Dataset Information

Species-specific quantitative proteomics profiles of sarcoma patient-derived models closely reflect their primary tumors　

ABSTRACT: Availability of patient-derived sarcoma models that closely mimic human tumors remains a significant gap in cancer research as these models may not recapitulate the spectrum of sarcoma heterogeneity seen in patients. To characterize patient-derived models for functional studies, we made proteomic comparisons with originating sarcomas representative of the three intrinsic subtypes by mass spectrometry. Human protein profiling was found to be retained with high fidelity in patient-derived models. Patient derived xenografts locally invade and colonize stroma in mice which enables unambiguous molecular discrimination of human proteins in the tumor from mouse proteins in the microenvironment. We characterized protein profiling of patient sarcoma tumors and mouse stroma by species-specific quantitative proteomics. We found that protein expression in mouse stroma was affected by the primary human tumor. Our results showed that levels of stromal proteins derived from the tumor were lowered in PDXs and cell lines and part of human stromal proteins were replaced by corresponding mouse proteins in PDXs. This suggests that the effects of the microenvironment on drug response may not reflect those in the primary tumor. This cross-species proteomic analysis in PDXs could potentially improve preclinical evaluation of treatment modalities and enhance the ability to predict clinical trial responses.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Tadashi Kondo

PROVIDER: PXD013185 | JPOST Repository | Sun Mar 22 00:00:00 GMT 2020

REPOSITORIES: jPOST

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	T56-KS-1-SC7-01-24-18.wiff.scan	Wiff
	T56-KS-1-SC7-02-02-18.wiff	Wiff
	T56-KS-1-SC7-02-08-18.wiff	Wiff
	T56-KS-1-SC9-02-23-18.wiff	Wiff
	T56-KS-2-SC7-01-24-18.wiff	Wiff

Items per page:

1 - 5 of 20

Similar Datasets

Project description:Osteosarcoma (OS) and Ewing’s sarcoma (EW) are the two most common pediatric solid tumors, after brain tumors. Multimodal treatments have significantly improved prognosis in localized disease but outcome is still poor in metastatic patients, for whom therapeutic options are often inadequate. Preclinical drug testing to identify promising treatment options that match the molecular make-up of these tumors is hampered by the lack of appropriate and molecularly well-characterized patient-derived models. To address this need, a panel of patient-derived xenografts (PDX) was established by subcutaneous implantation of fresh, surgically resected OS and EW tumors in NSG mice. Tumors were re-transplanted to next mice generations and fragments were collected for histopathological and molecular characterization. A model was considered established after observing stable histological and molecular features for at least three passages. To evaluate the similarity of the model with primary tumor, we performed a global gene expression profiling and tissue microarrays (TMA), to assess tumor specific biomarkers on tissues from OS/EW tumors and their PDXs (1st and 3rd passage). Moreover, we verified the feasibility of these models for preclinical drug testing. We implanted 61 OS and 29 EW samples: 14/38 (37%) primary OS and 9/23 (39%) OS lung metastases successfully engrafted; while among EW, 5/26 (19%) primary samples and 1/3 (33%) metastases were established. Comparison between patient samples and PDXs, highlighted that histology and genetic characteristics of PDXs were stable and maintained over passages. In particular, correlative analysis between OS and EW samples and their PDXs was extremely high (Pearson’s r range r=0.94-0.96), while patient-derived primary cultures displayed reduced correlation with human samples (r=0.90-0.93), indicating that in vitro adaptation superimpose molecular alterations that create genetic diversion from original tumors. No significant differentially expressed gene profile was observed from the comparison between EW samples and PDXs (fold change > 2, adjusted p <0.05 at paired t-test). In OS, the comparison between OS patient-derived tumors and PDX indicated differences in 397 genes, mostly belonging to immune system functional category. This is in line with the idea that human immune cells are gradually replaced by murine counterparts upon engraftment in the mouse. As proof-of concept, two EW PDX and one OS PDX have been treated with conventional and innovated drugs to test their value in terms of drug-sensitivity prediction. Overall, our study indicated that PDX models maintained the histological and genetic markers of the tumor samples and represent reliable models to test sensitivity to novel drug associations.

Project description:Hass2017-PanRTK model for single cell line The model structure comprises heterodimerization and receptor trafficking as described in detail in the article below. For ligand input, set a respective event. The illustrated event sets the EGF concentration to 2.5 nMol in the model file. This model is described in the article: Predicting ligand-dependent tumors from multi-dimensional signaling features. Hass H, Masson K, Wohlgemuth S, Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J, MacBeath G, Schoeberl B, Raue A. NPJ Syst Biol Appl 2017; 3: 27 Abstract: Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo. This model is hosted on BioModels Database and identified by: MODEL1708210000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Species-specific quantitative proteomics profiles of sarcoma patient-derived models closely reflect their primary tumors

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets