 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Mar 12, 2018 |
| Title |
Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative [cohort 3273] |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
|
| Summary |
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.
|
| |
|
| Overall design |
Illumina paired-end RNA-sequencing and expression estimation were performed for two cohorts: a training cohort with 405 samples (GSE81538), and a validation cohort with 3273 samples (of which 136 cases have technical replicates) (GSE96058). A comprehensive histopathological evaluation was performed on the 405-cohort to estimate inter-pathologist variability on original diagnostic slides as well as on repeat immunostains, leading to consensus scores for five clinical biomarkers. Subtyping was performed using the PAM50 gene list.
Please note that there are "primary sample" (e.g F30) and "technical replicate" (e.g. F30repl) of the same BioSample. The technical replicates in herein are primarily newly constructed and sequenced libraries from the same RNA extraction as the primary sample, with the remaining replicates being re-sequencings of the same library as the respective primary sample.
Due to the patient consent, Swedish law, the potential that the sequencing data contains personally-identifiable information and hereditary mutations, and the right to privacy, the submitter cannot make available the raw sequence data in a public repository.
|
| |
|
| Contributor(s) |
Brueffer C, Vallon-Christersson J, Grabau D, Ehinger A, Häkkinen J, Hegardt C, Malina J, Chen Y, Bendahl P, Manjer J, Malmberg M, Larsson C, Loman N, Rydén L, Borg Å, Saal LH |
| Citation(s) |
32913985, 32926574, 33937624, 35304506 |
|
Christian Brueffer, Johan Vallon-Christersson, Dorthe Grabau, Anna Ehinger, Jari Häkkinen, Cecilia Hegardt, Janne Malina, Yilun Chen, Pär-Ola Bendahl, Jonas Manjer, Martin Malmberg, Christer Larsson, Niklas Loman, Lisa Rydén, Åke Borg, and Lao H. Saal. Clinical Value of RNA Sequencing-Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative. JCO Precision Oncology 2018;2:1-18. DOI: 10.1200/PO.17.00135
|
| Submission date |
Mar 09, 2017 |
| Last update date |
May 04, 2022 |
| Contact name |
Lao H Saal |
| E-mail(s) |
lao.saal@med.lu.se
|
| Organization name |
Lund University
|
| Department |
Department of Oncology and Pathology
|
| Lab |
Translational Oncogenomics Unit
|
| Street address |
Scheelevägen 2, MV404B2
|
| City |
Lund |
| ZIP/Postal code |
22391 |
| Country |
Sweden |
| |
|
| Platforms (2) |
| GPL11154 |
Illumina HiSeq 2000 (Homo sapiens) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
|
| Samples (3409)
|
|
| This SubSeries is part of SuperSeries: |
| GSE81540 |
Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative [superseries] |
|
| Relations |
| BioProject |
PRJNA378692 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE96058_UCSC_hg38_knownGenes_22sep2014.gtf.gz |
19.8 Mb |
(ftp)(http) |
GTF |
| GSE96058_gene_expression_3273_samples_and_136_replicates_transformed.csv.gz |
564.3 Mb |
(ftp)(http) |
CSV |
| GSE96058_transcript_expression_3273_samples_and_136_replicates.csv.gz |
820.8 Mb |
(ftp)(http) |
CSV |
| Raw data provided as supplementary file |
| Processed data are available on Series record |
|
|
|
|
 |
Less...
More...