Quality of Cancer RNA Samples Is Essential for Molecular Classification Based on Microarray Results Application

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1 Quality of Cancer RNA Samples Is Essential for Molecular Classification Based on Microarray Results Application Pathology Author Jenny Xiao Agilent Technologies, Inc. Deer Creek Road MC U-7 Palo Alto, CA 9 USA Abstract There are four subgroups (normal-like, luminal, basallike, or Erb-B+/HER-) of breast cancer, as identified by gene expression profiling, which have a different prognosis and response to chemotherapy [, ]. Therefore, correct molecular identification of the subgroups is essential for breast cancer therapy. Recently, microarray-based gene expression analysis has been used for the molecular classification of breast cancer samples [] gaining widespread acceptance in its determination of subclassification of these tumors. However, isolating enough intact mrna, or even total RNA, from patients can be problematic. Degraded RNA can compromise the results and lead to misclassification. In this study, RNA samples from over breast cancer patients were compared, in a two-color microarray experiment, to a common reference sample on -mer oligo microarrays. The microarray results were then clustered into different subtypes. The quality of total RNA samples was tested using an Agilent Bioanalyzer prior to labeling and hybridization. Biochemical and molecular markers confirmed that more than 9% of the cancer samples were clustered correctly and had a clear breast cancer molecular subtype. However, some RNA samples used showed partial degradation on the Bioanalyzer trace. Their microarray results and analysis visually looked acceptable, however, instead of clustering with others of its subtype, they were clustered together, and we were able to remove or at least qualify the compromised profiles. These studies provide further proof that gene expression profiling for breast cancer classification is a valuable tool to the molecular pathologist but also suggest that researchers need to consider the potential risks of obtaining misleading classification results when RNA samples of lower quality are used. Strategies of how to avoid these pitfalls are discussed. Introduction Most researchers are aware that RNA samples of high quality are essential for good microarray experiments. For this reason, the Agilent Bioanalyzer is often used to test RNA quality prior to labeling and hybridization. The Bioanalyzer histogram of total RNA samples shows a low baseline and two clear ribosome RNA peaks (8s and 8s) indicating there is no degradation. However, it is hard to extract intact RNA from certain tissues and cell lines, for instance, pancreatic tissue is rich with RNAse which leaves the RNA susceptible to degradation. Improper handling, such as introducing RNAse contamination or storing biological samples at elevated temperature or lack of equipment in the operating rooms where biopsies are collected also put clinical samples at higher risk for degradation. Researchers must sometimes use RNA samples of lower quality in hopes of obtaining some valuable results. These results may give correct answers for the expression patterns of certain genes. However, when the microarray data are used for molecular identification purposes, it may lead to incorrect conclusions.

2 In this study, four partially degraded RNA samples were compared in a two-color microarray experiment to a common reference sample on Agilent oligonucleotide microarrays along with more than good breast cancer RNA samples. Cluster analysis has shown that instead of clustering with others of its subtype, they were all clustered together as a separate subgroup. Materials and Methods RNA Isolation Total RNA was isolated from mg of Snapfrozen Human breast tumor tissues by Qiagen RNeasy Midi Kit (Catalog Number 7). The quality of the isolated total RNA was checked using the RNA Nano LabChip kit on an Agilent Bioanalyzer (part number -7 and G9BA). Cluster Analysis The Genepix. software package was used for gridding and feature extraction. Lowess normalization algorithm was performed using University of North Carolina microarray databases. Experimental results were clustered using Eisen's gene clustering program. and TreeView.. Results and Discussion Bioanalyzer Results Seven RNA tumor samples were collected and tested on an Agilent Bioanalyzer. The Bioanalyzer histograms indicated that RNA from tumor samples,, and 7 were good quality, where RNA from tumors,,, and were degraded at different levels (Figures a g). RNA Labeling Purified total RNA samples were amplified and labeled by Agilent Low RNA Input Linear Amplification Kit (product number 8-), following the procedures described in the User's Manual. Cyanine - or Cyanine -CTP were purchased from Perkin-Elmer/NEN (NEL 8 and 8). Microarray and Hybridization Process Cyanine - and Cyanine -labeled crna were hybridized on an Agilent Human Oligo Microarray (Product number GB) following the Agilent oligonucleotide microarray hybridization user's manual using the Agilent in situ Hybridization Plus kit (product number 8-8). The hybridized microarray slides were disassembled in SSC,.% Triton X-, washed first with SSC,.% Triton X- for minutes at room temperature, then with. SSC,.% Triton X- for minutes on ice, then dried using a nitrogen-filled air gun. Processed microarray slides were then scanned by an Axon scanner.

3 Figure a. Tumor- (Intact RNA) Figure e. Tumor- (Degraded RNA) 7 7 Figure b. Tumor- (Intact RNA) Figure f. Tumor- (Partially degraded RNA) Figure c. Tumor- (Degraded RNA) Figure g. Tumor-7 (Intact RNA) Figure d. Tumor- (Partially degraded RNA)

4 Microarray Images The seven tumor RNA samples were labeled with Cyanine -CTP in parallel regardless of their quality. A common reference sample was labeled with Cyanine - CTP. The labeled Cyanine -tumor samples were then compared with the Cyanine - reference RNA on -mer oligonucleotide microarrays. Labeled RNA sample from tumor 7 was hybridized on two replicated microarrays to test the accuracy of the cluster algorithm. All eight microarray images were very similar upon visual comparison. Two microarray images are presented as examples (Figures a and b), showing results of the intact RNA vs. the degraded RNA. Figure a. Sample (intact RNA sample) vs. Reference Figure b. Sample (degraded RNA sample) vs. Reference

5 Cluster Results After feature extraction,.txt files from the eight microarrays (seven sample pairs) were clustered together along with microarray results from other breast cancer samples. Tumor samples 7 represent different subtypes and should be respectively clustered into different regions with their proper subtypes (Figure ). Among them, Tumors 7 and 7-repeated were clustered together indicating the profiles were very similar to each other. Tumors,, 7, and 7-repeated were clustered into correct regions and had clear breast cancer molecular subtype: Tumors and are blue, representing Luminal subtype; Tumor 7 and 7-repeated are in red representing Basal-like subtype. Tumors were clustered together in black as "weak signals" instead of going to their proper subtype regions. Basal cluster Proliferation cluster Her+ cluster Luminal cluster Figure. Tumor samples 7 clustered. Note that the displays on the right are exploded sectional views of the total display shown on the left.

6 Conclusion Microarray data plays an important role in the study of breast cancer classification. The results in this study, including Bioanalyzer data, microarray images, feature extracted results, clustering results, and biological expectations, suggest that researchers need to consider the potential risks of obtaining misleading classification results. For some reasons, researchers decide that even when the RNA quality is tested to be low, they still want to proceed to microarray experiments. In this case, the classification results require confirmation by other supporting biological and clinical data. If the above information is unavailable, the microarray data should be removed from the final cluster to avoid misleading conclusions. For More Information For more information on our products and services, visit our Web site at References. T. Sorlie, C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, et. al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. Sep ; 98(9): T. Sorlie, R. Tibshirani, J. Parker, T. Hastie, J.S. Marron, A. Nobel, et. al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. Jul 8; (): 88.. L. J. Van't Veer, H. Dai, et al. () Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer, Nature,. Agilent Technologies Bioresearch Solutions Unit Deer Creek Rd Palo Alto, CA 9 dna_microarray@agilent.com Agilent Gene Expression Microarrays Web site Agilent Lab-on-a-Chip Web site Acknowledgements We would like to thank Dr. Charles Perou and Dr. Zhiyuan Hu at the Departments of Genetics and Pathology Lineberger Comprehensive Cancer Center, the University of North Carolina at Chapel Hill for providing all the technical data. Agilent shall not be liable for errors contained herein or for incidental or consequential damages in connection with the furnishing, performance, or use of this material. Information, descriptions, and specifications in this publication are subject to change without notice. Please register online with Agilent to receive product updates at: Agilent Technologies, Inc. Printed in the USA May, 989-8EN