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Precision oncology is entering a new phase. Rather than simply profiling a tumor and matching it to a drug, physicians are increasingly able to follow a patient’s cancer over time, measuring treatment response, detecting residual disease, and adjusting therapy as the disease evolves. Jerry Radich of Fred Hutch Cancer Center has been at the forefront of this evolution. His pioneering work in chronic myeloid leukemia (CML) helped establish molecular monitoring as a critical part of cancer care and prefigured the rise we see in MRD testing today. Some CML patients can even discontinue treatment after achieving a sustained deep molecular response. Radich discusses how this remarkable success has become a model for other blood cancers. He’s now helping lead myeloMATCH, a national precision oncology initiative that brings together clinical trial groups across North America. With some 350 participating sites and molecular results typically available in less than 48 hours, the program is matching patients with acute myeloid leukemia to clinical trials based on their cancer’s genetic profile. Radich sees an opportunity not only to personalize treatment but to accelerate drug development by using molecular response as an earlier measure of efficacy. We also explore the limits of molecular profiling, the emerging role of the immune system, and why increasingly sensitive tests may transform how we understand cancer. Looking ahead, Radich imagines a future in which patients could monitor their disease burden almost continuously, perhaps even through wearable or implantable devices. Radich is co-chairing the upcoming APOAPE conference in Seattle on January 15, bringing oncologists and pathologists together to connect advances in molecular diagnostics with real-world treatment decisions. The conference is free for healthcare professionals. Learn more about the APOAPE conference This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

In the US, we have the All of Us Research Program. In the UK, they have Our Future Health, an ambitious new population health research program that aims to make healthcare more preventive than reactive. Launched just four years ago, it has already recruited nearly three million volunteers toward a goal of five million, making it one of the world’s largest health research initiatives. Today we’re joined by Raghib Ali, CEO and Principal Investigator of Our Future Health, to discuss how the program builds on the success of UK Biobank while pursuing a different goal. Rather than simply studying the causes of disease, Our Future Health aims to establish the evidence needed to move healthcare from diagnosis and treatment toward prediction and prevention. Ali discusses the program’s plans to combine polygenic risk scores with clinical and lifestyle data to develop integrated disease risk scores. A new clinical trials platform will test whether personalized prevention actually improves outcomes. The ultimate ambition is to bring successful approaches into routine NHS care. Perhaps most encouraging is the public response. More than a million participants have come from historically underrepresented groups, raising the possibility that precision medicine could help reduce health inequalities rather than widen them. As Ali puts it, the goal is to move from a national sickness service to a true national health service. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

In recent months on Mendelspod, we’ve explored how new sequencing and proteomics technologies are revealing biology in regions of the genome (and proteome) that have long escaped our attention. From repeat expansion disorders to new approaches in cancer diagnostics, the dark genome is increasingly becoming clinically relevant. Today we’re revisiting a conversation from earlier in the year that asks an even bigger question. What if much of this unexplored genomic territory is not simply functional, but a reservoir of evolutionary possibility? Last March, we talked with Sudhakaran Prabakaran, computational biologist at Northeastern University, founder of NonExomics, and author of Eclipsed Horizons: Unveiling the Dark Genome. Drawing on his lab’s work cataloging more than 250,000 non-canonical proteins, Prabakaran argued that regions outside traditional gene definitions may be continually generating new proteins with implications for adaptation, evolution, and disease. From rapidly diversifying cichlid fishes to human accelerated regions (HARs) of the human genome linked to schizophrenia, Sudhakaran makes the case that protein birth and death is continuous, cheap, and exploratory. In his framing, the “dark genome” functions less like debris and more like a flexible evolutionary sandbox. His company, NonExomics, is also exploring how these previously overlooked proteins might become biomarkers for cancer and other diseases. As our recent interviews have demonstrated, we’re entering a new era of genomic discovery. Prabakaran challenges us to think about just how much biology remains to be found. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Repeat expansions are places where the genome has remained stubbornly dark. Long-read sequencing is beginning to illuminate them, and now that new visibility is moving into clinical testing. Today we’re joined by Simon Sadedin, Head of Clinical Bioinformatics at Victorian Clinical Genetics Services in Australia, and Alex Lindell, Senior Director of Product Marketing at Oxford Nanopore. Until now, testing for repeat expansions has largely meant building a separate assay for each disorder. A patient might be tested for Fragile X, get a negative result, and then move on to another test, sometimes waiting months at each step. Some repeat expansions have been difficult to test for at all. Simon’s group is trying to replace that serial approach with a single assay covering roughly 60 repeat expansion loci, which is now being submitted for clinical accreditation. The promise goes beyond simply finding an expansion. Long reads can reveal its length, sequence, methylation and mosaicism, while adaptive sampling makes it possible to target these difficult regions in software rather than building a new wet lab assay for each one. Some say we are at a new threshold in genomics, where parts of the genome that have remained dark are finally becoming accessible to both discovery and clinical medicine. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Sometimes the best evidence that a new medical technology is working is simply that doctors start depending on it. For years, the promise of molecular medicine has been that better genomic information would lead to better patient care. At Roswell Park Comprehensive Cancer Center, Vice Chair of Molecular Pathology, Sean Glenn, offers a compelling example of that promise becoming routine clinical practice. Hematologic cancers can require information from many different kinds of genomic alterations and traditionally from multiple tests and laboratories. Glenn and his team developed a comprehensive pan-hematological assay that brings much of that information together, using both DNA and RNA to detect alterations across myeloid and lymphoid cancers. The panel targets nearly 400 genes on the DNA side and about 125 on the RNA side, while automation has helped Roswell Park achieve an average turnaround time of roughly two and a half days for the 50 to 60 tests it runs each week. And the oncologists are depending on it. Since introducing the panel, Glenn says Roswell Park has seen “a transformation and a resurgence of clinical care” in its leukemia and lymphoma clinics. His favorite measure of success is wonderfully practical. The oncologists are no longer calling the lab every few days asking where their results are. The information simply arrives in the patient’s record, in time to help make treatment decisions. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

