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.comA 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.comIs 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.comJennifer 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.comDigital 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.comIt 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.comAfter 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

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. 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