Every rare disease tells a story. Understanding how one unfolds — how it develops and evolves — is often the first step toward treating it. A small genetic error, a malfunctioning cell or a breakdown in a biological process can set disease in motion. But for many rare conditions, the course of progression remains unwritten. 

A groundbreaking natural history study led by researchers at Mayo Clinic's Center for Individualized Medicine and Department of Clinical Genomics aims to change that, advancing research for one rare disease with the potential to benefit many others. 

At the center of this effort is autosomal dominant leukodystrophy, a rare, hereditary neurodegenerative disorder. It is driven by a single genetic variant that causes an abnormal buildup of a protein. This disrupts nerve cell function and the brain's signaling network. Over time, this damage leads to a slow decline in movement, balance and essential body functions. 

A road map for research and treatment

This extensive study will track the disease from its earliest stages to advanced progression in patients from around the world. By analyzing patient medical records, brain imaging scans, genetic data, neurological assessments and more, researchers will develop a comprehensive dataset to identify key biological markers in people with the disease.  

These insights will serve as a foundation for future clinical trials. 

"Genetic therapies are increasingly becoming a reality for rare diseases, providing hope of a treatment that can address the root cause of the disease," says Margot Cousin, Ph.D., the study's lead investigator and director of the Center for Individualized Medicine's N-of-1 Therapeutics program. "We need to have systems in place to study these disorders so we can bring these therapies into clinical practice efficiently." 

"Genetic therapies are increasingly becoming a reality for rare diseases, providing hope of a treatment that can address the root cause of the disease."

mARGOT cOUSIN, pH.d.

What makes this disease so challenging?

Autosomal dominant leukodystrophy is classified as an ultra-rare disease, meaning it affects a small number of people worldwide. Fewer than 50 families have been identified, but researchers believe many cases go undiagnosed due to its similarities to other neurological conditions and limited access to genetic testing.  

Because it is so rare, most clinicians will never encounter a patient with autosomal dominant leukodystrophy (ADLD). When they do, the disease can be difficult to recognize, as its symptoms resemble other neurological disorders that affect the brain's protective nerve coating. This often leads to misdiagnosis and delays in care. 

Further, the type of genetic alteration that causes autosomal dominant leukodystrophy may not be detected using some common genetic testing methodologies, putting even more importance on the clinical recognition of the disease. Researchers emphasize the need to increase awareness of the disease among neurologists to help patients receive an accurate diagnosis sooner. 

How the study works

This natural history study is a collaboration with Children's Hospital of Philadelphia through the Global Leukodystrophy Initiative. This initiative has extensive expertise and infrastructure needed to capture and analyze data on autosomal dominant leukodystrophy at depth. 

Mayo Clinic researchers are working within this collaboration to improve how the disease is studied and to ensure data collection meets the highest clinical research standards.  

"We are creating a dedicated clinical and research infrastructure for identifying patients earlier so they can receive the highest level of multidisciplinary care."

Radhika Dhamija, M.B.B.S.

This study is also supported by the ADLD Center, a patient advocacy group, whose mission is to support research on the disease's pathology and therapy. The center provides patients, caregivers and families with essential resources and a strong support network.  

Improving diagnosis and clinical care

Alongside this natural history study, Mayo Clinic has established the Autosomal Dominant Leukodystrophy Clinical Care Center, dedicated to diagnosing and managing this disease. The center is led by Radhika Dhamija, M.B.B.S., a neurologist and medical geneticist in the Mayo Clinic Department of Clinical Genomics. This clinical care center coordinates care across neurology, urology, radiology, genomics and other specialties to provide the highest level of care for patients. 

"We are creating a dedicated clinical and research infrastructure for identifying patients earlier so they can receive the highest level of multidisciplinary care," says Dr. Dhamija. "This is about transforming the future of how we approach rare neurogenetic diseases." 

A model for future rare disease research

Mayo Clinic researchers see this natural history study on autosomal dominant leukodystrophy as a model for accelerating research in other ultra-rare conditions that remain understudied due to small patient populations. 