Plant biology too is benefiting from single cell resolution. Unlike animals, plants cannot move away from environmental stress. They have evolved tremendous biological complexity to stay rooted in one place. Their genomes are complex too, with repeated genome duplications producing families of related genes that can take on different functions. And within a single leaf are many specialized cell types, from photosynthetic and epidermal cells to vasculature and rare trichomes, each with its own patterns of gene expression. It’s early days for plant science. The field is only beginning to assemble the large, high quality single cell atlases needed to understand that complexity and eventually make better predictions and enable a new world of plant engineering. Robin Buell and Anita Giabardo join us from the University of Georgia to discuss one of those new atlases. Their BioPoplar project aims to engineer fast growing poplar trees to produce new biomaterials, bioproducts and biofuels. Using Illumina’s PIPseq, Anita and colleagues created an atlas of more than 159,000 cells across the developing poplar shoot. That resolution matters. The researchers were able to find rare trichome cells, the tiny hairs on leaves whose specialized metabolism makes them attractive little factories for engineered products. They then identified a promoter that can specifically direct gene expression to those cells. Robin has watched plant genomics progress from Northern blots to bulk RNA sequencing and now to single cell. She predicts that within ten years single cell could become the standard. The ability to see biology cell by cell is not only changing what scientists can discover about plants. It will mostly likely change what they can build. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

What new insights can we learn about cancer when researchers are able to look at the genome and the methylome together? Recently, Illumina launched its 5-base for Cancer Research Grant Contest to explore that question. Today, the three winners join us on Mendelspod with three very different problems in cancer research. Richard Tothill of the University of Melbourne is studying cancers of unknown primary and in collaboration with Hartwig Medical Foundation is combining DNA methylation and mutation profiling to reveal the primary origins of these aggressive cancers. Jacob Blum of Johns Hopkins is working with tiny amounts of circulating tumor DNA, combining genetic variants with methylation signals in hopes of improving detection of minimal residual disease and developing a model that other researchers could use. And Alfonso Bellacosa of Fox Chase Cancer Center is investigating what methylation can reveal about the mechanisms behind response and resistance to cancer immunotherapy. The projects are early stage, but together they give us a snapshot of how cancer researchers are using a new tool that brings epigenetics and genetics into the same experiment and analysis. Joining the discussion is Danielle Goldberg from Illumina, who talks about what the company saw across nearly 200 grant applications and the growing interest in combining genomic and methylation data in cancer research. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com A recent New York Times article highlighted a remarkable study showing that exercise can help keep colon cancer in remission. After eight years, 90 percent of patients in a structured exercise program were still alive, compared with 83 percent in the control group. Yet despite these results, such programs are rarely covered by insurance. It’s a striking example of something our guest today, Mayo Clinic physiologist Mike Joyner, has argued for years. Are we overlooking some of the most powerful ways to improve human health in our pursuit of molecular medicine? Over a decade ago, we invited Joyner on as a skeptic of the emerging precision medicine revolution. He questioned whether breaking biology into ever smaller molecular pieces would deliver the improvements in human health being promised. Ten years later, we begin by asking Mike a simple question. Was he right? “I think I was more right than wrong,” he says. Despite important successes in oncology and drug development, he argues that precision medicine has yet to make the broad impact on public health its advocates envisioned. Joyner remains as provocative as ever. He argues that biomedical researchers have become too dependent on engineered animal models and should pay more attention to nature’s own experiments. The discovery of GLP-1 drugs is a case in point. He welcomes their enormous potential for improving public health but worries that people taking them without exercising may lose valuable muscle mass and miss the additional benefits of physical activity. We also venture into the contentious debate over transgender athletes in women’s sports. Drawing on physiological research, Joyner argues that suppressing testosterone does not fully eliminate the athletic advantages associated with male puberty. For him, the evidence supports maintaining biological sex categories to preserve fair competition for women. Throughout the conversation, Joyner challenges us to think beyond the molecular and ask a bigger question. Are we doing the kind of science that will actually make people healthier?

