Breast Cancer Cell Metastatic State Characterized by Prrx1 Levels

A new study published in Nature Communications is reshaping how researchers think about metastasis, showing that the cells most likely to spread are not defined by extremes, but by a precise balance of biological states within the primary tumor.

The work, led by Raúl Jiménez Castaño, PhD, and colleagues in the Cell Plasticity in Development and Disease Laboratory headed by Ángela Nieto at the Instituto de Neurociencias in Spain, identifies a nonlinear relationship between expression of the transcription factor Prrx1 and metastatic potential in breast cancer. Tumors with intermediate levels of Prrx1—not low or high—were found to be the most metastatic.

“This is unusual,” Jiménez Castaño said. “You normally expect a linear correlation—either low or high expression being the most relevant. But here, the peak of metastasis is in the intermediate levels.”

From paradox to mechanism

The study builds on longstanding efforts to understand the epithelial-to-mesenchymal transition (EMT), a developmental program that enables cells to migrate and is co-opted by cancer cells during metastasis. While EMT has been widely linked to tumor dissemination, the new findings show that metastatic potential is not simply a function of how invasive a cell becomes. Instead, it depends on a finely tuned balance between invasion and proliferation—two processes that are often at odds.

Previous work from the group and others had produced conflicting results regarding the role of Prrx1. In some models, removing the gene reduced metastasis; in others, it appeared necessary for dissemination. To resolve this contradiction, the researchers turned to patient tumor samples, where they observed that metastatic incidence peaked in tumors with intermediate Prrx1 expression.

Modeling a metastatic “sweet spot”

To investigate, the team engineered mouse models with graded levels of Prrx1 expression, mimicking the spectrum observed in human tumors. The results closely mirrored patient data. Tumors lacking Prrx1 showed little ability to metastasize, while those with high expression were capable of invasion but produced relatively few metastases. In contrast, tumors with intermediate levels generated the highest metastatic burden.

At the invasive front of these tumors, the researchers identified a distinct population of cells capable of both migrating and adopting divergent fates—either proliferating or entering a dormant state. This balance proved to be the critical determinant of metastatic success.

To understand the underlying biology, the team applied a range of advanced techniques, including single-cell RNA sequencing, chromatin profiling, and spatial transcriptomics. These approaches allowed them to map cellular states within tumors and link Prrx1 expression levels to functional behavior.

The analyses revealed that Prrx1 plays a dual role: it promotes invasion while simultaneously activating a dormancy program that suppresses cell division.

“At the same time that Prrx1 is necessary for cancer cells to be invasive, it also activates a dormancy program,” Jiménez Castaño explained.

This creates a biological trade-off. At high Prrx1 levels, cells are highly invasive but largely non-proliferative, limiting their ability to form metastases. At low levels, cells retain proliferative capacity but cannot effectively disseminate. Only at intermediate levels do cells achieve both capabilities.

“If the cancer cell has these intermediate levels, it is both invasive and proliferative,” he said. “And therefore, these cells will create a lot of metastasis.”

Metastatic potential begins in the primary tumor

One of the study’s most significant implications is that metastatic potential is determined earlier than previously appreciated. Rather than being dictated solely by conditions at distant sites, the ability of cancer cells to form metastases appears to be encoded within specific cell states in the primary tumor.

“The big conclusion is that already in the primary tumor, the potential of the cancer cells to metastasize is defined,” Jiménez Castaño said.

This finding aligns with broader observations from the field that tumors contain heterogeneous populations of cells with distinct functional properties. In this case, a subset of cells with intermediate Prrx1 expression represents a particularly dangerous state—one that combines mobility with the capacity for sustained growth.

Implications for biomarkers and therapy

Although the study identifies Prrx1 as a potential marker of metastatic risk, translating this insight into clinical practice will require further validation. The researchers were able to stratify tumors into low, intermediate, and high expression groups using staining intensity and computational analysis, but defining precise thresholds remains a challenge.

“We cannot say at this moment it is a biomarker,” Jiménez Castaño noted.

Even so, the findings provide a conceptual framework for improving patient stratification and identifying tumors with a higher likelihood of metastasis.

They also suggest new therapeutic strategies. Rather than attempting to eliminate invasive behavior entirely, it may be possible to push tumor cells into states that are less capable of forming metastases. For example, maintaining high Prrx1 expression could promote invasion while simultaneously enforcing dormancy, preventing metastatic outgrowth.

