Identification of a plasma proteomic signature to distinguish pediatric osteosarcoma from benign osteochondroma

Abstract

Osteosarcoma (OS) is the most common malignant bone tumor in children. To identify a plasma proteomic signature that can detect OS, we used SELDI MS to perform proteomic profiling on plasma specimens from 29 OS and 20 age-matched osteochondroma (OC) patients. Nineteen statistically significant ion peaks that were differentially expressed in OS when compared with OC patients were identified (p < 0.001 and false discovery rate < 10%). Using the proteomic profiles, we constructed a multivariate 3-nearest neighbors classifier to distinguish OS from OC patients with a sensitivity of 97% and a specificity of 80% based on external leave-one-out crossvalidation. Permutation test showed that the classification result was statistically significant (p < 0.00005). One of the proteins (m/z 11 704) in the proteomic signature was identified as serum amyloid protein A (SAA) by PMF. The higher plasma level of SAA in OS patients was further validated by Western blotting when compared to that of osteochrondroma patients and normal subjects as reference. The classifier based on this plasma proteomic signature may be useful to differentiate malignant bone cancer from benign bone tumors and for early detection of OS in high-risk individuals.

Plasma proteome predicts chemotherapy response in osteosarcoma patients

“…Peaks were detected with the similar settings as previously reported (7). In this study, the m/z ranges for low-, medium-and high-molecular weight proteins were 2,000-10,000, 10,000-30,000 and 30,000-200,000, respectively.…”

“…In order to test whether plasma proteomic profiles can be used to detect which osteosarcoma patients would respond poorly to preoperative chemotherapy, 54 plasma samples of osteosarcoma patients collected at the time of initial diagnosis (pre-treatment, n=27) and during definitive surgery (post-treatment, n=27) were subjected to SELDI-TOF MS analysis using weak cationic protein arrays (CM10). All 783 protein peaks identified by SELDI-TOF MS from six different fractions and three laser power settings were combined to construct a multivariate classifier as previously described (7). As the histological response is measured clinically at the time of definitive surgery, we first tested whether we could identify a proteomic signature of chemotherapy response using the post-treatment plasma samples collected during definitive surgery.…”

“…As is shown in Fig. 1, 27 plasma samples were collected at the time of initial diagnosis (pre-treatment) and these were used as part of our previous study to distinguish osteosarcoma from benign osteochondroma (7). After diagnosis, all the osteosarcoma patients who had enrolled in this study received three-drug (cisplatin, doxorubicin and high-dose methotrexate) preoperative chemotherapy.…”

“…The SELDI-TOF MS analysis of the 27 pre-treatment plasma samples has been previously reported (7). For the post-treatment samples used in this study, a similar approach was carried out.…”

“…In this study, we used an alternative approach for identifying poor responders, known as surface-enhanced laser desorption/ ionization-time of flight mass spectrometry (SELDI-TOF MS), which is a rapid and high-throughput proteomic technique for the discovery of biomarkers in various cancers (5,6). We previously demonstrated that a plasma-based proteomic signature generated by SELDI-TOF MS can be used to distinguish osteosarcoma from benign osteochondroma patients (7). However, the problem of classifying good and poor responders was not addressed in that study.…”