Chareddy, Y. | bioRxiv Science

bioRxiv2022

An Integrated Model for Predicting KRAS Dependency

BackgroundThe clinical approvals of KRAS G12C inhibitors has been a revolutionary advance in precision oncology, but response rates are often modest. To improve patient selection, we developed an integrated model to predict KRAS dependency. Methods and FindingsBy integrating molecular profiles of a large panel of cell lines from the DEMETER2 dataset, we built a binary classifier to predict a tumors KRAS dependency. Monte Carlo cross validation via ElasticNet within the training set was used to compare model performance and to tune parameters. The final model was then applied to the validation set. We validated the model with genetic depletion assays and an external dataset of lung cancer cells treated with a G12C inhibitor. We then applied the model to several Cancer Genome Atlas (TCGA) datasets. The final "K20" model contains 20 features, including expression of 19 genes and KRAS mutation status. In the validation cohort, K20 had an AUC of 0.94 and accurately predicted KRAS dependency in both mutant and KRAS wild-type cell lines following genetic depletion. It was also highly predictive across an external dataset of lung cancer lines treated with KRAS G12C inhibition. When applied to TCGA datasets, specific subpopulations such as the invasive subtype in colorectal cancer and copy number high pancreatic adenocarcinoma were predicted to have higher KRAS dependency. ConclusionThe K20 model has simple yet robust predictive capabilities that may provide a useful tool to select patients with KRAS mutant tumors that are most likely to respond to direct KRAS inhibitors.

bioinformatics↗

bioRxiv2020

TLR4 signaling and macrophage inflammatory responses are dampened by GIV/Girdin

Sensing of pathogens by Toll-like receptor 4 (TLR4) induces an inflammatory response; controlled responses confer immunity but uncontrolled responses cause harm. Here we define how a multi-modular scaffold, GIV (a.k.a Girdin) titrates such inflammatory response in macrophages. Upon challenge with either live microbes or microbe-derived lipopolysaccharides (LPS, a ligand for TLR4), macrophages with GIV mount a more tolerant (hypo-reactive) transcriptional response and suppress pro-inflammatory cytokines and signaling pathways (i.e., NFkB and CREB) downstream of TLR4 compared to their GIV-depleted counterparts. Myeloid-specific gene depletion studies confirmed that the presence of GIV ameliorates DSS-induced colitis and sepsis-induced death. The anti-inflammatory actions of GIV are mediated via its C-terminally located TIR-like BB-loop (TILL)-motif which binds the cytoplasmic TIR-modules of TLR4 in a manner that precludes receptor dimerization; the latter is a pre-requisite for pro-inflammatory signaling. Binding of GIVs TILL motif to other TIR modules inhibits pro-inflammatory signaling via other TLRs, suggesting a convergent paradigm for fine-tuning macrophage inflammatory responses. SignificanceTo ensure immunity, and yet limit pathology, inflammatory responses must be confined within the proverbial Goldilocks zone. TLR4 is the prototypical sensor that orchestrates inflammatory responses through a series of well characterized downstream cascades. How TLR4 signals are confined remain incompletely understood. Using trans-scale approaches ranging from disease modeling in live animals, through cell-based interventional studies, to structure-guided biochemical studies that offer an atomic-level resolution, this study unravels the existence of a brake within the TLR4 signaling cascade, i.e., GIV; the latter is a prototypical member of an emerging class of scaffold proteins. By showing that GIV uses conserved mechanisms to impact multi-TLR signaling, this work unravels a multi-scale point of convergence of immune signaling of broader impact beyond TLR4.

immunology↗