Fordyce wants to change that. Her lab is developing ways to measure protein folding, binding, kinetics and function at a massive scale, using the quantitative language of physics. Her ambition is not simply to create more protein data. She wants measurements good enough to make biology more predictive.

Her favorite analogy is weather forecasting. Better computers and better models helped transform our ability to predict the weather. But so did thousands of weather stations around the world making standardized measurements of temperature, wind and precipitation. Biology now has extraordinary computational power and increasingly powerful models. Fordyce thinks it needs the equivalent of those weather stations.

Last year, Schmidt Sciences awarded Fordyce a Polymath Award worth up to $2.5 million to pursue that idea. Her lab has developed a new bead based technology that could allow ordinary laboratories to make high throughput measurements of protein function. Fordyce hopes scientists around the world will contribute those measurements to a new open resource she calls the Functional Protein Observatory.

The implications go well beyond building a database. Fordyce describes recent work from her lab on a protein involved in cancer and developmental disease. After making hundreds of thousands of measurements across human variants, the researchers found that the prevailing model for how drugs act on the protein may be wrong. The drugs appeared to stabilize a previously unseen form of the protein that was only partially closed. That could help explain why drugs designed around the old model have struggled. And it shows what can be discovered when scientists measure how proteins actually behave rather than relying on a static picture of their structure.

AI makes the project more timely. Computational models can now propose proteins and mutations far faster than scientists can experimentally test them. Fordyce believes that gap can be closed. Her new platform can go from receiving a library of DNA to functional measurements within 72 hours. This opens up the possibility of a continuous cycle in which AI can propose, experiments test, and the measurements make the models better.

He joins us today with a clear message: PacBio is choosing long reads. The company has moved on from its Onso short-read platform and is concentrating its resources on making HiFi sequencing cheaper, more scalable and the routine choice in certain clinical applications.

With the new SPRQ-Nx chemistry, PacBio has brought the list price of a HiFi whole genome down to $345. For Van Oene, that changes the problem. “It’s becoming less about economics for me right now.” The bigger constraint is scale, and PacBio expects a new high-throughput system next year.

The opportunities he keeps going to are in the clinic. In rare disease, long reads can consolidate multiple tests while revealing parts of the genome other approaches miss. He sees that eventually leading to long-read whole-genome sequencing at birth: “If you don’t know what you’re looking for, let’s use the most comprehensive analysis you can get.”

In this wide-ranging conversation, co-founder and CEO Serge Saxonov explains why. Despite decades of genomics, “we still understand very little biology.” For 10x, the way forward has been to build better tools for measuring it and to work backward from the biological questions rather than forward from a particular technology.

That philosophy helped drive the single-cell revolution and 10x’s move into spatial biology. Now the company has launched Atera, its new spatial platform designed to measure the whole transcriptome with single-cell sensitivity and at much greater scale. Customer shipments are expected to begin later this year. Saxonov argues that spatial is becoming something much bigger than another research niche. It’s a way of bringing molecules, cells and tissues into the same view.

Going beyond RNA? Saxonov discusses 10x’s recent moves into proteomics and why he believes binder-based approaches currently offer the best path to scale. And he says 10x is investing directly in clinical diagnostics, pointing to therapy selection in oncology and autoimmune disease as two early opportunities. Getting closer to patients, he says, should also help 10x understand what its next generation of technologies needs to do.

What would convince Saxonov that all this is succeeding? Not another instrument or another omic. It would be a measurable improvement in clinical-trial success.

“The next decade is going to be wild,” he says.

Read more

A few years ago, Lee Cronin, a chemistry professor at the University of Glasgow joined Mendelspod for a sprawling conversation about the origin of life, alien biology, assembly theory, and his conviction that chemistry could become programmable. Now Cronin is back, and that last idea has become a company that now has a “chemifarm.”

First let’s look at the problem. It is easy to assume that once a drug company has designed a promising molecule, the chemists more or less know how to make it. Not so. Chemistry remains surprisingly artisanal. A molecule that looks perfectly plausible on a computer may require too many steps to synthesize, depend on unstable intermediates, or require reactions that simply aren’t known. And chemists may not discover that until they’ve spent considerable time trying.

This becomes an even bigger problem in the age of AI. Algorithms can propose vast numbers of new molecules, but which ones can actually be made? And if one can’t, is there another molecule nearby in chemical space that could perform the same function but be dramatically easier to synthesize?

