Authorship:
S. Lou(1), B. Balluff(1), M. A. de Graaff(2), A.H. Cleven(2), I. Briaire-de Bruijn(2), J. V. M. G. Bovée(2) and L. A. McDonnell(1)
(1)Center for Proteomics and metabolomics, Leiden University Medical Center; (2)Department of Pathology, Leiden University Medical Center
| Short Abstract Ability of Matrix-assisted laser desorption/ionization Mass Spectrometry Imaging to distinguish between the most encountered but clinically challenging high grade Soft Tissue Sarcomas (four subtypes) were investigated (leading to diagnostic biomarkers discovery) and if there are individual proteins (signatures) that are statistically associated with patient survival and development of metastases were also investigated (thus would be prognostic biomarkers). Twenty protein peaks were found as diagnostic biomarkers. Fourteen protein peaks were found as prognostic biomarkers. Based on comparisons with databases, Acyl-CoA-binding protein, Macrophage migration inhibitory factor, Thioredoxin and Galectin-1 were tentatively assigned among diagnostic biomarkers; Thymosin beta-10, Proteasome activator complex subunit 1, two modified Histone H4 were tentatively assigned among prognostic biomarkers. |
Long Abstract
The combination of high heterogeneity, both intra-tumoral and inter-tumoral, with their rarity (comprising just 1% of all malignant tumors) has made diagnosis, prognosis, staging and treatment of soft tissue sarcomas (STS) difficult [1]. There is an urgent need for more objective molecular and biochemical protein biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets.
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry has been successfully used for the direct molecular assessment of tissue samples. The analysis is label-free, no a-priori knowledge is needed, and it is able to simultaneously measure hundreds of biomolecular ions. Spatially-correlated analysis, mass spectrometry imaging (MSI), can reveal how each of these biomolecular ions varies in tissue sections [3]. One of the distinct advantages of MSI for clinical research is that following data acquisition a histological image of the same tissue section can be recorded and registered to the MSI dataset. This seamless integration of MSI and histology enables the mass spectral signatures of distinct cells and groups of cells to be acquired within their correct histological context.
MSI has been previously used to investigate the molecular make-up of STS [5] and to investigate molecular markers to aid their differential diagnosis [6]. For other tumors MSI has amply demonstrated its ability to identify markers for patient survival, metastasis and response to therapy [7]. These examples indicate that MSI has great potential for providing novel abilities for improved differential diagnosis and patient management [2-4].
In this study we investigated the ability of MALDI MSI to distinguish between the most encountered (still infrequently) but clinically challenging high grade STSs, including osteosarcoma (OS, n=16), leiomyosarcoma (LMS, n=12), myxofibrosarcoma (MFS, n=13) and undifferentiated pleomorphic sarcoma (UPS, n = 12), leading to diagnostic biomarkers discovery. We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival and development of metastases , and thus may be prognostic biomarkers.
Two distinct approaches were used: the first follows the established clinical MSI protocol of using virtual-microdissection of the histological image to extract the average mass spectral profile of histologically specific tumor areas followed by statistical comparisons; The second approach was recently reported by Balluff et al.[8] and incorporated intratumor heterogeneity into the discovery pipeline.
Twenty protein peaks were found that were characteristic for specific tumors (p < 0.05) as diagnostic biomarkers. They were all highly expressed in LMS patients and lowly expressed in MFS patients. Acyl-CoA-binding protein (m/z 11162), Macrophage migration inhibitory factor(m/z 12350), Thioredoxin (m/z 11608) and Galectin-1 (m/z 14633) were tentatively assigned based on comparisons with databases of protein ions commonly detected by MALDI MSI.
Fourteen protein peaks were found to be associated (p < 0.05) with clinical outcome as prognostic biomarkers. Based on their intensities, nine peaks displayed significant differences for overall survival while the rest showed significant differences for metastases free survival. Tentative assignments were Thymosin beta-10 (m/z 4942, OS patients with metastasis), Proteasome activator complex subunit 1 (m/z 9753, non-OS patients with poor survival), modified Histone H4 (m/z 11314, m/z 11355, LMS patients with poor survival) based on comparisons with databases of protein ions commonly detected by MALDI MSI.
Several candidate biomarkers (not listed above) were found in a manner of limiting the analysis to specific differentiation grades (well differentiated grade, moderately differentiated grade and undifferentiated grade) that were not statistically significant or that exhibited a weaker statistical significance if the average mass spectrum of all differentiation grades were used instead. Which tumor areas are selected for the assay of a highly heterogeneous tumor, particularly in STSs has been shown the importance. Further investigation within histologically homogeneous tumor areas by applying routines from Balluff et al.[8] demonstrated the sub tumor populations which were statistically associated different overall survival.
Thus, our results confirm intratumor heterogeneity in high grade STSs, and using statistical methods, we identified diagnostic markers that could distinguish between the different entities, and prognostic biomarkers that were found statistically associated with patient survival and metastasis.
References:
[1] J. Martin-Liberal, C. Benson et al. (2013). Expert Rev Anticancer Ther 13(8): 975.
[2] B. Balluff, A. Walch et al. (2011). Am J Pathol 179(6): 2720.
[3] L. A. McDonnell, R. M. Heeren (2007). Mass Spectrom Rev 26(4): 606.
[4] L. A. McDonnell, A. M. Deelder et al. (2010). J Proteomics 73(10): 1921.
[5] R. L. Caldwell, R. M. Carprioli et al. (2006). Cancer Genomics & Proteomics 3: 279.
[6] S. M. Willems, L. A. McDonnell et al. (2010). J Pathol 222: 400.
[7] D. Baumhoer, A. Walch et al. (2011). Oncotarget 2: 970.
[8] Balluff, B, L. A. McDonnell et al. (2015). J Pathol 235(1): 3-13.