A landmark week for cardiovascular genetics opened with huge news from the gene-editing field: Eli Lilly’s experimental therapy VERVE-102, a single-dose PCSK9 base editor designed for patients with heterozygous familial hypercholesterolemia (FH) and premature coronary artery disease (CAD), demonstrated durable cholesterol-lowering effects in early studies. While the result marks one of the strongest signs yet that in vivo gene editing could become a practical treatment for inherited cardiovascular disease, it’s just scratching the surface of the enormous number of uncharacterized genetic variants that influence cholesterol levels and heart disease risk across global populations.
The largest rare variant association with blood lipids study ever reported may provide the roadmap to heart disease mechanistic and clinical insights. In a Nature Genetics study analyzing data from more than one million individuals, researchers identified thousands of rare coding variants linked to cholesterol and triglyceride (TG) levels, including several genes strongly associated with coronary artery disease. The findings could accelerate the development of precision medicines for dyslipidemia and improve the diagnosis of inherited lipid disorders such as familial hypercholesterolemia.
Diversity and scale
FH is caused by rare mutations that raise LDL-C, increasing the risk of early-onset CAD, a leading cause of premature death worldwide. Although statins and other lipid-lowering therapies can significantly lower cardiovascular risk, FH is underdiagnosed and undertreated. Many FH-associated variants have variable penetrance, so some carriers have severe disease and others have milder symptoms, adding to the complexity.
Accurate variant-specific risk assessment is becoming increasingly important as genetic screening expands. Most genetic databases are biased toward Europeans, making it difficult to classify disease-causing variants in non-Europeans.
To fill this gap, Satoshi Koyama, MD, PhD, led a research team across academic and medical institutions in the Boston area that analyzed exome sequencing and blood lipid data (total cholesterol, LDL-C, HDL-C, and TG) from Million Veteran Program, UK Biobank, and All of Us participants. More than 230,000 participants came from historically underrepresented populations, making this one of the most diverse large-scale lipid genetics studies conducted to date.
Mechanistic and clinical implications
Their analysis uncovered nearly three million rare coding variants, including more than 214,000 predicted loss-of-function mutations, 2.7 million missense variants, and over 23,000 cryptic splice variants that may disrupt gene processing. In total, the team evaluated over 10 million variant-phenotype associations. The results revealed 800 exome-wide significant additive associations across 184 genetic loci, along with 109 recessive associations involving 53 genes. Many of the strongest signals appeared in genes already known to regulate lipid metabolism and cardiovascular disease, including PCSK9, LDLR, APOB, NPC1L1, and APOC3.
The study also identified five lipid-associated genes significantly linked to CAD risk, highlighting potential therapeutic targets. One particularly intriguing gene was RORC, which encodes the transcription factor RORγ. Previous laboratory and animal studies suggested that suppressing RORγ improves metabolic health and reduces atherosclerosis. Consistent with those findings, the study showed that loss-of-function variants in RORC appeared protective against CAD in humans.
Another key finding involved cryptic splice variants, a class of mutations often overlooked in clinical genetics. The researchers used machine-learning-based splice prediction tools to show that these variants had biological effects similar to canonical loss-of-function mutations, suggesting that many clinically important variants may be underestimated.
The study also found that 13% of missense mutations produced hypermorphic alleles that increased gene activity, unlike most loss-of-function variants. Existing computational prediction tools frequently fail to identify these variants, potentially limiting the sensitivity of current genetic testing approaches.
Koyama and colleagues also detected strong recessive genetic effects that standard additive models may miss entirely. Because homozygous rare variants are uncommon, their contribution to disease has historically been difficult to measure. The findings suggest that recessive inheritance may explain part of the “missing heritability” in complex cardiovascular disease.
The researchers found that most rare variants exerted similar effects across populations, even when variant frequencies differed substantially between ancestries. The study identified 130 alleles observed primarily or exclusively in non-European populations, emphasizing the importance of expanding genetic research beyond European cohorts to improve equitable diagnosis and drug discovery.