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com Is biology headed back into the genome? While much of biology has been expanding outward into single cells, spatial biology and ever more biological context, Žiga Avsec and his team at Google DeepMind are making the case that there is still an enormous amount to learn by going deeper into DNA itself. Their new AlphaGenome Atlas uses AlphaGenome to predict the molecular effects of every possible single letter change in the human genome. That’s some nine billion variants. Now researchers can easily explore how a variant might affect gene expression, or splicing, or other layers of gene regulation across different cell types. The Atlas also introduces a variant impact score (AVI) designed to help researchers quickly identify which variants deserve a closer look. Avsec argues that the scale itself opens new possibilities. Researchers can use the Atlas to prioritize rare variants across enormous cohorts, but they can also work backward from the predictions to investigate something more fundamental, the regulatory grammar of the genome. The grammar of the genome—long a provocative idea, but still not cracked. Short DNA motifs could act something like words, and AlphaGenome may help reveal how those words work together to activate or repress genes across different biological contexts. There are important limitations. AlphaGenome predicts molecular consequences rather than phenotype, and Avsec says the distance between genotype and phenotype remains long and complex. The model also has more difficulty with regulatory elements that far from genes—a proximity issue—and has so far been trained largely on bulk tissue data rather than the much richer universe of individual cell types and cell states. For now, Avsec just wants researchers to use the Atlas. The portal is free for noncommercial research, and he explicitly invites scientists to tell the DeepMind team what works and what does not. That feedback matters because the Atlas is not being presented as a finished map. It is part of a cycle in which better models suggest better experiments, those experiments generate better data, and better data produce the next generation of models. Bioinformaticians always want two things. Better data and more data. This may be an argument for where genomics is headed. Rather than AI replacing experiments, Avsec hopes it will give researchers greater confidence about which experiments are worth doing and ultimately lead to more experiments with more positive findings. The AlphaGenome Atlas is available free for noncommercial research here: https://deepmind.google/science/alphagenome/

Mass spectrometry has been one of the most powerful technologies in clinical testing for decades. So why is it still largely confined to specialty labs? Don Mason has spent more than 25 years in clinical mass spectrometry. Now Senior Marketing Manager for Mass Spectrometry at Roche Diagnostics, he says the technology’s strength has also been its weakness: “Part of its power and part of its challenges are linked.” Mass spec is extraordinarily sensitive, selective and flexible, but traditionally requires specialized operators, complicated workflows and batch processing. That may finally be changing. Last year Roche launched their new cobas Mass Spec solution designed to automate the process from sample preparation through result reporting. It also brings random-access mass spectrometry into the routine clinical lab. Mason says a sample can now produce a numerical result in as little as 34 minutes, compared with turnaround times that can stretch into days with traditional workflows. He points to transplant drug monitoring and antibiotic and antifungal monitoring in critically ill patients as examples where that difference could matter clinically. But automation isn’t simply about replacing expertise. Mason argues that moving established assays onto standardized systems could free mass spectrometrists to develop the next generation of tests: “This in turn becomes the innovation engine for tomorrow’s routine mass spectrometry-based tests.” Roche currently has seven assays available in the U.S., with more expected beginning in 2027. Looking further ahead, Mason sees a much bigger transition. Just as MALDI-TOF transformed microbial identification, he predicts LC-MS will become standard equipment in more and more clinical laboratories over the coming decade. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com Jennifer Dionne, Stanford physicist and co-founder of Pumpkinseed, joins us to talk about a radically different way of reading proteins. Pumpkinseed’s deSIPHR technology combines nanophotonics with Raman spectroscopy to detect the molecular vibrations of individual amino acids. The goal is de novo protein sequencing that can read not only the 20 canonical amino acids, but potentially the enormous alphabet created by post-translational modifications and other forms of protein variation. Raman spectroscopy is super cool even if nearly a century old. Shine a laser on a molecule and a tiny fraction of the photons change color as they interact with the molecule’s vibrations, producing a characteristic molecular signature. The problem has always been sensitivity. As Dionne explains, only about one photon in a million undergoes this Raman scattering. Her work uses nanophotonics for specially patterned materials to amplify that faint signal by orders of magnitude. Why does that matter? Mass spectrometry has been the workhorse of proteomics, but it loses much of the sample during ionization and generally depends on existing catalogs for identification. Pumpkinseed wants to read what is actually there, including proteins and modifications we may never have seen before. One early application is particularly timely with the recent news of cancer vaccines. Working with Genentech, Pumpkinseed is studying immunopeptides, the protein fragments displayed on the surface of cells that allow the immune system to distinguish healthy from diseased tissue. Direct sequencing of these peptides is a way to improve personalized cancer vaccines by identifying the mutations actually present in an individual patient’s tumor. Ultimately, the ambition is much larger. “We want to be able to sequence all of the proteins that are in individual cells,” Dionne says. For biology and for AI models trying to learn biology, she argues, we first need to learn how to read much more of its language.

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.”

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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.

This is a free preview of a paid episode. To hear more, visit www.mendelspod.com 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 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe