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.

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Circulating tumor DNA (ctDNA) has already shown promise for detecting minimal residual disease (MRD), but its role in guiding treatment decisions during therapy remains largely unexplored. To address this gap, a research team led by Bill H. Diplas, MD, PhD, a radiation oncology fellow at MSKCC, investigated whether serial ctDNA measurements, paired with weekly MRI scans, could provide a more precise and dynamic view of tumor response. In collaboration with Labcorp and Biocartis, the MSKCC researchers developed a personalized ctDNA assay that combined two strategies: detection of patient-specific tumor mutations and quantification of DNA from high-risk HPV strains, particularly HPV-16 and HPV-18, using anchored multiplex PCR and high-throughput sequencing.

The study enrolled 158 patients with HPV-associated oropharyngeal cancer who had undergone primary tumor resection followed by risk-adapted chemoradiotherapy guided by hypoxia assessment. MRIs were performed pretreatment and weekly following treatment to determine tumor volume, and blood samples were collected before treatment and weekly during therapy, yielding nearly 1,000 samples from 119 patients (mean 8.2 samples/patient) up to 126 weeks.

At baseline, ctDNA was identified in 93.9% of patients—outperforming either mutation-based (89.4%) or HPV-based (80.3%) methods alone. ctDNA levels also correlated with tumor size and biological features such as cell death and viral load. Notably, ctDNA emerged as a faster and more sensitive indicator of treatment response than imaging. Changes in ctDNA levels appeared earlier and across a broader dynamic range than tumor size reductions observed on MRI. By the second week of therapy, ctDNA measurements could already distinguish patients likely to require more intensive treatment.

The identification of patients with high-risk disease was significantly improved by combining on-treatment ctDNA assessment with imaging in a multimodal model, outperforming any modality alone. These results underscore the complementary nature of molecular signals in blood and structural changes seen on imaging.

Similar multimodal strategies that integrate ctDNA with imaging have been explored in other cancers, including breast and lung, primarily in research settings. Studies suggest that combining these approaches can improve prediction of treatment response and enable earlier detection of resistance. Broader analyses across colorectal, lung, and breast cancers further support the value of integrating molecular and imaging data to refine models of response and survival. However, most of these approaches remain investigational, and the use of ctDNA to guide real-time treatment decisions is only beginning to be tested in prospective trials.

Although further validation is needed, this study establishes a framework for real-time, personalized treatment in oropharyngeal cancer. If translated into clinical practice, such an approach could accelerate the shift toward adaptive therapy—where decisions are guided not only by how tumors appear on imaging, but by how they respond at the molecular level throughout treatment.

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“Given the ever-increasing burden of Alzheimer’s disease, the urgent need for the identification of novel targets for the development of disease-modifying therapy is clear,” said senior author Andrea Kwakowsky, PhD, associate professor of pharmacology and lead researcher at the School of Medicine, University of Galway.

Alzheimer’s disease is characterized by progressive cognitive impairment and is associated with hallmark pathological features including β-amyloid (Aβ) plaques and neurofibrillary tangles. In addition to these, disruption of the brain’s excitatory/inhibitory (E/I) balance has gained traction as a central mechanism contributing to memory loss. Today, most approved therapies for AD target excitatory neurotransmitter systems such as cholinergic and glutamatergic pathways, but “the symptomatic relief provided by these therapies is only marginal, and the progression or underlying causes of the disease are not addressed,” the researchers noted.

For their work, the University of Galway team instead focused on the inhibitory side of this balance, specifically the role of gamma-aminobutyric acid (GABA), the brain’s main inhibitory neurotransmitter. GABA regulates neuronal activity and is essential for maintaining stable network function and memory processes. In AD, however, E/I balance becomes dysregulated with increased extracellular GABA—triggered in part by Aβ—leading to overactivation of certain GABA receptors, particularly α5-containing GABA type A receptors (α5-GABA ARs), which are abundant in the hippocampus. The result is a dampening of neuronal signaling and which impairs learning and memory.

“Our research is significant in that it demonstrates that if we block this GABA receptor activity in nerve cells we can reverse Alzheimer-like effects caused by amyloid beta and improve cognitive performance,” Kwakowsky said.

To test whether blocking a5-GGABA A could help restore E/I balance, the team investigated α5IA, an α5-GABA AR-selective inverse agonist. α5IA works by reducing the activity of α5-GABA ARs, which decreases excess tonic inhibition. The data showed that in experimental models of AD, the compound improved long-term potentiation (LTP), a mechanism of synaptic plasticity and memory, reduced abnormal inhibitory conductance, and restored spatial memory performance.