From screening to saving hearts
Beyond biological discovery, the study carries important clinical implications. By comparing their findings with curated pathogenic variant databases, the investigators confirmed many established classifications while identifying variants that may warrant reclassification. Two newly highlighted variants enriched in non-European populations may represent previously underrecognized causes of familial hypercholesterolemia.
Although the research focused mainly on rare coding variants rather than noncoding DNA, the authors argue that population-scale sequencing studies can now provide clinically actionable insights into disease mechanisms, pathogenicity, penetrance, and prognosis. Together, the findings offer a powerful new resource for cardiovascular genetics at a time when therapies such as PCSK9 gene editing are beginning to move from concept to clinic.
Hemato-Oncology Laboratory at Unidade Funcional de Hematologia Molecular Unidade Local de Saúde de Coimbra
Panelist
Margarida Coucelo, MSc, ErCLG
Margarida Coucelo, MSc, ErCLG, is head of the Hemato-Oncology Laboratory at Unidade Funcional de Hematologia Molecular, Unidade Local de Saúde de Coimbra, one of Portugal’s national reference centers for hemato-oncology. She has extensive experience investigating myeloid malignancies across the full disease spectrum, from chronic conditions, such as myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS), through to acute leukemias. She earned her master’s degree in cellular and molecular biology at the University of Coimbra.
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The 2022 World Health Organization (WHO) and International Consensus Classification updates for myeloid malignancies went beyond adjusting diagnostic thresholds and fundamentally redefined what constitutes a complete genomic profile. For many laboratories, keeping pace with these changes has required the adoption of broad-coverage NGS applications.
Margarida Coucelo, MSc, ErCLG, has led that transition at Unidade Funcional de Hematologia Molecular, Unidade Local de Saúde de Coimbra, one of Portugal’s national reference centers for hemato-oncology, where she has investigated myeloid malignancies across the full disease spectrum, from chronic conditions such as myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS) through to acute leukemias.
This IPM webinar will describe how expanded NGS workflows are being applied in a high-volume reference setting, and the implications for laboratories navigating the evolving landscape of myeloid malignancy testing.
Key takeaways include:
How a comprehensive DNA-based NGS approach can contribute to the identification of relevant variants and inform treatment options and risk stratification
Cases studies that cover the detection of rare fusion genes and somatic variants, and how to consolidate germline predisposition testing within a single workflow
A live Q&A session will follow the presentation offering you a chance to pose questions to our expert panelists.
Researchers from Taiwan have proposed a dual cut-off for the pancreatic tumor marker carbohydrate antigen 19-9 (CA19-9) in which the lower threshold identifies a small subgroup “nonproducers” with poor prognosis and the higher threshold detects the more common high-risk producers.
The approach “prevents the critical underestimation of disease severity and ensures more accurate risk stratification,” write Yung-Yeh Su, MD, from National Institute of Cancer Research in Tainan, and co-authors in Clinical Cancer Research.
Serum levels of CA19-9 (also known as sialyl Lewis antigen A) correlate with pancreatic cancer stage and prognosis in that higher levels indicate more advanced disease and a worse prognosis. Currently, a CA19-9 level below 37 units/mL is considered normal or, in the case of a known diagnosis of pancreatic cancer, prognostic for standard-risk disease.
However, approximately 10% of patients with pancreatic cancer do not have elevated CA19-9 levels, even in the presence of advanced disease. The Lewis antigen-negative status of these CA19-9 nonproducers comes from genetic polymorphisms in the FUT3 gene that impair the fucosyltransferase (FUT) activity required to produce CA19-9.
This is “clinically important because genotyping is rarely available, and Lewis-negative patients are often grouped with ‘low CA19-9’ cases, leading to an underestimation of risk,” Su et al. remark.
In 2012, a CA19-9 cut-off of five units/mL was proposed as a surrogate marker for CA19-9 nonproducers, but Su explained that this was largely an empirical observation based on clinicians noticing that Lewis-negative patients tended to exhibit extremely low baseline CA19-9 levels. It did not take genetic sequencing or genomic data into account.