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mRNA Uses Unconventional Pathways in CD8+T Cell Priming to Help Vaccines Work

mRNA vaccines scored a stunning win against SARS-CoV-2 in 2020, and now the Nobel-prize–winning technology is out to conquer some cancers. Several mRNA vaccines are already in clinical trials for melanoma, small cell lung cancer, and bladder cancer, among others. Recently, a pancreatic cancer vaccine grabbed headlines after researchers shared that most Phase I trial participants were still alive after several years—unprecedented in a disease that is considered incurable, and usually kills patients quickly.

But how exactly does mRNA work? A new study suggests a broader role for how T cells become activated after an mRNA vaccine. It’s a process that engages both cDC1 and cDC2 cells redundantly. The study was led by researchers at Washington University School of Medicine in St. Louis (WashU) and could lead to improvements in mRNA vaccine design. The findings were published in Nature. The work was powered by a novel mouse model developed by the WashU team.

“My lab made them in 2019 and 2022. We put all of them in Jackson labs [database] so anyone can get them, no strings, and study them,” senior author Kenneth M. Murphy, MD, PhD, told Inside Precision Medicine. “Thanks to them, we saw the question and were able to address it most quickly.”

Until now, scientists assumed that cDC1, which is a classical type 1 dendritic cell, was required for mRNA vaccination to activate the immune system. But, in a lab study, these researchers found that even without cDC1 cells, the mRNA vaccine still triggers strong cancer‑killing responses. That’s because they determined that cDC2, a cousin to cDC1, can also stimulate anti-tumor immune activity—an unexpected finding given that this related subtype is not involved in responses to other vaccines.

“There is a lot of interest in applying the mRNA vaccine approaches used during the COVID-19 pandemic to the problem of inducing anti-tumor immunity,” said Murphy, the Eugene Opie Centennial Professor in the department of pathology & immunology at WashU Medicine. “By dissecting which immune cells are involved and how they coordinate the response, we’re offering vaccine developers some additional mechanistic insights to consider in their goal of optimizing these vaccines against tumor proteins.”

Murphy is also a research member at Siteman Cancer Center, based at Barnes-Jewish Hospital and WashU Medicine.

mRNA vaccines work by delivering instructions, in the form of messenger RNA, for immune cells to produce bits of protein that trigger the immune system to destroy cells bearing these proteins. Dendritic cells produce the protein bits from the mRNA instructions, and T cells then find and destroy the invading proteins. To treat cancer, mRNA vaccines can be designed to generate protein bits unique to a tumor.

The work was done in collaboration with the study’s co-corresponding author, William E. Gillanders, MD, the Mary Culver Professor of Surgery at WashU Medicine. Gillanders, a physician-scientist and surgical oncologist who has also developed an investigational vaccine against triple-negative breast cancer, treats patients at Siteman Cancer Center.

Murphy and members of his lab used their mouse models, which lacked cDC1 or cDC2, to tease out the role that different groups of dendritic cells play in priming T cells after mRNA cancer vaccination.

One of their findings was that mice immunized with an mRNA vaccine generated strong T-cell responses even in the absence of cDC1s. In addition, they found that immunized mice without cDC1s were able to clear sarcoma tumors—cancers that develop in connective tissues such as fat, muscle, nerves, blood vessels, bone, and cartilage. This indicated that some other cell type must be stimulating the T-cell response.

Indeed, their study found that cDC2s also participate in generating an immune response from T cells and preventing tumor growth. Further, the study found that T cells turned on by cDC1s and cDC2s each showed slightly different molecular “fingerprints.” These differences could help scientists design better versions of vaccines in the future.

Similarly, immunized mice lacking cDC2s and mice that had both cell subtypes produced an immune response and rejected tumor growth, demonstrating that mRNA vaccination uses both dendritic cell subtypes to stop cancer.

“This work uncovers a new way mRNA vaccines engage the immune system—through both cDC1 and cDC2—which helps explain their power and gives researchers concrete targets for making future mRNA cancer vaccines more effective,” said Gillanders. “It could improve vaccine formulation and dosing, potentially explain why some patients respond better to vaccines than others, and guide strategies for making vaccines more effective.”

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Presurgery Pembrolizumab May Be the Future for Some Operable CRCs

Groundbreaking data from the Phase II NEOPRISM-CRC trial show that patients given pembrolizumab prior to surgery for certain types of high-risk, operable colorectal cancer (CRC) remain relapse-free for almost three years.

Furthermore, the response to treatment can be predicted by DNA and T cell biomarkers.

At present, the standard of care for people with high-risk stage II or III CRC with deficient DNA mismatch repair (dMMR) or microsatellite instability (MSI), like those included in the study, is surgery followed by chemotherapy, but relapse rates can range from 15% to 40% at three years.

Pembrolizumab is already given to patients with inoperable stage IV dMMR/MSI CRC to shrink the tumors and prolong life, but it is not yet available for patients with operable tumors.

