posted on 2023-08-01, 08:42authored byLing Cai, Ralph J. DeBerardinis, Yang Xie, John D. Minna, Guanghua Xiao
<p>Similar and distinct NE score–associated gene dependencies in SCLC and neuroblastoma cell lines. Selection of SCLC (<b>A</b>) and neuroblastoma (<b>B</b>) vulnerabilities based on the consistency (positive correlation) between CRISPR and RNAi data, and anticorrelation between dependency data and gene expression data. Pearson correlation coefficients from RNAi-CRISPR (left), RNAi-RNA expr (middle), and CRISPR-RNA expr (right) correlations were computed for all genes. The distributions of these coefficients are plotted as diagonal panels; pairwise correlations among these three sets of correlation coefficients were visualized as scatter plots in the lower triangular panels and the Pearson correlation coefficients are printed in the upper triangular panels. The four SCLC subtype driver TFs and the neuroblastoma oncogenic driver MYCN all have high consistency between CRISPR and RNAi data and high anticorrelation between dependency data and gene expression data. Areas with <i>r</i> > 0.4 from RNAi-CRISPR correlation, and <i>r</i> < −0.4 from RNAi-RNA expr and CRISPR-RNAi correlation were demarcated by light gray squares. <b>C,</b> Correlation between NE scores and effect scores of selected dependencies in SCLC and neuroblastoma. The upper part of the heat map displays selected vulnerabilities for SCLC and was ordered by correlations between NE scores and the effect scores in SCLC cell lines; likewise, the lower part of the heat map displays selected vulnerabilities for neuroblastoma. Genes with magenta arrows are showcased in <b>D–I</b>. Cell lines are ordered by their NE scores and annotated with NE score and SCLC driver TF expression. <b>D–I,</b> Comparison of selected gene dependencies in SCLC and neuroblastoma. In each plot, variable names are shown in the diagonal boxes, and scatter plots display relationships between each pairwise combination of variables. Lower triangular plots are colored by NE scores whereas upper triangular plots for SCLC figures are colored by TF classes. Pearson correlation coefficients are provided in lower triangular boxes for SCLC and upper triangular boxes for neuroblastoma. Refer to legends in <b>C</b> for color annotations.</p>
Funding
American Cancer Society (ACS)
National Institute of General Medical Sciences (NIGMS)
United States Department of Health and Human Services
Lineage plasticity has long been documented in both small cell lung cancer (SCLC) and neuroblastoma, two clinically distinct neuroendocrine (NE) cancers. In this study, we quantified the NE features of cancer as NE scores and performed a systematic comparison of SCLC and neuroblastoma. We found neuroblastoma and SCLC cell lines have highly similar molecular profiles and shared therapeutic sensitivity. In addition, NE heterogeneity was observed at both the inter- and intra-cell line levels. Surprisingly, we did not find a significant association between NE scores and overall survival in SCLC or neuroblastoma. We described many shared and unique NE score–associated features between SCLC and neuroblastoma, including dysregulation of Myc oncogenes, alterations in protein expression, metabolism, drug resistance, and selective gene dependencies.
Our work establishes a reference for molecular changes and vulnerabilities associated with NE to non-NE transdifferentiation through mutual validation of SCLC and neuroblastoma samples.