Abstract
Complex RNA-seq signatures involving the transcription factors ASCL1, NEUROD1, and POU2F3 classify Small Cell Lung Cancer (SCLC) into four subtypes: SCLC-A, SCLC-N, SCLC-P, and SCLC-I (triple negative or inflamed). Preliminary studies suggest that identifying these subtypes can guide targeted therapies and potentially improve outcomes. This study aims to evaluate whether the expression levels of these three key transcription factors can effectively classify SCLC subtypes, comparable to the use of individual antibodies in immunohistochemical (IHC) analysis of formalin-fixed, paraffin-embedded (FFPE) tumor samples. We analyzed preclinical models of increasing complexity, including eleven human and five mouse SCLC cell lines, six patient-derived xenografts (PDXs), and two circulating tumor cell (CTC)-derived xenografts (CDXs) generated in our laboratory. RT-qPCR conditions were established to detect the expression levels of ASCL1, NEUROD1, and POU2F3. Additionally, protein-level analysis was performed using Western blot for cell lines and IHC for FFPE samples of PDX and CDX tumors, following our experience with patient tumor samples from the CANTABRICO trial (NCT04712903). We found that the analyzed SCLC cell line models predominantly expressed ASCL1, NEUROD1, and POU2F3, or showed no expression, as identified by RT-qPCR, consistently matching the previously assigned subtypes for each cell line. The classification of PDX and CDX models demonstrated consistency between RT-qPCR and IHC analyses of the transcription factors. Our results show that single-gene analysis by RT-qPCR from FFPE-extracted RNA simplifies SCLC subtype classification. This approach provides a cost-effective alternative to IHC staining or expensive multi-gene RNA sequencing panels, making SCLC subtyping more accessible for both preclinical research and clinical applications.
Published Version
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