The NEOPRISM-CRC trial investigated whether pembrolizumab could benefit such patients.

For the study, 32 people with large, high-risk stage II or III dMMR/MSI CRC were given three cycles of intravenous pembrolizumab 200 mg followed by surgery.

The researchers, led by Kai-Keen Shiu, from University College London (UCL) Cancer Institute, have previously reported that that 59% of participants had a pathologic complete response (pCR) to pembrolizumab, indicating that there were no cancer cells in tissue samples removed from these patients during surgery.

The data presented at the American Association for Cancer Research Annual Meeting 2026 by Yanrong Jiang, a PhD student at UCL Cancer Institute, focused on survival outcomes and whether biomarkers could predict which patients respond to pembrolizumab.

She reported that, after a mean of 33 months of follow-up, all patients were alive and relapse-free.

Shiu said: “Seeing that no patients have experienced a cancer recurrence after almost three years of follow-up is extremely encouraging and strengthens our confidence that pembrolizumab is a safe and highly effective treatment to improve outcomes in patients with high-risk bowel cancers.”

Blood samples taken throughout the study were assessed for circulating tumor (ct)DNA using the highly sensitive whole genome tumor-informed Personalis NeXT Personal assay, which can track up to 1800 patient-specific variants.

The team found that all 25 patients with evaluable data had detectable ctDNA at baseline.

Remarkably, after one round of treatment with pembrolizumab, 24% of participants no longer had detectable ctDNA. The proportion increased to 43% and 58% after rounds two and three, respectively. Post-surgery, ctDNA was undetectable in all 25 patients.

When the researchers analyzed the ctDNA clearance profiles, they identified three distinct patterns. They designated the first group “super molecular responders.” All six patients in this group had undetectable ctDNA after one cycle of pembrolizumab.

The “dynamic molecular responder” group included 11 patients who cleared ctDNA at different rates—four after cycle two of pembrolizumab, five after cycle three, and the remainder post-surgery, even though the level was decreasing rapidly during immunotherapy.

The final group, termed “poor molecular responders,” included eight patients who showed stable, high levels of ctDNA throughout immunotherapy, with levels only becoming undetectable post-surgery.

Interestingly, the pCR rate varied across the three groups: It was 100% among the super molecular responders and 82% among the dynamic molecular responders, but 0% among the poor molecular responders.

Shiu told Inside Precision Medicine that measuring ctDNA using the Next Personal assay could “potentially trump all standard tests when it comes to informing decision making.”

He suggested that the super molecular responders could potentially consider forgoing surgery altogether, while the poor molecular responders could be considered for treatment intensification, such as the addition of a second immunotherapy agent.

Although ctDNA gives information on how the tumor is responding to treatment, it doesn’t explain why some patients respond and others don’t.

The researchers, therefore, also carried out T cell receptor (TCR) sequencing, which provides a readout of the immune environment within the tumor, specifically whether there are expanded T cell populations that may recognize cancer, explained Marnix Jansen, MD, a clinician scientist and consultant histopathologist who led the translational research on the trial from UCL Cancer Institute.

“We found that patients who achieved a complete response had a higher proportion of expanded T cell clones in their tumors, suggesting a more focused and effective anti-tumor immune response at baseline,” he said.

When the team combined the ctDNA results with the TCR sequencing data, they improved the ability to predict outcomes compared with using either biomarker alone.

“The key implication is that integrating immune and tumor biomarkers in a dynamic model may allow early, data-driven treatment decisions, such as identifying patients who are highly likely to benefit or, conversely, those who may need a change in therapy,” Jansen told Inside Precision Medicine.

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AI Could Help More Donor Hearts Reach Transplant Patients

Integrating artificial intelligence (AI) tools into transplant infrastructure could save a significant amount of available donor hearts from being discarded, according to research presented at the International Society for Heart and Lung Transplantation (ISHLT) 46th Annual Meeting and Scientific Sessions.

“There is a massive shortage of heart donors in the United States, with patients waiting months—if not longer—for a transplant, often on life support in the ICU. So the stakes are very high,” said Brian Wayda, MD, transplant cardiologist and assistant professor of medicine at NYU Grossman School of Medicine. 

Despite an ongoing shortage of donor hearts, only up to 40% of the hearts that become available are actually transplanted. Transplant teams will typically evaluate potential donors based on a series of donor risk factors, including the person’s age, disease history, and drug use record, among others. However, evidence is still limited on how each factor affects post-transplant outcomes, and decisions need to be made quickly to ensure any suitable hearts find a matching recipient on time. 