Mechanistically, α5IA appears to act by restoring physiological levels of inhibition in the hippocampus which is critical for memory formation. By reducing excessive tonic inhibition, it rebalances E/I signaling, which allows neuronal circuits to function more effectively. “The data presented here suggest that in both ex vivo and in vivo AD models, α5IA improves cognitive function by restoring CA1 tonic inhibition, thereby re-establishing E/I balance and ameliorating the abnormal hippocampal network activity induced by Aβ1-42,” the researchers wrote.

This new study is the latest to indicate that targeting inhibitory neurotransmission could be an effective treatment approach for AD. Earlier research has shown that α5-GABA AR modulation enhances memory and reduces inhibitory signaling in both animal models and humans. But most of these studies have not directly examined the effect of α5IA in chronic neurodegenerative disease models.

The researchers noted there are some limitations to their work, pointing out that while α5IA improved cognitive outcomes, it did not reverse neuronal loss in vivo, suggesting that its effects may be primarily functional rather than neuroprotective at later stages of AD. Also, variability in drug exposure and timing may influence outcomes. Finally long-term use of α5IA has also been associated with safety concerns at high doses, including renal toxicity, so further research is needed to determine toxicity and dosing regimens and limits.

Nonetheless, the implications of this research indicate there is potential to develop new AD therapies that directly target network dysfunction rather than focusing solely on amyloid accumulation or excitatory signaling. By restoring E/I balance, this approach shows the potential to improve cognitive function even when AD pathology has taken root. The findings could also benefit diagnostic methods, as biomarkers of inhibitory dysfunction or altered GABA signaling could help identify patients who would benefit an approach that rebalances E/I signaling.

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The research encompassing all G7 countries revealed multiple benefits from earlier intervention for time-critical infections through the extensive application of fast identification and antimicrobial susceptibility testing (Fast ID/AST).

The Value of Fast Diagnostics in Time-critical Infections report by the independent Office of Health Economics showed how systematic use of fast diagnostics would lead to far fewer sepsis-related deaths and significantly decrease long-term post-sepsis complications, thereby improving patients’ quality of life.

The multi-country economic evaluation, commissioned and funded by bioMérieux, also showed major savings in healthcare costs each year, with the magnitude dependent on country size, incidence, and cost structures.

“While the magnitude varies by country, the direction is consistent: the model demonstrates that early diagnostics reduce the likelihood that high-risk patients progress to sepsis,” said Julien Textoris, PhD, vice president of EMEA medical affairs at bioMérieux.

“Preventing cases of sepsis could therefore reduce the risk of long-term complications after hospital discharge, including recurrent infections, cognitive decline, psychological effects, and organ-specific complications.”

Sepsis is a life-threatening reaction to an infection responsible for 21 million deaths worldwide each year. The initial hours of management are crucial, with targeted antibiotic treatment a key predictor of survival.

However, conventional methods for diagnosis takes at least a couple of days deliver results and nearly one in five bloodstream infection patients receive an inappropriate initial treatment.

In a model-based health economic analysis, Shaheer Hassan and co-workers at the OHE examined what would happen if fast ID/AST were systematically used early in the care pathway before clinical deterioration occurs.

The study encompassed Canada, France, Germany, Italy, Japan, the United Kingdom, and the U.S. and used expert-validated clinical inputs, country-specific cost data, and conservative assumptions.

Results revealed consistent cost savings regardless of the structure or financing of the healthcare system.

More than half of all savings—between 53% and 83%—would occur during the initial hospitalization, when the clinical and economic consequences of deterioration are most evident.

This is because early diagnostic information would prevent the chances of patients progressing into one of the most resource intensive stages of sepsis care.

The savings per patient ranged from €500 in Canada to €3,800 in Japan. This was primarily driven by fewer admissions to the intensive care unit, shorter hospital stays, and educed management of severe complications.

Annual national savings ranged from €26 million in Canada to €2.5 billion in the U.S. and reflected both cost savings in the acute phase and from reduced long-term complications.

“To realize these benefits, hospitals must address structural and workflow barriers so fast results translate into faster therapy, alongside broader system reforms to correct the persistent undervaluation of diagnostics,” the report maintained.

“Overcoming the underutilization and undervaluation of diagnostics will require coordinated system-level action, with the G7 countries included in this analysis well-positioned to lead.”

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The mood at the recent 2026 AD/PD International Conference on Alzheimer’s and Parkinson’s Diseases and Related Neurological Disorders in Copenhagen was notably different from the mood that has hung over much of neurodegeneration research for the past decade.

There was still plenty of caution, and plenty of unanswered questions, and certainly no shortage of technical nuance. But there was also something more concrete than hope: a growing sense that the field now has enough tools, biological insight, and clinical momentum to start probing more deeply and stratifying pathologies of neurodegenerative diseases in patients, at varying stages of progression.