Su and colleagues therefore set out to provide genomically validated evidence, by correlating CA19-9 levels directly with Lewis antigen genotypes, of a CA19-9 cutoff that would capture Lewis antigen-negative patients and better predict their prognosis.
The study included 615 patients with pancreatic ductal adenocarcinoma who underwent germline whole-exome sequencing to determine their FUT2/FUT3 genotypes.
Overall, 10.1% of participants were classified as FUT3-null, 33.0% as FUT-low, 35.6% as FUT-intermediate, and 21.3% as FUT-high.
The researchers report that median CA19-9 levels increased progressively across the FUT functional groups, from 2.4 units/mL in the FUT3-null group to 348 units/mL in the FUT-low group, 453 units/mL in the FUT-intermediate group, and 1300 units/mL in the FUT-high group.
By contrast, median overall survival (OS) was similar among the groups, at 13.5, 15.1, 14.5, and 14.3 months, respectively.
Using receiver operating characteristic analysis, Su and team identified seven units/mL as the optimal CA19-9 cutoff capable of characterizing the FUT3-null population within the clinical reference range of 37 units/mL or lower. At this cut-off, the sensitivity was 76.5% and the accuracy was 87.9%, which the researchers say represents 7.9 and 1.7 percentage point improvements, respectively, on the literature-defined threshold of five units/mL.
The investigators then grouped the patients by CA19-9 cutoff, without genotyping, and showed that OS was lower at each end of the spectrum.
Specifically, median OS was 13.5 months in participants with a CA19-9 of seven units/mL or lower, 23.2 months among those with a level of >7 to 37 units/mL, 22.0 months in those with a CA19-9 of >37 to 200 units/mL, and 12.8 months in the group with a level above 200 units/mL.
After adjusting for confounding factors, the risk for death was a significant 1.96 times higher in participants with the lowest CA19-9 levels and 1.69-fold higher in those with the highest levels, relative to individuals that had a CA19-9 of >7 to 37 U/mL.
“These data tell us that the conventional normal CA19-9 range of less than 37 units/mL does not distinguish between true low tumor burden and Lewis-negative status,” Su said.
He told Inside Precision Medicine that, at present, Lewis antigen genotyping is not standard clinical practice for pancreatic cancer patients due to added costs, turn-around times, and limited availability of routine genetic testing in many centers.
“This clinical gap is precisely why our findings are highly relevant,” Su remarked. “Because genetic testing is often unfeasible or unavailable, proposing a readily accessible serum CA19-9 cut-off (such as seven units/mL) provides clinicians with a highly feasible, cost-effective, and immediate surrogate marker to identify Lewis-negative patients using standard laboratory assays.”
He explained that robust external validation is now needed before the proposed lower cut-off can be adopted into routine clinical practice. “Because our current cohort may reflect specific regional or demographic characteristics, validating this seven U/mL cut-off in diverse cohorts—particularly in Western populations—is critical to ensure its global applicability.”
To address this, an international external validation study is currently underway.
As genomic sequencing becomes routine in oncology, clinicians are increasingly inundated with tumor mutation data—but often without clear guidance on how to use it. A new artificial intelligence foundation model called MutationProjector may help bridge that gap by transforming complex tumor mutation profiles into clinically meaningful representations that can predict treatment response, classify cancer subtypes, and potentially guide therapeutic decisions across tumor types.
The study, led by researchers at the University of California, San Diego (UCSD) and published in Cancer Discovery, describes how the model was trained on more than 30,000 tumor genomes spanning 10 solid tumor types and then applied to a range of downstream clinical tasks, including prediction of immunotherapy and chemotherapy response.
“Right now, increasingly physicians will order gene sequencing panels, and in a couple of years it’ll be the whole genome,” senior author UCSD’s Trey Ideker, PhD. “What’s interesting is that as prolific as that information is becoming in the cancer clinic, relatively few treatment decisions are actually made based on it.”
