Molecular characterization of circRNA and miRNA networks in adagrasib resistance cells

• Grant number: 25/09594-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: August 01, 2025
• End date: July 31, 2026
• Field of knowledge: Biological Sciences - Genetics - Human and Medical Genetics
• Principal Investigator: Letícia Ferro Leal
• Grantee: Giovanna Maria Stanfoca Casagrande
• Supervisor: Miguel-Angel Molina-Vila
• Host Institution: Hospital do Câncer de Barretos. Fundação Pio XII (FP). Barretos , SP, Brazil
• Institution abroad: Pangaea Oncology, Spain
• Associated to the scholarship: 23/03918-0 - Identification of non-coding RNAs-based molecular signature for early detection of Lung Cancer, BP.DD

Abstract

KRAS-G12C inhibitors, such as adagrasib, have shown promising clinical activity in non-small cell lung cancer (NSCLC) patients harboring KRAS mutations. However, the emergence of resistance remains a significant clinical challenge, limiting long-term efficacy. Non-coding RNAs, including circular RNAs (circRNAs) and microRNAs (miRNAs), have been increasingly recognized as key regulators of gene expression and contributors to drug resistance mechanisms in cancer. Identifying specific non-coding RNA signatures associated with adagrasib resistance may offer novel insights into the biology of therapeutic resistance and support the development of predictive biomarkers. We aimed to determine circRNAs and miRNA profiles in adagrasib-resistant cell lines and parental cell lines by lung cancer patients. For this, KRASG12C mutant cell lines (H23, H2030, Mia-PaCa-2, and H358) and a KRAS wild-type line (H292) will be used to generate adagrasib-resistant models. Plasma samples from 68 patients enrolled in the ADDEPT phase II clinical trial (NCT05673187), all with KRASG12C-mutated NSCLC and treated with adagrasib, will be included. Total RNA was isolated from both cell lines and plasma, and expression profiling of circRNAs and miRNAs was performed using the NanoString nCounter® platform. Data will be normalized using the NanoStringNorm R package, and differential expression analysis was conducted with machine learning classifiers (Random Forest and K-Nearest Neighbors). Statistical analyses were performed using GraphPad Prism 9, with p-values < 0.05 considered statistically significant. We expected to identify a circRNA- and miRNA-based predictive signature in cell lines and plasma samples with high accuracy for potential resistance to adagrasib. This signature could be a non-invasive tool for predicting resistance in lung cancer patients, ultimately contributing to improved treatment outcomes and cure rates. (AU)