Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq

Abstract

There is a need for scientifically-sound, practical approaches to improve carcinogenicity testing. Advances in DNA sequencing technology and knowledge of events underlying cancer development have created an opportunity for progress in this area. The long-term goal of this work is to develop variation in cancer driver mutation (CDM) levels as a metric of clonal expansion of cells carrying CDMs because these important early events could inform carcinogenicity testing. The first step toward this goal was to develop and validate an error-corrected next-generation sequencing method to analyze panels of hotspot cancer driver mutations (hCDMs). The "CarcSeq" method that was developed uses unique molecular identifier sequences to construct single-strand consensus sequences for error correction. CarcSeq was used for mutational analysis of 13 amplicons encompassing >20 hotspot CDMs in normal breast, normal lung, ductal carcinomas, and lung adenocarcinomas. The approach was validated by detecting expected differences related to tissue type (normal vs. tumor and breast vs. lung) and mutation spectra. CarcSeq mutant fractions (MFs) correlated strongly with previously obtained ACB-PCR mutant fraction (MF) measurements from the same samples. A reconstruction experiment, in conjunction with other analyses, showed CarcSeq accurately quantifies MFs ≥10^-4 . CarcSeq MF measurements were correlated with tissue donor age and breast cancer risk. CarcSeq MF measurements were correlated with deviation from median MFs analyzed to assess clonal expansion. Thus, CarcSeq is a promising approach to advance cancer risk assessment and carcinogenicity testing practices. Paradigms that should be investigated to advance this strategy for carcinogenicity testing are proposed.

Keywords: ACB-PCR; cancer risk assessment; carcinogenesis; clonal expansion; next-generation sequencing.

FIGURE 1

Clonal expansion of cells carrying CDMs is a disease proximate biomarker of effect. A continuum of cancer‐related biomarkers is depicted. Because pathogenic mutations lead to clonal expansion of cells carrying CDMs during carcinogenesis, the variability in CD MF across individuals may be a sensitive metric for assessing cancer risk

FIGURE 2

Overview of methods used for EC‐NGS. (a) Standard versus optimized approaches for library preparation are shown. (b) Labeling of amplicons with index and UMIs is used to deconvolute samples and construct SSCSs, respectively, in the CarcSeq method

FIGURE 3

Frequency and distribution of mutations across amplicon targets for normal breast (a) and breast ductal carcinoma (b) samples

FIGURE 4

Frequency and distribution of mutations across amplicon targets for normal lung (a) and lung adenocarcinoma (b) samples

FIGURE 5

Comparison of CarcSeq and ACB‐PCR MF measurements. Bland–Altman plots of combined breast and lung samples illustrate little bias between CarcSeq and ACB‐PCR MF measurements when both are ≥10⁻⁴, bias = 0.02883 ± 0.4279, 95% limits of agreement = −0.8098 − 0.8674 (a) and ≥10⁻⁵ bias = 0.1700 ± 0.5155, 95% limits of agreement = −0.8404 − 1.180 (b). Linear regression analysis of combined breast and lung samples shows high concordance between CarcSeq and ACB‐PCR MF measurements when both are ≥10⁻⁴ (c) or ≥10⁻⁵ (d)

FIGURE 6

Define mixtures of PIK3CA E545K wild type and mutant were analyzed by CarcSeq and ACB‐PCR. The standards analyzed had MFs of 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, and 10⁻⁵. The relationship between expected and CarcSeq‐measured MFs ≥10⁻⁴ is shown in (a). The relationship between expected and CarcSeq‐measured MF ≥10⁻⁵ is shown in (b). ACB‐PCR analysis of the PIK3CA E545K MF standards is shown in (c)

FIGURE 7

Relationships between the sum of MF measurements in normal breast of different individuals and tissue donor age. The sum of MFs for different individual samples was correlated with age using MFs ≥10⁻⁴ for all targets (a) or only targets known to be drivers of breast cancer, PIK3CA and TP53 (b). SEER breast cancer incidence data was used to calculate a cumulative risk for each age (c), as the cumulative sum of incidence observed at the current and all previous years. Finally, the sum of PIK3CA and TP53 MFs ≥10⁻⁴ in normal breast were plotted relative to the cumulative risk expected based on the tissue donor's age (d)

FIGURE 8

Depiction of experimental paradigms that could be used to relate tumor incidence in human or rodent to a metric based on analyzing batteries of hCDMs