Representative Sequencing: Profiling Extreme Tumor Diversity

Introduction

While next-generation sequencing (NGS) has been applied to hundreds of thousands of solid tumors to date, there exists a fundamental undersampling bias inherent in current methodologies. This is caused by a biopsy input sample of fixed dimensions, which becomes grossly under-powered as tumor volume scales. Indeed analysis of pan-cancer data reveals that current protocols sample on average only 1.5% of cancer cells, decreasing to 0.3% for stage IV tumors. Failure to address this bias risks undermining the clinical utility of genomic medicine in cancer; through lack of sensitivity to detect actionable mutations, misasignment of subclonal variants as clonal, and unreliable estimate of tumor mutational burden.

Methods

Here we demonstrate Representative Sequencing (Rep-Seq), as a novel method to achieve unbiased sampling of solid tumor tissue. The Rep-Seq protocol comprises homogenization of all residual tumor material not taken for pathology into a well-mixed solution, coupled with NGS. Rep-Seq was implemented on a proof of concept basis in 15 tumors, and benchmarked against single and multi-region sequencing approaches.

Results

Rep-Seq achieved a linear rate of novel variant discovery in whole-exome sequencing across 0 to 5,000x coverage, detecting four-fold more mutations as compared to multi-region sequencing at equivalent total read depth. All variants were validated using custom panel and Ion Torrent platforms. Targeted panel Rep-Seq at 50,000x showed sensitivity to detect extreme parallel evolution, with 16 independent mutations in the gene SETD2 observed in a single tumor. Clonal clustering analysis revealed rapid convergence of cancer cell fraction estimates in Rep-Seq towards true values, as validated in >70 biopsies taken from a single tumor. As a consequence 97% of variants were correctly classified as clonal by Rep-Seq, compared to >85% in single biopsy sequencing. Rep-Seq was finally validated in a melanoma patient enrolled in the PEACE rapid autopsy study, a case with widely disseminated metastases across 9 distinct anatomical sites. Rep-Seq was able to accurately reconstruct the clonal phylogeny of advanced stage disease and recover a high proportion of all primary and metastatic variants, from deep sequencing of primary tissue alone.

Conclusions

Rep-Seq effectively implements an unbiased tumor sampling approach, drawing DNA molecules from a well-mixed solution of the entire tumor mass, hence removing spatial bias inherent in current approaches. As a result Rep-Seq detects more mutations, and achieves greater accuracy in determining clonal from subclonal variants.