According to the paper, FDA-approved targeted therapies currently match only about 8% of patients based on sequencing findings. Yet the average tumor contains roughly 11 distinct genomic alterations identified through clinical sequencing. “The mutations are there,” Ideker said. “People just don’t know what to do with them, or whether they’re informative.”
MutationProjector was designed to address that challenge with architecture inspired by large-scale AI systems used in natural language processing and computer vision. Rather than focusing on single-gene biomarkers, the model attempts to capture broader mutational patterns associated with hallmark cancer pathways.
“We’ve known for years that cancer is really a disease of pathways,” Ideker said. “If you could move beyond individual gene biomarkers and instead understand the overall state of the tumor from its mutations, then you could potentially treat that tumor better.”
The model translates tumor genomic information into a compact coordinate-based representation—similar to a UMAP embedding—that places tumors with similar molecular features near one another. Ideker described the output as an “XY coordinate system for a tumor” that integrates information from thousands of mutations simultaneously.
The approach relies heavily on pretraining, a hallmark of modern foundation models. MutationProjector first learned generalizable representations from large collections of tumor genomic data, even when detailed clinical outcome information was unavailable. Researchers then fine-tuned the model using smaller, highly annotated datasets tied to specific therapeutic responses.
Ideker compared the strategy to language learning. “If I want to learn Italian but I only have one book in Italian, I’m going to learn a lot first from English and Spanish, where I have much more data,” he said. “Then I can transfer that knowledge to Italian much more effectively.”
Similarly, the model learned from tens of thousands of tumor genomes before being tuned to predict outcomes such as immunotherapy response using comparatively small patient cohorts.
The researchers reported that the pretrained embeddings achieved strong performance across multiple downstream tasks, including immunotherapy response prediction, chemotherapy response prediction, and metastasis classification. In one analysis, the model predicted immunotherapy response after fine-tuning on only 94 samples, yet still outperformed competing approaches across independent cohorts.
Importantly, the same pretrained model was applied across multiple tasks rather than building separate models for each application. “These findings reflect the current movement toward general-purpose AI systems capable of addressing a broad range of challenges,” the authors wrote.
The study also highlighted the model’s interpretability. By examining how tumors clustered within the embedding space, researchers identified biologically meaningful patterns associated with treatment response and tumor subtype.
For example, MutationProjector distinguished HPV-positive from HPV-negative tumors in cervical and head and neck cancers, partly through pathway signatures involving apoptosis regulation and Wnt signaling. The model also differentiated basal and luminal subtypes in bladder and breast cancers.
Perhaps more significantly for clinical oncology, the system identified both individual and combinatorial biomarkers linked to immunotherapy outcomes.
Among the findings were associations involving KMT2A and SMARCA4 alterations, which may influence immune response through chromatin remodeling and DNA methylation pathways. The model also captured co-alteration patterns such as KRAS-STK11 and STK11-KEAP1, combinations previously associated with resistance to immunotherapy.
“Those mutations individually may not tell you very much,” Ideker said. “But together, they can become highly predictive of treatment response.”
The authors argue that this ability to model combinatorial mutation patterns could help address one of precision oncology’s central limitations: the difficulty of interpreting rare or co-occurring mutations that do not fit established single-gene biomarker frameworks.
Although the current study focused on 10 solid tumor types, the researchers see considerable room for expansion. Ideker said the team has already scaled training datasets from 30,000 to roughly 300,000 tumor genomes while also increasing the size of the neural network itself.
“We’re now scaling this idea substantially,” he said. “The original work was really a demonstration that the concept works.”
Future directions include expanding into additional tumor types such as pancreatic cancer, prostate cancer, and sarcoma, integrating multimodal data sources including transcriptomics and radiology, and potentially applying the framework to liquid biopsy analysis.
Ideker also envisions the technology eventually supporting molecular tumor boards, where oncologists currently debate difficult treatment decisions based on limited genomic evidence.
