Table 1.

Examples of SCLC-CellMiner Capabilities

	SCLC-CellMiner Explores and Validates	Method	Examples	Examples of Findings
1	cell line reproducibility and consistency	“Univariate Analyses: Plot Data:” expression of the same gene “across different datasets” (X and Y)	Figure 2	cell lines are highly reproducible across datasets
2	omic data robustness and reproducibility	“Univariate Analyses: Plot Data:” expression, copy number variation, promoter methylation, mutations for the same gene “across datasets” (X and Y)	Figures 1B, 1C, and 2	transcripts, promoter methylation, and gene copy number are highly reproducible across datasets
3	drug data robustness and reproducibility	“Univariate Analyses: Plot Data:” activity of the same drug “across datasets” (X and Y)	Figures 2E and 2F	warning: not all drugs are consistent across dataset
4	integrates all the SCLC cell line genomic datasets under SCLC-Global (NCI, GDSC, CCLE, CTRP, UTSW)	use the pull-down tabs for “Cell Line Sets” and choose “SCLC-Global”	Figures 4D, 6H, S2G, S2H, and S3C	the 119 SCLC cell lines can be classified in the four groups of NAPY; development of NAPY genomic signatures
5	integration with CellMinerCDB	open in parallel: https://discover.nci.nih.gov/cellminercdb	Figures 2, 4, and 5	POU2F3 is selective for SCLC; YAP1 is expressed widely beyond SCLC; ASCL1 is co-expressed with NEUROD1
6	select and compare subsets of cell lines based on tissue of origin or metadata: NAPY, TNBC, NSCLC	“Univariate Analyses:” select y axis: “Select Tissue/s of Origin” or “Select Tissues to Color” (NEUROD1, ASCL1, POU2F3, YAP1, NE)	Figures 2H, 5F, S3, and S6	NEUROD1 and ASCL1 are also selectively expressed in CNS cancer cell lines
7	test phenotypic data (mda): NE, APM, EMT	“Univariate Analyses:” select “Data Type mda: NE, APM, EMT;” additional selection can be done for subset (see #6)	Figures 4H and 6	NE cell lines have low antigen-presenting machinery (APM) score
8	tissue- or subset type-specific analyses (NAPY; NE)	“Select Tissue/s of Origin” or “Select Tissues to Color”	Figures 5, 6, and S4–S6	YAP1 cell lines have lower replication and highest APM score
9	epigenetics: promoter methylation for any given gene	“Univariate Analyses: Plot Data:” expression of a given gene versus its methylation (X and Y “Data Type”) within a given “Cell Line Set” or across datasets (independent datasets can be tested for missing “Data Type” and confirmation)	Figure S1	promoter methylation is a driver for gene expression (NAPY genes; SLFN11; MGMT; SMARCA1; CGAS)
10	gene amplification and deletions for any given gene	“Univariate Analyses: Plot Data:” expression of a given gene versus copy number (X and Y “Data Type”) within a given “Cell Line Set” or across datasets (independent datasets can be tested for validation and missing “Data Type”)	Figures 1, 3, and S1	MYC genes and other oncogenes are often driven by copy number variation (CNV)
11	integrate and complement different datasets for common cell lines	“Univariate Analyses: Plot Data:” plot different parameters (“Data Type for” genomic or drug response) across “Cell Line Sets” (X and Y) to counter missing data in one dataset	Figures 1, 2, and 6	drug response data in one dataset can be correlated with genomics of another dataset
12	genomic pathway discovery (coregulated genes and microRNAs)	“Univariate Analyses: Plot Data:” expression of a given gene (X or Y “Data Type”) within a given dataset or across datasets; also use the “Compare Patterns” tab	Figures 5, 6, S2, and S3	ASCL1 and YAP1 are integrated in tight genomic networks connected with the NOTCH pathway
13	discover determinants of drug response and targeted drug delivery	“Univariate Analyses: Plot Data: Compare Patterns:” coregulated genes for a given gene (X or Y) within a given “dataset” (independent datasets can be tested for confirmation)	Figures 6 and S6	resistance of YAP1 cell lines to chemotherapy and potential response to mTOR and immune checkpoint inhibitors; NAPY-specific antigen cell surface biomarkers
14	validate genomic determinant of drug response	“Univariate Analyses: Plot Data: Compare Patterns:” plot genomic parameter versus drug (X or Y “Data Type”)	Figure 6	validation of SLFN11 for DNA damaging chemotherapy
15	examine drug correlations: COMPARE analyses	“Univariate Analyses: Plot Data: Data Type:” drug versus drug (X or Y); also select “Compare Patterns” to identify drug-drug correlations	Figure S1	cell lines sensitive to etoposide are cross-sensitive to topotecan
16	multivariate models of drug response and genomic features	“Multivariate Analyses: Cell Line Set; Response Data Type; Predictor Data Type/s; Predictor Identifier:” enter drug and genomic parameters to be tested as identifier or use “LASSO” to discover additional non-redundant determinants of response	Figures 5B and 5D; Figure S3E	discover independent omic or drug parameters to build a molecular signature for drug response or gene expression
17	data download	“Univariate Analyses: View Data: Download” tabs or “Multivariate Analyses: Download” tab	Figure 6	allow further in-depth analyses and data download in Excel
18	drug identifier conversion	not applicable	Figures 2E and 2F	allow drug identification across different sources

Set off in quotation marks are the option tabs of SCLC-CellMiner (https://discover.nci.nih.gov/SclcCellMinerCDB/).