The online version of this article (doi:10.1186/s12885-017-3237-1) contains supplementary material, which is available to authorized users.
Bojana Jovanović and Quanhu Sheng contributed equally to this work.
Triple negative breast cancer (TNBC) is a heterogeneous disease that lacks unifying molecular alterations that can guide therapy decisions. We previously identified distinct molecular subtypes of TNBC (TNBCtype) using gene expression data generated on a microarray platform using frozen tumor specimens. Tumors and cell lines representing the identified subtypes have distinct enrichment in biologically relevant transcripts with differing sensitivity to standard chemotherapies and targeted agents. Since our initial discoveries, RNA-sequencing (RNA-seq) has evolved as a sensitive and quantitative tool to measure transcript abundance.
To demonstrate that TNBC subtypes were similar between platforms, we compared gene expression from matched specimens profiled by both microarray and RNA-seq from The Cancer Genome Atlas (TCGA). In the clinical care of patients with TNBC, tumor specimens collected for diagnostic purposes are processed by formalin fixation and paraffin-embedding (FFPE). Thus, for TNBCtype to eventually have broad and practical clinical utility we performed RNA-seq gene expression and molecular classification comparison between fresh-frozen (FF) and FFPE tumor specimens.
Analysis of TCGA showed consistent subtype calls between 91% of evaluable samples demonstrating conservation of TNBC subtypes across microarray and RNA-seq platforms. We compared RNA-seq performed on 21-paired FF and FFPE TNBC specimens and evaluated genome alignment, transcript coverage, differential transcript enrichment and concordance of TNBC molecular subtype calls. We demonstrate that subtype accuracy between matched FF and FFPE samples increases with sequencing depth and correlation strength to an individual TNBC subtype.
TNBC subtypes were reliably identified from FFPE samples, with highest accuracy if the samples were less than 4 years old and reproducible subtyping increased with sequencing depth. To reproducibly subtype tumors using gene expression, it is critical to select genes that do not vary due to platform type, tissue processing or RNA isolation method. The majority of differentially expressed transcripts between matched FF and FFPE samples could be attributed to transcripts selected for by RNA enrichment method. While differentially expressed transcripts did not impact TNBC subtyping, they will provide guidance on determining which transcripts to avoid when implementing a gene set size reduction strategy.
Additional file 1: Table S1. TCGA microarray and RNA-seq comparison. TCGA analysis of TNBC subtype using gene expression obtained from matched fresh-frozen specimens profiled by microarray and RNA-seq. (XLSX 106 kb)12885_2017_3237_MOESM1_ESM.xlsx
Additional file 2: Figure S1. TNBC subtype correlation between RNA-seq and microarray. Correlations to TNBC subtypes do not differ with data sets generated from microarray or RNA-seq platforms. Correlations to TNBC subtypes do not differ with data sets generated from microarray or RNA-seq platforms. Scatterplots show the gene expression correlation values to TNBC subtypes (A) BL1 (B) BL2 (C) M and (D) LAR for paired samples processed by microarray and RNA-seq platforms. (TIFF 1568 kb)12885_2017_3237_MOESM2_ESM.tif
Additional file 3: Table S2. Samples sequenced. Sample type, processing and sequencing. (XLSX 51 kb)12885_2017_3237_MOESM3_ESM.xlsx
Additional file 4: Figure S2. Comparison of read alignment for FF- and FFPE-derived RNA samples sequenced on MiSeq and HiSeq. Comparison of read alignment for FF- and FFPE-derived RNA samples sequenced on MiSeq and HiSeq. Distribution of unmapped, on-target and off-target (intronic and intergenic) reads for individual FF and FFPE paired samples sequenced on the (A) HiSeq or (C) MiSeq. Boxplots shows the number of mapped reads from FF an FFPE samples sequenced on the (B) HiSeq or (D) MiSeq. (E) Boxplot show distribution of unmapped reads (%) from FF and FFPE samples obtained from new (<4 y) or old (>10 y) samples. (TIFF 833 kb)12885_2017_3237_MOESM4_ESM.tif
Additional file 5: Figure S3. Specimen age correlation. Tumor specimen age decreases transcript correlation of sequenced FF- and FFPE-derived RNA samples. Tumor specimen age decreases transcript correlation of sequenced FF- and FFPE-derived RNA samples. Boxplots show correlation (Spearman) for (A) all transcripts or (B) non-differential transcripts between matched FF and FFPE specimens by sequencing method and age of sample (New <4y, Old >10y). (TIFF 606 kb)12885_2017_3237_MOESM5_ESM.tif
Additional file 6: Figure S4. Differential expression of paired samples using MiSeq. Comparison of gene expression from paired FF- and FFPE-derived RNA samples before and after differential transcript removal. Comparison of gene expression from paired FF- and FFPE-derived RNA samples before and after differential transcript removal. Heatmap displays (A) unsupervised hierarchical clustering, (B) sample-wise correlation coefficients and (C) principal component analysis (PCA) for all transcripts (n = 27,577) or (D) hierarchical clustering (E) sample-wise correlation coefficients and (F) PCA for transcripts remaining after removal of differentially expressed genes between FF and FFPE (n = 15,624). (TIFF 3206 kb)12885_2017_3237_MOESM6_ESM.tif
Additional file 7: Table S3. Differential gene expression. Differentially expressed genes between 21 paired FF- and FFPE-derived RNA samples from TNBC tumors. (XLSX 2255 kb)12885_2017_3237_MOESM7_ESM.xlsx
Additional file 8: Table S4. TNBC subtype comparisons after removal of differentially expressed genes. TNBC subtype comparison between FF and FFPE samples after differential gene removal. (XLSX 21 kb)12885_2017_3237_MOESM8_ESM.xlsx
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- Comparison of triple-negative breast cancer molecular subtyping using RNA from matched fresh-frozen versus formalin-fixed paraffin-embedded tissue
Robert S. Seitz
Kasey D. Lawrence
Stephan W. Morris
Lance R. Thomas
David R. Hout
Brock L. Schweitzer
Jennifer A. Pietenpol
Brian D. Lehmann
- BioMed Central
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