PD Dr. Niklas Klümper.

    Both study PIs also agree that without the great support of all the colleagues involved from the numerous oncology centers in Germany, Austria, the Netherlands and the USA, and of course without the patients’ consent to participate in the study, it could never have been carried out successfully.

    The study was funded and initiated by the German Research Foundation (DFG) as part of the DFG Young Investigator Academy UroAgeCare of the German Society of Urology (DGU). “This underlines the high relevance of funding clinician scientist programs for medical progress,” says Prof. Michael Hölzel, mentor of PD Dr. Klümper within the program.

    Source:

    University Hospital Bonn (UKB)

    Journal reference:

    Klümper, N., et al. (2024) NECTIN4 Amplification Is Frequent in Solid Tumors and Predicts Enfortumab Vedotin Response in Metastatic Urothelial Cancer. Journal of Clinical Oncology. doi.org/10.1200/JCO.23.01983.

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  • HEROS 2.0 trial results published

    HEROS 2.0 trial results published

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    Researchers at Texas Children’s Cancer Center and the Center for Cell and Gene Therapy at Baylor College of Medicine, Texas Children’s Hospital and Houston Methodist published results of a phase I clinical trial of a novel immunotherapy for high-risk sarcomas in the journal Nature Cancer.

    The therapy uses chimeric antigen receptor (CAR) T cells engineered to target the HER2 protein, which is overexpressed on the surface of sarcoma cells. The HEROS 2.0 trial showed that this therapeutic approach is safe and is associated with clinical benefit.

    CAR T cell therapy has been a highly successful strategy for recurrent or high-risk leukemias or lymphomas, but challenges remain in using this therapy for solid tumors. The results of this trial show that we are moving the dial in harnessing the power of CAR T cells as an effective anticancer therapy for sarcomas.”


    Dr. Meenakshi Hegde, first and corresponding author, associate professor of pediatrics – hematology and oncology at Baylor and pediatric oncologist at Texas Children’s Cancer Center

    In a previous clinical trial, the HEROS study, researchers found that CAR T cells directed at HER2+ tumor cells had a favorable safety profile, but clinical benefit was limited by poor CAR T expansion and persistence. In HEROS 2.0, researchers added successive HER2-CAR T cell infusions following lymphodepletion, which uses chemotherapy to deplete the patient’s own T cells, to make room for the infused therapeutic HER2-CAR T cells to expand.

    “We also increased the number of allowable HER2-CAR T infusions to sustain the exposure time of CAR T cells, with the goal of increasing the antitumor effect,” Hegde said. “This study showed that CAR T expansion and persistence was improved with lymphodepletion and repeat cycles of treatment.”

    Thirteen patients were enrolled in the HEROS 2.0 trial at Texas Children’s Cancer Center and Houston Methodist Hospital, and seven patients received multiple CAR T infusions. HER2-CAR T expansion occurred following 19 of 21 total infusions, and clinical benefit was seen in 50% of treated patients. An exceptional response in a patient with metastatic rhabdomyosarcoma was detailed in a publication in Nature Communications in 2020. The patient remains healthy and cancer free, more than five years after treatment.

    Nine patients in the first two cohorts developed low-grade cytokine release syndrome (CRS), an acute inflammatory response seen as a side effect of CAR T treatment. Two patients in the third cohort experienced dose-limiting CRS, which necessitated ending the dose-escalation.

    “We are now studying the tumors and the way we engineer the CAR T cells to better facilitate the safe delivery of higher doses, thereby enhancing antitumor activity by increasing the magnitude of CAR T cell expansion and persistence,” Hegde said.

    “HEROS 2.0, the second edition of the HEROS trials, exemplifies how the crosstalk between the bench and the bedside results in refinement of first-in-child studies and more durable clinical benefit,” said senior author Dr. Nabil Ahmed, professor of pediatrics – hematology and oncology at Baylor and pediatric oncologist at Texas Children’s Cancer Center.

    The researchers currently are recruiting for the HEROS 3.0 trial, which will evaluate the safety of giving HER2-CAR T cells in combination with chemotherapy and an immune checkpoint inhibitor drug. Find more information on the trial here.