That’s the problem Chemify is trying to solve.

“You cannot discover what you cannot make,” Cronin says in today’s interview.

He spent 15 years developing χDL, a programming language that reduces the work of chemistry to a set of basic operations that machines can execute. Chemify’s software can then work backward from a desired molecule to determine a possible route for making it. Its robotic systems execute those instructions in the physical world, including chemistry requiring unusual temperatures and conditions. And its Chemifarms bring large numbers of these systems together so the process can be repeated at scale.

The result is something Cronin calls a chemistry “hyperscaler.” A pharmaceutical company might bring Chemify a molecule proposed by its own AI system. Chemify can ask whether that molecule is realistically makeable, develop a route to it, physically attempt the synthesis, and feed what happened back into the system. If the molecule isn’t practical, the platform can suggest alternatives. Every success—and importantly, every failure—adds information about what parts of chemical space are actually accessible.

Cronin’s ambition is enormous. He wants Chemify to become a sort of utility, the “AWS for all drug discovery companies.” Not another company competing to discover the next drug, but infrastructure that allows pharmaceutical and AI companies to turn digital ideas into physical matter.

Read more

So what happens when a massive national research experiment begins to come of age?

Josh Denny, CEO of All of Us, joins us to talk about what the program has accomplished, what researchers are beginning to learn from the data, and what comes next. We discuss the extraordinary scale and diversity of the resource and the growing use of genetics alongside electronic health records and other forms of health data. All of Us is beginning to move from building infrastructure toward producing discoveries that could affect patient care.

Denny explains why the program’s diversity is not simply a matter of representation but a scientific necessity. The project has already identified roughly 1.3 billion genetic variants, including more than 275 million that had not previously been observed. That diversity becomes even more important, Denny argues, as medicine moves toward increasingly personalized predictions and treatments.

“We are capturing such a richer population and so much more kinds of data that we can’t actually reason through it as humans,” he says. “If the data underneath it are highly biased and not representative, then we’re going to make the wrong conclusions.”

We also discuss All of Us as a platform for a much broader picture of human health—from electronic health records and wearables to nutrition, the microbiome, multiomics, environmental exposures, and eventually pediatric data. Denny shares examples of participants whose lives have already been changed by medically actionable genetic results and describes how researchers can build new studies on top of the All of Us population.

What is the next phase of the program and that of precision medicine?

“We’re going to have to redefine our definition of disease,” Denny says. Rather than treating something like type 2 diabetes as a single condition, he imagines increasingly precise descriptions of an individual’s biology, exposures, risk, and response.

After years spent building one of the largest health datasets in the world, All of Us is beginning to show what we might actually do with it.

It’s hard to believe we’ve been doing this for just over 15 years, following first and foremost the people reshaping biology and medicine. But also the technologies and ideas. Our original vision was to feature the “people behind the headlines” and you’ll see that what we continue to do in this first show of the new season. A sincere thank you to everyone who has listened, watched, subscribed, sponsored, recommended guests, shared a show, told your friends, and simply kept coming back. We have a terrific season ahead, with some of the biggest names in the life sciences as well as some you may not know yet who are asking entirely new questions.

To begin, we wanted to go big picture.

Rob Phillips, professor of biophysics, biology, and physics at Caltech and co-author of Physical Biology of the Cell, thinks this could be the “Newtonian moment” for biology. We have extraordinary amounts of data. What biology needs now, he argues, are concepts that can organize those observations into a more predictive understanding of life.

“The data in biology is getting to the point where we have no excuse,” Rob says.

The conversation ranges from the limits of molecular reductionism to the meaning of prediction, the role of AI, and why better measurement still matters enormously. Phillips makes the case for explanations at many different scales. Understanding a wildebeest migration through molecules, he says, would be like explaining the Golden Gate Bridge through its iron atoms.

Rob is nothing if not provocative—in the best sense. His style is to pile up ideas and questions one on the other to propel his listeners into that creative space of wonder.

“We need more opinions. We need more authenticity,” Phillips says. “I want to hear how people think about things differently.”

What a year it has been.

We’ve explored how liquid biopsy is moving beyond cancer into metabolic disease. We’ve looked at how long-read sequencing is moving toward clinical applications, and how proteomics and multi-omics are filling in what the genome alone cannot tell us.