“We want to get models like this into those discussions to assist in decision-making,” he said.
The long-term goal, he added, is not simply prediction, but deeper biological understanding. “Once you can interpret the mutational patterns that place tumors into different regions of the map, you start learning the pathways of drug resistance,” Ideker said. “And understanding those pathways is ultimately what drives the next generation of cancer therapeutics.”
IntroductionMicroglia, the resident immune cells of the central nervous system, play a critical role in maintaining neural homeostasis and regulating inflammatory responses in the brain. Increasing evidence suggests that microglial dysfunction contributes to the progression of neurodegenerative diseases, including Alzheimer’s disease (AD). However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to characterize disease-associated molecular changes in microglia derived from induced pluripotent stem cells (iPSCs) of sporadic AD patients and healthy donors.MethodsiPSC-derived microglia from sporadic AD patients and healthy controls were analyzed using integrated multi-omics approaches, including total RNA sequencing, proteomics, and small non-coding RNA (sncRNA) sequencing. Gene Ontology (GO) analysis was performed to identify dysregulated biological pathways from transcriptomic and proteomic datasets. In addition, a modified T4 polynucleotide kinase (T4 PNK)-based sncRNA sequencing method was used to profile disease-associated sncRNAs and identify previously uncharacterized RNA species.ResultsComparative analyses revealed significant AD-associated alterations in mRNA, protein, and sncRNA expression profiles in iPSC-derived microglia. GO analysis demonstrated dysregulation of pathways related to extracellular communication, intracellular transport, cytoskeletal organization, and protein–protein interactions. Furthermore, the modified T4 PNK–sncRNA sequencing approach identified multiple disease-associated sncRNAs, including several novel and previously uncharacterized RNA species potentially linked to AD pathology.DiscussionThese findings demonstrate that iPSC-derived microglia provide a valuable model for studying molecular mechanisms associated with sporadic AD. The identified transcriptomic, proteomic, and sncRNA alterations highlight key pathways potentially involved in microglial dysfunction and neurodegeneration. In particular, the discovery of novel disease-associated sncRNAs may provide new insights into AD pathogenesis and reveal potential therapeutic targets for future investigation.
BOSTON — Thomas Bartlett’s life changed in 2019 when he was diagnosed with late-onset myasthenia gravis (MG). The 15-year veteran of the Bio-IT World Conference and Expo took to the stage on Tuesday as one of seven speakers in the opening plenary session of this year’s conference, which focused on various aspects of rare disease research and treatment.
The session offered a poignant, but often uplifting, launch to the annual conference, which celebrated 25 years from its inception in 2002, when the event was produced by the IDG World Expo Group.
Speaking with Susan Ward, PhD, founder and executive director of the Collaborative Trajectory Analysis Project (cTAP), Bartlett described an active life and fulfilling work prior to his diagnosis, and some of the debilitating physical and emotional impact of his disease. “I have to plan everything. If I’m going to go out, I plan ahead of time where I’m going to go [and] the amount of time,” he said. “I have to plan recovery.” Bartlett’s MG has prevented him from working a full-time job, as he would need a full day of rest just to recover from each day. “The math doesn’t work.”
Bartlett is now an ambassador for MG Uniter Myasthenia Gravis, an online platform designed to support some 70,000 patients living with the disease in the United States alone. Though there are some treatments that alleviate disease symptoms, there currently is no cure. Bartlett’s disease was diagnosed early thanks to a quick-thinking primary care provider. A recurring theme in the session was the stark reality that many in the rare disease community wait many years for a diagnosis.
Of the estimated 7–8,000 rare genetic diseases, many not well understood. About one in 10 people in the U.S. “either has or will have a rare disease at some point,” Ward noted. In a conference of about 2,700 attendees, “there are going to be about 270 people on average who might have a rare disease. So the magnitude of the problem is huge, even though the numbers of people are quite small.”