That shift was visible across the meeting. It was there in conversations about co-pathologies, the increasingly central role of inflammation and the rapid maturation of blood-based biomarkers. It also featured in the way industry and academia alike talked about therapy: not as a search for a single silver bullet, but as a move toward combination treatment strategies more familiar from the fields of oncology, cardiology, and other complex chronic diseases.

Henrik Zetterberg, PhD, Gothenburg University, University College London, and a guest professor at University of Wisconsin-Madison, one of the field’s most influential biomarker researchers, put the central theme plainly: “I think disease heterogeneity will be the mantra in the coming years, to dissect the molecular underpinnings of this heterogeneity.”

Heterogeneity moves from caveat to core concept

Zetterberg described how biomarker-enabled phenotyping is exposing just how different patient trajectories can be once amyloid begins to accumulate. Some people decline quickly. Others remain resilient for a decade or longer. Some cases that appear clinically similar may in fact be driven by very different molecular constellations.

Geoff Kerchner, MD, PhD, vice president, global head of neurodegeneration at Roche, made a similar point from the therapeutic side. In Alzheimer’s disease, he said, some features remain strikingly consistent across patients.

But once one moves beyond core pathology, “the rate at which that happens varies from person to person,” and that variance is shaped in part by co-pathologies, including alpha-synuclein, TDP-43, and vascular disease.

Steve Williams, MD, PhD, chief scientific officer at Alamar Biosciences, pushed the same logic further, arguing that mixed biology is not the exception but the rule. “Everyone with neurodegeneration is carrying around some combination of other pathologies,” he said. “It’s almost inevitable because it’s a feature of aging.”

That view has major consequences. It means the field is increasingly moving away from asking whether a patient is amyloid-positive or tau-positive in a binary sense and toward asking what additional pathological burden may be present, what that burden means for progression, and how it should influence treatment choice.

Betty M. Tijms, PhD, head of science Alzheimer Center Amsterdam at Amsterdam UMC, offered a useful example from discovery research. In her work integrating CSF proteomics and lipidomics, she described signals that shift depending on tau status and amyloid background. At one point, she noted that these patterns “will inform which type of patients may require their own, personalized therapies.” It captures the direction of travel: from broad molecular mapping to biologically meaningful subtyping.

Inflammation is no longer a side story

Andréa Lessa Benedet, PhD, University of Gothenburg, discussed findings showing that people with faster progression in tau-related pathology had “higher expression of many inflammatory markers in plasma and in CSF.” That observation alone is not enough to settle the longstanding question of whether inflammation is driving disease, responding to it, or doing both. But it adds to a growing body of work suggesting that immune biology is closely tied to the pace of progression.

What made Benedet’s description especially interesting was that the signal was not identical across biofluids. The proteins elevated in CSF were not the same as those elevated in plasma. Yet when her group mapped those proteins to cell types and pathways, the two compartments converged on similar biology. In other words, the field may not always be looking for one-to-one molecular matches between brain-adjacent and peripheral compartments. It may instead be learning to recognize pathway-level concordance.

Benedet pointed to evidence suggesting that amyloid pathology together with inflammation may influence how tau spreads through the brain. That “bit of both” view—driver and response, cause and consequence—may be unsatisfying if one wants a simple mechanism. It may also be closer to biological reality.

The therapeutic implication is obvious. If inflammatory processes help define faster-progressing biology, then they are not merely descriptive. They become candidates for stratification and, eventually, intervention.

Blood-based biomarkers as research infrastructure

Few topics drew more sustained attention in Copenhagen than blood-based biomarkers. Kerchner called blood-based biomarkers one of the biggest themes of the meeting saying they could “really democratize the diagnosis of Alzheimer’s disease.”

Democratization here is about health equity—geography, trial access, earlier identification, and the possibility of shifting neurodegeneration research beyond the relatively narrow populations that have historically been easiest to recruit and deeply phenotype. That broader perspective surfaced in a session on sex differences in neurodegeneration, where Jacob Vogel, PhD, assistant professor at Lund University and SciLifeLab, presented findings suggesting that brain cells responding to Alzheimer’s pathology have different expression patterns in men and women. Seen that way, the field needs tools that are sophisticated enough to capture the true biological complexity of disease across different patients.

However, one excellent blood-based biomarker, such as brain-derived p-tau217, does not solve the co-pathology problem. As Niranjan Bose, PhD, managing director at Gates Ventures put it, there is a growing “need to do better when it comes to co-pathologies so we can stratify participants better.” A strong single analyte may be enough to identify one core process very well; it is not enough to capture the layered biology of aging brains. That is why the discussion is shifting from singleplex to multiplex, from favorite markers to models.

Zetterberg spoke about the new NULISA Neuro 220 panel from Alamar Biosciences, as a research tool that can help the field probe lysosomal and synaptic biology, alpha-synuclein-related processes, and other pathways relevant to co-pathology. He also highlighted the importance of brain-derived tau readouts, arguing that they may reduce confounding from peripheral tau expression and make blood results easier to interpret in diseases where peripheral neuropathy or other non-CNS biology could muddy the picture.

Zetterberg said, “Those broader panels will be the engines for discovery.” In other words, the value of broad biomarker panels is not that every protein measured will someday be run routinely in a clinical lab. It is that broad panels can reveal reproducible patterns, identify hub biology, and narrow the search toward robust clinical assays.

Combination treatment is becoming the default future

The conference’s other major shift was therapeutically focused. Even where amyloid remained central, the discussion increasingly assumed that amyloid-directed therapy alone will not be the endpoint.

Michael Irizarry, MD, senior vice president and deputy chief clinical officer at Eisai US, put it bluntly: “Alzheimer’s is being used as the example of precision medicine.” That is a striking statement, because for years Alzheimer’s was more often framed as the place where precision medicine had failed to arrive. What changed is that biomarkers, imaging, and fluid measures have advanced enough to stage disease more accurately and begin matching interventions to biology and timing.

Irizarry also described an emerging combination logic already being tested clinically. “The hope is that by targeting multiple processes we can get a greater treatment effect,” he said, referring to efforts to combine anti-amyloid therapy with a tau-directed antibody strategy. The reasoning is straightforward: if amyloid clearance slows disease but does not stop it, then other mechanisms—including tau propagation—remain actionable targets.

Kerchner made the same point in even broader terms. “The combination of therapies attacking different aspects of Alzheimer’s disease and Parkinson’s disease is almost surely going to be needed,” he said. He compared the situation to hypertension, diabetes, and cardiovascular disease—complex chronic illnesses that are almost never controlled with one intervention alone.

If the field is moving toward a wider therapeutic lens, with multiple mechanisms and intervention points in play, then there is value in creating space for a broader range of emerging approaches. That was visible in the Startup Hub, now in its second year, where early-stage companies gave short five-minute pitches that often echoed the meeting’s main scientific themes. ScandBio was one example: its Phase III clinical trial of a combined metabolic drug targeting mitochondrial dysfunction in Alzheimer’s disease connected to the conference session on mitochondrial pathways in neurodegeneration and therapy.

Taken together, these developments pointed to the same conclusion: as the biology becomes more layered, the response from the field is becoming more layered too. Combination therapy only becomes rational if disease heterogeneity is measurable. It only becomes practical if blood-based biomarkers can help define stage, likely response, and co-pathology burden without requiring every patient to undergo repeated PET imaging. And it only becomes truly precise if inflammation, synaptic injury, lysosomal dysfunction, vascular change, can be integrated into the treatment model rather than treated as background noise.

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“Each person has a different brain structure, depending on their height, weight, age, sex and more,” said Tao Zhou, PhD, Wormley Family Early Career Professor and corresponding author of the study. “Despite this, we try to fit neural interfaces onto brains like they have identical structures. This motivated us to create electrodes that are tailored for each individual, based on the structure of their brain.”

The new electrodes are created using hydrogel, a water-rich material that has properties similar to brain tissue, and built in a honeycomb-like internal structure to help balance flexibility and strength. These soft electrodes, dubbed HiPGE for honeycomb-inspired printable gel electrodes, are fabricated using a 3-D printing method called direct ink writing, which allows for precise shaping at very small scales. The honeycomb design reduces stiffness allowing the electrodes to stretch and conform to the brain’s ridges and grooves without damaging brain tissue.

To individualize the design process, a patient would first have an MRI scan, which is used to create detailed simulations of each brain scanned. The simulations inform the how to create the shape of each electrode so it aligns with the patient’s specific cortical folds. The team then 3D prints both the electrode and a model of the brain to test how closely the device fits. In experiments involving 21 human brain models, the printed electrodes demonstrated improved conformity compared to traditional designs.

“The unique gyral patterns of the human brain demand patient-specific neural interfaces to achieve precise neuromodulation, mitigate adverse tissue responses, and optimize therapeutic efficacy and safety,” the researchers wrote, noting that conventional rigid electrodes “exhibit limited conformability to the brain’s heterogeneous cortical topography,” resulting in “poor electrode-tissue contact, signal loss, and foreign body responses.”

To evaluate HiPGE performance and biological compatibility, the researchers conducted 28-day in vivo tests in rat models. Results from the tests showed that electrodes maintained stable function for the entire the testing period and did not trigger an immune response. The flexible electrodes also provided consistent and accurate readings of electrical and physiological signals in the brain.

Prior research has studied soft-material neural interfaces, but customization to individual gyral patterns has been limited. The introduction of a combined imaging, modeling and printing design workflow has solved this limitation by enabling electrodes to be tailored at the patient level.