This next season, we’ll spend more time on mass spectrometry and proteomics. We’re following the rise and success of MRD testing. We’ll also be moving further into drug discovery. And, as always, we’re curious about how new tools are enabling new biology.

For fifteen seasons, we’ve built Mendelspod around the simple idea of bringing smart people together, asking good questions, and giving our guests enough time to actually say something.

But we can’t do it without our subscribers and sponsors. Mendelspod operates on a hybrid model of subscriptions and sponsorships, with our business partners providing most of the support that makes the show possible.

And we’ve always tried to make sponsorship work differently here. We don’t want to turn your message into an ad. We want those compelling, cutting-edge stories.

Our sponsors and subscribers bring us terrific guests. You introduce us to scientists we might never have found otherwise. That leads to conversations about emerging technologies before they’re widely known and work happening at the edges of fields we already cover.

Maybe there’s a scientist doing remarkable work with your platform whom the field should know about. Maybe you have new data that challenge the conventional wisdom. Or perhaps you’re launching a product or looking to build your brand among an audience that cares deeply about where biology is headed.

Bring us the story.

We’ll bring the curiosity, preparation, independent voice, and fifteen years of experience interviewing the people moving life science forward.

Average stats per show

• 1,300 listens
• 5,400 views
• Audience of 70% researchers
• Quickly growing clinical segment
• Social media promotion

Audience

• 44,000 unique monthly visits
• 18,500 subscribers
• 75% US, 5% UK, 4% France, 4% Japan, 4% China

Season 16 is underway, and we’re creating special sponsorship options on the calendar. If your company would like to be part of it, reply to this email, and receive a summer discount through the end of August. We’d love to hear what you’re working on.

Together let’s make more history!

Theral Timpson

Sezgin is a group leader at the Max Planck Institute of Molecular Cell Biology and Genetics, where he heads the Membrane Biophysics Group. His research focuses on one of the most familiar yet least understood structures in biology: the cell membrane.

In this conversation from April, Sezgin explains why the membrane isn’t a smooth sea of lipids, but a dynamic landscape of tiny molecular neighborhoods that constantly assemble, disappear, and reorganize. We discuss how he is using the tools of physics to better understand the membrane’s inner and outer lipid layers, each with distinct electrical properties.

Sezgin also talks about his collaboration with Pixelgen Technologies, where Molecular Pixelation was used to study how changes in membrane charge reshape the cell surface. By knocking out a lipid-regulating complex, Sezgin and his colleagues showed that living cells can adopt surface features that alter immune recognition and may help explain how cancer cells evade destruction.

It’s a reminder that major biological insights often arrive alongside new tools that make previously hidden phenomena measurable. Sezgin’s work is also a broader comment on scientific boundaries. Biology is not separate from physics or chemistry, but an expression of them in living systems.

“Cells don’t have physics, chemistry, biology... It is life,” he says.

In this conversation, Pier Federico Gherardini, VP of Computational Biology at Cellanome, joins Matthew Spitzer of UCSF, whose lab is putting the Cellanome platform to work in cancer immunology.

The new CellCage technology allows researchers to follow individual cells and their interactions over time, then pair that behavior with transcriptomic and other molecular readouts. As Gherardini explains, this creates “a new data type” that connects functional behavior directly to molecular biology.

For Spitzer, the breakthrough is linking phenotype and function in the same individual cell. His lab can watch dendritic cells activate T cells, or T cells interact with tumor cells, and then ask what was molecularly different about the cells that actually performed the function.

“Now we have measured the function of the cell and the phenotype for the same exact individual cell,” Spitzer says.

The result is a new way to study cell biology that could have implications for cancer immunology, cell therapy, target discovery, and functional screening.

Acute leukemia patients often wait days or even weeks for the full battery of molecular tests needed to guide treatment decisions. Dr. Parth Shah from Dartmouth believes long read sequencing can dramatically shorten that timeline. In this episode, Shah joins Agilent's Rita Shaknovich and Oxford Nanopore's Claire Attwooll to discuss some details of how long reads are beginning to move from research applications into routine clinical testing.

Along the way, we explore the role of targeted enrichment, quality control, automation, and informatics in making these workflows practical for real-world laboratories.

