Abstract C010: A comprehensive predictive biomarker analysis for MEK Inhibitors in Patient-derived Xenograft (PDX) models

Abstract

Abstract Introduction: The RAS-RAF-MEK-ERK pathway has been extensively studied for its prominent role in cancer development. Activating mutations in receptor tyrosine kinases, RAS, or RAF, are commonly observed in cancer. Pathway activation caused by these mutations is mediated through MEK kinases, making MEK inhibition an attractive therapeutic strategy. Although several in vitro studies have been carried out to identify predictive biomarkers for sensitivity to MEK inhibition, the results have not been validated in vivo. Methods: Cryopreserved or fresh PDX tumor tissues (~2–3 mm in diameter) were transplanted into NOD/SCID or BALB/c nude mice subcutaneously. Trametinib was administered orally daily at 0.3mg - 1mg/kg of body weight, starting when tumor size reached 100-300 mm3. Sequencing reads were aligned to both human and mouse reference genomes. Based on which alignment yielded fewer mismatches, they were subsequently sorted into human or mouse groups, representing cancer and stromal transcriptomes, respectively. Ambiguous readings were discarded. Transcript abundance was quantified using the Kallisto software tool. Nucleotide variants in human genes were detected using the GATK best practice workflow. Results: We systematically analyzed tumor growth data collected from 62 PDX models in 9 cancer types (colorectal, head and neck, esophageal, kidney, melanoma, lung, gastric, ovarian, and pancreatic) treated with Trametinib, an allosteric MEK1/2 inhibitor. The responses to Trametinib were classified into 3 categories based on the amount of tumor growth inhibition (TGI) achieved. About 20% of these models were sensitive to Trametinib treatment (TGI > 70% compared to vehicle), 35% were resistant (TGI ≤ 30% compared to vehicle), and the remaining showed a partial response (30% < TGI ≤ 70% compared to vehicle). Cross-group comparisons of PDX transcriptomic and mutation profiles allowed identification of novel biomarkers as well as validation of biomarkers previously reported in in vitro studies. Conclusions: The different sensitivity to MEK inhibition observed in PDX models mirrors the patient heterogeneity observed in clinic. Thus, our results highlight the need to identify the appropriate genetic background using predictive biomarkers when applying targeted therapies. Citation Format: Yanghui Sheng, Wubin Qian, Jingjing Wang, Sheng Guo. A comprehensive predictive biomarker analysis for MEK Inhibitors in Patient-derived Xenograft (PDX) models [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C010.