Proteomic Analysis of Neuroblastoma Subtypes in Response to Mitogen-Activated Protein Kinase Inhibition: Profiling Multiple Targets of Cancer Kinase Signaling

Targeted inhibition of MEK1 by cobimetinib leads to differentiation and apoptosis in neuroblastoma cells

Background

Neuroblastoma (NB) is one of the most common childhood malignancies. Currently, high risk NB carries a poor outcome and significant treatment related toxicities and, thus has been a focus for new therapeutics research in pediatric oncology. In this study, we evaluated the effects of the MEK inhibitor cobimetinib, as a single agent and in combinations, on the growth, survival and differentiation properties against a molecularly representative panel of NB cell lines.

Methods

In vitro anti-proliferative activity of cobimetinib alone or in combination was investigated by cell viability assays and its target modulatory activity was evaluated using phospho-kinases antibody arrays and western blot analysis. To determine the effect of combination with cis-RA on differentiation and resulting enhanced cellular cytotoxicity, the expression of glial fibrillary acidic protein (GFAP) and microtubule-associated protein 2 (MAP2) expression levels were examined by immuno-fluorescence.

Results

Our findings show that cobimetinib alone induced a concentration-dependent loss of cell viability in all NB cell lines. In addition, cobimetinib showed feedback activation of MEK1/2, and the dephosphorylation of extracellular signal-regulated kinases (ERK1/2) and c-RAF, providing information on the biological correlates of MEK inhibition in NB. Combined treatment with cis-RA, led to differentiation and enhanced sensitization of NB cells lines to cobimetinib.

Conclusion

Collectively, our results provide evidence that cobimetinib, in combination with cis-RA, represents a feasible option to develop novel treatment strategies for refractory NB.

Sorafenib inhibits neuroblastoma cell proliferation and signaling, blocks angiogenesis, and impairs tumor growth

BACKGROUND

More effective therapy for children with high-risk neuroblastoma is desperately needed. Preclinical studies have shown that neuroblastoma tumor growth can be inhibited by agents that block angiogenesis. We hypothesized that drugs which target both neuroblastoma cells and tumor angiogenesis would have potent anti-tumor activity. In this study we tested the effects of sorafenib, a multi-kinase inhibitor, on neuroblastoma cell proliferation and signaling, and in mice with subcutaneous human neuroblastoma xenografts or orthotopic adrenal tumors.

PROCEDURE

Mice with subcutaneous neuroblastoma xenografts or orthotopic adrenal tumors were treated with sorafenib, and tumor growth rates were measured. Blood vessel architecture and vascular density were evaluated histologically in treated and control neuroblastoma tumors. The in vitro effects of sorafenib on neuroblastoma proliferation, cell cycle, and signaling were also evaluated.

RESULTS

Sorafenib inhibited tumor growth in mice with subcutaneous and orthotopic adrenal tumors. Decreased numbers of cycling neuroblastoma cells and tumor blood vessels were seen in treated versus control tumors, and the blood vessels in the treated tumors had more normal architecture. Sorafenib treatment also decreased neuroblastoma cell proliferation, attenuated ERK signaling, and enhanced G(1) /G(0) cell cycle arrest in vitro.

CONCLUSIONS

Our results demonstrate that sorafenib inhibits the growth of neuroblastoma tumors by targeting both neuroblastoma cells and tumor blood vessels. Single agent sorafenib should be evaluated in future phase II neuroblastoma studies.

Application of proteomics to soft tissue sarcomas

Soft tissue sarcomas are rare and account for less than 1% of all malignant cancers. Other than development of intensive therapies, the clinical outcome of patients with soft tissue sarcoma remains very poor, particularly when diagnosed at a late stage. Unique mutations have been associated with certain soft tissue sarcomas, but their etiologies remain unknown. The proteome is a functional translation of a genome, which directly regulates the malignant features of tumors. Thus, proteomics is a promising approach for investigating soft tissue sarcomas. Various proteomic approaches and clinical materials have been used to address clinical and biological issues, including biomarker development, molecular target identification, and study of disease mechanisms. Several cancer-associated proteins have been identified using conventional technologies such as 2D-PAGE, mass spectrometry, and array technology. The functional backgrounds of proteins identified were assessed extensively using in vitro experiments, thus supporting expression analysis. These observations demonstrate the applicability of proteomics to soft tissue sarcoma studies. However, the sample size in each study was insufficient to allow conclusive results. Given the low frequency of soft tissue sarcomas, multi-institutional collaborations are required to validate the results of proteomic approaches.

Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

Background

Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.

Methods

Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.

Results

We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.

Conclusions

The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.

Serum biomarkers which correlate with failure to respond to immunotherapy and tumor progression in a murine colorectal cancer model

PURPOSE

To advance our understanding of mechanisms involved in tumor progression/regression, a CT26 colorectal mouse model treated intra-tumorally with DISC-herpes simplex virus as immunotherapy was used in the discovery and validation phases to investigate and ultimately identify biomarkers correlating with the failure to respond to immunotherapy.