For Shah, the field has reached an inflection point. After more than a decade of development, he argues that long reads are finally positioned to make the leap into clinical genomics. “As we ask more complex questions of human biology, long read is probably going to be the best ammunition that we have,” he says. His team at Dartmouth has already demonstrated the potential in acute myeloid leukemia, where a long-read workflow can now generate a comprehensive molecular profile within 24 hours rather than the weeks often required by conventional testing.

Shaknovich emphasizes that the opportunity is not simply generating more data, but generating better data. Long reads, she notes, can simultaneously capture mutations, structural variants, and epigenetic information, creating a richer biological picture than many existing approaches.

Attwooll highlights the flexibility that has emerged in the long-read ecosystem. Researchers can now choose among whole-genome sequencing, targeted enrichment, and Oxford Nanopore’s adaptive sampling approaches depending on the clinical question. She argues that the field is moving from a niche technology toward a mainstream platform for translational and clinical applications.

A recurring theme throughout the conversation is that no single technology will dominate every application. Whole-genome long reads, targeted enrichment, and adaptive approaches each have a role to play. As these methods move from research into routine testing, success will depend on more than sequencing alone. Agilent's established customer base, automation capabilities, quality-control tools, and experience supporting laboratories help provide the infrastructure needed to bring Oxford Nanopore's rapidly advancing long-read technology into practical clinical workflows.

To do that we’re joined by Dr. Vincent Miller, an oncologist and former founding Chief Medical Officer of Foundation Medicine, and Dr. Mirna Jarosz, CEO of LiquidCell Dx.

The work introduces a striking idea. Rather than focusing only on mutations inside tumor cells, it identifies recurring spatial ecosystems within tumors and then shows that their signatures can be recovered from plasma cell-free DNA using methylation patterns. The implication is that liquid biopsy may soon reveal not only what mutations a tumor carries, but how its surrounding biology is organized before treatment ever begins.

But wait. How can blood possibly contain information about spatial organization inside a tumor? That answer unfolds gradually on today’s show, making the final portion of the discussion particularly rewarding.

As Jarosz explains, “We’ve condensed spatial biology to really critical and recurring biological programs. And then we can measure those in blood. So now we have that spatial insight of the tumor microenvironment in a liquid biopsy.”

For Miller, the significance is ultimately clinical. “The tumor is almost like an organ,” he says. “The ability to understand how that organ is constructed and what structures are near one another and how they’re functioning... is really the underpinning” of why patients with seemingly similar cancers can have dramatically different responses to therapy.

If this approach continues to hold up in larger clinical studies, liquid biopsy may expand from reading the genetics of cancer to reading its ecosystem. This shift could improve immunotherapy selection, longitudinal monitoring, and our understanding of cancer biology itself.

Read more

Read more

On today’s show, Dr. Mohamed Alameri of the UAE Department of Health and Albarah El-Khani of M42 describe one of the most ambitious precision medicine efforts underway anywhere in the world: the Emirati Genome Program, which has already sequenced more than 900,000 genomes and is rapidly integrating that data into everyday healthcare.

The UAE program is not only a large sequencing effort and database—soon to be made available for research anywhere—but a coordinated national strategy built on prevention, diagnosis, and long-term population health. Particularly striking is the UAE’s focus on inherited and autosomal recessive diseases, which occur at significantly higher prevalence in the region than in many Western populations. Rather than treating genomics as an isolated research exercise, the program has pushed aggressively into premarital screening, newborn genomic screening, pharmacogenomics, hereditary cancer risk assessment, and rare disease diagnosis.

“We truly believe in the philosophy of ‘sequence once, analyze for life,’” says El-Khani. “Imagine a society where every individual from birth holds a whole genome sequence throughout their life. How powerful is that tool at every intersection of public health, clinical care, and screening?”

The scale of the project is already yielding discoveries difficult to achieve elsewhere. According to Alameri, roughly 12% of the variants identified in the Emirati population are not represented in existing global databases, underscoring just how underrepresented Middle Eastern populations remain in genomics research. In some cases, variants previously considered pathogenic in European populations appear to behave differently in Emirati patients, opening entirely new biological questions.

Perhaps the most impressive aspect of the program is the degree to which genomics has been operationalized across the healthcare system. The UAE has invested heavily in physician education and public engagement to move genomics from bench to bedside. Our guests describe a healthcare ecosystem where genomic reports, pharmacogenomic guidance, and hereditary risk assessments are increasingly available directly within clinical workflows.

