Comparison of Triple Negative Breast Cancer between Asian and Western Data Sets

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1 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops Comparison of Triple Negative Breast Cancer between Asian and Western Data Sets Lee H. Chen Bioinformatics and Biostatistics Core, Research Center for Medical Excellence, National Taiwan University Liang-Chuan Lai Graduate Institute of Physiology, National Taiwan University Chuhsing K. Hsiao Department of Public Health, Institute of Epidemiology & Research Center for Gene, Environment, and Human Health, National Taiwan University Pei-Chun Chen Bioinformatics and Biostatistics Core, Research Center for Medical Excellence, National Taiwan University Mong-Hsun Tsai Institute of Biotechnology, National Taiwan University Wen-Hung Kuo Department of Surgery, National Taiwan University Hospital King-Jen Chang Department of Surgery, National Taiwan University Hospital Eric Y. Chuang Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University Abstract The prognosis of triple negative breast cancer remains poor compared with other subtypes of breast cancer, such as hormone receptor positive or human epidermal growth factor receptor 2 positive breast cancers, for the application of possible adjuvant therapy of hormone therapy for hormone receptor positive breast cancer and target therapy for human epidermal growth factor receptor 2 positive breast cancers. Searching for adjuvant therapy for triple negative breast cancer is still the main goal for clinical practice. Moreover, it is believed the prognosis seems different between Asian and western people. Here, we focused on the signature of breast cancer to differentiate different races by comparing previous published literatures. By this method, obvious cluster of triple negative breast cancer formed, but minor clusters between Asian and western samples were also noted. The slight difference might be the first step to solve the dilemma of lacking treatment of triple negative breast cancer. 1. Introduction Breast cancer is the most common female solid tumor in Taiwan, and is the top five leading cause of cancer related death among women in Taiwan. It can be classified as different subtypes on the basis of cellular morphology and the presence of several receptors, i.e. estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (erbb2/her-2). Triple negative breast /10/$ IEEE 489

2 cancer (TNBC), that is ER negative, PR negative, and HER-2 negative, comprises approximately 10 17% of breast cancer cases, without appropriate adjuvant therapy till now. And according to the clinical response, it is believed that there might be slight differences of TNBC between Asian and western people [1, 2]. Based on previous studies, different subtypes of breast cancer can be matched well to its gene expression profiles. The signature of breast cancer gene expression profile data has been identified across different platforms by different research groups.[3-6] One hundred and eight-five breast cancer patients have been collected and followed in Taiwan since For possible difference between Asian and Western people, comparison of gene expression profile was performed to identify essential difference of genes. 2. Material and method Table 1 Summary statistics for patient profile (n=185) Clinical characteristics Sample size (n) Percentage Clinical characteristics Sample size (n) Percentage Histological Types Stage Luminal A 49 26% Stage I 33 18% Luminal B 31 17% Stage IIA 42 23% HER % Stage IIB 42 23% Triple negative 51 28% Stage IIIA 29 16% Others 19 10% Stage IIIB 3 2% Grade Stage IIIC 26 14% Grade % Stage IV 10 5% Grade % Recurrence Grade % Yes 40 22% Missing data 13 7% No % One hundred and eighty-five female breast cancer samples were collected from National Taiwan University Hospital from 1995 to Table 1 summarizes detailed information of patients. Based on the result of immunohistochemistry (IHC) stains of ER and HER-2, that is ER/PR positive HER-2 negative for Luminal A, ER/PR positive HER-2 positive for Luminal B, ER/PR negative HER-2 positive for HER-2, and ER, PR, HER-2 negative for Triple negative, there were 49 luminal A, 31 luminal B, 35 HER-2, and 51 triple negative cases. All these patients received surgery, adequate chemotherapy and adjuvant therapy by protocol. Agilent Human 1Av2 oligo microarray chips were used, while breast cancer samples were dyed as cyt5, reference samples (Stratagene common reference RNA) dyed as cyt3. Log ratio for gene expression after rank invariant lowess normalization was used before formal statistical analysis, as Agilent Company recommended. The data published by van de Vijver et al. [7] with Agilent chips and by Herschkowitz et al., Hennessy et al., Parker et al. Perou et al. (GEO GSE18229) with Agilent Human 1Av2 oligo microarray chips were used for comparison. Because the data (from van de Vijver et al. [7]) were from different versions of Agilent chips, systemic name of genes were checked to filter overlapping genes with 8843 genes left, while the other (from Herschkowitz et al., Hennessy et al., Parker et al. Perou et al.) with the same version of Agilent chips. Because the choice of reference sample by van de Vijver at al. [7] was mixing of all breast cancers, baseline of our data was corrected to average expression of all breast cancer samples, that is, expression ratio was adjusted to compare between breast cancer and average gene expression, not reference sample originally used. Quantile normalization was applied to the combined data to adjust the possible batch effect. Because of insufficiency of HER-2 and ER IHC status, gene expression profile (ERBB2 and ESR1) was used for simulating HER-2 and ER status. After assigning HER-2 or ER status, combined data were analyzed by 490

