Biomarker discovery for colon cancer using a 761 gene RT-PCR assay

Abstract

Background: Reverse transcription PCR (RT-PCR) is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE) clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis) and the likelihood of tumor response to standard chemotherapy regimens (prediction). We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application.

Results: RNA was extracted from formalin fixed paraffin embedded (FPE) tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery.

Conclusion: We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of genes analyzed while maintaining the high specificity, sensitivity and reproducibility that are characteristics of RT-PCR. Biomarkers discovered using this approach can be transferred to a clinical reference laboratory setting without having to re-validate the assay on a second technology platform.

Figure 1

Effect of increasing priming complexity in reverse transcription using high quality RNA template. Eight gene specific primer (GSP) pools each containing from 94 to 96 unique primers were used to prime separate RT-PCR reactions with high quality commercial RNA template. These 8 GSP pools were then combined to make a single GSP pool that was used to prime one RT-PCR reaction using the same template RNA. Both priming methods were performed twice and the average C_T value for each gene was determined for this analysis. The solid line represents the least squares line fit and the dashed line represents the line of concordance.

Figure 2

Effect of increasing priming complexity reverse transcription using FPE tissue RNA template. Eight GSP pools containing from 94 to 96 unique primers were prepared. Two pools were selected at random to prime separate RT-PCR reactions using RNA from FPE tissue. The 8 GSP pools were then combined to make a single GSP pool that was used to prime one RT-PCR reaction using the same FPE RNA template. The data therefore represents concordance between primings for the subset of gene assays represented with those two GSP pools. Both priming methods were performed twice and the average C_T values determined for this analysis. The solid line represents the least squares line fit and the dashed line represents the line of concordance.

Figure 3

Boxplots for reference sample CT distributions of the 761 genes at time points throughout the study. These boxplots show averaged expression values of all 761 genes over the duration of the study. The bottom edge of the box represents the 25^th percentile of the data while the top edge of the boxplot represents the 75^th percentile. The line inside the box represents the 50^th percentile of the data or the median and the symbol + represents the mean. The distance between the 25^th and 75^th percentiles is defined as the Inter Quartile Range (IQR). A whisker extends from the upper edge of the box to the largest value that is inside a distance of 1.5*IQR. Similarly, a whisker extends from the lower edge of the box to the smallest values inside a distance of 1.5*IQR. Observations outside the fences of 1.5*IQR are marked by a square. The first boxplot represents data from the 3 assay repeats used to set a baseline, all other boxplots represent a single assay run. Overall, the C_T distributions of the 761 genes assayed in the reference sample were stable throughout the study.

Figure 4

Average C_T value versus standard deviation for the reference RNA sample. Plotted here is the average C_T value for each gene versus the standard deviation (SD) for each gene from a total of 11 assay runs. The "△" symbols represent genes where a single run had an empty assay well and was assigned a value of zero. The "□" symbol represents a gene where one out of the 11 runs resulted in a spurious "failed" well and was assigned a C_T value of 40. These data show great consistency over the study.

Figure 5

Comparison of RT-PCR results for the reference RNA sample assayed on two different machines. Two separate RT-PCR reactions were set up and assayed on different ABI 7900 HT machines, using the reference RNA sample. The graph represents paired raw C_T values for each of the 761 assays obtained from each of these two machines. The solid line represents the least squares line fit and the dashed line represents the line of concordance.

Figure 6

Comparison of paired whole plate average C_T values for all patients. Expression analysis for 761 unique genes required each patient RNA sample to be divided between two 384 well plates. Shown here is the average raw C_T value for all wells of data obtained for plate one plotted against the average raw C_T obtained for plate two, for each patient. The patient sample which appears to be an outlier was re-assayed through RT-PCR and on repeat analysis, fell into alignment with the other samples. The solid line represents the least squares line fit and the dashed line represents the line of concordance.

Figure 7

Comparison of paired raw C_T values for reference normalization gene RPLPO for all patients. RPLPO was one of 6 normalization genes. The graph shown here represents the paired raw C_T values for RPLPO on both assay plates for all patients in the study. Paired plates for each patient sample were assayed on the same ABI 7900 HT machine. The solid line represents the least squares line fit and the dashed line represents the line of concordance.

Figure 8

Proliferation. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Figure 9

Epithelial/secreted. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Figure 10

Focal adhesion. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Figure 11

Stromal response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Figure 12

Early response. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.

Figure 13

Immune/interferon-inducible genes. Gene groups identified by clustering analysis. Clustering analysis was performed using the 1-Pearson's R distance and unweighted pair-group average amalgamation method. Clustering was performed using all 761 genes. Figures 8-13 represent selected clusters from the entire 761 gene dendogram.