“Our strategy takes advantage of the immune system’s innate ability to ramp up broadly in response to a variety of components found in and on pathogens,” says Ivan Zanoni, PhD chair in immunology at Boston Children’s. “Even though the mRNA technology is the biggest breakthrough for vaccine technology in the last two decades, we thought that there was still room for us to improve this platform.”

The research focused on whether tuning inflammation through activation of pattern recognition receptors, or PRRs, could improve the effectiveness and durability of mRNA vaccines. The researchers tested whether stimulation of these receptors using fungal mannan could enhance immune responses generated by an mRNA vaccine targeting the ancestral SARS-CoV-2 spike protein.

The current research builds on earlier findings from Zanoni’s lab studying the same adjuvant in protein-based influenza vaccines. In the current study, investigators evaluated whether the mannadjuvant could improve performance of the original monovalent BNT162b2 Comirnaty vaccine encoding the spike protein from the ancestral WA1 strain of SARS-CoV-2.

To conduct the study, the team first confirmed that the mRNA vaccine remained stable after being mixed with the adjuvant. Researchers then vaccinated mouse models with either the mRNA vaccine alone or the vaccine combined with mannadjuvant. Additional testing was later conducted in non-human primates and human immune cells.

The research evaluated durability of antibody responses, T-cell activity, inflammatory signaling, and immune recognition of SARS-CoV-2 variants. Vaccinated mice were also exposed to mutated spike proteins from omicron variants of the virus to find out if the immune response generated by the adjuvant-enhanced vaccine could also recognize strains that emerged after the original vaccine design.

The data showed that mice that received the vaccine with the mannadjuvant retained antibodies to the viral spike protein for up to two years, compared with only a few months for animals given the vaccine alone. The adjuvant combined with the original vaccine was also shown to create a strong immune responses against omicron variant spike proteins.

“In mice and non-human primates, mannadjuvant increased the magnitude and durability of the response elicited by the mRNA-based vaccine, and it also led to the induction of neutralizing antibodies directed against variants of concern with a high escape capacity, overcoming antigenic imprinting,” the researchers wrote adding that their findings suggested antifungal PRRs could be used to create more potent and durable mRNA-based vaccines.

The researchers also evaluated the safety of the combination in mice and non-human primates and found that the adjuvant improved immune response “without increasing reactogenicity or breaking self-tolerance in mice and NHPs.”

The clinical implications of this finding shows that adjuvants targeting innate immune pathways could make mRNA vaccines more durable and less dependent on repeated reformulation or booster administration. Rather than updating vaccines to match each new variant, the researchers believe that the broader immune training elicited by combining the vaccine with an adjuvant may allow vaccines to remain effective as viruses evolve.

Future studies will focus on clarifying the molecular mechanisms by which the adjuvant stimulates immunity, particularly because immune responses to fungi are not fully understood. The team also plans to further investigate how mannadjuvant regulates interferon and interleukin-1 signaling and controls inflammasome activation.

The post Fungal Adjuvant Mannadjuvant Extends mRNA COVID-19 Vaccine Protections appeared first on Inside Precision Medicine.

Researchers at University of Texas MD Anderson Cancer Center and Weill Cornell Medicine have identified key mechanisms that allow colorectal tumors to resist KRAS inhibitors, uncovering both genetic and non-genetic pathways that help cancer cells survive treatment.

The findings, published in Cancer Cell, suggest that combining KRAS inhibitors with therapies targeting early inflammatory responses may improve outcomes for patients with KRAS-mutant colorectal cancer.

KRAS mutations remain difficult to treat in colorectal cancer

KRAS is one of the most commonly mutated oncogenes in colorectal cancer, occurring in nearly half of all patients. Mutations in the gene drive tumor growth by continuously activating signaling pathways involved in cell proliferation and survival.

Although KRAS inhibitors such as Adagrasib and Sotorasib have transformed treatment options for some lung cancers, their clinical benefit in colorectal cancer has been more limited. Many patients either fail to respond or rapidly develop resistance after an initial response.

Previous studies have suggested that tumors may escape treatment by acquiring secondary pathway mutations, but the full picture of resistance has remained unclear.

To investigate this further, the research team analyzed patient-matched tumor samples collected before treatment, during KRAS inhibitor therapy, and after disease progression. The researchers combined targeted gene sequencing with single-cell spatial transcriptomics to examine how tumor cells evolved over time.

