Head and Neck Cancer Biology and Translational Therapeutics
Devraj Basu Laboratory
Dept. Otorhinolaryngology - Head and Neck Surgery
University of Pennsylvania Perelman School of Medicine
Philadelphia, PA USA
Varun Sahu, B.S. After graduating from Rutgers University, Mr. Sahu served the lab from 2014 until 2019 as a Research Specialist. In addition to participating in all basic studies in the laboratory, he played a central role in developing the unique patient-derived models now available to the lab, including the multiple PDXs, organoids, and cell lines derived from HPV+ head and neck cancers. He is presently working in the lab of Anil Rustgi, MD at Columbia University and applying to medical school.
OUR DISEASE FOCUS AND LONG-TERM MISSION
Human papilloma virus-related (HPV+) head and neck squamous cell carcinomas (HNSCCs) have emerged over the past decade as a newly recognized disease entity. These aggressive tumors afflict a relatively young patient demographic and are rapidly growing in incidence the US. Our lab’s long-term mission is to improve two aspects of care of HPV+ HNSCC patients:
1. Increasing cures for the subset of HPV+ HNSCCs with high lethal potential.
2. Preventing the lifelong disabilities created by toxicity of current therapies.
OUR SCIENTIFIC FOCUS
Standard treatment for HNSCCs and many other cancers relies heavily upon radiation therapy and cisplatin, which both kill tumors largely by inducing reactive oxygen species (ROS) to create DNA damage. Whereas HPV+ HNSCCs are typically sensitive to radiation and cytotoxic drugs, some tumors successfully adapt to the oxidative stress induced by these therapies and progress to lethal outcome. These differences in therapy response among HPV+ HNSCCs appear linked to a high degree of diversity in their expression of the mitochondrial anti-oxidant machinery that buffers ROS levels to protect from oxidative injury. Fully understanding differences in response to oxidative stress among these cancers is needed to target resistance mechanisms of the most aggressive tumors and guide use of less toxic therapies for readily curable cases. Thus, the overarching focus our lab has become to define and target the mechanisms underlying resistance of some HPV+ cancers to oxidative stress. A related secondary focus is the identification of molecular biomarkers that precisely distinguish therapy-resistant HPV+ HNSCC cases on a prospective basis in order to optimize patient selection for future clinical trials. These efforts integrate data from large patient cohorts with advanced patient-derived preclinical models to elucidate molecular subgroups within this cancer type that merit distinct treatments. Biomarkers with potential for predictive utility are used to guide rational application of established and emerging therapeutics to the preclinical models with the goal of optimizing future clinical trial design for maximal impact on patient care.
MODEL SYSTEMS AND APPROACHES
Our efforts to overcome the paucity of experimental models that accurately recapitulate the biology of HPV+ HNSCCs has produced a unique set of resources in our lab. Existing HPV+ cell lines fail to capture the canonical PIK3CA activating mutations and multiple other molecular features characteristic of the HPV+ subtype of HNSCC. Our intensive effort to generate patient-derived xenografts (PDXs) and organoids from HPV+ OPSCCs has created a unique set of models that have been categorized based on high vs. low oxidative metabolism. Detailed genetic and transcriptomic characterization of these tools has also shown them to retain PIK3CA hotspot mutants and other genetic and transcriptomic features of HPV+ HNSCC that are lost in cell lines. While creating these models, we have developed the expertise to analyze heterogeneity and cell state plasticity within HPV+ cancer organoids, PDXs, and patient tumor samples based on molecular and functional criteria. Mechanistic and preclinical studies using these materials combine standard genetic and pharmacologic approaches with modern single cell analysis tools and assays employing high throughput image analysis.