Perturb-MARS: Reading mouse experiments through a human lens

IntroductionWhat is Perturb-Map?Why do we think Perturb-Map is better than most animal studies?How do we use Perturb-Map to find the overlap between mouse biology and human biology?Conclusion, and the active-learning loop TL;DR: Human data is directly related to what drug development demands, but is difficult to generate and perturb. Mouse data allows us to discover causality and is easy to generate, but is only a proxy for what matters. What has never existed is causal data that speaks in human terms. We’ve built the system that creates it: a combination of wet-lab innovations and machine-learning innovations. The first, Perturb-Map, is a multiplexed in vivo perturbation platform that can test hundreds of genetic knockouts in the same mouse with full spatial resolution. The second is TARIO-2, a foundation model we’ve trained exclusively on human cancer tissue, that can convert H&E to whole-genome spatial transcriptomics. We’ve found that we can apply TARIO-2 directly to H&E derived from Perturb-Map experiments, yielding human-centric tumor microenvironment characterizations from animal data, with no re-training required.In other words, we can read the results of multiplexed mouse experiments through a lens that only understands human biology.The combination of Perturb-Map and TARIO-2 is something we call ‘Perturb-MARS’ (Multi-species Alignment and Reasoning on Spatial data), and it allows us to answer not only typical preclinical target discovery questions, but also ones that the current preclinical apparatus is structurally incapable of answering, such as combination therapy exploration; e.g. ‘what is the next PD-1 bispecific target after VEGF and what population is it active in?’. All the while, the readout stays grounded in human-specific genes. We are actively looking for partners here—reach out to partnerships@noetik.ai to learn more. Every life-saving oncology drug on the market first proved itself in a mouse. And yet, the dominant instinct in the field is to abandon animal models entirely, because 95% of the time, what works in a mouse doesn’t work in a human. Why is this? The standard diagnosis is that mouse biology is a bad model for human biology. This isn’t entirely wrong, but we think the field has it backwards. The problem isn’t the mouse, not entirely.

The bigger problem is that we’ve always read mouse experiments with mouse-native readouts—mouse gene expression, mouse protein levels, mouse immune cell counts—and then hoped the translation would sort itself out.Rather than making the animal more human, what if we changed the lens by which we interpret the animal data?Last month, we released a post on TARIO-2, an internal foundation model trained exclusively on human cancer tissue. Given an H&E-stained human tumor section, TARIO-2 can predict the spatial gene expression, telling you what genes are expressed across the tissue.We’ve discovered that TARIO-2 generalizes from human to mouse H&E.In other words, we’re able to feed it the H&E histology of a mouse tumor, and the model is able to predict what a human tumor with similar morphological features would express at the transcriptomic level. What comes out of the model is not a mouse gene readout, but rather the projection of that mouse experiment into a human biological coordinate system. And when combined with an in vivo genetic perturbation platform called ‘Perturb-Map’, we’ve found that the resulting human-space projections of the mouse H&E are both accurate and have clear use-cases for several clinically relevant tasks.In this essay, we plan to explain this whole process.First, how Perturb-Map works. Second, why we believe the method gives far more useful results than most oncology animal studies. Third, and most excitingly, via TARIO-2, we’ve used the method alongside our human tumor foundation models to view mouse data with a human-centric lens; something we’re calling ‘Perturb-MARS’ (Multi-species Alignment and Reasoning on Spatial data). Finally, we’ll end with how this methodology can be used to tighten the currently slow feedback loops between wet-lab and computational predictions, allowing us to train even better generative models of human biology.The original paper for Perturb-Map was published in Cell in March 2022 by Maxime Dhainaut and colleagues. He was recruited to Noetik shortly after and has directed work on the platform since then, massively scaling it up.The process starts with cancer cells that are engineered to express unique protein barcodes called “Pro-Codes”. These barcodes consist of triplet combinations of peptide tags, and each unique triplet combination identifies a specific ‘perturbation’, or deliberate genetic change.