    Hegde and Ahmed both are members of the Dan L Duncan Comprehensive Cancer Center at Baylor. Other study authors include Shoba Navai, Christopher DeRenzo, Sujith K. Joseph, Khaled Sanber, Mengfen Wu, Ahmed Z. Gad, Katherine A. Janeway, Matthew Campbell, Dolores Mullikin, Zeid Nawas, Catherine Robertson, Pretty R. Mathew, Huimin Zhang, Birju Mehta, Raksha R. Bhat, Angela Major, Ankita Shree, Claudia Gerken, Mamta Kalra, Rikhia Chakraborty, Sachin G. Thakar, Olga Dakhova, Vita S. Salsman, Bambi Grilley, Natalia Lapteva, Adrian Gee, Gianpietro Dotti, Riyue Bao, Ahmed Hamed Salem, Tao Wang, Malcolm K. Brenner, Helen E. Heslop, Winfried S. Wels, M. John Hicks and Stephen Gottschalk. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Texas Children’s Cancer and Hematology Center, the Center for Cell and Gene Therapy, the Dan L Duncan Comprehensive Cancer Center, Dana Farber Cancer Institute, University of North Carolina at Chapel Hill, University of Pittsburgh, UPMC Hillman Cancer Center, Ain Shams University, Georg-Speyer-Haus Institute for Tumor Biology and Experimental Therapy, German Cancer Consortium, Frankfurt Cancer Institute and St. Jude Children’s Research Hospital.

    This work was supported in part by Stand Up To Cancer (SU2C) – St. Baldrick’s Pediatric Cancer Dream Team Translational Research Grant (SU2C-AACR-DT1113), the V Foundation for Cancer Research, Triumph Over Kids Cancer Foundation (TOKC), Cookies for Kids’ CancerTM Foundation, Alex’s Lemonade Stand Pediatric Cancer Foundation, the Faris Foundation, National Cancer Institute and the Cancer Prevention and Research Institute of Texas. See the publication for a complete list of funding sources.

    Source:

    Baylor College of Medicine

    Journal reference:

    Hegde, M., et al. (2024). Autologous HER2-specific CAR T cells after lymphodepletion for advanced sarcoma: a phase 1 trial. Nature Cancer. doi.org/10.1038/s43018-024-00749-6.

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  • Anesthetic midazolam boosts survival after cardiac arrest

    Anesthetic midazolam boosts survival after cardiac arrest

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    If a patient is successfully resuscitated after a cardiac arrest and circulation resumes, they are not out of the woods yet. A number of factors can influence whether and how they survive the trauma in the subsequent phase. The administration of the anesthetic midazolam has a positive effect, as shown by a multicenter study of 571 patients conducted by the Research Association for Emergency Medicine Ostwestfalen-Lippe led by the University Clinic for Anesthesiology, Intensive Care and Emergency Medicine at the Johannes Wesling clinic in Minden, hospital of Ruhr University Bochum, Germany, at the Chair of Emergency Medicine headed by Professor Jochen Hinkelbein.

    In cases where the patient required anesthesia after successful resuscitation, midazolam improved the chances of optimal oxygen saturation and CO2 levels in the blood. The risk of a renewed drop in blood pressure or a renewed circulatory arrest didn’t increase. “This specific group of patients who have been successfully resuscitated should definitely be included in the guidelines for pre-hospital anesthesia. Midazolam has proven to have a particularly positive effect in this group,” concludes Dr. Gerrit Jansen, lead author of the study, which was published in the journal Deutsches Ärzteblatt International on April 8, 2024.

    Concerns about increased risk prove unfounded

    In the event of a cardiac arrest, rapid intervention is essential: If first aiders carry out resuscitation measures in good time, the patient’s circulation can be restarted in the best-case scenario. “However, it’s often the case that the patient hasn’t yet regained consciousness,” explains Gerrit Jansen. In this phase, there are various factors that can affect the chances of survival and subsequent permanent limitations due to the circulatory arrest.

    Some patients display protective reflexes after resuscitation, such as coughing or defensive movements, which make the emergency responders’ work more difficult. They often have to perform extended airway management, for example by intubating the patient in the same way as during surgery. This frequently requires sedation or anesthesia.”


