MATCH PATIENTS

“The biomarkers that, you know, people have been using are, you know, biased toward simplicity, you know, does the patient have this particular mutation? Sometimes, like, staying for this single protein, or, you know, do transphotomics, like, to look for a particular gene signature. But, like, there's no reason to think that biology or, like, biology of cancer is that simple that you're going to capture, you know, most of the meaningful variation with such simple biomarkers.”

“We all know the numbers at 90 percent, 95 percent of the cancer drugs fail in the clinic. Why do they fail? So our thesis is they fail not because we're bad at pharmacology, not because we're bad at target selection, you're making the drug. We're actually better at that process than we have ever been in the history of drug development. Most of those drugs fail, we'd argue, because we're bad at selecting which patients those drugs are in our work.”

“Whenever you make a new drug, you do a set of experiments in cell culture, cells in a dish, those cells are often cell lines. These cell lines have existed for 40, 50 years, and they're immortalized. So they have genomes that allow them to persist, that have abnormal numbers of chromosomes. They have gene expression patterns that don't represent any known cell in the human body, really. These are Frankensteinian cells, a cancer drive that are ruinlessly hit by it. They're mostly cancer.”

“Our thesis is kind of that if you look at the data, a much richer kind of data, so the multimodal data that we're generating in our lab, we're going to see that actually what people thought was one subtype of lung cancer is really three distinct subtypes of cancer, and that is going to be critical for figuring out which patients should get which drugs. Nobody actually knows what the subtypes are. There are cancers that originate in a certain tissue like the lung that have been classified into subtypes based on pathologists looking at them for more than a century.”

“We generate all our data in the lab. Everything from sourcing tumor samples themselves to processing them and generating the data. Maybe another hot take I have, just in AI and bio, is you're sort of not at the order of magnitude of data that you are in other spaces of building training models. And so it becomes really hard to brute force these problems just by collecting data. We have a couple pretty good examples of where someone has designed a data set.”