Abstract
BackgroundDiagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.Methodology/Principal FindingsWe analyzed 5 independent datasets (325 tumor arrays). We developed and validated a predictor, which was used to reclassify MFH and NOS sarcomas. The molecular “match” between MFH and their predicted subtypes was assessed using genome-wide hierarchical clustering and Subclass-Mapping. Findings were validated in 15 paraffin samples profiled on the DASL platform. Bayesian models of oncogenic pathway activation and chemotherapy response were applied to individual STS samples. A 170-gene predictor was developed and independently validated (80-85% accuracy in all datasets). Most MFH and NOS tumors were reclassified as leiomyosarcomas, liposarcomas and fibrosarcomas. “Molecular match” between MFH and their predicted STS subtypes was confirmed both within and across datasets. This classification revealed previously unrecognized tissue differentiation lines (adipocyte, fibroblastic, smooth-muscle) and was reproduced in paraffin specimens. Different sarcoma subtypes demonstrated distinct oncogenic pathway activation patterns, and reclassified MFH tumors shared oncogenic pathway activation patterns with their predicted subtypes. These patterns were associated with predicted resistance to chemotherapeutic agents commonly used in sarcomas.Conclusions/SignificanceSTS profiling can aid in diagnosis through a predictor tracking distinct tissue differentiation in unclassified tumors, and in therapeutic management via oncogenic pathway activation and chemotherapy response assessment.
Highlights
Soft tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors traditionally classified according to their morphological resemblance to presumptive cells of origin such as fibroblasts, muscle cells, adipocytes or peripheral nerve-sheath cells [1,2,3]
Precise classification is only partially possible, because current histopathologic classification criteria are often inconclusive reflecting the overlapping boundaries between conventional diagnostic groups [6]. This is best exemplified in the case of malignant fibrous histiocytoma (MFH), the second largest subtype by conventional criteria, a controversial diagnosis which has lately been called in doubt [2,8]
We examined expression of tissue specific genes in the 4 not otherwise specified’’ (NOS) samples classified as LIPO, and found that they appropriately overexpressed genes associated with adipocyte differentiation including adiponectin, insulin-like growth factor 1, and adipocyte fatty acid binding protein 4 (3.1 fold, 2.4 fold, and 1.5 fold upregulated (t test p = 0.06, 0.15 and 0.07 respectively), respectively, as compared to the known non-LIPO samples, (3 LEIO, 2 SYN and 2 malignant peripheral nerve sheath tumors (MPNST))
Summary
Soft tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors traditionally classified according to their morphological resemblance to presumptive cells of origin such as fibroblasts, muscle cells, adipocytes or peripheral nerve-sheath cells [1,2,3]. Precise classification is only partially possible, because current histopathologic classification criteria are often inconclusive reflecting the overlapping boundaries between conventional diagnostic groups [6] This is best exemplified in the case of malignant fibrous histiocytoma (MFH), the second largest subtype by conventional criteria (approximately 20% of cases [7]), a controversial diagnosis which has lately been called in doubt [2,8]. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response
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