Abadie et al. present prime‑SGE, a pooled prime‑editing framework that installs thousands of precise point mutations across multiple oncogenes and identifies drug‑resistance variants by sequencing integrated pegRNAs after positive‑selection with kinase inhibitors. The method resolved known resistance mutations (e.g., EGFR C797S, KRAS G12 variants), uncovered less-characterized candidates, compared resistance landscapes across covalent and non‑covalent EGFR inhibitors, and validated resistant edits in vivo.
0:00Welcome to Base by Base, the paper cast that brings Genomics to wherever you are. Thanks for listening, and don't forget to follow and rate us in your podcast app. Yeah, thanks so much for joining us. You know, there is this, um, really heartbreaking cycle in modern medicine, specifically when we look at how we treat cancer today.
0:17Yeah, it's honestly one of the most devastating challenges in all of oncology. Right, because, I mean, we have access to these incredibly advanced targeted therapies. And for a lot of patients, these drugs act almost like a miracle at first.
0:30Right, absolutely. The tumors shrink. The scans come back clear. Yeah, and it feels for a moment like we've completely won the battle. But then, you know, 6 months or a year later, the therapy just suddenly stops working.
0:42Right. The cancer comes back and it's almost always more aggressive. Exactly. Because tumors, well, they learn. They adapt through genetic mutations. I always try to visualize this to make sense of it.
0:53It's like, if a targeted drug is a perfectly cut key fitting into a lock. I like that with the cancer protein being the lock. Exactly. The tumor basically survives by subtly changing the shape of the lock.
1:06So suddenly the key just doesn't fit anymore. And, you know, the real tragedy there is that finding out how the lock changed usually happens retrospectively. Right, like after the patient has already relapsed.
1:18Exactly. But what if we could test 1000s of potential life changes at once, like before they even happen? Wow, yeah. Which sets up the core question of this deep dive. What really happens when a targeted cancer therapy suddenly stops working.
1:33And how could predicting that evolution change the way we treat the disease entirely? It's such a massive leap forward. It really is. But before we get into the weeds. Today we celebrate the work of the collaborative teams at the University of Washington.
1:46Fred Hutchinson Cancer Center, and Brottman Baddy Institute, who have advanced our understanding of cancer drug resistance. Yeah, and to really grab the magnitude of what those collaborative teams accomplished, we need to 1st look at the clinical challenge they're addressing.
2:01They focused on non-small cell lung cancer. And specifically, a class of targeted drugs called tyracine kinase inhibitors, or TKIs. Okay, let's unpack this, because Tyracine Kinus Inhibitor is a mouthful.
2:14What exactly is a kinis and why is it so important to a lung cancer cell? So think of a kine as a cellular communication tower. In a healthy cell, it basically relays signals telling the cell when to divide.
2:26Okay, pretty straightforward. But in non-small cell lung cancer, one of these specific towers, a protein called EGFR gets permanently stuck in the on position. So it's just constantly broadcasting the signal to multiply.
2:39Exactly. Runaway multiplication is your cancer. So scientists designed TKIs, like the drug awesome routine, to act as a chemical jammer. Got it. But cancer develops resistance in 2 main ways. Either the target itself mutates, like the EGFR protein, mutating at a specific spot called residue C797 to physically block the drug.
2:58So the jammer basically just bounces right off. Exactly. Or the 2nd way is the cancer activates what we call a bypass pathway. Oh, like mutating a completely different encogene entirely. Right, like KRAS.
3:10If EGFR is the general at headquarters, KRAS is a field commander, if the drug gags the general, the cancer just mutates the field commander to start shouting the division orders instead. That is incredibly sneaky.
3:23Now, in the past, scientists use saturation genome editing or SGE to test which mutations cause this resistance, right? You did, yeah. SGE sounds really comprehensive. So why do we need a new framework?
3:35Well, the bottleneck with traditional SGE was just massive. To find out which mutation you successfully installed, you had to physically sequence the edited DNA locus itself? Wait, like the actual genomic neighborhood where the cut was made?
3:48Exactly. And because that's so labor intensive, scientists could only really study one tiny region, like a single Exxon at a time. Oh, wow. But tumors have the whole genome to play with. Right. So this method was simply too slow.
4:02We needed a way to decouple the edit from the readout. Which directly leads us to the core methodology of this near research, a framework they call Prime SGE. Yes, saturation genome editing, using prime editing.