The team also used organoid models of KRAS inhibitor-resistant colorectal cancer to validate their findings experimentally.

Tumors evade KRAS inhibition through multiple mechanisms

The researchers discovered that resistant tumors do not rely on a single escape mechanism. Instead, colorectal cancer cells use both genetic mutations and adaptive cell-state changes to survive therapy.

Some resistant cancer cells acquired secondary mutations that bypassed KRAS blockade, while others altered their cellular behavior without acquiring new mutations. According to the authors, these non-genetic adaptations enabled tumor cells to tolerate treatment and remain viable despite continued KRAS inhibition.

Both resistance mechanisms could coexist within the same tumor, highlighting the complexity and heterogeneity of treatment resistance in colorectal cancer.

“Our findings uncovered the genetic and cell-state shifts that colorectal tumors use to escape KRAS inhibition,” said co-lead author Salvador Alonso Martinez, MD, assistant professor of Gastrointestinal Medical Oncology at MD Anderson.

“Targeting the adapted early inflammatory response may be the key to stopping resistance and improving the effectiveness of KRAS therapies for these patients,” he added.

Early inflammatory signaling helps cancer cells survive

One of the study’s most significant findings was the identification of an early inflammatory response triggered shortly after KRAS inhibitor treatment begins.

In tumor samples collected early during therapy, the researchers observed activation of inflammation-related signaling programs that appeared to help cancer cells adapt to therapeutic stress.

Rather than immediately dying in response to KRAS blockade, some tumor cells entered a survival state driven by inflammatory signaling pathways. This adaptive response allowed them to persist and eventually contribute to disease progression.

The investigators identified the protein kinase TBK1 as a central mediator of this inflammatory adaptation.

TBK1 is known to regulate innate immune and inflammatory signaling pathways, but the new findings suggest it may also play a critical role in enabling colorectal tumors to survive KRAS inhibition.

When the researchers blocked TBK1 activity in preclinical models, the inflammatory survival response was suppressed and tumor cells became more sensitive to KRAS inhibitors.

Combination therapy may improve treatment durability

The results suggest that combining KRAS inhibitors with TBK1 blockade could represent a new therapeutic strategy for patients with KRAS-mutant colorectal cancer.

Rather than focusing exclusively on secondary genetic mutations after resistance has already emerged, targeting the early adaptive inflammatory response may help prevent or delay resistance from developing in the first place.

In preclinical organoid models, dual inhibition strategies enhanced treatment sensitivity and overcame resistance mechanisms that limited the effectiveness of KRAS inhibition alone.

The study’s authors believe this approach could potentially produce more durable responses in patients, though clinical validation will still be required.

Implications for future colorectal cancer treatment

The findings add to growing evidence that cancer drug resistance is often driven not only by permanent genetic evolution, but also by reversible adaptive cell states that emerge under therapeutic pressure.

Understanding these early adaptive mechanisms may help researchers design combination therapies that suppress tumor survival pathways before resistant clones become dominant.

The study also demonstrates the increasing importance of single-cell and spatial transcriptomic technologies in oncology research, allowing scientists to observe how tumors dynamically respond to treatment at high resolution.

While TBK1 inhibitors are not yet standard therapies for colorectal cancer, the researchers believe their work provides a strong rationale for future clinical trials combining TBK1 blockade with KRAS-targeted therapies.

If successful, these strategies could expand the clinical impact of KRAS inhibitors in colorectal cancer, where durable responses have historically been difficult to achieve.

The post How Colorectal Tumors Escape KRAS Inhibitors Identified appeared first on Inside Precision Medicine.

“Datopotamab deruxtecan is the first and only medicine to significantly prolong overall survival in the first-line setting compared to chemotherapy in patients with metastatic triple-negative breast cancer who are not candidates for immunotherapy,” said Tiffany A. Traina, MD, medical oncologist at Memorial Sloan Kettering Cancer Center. “This approval will bring a much-needed treatment option for these patients.”

Datroway is an antibody-drug conjugate (ADC) that targets the TROP2 protein, which is broadly expressed in several types of solid tumors and is associated with an increased risk of tumor progression and lower survival rates in breast cancer patients. With this approval, the ADC is now commercially available in the U.S. for three indications, including HR+/HER2– breast cancer and EGFR-mutated non-small cell lung cancer. Regulators across the world are now reviewing applications to expand this approval to Australia, Canada, China, the EU, Singapore, and Switzerland. 