“It’s an extremely complex judgment call that must be made in a very short time window, often in the middle of the night,” said Wayda. “AI can support these life‑and‑death decisions made under extreme time constraints.”

Together with scientists at Stanford and other leading U.S. research centers, Wayda has developed a web-based prediction tool called TOPHAT (Tool Predicting Heart Acceptance for Transplant). This machine learning algorithm evaluates 20 donor characteristics to estimate how likely a transplant center is to accept a donor heart, based on historical data from over 78,000 potential donors.

Using this tool could help experts make decisions in a more data-driven, consistent, and efficient way. This could reduce the likelihood that a suitable donor heart gets discarded due to time running out before a matching recipient is found. 

“The tool doesn’t say ‘this is a good heart’ or ‘this is a bad heart,’” Wayda explained. “Instead, it quickly shows how a donor compares to the national experience. An older donor, or one with a single risk factor like cocaine use, may look high-risk at first glance. But when you consider all the variables at once, that donor may not be any riskier than a typical heart we already use.” 

There are currently over 4,000 patients waiting for a heart transplant in the United States. Even a relative increase of 500 additional hearts becoming available each year would be enough to reduce wait time substantially, said Wayda.

Going forward, the researchers are working toward developing a unified decision support system that brings together output from TOPHAT and other AI tools, as well as the broader donor medical record, to generate a single, easy-to-digest summary for clinicians making time-sensitive decisions about a potential transplant. 

“The real value of AI is helping us synthesize a huge amount of data quickly and objectively so clinicians can make better-informed choices,” said Wayda. “With this kind of integrated view, doctors would be less likely to anchor their decision on a single ‘red flag’—such as donor age over 50—and decline hearts that could have performed well.”

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Gut Microbiome Signatures Predict Melanoma Response to ICB Treatments

Researchers at NYU Langone Health’s Perlmutter Cancer Center have found that patterns in the populations of bacteria in the gut microbiome can predict which melanoma patients are more likely to benefit from immunotherapy. The study, published in Cell, showed that specific bacterial signatures, when analyzed in the context of a patient’s overall microbiome profile, can forecast cancer recurrence after immune checkpoint blockade (ICB) with accuracy as high as 94%. The findings suggest that using this information could help identify which patients will respond to ICB treatment and which are more likely to relapse.

“Our study identified for the first time gut bacterial types that can serve as markers of increased recurrence risk in these specific patients, which will help to tailor treatment,” said study senior author Jiyoung Ahn, PhD, a professor of population health at NYU Grossman School of Medicine and associate director of population research at NYU Langone’s Perlmutter Cancer Center.

ICB is a form of cancer treatment that enhances the immune system’s ability to recognize and attack tumor cells. Drugs such as nivolumab and ipilimumab work by inhibiting molecular “checkpoints” that normally restrain T cell activity to allow immune cells to mount an anti-tumor response. Because of the success of ICBs in advanced cancer, this form of treatment is now expanding into earlier-stage, higher-risk patients following surgery.

“Immune checkpoint blockade (ICB) therapy has transformed the management of advanced, unresectable melanoma,” the researchers wrote. However, it is not effective for all patients. “Clinical benefit remains unpredictable, with approximately 25%–40% of patients experiencing disease recurrence despite therapy,” they added.

In their search for biomarkers that could stratify responders from non-responders, the NYU investigators analyzed stool samples from 674 melanoma patients enrolled in the Phase III CheckMate 915 clinical trial. Participants had undergone surgical tumor removal and then received either a combination of nivolumab plus ipilimumab or nivolumab alone for up to one year. Using shotgun metagenomic sequencing, the researchers characterized the gut microbiome at strain-level resolution before treatment and, in a subset of the patients, during therapy.

Their analysis identified bacterial taxa, including Eubacterium, Ruminococcus, Firmicutes, and Clostridium, that were associated with recurrence risk.

An important finding was that predictive accuracy was dependent on matching patients by their overall microbiome composition. “Recurrence prediction was strongest when the validation cohort exhibited GMB profiles similar to those in the discovery cohort,” the researchers wrote. When patients were closely matched based on microbial similarity, prediction performance reached area under the curve (AUC) values between 0.78 and 0.94. “This evidence indicates that taxonomic markers for prediction of recurrence are generalizable across regions for individuals with similar GMB composition,” the researchers noted.

The study’s design sought to address a longstanding challenge in microbiome research, notably that earlier studies had shown bacterial markers linked to immunotherapy response varied widely by geography.

“Past studies have struggled because the gut bacteria that predict treatment success seemed to change from one region to another,” Ahn said. “Our study provides a new method that overcomes this barrier, showing that these markers are indeed generalizable if we account for the person’s underlying microbiome.”

