13 research outputs found

    Additional file 1: Table S1. of Response and survival of breast cancer intrinsic subtypes following multi-agent neoadjuvant chemotherapy

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    Cox model DRFS analyses including intrinsic subtype in all patients from the MDACC-based cohort (GSE25066). Table S2. Cox model DRFS analyses including ROR-P in all patients from the MDACC-based cohort (GSE25066). Table S3. Cox model DRFS analyses including intrinsic subtype in patients that achieved a pCR from the MDACC-based cohort (GSE25066). Table S4. Cox model DRFS analyses including ROR-P in patients that achieved a pCR from the MDACC-based cohort (GSE25066). Table S5. Cox model DRFS analyses including ROR-P in patients with residual disease from the MDACC-based cohort (GSE25066). Table S6. Distribution of the PAM50 subtypes within the TNBCtype groups and vice versa. Table S7. Association of the TNBCtype subtypes with chemotherapy response in triple-negative breast cancer. Figure S1. CONSORT diagram of the various cohorts evaluated in this study. Figure S2. Kaplan-Meier distant relapse-free survival analysis in MDACC-based (GSE25066 [13]) dataset set. (A) Survival outcomes of the ROR-P groups in all patients. (B) Survival outcomes of the ROR-P groups in patients with clinically node-negative disease. Figure S3. Levels of ESR1 across TNBCtype ESR1-low group, TNBCtype ESR1-high group and ER+ group. Median expression of ESR1 in the PAM50 training dataset reported in Parker et al. [24] has been set to zero. Figure S4. Distribution of the TNBCtype subtypes and ESR1-high group within the PAM50 subtypes in TNBC. Figure S5. Distribution of the TNBCtype subtypes and ESR1-high group within the PAM50 + Claudin-low subtypes in TNBC. Figure S6. Training and testing gene expression-based models predictive of pCR in all patients. Figure S7. Training and testing gene expression-based models predictive of pCR in patients with Basal-like disease. Figure S8. Training and testing gene expression-based models predictive of pCR in patients with luminal (A/B) disease. (DOCX 819 kb

    Comparison of gene expression between FF and FFPE samples.

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    <p>(A) Correlation plots of gene expression in FF-FFPE pairs. In general, the correlation was high (R<sup>2</sup>~0.9), with the exception of the FF_AA6361-FFPE_AA6365 pair, where the FFPE sample was highly degraded. Higher variability was observed for more degraded samples. (B) Results of the principal component analysis. FF-FFPE pairs clustered together. The most degraded sample (FFPE_AA6365) was not included in the plot.</p

    Degradation quality metrics.

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    <p>(A) Gene coverage heatmap. More degraded regions are depicted blue. All samples were affected at the 5’ end of the gene body but this effect was more prominent for FFPE samples. The most degraded FFPE sample (AA6365) also showed degradation at the 3’ end and across the gene body. (B) Line graphs (FF, blue; FFPE, red) showing the mean per-base coverage of RNA transcripts for all paired samples. Strong coverage unevenness was observed for the most degraded sample (FFPE_AA6365).</p

    Boxplots of PSI values for intron retention events.

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    <p>Results for FF samples are shown in blue and those for FFPE samples in red. The PSI value was defined as the number of reads supporting the inclusion divided by the number of reads supporting the inclusion or the exclusion. The median PSI value for intron retention events was higher in FFPE samples, suggesting a greater abundance of transcripts with unspliced introns, such as pre-mRNAs or linc-RNAs.</p

    Number of mismatches across the read length.

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    <p>Mismatch profiles changed dramatically mainly due to G>A and C>T changes, which were substantially more frequent in FFPE samples (top pink and blue lines). Sample FFPE_AA6365, which was highly degraded, showed a totally different pattern, not matching with any other sample.</p
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