SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

Abstract

SummaryModel systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated.Graphical AbstractHighlightsSCLC-CellMiner provides the most extensive SCLC resource in terms of number of cell lines (118 cell lines), extensive omics data (exome, microarray, RNA-seq, copy number, methylomes and microRNA) and drug sensitivity testing.We find evidence of distinct epigenetic profile of SCLC cell lines (global hypomethylation and histone gene methylation), which is consistent with their plasticity.Transcriptome analyses demonstrate the coherent transcriptional networks associated with the 4 main genomic subgroups (NEUROD1, ASCL1, POU2F3 & YAP1 = NAPY classification) and their connection with the NOTCH and HIPPO signaling pathways.SCLC-CellMiner provides a conceptual framework for the selection of therapies for SCLC in a personalized fashion allowing putative biomarkers according molecular classifications and molecular characteristics.SCLC-Y cell lines differ from the other cancer cell lines; their transcriptome resemble NSCLC cell lines. YAP1 cell lines while being the most resistant to standard of care treatments (etoposide, cisplatin and topotecan) respond to mTOR and AKT inhibitors and present native immune predisposition suggesting sensitivity to immune checkpoint inhibitors.

Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

“…The availability of both expression and methylation data, along with drug response measures for this rich dataset, provides opportunities for further integrative analyses. In a parallel effort, we have been developing a public online resource, SCLC-CellMiner [72] which Fig. 4 Distribution of a DNA methylation and b mRNA expression levels of TREX1 among different cancer categories in the CCLE dataset.…”

MYC Drives Temporal Evolution of Small Cell Lung Cancer Subtypes by Reprogramming Neuroendocrine Fate

“…Many MYC-expressing tumors with intact NOTCH exhibit low NE scores, whereas all of the NOTCH-damaging mutant tumors are NE-high (Figure 7G, right panel). We mined a recent cell line genomics database (SCLC_CellMiner) (Tlemsani et al, 2020), which allowed a similar analysis with 50 additional human SCLC cell lines (Figure 7H). There was a significant difference in the quantity and proportion of MYC-high NOTCH WT versus MYC-high NOTCH mutant samples that were NE-low, consistent with our model that MYC promotes non-NE progression by activating NOTCH signaling.…”

“…Figure 7H represents expanded analysis of human cell lines (n = 98, excluding POU2F3 + samples) that had publicly available expression and matching mutation data on the newly developed SCLC_Cellminer databse (https://discover.nci.nih.gov/ SclcCellMinerCDB/), as well as the same human tumors (n = 70) used in Figure 7G. Cell line expression was downloaded from the Global SCLC dataset on the SCLC_Cellminer database (Tlemsani et al, 2020). In this dataset, global expression, including MYC expression, was determined based on average z-score intensity of gene expression from 5 sources (NCI SCLC, CCLE, CTRP, GDSC and UTSW).…”