The study also showed that the gut microbiome remains stable during treatment, a finding that suggests the potential to manipulate the gut microbiome before therapy begins. “This stability suggests an important window of opportunity before treatment begins,” Ahn told Inside Precision Medicine. “We are currently planning diet-based intervention trials aimed at actively modifying the microbiome prior to immunotherapy. The goal is to move beyond observational associations toward actionable strategies that can improve treatment response.”

The biological mechanisms underlying these associations may relate to how gut bacteria influence immune activity. “These taxa are largely fiber-metabolizing bacteria that produce short-chain fatty acids, such as butyrate,” Ahn said. “These metabolites are known to play important roles in modulating immune function, including enhancing anti-tumor immune responses and regulating inflammation.” The researchers also noted links between these bacteria and metabolic pathways such as “glycolysis/gluconeogenesis” and the “pentose phosphate pathway,” which prior research has shown can affect cancer treatment outcomes.

Evidence supporting the microbiome’s role in immunotherapy response has been accumulating. Prior studies in metastatic melanoma have shown that fecal microbiota transplantation can restore responsiveness to ICB in some patients, via activation of CD8+ T cells. But earlier research has been limited by small sample sizes and regional variability.

The current study, however, examines the influence of the microbiome during adjuvant therapy and provides a potential method for overcoming geographic differences.

“The main challenge is that prediction models may be limited to subsets of populations with similar underlying microbiome structures,” Ahn noted. “Moving forward, we will need well-characterized, large-scale microbiome reference datasets that allow appropriate matching across populations and regions.”

Additional work is needed in order to use these signatures in the clinic. “The next steps include validation in independent cohorts and prospective trials,” Ahn noted. “Ultimately, these biomarkers have the potential to guide patient stratification and optimize immunotherapy outcomes in clinical settings.”

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Hearing Loss Gene Therapy Lasts More than Two Years

A trial of a gene therapy to treat people with hearing loss related to recessive mutations in the OTOF gene shows the treatment is effective and safe for at least 2.5 years.

The study, published in Nature, showed around 90% of those who received the adeno-associated viral (AAV) vector gene therapy showed at least some restoration in hearing.

Improvement was rapid in the first six weeks, improved further by 26 weeks and in a small subset of patients remained stable for 2.5 years of follow-up.

“It’s remarkable to see patients go from complete deafness to being able to hear,” said the study’s co-lead author, Zheng-Yi Chen, PhD, the Ines and Fredrick Yeatts Chair in Otolaryngology and an associate scientist at Massachusetts Eye and Ear hospital, in a press statement. “For many patients, that also means the ability to develop and use speech.”

The OTOF gene encodes the otoferlin protein, which is critical for normal hearing. When otoferlin is missing or nonfunctional, inner‑ear hair cells can’t relay sound information to the brain, leading to severe or complete deafness. This kind of hearing loss is rare and inherited in a recessive manner, needing mutations from both parents for a child to be affected.

As of this year there are at least five gene therapies being developed to treat this kind of deafness, for example, by Akouos/Eli Lilly and Decibel/Regeneron in the U.S., Sensorion in France, and at least two additional programs in China.

The current study took place in China and included 42 people between the age of eight months and 32 years (average age six years) and is the largest cohort of OTOF gene‑therapy patients reported so far, as well as the longest study follow-up period.

The participants received one of three doses of the AAV gene therapy injected into their cochlea’s and were followed up for 13 weeks to 2.5 years (median 52 weeks) to assess the impact of the therapy on hearing and also to evaluate safety.

Overall no serious adverse events or dose-limiting toxicities occurred. Around 90% of participants experienced hearing restoration to some degree with fast improvements seen in the first six weeks after treatment and slower improvements after that. A subset of patients (seven ears from seven patients) were included in the 2.5 year follow-up group and results were similar to those seen at two years.

Some groups did better than others. For example, hearing restoration was 100% in children aged up to three years and 92% in those aged 3-8 years. Improvement was seen in older children and adults, but to a lesser degree than that seen in young children in the study. Participants with better outer hair cell function on enrollment also responded better to the therapy than those with greater functional loss.

“It is very encouraging to see meaningful improvements in some adult patients. It suggests there may be more flexibility in the human auditory system than we expected,” said Chen, who is also the scientific founder of Salubritas Therapeutics, a Massachusetts based biotech focusing on hearing loss correction.

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Jurgi Camblong: Data-Driven Doctors Without Borders

Jonathan D. Grinstein, PhD, North American Editor of Inside Precision Medicine, hosts a new series called Behind the Breakthroughs that features the people shaping the future of medicine. With each episode, Jonathan gives listeners access to his guests’ motivational tales and visions for this emerging, game-changing field.

