Bias-Corrected Targeted Next-Generation Sequencing for Rapid, Multiplexed Detection of Actionable Alterations in Cell-Free DNA from Advanced Lung Cancer Patients

Abstract

Purpose: Tumor genotyping is a powerful tool for guiding non-small cell lung cancer (NSCLC) care; however, comprehensive tumor genotyping can be logistically cumbersome. To facilitate genotyping, we developed a next-generation sequencing (NGS) assay using a desktop sequencer to detect actionable mutations and rearrangements in cell-free plasma DNA (cfDNA).

Experimental design: An NGS panel was developed targeting 11 driver oncogenes found in NSCLC. Targeted NGS was performed using a novel methodology that maximizes on-target reads, and minimizes artifact, and was validated on DNA dilutions derived from cell lines. Plasma NGS was then blindly performed on 48 patients with advanced, progressive NSCLC and a known tumor genotype, and explored in two patients with incomplete tumor genotyping.

Results: NGS could identify mutations present in DNA dilutions at ≥ 0.4% allelic frequency with 100% sensitivity/specificity. Plasma NGS detected a broad range of driver and resistance mutations, including ALK, ROS1, and RET rearrangements, HER2 insertions, and MET amplification, with 100% specificity. Sensitivity was 77% across 62 known driver and resistance mutations from the 48 cases; in 29 cases with common EGFR and KRAS mutations, sensitivity was similar to droplet digital PCR. In two cases with incomplete tumor genotyping, plasma NGS rapidly identified a novel EGFR exon 19 deletion and a missed case of MET amplification.

Conclusions: Blinded to tumor genotype, this plasma NGS approach detected a broad range of targetable genomic alterations in NSCLC with no false positives including complex mutations like rearrangements and unexpected resistance mutations such as EGFR C797S. Through use of widely available vacutainers and a desktop sequencing platform, this assay has the potential to be implemented broadly for patient care and translational research.

Fig 1. Key differences between standard hybrid capture (left) and bias-corrected NGS (right)

(A) Mono-, di- and trimeric nucleosome cfDNA fragments ranging from 130-480 basepairs are isolated. (B) In standard hybrid capture, cfDNA fragments are end-repaired and ligated with single primers. In contrast, bias-corrected NGS uses multifunctional adaptors that include sequences for single-primer amplification (red), tags for sample identification (green), and sequence identification tags (blue) that, in conjunction with the fragmentation site (blue dot) identify unique sequence clones. (C) In standard hybridization cfDNA fragments are captured with large capture probes (up to 120 bp) that span the genetic region of interest and may result in off-target fragments being isolated (e.g., daisy-chaining off-target DNA). Bias-corrected NGS uses small capture probes (∼40 bp) that are designed to be adjacent to the region of interest. Primer extension of fragments copies genomic and adaptor sequences. Lastly, amplification with tailed PCR primers create sequencing ready clones. (D) While both approaches allow sequencing of gene re-arrangements, large capture probes designed to target one gene will inefficiently target fragments containing a large amount of fusion partner gene sequence, resulting in poor sensitivity. In bias-corrected NGS, gene junction and partner gene sequence is replicated during primer extension. E: In standard hybrid capture all pulled-down cfDNA (specific and non-specific) is amplified and sequenced without knowing the exact read or probe which captured the fragment. In bias-corrected NGS, READ_1 identifies the sample ID and the unique sequence identifiers, while READ_2 identifies the probe that pulled down each clone, facilitating read analysis and probe optimization.

Fig. 2

Plasma NGS compared to known tumor genotype across a range of genomic equivalents (GE) in the sequencing library. The mutant allele frequency is provided when detected by the plasma genotyping assay (green circle) but not if undetected (red circle). In patients with common EGFR and KRAS mutations (A) plasma NGS has similar sensitivity to plasma ddPCR. Quantification of allelic frequency with plasma NGS and plasma ddPCR are closely correlated (B). In patients with rare genotypes (C), plasma NGS is able to detect a wide range of genomic alterations. In both groups of patients, the rate of detection by plasma NGS increases as the number of GE increases (A, C).

Fig. 3

Bias-corrected NGS of cfDNA identifies complex genomic alterations. (A) Sequencing of the intronic region of RET detects reads extending into KIF5B (inset), predicting a fusion of these two genes. (B) In cfDNA from a case with known MET amplification (case 105), MET copy number is significantly increased compared to control probes (p<0.001), which is not seen in cases without MET amplification. (C) Two mutations encoding for EGFR C797S are detected in cis with EGFR T790M after resistance to AZD9291. (D) In a case with acquired T790M despite no apparent EGFR sensitizing mutation, plasma NGS detects a novel double-deletion in exon 19 of EGFR which would have been missed with many PCR-based genotyping assays.