    Dr. Gerrit Jansen, lead author of the study

    Until now, there has been concern that anaesthetic drugs could have a negative impact on the circulatory system, which has only just been restored. According to the study, however, this is not the case.

    Groundbreaking research

    Of the 571 people included in the study who survived a cardiac arrest and were admitted to hospital, 395 were sedated, 249 of them with midazolam. The chance that their blood oxygen saturation levels were in the optimal range following a cardiac arrest increased twofold when midazolam was administered. The chance that carbon dioxide was effectively exhaled increased by a factor of 1.6 with the drug. “Our statistical methods confirmed a correlation between these results and the administration of midazolam, without any indication of negative circulatory effects,” says Gerrit Jansen.

    “The European guidelines of the European Resuscitation Council don’t yet set out any specific recommendations for possible anesthetic drugs,” explains Jansen. “The German guideline for pre-hospital anesthesia for patients with cardiovascular risk doesn’t mention patients in cardiac arrest. We’ve therefore carried out pioneering research in this field, the results of which should be incorporated into the recommendations for the benefit of the patients.”

    Source:

    Journal reference:

    Jansen, G., et al. (2024) Midazolam for post-arrest sedation in pre-hospital emergency care, A multicenter propensity score analysis. Deutsches Ärzteblat. doi.org/10.3238/arztebl.m2023.0277.

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  • Researchers aim to use AI for early screening and prognosis of Dry Eye Disease

    Researchers aim to use AI for early screening and prognosis of Dry Eye Disease

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    Dry Eye Disease (DED) is one of the more common eye diseases, affecting up to 30% of the world’s population. This disease can affect many different types of people and can wind up being a great hindrance to their overall quality of life. Early screening and prognosis is vital to the patient’s progression with the disease. However, this can be difficult. In this study, researchers aim to use artificial intelligence (AI) to aid in early screening and prognosis of DED. Not only can the use of AI make screening more accessible for individuals, but it can also aid patients in personalized therapeutic intervention.

    Researchers published their results in Big Data Mining and Analytics on April 22.

    DED can affect a wide array of people, including those who wear contact lenses, makeup, stay up late, look at screens for a long time and are over 30 years old. Symptoms of this disease are dry eyes, irritation and burning, tears, eye fatigue and pain. One can easily see how this disease has the potential to drastically impact a large portion of the modern world’s population. Here is where the combined efforts of ophthalmic disease detection and the world of computer scientists and engineers can help.

    By addressing challenges, imparting insights, and delineating future research pathways, it contributes substantially to the advancement of ophthalmic disease detection through sophisticated technological modalities.”


    Mini Han Wang, author and researcher

    There are seven facets to this AI-based disease detection. Timely intervention via the AI screening process and correct prognosis is the first part. The use of exhaustive surveys for DED through AI is another, and this is a supporting principle to ensure a level of thoroughness and trustworthiness throughout the process. A systematic approach follows, as well as the marriage of computer science and engineering with ophthalmology. Then, the standards for DED detection must be devised and upheld for future researchers and practitioners, which will naturally lead to the advancement of the field. Finally, all the research, methodologies and tools must be compiled so researchers, scholars and practitioners can have all of the information currently out there available to them.

    While the ophthalmologists set the guidelines regarding the framework of the disease and flags for diagnosis, the AI does a lot of the heavy lifting. Ideally, this AI would use images and videos taken from a user’s cell phone to help reach users across the world. The AI can then utilize these images, as well as risk factors in the patient’s life, to make a smart and well-informed prognosis. Further, AI continuously learns and can help propel research forward by contributing to predictive models for DED.

    The use of AI detection for DED holds a lot of promise, especially considering the risk factors are often normal activities in many people’s everyday lives. To make the detection methods accessible enough and accurate enough, further research needs to be done.

    “However, there are still challenges for engineers to select the diagnostic standards and combinations of different types of datasets. By using trustworthy algorithms, images and videos captured from phones for accessibility purposes, a holistic approach to healthcare for early screening is possible,” said Wang.

