Sridhar Hannenhalli

Cancer is characterized by major transcriptional and epigenetic changes not only in the cancer cells but also in other cell types that jointly constitute the tumor microenvironment (TME). The cancer phenotype is ultimately a result of dynamically changing interactions between various cell types. While much of the focus of research in oncogenesis, metastasis, drug resistance and relapse has been on protein-coding mutations, a growing body of literature clearly points to an alternate mechanism centered on transcription heterogeneity and cellular plasticity that sets the stage for oncogenesis. Broadly, cancers hijack several developmental and homeostatic processes for proliferation, immune evasion, and therapy resistance. Precise molecular links between cancer and the developmental/homeostatic processes is of great interest. As mentioned above, while most of the focus has been on the protein-coding mutations, a vast majority of mutations and germline variation fall in the non-coding portion of genome, however, identifying the functional subset of such non-coding variations and mutations has been challenging due to lack of appropriate models.  Further complicating functional investigation of cancers is that the very notion of a gene’s or a cell’s function may be highly context-specific and the knowledge of context-specific functions is highly incomplete. The research in Hannenhalli lab is broadly focused on Cancer Gene Regulation, and is organized into four broad areas: 

• Transcriptional heterogeneity, cellular plasticity, and regulatory mechanisms underlying oncogenesis, metastasis, and therapy response
• Developmental and homeostatic origins of cancer
• Development of deep learning models to identify functional non-coding polymorphisms and mutations underlying cancer
• Intrinsic and extrinsic context-specific functionality of genes and cells, and

We are a purely dry lab with expertise in computational and statistical methods, machine learning, and analysis of a variety of high-throughput omics data. We strongly believe in team science and welcome collaborations.