"We're building a framework that brings scientific clarity to diseases that have remained long overlooked," Dr. Cousin says.  

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(Video: Filippo Pinto e Vairo, M.D., Ph.D., medical director of Mayo Clinic’s Program for Rare and Undiagnosed Diseases, analyzes genetic data on screen, uncovering insights into rare disease.)

February 28 is Rare Disease Day, a time to recognize the millions of people worldwide living with conditions that often go undiagnosed or untreated. In the U.S., a disease is considered rare if it affects fewer than 200,000 people.

More than 7,000 rare diseases have been identified, many of them genetic. These conditions can affect vital organs, the nervous system, the immune system and other critical functions. 

At Mayo Clinic's Center for Individualized Medicine, researchers and clinicians are advancing precision medicine to improve diagnoses and develop treatments once thought impossible. A key part of this effort is the center's Rare Disease Omics strategy. This innovative approach moves discovery from the lab to patient care. 

By combining multiple layers of biological omics data, including genomics (study of genes), proteomics (study of proteins), and other molecular insights, scientists uncover how rare diseases form at their earliest stages and progress over time. Using omics tools, they identify mutations, examine disruptions in cellular function and trace how small errors in biological processes can lead to disease. 

"Our goal is to build a discovery pipeline," says Timothy Curry, M.D., Ph.D., the William O. Lund, Jr. and Natalie C. Lund Program Director for Clinomics, and the associate director of Practice Implementation at the Center for Individualized Medicine. Dr. Curry leads the center's Rare Disease Omics pillar.  

"Every breakthrough we make brings us closer to the day when rare diseases once thought untreatable can be managed or even cured," he says. 

Artificial Intelligence helps accelerate research

Artificial intelligence helps researchers identify genetic mutations and patterns that are difficult to detect through traditional methods. AI also helps teams search for existing drugs that might be repurposed for rare conditions.  

These advances are unlocking new treatment possibilities. Scientists are developing personalized therapies, including RNA-based treatments that target genetic errors without altering DNA. 

"Every breakthrough we make brings us closer to the day when rare diseases once thought untreatable can be managed or even cured."

Timothy curry, m.d. ph.d.

At the same time, Mayo Clinic is expanding efforts to provide answers for patients with unexplained conditions. The Program for Rare and Undiagnosed Diseases has delivered in-depth genomic analysis to more than 3,200 patients since its launch in 2019. 

Now, the next step is bringing these discoveries to more patients. Mayo Clinic is expanding genetic testing and counseling to give more people with complex conditions access to expert care and research.

Special presentations spotlight rare disease experts

To mark Rare Disease Day, Mayo Clinic will host a virtual event featuring a global leader in rare disease advocacy. 

February 27 at 12 p.m. CST – A Conversation with Dr. Durhane Wong-Rieger 

Durhane Wong-Rieger, Ph.D., president of the Canadian Organization for Rare Disorders and a key figure in the International Rare Diseases Research Consortium, will share insights on improving treatment access and driving innovation. She has been instrumental in shaping policies that expand options for patients. With expertise spanning global initiatives and groundbreaking research, she will discuss how advances in genomics and AI can bridge — or deepen — the divide in healthcare equity for rare disease patients worldwide. Learn more. 

Related story

Advancing rare disease breakthroughs with genomics, AI and innovation

Patient by patient, researchers at Mayo Clinic's Center for Individualized Medicine are making notable progress in rare disease diagnostics and treatments. Read more.

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In a study published in Science Advances, Mayo Clinic researchers found a new immunotherapy target called a cryptic antigen that may be key in helping the immune system fight tumors in ovarian cancer.

Cryptic antigens are part of a protein — known as epitopes — that are usually hidden or inaccessible to the immune system and may be present in tumor cells.

"These findings underscore the need to look at alternate sources of target antigens for ovarian cancer," says Marion R. Curtis, Ph.D., a Mayo Clinic senior associate consultant in immunology and senior author of the study.

Dr. Curtis explains that discovering tumor-associated antigens that T cells recognize is crucial for the success of immunotherapeutic approaches in ovarian cancer, where the growth of cells that form in the ovaries multiply quickly and can invade and destroy healthy body tissue.