“The vision was not sequencing everyone for its own sake,” says Dr. Alameri. “It was to build a national asset that could support more predictive, preventative, personalized healthcare for our population and for future generations.”

There is always hype in genomics, as with other emerging technologies. But the UAE effort is already very comprehensive and clinically grounded. This is genomics functioning as healthcare infrastructure in real time.

As we’ve been hearing on the program, biology has largely been a science of inventory. Throughout today’s conversation, Flynn argues that molecular organization itself may be a fundamental biological variable. Not simply whether a molecule exists, but where it exists, what it is adjacent to. Using technologies such as Pixelgen’s Proximity Network Assay, his lab is beginning to map the “cell surface architecture,” or the arrangement of proteins, glycans, and nucleic acids that together govern signaling and cellular behavior.

The implications stretch across biology. Flynn describes early evidence that extracellular RNA can tune classical signaling pathways such as VEGF-mediated angiogenesis by physically modulating how growth factors engage receptors on endothelial cells. Remove the RNA, and growth factor binding changes dramatically. Rather than acting as a simple on/off switch, the RNA appears to function as a finely tuned regulatory layer controlling signaling strength.

In cancer, where cell-surface signaling drives growth, invasion, and immune escape, looking at the organization of the cell surface may determine whether therapies can physically access their targets. Flynn points to bispecific antibodies and T-cell engagers as examples of drugs whose function already depends on proximity and molecular arrangement, even if work in biology has not fully measured those variables before.

For decades, biology has been driven by the powerful notion that if we could sequence enough genomes, transcriptomes, epigenomes, then we could finally explain the cell. On today’s show, Gary Schroth, the Chief Scientific Officer at Cellanome, argues that something essential was still missing.

Schroth spent nearly two decades at Illumina helping build the sequencing revolution. He has now joined Cellanome to pursue an expanded vision of biology that connects transcriptomics with live-cell imaging. Our conversation centers around two newly released preprints describing the company’s platform and its application to CRISPR screening, where imaging and transcriptomic data are explicitly linked in the very same cells.

“What we show in a few examples in both papers,” Schroth explains, “is that it’s the combination of transcriptome information and imaging information that really gives us the complete story of what that cell is doing.”

That idea—linking what researchers literally see under the microscope with the molecular state of the exact same cell—emerges as the core concept of the interview. Rather than treating imaging and transcriptomics as separate measurements, Cellanome brings them together in a longitudinal workflow where cells can be observed alive over time and then profiled at the transcriptomic level.

“Sequencing has certainly taught us a lot about cells and sort of the parts list inside cells,” he says. “But it doesn’t really explain biology.”

Will this be the next phase of post-genomic biology where the field moves beyond static snapshots toward directly observing cellular function as it unfolds?

Today on the show, we’re discussing a new study just presented at ASCO 2026 that could change how chemotherapy decisions are made for a large group of breast cancer patients.

During ASCO we spoke with Phil Febbo, Chief Scientific and Medical Officer at Veracyte, and John Leite, the company’s Chief Commercial Officer, looking at the results from the OPTIMA study, a large prospective trial involving roughly 4,500 patients with clinically high-risk ER-positive, HER2-negative breast cancer. The study found that about two-thirds of these patients could safely avoid chemotherapy when treatment decisions were guided by the Prosigna test.

“What the Optima study shows definitively is that those women with low Prosigna score do not benefit from chemotherapy,” Febbo explains. “They get all the side effects… without any benefit.”

The data generated favorable attention at ASCO. The study produced prospective level 1A evidence, the highest standard for predictive testing, and addressed one of the central problems in breast cancer care: determining which patients actually benefit from chemotherapy and which patients may be exposed to toxic treatment unnecessarily.

Our show also looks at the broader evolution of molecular diagnostics in oncology. Prosigna runs on whole transcriptome sequencing, creating opportunities not only for current clinical decision-making, but also for future translational research into tumor biology and treatment response.

“We need the full complement of the transcriptome to understand what is the faulty circuitry and how do we shut it off therapeutically,” Leite says.

Veracyte has moved quickly from clinical validation to rollout. The company already has the assay prepared for U.S. launch immediately following the ASCO presentation. If only it worked out this way every time. It’s the kind of direct through-line between biology, clinical evidence, and improvement of human life that molecular diagnostics companies strive for each year.

