An integrated model for predicting KRAS dependency
Overview
The development of KRAS G12C inhibitors in precision oncology has been a significant advancement, but response rates are often limited. To improve patient selection, researchers have created an integrated model called “K20” that predicts KRAS dependency. The model was built using molecular profiles of cell lines and features 19 genes, KRAS mutation status, and other factors. It accurately predicted KRAS dependency in validation cohorts and demonstrated effectiveness in lung cancer cells treated with KRAS G12C inhibitors. When applied to various cancer datasets, the model identified specific subpopulations with higher KRAS dependency. The K20 model offers a simple and robust tool to select patients with KRAS mutant tumors who are likely to respond to KRAS inhibitors.
Background
Dysregulation of the RAS family of GTPases, including KRAS, is responsible for driving nearly 30% of all cancer types. Mutated KRAS, particularly the G12C mutation, is prevalent in aggressive tumor types such as lung, colorectal, and pancreatic cancer. Traditional small-molecule inhibitors have been ineffective against KRAS due to its structure, but recent advances have led to the development of direct KRAS G12C inhibitors like MRTX849 and AMG510, which show promise in lung cancer treatment. However, not all KRAS-mutant cancers are KRAS-dependent, and resistance to KRAS inhibitors can arise due to various mechanisms. Therefore, there is a need for biomarker signatures to identify patients most likely to benefit from KRAS inhibitors beyond the presence of the KRAS mutation. The integrated K20 model, developed using publicly available datasets, improves the prediction of KRAS dependency by considering genomic features and tumor-specific transcriptional profiles. This model can aid in selecting patients who are most likely to respond to KRAS inhibitors across different cancer types.
Results
The model was built using a dataset called DEMETER2, which integrated three large-scale RNAi screen datasets with model-based normalization. The dataset included 712 cell lines, and the KRAS dependency scores were significantly lower in KRAS-mutant cells compared to KRAS wild-type cell lines.
To develop the KRAS dependency classifier, the cell lines were divided into sensitive, intermediate, and refractory clusters using K-means clustering. The aim was to classify the cell lines into binary groups (non-refractory vs. refractory) based on their KRAS dependency. The training and validation sets were randomly divided, and different feature sets were compared to determine the best prediction accuracy. The final model, named K20, included gene expression data of 19 genes and the mutation status of KRAS.
External validation of the K20 model was performed using in vitro RNAi-mediated dose-response assays and an external dataset of KRAS G12C-mutant lung cancer cell lines treated with a G12C inhibitor. The model’s prediction scores were highly correlated with the actual KRAS dependencies observed in the experiments, demonstrating its predictive capabilities.
The K20 model was then applied to the TCGA-PANCAN dataset to analyze the molecular features of predicted KRAS dependency in human tumors. Patients with KRAS mutations across different cancer types had significantly lower prediction scores, indicating higher KRAS dependency. Gastrointestinal tract cancers with KRAS mutations, such as esophageal carcinoma, stomach adenocarcinoma, colon adenocarcinoma, rectum adenocarcinoma, and pancreatic adenocarcinoma, had the lowest prediction scores. Melanoma patients, regardless of KRAS mutation status, were predicted to be the most resistant to KRAS dependency.
Overall, the K20 model showed high predictive accuracy and provided insights into the molecular features associated with KRAS dependency in different cancer types.
Discussion
This paper discusses the complexity of defining oncogene dependency, specifically focusing on KRAS in different cancer types. It introduces the K20 model, which uses 20 features to predict KRAS dependency in carcinomas, allowing for characterization and discrimination of differences across cancer subtypes and genotypes. The paper highlights various features within the model that are linked to KRAS and its promotion of tumor progression. It also mentions features that downregulate KRAS or compensate for its loss. The paper describes the validation of the K20 model using in vitro dose-response studies and external validation with lung KRAS G12C mutant cell lines. It emphasizes the importance of a predictive tool to determine tumor response to direct KRAS inhibition. The paper further discusses the findings of the K20 model across lung, colorectal, and pancreatic cancers, identifying specific molecular subsets with increased KRAS dependency. It also mentions the potential implications of KRAS dependency in different subtypes, such as CMS4 tumors in colorectal cancer and the CIMP-L subtype. The paper concludes by emphasizing the need for predictive biomarker models to define patients who are most likely to benefit from KRAS inhibitors beyond the presence of a KRAS mutation.
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