Table 1.

Accuracy of classification of the nine NCI-60 panel cancer types using various profiling technologies^*

		Adjusted P value threshold for gene selection
Genes analyzed	Gene expression profiling assays	0.25	0.1	0.05	0.02	0.01	0.005	0.001
380 MDR-linked genes	TLDA	0.73	0.69	0.71	0.68	0.66	0.68	0.69
	HG-U133A array	0.71	0.64	0.66	0.66	0.61	0.59	0.61
ABC transporter genes	TLDA	0.59	0.61	0.53	0.42	0.46	0.44	0.29
	HG-U133A array	0.37	0.39	0.36	0.37	0.31	0.18†	0‡
	SybrGreen-based qRT-PCR	0.43	0.45	0.40	0.40	0.32	0.23	0.25
	Biomark 48.48	0.53	0.53	0.42	0.42	0.47	0.46	0.44
SLC genes	HG-U133A array	0.61	0.63	0.64	0.63	0.63	0.54	0.58
14,500 genes	HG-U133A array	0.20	0.20	0.22	0.25	0.31	0.32	0.47

*Seventy-one percent of the cell lines were correctly classified at P = 0.05 and 69% at P = 0.001 with the TLDA 380 gene MDR set, whereas the expression profiles of the same genes obtained from HG-U133A oligonucleotide microarray analysis classified the 60 cancer cell lines with only 66% accuracy at P = 0.05 and 61% at P = 0.001. Confining the analysis to only ATP-Binding Cassette (ABC) transporter genes, some of the major mediators of multidrug resistance in cultured cells, generates less accurate classification. Only 53% of cell lines were correctly classified at P = 0.05 and 29% at P = 0.001, whereas microarray analysis of the same genes provides the worst results, with 36% accuracy at P = 0.05, with no classification achievable at P = 0.001. ABC transporter gene expression profiling using Sybr Green-based qRT-PCR provides intermediate results with 40% of cell lines properly classified at P = 0.05 and 25% at P = 0.001. Using Biomark 48.48, a high-throughput nanofluidic TaqMan-based qRT-PCR platform, the classification accuracy reaches 44% at P = 0.001. Solute carriers belong to a large family of uptake transporters that are also important MDR mediators. Their expression profiles measured by HG-U133A provide more accurate classification than the ABC transporter genes, with 64% at P = 0.05 and 58% at P = 0.001. Interestingly, the expression profiles of the 14,500 genes on the HG-U133A array do not improve the classification accuracy of the 9 cancer types, as only 22% of the cancer cell lines are correctly classified at P = 0.05, whereas an accuracy of 47% is achieved at P = 0.001. The reason for this apparent paradox is that at lower statistical significance (P < 0.05), more genes are being analyzed and the background noise is greater than at P < 0.001, which reduces the accuracy.

^†Three samples unclassified.

^‡Fifty-four samples unclassified.