Use of unique molecular identifiers to gain insight about the true positive mutations in cfDNA of breast cancer patients for implementation of personalized treatment

Background:

Blood analytes, as liquid biopsies, are discussed to be surrogate markers for therapy stratification, as serial sampling enabled by the minimal invasive nature of blood draw facilitates monitoring of clonal evolution. Mutational analysis of cell-free DNA (cfDNA) in plasma of breast cancer (BC) patients (pts) may predict the ideal therapy strategy. In this regard, PIK3CA mutations in BC pts’ cfDNA were already shown to correlate with treatment response. However, in Next Generation Sequencing (NGS) workflows, used for cfDNA analysis, artifacts are often induced during library construction. Here we used unique molecular identifiers (UMIs) to verify true positive PIK3CA mutations in cfDNA of BC pts.

Patients and methods:

cfDNA was isolated from 54 plasma samples of 38 BC patients by affinity-based binding to magnetic beads (QIAamp MinElute ccfDNA Kit, QIAGEN, Germany). 30–60 ng cfDNA was used for library construction with the QIAseq Targeted DNA Panel for Illumina (QIAGEN, Germany) with integrated UMIs. Sequencing was executed on the NextSeq 500 platform (Illumina, US). Data analysis was performed by QIAseq Targeted Sequencing Data Analysis Portal and the Biomedical Genomics Workbench. As a reference, the PIK3CA mutational status of matched tumor tissue DNA (analyzed by Sanger sequencing) was consulted.

Results:

Library preparation was successful (yield >15 ng) in 52/54 cases. Mean coverage was ~20,000x (mean UMI coverage ~2,500x) and >10,000x in 48/52 cases. The minimal allele frequency found for PIK3CA hotspot mutations (P539S, E545K, H1047R) by UMI analysis was 0.72%. In total, 133 mutations of the PIK3CA gene were identified as true positive mutations by UMI analysis in all 52 samples, which is a reduction of 69% (294/427) of all PIK3CA mutations incorrectly called by conventional data analysis. 59% of all different PIK3CA mutations called by UMI analysis appeared in over 5% of all pts. In the cohort of pts with PIK3CA hotspot mutant tumor tissue, 32% (6/19) showed the mentioned mutations also in matched cfDNA, whereas 16% of pts (3/19) without PIK3CA mutant tumor tissue were identified with true positive PIK3CA hotspot mutations in their plasma. Longitudinal analysis across two years during therapy revealed the increase in allele frequency (0%;11%;39%) of the PIK3CA H1047R mutation in one pt, whereas another pt showed a stable allele frequency of the PIK3CA P539S mutation (52%; 51%; 48%). Results will be expanded by consideration of mutations in BC hotspot genes despite PIK3CA and correlation to clinical parameter.

Conclusions:

Unique molecular identifiers enable the identification of true positive mutations in cfDNA and can thus, be used in clinical practice to determine molecular drivers of individual cancer progression and to employ personalized therapy.

Corinna Keup (University Hospital of Essen), Karim Benyaa (QIAGEN), Peter Hahn (QIAGEN), Siegfried Hauch (QIAGEN), Pawel Mach (University Hospital of Essen), Mitra Tewes (University Hospital of Essen), Hans-Christian Kolberg (Marienhospital Bottrop), Sabine Kasimir-Bauer (University Hospital of Essen)

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