Enhanced specificity of clinical high-sensitivity tumor mutation profiling in cell-free DNA via paired normal sequencing using MSK-ACCESS

Abstract

Circulating cell-free DNA from blood plasma of cancer patients can be used to non-invasively interrogate somatic tumor alterations. Here we develop MSK-ACCESS (Memorial Sloan Kettering - Analysis of Circulating cfDNA to Examine Somatic Status), an NGS assay for detection of very low frequency somatic alterations in 129 genes. Analytical validation demonstrated 92% sensitivity in de-novo mutation calling down to 0.5% allele frequency and 99% for a priori mutation profiling. To evaluate the performance of MSK-ACCESS, we report results from 681 prospective blood samples that underwent clinical analysis to guide patient management. Somatic alterations are detected in 73% of the samples, 56% of which have clinically actionable alterations. The utilization of matched normal sequencing allows retention of somatic alterations while removing over 10,000 germline and clonal hematopoiesis variants. Our experience illustrates the importance of analyzing matched normal samples when interpreting cfDNA results and highlights the importance of cfDNA as a genomic profiling source for cancer patients.

Fig. 1. Design, characterization, and validation of MSK-ACCESS.

a The MSK-ACCESS panel was designed using data from 25,000 tumors analyzed using MSK-IMPACT tumor sequencing assay to identify at least one mutation in 94% of lung cancers, 91% of breast, and 84% of all cancers. b The laboratory workflow includes the extraction of cfDNA from plasma and genomic DNA from WBC originating from the same tube of blood. The addition of UMIs during library construction enables the identification of original cfDNA molecules during analysis and error suppression. c The analysis pipeline is modified from the standard MSK-IMPACT pipeline to incorporate UMI clipping and the generation of simplex and duplex consensus reads. d The sequencing of healthy donors to a mean raw coverage of 18,818× yielded a mean duplex coverage of 1103× and a mean simplex coverage of 658× across 47 samples. e The background error rate of non-reference sites demonstrates the reduction of overall and substitution specific errors via consensus read generation. Only the genomic position with non-reference reads are used; error rate is defined as the percentage of reads that support non-reference alleles. N = 47 for each boxplot. f A heatmap of error rate at all positions demonstrates how effective consensus read generation is at decreasing the error to zero at over 85% of sites. g Comparison of orthogonal and validated testing (expected VAF) to MSK-ACCESS (observed VAF) in the accuracy analysis showed high concordance (R² = 0.98). All boxplots show the median (center line) and 25th and 75th percentiles (bounding box) along with the 1.5 interquartile range (whiskers).

Fig. 2. Clinical experience with MSK-ACCESS.

a Distribution of cancer types amongst the first 617 patients sequenced with MSK-ACCESS. Colors indicate the highest OncoKB level ascribed to each patient’s genomic findings. b Distribution of all alterations found in each ctDNA sample (n = 681). c Variant allele frequencies (VAF) of all mutations found in ctDNA samples from MSK-ACCESS. Samples were sorted by the median VAF and each mutation was colored based on whether prior evidence was found for the mutation. De novo: mutations were called in ctDNA and were not reported in tissue testing or tissue testing was not performed; De novo and prior evidence: mutations were called in ctDNA and also were present in tissue testing; Genotyped from prior evidence: mutations were not detected in ctDNA by genotyping based on tissue results. d Same mutations in c showing the distribution of total collapsed coverage and VAF. Dotted line indicates the theoretical limits of calling threshold. e Oncoprint of genomic alterations found in lung, biliary, bladder, breast, prostate and pancreatic cancer samples with reported alterations. Colors indicate the OncoKB levels as in (a). f Comparison of cohort alteration rate of tumor types in (e) for genes where the alteration rate was greater than 3% by both MSK-ACCESS and MSK-IMPACT.

Fig. 3. Comparison of mutation calls between ctDNA and tissue.

Comparison of mutation calls between ctDNA and tissue. a Venn diagrams indicating the number of samples with concurrent cfDNA and tissue testing (n = 383) and the number of mutation calls identified in each (total n = 1206). b VAF distribution of mutations identified by MSK-ACCESS-only, shared by both MSK-ACCESS and MSK-IMPACT, and by MSK-IMPACT only (p = 2.06 × 10⁻¹⁸). The p value was obtained from pairwise comparisons using two-sided Mann–Whitney U-tests and adjusted for multiple testing using the Bonferroni method. c Comparison of VAF distributions of mutations identified in both the ctDNA and tissue from both MSK-ACCESS and MSK-IMPACT. d Tumor purity distribution of MSK-IMPACT tissue samples of (i) all patients in the concordance analysis, (ii) samples belonging to patients presenting actionable mutations on both assays, (iii) samples belonging to patients with actionable mutations detected in MSK-IMPACT only, and (iv) samples belonging to patients with actionable mutations detected in MSK-ACCESS only. e Clonality of all and actionable mutations detected in MSK-IMPACT only and in both assays (all mutations: p = 8.10 × 10⁻¹³, actionable mutations: p = 6.06 × 10⁻³). The p values were obtained from two-by-two Fisher’s exact tests and adjusted for multiple testing using the Bonferroni method. f Absolute time difference (ΔDOP) between MSK-IMPACT tissue sample and MSK-ACCESS blood sample collection for patients with actionable mutations in MSK-IMPACT only (p = 3.63 × 10⁻¹⁴), in both assays, and in MSK-ACCESS only (p = 1.33 × 10⁻⁹). The p values were obtained from pairwise comparisons using two-sided Mann–Whitney U-tests and adjusted for multiple testing using the Bonferroni method. All boxplots show the median (center line) and 25th and 75th percentiles (bounding box) along with the 1.5 interquartile range (whiskers).

Fig. 4. Use of WBC sequencing data to classify variants found in cfDNA.

a VAF distribution of all mutations called in plasma from cfDNA and WBCs. Colors indicate the origin of mutations. Boxes indicate different populations of mutations: I: Variants only present in cfDNA, II: Variants present in cfDNA at high VAF but also present in WBC at lower VAF, III: Variants present in both cfDNA and WBCs with VAFs in the presumed germline range (35–65%), IV: Variants present in both cfDNA and WBCs with VAFs lower than 10% in both. b Insert size distribution of sequencing reads (fragment size) in healthy donors with characteristic peaks at 161 bp and 317 bp c Fragment size distribution for reads encompassing the variants highlighted by the boxes and labels in (a) for both reference and alternate alleles. Clear differences are observed for reads originating from ctDNA vs normal tissue.