T cells are a critical component of the adaptive immune system. Their ability to recognize and respond to specific targets is fundamental to their function. They play a significant role in developing and treating cancer and are vital in the immune system's fight against infections.

The researchers characterized the tumor antigens generated from ovarian cancer using multi-omics approaches to determine their ability to trigger an immune response. Multi-omics encompasses using multiple “omes” (i.e., genome, proteome, microbiome, epigenome) to better understand the mechanisms of disease processes, detection, potential prevention and more focused therapies.

Researchers have previously focused on discovering newly formed antigens (neoantigens). However, a previous study revealed that neoantigens are rarely found in ovarian cancer samples, making them unattractive targets. While targeted immunotherapies and immune checkpoint inhibitors have been highly successful in treating multiple cancer types, patients with ovarian cancer have limited benefit from these approaches.

What lies ahead?

Researchers will explore cryptic antigens in designing new treatments, such as vaccines or immunotherapies, that could help the immune system target tumors more effectively. They plan to test these approaches in laboratory models and clinical trials to see if they work safely.

The next steps include larger-scale studies to identify the abundance of cryptic antigen expression across different tumor types and to understand how those levels of cryptic antigens may correlate with patients' responses to immunotherapy.

Such studies could pave the way for developing immunotherapies targeting cryptic antigens in other tumor types. Moreover, understanding how cryptic antigens are processed and presented could offer new strategies to help the body's immune system fight cancer. In the long term, these efforts could broaden the reach of immunotherapy to other forms of cancer that currently lack effective treatment options.

Review the study for a complete list of authors, disclosures and funding.

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Imagine knowing your risk for disease long before symptoms appear. With early detection and targeted interventions, this knowledge could transform how complex healthcare challenges are addressed. Researchers are now leveraging genetic data to enhance disease risk prediction through an innovative tool known as a polygenic risk score. Learn more on a new episode of Tomorrow's Cure.

The podcast's latest episode features Victor Ortega, M.D., Ph.D., associate director of the Mayo Clinic Center for Individualized Medicine, and Louise Wain, Ph.D., professor of respiratory research, University of Leicester in the U.K.

Scientists have developed polygenic risk scores to analyze multiple genetic variants across a person's genome. These scores assess the likelihood of developing conditions such as heart and lung diseases, diabetes, asthma, and certain cancers.

"There's really no one singular variant in the genome that causes common diseases. It's really a lot of different variants across the genome, across our genomes that have weaker or milder effects on disease risk, but all together give us this increased risk," explains Dr. Ortega.

"With those discoveries, we've developed genetic risk scores, polygenic risk scores, where we bring together the effects or associations across variants, across entire genomes into a singular score," he continues.

Clinicians can then utilize these risk scores to potentially customize prevention and treatment strategies.

"These are quite early in development, but are potentially really, really important," says Dr. Wain. "In the future, we could use these to improve diagnosis where somebody is already presenting with the disease, but they need a specific diagnosis in order to access the medicines that are going to work for them." Dr. Ortega says the potential of polygenic risk scores is both exciting and promising. Find out more on the latest episode of Tomorrow's Cure. To view the complete list of episodes and featured experts, visit tomorrowscure.com.

Related content:

• What's your health forecast? Expert explains science behind personal guides to well-being
• Mayo pioneers population science to advance personalized medicine

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Antibody-drug conjugates are targeted cancer therapies. They deliver potent drugs directly into cancer cells while minimizing harm to healthy tissue. These therapies work by recognizing specific proteins found on the surface of cancer cells.

The proteins function like a lock and the therapy acts as a key, latching onto the cancer cells with precision to deliver the treatment.

But precision alone isn’t enough. Even when the key fits the lock, the door often stays closed. This prevents the drug from entering the cell to attack the cancer. 

Now, Mayo Clinic researchers have developed a pioneering strategy that may help overcome this barrier. The approach combines antibody-drug conjugates with proteolysis-targeting chimeras. These molecular tools break down specific proteins to improve how cancer cells internalize the cancer-fighting therapy.  

