Reverse phase protein array (RPPA) combined with computational analysis to unravel relevant prognostic factors in non- small cell lung cancer (NSCLC): a pilot study

Integrative Multi-OMICs Identifies Therapeutic Response Biomarkers and Confirms Fidelity of Clinically Annotated, Serially Passaged Patient-Derived Xenografts Established from Primary and Metastatic Pediatric and AYA Solid Tumors

Establishment of clinically annotated, molecularly characterized, patient-derived xenografts (PDXs) from treatment-naïve and pretreated patients provides a platform to test precision genomics-guided therapies. An integrated multi-OMICS pipeline was developed to identify cancer-associated pathways and evaluate stability of molecular signatures in a panel of pediatric and AYA PDXs following serial passaging in mice. Original solid tumor samples and their corresponding PDXs were evaluated by whole-genome sequencing, RNA-seq, immunoblotting, pathway enrichment analyses, and the drug−gene interaction database to identify as well as cross-validate actionable targets in patients with sarcomas or Wilms tumors. While some divergence between original tumor and the respective PDX was evident, majority of alterations were not functionally impactful, and oncogenic pathway activation was maintained following serial passaging. CDK4/6 and BETs were prioritized as biomarkers of therapeutic response in osteosarcoma PDXs with pertinent molecular signatures. Inhibition of CDK4/6 or BETs decreased osteosarcoma PDX growth (two-way ANOVA, p < 0.05) confirming mechanistic involvement in growth. Linking patient treatment history with molecular and efficacy data in PDX will provide a strong rationale for targeted therapy and improve our understanding of which therapy is most beneficial in patients at diagnosis and in those already exposed to therapy.

Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis.

RESULTS

Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma.

CONCLUSIONS

We summarize the whole process of the experiment and discuss how to expand our experiment in the future.