138P - Analysis of master regulatory transcription factors and their associated transcriptomic profiles in SCLC patients

Background

Despite extensive research small cell lung cancer (SCLC), a highly aggressive neuroendocrine tumor, is characterized by a poor clinical outcome and limited response to conventional chemotherapy. Gene expression analyses have recently defined subgroups of SCLC and helped to understand intertumoral heterogeneity and plasticity. In particular the master regulatory transcription factors (TF) of neuroendocrine differentiation ASCL1, NEUROD1 and POU2F3 show differential expression in SCLC cohorts. Preclinical models demonstrate that variations in the activity of these TF are associated with significant differences regarding morphology and biology, including response to therapy.

Methods

Resection specimen from 30 patients with SCLC as defined by standard morphological and immunohistochemical criteria were retrieved from the archive and tissue microarrays (TMA) constructed. All cases were classified based on the immunohistochemical expression of the dominant master regulatory TF, namely NEUROD1-exclusive, ASCL1-exclusive, NEUROD1-ASCL1-hybrid, POU2F3-exclusive, and negative (Null) phenotypes. Using the GeoMx Digital Spatial Profiler (DSP) platform, we performed transcriptomic analysis on 15,359 mRNA transcripts in multiple TMA tumor spots from each SCLC case.

Results

SCLC tumors were subclassified by IHC into 4 NEUROD1-exclusive, 8 ASCL1-exclusive, 4 NEUROD1-ASCL1-hybrid, 8 POU2F3-exclusive, and 6 Null phenotypes. Transcriptomic analysis revealed unique signatures: 172 transcripts for POU2F3 (e.g. SLC45A4), 48 for ASCL1-NEUROD1-hybrid (e.g. INSM1), and 92 for Null (e.g. FOLR1). Common signatures in ASCL1- and NEUROD1-exclusive phenotypes overlapped with Null and ASCL1-NEUROD1-hybrid phenotypes for 13 transcripts (e.g. CDH7). Subsequent analysis highlighted respective biological pathways.

Conclusions

SCLC subgroups as defined by IHC of the neuroendocrine master regulators show specific transcriptomic signatures. Using these signatures SCLC biology can be better elucidated and new targets for therapy defined.

Editorial acknowledgement

During the preparation of this work the author used ChatGPT in order to proofread. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Clinical trial identification

Legal entity responsible for the study

RWTH Uniklinik Aachen.

Funding

RWTH Uniklinik Aachen.

Disclosure

All authors have declared no conflicts of interest.