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1.
PLoS Comput Biol ; 19(5): e1011095, 2023 05.
Article in English | MEDLINE | ID: mdl-37141389

ABSTRACT

The clinical approvals of KRAS G12C inhibitors have 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. By integrating molecular profiles of a large panel of cell lines from the DEMETER2 dataset, we built a binary classifier to predict a tumor's KRAS dependency. Monte Carlo cross validation via ElasticNet within the training set was used to compare model performance and to tune parameters α and λ. 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. The 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.


Subject(s)
Adenocarcinoma , Lung Neoplasms , Pancreatic Neoplasms , Humans , Adenocarcinoma/genetics , Proto-Oncogene Proteins p21(ras)/genetics , Proto-Oncogene Proteins p21(ras)/metabolism , Precision Medicine , Lung Neoplasms/pathology , Mutation
2.
Proc Natl Acad Sci U S A ; 117(43): 26895-26906, 2020 10 27.
Article in English | MEDLINE | ID: mdl-33055214

ABSTRACT

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 multimodular scaffold, GIV (a.k.a. Girdin), titrates such inflammatory response in macrophages. Upon challenge with either live microbes or microbe-derived lipopolysaccharides (a ligand for TLR4), macrophages with GIV mount a more tolerant (hypo-reactive) transcriptional response and suppress proinflammatory 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 dextran sodium sulfate-induced colitis and sepsis-induced death. The antiinflammatory 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; such dimerization is a prerequisite for proinflammatory signaling. Binding of GIV's TILL motif to TIR modules inhibits proinflammatory signaling via other TLRs, suggesting a convergent paradigm for fine-tuning macrophage inflammatory responses.


Subject(s)
Microfilament Proteins/metabolism , Toll-Like Receptor 4/metabolism , Vesicular Transport Proteins/metabolism , Animals , Colitis/metabolism , Disease Models, Animal , Female , HEK293 Cells , Humans , Macrophages/metabolism , Mice , Mice, Knockout , RAW 264.7 Cells , Sepsis/metabolism , Signal Transduction
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