The researchers wrote that the folded structure of the brain “creates a unique ‘fingerprint’ for each brain.” They also noted that current rigid devices can lead to “signal degradation due to scar formation,” and can create instability caused by the mismatch between the stiff neural interface materials and soft brain tissue.

Clinically, the improved contact between electrode and brain tissue could provide more reliable monitoring of neural activity, an important improvement for diagnosing and managing neurological conditions. Better signal fidelity could enhance applications such as brain-computer interfaces, neuroprosthetics, and neuromodulation therapies. The soft, conformable design may also reduce complications associated with long-term implantation, including inflammation and tissue damage.

“This tailored design ensures robust electrode-tissue integration, minimizing mechanical mismatch and improving signal fidelity during in vivo neural activity recording,” the researchers wrote. They added that studies “confirmed HiPGE’s biocompatibility, revealing no significant immune response or structural disruption to brain tissue.”

Future research will seek refine the technology for specific disease monitoring and exploring its use in clinical settings. They also aim to optimize the devices for targeted neurological conditions, which could inform the development of more precise and individualized care strategies.

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Chimeric antigen receptor (CAR) T cell therapies have transformed the treatment of certain blood cancers, yet translating this success to solid tumors has remained a major challenge. One of the key obstacles has been T cell exhaustion, a state in which engineered immune cells lose their ability to sustain an effective anti-tumor response.

Now, early clinical data from a first-in-human Phase I trial suggest a new approach may help overcome this limitation. Presenting at the AACR annual meeting in San Diego, researchers from the Perelman School of Medicine at the University of Pennsylvania report that a novel “KIR-CAR” T cell therapy shows promising safety and early efficacy signals across multiple solid tumor types.

New design inspired by natural killer cells

The investigational therapy, SynKIR-110, represents a departure from traditional CAR T designs. Rather than using a single-chain receptor, the therapy is modeled after natural killer (NK) cell receptors and uses a “multi-chain” architecture.

This design separates tumor recognition from activation, effectively creating an intrinsic “on-off” mechanism. The T cell remains in a resting state until it encounters its target, at which point the receptor components assemble to trigger an immune attack.

“The KIR-CAR design provides a natural ‘on-off’ mechanism, which helps avoid the problem of T cell exhaustion,” said Janos L. Tanyi, MD, PhD, principal investigator of the study. “The CAR turns on when it finds its target, kills it, and then rests, rather than constantly burning energy.”

This contrasts with conventional CAR T cells, which remain continuously active and can become depleted over time, limiting their effectiveness—particularly in the more complex microenvironment of solid tumors.

Early clinical signals in difficult-to-treat cancers

The Phase I dose-escalation trial enrolled nine patients with advanced, mesothelin-expressing cancers, including ovarian cancer, mesothelioma, and cholangiocarcinoma. These patients had limited treatment options, having received an average of four prior lines of therapy.

Although the primary goal of the study was to assess safety, early signs of efficacy were observed. Disease stabilization was reported in four patients, and one patient in the highest dose cohort achieved an ongoing partial response.

“These are cancer types that have never had an approved cell therapy,” Tanyi said. “We’re seeing good efficacy signals, even at low doses, and limited toxicity.”

The results suggest that the therapy may be able to generate meaningful anti-tumor responses even in heavily pretreated populations.

Favorable safety profile

Safety has been another major barrier for CAR T therapies, particularly in solid tumors. However, the KIR-CAR approach appears to mitigate some of these concerns.

No dose-limiting toxicities were observed in the initial cohorts. Cytokine release syndrome (CRS), a common side effect of CAR T therapy, occurred in 33% of patients but was limited to low-grade events. Notably, there were no cases of immune effector cell-associated neurotoxicity syndrome (ICANS), a more severe complication sometimes seen with CAR T therapies.

The ability to limit toxicity while maintaining activity is a key step toward broader application of cell therapies in solid tumors.

Targeting mesothelin across tumor types

SynKIR-110 targets mesothelin, a protein expressed on the surface of several solid tumors but largely absent from normal tissues. This makes it an attractive target for immunotherapy, particularly in cancers such as ovarian cancer and mesothelioma, where treatment options are limited.

The trial results indicate that the therapy’s activity is not confined to a single tumor type, raising the possibility of broader applicability across mesothelin-expressing cancers.

Expanding CAR T into solid tumors

The findings come amid growing efforts to adapt CAR T technology for solid tumors. While the approach has revolutionized hematologic malignancies, solid tumors present additional challenges, including immunosuppressive microenvironments, physical barriers to T cell infiltration, and antigen heterogeneity.

Researchers are exploring multiple strategies to address these barriers, including improved targeting, combination therapies, and next-generation receptor designs such as KIR-CAR.

