Project description:We report transcriptomes from 430 single glioblastoma cells isolated from 5 individual tumors and 102 single cells from gliomasphere cells lines generated using SMART-seq. In addition, we report population RNA-seq from the five tumors as well as RNA-seq from cell lines derived from 3 tumors (MGH26, MGH28, MGH31) cultured under serum free (CSC) and differentiated (FCS) conditions. This dataset highlights intratumoral heterogeneity with regards to the expression of de novo derived transcriptional modules and established subtype classifiers. Operative specimens from five glioblastoma patients (MGH26, MGH28, MGH29, MGH30, MGH31) were acutely dissociated, depleted for CD45+ inflammatory cells and then sorted as single cells (576 samples). Population controls for each tumor were isolated by sorting 2000-10000 cells and processed in parallel (5 population control samples). Single cells from two established cell lines, GBM6 and GBM8, were also sorted as single cells (192 samples). SMART-seq protocol was implemented to generate single cell full length transcriptomes (modified from Shalek, et al Nature 2013) and sequenced using 25 bp paired end reads. Single cell cDNA libraries for MGH30 were resequenced using 100 bp paired end reads to allow for isoform and splice junction reconstruction (96 samples, annotated MGH30L). Cells were also cultured in serum free conditions to generate gliomasphere cell lines for MGH26, MGH28, and MGH31 (CSC) which were then differentiated using 10% serum (FCS). Population RNA-seq was performed on these samples (3 CSC, 3 FCS, 6 total). The initial dataset included 875 RNA-seq libraries (576 single glioblastoma cells, 96 resequenced MGH30L, 192 single gliomasphere cells, 5 tumor population controls, 6 population libraries from CSC and FCS samples). Data was processed as described below using RSEM for quantification of gene expression. 5,948 genes with the highest composite expression either across all single cells combined (average log2(TPM)>4.5) or within a single tumor (average log2(TPM)>6 in at least one tumor) were included. Cells expressing less than 2,000 of these 5,948 genes were excluded. The final processed dataset then included 430 primary single cell glioblastoma transcriptomes, 102 single cell transcriptomes from cell lines(GBM6,GBM8), 5 population controls (1 for each tumor), and 6 population libraries from cell lines derived from the tumors (CSC and FCS for MGH26, MGH28 and MGH31). The final matrix (GBM_data_matrix.txt) therefore contains 5948 rows (genes) quantified in 543 samples (columns). Please note that the samples which are not included in the data processing are indicated in the sample description field.

Project description:PURPOSE In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. METHODS In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. RESULTS Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). CONCLUSION Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

Project description:This SuperSeries is composed of the SubSeries GSE81538 [cohort 405] and GSE96058 [cohort 3273] linked to below. PURPOSE In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. METHODS In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. RESULTS Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). CONCLUSION Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

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Single cell RNA-seq of primary human glioblastomas

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