Precision medicine is often framed as imminent: gather more data, refine analytics, and individualized care will naturally follow. In reality, progress has been uneven. Genomic, imaging, pathology, and clinical data remain fragmented across systems and poorly integrated into clinical workflows. The core challenge is not data scarcity but the ability to interpret complex, heterogeneous inputs quickly enough to guide real medical decisions. To address this, Jurgi Camblong founded SOPHiA Genetics with a focus on building infrastructure rather than isolated tools—aiming to turn multimodal health data into actionable insights, a goal far more difficult in practice than in theory.

In Behind the Breakthroughs, Camblong highlights persistent structural and technical barriers limiting data-driven healthcare. Genomic standardization, for example, remains inconsistent, with approaches ranging from targeted panels to whole-genome sequencing, each balancing cost, sensitivity, and speed. The field is also shifting from single mutations to complex interactions among variants. Expanding beyond genomics adds further complexity, as transcriptomics, radiology, liquid biopsy, and computational pathology each involve distinct methods and clinical uses. Rather than enforcing uniformity, SOPHiA Genetics works across this diversity to produce consistent, clinically usable outputs despite technological and regulatory variation.

Ultimately, success depends on integrating statistical, machine learning, and deep learning methods while staying grounded in biology. A major limitation is the lack of robust feedback loops: precision medicine requires long-term patient outcomes, which many systems fail to capture. Without this, even advanced models are constrained. The central challenge is execution—translating existing data into meaningful insights that improve individual patient care.

This interview has been edited for length and clarity.

 

IPM: What types of multi-omics datasets are currently workable and applicable in a clinical setting, and how do you see their role evolving in routine patient care?

Camblong: When we started in 2015 and launched the platform into the market, people were just analyzing CFTR for cystic fibrosis and BRCA1 and BRCA2, two genes for hereditary cancer. To be honest, there were some efforts around whole genome analysis, but it was very, very rare. Our intent was always not to be a research tool but a tool that brings real benefit to most patients routinely and safely, and things evolved over time.

Now, probably the mean number of genes analyzed when producing genomic information for a patient is around 100 genes. Then you have some solutions that require analyzing only 30 genes because you want to be extremely precise, cost-effective, and rapid. There are other solutions that require sequencing the whole genome. But getting full information with the same sensitivity you can have with smaller panels is not an easy task, and this is where algorithms are really important.

In our case, the fact that we have grown along this journey with the field gives us an advantage today, enabling people to produce more genomic information with the same sensitivity as smaller panels. Genomics is continuously evolving. In the past, people did not necessarily look at copy number variations. Now we are even talking about partial copy variations, like in a gene called PTEN, which is a driver gene, and where a partial CNV can be very important.

What I am trying to explain is that it is not yet simple. It is not streamlined. Lab protocols are different; sequencing approaches are different; it is a constant evolution. In our case, being an operating system that supports thousands of hospitals, we are privileged to be exposed to this complexity, which enables us to improve our algorithms more rapidly and deliver them back to users who can benefit from new capabilities.

Transcriptomics is becoming a very interesting data modality. Initially, it was used to detect so-called gene fusions, specific genomic features that are hard to detect from DNA and require RNA. I am quite bullish on transcriptomics. I believe it will enable cancer subtyping at scale, possibly with more efficient methodologies than what is done today on tissue. It may not replace tissue, but it may allow us to go further and, in some cases, provide more objective outcomes than staining protocols.

Along those lines, radiomics is also very important. By radiomics, I mean data produced by radiologists, CT scans, PET scans, and MRI. There is a signal in this data. For example, you can see if cells are necrotic. You get additional information based on tissue composition and imaging. You can automatically measure tumor volume.

In metastatic cases, where tumors are spread, measuring them is not necessarily easy. You can identify where tumors are, and this information, feature extraction from images, is very powerful. It is also the only data modality that is used longitudinally today in cancer to monitor response to treatment.

Another modality that will become important is liquid biopsy testing to follow patients longitudinally, based on molecular profiles and minimal residual disease (MRD). If you think about computational pathology, H&E staining in particular will be important. I am more skeptical about immunohistochemistry at scale, given feedback from pathologists; multiplexing may introduce too much signal and create confusion. Proteomics has potential, but clinically, it is not quite there yet. Even the most advanced actors are not fully at clinical utility.

Over time, we will need to combine these modalities and apply smart algorithms to extract signals and support decision-making. In the end, this is what matters: not computing data unless it brings value to the oncologist, pathologist, biologist, or geneticist.

 

IPM: How is the SOPHiA interface designed for clinicians in practice? What does the user experience look like across different use cases, such as oncology or liquid biopsy workflows?