Note: For more in-depth discussion on the OPTIMA study and the launch of Prosigna, sign up for an upcoming webinar at GenomeWeb here.

For years, precision oncology has largely been discussed through the lens of breakthrough drugs. But there’s another story running underneath modern cancer care: the quiet rise of companion diagnostics. These tests are increasingly deciding who receives those therapies in the first place. In many cases, the real bottleneck is no longer discovering a drug target. It’s building a reliable system for identifying the right patient at the right moment in the disease. That challenge sits at the center of this conversation with Rita Shaknovich, Chief Medical Officer for Life Sciences and Diagnostis, and Karina Kulangara, Associate Vice President of R&D in Companion Diagnostics at Agilent Technologies.

Agilent has always had a major role in this field. Rita and Karina explain how companion diagnostics evolved from the original Herceptin test into a vision for a much broader ecosystem spanning pathology, automation, regulation, and global clinical deployment.

We dive into Agilent’s recent FDA approval expanding PD-L1 IHC 22C3 PharmDx into ovarian cancer1, a development both guests describe as particularly meaningful given the historically poor outcomes associated with the disease. As Rita puts it: “Precision medicine is based fundamentally on scientific truth . . . it brought real results for patients. It brings better survival for patients, fewer side effects from the medication.”

Karina offers one of the clearest explanations we’ve heard for why immunohistochemistry or IHC has endured so long in modern oncology. “It’s the ability to detect protein biomarker in the spatial context of the tissue,” she explains, emphasizing that location and cellular context can fundamentally shape how therapies work.

What emerges is a picture of precision oncology that is becoming less exotic and more routine. We’re talking not just new drugs, but an entire clinical and technological infrastructure which is designed to match therapies to biology more effectively and over time.

Green takes us on a tour around the world of multi-omics, which he says is not a branding exercise but a practical response to the limits of sequence data by itself. Genomics remains foundational, but many clinical questions require additional layers of biology, including RNA, epigenomics, proteomics, and single-cell analysis. As he puts it, “DNA sequence alone may not reveal it.”

The discussion highlights rare disease as one of the clearest examples. Genome sequencing can solve roughly half of suspected cases, Green notes, but many patients remain undiagnosed because the relevant signal may lie in RNA splicing, epigenetic regulation, structural variation, or downstream protein effects. In those settings, multi-omic approaches can provide the missing evidence needed to move from uncertainty to diagnosis.

In oncology, the challenge is different. Cancer genomes can be highly complex and heterogeneous, making it difficult to distinguish driver events from background noise. That is one reason why researchers and clinicians are increasingly incorporating methylation markers, transcriptomic data, and proteomic signals into early detection, disease sub typing, and monitoring strategies.

Green also emphasizes that the next bottleneck may be less about generating data than interpreting it. “The human brain is not going to be the thing that’s going to crack this nut,” he says. “It’s going to be AI and computational biology.”

The result is an overall picture of where the field may be headed as we go from genomic medicine to a broader molecular medicine with multiple data types that will improve diagnosis, stratify disease, and guide care worldwide.

That may finally be changing.

In today’s episode Theral speaks with Dr. Yan Zhang, President of Proteomic Sciences at Thermo Fisher Scientific, about the rapid evolution of large-scale p…

Read more

On today’s show, Rob Osborne, Senior Vice President of R&D at Biomodal, discusses new evidence that separating two epigenetic marks—5-methylcytosine and 5-hydroxymethylcytosine—can improve early cancer detection from liquid biopsy. In a recent Nature Communications Medicine study, his team showed that analyzing these signals independently in circulating DNA significantly enhanced detection of Stage I colorectal cancer compared with approaches that combine them.

The advance does not require new sequencing hardware. Biomodal’s approach uses a sample preparation kit compatible with existing platforms, paired with bioinformatics tools, potentially lowering the barrier to adoption while expanding the information content of standard sequencing workflows.

The underlying insight is biological as much as technical. Most methylation assays collapse 5mC and 5hmC into a single signal, masking early transitions in gene regulation. Osborne describes this as “squishing them into one output,” a simplification that can obscure meaningful changes in disease onset and progression.

By separating the signals, the study identified patterns that emerge earlier in tumor development, offering a more sensitive window into disease biology.

But the deeper message of the interview is that this work may only scratch the surface. “I think that we’re just at the beginnings of really understanding this biology,” Osborne says.