Modeling diverse genetic subtypes of lung adenocarcinoma with a next-generation alveolar type 2 organoid platform.

Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD), the most common histological subtype, accounts for 40% of all cases. While existing genetically engineered mouse models (GEMMs) recapitulate the histological progression and transcriptional evolution of human LUAD, they are time-consuming and technically demanding. In contrast, cell line transplant models are fast and flexible, but these models fail to capture the full spectrum of disease progression. Organoid technologies provide a means to create next-generation cancer models that integrate the most advantageous features of autochthonous and transplant-based systems. However, robust and faithful LUAD organoid platforms are currently lacking. Here, we describe optimized conditions to continuously expand murine alveolar type 2 (AT2) cells, a prominent cell of origin for LUAD, in organoid culture. These organoids display canonical features of AT2 cells, including marker gene expression, the presence of lamellar bodies, and an ability to differentiate into the AT1 lineage. We used this system to develop flexible and versatile immunocompetent organoid-based models of KRAS, BRAF, and ALK mutant LUAD. Notably, organoid-based tumors display extensive burden and complete penetrance and are histopathologically indistinguishable from their autochthonous counterparts. Altogether, this organoid platform is a powerful, versatile new model system to study LUAD.

Thermodynamic characterization of the multivalent interactions underlying rapid and selective translocation through the nuclear pore complex.

Intrinsically disordered proteins (IDPs) play important roles in many biological systems. Given the vast conformational space that IDPs can explore, the thermodynamics of the interactions with their partners is closely linked to their biological functions. Intrinsically disordered regions of Phe-Gly nucleoporins (FG Nups) that contain multiple phenylalanine-glycine repeats are of particular interest, as their interactions with transport factors (TFs) underlie the paradoxically rapid yet also highly selective transport of macromolecules mediated by the nuclear pore complex. Here, we used NMR and isothermal titration calorimetry to thermodynamically characterize these multivalent interactions. These analyses revealed that a combination of low per-FG motif affinity and the enthalpy-entropy balance prevents high-avidity interaction between FG Nups and TFs, whereas the large number of FG motifs promotes frequent FG-TF contacts, resulting in enhanced selectivity. Our thermodynamic model underlines the importance of functional disorder of FG Nups. It helps explain the rapid and selective translocation of TFs through the nuclear pore complex and further expands our understanding of the mechanisms of "fuzzy" interactions involving IDPs.

A prime editor mouse to model a broad spectrum of somatic mutations in vivo.

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

Multiscale profiling of protease activity in cancer.

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.