Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Abstract

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.

Figure 1.

Mutation detection performance in simulated tumor genomes. Model performance is displayed as receiver operating characteristic curves. Sensitivity plateaus below 1 because simulated mutations include sites with zero tumor sequencing depth in DNA and/or RNA (see ‘Simulation analysis’ methods).

Figure 2.

Validation of mutation detection by whole genome sequencing. The number of true positives and false positives of mutation detection models are plotted as step functions. At fixed false positive totals (250, 500 or 1000), each pair of models was compared for differences in number of true positives (*). The published mutation set , did not include mutation rankings and was not amenable to rank-based statistical analysis.

Figure 3.

Mutation signal in RNA versus DNA. Mutant allele fraction distributions of UNCeqR_META expressed mutations from the lung triplet cohort tumor sequencing (A). Germline variant allele fraction distributions of expressed germline variants from lung quadruplet cohort germline sequencing (B). Diagonal lines indicate equal allelic fraction between DNA and RNA, with points above the diagonal having greater allelic fraction in RNA, below the diagonal greater allelic fraction in DNA. Breast cancer somatic mutation and germline allele distributions in Supplementary Figure S6. Distributions of MAF difference among driver genes having a significant difference in MAF over all mutations (C). MAF distributions for all TP53 UNCeqR_META mutations, expressed and unexpressed (C and D).

Figure 4.

Tumor purity effects on mutation detection. Lines summarize breast and lung triplet cohorts, displaying total mutation ratios (A) or mean mutant allele fraction difference within expressed mutations (B) among tumors, binned by tumor purity quintile and plotted at midpoint. Pearson's correlation tests compared the association of mutation ratio and MAF associations among triplet cohort tumors (P). MAF distributions from two exemplar low purity tumors’ mutations (C and D). Diagonal lines indicate equal MAF in DNA-WES and RNA-seq, with mutations above the diagonal having greater MAF in RNA, below the diagonal greater MAF in DNA. Unexpressed mutations are marked along the horizontal axes in (C and D).

Figure 5.

Example of somatic mutation only detectable by RNA and DNA integration.  Mutation detected by UNCeqR_METAP = 1e-16. Read alignment display from integrative genomics viewer (43) for a low purity breast tumor at the major mutational hotspot of PIK3CA (47).

Figure 6.

Novel mutation discoveries in cancer-relevant genes. Increases in mutation absolute count versus relative increase are displayed for selected genes (A and B). Percentage increase is the number of novel UNCeqR_META mutations over the number of published mutations ,or a gene. Absolute counts for select genes among breast (C) and lung (D) cancer expression subtypes.