The FDA decision is based on Phase III results from the TROPION-Breast02 clinical trial, where Traina was one of the investigators. In this trial, Datroway improved the median overall survival by five months and reduced the risk of disease progression or death by 43% compared to chemotherapy as a first line treatment. The ADC also achieved an objective response rate of 64% compared to 30% for chemotherapy. 

“Triple-negative breast cancer is notoriously difficult to treat,” said Dave Fredrickson, executive vice president of the oncology hematology business Unit at AstraZeneca. “Patients with metastatic disease, especially those who are unable to receive immunotherapy, urgently need more effective, durable and tolerable treatment options, which extend survival. With today’s approval, we are proud to bring Datroway to a broad population of advanced triple-negative breast cancer patients and we continue to study its promise as a mainstay treatment across tumors, stages, and settings.”

Accounting for about 15% of all cases of breast cancer, triple-negative breast cancer is the most aggressive type of breast cancer, with only 15% of patients surviving five years after their diagnosis. Because it doesn’t express any of the three most common targets for established breast cancer treatments, patients with triple-negative breast cancer have limited treatment options. 

Those patients whose tumors express PD-L1 are eligible to receive checkpoint inhibitor immunotherapy as a first line treatment in addition to chemotherapy. However, as many as 70% of patients with metastatic triple-negative breast cancer are not eligible for this form of immunotherapy, whether because their tumors do not express PD-L1 or because of comorbidities. 

“For seven out of 10 patients with metastatic triple-negative breast cancer who are not candidates for immunotherapy, chemotherapy has remained the only treatment option,” said Arlene Brothers, executive director of the Triple Negative Breast Cancer Foundation. “Today’s approval of Datroway means that for the first time, these patients will have a new standard of care beyond traditional chemotherapy at the outset of their treatment.”

The post FDA Approves Datroway as First-Line Therapy for Triple-Negative Breast Cancer appeared first on Inside Precision Medicine.

A common precursor to pancreatic cancer is the presence of lesions called pancreatic intraepithelial neoplasia (PanIN). Researchers from the University of Michigan and the University of Maryland have been studying these precursor lesions and previously identified metabolic biomarkers involved with the development of PanIN to cancer. In their new study, published in Cancer Discovery, they expand their work into the PanIN microenvironments—the cells and tissues directly surrounding the lesions—to understand the cellular changes in an effort to find markers to use in diagnostics.

To examine the microenvironments, the team utilized single-cell RNA sequencing and spatial transcriptomics, isolating single cells and mapping their gene expression.

“These lesions are like needles in a haystack,” said co-senior author Timothy Frankel, MD, professor of surgical oncology and co-director of the Rogel and Blondy Center for Pancreatic Cancer at the University of Michigan. “The prior way of looking at this was to look at the entire haystack. You get a lot of information about hay and very little information about the needle.” He explained that using these precise techniques to examine cells individually is both more efficient and allows the researchers “to just focus in on the needle so we can look at multiple needles using the same amount of computing power and resources.”

The researchers used whole donated human pancreases to map the progression from PanIN to cancer, both for the precancerous and cancerous cells themselves, and the microenvironments surrounding them.

The pancreatic cancer microenvironment contains a highly diverse group of interactive cells, including fibroblasts and immune cells. The researchers found that while the epithelial tissue gene expression changes on a continuum from PanIN to cancer, the cells in the microenvironment are much more dynamic.

“Progression to cancer is accompanied by profound geographical reorganization of myeloid cells and lymphocytes and the formation of a cancer-specific fibroblast population characterized by high levels of Smooth Muscle Actin, LRRC15, and the WNT signaling component LEF1,” they wrote.

“It turns out, the microenvironment of these precursor lesions is the same as the microenvironment of the normal pancreas,” explained co-senior study author Marina Pasca di Magliano, PhD, professor of surgical immunology and co-director of the Rogel and Blondy Center for Pancreatic Cancer at the University of Michigan.

“The lesions have not convinced any of the cells around them to change. That’s not what we were expecting. We were expecting the two components, the cells and the microenvironment, to evolve in lockstep. They did not.”

These unexpected results indicate that there are other factors or stressors impacting the microenvironment. This is also in line with prior data showing that while pancreatic cancer is preempted by PanIN, not all, and in fact most, PanIN occurrences do not lead to a cancer diagnosis.

