Mendelspod Podcast

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Digital technology has been promising to transform pathology for years. But the last year looks different. Roche paid roughly $1 billion for PathAI. Tempus acquired Paige. Other deals are adding to a sudden wave of consolidation. And AstraZeneca is developing a computational pathology algorithm for TROP2 that could become a companion diagnostic used to determine which patients receive a drug.
DeciBio partner Katie Maloney says these are signs that digital pathology may finally be reaching an inflection point.
The important shift is from digital to computational pathology. Until recently, much of the value proposition was about making an existing workflow more efficient. Now algorithms are beginning to extract clinical information that a pathologist could not simply determine by eye. Maloney points to tools that can predict prognosis, stratify patients and potentially predict drug response. Computational pathology is beginning to compete with, and increasingly complement, molecular diagnostics.
The transition is still early. Maloney estimates that only 20 to 30 percent of US labs have adopted even a slide scanner. Reimbursement remains a major obstacle, with labs generally not paid for scanning slides, using image management software or deploying computational algorithms. And some of the hardest problems are surprisingly basic. Different labs stain the same tissue differently, creating variability that algorithms must accommodate if they are going to work across thousands of clinical sites.
But pharma may change the equation. Maloney is watching to see whether AstraZeneca’s work proves to be an isolated example or the beginning of something much larger. If computational pathology becomes important across a significant share of new drugs, particularly antibody drug conjugates, pathology images become another rich source of patient data that can be layered with clinical and molecular information. As Maloney puts it, computational pathology is becoming “not just a tool for pathologists, but it’s a precision medicine tool.”

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It was the heady days of the CRISPR revolution and of the gene therapy Casgevy. And when longtime genomics editor and author Kevin Davies went looking for a book that told the story of sickle cell disease and could not find one, he was perplexed and then inspired.
A few years later, the result is Curved Air, a biography of sickle cell anemia that traces one of the most remarkable arcs in modern biology. Davies begins with Victoria Gray, the first sickle cell patient treated with the CRISPR therapy that became Casgevy. Her transformation leads him backward through more than a century of discovery, from the first description of sickled blood cells to the identification of sickle cell as the first molecular disease. He writes of the extraordinary biology of fetal hemoglobin that made today’s therapy possible.
This is also a story about the gap between biology and medicine. Davies explores the neglect and discrimination endured by sickle cell patients and the difficulty of bringing a multimillion dollar therapy to those who need it. With a list price of $2.2 million for Casgevy, there is an enormous challenge of extending this advanced therapy to the millions of patients around the world who are in need.

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After being pursued for more than 150 years, cancer vaccines may finally be having their moment.
The recent positive Phase III results from Moderna and Merck offer what today’s guest Dr. Elias Sayour calls the first “bona fide evidence” that a therapeutic personalized cancer vaccine can work. For Sayour, a pediatric oncologist and cancer researcher at the University of Florida, the result is not the culmination of the field. It is “just the tip of the iceberg.”
Sayour explains why cancer has been such a difficult target for vaccines. Cancer is heterogeneous and constantly evolving. Yet the immune system evolves too. Sayour describes the contest as an “epic battle between an evolutionary foe and an evolutionary guardian.”
His own research points toward an intriguing next step. Sayour’s lab has found that an mRNA vaccine may not always need to carry a cancer specific target. Nonspecific mRNA can wake up a dormant immune response and potentially make any tumor more responsive to checkpoint inhibitors. Retrospective observations in cancer patients receiving COVID mRNA vaccines have strengthened that hypothesis, and Sayour says his group expects to begin a prospective clinical trial shortly.
The larger vision is striking. Sayour imagines combining universal immune activation, personalized vaccines and eventually therapies that anticipate where an evolving cancer is going next. After decades of frustration, cancer vaccines have finally delivered a major clinical success. The question now may be not whether they can work, but how far this new way of programming the immune system can take us with cancer and other diseases.

Single cell sequencing has given researchers extraordinary new maps of human biology. For Nick Banovich of TGen, the burning question is how to turn those maps into something that matters for patients.
Banovich has spent much of his career studying pulmonary fibrosis, and single cell sequencing has changed the field’s understanding of the disease. Instead of looking at an average signal from diseased lung tissue, researchers can now separate molecular changes from changes in the populations of cells themselves. That has helped point drug developers away from simply targeting fibrosis and toward earlier changes in epithelial and endothelial cells. Banovich sees spatial technologies as the next step.
Late in the conversation, Banovich tells of his group discovering a population of cells found almost exclusively in patients with pulmonary fibrosis, cells that had never been described before single cell sequencing. Later, using spatial transcriptomics, Banovich and his team were able to locate those same cells directly in diseased lung tissue, to physically see their finding.
We also discuss perturbation experiments, organoids, AI and virtual cells. Throughout the conversation, Banovich returns to the reason he came to TGen in the first place. Discovery is exciting, but ultimately he wants these technologies to affect disease and improve patient care.
Note: Nick will continue the conversation as a panelist in GenomeWeb’s virtual roundtable, “Single-cell Sequencing in the Era of Translational Medicine.”
Register for the GenomeWeb virtual roundtable


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Stanford physicist and bioengineer Polly Fordyce has a big vision. She’s attempting to measure protein function at the scale at which we learned to measure DNA. We can sequence proteins. We can increasingly predict their structures. But we still have a surprisingly difficult time measuring what proteins actually do.
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.



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