    With continued testing and collaboration between engineers and ophthalmologists, there is great potential for this method of testing to be useful in contributing to early screening of DED and subsequent therapeutic actions taken for the patient to reduce a worsening condition or to recover some quality of life.

    Mini Han Wang and Xiangrong Yu of the Zhuhai People’s Hospital with Mini Han Wang also of the Department of Ophthalmology and Visual Sciences at the Chinese University of Hong Kong, The Faculty of Data Sciences at City University of Macau and the Department of big data at the Zhuhai Institute of Advanced Technology at the Chinese Academy of Sciences, Lumin Xing of the First Affiliated Hospital of Shandong First Medical University, Yi Pan of the Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Feng Gu of the College of Staten Island at the City University of New York, Junbin Fang at the Department of Optoelectronic Engineering at Jinan University, Chi Pui Pang, Kelvin KL Chong, Carol Yim-Lui Cheung and Xulin Liao of the Department of Ophthalmology and Visual Sciences at The Chinese University of Hong Kong, Xiaoxiao Fang with the Zhuhai Aier Eye Hospital, Jie Yang of the College of Artificial Intelligence at Chongqing Industry and Trade Polytechnic, Ruoyu Zhou and Wenjian Liu with the Faculty of Data Science at City University of Macao, Xiaoshu Zhou with the Centre for Science and Technology Exchange and Cooperation between China and Portuguese-Speaking Countries, and Fengling Wang with the School of Artificial Intelligence at Hezhou Univeristy contributed to this research.

    The National Natural Science Foundation of China Natural, the Shenzhen Key Laboratory of Intelligent Bioinformatics, the Shenzhen Science and Technology Program, the Guangdong Basic and Applied Basic Research Foundation, the Zhuhai Technology and Research Foundation, the Project of Humanities and Social Science of MOE, the Science and Technology Research Program of Chongqing Municipal Education Commission and the Natural Science Foundation of Chongqing China made this research possible.

    Source:

    Tsinghua University Press

    Journal reference:

    Wang, M. H., et al. (2024) AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions. Big Data Mining and Analytics. doi.org/10.26599/BDMA.2023.9020024.

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  • Immune dysfunction mechanism discovered in stroke and heart attack patients

    Immune dysfunction mechanism discovered in stroke and heart attack patients

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    Every year, between 250,000 and 300,000 people in Germany suffer from a stroke or heart attack. These patients suffer immune disturbances and are very frequently susceptible to life-threatening bacterial infections. Until now, little was known about the underlying mechanisms of this immune dysfunction. Research teams from the Faculty of Medicine at the University Hospital of the UDE and the Leibniz Institute for Analytical Sciences in Dortmund have now uncovered a previously unknown cause – and a therapeutic approach. These findings are published in the May 2024 issue of the Journal Nature Cardiovascular Research.

    The study was led by Prof. Matthias Gunzer, Director of the Institute of Experimental Immunology and Imaging (IEIB) at the UDE and Head of the Biospectroscopy Department at the Leibniz Institute for Analytical Sciences (ISAS), and Dr. Vikramjeet Singh, Head of the Stroke Immunology Unit at the IEIB. They found that in patients one to three days after a stroke or heart attack, the amount of IgA antibodies in the blood decreases drastically – these are essential for defense against infections. Antibodies come in several subtypes, collectively known as immunoglobulins (Ig), which are produced by specialized plasma cells.

    To track down the mechanism behind the loss of antibodies – and to improve the treatment of patients with these findings – the researchers used disease mouse models. Mice also experienced a loss of IgA in their blood and stool after a stroke or heart attack. The researchers discovered that specialized DNA fibers released in blood are a factor in the loss of immune defense. These DNA fibers, known as neutrophil extracellular traps (NETs), originate from the nuclei of another type of immune cell, neutrophils. NETs are released into the blood in large quantities by highly activated neutrophils after a stroke or heart attack and can directly kill plasma cells in the intestine. Probably an even more important effect of NETs is the formation of hundreds of small clots in the blood vessels that supply energy to plasma cells in the intestine. This results in a lack of nutrient and oxygen supply and the Ig-forming cells die off in large numbers.