EXPERIMENTAL DESIGN

For the discovery phase, serum protein/peptide profiles of a retrospective sample collection (total n=70) were analyzed using MALDI-TOF-MS combined with artificial neural networks. Following identification of the key predictive peptides using ESI-MS/MS, validation of the identified proteins was carried out on serum and tissues collected in an independent sample set (total n=60).

RESULTS

Artificial neural network analysis resulted in four discriminatory peaks with an accuracy of 86%, sensitivity of 90% and specificity of 81% between the progressor/regressor groups. Three of the identified discriminatory markers were upregulated and demonstrated a positive correlation with tumor progression following DISC-herpes simplex virus therapy. Immunovalidation studies corroborated the MALDI-TOF-MS findings. Immunohistochemistry revealed that serum amyloid A-1 and serum amyloid P produced in the liver localized intracellularly in CT26 tumor tissue.

CONCLUSIONS

MALDI-TOF-MS and BI analysis of the serum proteome of tumor-bearer mice undergoing immunotherapy, identified biomarkers associating with "failure to respond" and biological arrays confirmed these findings.

Proteomic approaches in neuroblastoma: a complementary clinical platform for the future

Neuroblastoma (NB) is one of the most common solid tumors of childhood and displays a remarkable diversity in both biologic characteristics and clinical outcomes. Availability of high-throughput 'omics technologies and their subsequent application towards oncology has provided insight into the complex pathways of tumor formation and progression. Investigation of NB 'omics profiles may better define tumor behavior and provide targeted therapy with the goal of improving outcomes in patients with high-risk disease. Utilization of these technologies in NB has already led to advances in classification and risk stratification. The gradual emergence of NB-directed proteomics adds a layer of intricacy to the analysis of biologic organization but may ultimately provide a better comprehension of this complex disease. In this review, we cite specific examples of how NB-directed proteomics has provided information regarding novel biomarkers and possible therapeutic targets. We finish by examining the impact of high-throughput 'omics in the field of NB and speculate on how these emerging technologies may further be incorporated into the discipline.

Alterations in Barrett's-related adenocarcinomas: a proteomic approach

In this study, we applied high-resolution, two-dimensional, gel electrophoresis and matrix-assisted laser desorption/ionization, time-of-flight and tandem mass spectrometry analysis (MALDI TOF MS) to identify novel proteins that are involved in Barrett's tumorigenesis. We analyzed 12 primary tissue samples that included 8 Barrett's-related adenocarcinomas (BA) and 4 normal mucosae samples. Twenty-three spots were consistently altered (>or=2-fold) in at least half of the tumors when compared with all normal samples and thus subjected to further analysis. The MALDI TOF MS analysis demonstrated biologically interesting upregulated proteins such as ErbB3, Dr5 and Cyclin D1 as well as several members of the zinc finger proteins (Znf146, Znf212 and Znf363). Examples of downregulated proteins included Lgi1 and Klf6. We selected four proteins (ErbB3, Dr5, Znf146 and Lgi1) that are novel for BAs for validation using quantitative real-time reverse-transcription PCR on 39 BA tissue samples when compared with normal samples. We demonstrated mRNA upregulation of ERBB3 (51.3%), DR5 (41%) and ZNF146 (30.7%) and downregulation of LGI1 (100%) in BA. We have further validated the protein overexpression of ErbB3, Dr5 and Znf146, using immunohistochemical (IHC) analysis on a tissue microarray that contained 75 BAs and normal gastric and esophageal mucosae samples. BA tissue samples demonstrated overexpression of ErbB3 (42%), Dr5 (90%) and Znf146 (30%) when compared with normal tissues. In conclusion, we have identified and validated several novel proteins that are involved in Barrett's carcinogenesis.

Identification of novel proteins induced by estradiol, 4-hydroxytamoxifen and acolbifene in T47D breast cancer cells

Tamoxifen is currently used as adjuvant therapy for estrogen receptor (ER) positive breast cancer patients and as a chemopreventative agent. Although ER is a predictive marker for tamoxifen response, ER status fails to predict tamoxifen response in a significant number of patients highlighting the need to identify new pathways for tamoxifen sensitivity/resistance. To identify novel proteins induced by tamoxifen in breast cancer cells sensitive to tamoxifen growth inhibition, two-dimensional (2D) gel electrophoresis was used to profile proteins in T47D breast cancer cells. Six proteins were identified that were differentially regulated by 17beta-estradiol, 4-hydroxytamoxifen and the pure antagonist acolbifene (EM-652); calreticulin, synapse associated protein 1 (SYAP1), CD2 antigen binding protein 2 (CD2BP2), nucleosome assembly protein 1 like 1 (NAP1L1), d-3-phosphoglycerate dehydrogenase (3-PHGDH) and pyridoxine 5' phosphate oxidase (PNPO). At the mRNA level, these ligands differentially regulated expression of mRNAs encoding the identified proteins in T47D and MCF7 cells but had no effect on mRNA in ERalpha-negative MDA-MB-231 breast cancer cells. These novel SERM-regulated proteins may participate in new or existing pathways for sensitivity or resistance to SERMs.