“It is incredible to see how we can uncover the fundamental cellular mechanisms of disease etiology by blending new computational methods and cutting-edge spatial transcriptomics technologies,” said co-corresponding author Elana J. Fertig, PhD, director of the Institute for Genome Sciences at the University of Maryland School of Medicine and associate director of quantitative sciences at the University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center. “Through careful study design, we can use the spatial information to start delving into the unknown dynamics of pancreatic tumor evolution.”

Moving forward, more research is needed to identify those stressors that may impact the microenvironment surrounding PanINs, which would allow the lesions to turn into cancers. Understanding those switches may open the door for developing earlier diagnostics or therapies to help prevent cancer development in the first place.

The post Precancerous Pancreatic Lesions May Not Always Lead to Cancer appeared first on Inside Precision Medicine.

The system, outlined in Nature Communications, sets the scene for multimodal learning approaches to be further integrated into medical imaging, with the potential to improve diagnoses and patient outcomes.

The vision-language self-supervised learning framework for cardiac magnetic resonance (CMR) imaging uses contrastive language image pretraining (CLIP) and treats scans as videos of the beating heart.

The novel CMR-CLIP system outperformed existing models by 35% and was better at identifying common pathologies such as myocardial fibrosis and left ventricular hypertrophy.

It also beat other models at common computational tasks such as the retrieval of CMR studies or radiology reports and downstream disease classification tasks.

“Systems like CMR-CLIP have the potential to support clinicians through automated screening, and interpretation support, particularly in settings where expert readers are limited,” explained researcher David Chen, PhD, of Cleveland Clinic.

“Such reader assistant tools are critical to improving patient access to this powerful diagnostic technology.”

Cardiac magnetic resonance imaging is the definitive way to diagnose several cardiac diseases including valvular pathologies, cardiomyopathies, pericardial and aortic diseases.

However, interpreting and documenting each exam takes a great deal of time due to the amount of information collected in each CMR exam—often more than 40 minutes per study.

Vision-language models trained using self-supervised learning are therefore crucial to reduce dependency on large volumes of labeled data.

However, conventional self-supervised approaches that rely on precise image-text pairing are not always feasible for CMR, given that it is able to visualize cardiac anatomy, physiology, and microstructure in a single exam.

Unlike either generalists and other biomedical domain-specific models, which are trained using individual images or limited views, CMR-CLIP incorporates a wide variety of standard cardiac views and image types that represent of morphology, function, and myocardial viability.

The vision language model connects images and associated reports, treating the various views of the heart and image types as a sequence of images in video format.

The model was trained on over a million images from over 10,000 unique studies at a single institution and performed well on evaluation in both on internal and external datasets.

The researchers said it achieved “remarkable performance” at real-world clinical tasks, reaching accuracies of 88.5% for non-ischemic cardiomyopathy, 88.0% for ischemic cardiomyopathy, 96.2% for cardiac amyloidosis, and 98.6% for hypertrophic cardiomyopathy.

“This work demonstrates that domain-specific foundation models can significantly outperform general-purpose AI systems in specialized clinical applications,” said researcher Ding Zhao, PhD, Carnegie Mellon University.

“By designing models that reflect the structure and complexity of cardiac MRI data, rather than adapting generic image models, we can unlock new levels of performance and clinical utility.”

The post Next-Generation Cardiac AI System Outperforms Existing Models appeared first on Inside Precision Medicine.

“Across the reproductive life stage, a woman living in the United States would have, on average, 450 menstrual cycles, out of which [approximately] 3.2 cycles result in pregnancy. Yet, most of the focus on menstrual health—including research, medical training, customers apps, and patents—are centered solely on the reproductive aspect, and fail to leverage these 99% non-conceptive menstrual cycles as health indicators,” writes Marie Gombert-Labedens, PhD, postdoctoral researcher at SRI International and lead author of the study. 

The menstrual cycle is a complex process that is tightly connected to an individual’s health, influencing many physiological processes including metabolic and immune functions. Gombert-Labedens and colleagues believe that tracking the rhythms of the menstrual cycle can be a valuable yet underexplored diagnostic tool—similar to how cardiac rhythms are routinely monitored to diagnose a wide range of cardiovascular conditions or how circadian rhythms can indicate metabolic disorders. 

However, more work needs to be done in menstrual health research to identify the most relevant metrics, their relationship with health conditions, and the extent to which individual variations may require a personalized approach. To aid the research community in this endeavor, the team developed the WAVES algorithm, which stands for ‘women’s health assessment through variability in endocrine-related signals.’

