Abstract 163P
Background
Small cell lung cancer (SCLC) accounts for 14% of lung cancer diagnoses and is characterized by rapid onset of chemoresistance and poor clinical outcomes. SCLC has four major subtypes driven by transcription factors ASCL1, NEUROD1, POU2F3, and YAP1. Recent studies have also shown intratumoral heterogeneity with respect to ASCL1/NEUROD1 balance and MYC amplification – which are potential mechanisms underlying SCLC's aggressive and refractory biology. Unfortunately, patient-derived models of SCLC with which to better characterize the molecular profiles of refractory SCLC are scarce.
Methods
We generated 46 patient-derived (PDX)/circulating tumor cell-derived xenograft (CDX) models derived from 33 patients with treatment-naïve or relapsed SCLC. We performed multi-omic analyses to deconvolute the mutational landscapes, global expression profiles, and molecular subtypes of these SCLC models.
Results
Our models revealed mutations typical of SCLC (e.g. TP53, RB1), which were maintained in vivo over multiple passages. Consistent with the known distribution of subtypes, most of our samples express ASCL1 or both ASCL1 and NEUROD1. We looked into an inflamed gene signature, including immune checkpoint genes and human leukocyte antigens (HLAs). Seven models showed high expression of HLAs and related antigen presentation genes such as HLA-DRA or HLA-DBP1. To date, there are no reports of an animal model representing POU2F3 subtype. Our cohort included 10 POU2F3-driven models from primary and metastatic tumors from a patient with ES-SCLC. These novel models include high POU2F3 and MYC expression by IHC and RNA-seq; low expression of neuroendocrine (NE) markers; notably high expression of mitochondrial genes such as MT-RNR2 or MT-CO3/1; high expression of REST and BACH2; low expression of DLL3 and ATOH1; and high expression of metabolic genes in comparison to the non-SCLC-P samples such as ABCB6, PGD, or G6PD, highlighting metabolic heterogeneity in our SCLC samples.
Conclusions
Our PDX/CDX models and the multi-omic characterization of these models provide a unique system and resource to characterize SCLC biology and inform clinical research treatment strategies for patients with SCLC.
Legal entity responsible for the study
The author.
Funding
Has not received any funding.
Disclosure
The author has declared no conflicts of interest.