Proteomic patterns associated with response to breast cancer neoadjuvant treatment

Abstract Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometry‐based proteomic approach and analyzed a breast cancer cohort of 113 formalin‐fixed paraffin‐embedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumor‐adjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of post‐treatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ER‐positive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy

mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Abstract Background The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. Results In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3′-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. Conclusions Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2–3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies

Additional file 3: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Table S3. Comparison of the mean % of reads mapped on exons in other studies. (PDF 147 kb

Additional file 1: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Table S1. RNA integrity number for all samples. (PDF 212 kb

Additional file 2: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Table S2. Number of exonic and intronic reads. (PDF 167 kb

Additional file 5: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Figure S2. Comparison of fold-changes measured for FFPE samples vs. matched FF samples using mRNA-seq. (A) Scatter plot for the expression fold changes (log2 scale) of genes measured in T1 vs.T3, obtained from FF samples (x-axis) compared to matched FFPE samples (y-axis) by mRNA-seq protocol (purple). r-square and correlation coefficient are presented at the plot. B) Scatter plot for the expression fold changes (log2 scale) of genes measured in T2 vs.T3, obtained from FF samples (x-axis) compared to matched FFPE samples (y-axis) by mRNA-seq protocol (purple). r-square and correlation coefficient are presented at the plot. (PDF 936 kb

Additional file 6: of mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues

Figure S3. Expression of non-coding RNAs in FFPE samples by mRNAseq and RiboZero protocols. (A) Scatter plot of the expression levels of annotated lincRNAs as measured on T1 FFPE sample by mRNAseq (x-axis) versus RiboZero protocol (y-axis). Correlation coefficients between the two protocols for the expression of these lincRNAs are indicated at the fig. (B) Same as (A) for miRNAs expression. (PDF 185 kb