Camblong: It is a web-based interface you log into. For example, if you are at Moffitt Cancer Center in Florida, using the platform for hematological malignancies, you will see which mutations are detected with high sensitivity and how actionable they are. If you are in a hospital in the U.K. using it for liquid biopsy testing, you will see the mutations identified for those patients.

We also have customers using it from a multimodal perspective, more from an oncologist’s point of view, where they can see how similar patients with similar molecular profiles respond to treatments elsewhere. For us, this includes partnerships with major clinical genomic databases. Through these, we provide access to additional data layers for institutions, even when the patient data originates locally.

The interface is always web-based. In the backend, we use microservices to compute data using AI, deep learning, machine learning, statistical inference, and pattern recognition. The user then leverages this information to make decisions and answer clinical questions.

 

IPM: Given the diversity of data sources and technologies, how do you approach standardization and harmonization across datasets, particularly in a global context?

Camblong: We operate in over 70 countries. We support local data production and management, but within a framework of collective knowledge. It is important to align solutions with regulations. In some countries, we operate in research mode only. In Europe, some applications are IVD, and in the future possibly In Vitro Diagnostic Regulation (IVDR) or companion diagnostic solutions.

The key is to build technology with optionality, documenting how it is built and its intended use. If you want to make clinical claims, you must conduct clinical studies. The foundation is design control, like in aviation, so that you ensure sensitivity, specificity, reproducibility, repeatability, and robustness, regardless of regulatory frameworks.

 

IPM: How does your platform adapt to the wide variety of user systems, including different sequencing instruments, workflows, and laboratory environments?

Camblong: The backend is fully engineered and automated. But workflows differ across hospitals due to global constraints and complexities. Managing this heterogeneity while delivering consistent outputs means adapting to different workflows. This is not easy, but we have demonstrated strong performance. For example, with Memorial Sloan Kettering, we accessed both their data and their applications, MSK-IMPACT and MSK-ACCESS. We industrialized these within SOPHiA without infringing on IP, enabling hospitals to produce data locally and leverage our algorithms. We achieved over 98% concordance across sites, comparable to repeating sequencing within a single workflow.

We also work with multiple sequencing vendors to ensure compatibility across instruments and consumables. Because we process large volumes of data, we can also advise on optimal workflows for specific applications. Since we are paid per use, our incentives are aligned with hospitals; better workflows mean more patient cases and better outcomes.

On AI: it is a toolbox. Different models suit different problems. Large language models are useful for text and sometimes images, but not everything. Understanding biology and data diversity is key to selecting the right mathematical model that scales effectively.

 

IPM: As you expand into adjacent domains like radiology, how do you approach entering new clinical areas while ensuring relevance and usability?

Camblong: Always with partners, healthcare institutions. We are strong in software, AI, and biology, but not medical practice. We co-develop with clinicians to ensure integration into workflows and real clinical benefit. For example, with MD Anderson, we collaborate on translational and routine lab work to move technologies into clinical practice, such as transcriptomics for cancer subtyping and MRD.

In multimodality, we work case by case. For instance, in kidney cancer in France, we partnered with the UroCCR network, analyzing 27,000 patient cases. This allowed us to identify signals and predict responses to immunotherapy. Innovation only matters if it is adopted in practice.

 

IPM: How actionable are your clinical decision-support tools today, and how do you incorporate real-time or longitudinal data?

Camblong: It depends on regulations. In some places, like the U.K., the platform provides information to oncologists, who then interpret it. For multimodality, feedback loops are essential, linking molecular data, treatment, and outcomes.

With UroCCR, we continuously improve algorithms using real-world data. We should be leveraging post-market data more systematically to refine treatment decisions. Real-world complexity can reveal which patients truly benefit from therapies. Longitudinal data is critical, not just for outcomes, but also for avoiding adverse effects. For example, some ovarian cancer patients benefit from PARP inhibitors but may develop leukemia. Understanding these patterns requires real-world data loops.

 

IPM: How do you think about data ownership, access, and control?

Camblong: Ownership does not exist in a strict sense. Individuals are the ultimate controllers. Hospitals and companies are processors. Data is critical for AI, but our model is decentralized: hospitals retain control of their data. Algorithms learn from data, but once trained, they can deliver insights without retaining raw data, enhancing privacy.

Also, oncology data does not age well because treatments and technologies evolve rapidly. What matters is continuous exposure to new data. Collective intelligence through networks and platforms is essential for precision medicine.

 

IPM: How does SOPHiA approach cross-border collaboration and democratization?

