Home » Key Scientific Articles » Functional genomics analysis reveals a MYC signature associated with a poor clinical prognosis in liposarcoma

Functional genomics analysis reveals a MYC signature associated with a poor clinical prognosis in liposarcoma

Significance statement

As scientists and clinicians, we are currently faced with a promising opportunity, yet challenging conundrum in the classification of tumor types as new omics-based technologies are revealing the remarkable genomic, proteomic, and metabolomic diversity present across all cancers.  Historically, we have relied solely on pathological examination of tumors for proper diagnosis, but improved cost effectiveness and increased accessibility of omics-based approaches have revealed that particular tumor types which we have previously lumped into a single category are, in actuality, composed of distinct subsets of tumors with their own unique etiology, disease progression, drug susceptibilities, and prognostic outcomes.  In this study, we used a genomics- and Boolean statistics-based approach to reveal that the global gene expression profiles of the four main liposarcoma subtypes do not clearly correlate with histological type or clinical staging.  Our observed discordance between genomic features and pathological and clinical classification may explain, in part, the considerable variation in therapeutic susceptibility, drug resistance, and patient outcome observed across all liposarcoma subtypes.  We furthermore revealed that the expression of the Myc oncogene was a significantly more accurate predictor of patient mortality than pathological identification of liposarcoma subtype.  These data provide strong support for the inclusion of omics based analyses in conjunction with pathological and clinical classification when diagnosing patients with liposarcoma and very likely other tumor types.

Functional Genomics Analysis Reveals a MYC Signature Associated with a Poor Clinical Prognosis in Liposarcomas. Global Medical Discovery

 

 

 

 

 

 

 

 

 

 

 

Journal Reference

Tran D1, Verma K1, Ward K1, Diaz D1, Kataria E1, Torabi A2, Almeida A3, Malfoy B3, Stratford EW4, Mitchell DC1, Bryan BA5. Am J Pathol. 2015 Mar;185(3):717-28.

Show Affiliations

1Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.

2Department of Pathology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.

3Curie Institute Research Center, Paris, France.

4Cancer Stem Cell Innovation Centre and the Department of Tumor Biology, Institute of Cancer Research, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway.

5Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.

Electronic address: [email protected]

Abstract

Liposarcomas, which are malignant fatty tumors, are the second most common soft-tissue sarcomas. Several histologically defined liposarcoma subtypes exist, yet little is known about the molecular pathology that drives the diversity in these tumors. We used functional genomics to classify a panel of diverse liposarcoma cell lines based on hierarchical clustering of their gene expression profiles, indicating that liposarcoma gene expression profiles and histologic classification are not directly correlated. Boolean probability approaches based on cancer-associated properties identified differential expression in multiple genes, including MYC, as potentially affecting liposarcoma signaling networks and cancer outcome. We confirmed our method with a large panel of lipomatous tumors, revealing that MYC protein expression is correlated with patient survival. These data encourage increased reliance on genomic features in conjunction with histologic features for liposarcoma clinical characterization and lay the groundwork for using Boolean-based probabilities to identify prognostic biomarkers for clinical outcome in tumor patients.

Copyright © 2015 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Go To PubMed