As noted by CAR T pioneer Carl June, MD, advancing cellular therapies into solid tumors remains a central goal for the field.

Looking ahead

The Phase I study is ongoing, with plans to enroll up to 42 patients and identify the maximum tolerated dose before advancing to a Phase II trial. Early data indicate that CAR T cell expansion in the blood increases with dose, suggesting that higher doses may further enhance efficacy.

While still preliminary, the results highlight the potential of multi-chain CAR designs to address one of the most persistent challenges in cell therapy: maintaining durable activity without excessive toxicity.

If confirmed in larger studies, KIR-CAR therapies could represent a new generation of engineered immune cells, ones that more closely mimic natural immune regulation while retaining the precision of targeted cancer therapy.

For now, the data offer an encouraging signal that the next wave of CAR T innovation may finally extend the reach of cell therapy into solid tumors, where the need remains greatest.

The post New KIR-CAR T Cell Therapy Shows Promise in Solid Tumors appeared first on Inside Precision Medicine.

Writing in Nature Medicine, the researchers also found that people carrying a genetic mutation in the GBA1 gene that put them at risk of developing Parkinson’s disease had gut microbiomes similar to people with the condition.

Parkinson’s is the second most common neurodegenerative disease in the U.S. after Alzheimer’s disease affecting more than one million people across the country. By the time full-blown motor symptoms emerge, a large degree of neurological damage has already occurred, so much work is underway to find ways to predict and diagnose early disease, as well as to develop more effective treatments.

“In recent years there has been a growing recognition of the links between Parkinson’s disease—a brain disorder—and gut health,” said co-lead author Anthony Schapira, MD, a professor at UCL Queen Square Institute of Neurology, in a press statement.

“Here we have strengthened that evidence and shown that microbes in the gut can reveal signs of Parkinson’s and may be an early warning signal… years before symptom onset.”

For this study, the researchers evaluated gut microbiome samples from 271 Parkinson’s disease patients, 43 people carrying GBA1 risk variants who did not yet have disease symptoms and 150 healthy controls. They also validated their findings in a further 638 people with Parkinson’s and 319 healthy controls from the U.K., Korea, and Turkey.

Schapira and team used DNA sequencing to see which bacterial species were present in each person’s gut. Comparing people with Parkinson’s disease to healthy controls, they found 176 bacterial species that were more or less common in people with the condition.

For example, people with Parkinson’s had more potentially pro‑inflammatory bacteria, including Bifidobacterium longum and B. dentium, Streptococcus mutans, and Lactobacillus paragasseri, than healthy controls.

In contrast, healthy controls had more helpful, butyrate‑producing gut bacteria from including Roseburia intestinalis, R. inulinivorans and some Faecalibacterium species and less pro-inflammatory species.

Notably, people in the at-risk group who carried a GBA1 risk variant had a gut microbiome somewhere between healthy controls and people with Parkinson’s, suggesting that the composition of microbes in the gut may change over time as the disease develops. In this group, 142 of the 176 species that differed in people with Parkinson’s versus healthy controls also showed changed abundance.

“For the first time we identify bacteria in the gut of people with Parkinson’s that can also be found in those with a genetic risk for the disease, but before they develop symptoms. Importantly, these same changes can be found in a small proportion of the general population that may put them at increased risk for Parkinson’s,” said Schapira.

“This discovery opens the way not only to see if the bacteria are a way to identify those at risk of Parkinson’s, but also to see if changing the bacterial population, through dietary changes or medication, can reduce a person’s risk for Parkinson’s.”

The post Distinct Nature of Parkinson’s Disease Gut Microbiome Identified appeared first on Inside Precision Medicine.

“In Canada, there were no medical oncologists,” Parkinson told Inside Precision Medicine. “Radiation therapists administered what little chemotherapy existed. They resisted the development of medical oncology as a specialty.”

In reflecting on his remarkable career, which was recognized with the 2026 AACR Outstanding Achievement Award for Service to Cancer Science and Medicine, Parkinson said, “I’ve essentially grown alongside the field.”

From scarcity to structure: Oncology’s early years

When Parkinson arrived in Montreal, there were only a handful of chemotherapeutics available. “In those days, there were only one or two drugs available for hematologic malignancies across the entire field,” Parkinson said. “The main treatments were cyclophosphamide and nitrosoureas.”

Even supportive care lagged. “Initially, we had no effective way to control chemotherapy-induced nausea,” he noted of the standard of care for testicular cancer. “Some patients stopped treatment because they couldn’t tolerate it.”

Parkinson explained that early cancer drugs worked best on rapidly dividing tumors, like leukemias and testicular cancers, because that’s what the animal models represented. These therapies targeted DNA and cell division broadly, often with severe toxicity, and were far less effective against slower-growing solid tumors.

