Translating cancer proteogenomics data into biological and clinical insights 

Advancements in high-throughput omics technologies have provided an unprecedented opportunity for cancer studies. At the same time, advanced technologies have led to an increasing gap between data generation and our ability to interpret the vast amount of interconnected data. My lab develops and uses integrative bioinformatics approaches that help translate omics data into biological and clinical insights. In this talk, I will briefly introduce our established computational workflow for cancer proteogenomics data analysis¹. I will use data generated by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) on HPV-negative head and neck squamous cell carcinoma (HNSCC) as an example to demonstrate the utility of proteogenomics data in driving therapeutic hypothesis generation for precision oncology². I will also present our efforts on improving phosphoproteomics data analysis³ and making proteogenomics data accessible and useful to non-computational biologists^{4, 5}. 

References

1. Wen, B., Li, K., Zhang, Y. & Zhang, B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat Commun 11, 1759 (2020).

2. Huang, C. et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 39, 361-379 e316 (2021).

3. Jiang, W. et al. Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification. Mol Cell Proteomics 20, 100171 (2021).

4. Vasaikar, S.V., Straub, P., Wang, J. & Zhang, B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 46, D956-D963 (2018).

5. Wen, B., Wang, X. & Zhang, B. PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res 29, 485-493 (2019)