Bin Zhu Awarded Scientific Tenure by the NIH
, by Michelle Dwyer, M.P.H.
In March 2023, Bin Zhu, Ph.D., was awarded scientific tenure by the NIH and promoted to senior investigator in the Biostatistics Branch. Dr. Zhu is an internationally recognized expert in the development of statistical methods to uncover and characterize cancer etiology. His multidisciplinary approach has resulted in major contributions to our understanding of germline and environmental exposures on molecular features of the cancer genome and the role of mutational signatures in prevention, diagnosis, and treatment, as well as implications for risk prediction and survival.
Among his seminal findings, Dr. Zhu and his team were the first to identify an inverse association between common germline risk variants and somatic mutation burden in breast cancer. In a collaborative study of breast cancer in Hong Kong, he identified a subset of immunologically “hot” luminal tumors that may respond particularly well to treatment with immunotherapy in Asian populations. In another collaboration, he discovered a germline deletion polymorphism associated with APOBEC mutational signatures across cancer types. At present, Dr. Zhu is conducting similar studies using registry data from the Genomics Evidence Neoplasia Information Exchange (GENIE), curated by the American Association for Cancer Research (AACR); the population diversity in GENIE has the potential to increase the generalizability of mutation signature research findings to populations outside of Asia.
In an extension of this work to carcinogenic viruses, Dr. Zhu and collaborators conducted a large genomic analysis of human papillomavirus (HPV) and observed that a proportion of rare variants were likely caused by APOBEC deaminases, human DNA editing enzymes with antiviral effects thought to be part of the innate immune system. In a later study, he demonstrated that those HPV infections with a greater burden of viral APOBEC mutations were less likely to persist and cause cervical cancer.
Dr. Zhu has also developed a number of research tools to support his research: SKIT (Semiparametric Kernel Independence Test), an association test to identify potential causes of novel mutational signatures by utilizing information from associated signatures with known etiology, and SUITOR (Selecting the nUmber of mutatIonal signaTures thrOugh cRoss-validation), an approach for cross validation to identify how many mutation signatures should be selected from a tumor dataset for downstream analyses of etiology or potential therapeutic targets.
In parallel to his mutational signatures work, Dr. Zhu has distinguished himself as an expert in methods to understand the effects of both inter- and intra-tumor heterogeneity to improve effectiveness of cancer diagnosis and treatment. He has led several important studies, including the development of a multi-omic kernel machine learning approach for high-throughput heterogeneous genomic, epigenomic, and transcriptomic profiles to predict survival outcomes across 14 cancer types. For example, his study of the evolution of papillary renal cell carcinoma (pRCC), a cancer known from TCGA studies to have a relatively low tumor mutation burden, showed that pRCC also has low clonal heterogeneity, implying that limited biopsy sampling would be sufficient to capture the clonal diversity of the tumor.
As a senior investigator, Dr. Zhu will continue to design and conduct analyses of tumor heterogeneity across the DCEG portfolio of cancer genomic studies.