After his residency at McGill, Parkinson moved to Boston, first to Tufts New England Medical Center on a modest Canadian fellowship that placed him at the edge of a field just beginning to coalesce. “I was on a Canadian fellowship earning $12,000 a year,” he said. “The exchange rate fluctuated significantly, which made things difficult, and I couldn’t work due to my student visa.”

What he found, however, was momentum. Through connections with Dana-Farber, Parkinson entered formal training in medical oncology as the specialty began to take shape. “I connected with Dana-Farber and took their introductory course for fellows—that was my entry into medical oncology.”

At the same time, breakthroughs in specific cancers hinted at what might be possible. “What really shaped my thinking was the emergence of treatments for testicular cancer just as I entered oncology,” he said. “Platinum-based therapies—and later combination regimens—felt like miracles. We had never seen anything like it. These were often young patients, difficult to manage, but suddenly there were real cures.”

Targeted therapy and the Gleevec moment

Parkinson’s career soon intersected with early efforts to harness the immune system against cancer—decades before immunotherapy became a dominant paradigm. “I became deeply involved in immunotherapy, particularly interleukin-2 and early tumor-infiltrating lymphocyte studies,” he said.

Working at the National Cancer Institute (NCI), he collaborated with leaders, including immunotherapy pioneer Steven Rosenberg, MD, PhD, maintaining a hybrid role that combined research with clinical care. “At the same time, I continued clinical work for a couple of months each year, collaborating with Steve Rosenberg in the surgical branch.”

These early approaches were technically challenging and often unpredictable, but they laid the groundwork for later advances. “We started with basic approaches, moved to tumor-infiltrating lymphocytes, and eventually to engineered CAR T cells,” Parkinson said. “Progress has been steady, though often slower than those treating patients would like.”

If immunotherapy represented one trajectory, targeted therapy represented another—one that depended on a deeper understanding of cancer biology.

“When I joined Novartis in the late 1980s, we were among the first developing kinase inhibitors,” Parkinson said. At the time, the idea was controversial. “Early skepticism suggested kinase inhibitors wouldn’t work due to high intracellular ATP levels and structural challenges.”

But advances in molecular biology were beginning to change the landscape. The discovery of the Philadelphia chromosome and its associated oncogene created a clear therapeutic target. “The Philadelphia chromosome had been known since the 1960s, and by the 1980s the responsible gene was identified,” Parkinson explained.

The result was imatinib (Gleevec), a drug that would become a prototype for precision oncology. “Eventually, a small molecule inhibitor was developed that targeted it precisely.”

The clinical results were extraordinary. “By the third cohort in a Phase I trial, patients with chronic myelogenous leukemia showed dramatic responses—some within 24 hours,” Parkinson said. “It’s probably the only Phase I oncology trial where essentially every patient achieved remission.”

For Parkinson, the implications extended far beyond a single drug. “Of course, [Gleevec] was a unique case,” he said. “But it proved an important point: what once seemed impossible can become possible.”

Since then, the field has expanded dramatically. Hundreds of kinase inhibitors have been developed, with thousands more explored, reflecting a broader shift toward therapies grounded in specific molecular mechanisms.

Precision medicine—and its limits

As oncology evolved, so too did its language. “For years, we called it ‘personalized medicine,’” Parkinson said. “I used to joke that medicine has always been personalized—you’re always trying to determine what’s best for a specific patient in a specific context.”

He credits industry with popularizing a more precise term. “Although Pfizer popularized the term ‘precision medicine,’ I think it’s a better term,” he added, with a note of humor: “I have a few good Pfizer jokes—best shared over a drink.”

Yet the reality of precision medicine has proven more complex than its promise. “The evolution of therapeutics mirrored the models and biological understanding available,” Parkinson said. “Targeted therapies only emerged once we understood the biology. Diagnostics, however, lagged by about two decades.”

That lag remains a structural challenge. Parkinson founded a diagnostics company based on single-cell signaling technology developed at Stanford. “Technically, it worked—we solved major challenges in instrumentation, standardization, and analysis,” he said. “But we couldn’t establish a viable business model.”

The core issue was reimbursement. “Without adequate reimbursement from Medicare, even highly sophisticated diagnostics struggle commercially,” said Parkinson. “Better diagnostics can reduce the use of expensive drugs by identifying who won’t benefit—something that doesn’t always align with pharmaceutical business models.”

In recent years, Parkinson has focused increasingly on large-scale data integration, including his involvement with the GENIE consortium. The initiative aggregates genomic and clinical data across institutions, aiming to accelerate discovery and improve clinical decision-making. “GENIE has been a technical success,” he said. “But its long-term sustainability remains uncertain.”

