Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome

By R. Tothill, A. Tinker, J. George, Robert Brown, S. Fox, S. Lade, Daryl Johnson, M. Trivett, D. Etemadmoghadam, Bianca Locandro and 8 more. Clinical Cancer Research, 2008

1,415 citations recorded by the source corpus.

Purpose: The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. Experimental Design: Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results. Results: Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends. Conclusion: Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

Subfields: Obstetrics and Gynecology, Pulmonary and Respiratory Medicine, Reproductive Medicine

View Paper: https://www.semanticscholar.org/paper/0195896baddb78323ae90b5646ec3ccfee946d7a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Obstetrics and Gynecology

Daniel C. Koboldt

In Pulmonary and Respiratory Medicine

In Reproductive Medicine

Citations in the corpus

Evaluating deep variational autoencoders trained on pan-cancer gene expression (2017, methodology): “The mesenchymal subtype is deﬁned by poor prognoses, overexpression of extracellular matrix genes, and increased desmoplasia, while the immunoreactive subtype displays increased survival and immune cell inﬁltration [24, 25].”
Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. (2012, positive): “Mean classification error when applying the 100-gene signature was 8% (CLOVAR Differentiated, 9%; CLOVAR Immunoreactive, 9%; CLOVAR Mesenchymal, 6%; CLOVAR Proliferative, 8%), which indicates that classification can be performed with small error rates using the 100-gene signature.”
Metagenomic biomarker discovery and explanation (2011, background): “Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample) [1-4], and recent metagenomic assays have shown that human microbial communities ca”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, neutral): “[87], ovarian cancer [88] and sarcomas [89].”