Genetically-engineered mouse models of small cell lung cancer: the next generation.

Small cell lung cancer (SCLC) remains the most fatal form of lung cancer, with patients in dire need of new and effective therapeutic approaches. Modeling SCLC in an immunocompetent host is essential for understanding SCLC pathogenesis and ultimately discovering and testing new experimental therapeutic strategies. Human SCLC is characterized by near universal genetic loss of the RB1 and TP53 tumor suppressor genes. Twenty years ago, the first genetically-engineered mouse model (GEMM) of SCLC was generated using conditional deletion of both Rb1 and Trp53 in the lungs of adult mice. Since then, several other GEMMs of SCLC have been developed coupling genomic alterations found in human SCLC with Rb1 and Trp53 deletion. Here we summarize how GEMMs of SCLC have contributed significantly to our understanding of the disease in the past two decades. We also review recent advances in modeling SCLC in mice that allow investigators to bypass limitations of the previous generation of GEMMs while studying new genes of interest in SCLC. In particular, CRISPR/Cas9-mediated somatic gene editing can accelerate how new genes of interest are functionally interrogated in SCLC tumorigenesis. Notably, the development of allograft models and precancerous precursor models from SCLC GEMMs provides complementary approaches to GEMMs to study tumor cell-immune microenvironment interactions and test new therapeutic strategies to enhance response to immunotherapy. Ultimately, the new generation of SCLC models can accelerate research and help develop new therapeutic strategies for SCLC.

Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions.

We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.