Combined Targeted DNA Sequencing in Non-Small Cell Lung Cancer (NSCLC) Using UNCseq and NGScopy, and RNA Sequencing Using UNCqeR for the Detection of Genetic Aberrations in NSCLC

Abstract

The recent FDA approval of the MiSeqDx platform provides a unique opportunity to develop targeted next generation sequencing (NGS) panels for human disease, including cancer. We have developed a scalable, targeted panel-based assay termed UNCseq, which involves a NGS panel of over 200 cancer-associated genes and a standardized downstream bioinformatics pipeline for detection of single nucleotide variations (SNV) as well as small insertions and deletions (indel). In addition, we developed a novel algorithm, NGScopy, designed for samples with sparse sequencing coverage to detect large-scale copy number variations (CNV), similar to human SNP Array 6.0 as well as small-scale intragenic CNV. Overall, we applied this assay to 100 snap-frozen lung cancer specimens lacking same-patient germline DNA (07-0120 tissue cohort) and validated our results against Sanger sequencing, SNP Array, and our recently published integrated DNA-seq/RNA-seq assay, UNCqeR, where RNA-seq of same-patient tumor specimens confirmed SNV detected by DNA-seq, if RNA-seq coverage depth was adequate. In addition, we applied the UNCseq assay on an independent lung cancer tumor tissue collection with available same-patient germline DNA (11-1115 tissue cohort) and confirmed mutations using assays performed in a CLIA-certified laboratory. We conclude that UNCseq can identify SNV, indel, and CNV in tumor specimens lacking germline DNA in a cost-efficient fashion.

Fig 1. The UNCseq project.

(A) The UNCseq project is an initiative that involves clinicians and patients interested to participate in a non-therapeutic clinical trial conducted through the Lineberger Comprehensive Cancer Center (IRB-approved protocol 11–1115), as well as a multidisciplinary team that involves clinical and research faculty (medical oncologists, pathologists, bioinformaticians, and molecular biologists) who generate, critically assess, and discuss NGS data in relation to patients’ clinical history and review previously identified genetic aberrations to determine which are potentially clinically actionable and targeted for downstream validation using validated methods in a CLIA-certified laboratory. (B) Following consent to 11–1115, tumor tissues and peripheral blood are collected from cancer patients. Hematoxylin and eosin (H&E)-stained representative tissue sections from tumor samples (SF or FFPE) are assessed by a certified pathologist for the percentage of viable tumor/stroma content and presence/absence of necrosis (sample QC). Extracted DNA from tumor samples is processed through various steps (fragmentation, DNA library preparation, in-solution capture of DNA fragments of interest, small-scale amplification of captured DNA fragments) prior to Illumina NGS. Data generated are discussed in a multidisciplinary Molecular Tumor Board meeting. Following validation in a CLIA-certified laboratory, these genetic aberrations are reported in patients’ personal electronic medical records.

Fig 2. Depth and Breadth of On-Target Coverage of the 100 Lung Cancer Samples.

Shown for each tumor specimen is the percentage of targeted bases covered at given coverage depth (1x, 20x, 50x, 100x) and sequenced under different lane settings in the HiSeq 2000 instrument (2, 4, and 8 DNA libraries per lane, Lib/Ln).

Fig 3. SNV Calling of KRAS Hotspots Using First- and Next-Generation Sequencing.

(A) Sequencing chromatograms (Finch TV trace viewer v1.4.0) obtained from two tumor tissue examples showing concordance (sample 24) or discordance (sample 38) in KRAS SNV calling. (B) SNV calling at hot-spot loci in KRAS codon 12 and 13 for all 16 tumors using either of the two sequencing strategies. Calls by Sanger and NGS are colored in orange and blue, respectively. Calls by both platforms are colored in half orange and half blue. NGS coverage depth, purity, and MAF are also shown. (C) Boxplots of MAF, tumor purity, and coverage depth between discordant and concordant SNV calls are shown (p-value = 0.0006, two-sided Wilcoxon test).

Fig 4. SNV Calling in Lung Cancer Specimens Using the UNCseq Assay for SNV Listed in the OncoMap System (‘Conservative’ SNV).

Percentage, actual number of significantly mutated genes, and particular SNV types, nonsynonymous (nonsense, missense) and synonymous, are shown for each tumor sample in relation to its tumor histology and tumor purity. Abbreviations: SqCC: Squamous Cell Carcinoma; SmCC: Small Cell Carcinoma; ADC/BAC: Adenocarcinoma or Bronchio-alveolar Carcinoma; LCC: Large Cell Carcinoma; AD-SqC: Adenosquamous Carcinoma or Combined/Mixed; Carcinoid/NSmCC: Carcinoid-Atypical, Carcinoid-Typical, or Non-small cell carcinoma.

Fig 5. DNA Copy Number Analysis Using Affymetrix Human SNP Array 6.0 Microarray (Panel A) and UNCseq (Panel B, C) or Both (Panel D) of Lung Cancer Samples.

(A) Copy number gains (red) and losses (blue) are plotted along the normal genome per each chromosome for each of the 60 completed tumor samples in relation to tumor histology and tumor purity. (SqCC: Squamous Cell Carcinoma; SmCC: Small Cell Carcinoma; ADC/BAC: Adenocarcinoma or Bronchio-alveolar Carcinoma; LCC: Large Cell Carcinoma; AD-SqC: Adenosquamous Carcinoma or Combined/Mixed; Carcinoid/NSmCC: Carcinoid-Atypical, Carcinoid-Typical, or Non-small cell carcinoma) (B) Examples of chromosome-level CNV in various chromosomes (6, 14, and 19) using UNCseq in two tumor samples (27 and 90). Black dots represent the per nucleotide relative copy number ratios (CNRs) in log₂. Segmentation-derived regions of equal copy number are indicated in red lines. A red triangle at 10.6 Kbp position of chromosome 19 in the sample (ID: 90) indicates the zoomed regions in panel C. (C) Example of small (gene-level) structural variations across exons (from 5’ to 3’) of the KEAP1 gene (RefGene ID: NM_203500) for all (black) but one (red) tumor samples. Markers from the Genome-Wide Human SNP Array 6.0 corresponding to the chromosome area where KEAP1 gene is located are highlighted in red triangles. (D) Boxplot analysis illustrating the SNP array signals at these two markers in C. Signals of tumor sample 90 are in red.