"The ability to target specific proteins and improve drug absorption into tumor cells represents a significant step forward in how we approach aggressive cancers."

Aaron Mansfield, M.D.

In a preclinical study, published in Communications Biology, the combined therapy improved the internalization of these treatments by up to 1.9 times in certain models.  

"The ability to target specific proteins and improve drug absorption into tumor cells represents a significant step forward in how we approach aggressive cancers," says Aaron Mansfield, M.D., a medical oncologist and lead author of the study at the Center for Individualized Medicine and the Mayo Clinic Comprehensive Cancer Center. 

Dr. Mansfield and his team tested the approach using breast cancer organoids. They created these miniaturized, lab-grown versions of breast tissue to mimic the complexity of real tumors. Unlike traditional cell cultures, organoids better replicate how tumors behave in the body.

Specifically, the research focused on three proteins often found in aggressive cancers. These include human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and mesenchymal-epithelial transition factor (MET). These proteins are associated with breast, lung and pancreatic cancers.  

Overall, the study highlights the versatility of this approach by demonstrating potential benefits across multiple cancer types and protein targets. 

For a complete list of authors, disclosures and funding, review the study. 

Other recent research from Dr. Mansfield

Innovative Mayo Clinic test may improve mesothelioma detection rate in blood

Dr. Mansfield and his team have developed an innovative testing strategy for mesothelioma that could potentially increase the detection rate of cancer DNA in the blood. Read more.

Researchers predict immunotherapy response in mesothelioma cancer

Dr. Mansfield and his team have discovered a potential genomic signature to predict which patients with mesothelioma could benefit from immunotherapy.  Read more.

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Mayo Clinic researchers have developed organoid models to study uveal melanoma, one of the most common types of eye cancer in adults. Their goal is to use these models to better understand how this disease works and develop treatments for unmet patient needs.

Organoids are 3D models grown from patient tissue that accurately reflect a patient's unique genetic and biological characteristics, also known as "avatars." When derived from a patient's cancer tumor, an organoid will behave and respond to treatments outside the body in a lab (in vitro) just like the original tumor would inside the body (in vivo).

In 50% of patients, uveal melanoma metastasizes, spreading to other parts of the body, leading to a poor prognosis and average survival of less than two years.  Unfortunately, current treatments for this condition often have limited effectiveness, leaving patients and their doctors with few options.

"The hope is that these patient-derived organoid models better represent human cancer in the laboratory," says Lauren Dalvin, M.D., a Mayo Clinic Comprehensive Cancer Center ocular oncologist and surgeon-scientist who is one of the lead researchers. "Using these models as a foundation for drug testing will facilitate new treatment discoveries with higher success rates in clinical trials, ultimately translating to improved outcomes for patients with uveal melanoma."

Growing an eye cancer biobank

In the past, the lack of human disease models representing the entire spectrum of uveal melanoma has created a bottleneck, limiting the ability of scientists to identify effective targets for treatment and prevention. Most laboratory studies have drawn from the same set of commercially available cell lines, which are not representative of the disease and often differ in important ways from the original tumors.

To blast through this bottleneck, a study team led by Dr. Dalvin, in collaboration with Martin Fernandez-Zapico, M.D., a cancer biologist with Mayo Clinic Comprehensive Cancer Center, decided to develop a new, uveal melanoma patient-derived organoid biobank. Their goal is to create a research resource representing the real-world variability of this cancer.

In a paper published in Investigative Ophthalmology & Visual Science, they described the initial development of this biobank. The researchers successfully created organoids derived from Mayo Clinic ocular oncology patients who enrolled in a prospective study involving the collection of tumor tissue for research from July 1, 2019, through July 1, 2024. Their study determined that these organoid models:

• Could be generated, retained their stability through many uses and were a renewable living resource capable of being regenerated at need.
• Retained the clinically relevant features of the original tumors, clustered into appropriate molecular groups based on validated prognostic markers and resembled human disease when compared to in vivo animal models.
• Served as suitable human models for drug screening.

