Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Overcoming osimertinib resistance with AKT inhibition in EGFRm-driven Non-Small-Cell-Lung-Cancer with PIK3CA/PTEN alterations.

PURPOSE: Osimertinib is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) indicated for the treatment of EGFR mutated (EGFRm)-driven lung adenocarcinomas. Osimertinib significantly improves progression-free survival in first-line treated patients with EGFRm advanced NSCLC. Despite the durable disease control, the majority of patients receiving osimertinib eventually develop disease progression. EXPERIMENTAL DESIGN: ctDNA profiling analysis on-progression plasma samples from patients treated with osimertinib in both first (Phase 3, FLAURA trial) and second-line trials (Phase 3, AURA3 trial) revealed a high prevalence of PIK3CA/AKT/PTEN alterations. In vitro and in vivo evidence using CRISPR engineered NSCLC cell lines and PXD models support a functional role for PIK3CA and PTEN mutations in the development of osimertinib resistance. RESULTS: These alterations are functionally relevant as EGFRm NSCLC cells with engineered PIK3CA/AKT/PTEN alterations develop resistance to osimertinib and can be re-sensitized by treatment with the combination of osimertinib and the AKT inhibitor capivasertib. Moreover, xenograft and PDX in vivo models with PIK3CA/AKT/PTEN alterations display limited sensitivity to osimertinib relative to models without alteration, and in these double mutant models capivasertib and osimertinib combination elicits an improved anti-tumor effect versus osimertinib alone. CONCLUSIONS: Together, this approach offers a potential treatment strategy for patients with EGFRm-driven NSCLC that have a sub-optimal response, or develop resistance, to osimertinib through PIK3CA/AKT/PTEN alterations.

Genome-wide CRISPR screens identify the YAP/TEAD axis as a driver of persister cells in EGFR mutant lung cancer.

Most lung cancer patients with metastatic cancer eventually relapse with drug-resistant disease following treatment and EGFR mutant lung cancer is no exception. Genome-wide CRISPR screens, to either knock out or overexpress all protein-coding genes in cancer cell lines, revealed the landscape of pathways that cause resistance to the EGFR inhibitors osimertinib or gefitinib in EGFR mutant lung cancer. Among the most recurrent resistance genes were those that regulate the Hippo pathway. Following osimertinib treatment a subpopulation of cancer cells are able to survive and over time develop stable resistance. These 'persister' cells can exploit non-genetic (transcriptional) programs that enable cancer cells to survive drug treatment. Using genetic and pharmacologic tools we identified Hippo signalling as an important non-genetic mechanism of cell survival following osimertinib treatment. Further, we show that combinatorial targeting of the Hippo pathway and EGFR is highly effective in EGFR mutant lung cancer cells and patient-derived organoids, suggesting a new therapeutic strategy for EGFR mutant lung cancer patients.