Personalized Cancer Care for Head and Neck Squamous Cell Carcinoma

Abstract

Objectives:

Tumor resistance to single-agent chemotherapy is ubiquitous, and recurrence after initial therapy remains a therapeutic challenge. The objectives of our study are: 1) Characterize functional therapeutic targets for primary tumor cells from a recurrent head and neck squamous cell carcinoma (SCCa) patient. 2) Determine a personalized drug combination accomplished by mathematical modeling.

Methods:

A patient with stage IVa floor of mouth SCCa refused surgery and received cytotoxic chemoradiation in 2012. Following progression, the patient underwent radical excision at OHSU. A portion of the specimen was converted to a primary-cell culture. Tumor cells were screened for functional therapeutic targets with a custom high-throughput 60-drug panel of molecularly-targeted agents. A personalized drug combination was generated using a probabilistic Boolean algorithm which cross-correlates drug-screen results with known drugs targets.

Results:

Twelve drugs decreased tumor cell growth to less than 20% of control. Inhibitors of cyclin-dependent kinases, proteasomes, Hsp90, vascular endothelial growth factor, and platelet-derived growth factor receptor had the lowest IC50 values. Dinaciclib was the most potent (absolute IC50 of 4nM). The Boolean algorithm predicted 7 two-target, synergistic combinations that should result in tumor cell death. An example is ALK inhibition simultaneously with WEE1 or PRCKD. Absolute IC50 for crizotinib (ALK inhibitor) and pelitinib (PRCKD and WEE1 inhibitor) were 1.57uM and 1.43uM, respectively.

Conclusions:

Molecularly-targeted drug screening and mathematical modeling may provide a feasible mechanism for choosing combinations of targeted therapies that abrogate tumor cell resistance. Once validated, this treatment paradigm may be a powerful method for guiding therapy when a patient develops resistance to the standard-of-care.