Recognizing the immense value of this organoid biobank, the investigators have already begun expanding it to include other research centers. Their goal is to create a resource capable of representing the global epigenomic variability of uveal melanoma. In the future, they hope this biobank will serve as a comprehensive platform for drug screening and other types of lab research on uveal melanoma. This collaborative effort will accelerate research and pave the way for improved treatments and outcomes for patients with this disease.

Read the paper to learn more about the study, including funding and disclosures.

Mayo Clinic organoid research across the spectrum of disease

Organoids are transforming the landscape of biomedical research. Scientists are using this innovative approach to model diseases, track their progression and identify and characterize potential treatments. Mayo Clinic is at the forefront of organoid research, applying this approach to study a wide range of health conditions including:

• Neurodegenerative and neurological disorders, such as Lewy body dementia, Alzheimer's disease, autism, opioid addiction and alcohol use disorder.
• Measles and other infectious diseases.
• Many types of cancer including breast cancer.
• Inflammatory bowel disease.

The goals of this research extend far beyond its current applications. Mayo Clinic researchers aim to develop organoids representing organs throughout the human body to track disease, screen drugs and regenerate tissues. This approach holds the promise of accelerating research in precision medicine and the search for cures in other areas of biomedical research.

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ROCHESTER, Minn. — Mayo Clinic today unveiled separate groundbreaking collaborations with Microsoft Research and with Cerebras Systems in the field of generative artificial intelligence (AI), designed to personalize patient care, significantly accelerate diagnostic time and improve accuracy.

Announced during the 43^rd Annual J.P. Morgan Healthcare Conference, the projects focus on developing and testing foundation models customized for various applications, leveraging the power of multimodal radiology images and data (including CT scans and MRIs) with Microsoft Research and genomic sequencing data with Cerebras. The innovations have the potential to transform how clinicians approach diagnosis and treatment, ultimately leading to better patient outcomes. 

Foundation AI models are large, pre-trained models capable of adapting to and carrying out many tasks with minimal extra training. They learn from massive datasets, acquiring general knowledge that can be used across diverse applications. This adaptability makes them efficient and versatile building blocks for numerous AI systems.

Mayo Clinic and Microsoft Research advance AI for chest X-rays

Mayo Clinic and Microsoft Research are collaboratively developing foundation models that integrate text and images. For this use case, Mayo and Microsoft Research are working together to explore the use of generative AI in radiology using Microsoft Research’s AI technology and Mayo Clinic’s X-ray data.

"Multimodal foundation models hold immense promise in tackling significant roadblocks across the radiology ecosystem. The innovations we’re creating with Microsoft Research will help unlock valuable insights for the future of medical imaging to improve how radiologists work and how patients are cared for," says Matthew Callstrom, M.D. Ph.D., chair of Mayo Clinic Radiology in the Midwest and medical director for Generative AI and Strategy. "Focusing on chest X-ray reports, Mayo's clinical teams and Microsoft researchers will collaborate to advance the state-of-the-art in multimodal AI radiology, helping bring innovation to real-world application faster and at scale, which is key to making exemplary healthcare more accessible." 

Empowering clinicians with instant access to the information they need is at the heart of this research project. Mayo Clinic aims to develop a model that can automatically generate reports, evaluate tube and line placement in chest X-rays, and detect changes from prior images. This proof-of-concept model seeks to improve clinician workflow and patient care by providing a more efficient and comprehensive analysis of radiographic images.

"This collaboration is a crucial step towards our mutual goal of developing generative AI that improves patient outcomes and the clinician experience," says Jonathan Carlson, Ph.D., managing director, Health Futures at Microsoft Research. "The fusion of Microsoft's recognized research innovations in biomedical AI and Mayo Clinic's radiology excellence will empower clinicians with the tools they need to deliver more precise and accessible care and furthers Microsoft’s commitment to bringing the power of emerging AI to clinical researchers worldwide."

