NanoString nCounter with the Oxford Classifier of Carcinoma of the Ovary
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PROVIDER: PRJNA635505 | ENA |
REPOSITORIES: ENA
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Project description:Using RNA-seq, we recently developed the 52-gene-based Oxford Classifier of Carcinoma of the Ovary (Oxford Classic, OxC) for molecular stratification of serous ovarian cancers (SOCs) based on the molecular profiles of their cell-of-origin in the fallopian tube epithelium. Here, we developed a 52-gene NanoString panel for the OxC to test the robustness of the classifier. We measured the expression of the 52 genes in an independent cohort of prospectively collected SOC samples (n = 150) from a homogenous cohort who were treated maximal debulking surgery and chemotherapy. We performed data mining of published expression profiles of SOCs and validated the classifier results on tissue arrays comprising 137 SOCs. We found evidence of profound non-genetic heterogeneity in SOC. ~20% of SOCs were classified as epithelial-mesenchymal-transition-high (EMT-high) tumors, that were associated with poor survival. This was independent of established prognostic factors such as tumor stage, tumor grade and residual disease after surgery (HR = 3.3, p = 0.02). Mining expression data of 593 patients revealed a significant association between the EMT scores of tumors and the estimated fraction of alternatively activated macrophages (M2) (p < 0.0001) suggesting a mechanistic link between immunosuppression and poor prognosis in EMT-high tumors. The OxC-defined EMT-high serous ovarian cancers carry particularly poor prognosis independent of established clinical parameters. These tumors are associated with high frequency of immunosuppressive macrophages suggesting a potential therapeutic target to improve clinical outcome.
2020-12-14 | GSE151335 | GEO
Project description:Purpose: We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens. Methods: Tumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n=63). The training and validation datasets accurately classified 87% and 93% of samples, respectively. Results: We have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier. Conclusions: The NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.
2020-11-03 | GSE160693 | GEO
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2013-12-30 | E-GEOD-49600 | biostudies-arrayexpress
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