By combining base editing with transcriptomic profiling, we established a scalable framework to:

🧬 Correct mutations
📊 Measure downstream gene expression
💡 Reveal functional consequences
All in endogenous contexts.

🧫 Our platform can be extended to other cancer driver mutations

Repairing mutations in KRAS, PTEN or SMAD4, we found gene-specific differences in how mutations drive cancer cell fitness

Reinstating brakes (TP53) seems more effective than releasing a pedal stuck on gas (KRAS)

These effects were:
✔️ Consistent across tissue types
✔️ Independent of co-occurring mutations
✔️ Uniform across TP53 mutations (R273H vs R175H)

That suggests a conserved, mutation-independent p53 regulatory network that controls cancer cells. 🧬🛡️

🔬 What did we find?

🎯 Cancer cells are dependent on the mutant TP53 allele for survival.

When we corrected these mutations, we saw:
✅ Loss of oncogenic proliferation
✅ Reactivation of tumor suppressive transcriptional programs

Using adenine base editing (ABE), we precisely corrected TP53 hotspot mutations in a range of human cancer cell lines from different tissues.

No overexpression. No exogenous DNA. Just editing the native genome.

Excited to share my first Last-author paper out now in GenomeBiology @bmc.springernature.com 🥳

We present a base editing platform to functionally & transcriptionally profile cancer hotspot mutations, starting with TP53 - the most frequently mutated gene in cancer. Let's dive in 👇🧵