Given the computational nature of the Bio-IT conference, Bartlett and Ward soon turned to data and the challenges with collecting and aggregating information from rare disease populations. Ward noted that centers of excellence in rare disease may have several patients but “no one center that has enough data for anybody to really learn much.” Aggregating data from multiple centers and across geographies is one possibility but due to the differences that exist between centers across states and countries, “you need a really rich and deep ontology” as well as “context for what those data mean,” she said.
And that’s not the only challenge. In many cases, rare diseases can present and progress differently in patients with the same condition. “Imagine you’re trying to design a clinical trial. You’ve got patients who are fluctuating [while] you’re really looking for patients who are slowly declining” and “patients who have intermittent remissions,” Ward noted. With that mix, “you’re going to have a very noisy trial.”
There are also other data sources that could provide value. Bartlett noted that wearable devices such as the Apple watch capture useful health-related data, but as it is not clinical data, physicians cannot use it. As someone with decades of tech experience—including a stint at Apple—Bartlett asked: “How do we change that?” How do we prove the patient’s experience with the data that we can collect, and work with companies and legislation” to “include real world data and evidence and compare that … with the clinical data and get a much broader picture.”
Bartlett’s closing comments focused on hope for people living with rare diseases. And with good reason given recent successes in development of gene therapies and other therapeutics. As a patient, “you need to have something that you can look forward to today,” he said. For now, MG is incurable but “we will find ways to get to that end game and ultimately have a cure or at least a high level of quality of life.”
Shortening the rare disease diagnostic journey
Sebastien Lefebvre, head of technology, data and AI at Aurelis Insights, has spent 10 years in the rare disease space in different capacities including developing platforms for digital decision support or “data-driven and AI-assisted support decisions.” Speaking with William Van Etten, PhD, co-founder, CEO and principal scientist, StarfleetBio, Lefebvre described Rare Answers, a clinical decision support platform for rare disease diagnostics that he worked on while at Alexion Pharmaceuticals. The platform was designed in collaboration with two children’s hospitals as well as a number of technology and data science companies.
Sebastien Lefebvre, head of technology, data and AI, Aurelis Insights (right) and William Van Etten, PhD, co-founder, CEO & Principal Scientist, StarfleetBio (left). [Uduak Thomas]
He also described a second project in 2022 with the Rare-X program that analyzed data from public databases of rare and inherited diseases, drugs, and genes. Lefebvre and his colleagues hoped to produce an accurate assessment of the total number of rare diseases. Following extensive data cleaning and normalization—now made much simpler with advances in AI and machine learning—they arrived at a figure closer to 12,000. Of that number, between 80–87 percent have a genetic basis and about 80 percent had at least three associated phenotypic descriptors. That kind of information provides a viable starting point for applying AI-assisted data-driven diagnostic approaches. This is important because, as Bartlett noted, many patients with rare diseases wait years for a diagnosis. “It all starts with a diagnosis,” Lefebvre said. “[If] you don’t known what you’ve got, how can you [treat it].”
Van Etten is focused on making individual genomes truly private. The emergence of commercial personal genomics companies created a data privacy problem. Customers pay for their genomes to be sequenced by a company that holds the data, reads it, and sends periodic reports. He has developed an app called DNAVault that lets people host their genomes on their smartphones, putting data control back into their hands.
Van Etten’s new company, StarfleetBio, is partnering with his former consulting firm, BioTeam, and the Hubbard Center for Genomic Studies at the University of New Hampshire, to provide sequencing services.
“It used to be that we needed to centralize all the human genome data because you need a lot of compute to perform the analysis, but it’s really not required anymore,” he said. “We decided to decentralize it, where your genome is on your phone, you can generate your own reports, and nobody has access to it but you.”
Each encrypted genome is only accessible with a key unique to the individual’s phone. This way, only they individual can download their data and read it. Some audience members clearly approved of Van Etten’s app, with shouts of “Bravo!” from the back of the hall. (The app was later named one of three “Best of Show” winners at this year’s meeting.)