4:13And prime editing is a pretty big deal, right? Unlike older CRISPR methods that act like, you know, molecular scissors causing double strand brakes. Exactly. Old CRISPR is brute force. Crime editors use a nicking Kaz9 fused to a reverse transcript ace.
4:29So they act much more like word processors. Right, just seamlessly rewriting the code instead of shredding the DNA. Exactly. And the stroke of genius here is the Peg RNA. The prime editing guide RNA. It contains both the address of the target site and the exact instruction for the program to edit.
4:46Let me try an analogy here. It's like instead of sending out 1000s of spies with specific instructions and then searching every single house in the genome to see if the instruction was followed. Prime SGE allows us to just check the ID badges of the surviving spies.
5:00That's a perfect way to put it. Because by sequencing the integrated PEG RNAs from the surviving cells, researchers know exactly which mutation was programmed. What's fascinating here is this means scientists can multiplex 1000s of mutations across multiple genes all at once in a single experiment, literally just by reading the page RNA barcodes.
5:20That scale is just staggering. I mean, the proof of concept screen was massive. They designed 3825 page RNAs, right? Yeah, programming 12,220 single nucleotide mutations across 7 different onca genes, like EGFR, KaryS, Met, and others.
5:35And they tested these in PC 9 lung cancer cells against 3 different drugs. Right. Awesome routineib and SunVosertineib, which are covalent binders. Meaning they form a permanent chemical bond. Exactly.
5:46And then CH7233163, which is a non-covalent binder. And the findings were super clear. I mean, the KRAS bypass pathway we talked about. The KRS G 12 variants provided resistance to all 3 drugs. Which totally makes sense biology wise.
5:59Yeah. But the structural differences were amazing too. The EGFR, C797S mutation, beat the colon drugs, Austin Martineb, and Sunvoser to Nib, but completely failed against the non-covalent drug. Wow, so mapping perfectly to how these drugs physically bind differently.
6:14Precise. And they found novel discoveries too, like less characterized resistance mutations at EGFR, Q791 and Y 801. They didn't just stop at lab cells either. They injected these into mice, right? Mouse xenographs.
6:25Yeah. Treated with awesome routinib, the unedited tumors died, but tumors engineered with the KRAS, G 12C, and EGFR, C 787 S mutations, grew aggressively. So it proves it works in Vivo. And they even confirmed it in other cancers.
6:42They tested 498 KRAS variants in A375 melanoma cells against vamurafinib. Right, identifying 87 resistant variants. It's an incredibly versatile system. Okay, but here's where it gets really interesting, and I have to push back a little.
6:56Wait, just because the Peg RNI spy is inside the cell, does that actually guarantee the word processor successfully edited the DNA? No. It absolutely doesn't. Hey, really? It doesn't guarantee the edit?
7:08Not at all. Prime editing has notoriously low efficiency rate. We're talking single digits. Oh, wow. Yeah. And this brings up the study's major limitation. False negatives. The researchers estimate a 40% false negative rate.
7:2040%. huge. It is. A perfect example in their data is KRASG-13D, which is a massive, well-known cancer driver. Right, but it didn't show up in the results. Exactly. Simply because the prime editor struggled to efficiently install that specific edit.
7:34That seems like a pretty catastrophic flaw at first glance. Well, if we connect this to the bigger picture. It explains why the study's positive selection framework is so vital. Ah, positive selection.
7:46Because only the cells that actually got the resistance conferring edit can survive the toxic drug bath. Exactly. So, false positives are virtually eliminated. If it survives, it's genuinely resistant.
7:59Oh, I see. And as prime editing tech inevitably improves. This framework will allow us to prospectively map out drug resistance during early drug development. Right, allowing doctors to deploy combination therapies before a tumor can even adapt.
8:12It completely shifts oncology from reactive to proactive. So to wrap this up, The prime SGE framework fundamentally transforms our ability to predict cancer drug resistance by allowing scientists to multiplex 1000s of specific mutations across the genome in a single experiment.
8:29By relying on positive selection and reading the guide RNAs of surviving cells, researchers can prospectively map out how tumors might evade targeted therapies. It's an absolute game changer for the field.
8:41What does this mean for the future of personalized oncology? Could we one day hand a patient a treatment plan that already accounts for every evolutionary move their specific tumor might try to make? This episode was based on an open access article under the CCBY4.0 license.
8:57You can find a direct link to the paper and the license in our episode description. If you enjoyed this, follow or subscribe in your podcast app and leave a 5 star rating. If you'd like to support our work, use the donation link in the description.
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