    The immunologists and their teams not only succeeded in proving a causal link between stroke, heart attack and immunodeficiency, but they were also able to demonstrate a new treatment approach: If the NETs were destroyed with the enzyme DNase or their release was prevented by a substance with a novel mode of action, the immune defense remained intact. The researchers were able to demonstrate this both in the mouse model and – in the case of DNase – in later clinical studies.

    Until now, no therapeutic approaches could be developed because the cause of the immune deficiency was unclear. A treatment that breaks down the NETs or even prevents them from forming in the first place could be a promising new approach to maintaining the immune defense in patients after a stroke or heart attack. It may be possible to prevent serious secondary infectious diseases or even death.”


    Prof. Matthias Gunzer, Director of the Institute of Experimental Immunology and Imaging (IEIB) at the UDE and Head of the Biospectroscopy Department at the Leibniz Institute for Analytical Sciences (ISAS)

    Source:

    Universität Duisburg-Essen

    Journal reference:

    Tuz, A. A., et al. (2024). Stroke and myocardial infarction induce neutrophil extracellular trap release disrupting lymphoid organ structure and immunoglobulin secretion. Nature Cardiovascular Research. doi.org/10.1038/s44161-024-00462-8.

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  • Investigating the efficacy and safety of existing drugs in patients with rare immune diseases

    Investigating the efficacy and safety of existing drugs in patients with rare immune diseases

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    This month the first study within the DRIMID consortium (DRIMID stands for Drug Rediscovery for Rare Immune Mediated Inflammatory Diseases) has started. This study will investigate the efficacy and safety of the drug filgotinib (approved for treatment of rheumatoid arthritis and ulcerative colitis) in three rare immune diseases (Behçet’s disease, idiopathic inflammatory myositis, IgG4-related disease). DRIMID aims to investigate whether this drug – despite the absence of formal drug approval – can also be used to treat these rare immune diseases.

    New drugs are usually developed (and therefore faster accessible) for conditions involving large patient groups. However, for rare diseases, drug development is more difficult. With the establishment of the DRIMID partnership, major steps have now been taken to make new drugs available to such patient groups. The project is a collaboration between ARCH foundation, ReumaNederland, drug company AlfaSigma and a number of Dutch hospitals with the aim of (re)developing drugs for rare disorders.

    Rare immune diseases

    Rare immune-mediated inflammatory diseases usually have an unknown cause, and are often associated with the formation of autoantibodies (the immune system attacks its own body). Examples of such diseases include granulomatosis with polyangiitis, inflammatory myositis, vasculitis of the great vessels, IgG4-related disease, Behçet’s disease, Sjögren’s disease and systemic sclerosis. A major problem in clinical practice is that many patients do not respond adequately to common anti-inflammatory drugs over time. Thus, there is an unmet medical need for adequate treatment options for this group.

    The study

    The aim of the study is to investigate if filgotinib is efficacious when used by patients with rare immune diseases and whether the drug is well tolerated. Now that the first study within the DRIMID framework has been approved by the medical ethics review committee, patients are being sought with one of the following rare immune diseases:

    • Behçet’s disease

    • idiopathic inflammatory myositis

    • IgG4-related disease

    It is important that participants have active symptoms of the disease at the start of the study. In addition, it is important that they have first tried regular treatment methods, such as prednisone and at least one other anti-inflammatory agent. If the disease did not or insufficiently respond to these drugs, or if the patient was hypersensitive to these drugs, the patient may be eligible for this new study.

    The study will follow up patients for 26 weeks. At several points during the study, the effect of the drug on symptoms will be measured by a physician via physical examination, blood tests and questionnaires. If the drug appears to be working well for the patient at the end of the study, the patient will be allowed to continue taking it. Participation in the study is free of charge.