Among the features in the app is a fun kinship feature, which lets two people determine if they are related by placing their phones in close proximity as if sharing a Wi-Fi password. Another feature dubbed “origins” lets people track their ancestry over thousands of years via their Y-chromosome or mitochondrial DNA. Van Etten was particularly moved by the kind of insights this feature revealed about human relationships. “We found that all humans are far more closely related than we thought,” he said. “We all really [came from] the same 5,000–10,000 people from 50,000–70,000 years ago.”
Another app feature screens for the 81 ACMG medically actionable genes to provide health reports, while a final feature lets people ask questions about their genome and get answers much the same way one might enter a question into Google or ChatGPT.
Tying this to rare diseases, Van Etten is working on ways for app users to opt into participating in relevant research studies and clinical trials. The idea is that users could “toggle a switch” that would let alert the relevant researchers and then answer questions to help gauge eligibility. Importantly, this would all be done without people having to share their primary information.
Learning from rare diseases to treat common conditions
Another plenary conversation took place between Morgan Cheatham, MD, partner, head of healthcare & life sciences, Breyer Capital, and Catherine Brownstein, PhD, manager of the Molecular Genomics Core Facility at Boston Children’s Hospital and scientific director of the Manton Center for Orphan Disease Research Gene Discovery Core.
Morgan Cheatham, MD, Partner, Head of Healthcare & Life Sciences, Breyer Capital (left), and Catherine Brownstein, PhD, Manager of the Molecular Genomics Core Facility at Boston Children’s Hospital and Scientific Director of the Manton Center for Orphan Disease Research Gene Discovery Core (right). [Uduak Thomas]
Brownstein and Cheatham use OpenAI’s generative AI to help diagnose patients who in some cases had been waiting decades for answers. Importantly, “this was a zero-shot model,” Cheatham noted. “We didn’t do any specialized training of GPT-3, we just deployed the existing models and we’re able to return answers to families who have been waiting for sometimes over a decade.”
He also acknowledged the contributions of people living with rare diseases to many major drug modalities including CAR Ts and RNA medicines. “Many of those modalities were actually validated” with “the help of rare patients who were willing to participate in trials that allowed us to show the efficacy, the safety, and the durability of these modalities.”
According to Brownstein, a deeper understanding of rare conditions often has implications for more common conditions. “As someone who spent six years studying hypophosphatemic rickets … it’s these extreme cases, these rare presentations of disorders where you don’t know the underlying etiology [that] inform the common diseases,” she said. Understand the biology behind hypophosphatemic rickets “has implications for bone density and osteoporosis that affects a ton of us in this room.”
Many opportunities were highlighted where some form of AI is already being used or could be applied. One company that Cheatham mentioned is applying AI to colonoscopies to characterize inflammation levels in the bowel in a standardized way with an eye towards connecting patients with ulcerative colitis and Crohn’s disease to relevant clinical trials. There are also opportunities in cardiology, neurology, pathology and more.
Giving more patients the right to try
In the closing conversation, Van Etten spoke with Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI). Livingston is at the forefront of efforts aimed at implementing policies in different states that allow patients with rare diseases to try treatments that may benefit before they have been approved.
The story of how Livingston, still in his 20s, got involved in healthcare policy is interesting. As a college senior during the Covid-19 lockdowns, “I started thinking about COVID as it relates to age [and] why … [I] would be pretty much completely unaffected by COVID and why my grandfather at 92 would most likely die,” he recalled. “It all comes back to aging, your immune response to these diseases and your immune response to chronic diseases overall.” That got him interested in the field of aging and longevity more broadly.
Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI) (left) and William Van Etten, PhD (right) in conversation at Bio-IT World 2026 [Uduak Thomas]
Livingston and his group have worked to pass laws in the state of Montana that extend eligibility under the Right to Try Act, a piece of federal legislation that lets people with terminal illnesses try therapeutics that may help them which are not yet fully approved. The issue with the Right To Try Act as it stands is that “the definition of who is eligible is very narrow” and restricted to people with months left to live “which made no sense to me” Livingston said. From his perspective, people just diagnosed with conditions like Alzheimer’s or Parkinson’s should also have the chance to access treatments which could potentially help them earlier in their journeys as those who are further along in their journeys.