The broader challenge, he argues, is conceptual as much as technical. “Looking forward, the field is evolving toward integrating multiple data types—genomics, transcriptomics, imaging, and more—to better understand tumor biology,” he said. “Sequencing alone isn’t enough. The challenge now is not a lack of data, but making sense of it—something where artificial intelligence will play an increasingly important role.”

Back to basics

Across academia, government, and industry—including roles at the NCI, Novartis, Amgen, and Biogen Idec—Parkinson sees a single throughline. “I remember an interview with a biotech company where an HR representative told me, ‘You seem to have done a lot of different things,’” he said. “I responded that I had really only done one thing: trying to improve cancer treatment, just from many different angles.”

Not every effort succeeded. “In one case, we developed a drug that performed beautifully in mice but failed in human trials,” he said. “That’s common in oncology—most ideas don’t translate. You don’t think of it as failure but as learning. Still, there’s a limit to how many ‘learnings’ one can appreciate.”

Reflecting on decades of progress, Parkinson emphasizes both how far the field has come and how much remains unresolved. “Outcomes have improved dramatically across several cancers, especially hematologic ones,” he said.

Yet he underscores a fundamental principle: that progress in cancer treatment comes down to understanding biology. “The better we understand it, the more effectively we can develop targeted therapies,” said Parkinson. “Without that understanding, we’re essentially guessing.”

At AACR 2026, Parkinson’s recognition underscores not just past achievements but a continuing trajectory—one shaped by the interplay of discovery, failure, and persistence. “Despite all the challenges,” he said, “[precision medicine] is still the most promising path forward.”

The post AACR 2026: David Parkinson and the Arc of Modern Cancer Therapy appeared first on Inside Precision Medicine.

Meconium, the first stool passed by newborns, was traditionally thought to be sterile, explained study lead Elias Iosifidis, MD, PhD, from Aristotle University of Thessaloniki in Greece. However, recent molecular studies have detected microbial genetic material in meconium samples, indicating that the neonatal gut may be exposed to bacteria during pregnancy.

It has been suggested that this early microbial exposure could contribute to the development of antibiotic resistance. Indeed, ARGs have been detected in meconium samples, and their presence at this early stage may facilitate the spread of resistance through horizonal gene transfer between bacteria.

To investigate further, Iosifidis and colleagues screened 105 meconium samples that were collected from newborns admitted to a neonatal intensive care unit within 24 hours of birth for 56 different resistance genes associated with commonly used antibiotics.

“This is the largest study of its kind exploring the effect of hospital environment on the collection of ARGs in the neonatal gut,” said lead author Argyro Ftergioti. “We analyzed meconium samples within the first 72 hours of life to capture the earliest snapshot of microbial and genetic exposure in newborns. At this stage, the collection of resistance genes is mainly shaped by maternal transmission, delivery mode, and very early hospital exposures.”

The most frequently detected ARGs were oqxA, a gene that causes resistance to fluoroquinolones (like ciprofloxacin), olaquindox, chloramphenicol, and tigecycline, and qnrS, which contributes to decreased susceptibility to quinolones. These were found in 98% and 96% of samples, respectively.

The study also identified several genes encoding beta-lactamases, enzymes that cause resistance to beta-lactam antibiotics such as penicillins and cephalosporins. Among these, the most prevalent were blaCTXM (55%), blaCMY (51%), and blaSHV (39%). Genes linked to carbapenem resistance (KPC/NDM/GES/VIM), a last-line class of antibiotics, were detected in 21% of samples.

Each sample contained a median of eight resistance genes.

“This finding suggests that a pattern of ARGs is already established at this stage. The neonatal gut harbors a diverse resistome, and the presence of clinically important ARGs so early in life is concerning,” said Ftergioti. “Although some ARGs were expected, their high prevalence across the majority of samples was striking—particularly for clinically critical genes offering carbapenem resistance.”

The team also found associations between resistance genes and several maternal and neonatal factors. The presence of the msrA (macrolide-streptogramin resistance) gene was linked with maternal hospitalization during pregnancy, while a higher number of resistance genes was associated with central venous catheter placement within the first 24 hours of life. Both findings likely reflect exposure to healthcare-associated microbes in hospital settings.

“Surprisingly, resuscitation shortly after birth was associated with fewer resistance genes,” noted Ftergioti. He cautioned, however, that “this finding should be interpreted carefully, as it may reflect differences in early microbial exposure or other clinical factors.”

Overall, the data suggest that both maternal transmissions and early exposure to the hospital environment may contribute to the establishment of ARGs in the neonatal gut.

“While further research is needed to understand how early carriage of resistance genes affects microbiome development and infection risk, these findings highlight the importance of surveillance, infection prevention and control in neonatal care,” Ftergioti concluded.

The post Antibiotic Resistance Genes Established Early in Newborns appeared first on Inside Precision Medicine.