Mayo Clinic and Cerebras create a world-leading genomic foundation model

Mayo Clinic and Cerebras have created a genomic foundation model that combines publicly available human reference genome data that represents an ideal version of the human genome, with Mayo's comprehensive patient exome data and the power of its Mayo Clinic Platform. Exome data focuses on the protein-coding region of the genome where several disease-causing mutations take place. Cerebras's high-powered computing and generative AI capabilities make it possible to train and develop the model at scale, positioning it to be further refined for more specific uses.

"The genomic foundation model represents a significant advancement in personalized medicine," says Dr. Callstrom. "Its ability to analyze genomic data and compare them in almost real-time with patients with similar traits allows for more precise prediction of disease and treatment response, leading to faster diagnoses and the selection of targeted therapies for patients."

For example, rheumatoid arthritis (RA) is a debilitating autoimmune disease, and the standard treatment approach often requires trials of different therapies to achieve disease remission. It can take several months to know if a therapy is working. A new genomic model developed by Mayo Clinic and Cerebras offers a potential solution to shorten the time to identify effective treatment and avoid long-term morbidity associated with the untreated disease. Early findings demonstrate high performance against benchmarks and show early promise in identifying patient response to therapy. As more patient data is added, the model's predictive power is expected to increase, leading to faster, more effective personalized treatment for RA patients.   

"Mayo’s genomic foundation model sets a new bar for genomic models, excelling not only in standard tasks like predicting functional and regulatory properties of DNA but also enabling discoveries of complex correlations between genetic variants and medical conditions," says Natalia Vassilieva, Field CTO at Cerebras Systems. "Unlike current approaches focused on single-variant associations, this model enables the discovery of connections where collections of variants contribute to a particular condition."

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About Mayo Clinic
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.

Media contact:

• Terri Malloy, Mayo Clinic Communications, newsbureau@mayo.edu

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ROCHESTER, Minn. — Against the backdrop of the 43^rd Annual J.P. Morgan Healthcare Conference, Mayo Clinic announced the formation of Mayo Clinic Digital Pathology, designed on a platform architecture to boldly unlock the power of its extensive archive of digital slides to revolutionize pathology and accelerate medical breakthroughs.

Mayo Clinic's expertise, de-identified clinical data, and its Platform architecture, combined with technical expertise and resources of NVIDIA, a world leader in accelerated computing, is powering the ability to accelerate this transformation. In addition, to improve performance and scalability of generative AI in pathology, Mayo Clinic is collaborating with Aignostics, an industry leader in building artificial intelligence (AI) models for digital solutions in precision medicine in a way that mirrors its established approach to patient care. This collaborative, multidisciplinary effort leverages both medical and technological strengths, and the early achievements are promising.

"Mayo Clinic is reimagining what is possible in disease detection and prediction, both within its own system and globally. We are doing this by using large, diverse datasets to build powerful artificial intelligence models in pathology. This will make diagnoses faster, more accurate, and more efficient, improving treatment approaches and speeding new cures to patients," says Jim Rogers, CEO, Mayo Clinic Digital Pathology.

"AI-driven insights can accelerate diagnostics, enhance precision medicine and revolutionize patient care," said Kimberly Powell, VP of Healthcare and Life Sciences, NVIDIA. "By digitizing and harnessing the power of vast datasets through its Digital Pathology platform, powered by NVIDIA's accelerated computing, Mayo Clinic is helping pave the way for a future with faster medical breakthroughs, better treatments and improved outcomes for patients across the globe."

"Merging Mayo Clinic's data and expertise with our advanced machine learning capabilities will produce breakthrough foundation models and AI products that advance the field of precision medicine and meaningfully improve patient care," says Viktor Matyas, CEO of Aignostics.

The vast majority of pathology practices remain tethered to analog processes, hindering access to critical diagnostic data that could be used to expand diagnostics and treatments and speed the development of new therapies to benefit patients. To address this challenge, Mayo Clinic has moved quickly, investing in digitizing its pathology practice and by scanning its extensive archive of pathology slides, as well as prospectively scanning pathology slides from current patients. To date, Mayo Clinic Digital Pathology has leveraged 20 million digital slide images linked to 10 million patient records that incorporate treatments, medications, imaging, clinical notes, genomic data and more.