Camblong: Democratization means making technology accessible and usable. For example, in India, a hospital previously sent samples to the U.S., with high costs and six-week turnaround times. We enabled local testing within months, reducing turnaround to under two weeks and building internal expertise. This increased testing volumes and improved clinical adoption.

 

IPM: Are there areas less amenable to your approach?

Camblong: About 80% of our work is in cancer, 20% in rare disorders. Rare diseases require even more collaboration due to limited data. We support peer networks where clinicians share insights, for example, variant classifications, helping others make faster decisions. As medicine becomes more precise, collaboration becomes even more critical.

The post Jurgi Camblong: Data-Driven Doctors Without Borders appeared first on Inside Precision Medicine.

Tideglusib improves novel object recognition memory in the preclinical DBA/2J mdx mouse model of Duchenne muscular dystrophy

IntroductionDuchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disorder characterized by progressive muscle wasting. Approximately 1 in 3 DMD patients experience cognitive dysfunction, with research suggesting an Alzheimer’s disease (AD)-like pathology. We have previously shown that treatment with the glycogen synthase kinase 3β (GSK3) inhibitor, tideglusib, improves muscle quality, function, and insulin sensitivity in the DBA/2J (D2) mdx mouse model of DMD. In this brief follow-up study, we report the effects of tideglusib treatment on cognitive function.MethodsMale D2 WT and mdx mice were purchased from Jackson Laboratories. Mice were separated into the following groups: (1) WT, (2) mdx-vehicle, and (3) mdx-tideglusib (10 mg/kg/day via oral gavage for 4 weeks). A novel object recognition test was performed to assess recognition memory. Hippocampus and serum samples were collected for BACE1 activity assays, amyloid beta (Aβ) ELISAs, and western blotting.ResultsCompared to vehicle-treated mdx mice, tideglusib-treated mdx mice demonstrated improved recognition memory. These changes to recognition memory were accompanied by greater expression of beta-catenin, an indirect downstream marker of GSK3 inhibition. While there were no changes in BACE1 activity, tideglusib-treated mdx mice had higher concentrations of Aβ in the serum and lower protein levels of receptor of advanced glycation end products.DiscussionThe results from this brief follow-up study offer preliminary support for tideglusib as a treatment for both muscle and brain impairments in mdx mice, potentially improving cognitive function through enhanced vascular Aβ clearance.

Oral administration of kratom leaf extract alleviates anxiety-like behavior, urinary bladder pain, voiding dysfunction, and bladder hypercontractility via attenuating muscarinic receptor response in male mice exposed to chronic water avoidance stress

Psychological stress causes and deteriorates interstitial cystitis/painful bladder syndrome with urinary frequency, incontinence, bladder pain and urgency. The major alkaloid of kratom (Mitragyna speciosa), mitragynine, shows analgesic, anxiolytic, and smooth muscle relaxant effects. However, the effects of kratom leaf extract on stress-induced anxiety-like behavior, urinary bladder pain and urinary bladder dysfunction remain unknown. Therefore, this study aims to examine the effect of kratom leaf extract administration on anxiety-like behaviors, bladder pain, bladder contractile properties, and mast cell number in mice exposed to water avoidance stress. Male C57BL/6 mice were exposed to water avoidance stress (WAS) protocol for 10 consecutive days and compared with the stress-exposed mice receiving oral administration of kratom leaf extract (2.5 and 5 mg/kg of mitragynine) or solifenacin (10 mg/kg). Anxiety-like behaviors were assessed using open field test. Bladder pain sensitivity was evaluated with von Frey test, while voiding behavior was analyzed using voiding pattern analysis. Bladder contractility was examined using an in vitro organ bath technique, and urinary bladder mast cell infiltration was assessed by toluidine blue staining. Results show that mice receiving WAS had a reduction in the total duration and number of unsupported rearing behaviors, reduced voiding area, and increased bladder pain responses; however, these effects were reversed by treatment with kratom leaf extract (2.5 and 5 mg/kg of mitragynine). Interestingly, the WAS group also exhibited markedly increased tonic contractions in response to carbachol, a muscarinic agonist; these responses were attenuated in mice treated with kratom leaf extract (2.5 and 5 mg/kg) The enhanced tonic contractile response to carbachol was abolished by pre-incubation with ondansetron (a 5-HT₃ antagonist). The WAS group showed an increased total number of mast cells in the urinary bladder, which was reduced by treatment with kratom leaf extract at both 2.5 and 5 mg/kg. Our results indicate that treatment with kratom leaf extract attenuated chronic stress–induced bladder pain responses, voiding abnormalities, and mast cell numbers, and was associated with reduced contractile response to muscarinic stimulation, suggesting a potential modulatory effect on stress-induced bladder dysfunction.