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Published on: April 7, 2017
Analysis of multiple sarcoma expression datasets: implications for classification, oncogenic pathway activation and
Panagiotis A Konstantinopoulos1, Elena Fountzilas, Jeffrey D Goldsmith
1Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Plos One
|April 7, 2010
Summary
A new 170-gene predictor accurately reclassifies unclassified soft tissue sarcomas (STS), including malignant fibrous histiocytoma (MFH). This molecular classification aids diagnosis and predicts chemotherapy response based on pathway activation.
Area of Science:
- Oncology
- Genomics
- Molecular Diagnostics
Background:
- Diagnosis of soft tissue sarcomas (STS) is challenging, with many tumors classified as not-otherwise-specified (NOS) or malignant fibrous histiocytoma (MFH).
- These unclassified or controversially categorized STS lack clear therapeutic guidance.
Purpose of the Study:
- To identify a molecular predictor for classifying unclassifiable STS.
- To reclassify MFH and NOS sarcomas using the developed predictor.
- To assess oncogenic pathway activation and chemotherapy response in relation to STS subtypes.
Main Methods:
- Analysis of 5 independent microarray datasets (325 tumor arrays).
- Development and validation of a 170-gene predictor for STS classification.
- Genome-wide hierarchical clustering and Subclass-Mapping to assess molecular similarity.
- Application of Bayesian models for pathway activation and chemotherapy response prediction.
- Validation in 15 paraffin-embedded samples using DASL profiling.
Main Results:
- A 170-gene predictor was developed and validated with 80-85% accuracy across datasets.
- Most MFH and NOS tumors were reclassified into distinct subtypes like leiomyosarcomas, liposarcomas, and fibrosarcomas.
- Molecular classification revealed previously unrecognized tissue differentiation lines.
- Distinct oncogenic pathway activation patterns were identified for different STS subtypes.
- Reclassified MFH tumors showed pathway activation patterns similar to their predicted subtypes, associated with predicted chemotherapy resistance.
Conclusions:
- Molecular profiling of STS can significantly improve diagnostic accuracy for unclassified tumors.
- A validated predictor aids in identifying distinct tissue differentiation lines within STS.
- Assessment of oncogenic pathway activation provides insights into therapeutic management and chemotherapy response.