    Participating hospitals

    The study, coordinated by rheumatologist Prof. Jaap van Laar MD PhD (Department of Rheumatology & Clinical Immunology, UMC Utrecht), will start in six Dutch hospitals. Patients can be approached by their physician to participate, but they can also inquire about eligibility themselves. They can do so by sending an e-mail to [email protected]. The research nurse at UMC Utrecht will then contact the hospital most suitable for the patient. The study will be conducted at the following locations:

    – UMC Utrecht

    – Amsterdam UMC

    – Erasmus MC (Rotterdam)

    – Radboudumc (Nijmegen)

    – Haga Hospital (The Hague)

    – Zuyderland Medical Center (Heerlen)

    DRIMID consortium

    DRIMID is an acronym and stands for ‘Drug Rediscovery for Rare Immune Mediated Inflammatory Diseases’. ARCH (Arthritis Research and Collaboration Hub, a Dutch medical expertise platform for rare autoimmune diseases), ReumaNederland and UMC Utrecht (the first academic partner within DRIMID) established the public-private DRIMID consortium in 2021. The consortium is funded by ReumaNederland and Health Holland. In time, the investigators within the consortium intend to expand the project to other immune diseases and also to other drug companies and drugs.

    Source:

    University Medical Center Utrecht

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  • Ophthalmological method can be used to monitor neurodegeneration in Parkinson’s patients

    Ophthalmological method can be used to monitor neurodegeneration in Parkinson’s patients

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    A study by the University of the Basque Country (UPV/EHU) and Biobizkaia proposes using an available, simple, non-invasive tool to monitor this neurodegeneration.

    Although there are still some aspects pending confirmation for its use in the clinical setting, and its resolution needs to be improved slightly, a study by the UPV/EHU and Biobizkaia has shown that a method routinely used to carry out ophthalmological tests can also be used to monitor the neurodegeneration that occurs in Parkinson’s patients. In the course of the research it was found that the neurodegeneration of the retina probably precedes cognitive impairment.

    When Parkinson’s or another neurodegenerative disease is diagnosed, patients always ask: “And now what? What will happen? What can be expected from the disease?” For neurologists, however, it is not possible to answer these questions precisely, as “the evolution of patients tends to be very varied: some experience no change over the years, while others end up with dementia or in a wheelchair”, explained Ane Murueta-Goyena, researcher in the UPV/EHU’s department of Neurosciences.

    Today, identifying Parkinson’s patients at risk of cognitive impairment poses a major challenge, yet this is necessary when it comes to providing more effective clinical treatments and stepping up clinical trials. In fact, Dr. Ane Murueta-Goyena, in collaboration with Biobizkaia’s research staff, wanted to see “whether the visual system can enable this deterioration to be predicted, in other words, what future the patient can expect within a few years”. The thickness of the retina was used for this purpose.

    The retina is a membrane located at the back of the eyeball, it is related to the nervous system and comprises several layers. During the study, a cohort of Parkinson’s patients had the thickness of the innermost layer of their retinas measured using optical coherence tomography. This type of tomography is a routinely used instrument in ophthalmological tests, as it allows high-resolution, repeatable and accurate measurements to be made. So the evolution of this retinal layer was analysed and compared in people with and without Parkinson’s disease over the 2015-2021 period. The results of the analysis of the images of the retinal layers of Parkinson’s patients were also confirmed in a UK hospital.

    The results showed that the retinal layer is noticeably thinner in Parkinson’s patients. It was also observed that “during the initial phases of the disease it is in the retina where the greatest neurodegeneration is detected, and, from a given moment onwards, when the layer is already very thin, a kind of stabilising of the neurodegeneration process takes place. Retinal thinning and cognitive impairment do not occur simultaneously. The initial changes in the retina are more evident and then, over the years, patients are observed to worsen clinically in both cognitive and motor terms”, explained Murueta-Goya. In other words, the slower retinal layer thickness loss is associated with faster cognitive decline; this slowness is linked to greater severity of the disease”.

    The researcher has attached great importance to the results: “We have obtained information on the progression of the disease, and the tool we are proposing is non-invasive and available at all hospitals.” The results need to be validated internationally and “by slightly improving the resolution of the technology, we will be closer to validating the method for monitoring the neurodegeneration that takes place in Parkinson’s disease”. The researcher also revealed that they are continuing the research on another cohort of patients and that funding is the key.

    Source:

    University of the Basque Country

    Journal reference:

    Murueta-Goyena, A., et al. (2024). Association of retinal neurodegeneration with the progression of cognitive decline in Parkinson’s disease. Npj Parkinson’s Disease. doi.org/10.1038/s41531-024-00637-x.

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