Using the WAVES algorithm, the researchers analyzed data from 5,674 menstrual cycles from 753 participants between 18 and 42 years old, including daily temperature measurements, vaginal secretions, age, reproductive history and sexual activity. Results showed that aging was associated with measurable changes in the menstrual cycle, including higher average temperatures, shorter cycles, and a decrease in regularity across multiple metrics. 

Gombert-Labedens and colleagues also looked at individual variability across all metrics, as menstrual cycles are known to vary widely from person to person both in terms of length and regularity. “Although the menstrual cycle is typically described as 28 days long, research based on large datasets indicate that this is more the exception than the rule, as only 12.4% of individuals present 28-day cycles,” she stated. 

Their analysis revealed that each participant showed individual patterns concerning mean body temperature across cycles, minimum and maximum temperature measurements, and the duration of both the full cycle and its phases. “These findings suggest that, across cycles from the same individual, each person has their own temperature baseline measurement around which the menstrual fluctuations are organized and are highly stable,” noted Gombert-Labedens. 

As an open-source platform, the WAVES algorithm is now available to researchers worldwide studying menstrual cycle patterns, helping them parse through large amounts of data to identify relevant biomarkers associated with health, disease, and treatment response. 

“The menstrual cycle is a rich yet underused source of physiological information,” Gombert-Labedens concludes. “This work suggests that the WAVES algorithm can be used for advancing digital biomarker discovery, and highlights the relevance of a personalized approach in the development of next-generation tools for women’s health.” 

The post Open-Source Algorithm Advances Precision Menstrual Health Beyond Fertility appeared first on Inside Precision Medicine.

The race to operationalize artificial intelligence (AI) across oncology research has entered a new phase. After years of building massive catalogs of real-world data (RWD) from electronic health records (EHRs), molecular testing, and longitudinal patient outcomes, healthcare technology companies are now competing to transform those datasets into interactive intelligence systems capable of answering complex clinical and commercial questions in real time.

That convergence has fueled a wave of oncology AI platform development from companies including SOPHiA GENETICS, Ontada, COTA Healthcare, and now Flatiron Health. “As oncology becomes more complex, the ability to quickly identify the right patients and answer critical research questions is no longer a nice-to-have, it’s essential,” Kate Estep, chief product officer at Flatiron Health, told Inside Precision Medicine.

The move by Flatiron Health supports the continuing trend of data companies in oncology and across healthcare positioning themselves beyond simply aggregating clinical datasets toward creating AI-native research environments where clinicians, commercial strategists, and researchers can interact directly with data using natural language.

The need for speed

Historically, RWE generation has been labor-intensive. Pharmaceutical teams often rely on analysts or biostatistics groups to construct cohorts, validate inclusion criteria, and generate feasibility assessments—a process that can take days or weeks before a research question even begins to take shape. That workflow is increasingly incompatible with modern oncology development, where therapies are often targeted toward highly specific molecular subpopulations.

Cancer research may be uniquely suited for AI-native evidence generation systems. Compared with many therapeutic areas, oncology already produces unusually data-dense patient journeys involving pathology reports, genomic sequencing, imaging, biomarker testing, treatment lines, progression tracking, and survival endpoints. Oncology drug development is also increasingly dependent on identifying narrow molecular populations quickly and accurately. That complexity creates ideal conditions for conversational AI systems capable of navigating structured and unstructured clinical data simultaneously.

Flatiron Telescope attempts to address that bottleneck by giving users a conversational interface layered on top of Flatiron’s oncology-specific datasets. Researchers can describe inclusion and exclusion criteria in natural language, dynamically refine cohorts, and immediately view patient counts, attrition curves, treatment patterns, and survival analyses without writing code. “We were chatting with one of our early access partners last week, and this person was remarking, ‘I could answer my question in 30 minutes, and that would have taken me two days before waiting for my data team to come back to me,’” Estep said during a media briefing ahead of launch.

That acceleration may ultimately become the defining metric in the AI healthcare infrastructure market: not simply the size of a company’s dataset, but how quickly actionable insight can be extracted from it.

From data vendors to research platforms

But the challenge is not merely access to information. Trust and scientific validity remain central concerns. “One of the things our head of data science was sharing is that off-the-shelf models are roughly 60% accurate,” Estep said. “When built and trained with the clinical and scientific best practices that we have applied to model context because we have been asking cohort questions of our data for 15 years, that’s 90% plus accuracy.”