For instance, a cell expressing tag 1, 2, and 3 together might carry a CRISPR knockout of gene X, while a cell expressing tags 1, 2, and 4 carries a knockout of gene Y. We do this at immense scales, creating hundreds of tumors that each carry their own, distinct mutation; alongside some control cancer cells.Next, we pool the sum total of these cancer cells together, and inject them into a single mouse in a way that gets them to the tissue of origin from which they were derived (e.g. for lung cancer cells, injection into the tail vein lets them seed in the lung; for breast cancer lines, cells are injected directly into the breast tissue). If this is in a ‘treated’ mouse, we could also administer any arbitrary cancer drug shortly afterwards. After several weeks, the organ—now seeded with vast numbers of genetic mutant cancers—is resected.Finally, we stain the lung tissue with antibodies that recognize the Pro-Codes, allowing us to directly visualize each genetically perturbed tumor population and trace the growth behavior of every single mutation, using the aforementioned control cancer cells as a baseline. And, if we elected to treat the mouse with an anti-cancer drug, that’d give us additional information as to how the drug affected the tumor microenvironment.Figure 1. Perturb-Map steps.Figure 2. Each color is unique to a genetically distinct subpopulation of tumors, imaged from a mouse lung resection.In one fell swoop, this allows us to understand the heterogeneity of each tumor’s response to drugs, assess the relative fitness of different genetic alterations, and potentially identify new cancer targets entirely. All this, at orders-of-magnitude higher scales than typical, single-throughput animal experiments.Animal data does not have a good reputation. In fact, it has an awful reputation, with many papers’ conclusions often being waved off because ‘it’s based on mouse data’. This is understandable. But we think that its tarnished reputation is sometimes conflating two, completely independent things.The first one is the inability for mouse physiology to capture human physiology, which, yes, is difficult to solve. We’ll explain more in the next section how we think we’ve put a dent in this problem, but also realize that there are absolute limits in the pharmacologic transferability from one organism to another.The second is the animal data itself being captured badly, either through flawed experimental design, difficulty in replication, and inability to scale.

We think that this second bit is both entirely fixable and accounts for a fair bit of why so many researchers distrust animal data. This section is meant to offer our take on this.So, why do we think Perturb-Map is better than most animal studies?First, and biggest, the ability to multiply speed and reduce cost. Traditional animal studies test one, or maybe two, genetic perturbations per cohort of mice. This means that if a researcher wants to understand how 50 different cancer genes affect tumor growth, they’d traditionally need 50+ separate groups of mice. Hundreds of animals, years of work, and enormous costs. With Perturb-Map, all 50 mutations (plus controls) can be tested simultaneously in the same mouse, in the same organ, at the same time. And there is evidence that we can scale this up to hundreds of mutations, allowing us to explore combinatorial mutational space.Second, and relatedly, this method dramatically attenuates inter-animal variability. In a traditional study, if you want to compare fifty knockouts, you need fifty cohorts of mice, and any differences you observe are entangled with per-animal confounders: vascular supply, microbiome, stress levels, injection technique, and more. With Perturb-Map, all fifty perturbations coexist in the same host, so their relative rankings are controlled by construction. Drug-treatment comparisons still involve a treated and an untreated animal, so some inter-mouse variability re-enters there, but each mouse carries hundreds of knockouts acting as an internal control panel. This makes global per-mouse offsets identifiable and correctable in ways that a single-knockout-per-mouse design cannot match.Third, this method allows you to retain in vivo spatial resolution. You may realize that Perturb-Map sounds a lot like Perturb-Seq. Perturb-Seq is a related, older method that also allows for multiplexed genetic perturbations. So why aren’t we using that? The problem with Perturb-Seq is that it forces one to dissociate tumor cells to tell which one has which mutation. This is a big problem! A tumor is not a bag of cancer cells floating around. There is an incredibly important, delicate structure to the whole thing. Perturb-Seq, by necessity, destroys all of that.

Perturb-Map, in contrast, preserves that spatial information about tumor microenvironments, and the original paper behind it compiled a fairly large list of insights that literally could not have been gained without it.Fourth, and finally, we place the tumors in their natural environment. There is a somewhat bizarre practice in the oncology field of doing animal studies by injecting tumors just underneath the skin, or what is often referred to as ‘subcutaneous tumors’. This is opposed to ensuring the tumor eventually reaches whatever the tissue of origin is: lungs for lung cancer, pancreas for pancreas cancer, and so on. Using subcutaneous tumors is cheap, fast, and easy to do. It also, unfortunately, yields a cancer microenvironment state that barely resembles that of a tumor grown in its natural environment. We simply don’t do subcutaneous tumors. This, to be fair, is not really related to Perturb-Map; it’s just good experimental hygiene that we enforce across our entire platform.And that’s why we think Perturb-Map is worth using.Just because Perturb-Map has good experimental design, reduces variability, is scalable, and retains spatial resolution does not allow it to magically recapitulate human biology. Where are we deriving this understanding of where mouse biology—derived via Perturb-Map—actually maps onto human biology?A few weeks back, we had a post about TARIO-2, a foundation model trained entirely on human cancer tissue. It can predict whole-plex spatial transcriptomics from H&E-stained human tumor sections. And when we ran the perturbed mouse tumor H&Es from Perturb-Map through TARIO-2, it worked. The model predicted human gene expression patterns from mouse tumors, despite never having seen a mouse cell during training. Here is an example of it predicting CEACAM6 in mouse H&E.Figure 3. TARIO-2 predictions on H&E derived from a Perturb-Map sample.Importantly, CEACAM6 is a human gene that has no functional ortholog in mice. And you’ll notice it was predicted to be especially high in only small fractions of this H&E slide, which is composed of many genetically different tumors. In other words, the model is likely recognizing the existence of multiple different phenotypes.Are these phenotypes correct? We’ll discuss that in a bit, but it’s important to first grasp what is actually going on here.