Functional precision medicine identifies novel druggable targets and therapeutic options in head and neck cancer.

Heat map visualizing functional patterns in large scale data sets. These patterns are brought into biological context by data mining.

Heat map visualizing functional patterns in large scale datasets. © 2018 The Jenner Institute Laboratories

Xu C, Nikolova O, Basom R, Mitchell RM, Shaw R, Moser R, Park H, Gurley KE, Kao M, Green CL, Schaub FX, Diaz RL, Swan HA, Jang IS, Guinney J, Gadi VK, Margolin AA, Grandori C, Kemp CJ, Méndez E

Clinical Cancer Research

June 15, 2018

Purpose: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide with high mortality and a lack of targeted therapies. To identify and prioritize druggable targets, we performed genome analysis together with genome-scale siRNA and oncology drug profiling using low passage tumor cells derived from a patient with a treatmentresistant HPV-negative HNSCC.

Experimental Design: A tumor cell culture was established and subjected to whole exome sequencing, RNA sequencing, comparative genome hybridization, and high-throughput phenotyping with siRNA library covering the druggable genome and an oncology drug library. Secondary screens of candidate target genes were performed on the primary tumor cells and two non-tumorigenic keratinocyte cell cultures for validation and to assess cancer-specificity. siRNA screens of the kinome on two isogenic pairs of p53-mutated HNSCC cell lines were used to determine generalizability. Clinical utility was addressed by performing drug screens on two additional HNSCC cell cultures derived from patients enrolled in a clinical trial.

Results: Many of the identified copy number aberrations and somatic mutations in the primary tumor were typical of HPV(-) HNSCC, but none pointed to obvious therapeutic choices. In contrast, siRNA profiling identified 391 candidate target genes, 35 of which were preferentially lethal to cancer cells, most of which were not genomically altered. Chemotherapies and targeted agents with strong tumor specific activities corroborated the siRNA profiling results and included drugs that targeted the mitotic spindle, the proteasome and G2/M kinases WEE1 and CHK1. We also show the feasibility of ex-vivo drug profiling for patients enrolled in a clinical trial.

Conclusions: High-throughput phenotyping with siRNA and drug libraries using patient derived tumor cells prioritizes mutated driver genes and identifies novel drug targets not revealed by genomic profiling. Functional profiling is a promising adjunct to DNA sequencing for precision oncology.

Last updated: October 21, 2020