Those comments point toward an increasingly important divide in healthcare AI: the distinction between general-purpose AI models and clinically fine-tuned systems trained on domain-specific workflows. For companies like Flatiron, the competitive moat may ultimately come less from the underlying language models themselves and more from proprietary clinical context, curation methodologies, and validated evidence-generation pipelines.

The emergence of platforms like Telescope also reflects a broader transformation occurring across healthcare AI. The first generation of healthcare data companies focused primarily on aggregation, assembling electronic health record (EHR) data, claims data, genomic profiles, and imaging repositories into structured datasets. The second generation is now focused on orchestration: enabling users to interrogate those datasets continuously through AI-driven systems.

Flatiron is betting that domain specificity will matter more than generic AI capability. “Most people in the space either give you data, they give you analytics, or they give you a platform,” Estep said. “Very few cut across all three buckets.”

That positioning distinguishes Flatiron somewhat from competitors. Tempus AI has built a broad precision medicine tech ecosystem for both providers and life sciences companies. SOPHiA GENETICS has emphasized multimodal analytics and genomic interpretation. Ontada, backed by McKesson, combines oncology data assets with point-of-care tools and network analytics.

Flatiron, by contrast, is leaning heavily into its reputation for longitudinal oncology RWE and EHR-derived clinical depth. The company says Telescope is powered by more than 15 years of oncology-specific data infrastructure spanning over 4,700 providers and 1,600 clinical sites in the United States, representing approximately 40% of U.S. community oncology practices. Globally, the company now manages data from more than five million patient journeys across the U.S., U.K., Germany, and Japan.

That scale matters because oncology AI systems depend heavily on context-rich longitudinal data. Large language models alone are insufficient if the underlying clinical infrastructure lacks standardized outcomes, biomarker histories, treatment sequences, or progression events. “Flatiron has spent the last decade and a half building high-quality, oncology-specific real-world datasets,” Estep said. “Telescope really sits at the epicenter of that.”

Global oncology intelligence

Another major shift underway in oncology AI involves international interoperability. Historically, most RWE systems were fragmented geographically, with datasets built independently for different markets. But as pharmaceutical companies globalize clinical development programs, pressure is increasing to harmonize datasets across countries.

Flatiron says it is now building globally interoperable oncology datasets across the U.S., U.K., Germany, and Japan, beginning with prostate cancer data expected later this year. “We are ensuring that our datasets are interoperable from a global perspective,” Estep said. “Conclusions drawn on definitions of variables and data models in one market can easily be applied or explored in another.”

The long-term implications could be substantial. Globally harmonized oncology datasets would allow researchers to study treatment variation, biomarker prevalence, and outcomes across healthcare systems at a scale previously difficult to achieve. It may also help address longstanding concerns around representativeness in RWE generation. “Representativeness of a RWD is probably one of the single biggest requirements for us as we think about whether this dataset is considered reliable,” Estep said.

Perhaps the most important industry trend underlying Telescope’s launch is the democratization of advanced analytics. Historically, sophisticated oncology data analysis required teams of data scientists, epidemiologists, or biostatisticians. AI interfaces are beginning to collapse those barriers, enabling clinical operations leaders, medical affairs teams, and commercial strategists to interact directly with research-grade datasets. “There’s no coding required,” Estep said. “Any team member can use it, not just your data analysts.”

That shift could fundamentally change how oncology organizations make decisions, reducing delays between hypothesis generation and evidence generation while broadening access to sophisticated analytical capabilities across enterprise teams.

Whether Telescope ultimately becomes a dominant platform remains to be seen. But its launch reflects a broader reality now reshaping healthcare: in oncology, the future competitive advantage may belong not simply to companies with the most data but to those capable of turning clinical complexity into usable intelligence fastest.

The post Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight appeared first on Inside Precision Medicine.

“The study gives us a better idea of how sensitive human gut microbes are,” said senior author Maria G. Dominguez-Bello, PhD, a professor of microbiome and health at Rutgers. “It opens the door for future research on how we can restore our microbiota after using medicines like antibiotics, which can deplete organisms in our gut.”

Prior research has established that urbanization-related changes in diet, lifestyle, and environment can alter the gut microbiome, but those factors are often intertwined. In this research, the unique setting and population allowed the Rutgers team to isolate the effects of repeated medical exposure in populations with minimal prior contact with modern healthcare.