Expanding the Right to Try provides a possible pathway to those treatments without requiring approval from the U.S. Food and Drug Administration, which may be years away.
Livingston and his team have been successful in expanding the law in Montana to cover people with age-related ailments as well as people with rare diseases, people recently diagnosed with terminal diseases, and people with disease that will eventually become terminal. Now he and his team are working on getting similar changes in place in New Hampshire. There are safeguards in place: the proposed treatment has to be prescribed by two physicians, pass through IRB review, and the therapy must have passed a Phase I testing. “What we’re trying to do is create a system that is safe enough to prevent as many tragedies as possible while also opening up access to as many people as possible,” Livingston said.
As an example of the benefit of changing the law, Livingston shared a story of a father whose son had died from a rare mitochondrial disease. The father has since had the genomes of his two other children sequenced, only to discover that they carry the same mitochondrial mutation. In this scenario, Montana’s model would allow the father in this instance to bypass the strict requirements of a drug trial and access treatments that could potentially help his children.
“Maybe it’s not as great in terms of a data collection standpoint for companies, but what we’re offering here [are] options for people that don’t have any other options.”
*Bio-IT World Conference & Expo, Boston; May 19-21, 2026.
Analyses of sequencing data from 75,602 children referred for genetic testing from 2016 to 2025 provide insights on the association between pathogenic germline variants and childhood cancer.
Newman et al. review the evolving landscape of antibody-drug conjugates (ADCs) in breast cancer, including approved agents, resistance mechanisms, and combinatorial strategies. The authors highlight novel agents, antigen targets, and next-generation platforms, underscoring the need for predictive biomarkers and optimized sequencing strategies to improve patient selection and efficacy.
BackgroundTic disorder (TD) is a common chronic neuropsychiatric condition manifesting during childhood and adolescence. Jujuboside A (JuA) may alleviate TD symptoms; however, the mechanisms underlying its therapeutic effects remain unclear.MethodsWe established a rat model of TD and used histological techniques to evaluate the effects of JuA on pathological changes. We also measured 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA) levels and assessed tryptophan hydroxylase 1 (TPH1) mRNA expression. Finally, we analyzed the gut microbiota composition in fecal samples using 16S rRNA metagenomic sequencing.ResultsJuA administration alleviated pathological changes in rats with TD, increased 5-HT and 5-HIAA levels, and upregulated TPH1 mRNA expression. Compared with no treatment, JuA treatment increased the proportion of Bacteroidia, Muribaculaceae, Bacteroidales, and Bacteroidota, while reducing that of Bacilli, Lactobacillaceae, Lactobacillus, Lactobacillales, and Firmicutes.ConclusionThese findings indicate that JuA mitigates TD progression, potentially by remodeling the gut microbiota and regulating 5-HT levels.
![An image showing Sebastien Lefebvre, head of technology, data and AI, Aurelis Insights (right) and William Van Etten, PhD, co-founder, CEO & Principal Scientist, StarfleetBio (left) in conversation at Bio-IT World 2026. [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Seb-Bill-300x233.jpg)
![Morgan Cheatham, MD, Partner, Head of Healthcare & Life Sciences, Breyer Capital (left), and Catherine Brownstein, PhD, Manager of the Molecular Genomics Core Facility at Boston Children's Hospital and Scientific Director of the Manton Center for Orphan Disease Research Gene Discovery Core (right). [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Cheatham-Bownstein-300x232.jpg)
![Dylan Livingston, founder and president of The Alliance for Longevity Initiatives (A4LI) (left) and William Van Etten, PhD (right) in conversation at Bio-IT World 2026 [Uduak Thomas]](https://www.genengnews.com/wp-content/uploads/2026/05/Livingstone-Van-Etten-300x225.jpg)