In less than two months, Mayo Clinic and Aignostics developed a leading foundation model built on 1.2 million deidentified slides from Mayo Clinic and Charité – Universitätsmedizin Berlin, findings of which were published in a paper on Jan. 9. Current efforts include developing and deploying new solutions enabled by this model. Future plans are focused on building new models, including one being trained on 5 million slides.   

The NVIDIA collaboration will create a first-in-class infrastructure for building and deploying foundation models to accelerate generative AI advances in pathology and beyond. With NVIDIA's healthcare-specific full stack computing architecture for artificial intelligence, NVIDIA Clara, Mayo Clinic is building models that will open new frontiers in medicine and lay the foundation for more personalized patient experiences.      

The development of Mayo Clinic Digital Pathology has been a planned process over many years. The pathology platform takes advantage of the portfolios of Mayo Clinic Platform, a global network that drives digital innovation around diagnosis, treatment and operational improvements worldwide, and Mayo Clinic Laboratories, which provides advanced testing and pathology services for healthcare organizations worldwide. Continuing its efforts to transform healthcare, Mayo Clinic is working with investors and data providers to continue building a pathology platform that spurs innovation and transforms the medical practice for the benefit of healthcare systems and patients worldwide.

"These new capabilities using digital pathology data will unlock this critically important clinical information for building AI solutions for advanced diagnosis and care of patients and that will improve the lives of patients globally," says Matthew Callstrom, M.D. Ph.D., chair of Mayo Clinic Radiology in the Midwest and medical director for Generative AI and Strategy.

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About Mayo Clinic
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.

Media contact:

• Terri Malloy, Mayo Clinic Communications, newsbureau@mayo.edu

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Each year, many women undergo surgery for pelvic organ prolapse, a condition where weakened muscles and tissues allow organs like the bladder or uterus to shift and press against the vaginal wall. This typically results in feelings of tissue bulging from the vagina, pelvic pressure or urinary issues.

While the surgical procedure often alleviates symptoms, many women experience a common post-surgical complication: urinary tract infections.  

Now, researchers at Mayo Clinic, led by Marina Walther-Antonio, Ph.D., and John Occhino, M.D., have found a way to potentially predict who will develop these infections before surgery even begins.

Their study reveals that specific patterns in the vaginal microbiome — a community of microorganisms, including bacteria, fungi and viruses — could serve as early indicators of urinary tract infection risk. Their findings are published in Nature Scientific Reports.

Zeroing in on key bacteria

Their findings show that women with lower levels of the protective bacterial species Lactobacillus were more likely to develop urinary tract infections following pelvic organ prolapse surgery. In addition, they found that higher levels of Prevotella and Gardnerella — bacteria linked to inflammation and infection — are also associated with an increased risk of postoperative complications. 

The research also provides insights on how surgery itself impacts the microbiome. Samples taken after surgery showed notable disruptions to both the vaginal and urinary microbiomes. These changes included lower microbial diversity, which can leave the body more vulnerable to infection.  

The reasons for the changes are likely multifactorial and may include preoperative antibiotic administration, vaginal cleansing prior to surgery and surgery itself. 

"Our ability to detect these microbiome patterns provides a potential opportunity to personalize treatment strategies and improve outcomes."

Marina Walther-Antonio, Ph.d.

"Our ability to detect these microbiome patterns provides a potential opportunity to personalize treatment strategies and improve outcomes," says Dr. Walther-Antonio, a microbiome researcher at Microbiomics Program at Mayo Clinic's Center for Individualized Medicine. Dr. Walther-Antonio also conducts research in the Departments of Surgery and Obstetrics and Gynecology, as well as the Mayo Clinic Comprehensive Cancer Center. 

Dr. Walther-Antonio's collaboration with Dr. Occhino bridges a gap between cutting-edge research and clinical practice. 

"Understanding the microbiome's role in post-surgical infections gives us a unique opportunity to possibly prevent these complications altogether," says Dr. Occhino, a Mayo Clinic urogynecologic surgeon. Dr. Occhino specializes in treating pelvic floor disorders, including conditions like pelvic organ prolapse and urinary incontinence.  

Turning microbiome data into action 

The study analyzed microbiome samples from postmenopausal women undergoing a vaginal hysterectomy with pelvic reconstruction. Researchers collected samples using vaginal swabs or urine collection at five stages, including before surgery, immediately after and during recovery. Advanced sequencing techniques were used to map the genetic material of the microbes and identify changes in the microbial communities. 

"Understanding the microbiome's role in post-surgical infections gives us a unique opportunity to possibly prevent these complications altogether."

John Occhino, M.D.

Dr. Walther-Antonio and Dr. Occhino plan to expand their research to include larger and more diverse patient groups. This effort aims to confirm their findings and explore interventions. This could include using probiotics to restore microbial balance before surgery or adjusting surgical timing for at-risk patients. 

Dr. Walther-Antonio's other recent microbiome discoveries

Dr. Walther-Antonio's expertise in microbiome research extends beyond surgical outcomes. Her work also focuses on specific microbial communities associated with gynecologic cancers, including endometrial and ovarian cancers. She has also investigated how these microbes may contribute to disease progression and influence treatment responses. These discoveries have driven the development of noninvasive swab tests designed for earlier detection to improve outcomes. 

Review the study for a complete list of authors, disclosures and funding.   

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Mayo Clinic researchers have pioneered an artificial intelligence (AI) tool, called OmicsFootPrint, that helps convert vast amounts of complex biological data into two-dimensional circular images.  

Omics is the study of genes, proteins and other molecular data to help uncover how the body functions and how diseases develop. By mapping this data, the OmicsFootPrint may provide clinicians and researchers with a new way to visualize patterns in diseases, such as cancer and neurological disorders, that can help guide personalized therapies. It may also provide an intuitive way to explore disease mechanisms and interactions. 

The details of the tool are published in a new study in Nucleic Acids Research.  

"Data becomes most powerful when you can see the story it's telling," says Krishna Rani Kalari, Ph.D., lead author of the study and associate professor of biomedical informatics at Mayo Clinic's Center for Individualized Medicine. "The OmicsFootPrint could open doors to discoveries we haven't been able to achieve before." 

Simplifying complex data 

Genes act as the body’s instruction manual, while proteins carry out those instructions to keep cells functioning. Sometimes, changes in these instructions — called mutations — can disrupt this process and lead to disease. The OmicsFootPrint helps make sense of these complexities by turning data — such as gene activity, mutations and protein levels — into colorful, circular maps that offer a clearer picture of what’s happening in the body. 

In their study, the researchers used the OmicsFootPrint to analyze drug response and cancer multi-omics data. The tool distinguished between two types of breast cancer — lobular and ductal carcinomas — with an average accuracy of 87%. When applied to lung cancer, it demonstrated over 95% accuracy in identifying two types: adenocarcinoma and squamous cell carcinoma. 

Small data sets bring big impacts 

The study showed that combining several types of molecular data produces more accurate results than using just one type of data. 

The OmicsFootPrint also shows potential in providing meaningful results even with limited datasets. It uses advanced AI methods that learn from existing data and apply that knowledge to new scenarios — a process known as transfer learning. In one example, it helped researchers achieve over 95% accuracy in identifying lung cancer subtypes using less than 20% of the typical data volume.  

"This approach could be beneficial for research even with small sample size or clinical studies," Dr. Kalari says. 

To enhance its accuracy and insights, the OmicsFootPrint framework also uses an advanced method called SHAP (SHapley Additive exPlanations). SHAP highlights the most important markers, genes or proteins that influence the results to help researchers understand the factors driving disease patterns. 

From research to clinical practice 

Beyond research, the OmicsFootPrint is designed for clinical use. It compresses large biological datasets into compact images that require just 2% of the original storage space. This could make the images easy to integrate into electronic medical records to guide patient care in the future. 

The research team plans to expand the OmicsFootPrint to study other diseases, including neurological diseases and other complex disorders. They are also working on updates to make the tool even more accurate and flexible, including the ability to find new disease markers and drug targets. 

Review the study for a complete list of authors, disclosures and funding. 

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