3 Figure 1. Gene profiling of triple negative breast cancer is different between Caucasian and Asian populations. A. TNBC samples (marked by yellow: our samples and blue: western samples) were clustered regardless of different data sources. Minor clusters were noted within TNBC cluster. B. Only basal-like breast cancer samples were used in clustering (yellow: our samples, blue: western samples) and it reveals apparent difference between different races. These results indicate the genetic signatures of TNBC share similar characteristic between different races but with still minor differences, which might be the essential differences between races. complete-linkage hierarchical clustering to see the similarity or differences between different races. A list of 506 genes (about 5.7% of the 8843 genes) from the data set of van de Vijver et al. was acquired for hierarchical clustering, whose expression ratio varied at least fourfold from their medians in this sample set in at least fifteen of the samples. Another list of 543 genes (about 2.7% of the genes) combined from the data set of Herschkowitz et al., Hennessy et al., Parker et al. and Perou et al. was 491

4 Figure 2. Gene profiling of triple negative breast cancer is different between Caucasian and Asian populations. A. TNBC samples (marked by yellow: our samples and blue: western samples) were clustered regardless of different data sources. B. Only TNBC samples were used in clustering (yellow: our samples, blue: western samples). These results as figure 1 also indicate different races of TNBC share similar signatures but with still minor differences. acquired for another hierarchical clustering, with at least thirty samples whose expression ratio varied at least fourfold from their medians. While applying HER-2 status by ERBB2 gene expression to the data set from van de Vijver et al. [7], there seems a clear gap between and (Figure 3), when HER-2 positive was assigned to sample whose ERBB2 was greater than 0.081, negative to sample whose ERBB2 expression less than 0.081, a maximal 88% accuracy was achieved applying to our data. There was also an obvious gap in western data that no ERBB2 expression of sample fell on the interval, 492

5 the same criteria was applied to western data to simulate without equivocal judgments. Figure 3. Correlation between ERBB2 expression and HER-2 IHC Similar approaches were applied for another data set to determine the lacking status of ER or HER Results Figure 1 showed that hierarchical clustering was applied to combined data (combined with data from van de Vijver et al.[7]. Figure 1A revealed TNBC samples (marked by yellow: our samples and blue: western samples) were clustered regardless of different data sources, and minor clusters were noted within TNBC cluster. Figure 1B used only TNBC samples to cluster (yellow: our samples, blue: western samples) and it reveals apparent difference between different races. Figure 2 showed similar results with different combined data set (combined with data from Herschkowitz et al., Hennessy et al., Parker et al. Perou et al.). Figure 2A revealed hierarchical clustering of combined data. Patent clustering of TNBC was noted and minor clusters were still obvious within triple negative subtypes. Figure 2B showed only TNBC samples. Different races of TNBC samples were easily differentiated from the result of clusters. Both figures revealed that TNBC samples were clustered regardless of different data sources, but minor clusters matched with different data sources were noted within TNBC cluster, which indicates the characteristic genes expression of TNBC were similar across races, but mild differences still exist, enough to distinguish different minor clusters. could be differentiated. The results suggested the essential differences might exist between different races. Although it might be controversial that the essential differences could be attributed to the batch effect of experiment, samples of the same subtype TNBC were clustered within the same cluster, which indicated that these gene expression profiles might not fluctuate under different batch and environment, and indicated that if batch effect did exist, the cluster of TNBC might not be present due to the confounding batch factors. Because our samples used Agilent chips, to reduce the possible batch effect, meta-analysis was performed on Agilent chips too, one of the comparisons even with the same version of chips. Moreover, by adequate normalization, the probable batch effect was reduced as possible. With this signature of differences, advanced pathway analysis could be performed and the possible mechanism of different prognosis between different races could be studied. Reference [1] C. Lin, et al., "Triple negative breast carcinoma is a prognostic factor in Taiwanese women," BMC Cancer, vol. 9, p. 192, [2] J. Kurebayashi, et al., "The prevalence of intrinsic subtypes and prognosis in breast cancer patients of different races," Breast, vol. 16 Suppl 2, pp. S72-7, Dec [3] T. Sørlie, et al., "Repeated observation of breast tumor subtypes in independent gene expression data sets," Proceedings of the National Academy of Sciences of the United States of America, vol. 100, pp , July 8, [4] C. M. Perou, et al., "Molecular portraits of human breast tumours," Nature, vol. 406, pp , [5] T. Sørlie, et al., "Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications," Proceedings of the National Academy of Sciences of the United States of America, vol. 98, pp , September 11, [6] Z. Hu, et al., "The molecular portraits of breast tumors are conserved across microarray platforms," BMC Genomics, vol. 7, p. 96, [7] M. J. van de Vijver, et al., "A Gene-Expression Signature as a Predictor of Survival in Breast Cancer," N Engl J Med, vol. 347, pp , December 19, Discussion From the dendrogram of hierarchical clustering, TNBC was matched well with gene expression profiles and TNBC samples from Asian and western samples were clustered within same cluster but minor clusters 493