Show Notes
The tumor microenvironment (TME), composed of immune cells, fibroblasts, and endothelial cells, has emerged as critical to pathogenesis, prognosis and treatment for cancer (schematic Figure below). There’s no such thing as TMI (too much information) for TME! Yet there’s still no way of assessing it in the clinic by a blood test, and even by invasive biopsy the sampling bias for TME without broader geographic context can lead to important mistakes. That isn’t used for clinical decision making. The TME field is about to change. Recently, Professor Aaron Newman at Stanford and colleagues from many US leading centers published a landmark paper in Nature. It may ultimately be considered one of the most important leaps in cancer diagnostics in decades. Ground Truths is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
This was a massive undertaking involving 10 million single-cell RNAseq in 10 types of cancer to define 9 TME types by spatial omics which they called “spatial ecotypes”. It was AI-enabled by machine learning (Spatial Ecotyper from biopsy samples) and extrapolating that to knowledge to deep learning AI (Liquid Ecotyper from blood samples). The whole project took several years. Here’s an infographic summary I made with the help of NotebookLM along with the Figures that follow. Some of the key points from our conversation: —TME is the soil in which the cancer cells live. It strongly influences whether the cancer cells will thrive or not, spread, respond to therapy, and determine the fate pf pre-cancerous lesion (as seen in recent important paper in pancreatic cancer evolution and colon cancer development). —Ecotypes, a term coined in 1922, to get at the local environment of tissue and cancer, was repurposed in this new project. Good metaphor: neighborhoods. And emphasis on location , location, location like real estate. —In the clinic today, the only window to TME is via a biopsy. But decisions for treatment are not based on TME, they are based on biomarkers like tumor mutation burden (TMB), PD-L1 levels , or volume of tumor DNA by liquid biopsy.
—TME is dynamic and changes over time so a single snapshot won’t cut it. —Spatial 3D maps of TME from 10 million cells, 10 cancer types led to 9 spatial ecotypes (with the Spatial Ecotyper machine learning). Validation across 3D grids (such as Visium) and multiple cohorts. The cell location base within TME is striking. —These 9 neighborhoods are conserved and very different. SE4-immune system is asleep, hypoxic; SE 7/8 are “war zones” rich with immune cells, hot tumor; SE 5 exhibits intense immunosuppression, hostile vs immune system, portends poor survival, SE9 deep in core, driving angiogenesis (new blood vessels) —Now rich information from these 9 SEs needs to be extrapolated to a holistic blood test of TME. It turns out an extension of tapping into methylation as used by some liquid biopsy cell-free DNA tests works very well, providing cell identity from the DNA fragments leaked into the blood. To get to TME spatial biology in a tube of blood. Akin to wastewater surveillance form municipals for detection of infectious disease pathogen sequence variants and circulating levels. A triumph of AI analytics made this possible! —The Liquid Ecotyper was validated in 78 patients with malignant melanoma pre-treatment from Yale University. SE 7/8 benefited from immune checkpoint therapy; SE4 resisted treatment, shorter survival. Matched with tissue biopsy work. —Work beyond publication ongoing in many types of lung cancer, bladder cancer, mesothelioma, and response to other therapies like CAR T and bispecific antibodies. This is critical since we have slew of different cancer immunotherapies (see my pyramid Ground Truths review) and we’re not sure who, when, how to use them to drive optimal cancer outcomes in patients. May also help predict other types of therapy (not just immunotherapy) for cancer. —The blood te
More from Ground Truths
Michael Lewis is one of the best storytellers of our era. You know and likely have read his books Moneyball, The Big Short, The Blind Side, Flash Boys, Liar’s Poker, and The Undoing Project, to name a few. He has long been one of my favorite authors. This was the first interview that Michael did on his new book, BLOCKERS, published on October 6th, streamed live on Substack Sept 29th. It’s his 21st book and 4 of the last 5 have been about the US government: The Fifth Risk (2019), Premonition (2021), and Who Is Government? (2025). His ability to talk with the silent civil servant heroes of multiple government agencies (the Blockers), members of the DOGE destruction team, and much more from the current US government administration.
In recent weeks the results (announced by press release) of 2 major placebo-controlled randomized trials—ZEUS, testing an interleukin-6 (IL-6) blocker vs inflammation, and HORIZON, testing a drug to lower Lp(a), indicated failure to achieve their primary endpoint. Are we maxxed out for new treatments vs heart disease, as this article in the WSJ (←gift link) today asserts? “Cardiovascular drug development is starting to look a bit like the modern oil industry. The easy wells have been tapped. What’s left is in deep water: expensive, technically perilous and less likely to strike oil.”
This podcast is meant to complement my recent Ground Truths post Why Our Memory Slips After Age 50 which has all the relevant links to the 2 papers, key graphics, and other related publications. If you haven’t had a chance to read that, it would be helpful to spend a few minutes to do so before listening to this podcast. Links to the 2 papers for those who don’t want to look back Zemke et al in Science https://www.science.org/doi/10.1126/science.adt8307 Belk et al in Nature https://www.nature.com/articles/s41586-026-10939-0
Deb Cohen is an award winning investigative journalist and correspondent who trained as a physician and has worked with the BBC and BMJ. She just published BAD INFLUENCE, a book which takes us deep into the wellness-industrial complex—the confluence of the supplement, testing, tech, and pharma industries, and how they sell health anxiety to healthy people.
Sarah Urbut is a physician-scientist at Harvard Medical School and the Massachusetts General Research Institute. She and her colleagues published a landmark paper in Nature on patient health journeys for 3 different cohorts—the Mass General Brigham patients, the UK Biobank, and the All of Us research program—with a cumulative 683,000 individuals, 348 diseases, and up to 52 years of follow-up. Integrating electronic medical records and polygenic risk scores (N=36). An AI model was developed and validated called ALADYNOULLI, with each individual represented latent disease signatures (N=21), mathematically and biologically driven. An example of a patient’s dynamic life journey (health arc) is provided in the Figure below (adapted from the paper, made with NotebookLM). The longitudinal story for each individual is key here. Using a Gaussian process, the risk continually shifts like a GPS navigation when you make a wrong turn and you are re-routed. Considerable effort was dedicated to addressing selection bias with inverse probability weighting.