“Many factors contribute to reduced microbial diversity associated with Westernization, complicating efforts to identify early drivers of microbiome change,” the researchers noted, adding that the Amazonian villages provided a setting where “low-exposure villages show higher baseline gut microbiota diversity than the medium-exposure village, and microbiota diversity declines over time in association with repeated exposure, particularly in children.”

The study followed 335 participants across multiple villages, collecting fecal samples and body-site swabs before and during repeated medical visits between October 2015 and February 2016. The villages were categorized by prior exposure to outsiders and medicine, allowing comparisons between low-exposure communities and a medium-exposure village that had a longer history of modern medical services. Researchers collected samples from the gut, mouth, nose, and skin in conjunction with the WHO’s quarterly visits that delivered antiparasitic treatments and basic care.

“This study leverages a rare longitudinal dataset from remote Amazonian Indigenous communities to examine how the human microbiome shifts during the earliest stages of contact with external institutions, prior to major dietary or lifestyle urbanization,” the researchers wrote.

In the gut microbiome, data from the collected samples showed a decline in taxa commonly associated with fiber metabolism and an increased abundance of bacterial groups more commonly seen in industrialized populations. Functional gene profiles also shifted, with increased representation of genes linked to simple carbohydrate metabolism and antimicrobial resistance, and reduced representation of genes involved in fiber fermentation and other metabolic processes.

The study also found that microbial changes were most pronounced in children, pointing toward a heightened sensitivity of developing microbiomes to repeated medical exposure. In addition to gut changes, oral, nasal, and skin microbiomes also shifted: oral microbial diversity declined; nasal diversity increased after initial visits; and skin communities showed reductions in diversity and shifts in composition.

Previous research cited by the authors has linked industrialized lifestyles with reduced microbial diversity, altered microbial networks, and increased prevalence of genes associated with antimicrobial resistance. However, the current study provides a unique glimpse at the effects of the earliest stages of treatment by modern medicine before shifts in diet or infrastructure occur.

This new data highlights the need to find the balance between essential medical interventions and preservation of microbial diversity. While treatments such as those for river blindness remain critical for reducing infectious disease burden, the researchers suggest that repeated exposure to medical care may coincide with microbial restructuring that reduces diversity and alters functional capacity.

“Understanding and navigating this balance is essential not only for microbiome science but also for ethical, culturally informed approaches that respect both biological and social dimensions of wellbeing,” the researchers noted.

Future research will examine ways to protect microbial diversity during necessary medical interventions and expanding longitudinal studies that assess resilience and recovery of the microbiome after repeated exposure to medicines.

The post Early Medical Care Linked to Rapid Gut Microbiome Shifts in Amazonian Indigenous Communities appeared first on Inside Precision Medicine.

The first study, in Science Translational Medicine, revealed that AI could outperform standard clinical risk tools at identifying women of European ancestry who were at greatest risk of developing breast cancer within the next decade.

Because current AI-based models mostly predict breast cancer risk in the short term, Mikael Eriksson, PhD, from the Karolinska Institute, and fellow researchers created a model for invasive and in situ breast cancer that covered a 10-year period.

“Considering that a tumor can take five to 20 years to develop into a screen- or clinically detected cancer, a 10-year or lifetime risk projection time is reasonable,” they noted.

The researchers validated their image-derived AI-based 10-year risk model using digital mammograms from 8676 women in two cohorts from the U.S. and Sweden, of whom 1633 had breast cancer.

They compared their model with three other risk assessment tools: the Tyrer-Cuzick-v8, which uses personal and family history to determine the lifetime risk of breast cancer; the Breast Cancer Surveillance Consortium v3 tool, which estimates the risk of developing invasive risk cancer over five years; and the Mirai AI algorithm for predicting breast cancer risk.

The model calculated that the 10-year risk of breast cancer was 3.83% for the North-American group and 3.14% for the Scandinavian group, and it showed promise for predicting the risk of invasive tumors.

The AI model performed significantly better at predicting invasive breast cancer than the three comparator models both overall, in women aged 50 to 69 years and those with estrogen-receptor-positive breast cancers.

“An image-derived AI-based risk model developed for 10-year risk assessment for identifying individuals in mammographic screening who may benefit from primary prevention strategies identifies up to 40% of breast cancers to be at high risk at study entry in U.S. and Swedish validation case cohorts per clinical guidelines,” summarized Eriksson and co-workers.

The second study, in Science Advances, revealed how AI could be of value for women from more diverse backgrounds, and that it had value regardless of race and ethnicity.

The research was driven by the observation that conventional prediction models incorporating genetic and clinical factors including breast density underperform in women who do not have European ancestry.

Shu Jiang, PhD, from Washington University School of Medicine in St Louis, and co-workers set out to evaluate the generalizability of an AI-derived mammogram risk score (MRS), a summary of texture features which captures intrinsic breast-tissue characteristics that are the basis for cancer to develop.

To do this, they used data from two large North American cohorts that represented more than 226,000 racially diverse women undergoing routine breast screening with mammography.

This was then used to examine the generalizability of MRS with breast cancer risk across non-Hispanic white (NHW), non-Hispanic Black (NHB), East Asian, South Asian, and Indigenous women.

Jiang and team found that the MRS was a strong predictor of breast cancer risk independent of race or ethnicity, demonstrating its potential for broader clinical utility.

Across the two independent screening cohorts, the MRS increased with age in line with rising breast cancer risk. It was a strong and consistent predictor of breast cancer risk across race and ethnic groups as shown by the association study and comparison of distributions and demonstrated excellent calibration in all groups.

Its remained robust across full-field digital mammograms or synthetic two-dimensional digital breast tomosynthesis, although the limited numbers of images precluded an analysis of how performance varied across manufacturers.

“Overall, these results support that MRS is a powerful breast cancer risk predictor that does not depend on race or ethnicity, supporting its potential for broader clinical adoption and use in varied populations of women undergoing routine screening mammography to identify those at increased risk,” Jiang and coworkers concluded.

The post AI Shows Value for Breast Cancer Prediction appeared first on Inside Precision Medicine.

“Noninvasive and accurate diagnosis of kidney fibrosis remains unavailable in clinics, limiting effective patient management,” write the authors of the study, led by Zhou Jiaguo, PhD, professor of pharmacology at Sun Yat-sen University in China. “Although biopsy is considered the gold standard for assessing fibrotic progression, this invasive approach is subject to sampling error and interobserver variability, which may bias the diagnosis and is improper for repeated monitoring.”

Chronic kidney disease affects approximately 12% of the global population, with rates steadily rising as the world’s population ages. Characterized by the excessive formation of scar tissue, kidney fibrosis is a hallmark of chronic kidney disease that causes the proggressive impairment of kidney function, eventually leading to end-stage renal disease that requires dialysis or transplantation. 

Diagnosing kidney fibrosis at early stages can be critical for preventing irreversible damage to the kidneys. However, current diagnostic methods present some major limitations. While biopsies are invasive and difficult to accurately interpret, blood biomarker tests cannot distinguish kidney fibrosis from other kidney conditions or from fibrosis in other organs.

To circumvent these limitations, Jiaguo’s team identified two biomarkers that are specific to kidney fibrosis and can accurately differentiate this diagnosis from other related conditions. These are the transglutaminase 2 (TG2) enzyme and its substrate, lysyl oxidase–derived allysine (LysAld), which are both upregulated during the formation of fibrotic kidney tissue. 

The researchers developed a fluorescent reporter that binds to LysAld in the kidney and is then cleaved by TG2, turning on its fluorescence. Higher biomarker levels result in a stronger signal, providing an indication of how advanced fibrosis is in the patient’s kidneys. 

In a small cohort of 35 participants, the molecular imaging test could distinguish patients with kidney fibrosis from healthy controls with 84% sensitivity and 94% specificity. In addition, the test could differentiate mild and severe cases of fibrosis, with results confirmed with histological imaging. In contrast, traditional clinical measurements such as glomerular filtration rate, serum creatinine, and blood urea nitrogen were not able to classify patients according to the severity of the condition. 

Large-scale studies will be needed to establish this novel approach as a diagnostic and prognostic. Going forward, Jiaguo and colleagues are also interested in exploring using this test to monitor disease progression over time as a potential tool to evaluate treatment response and help clinicians make critical decisions to improve patient outcomes. 

“This noninvasive activatable reporter holds translational potential for early identification of renal fibrosis and patient stratification of [chronic kidney disease] to improve clinical management and patient outcome,” write the researchers. “We do not envision the probe as a standalone replacement for current standards; synergistic combinations with other biomarkers could further enhance its clinical utility.”

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