Whole-Exome Sequencing and Targeted Deep Sequencing of cfDNA Enables a Comprehensive Mutational Profiling of Multiple Myeloma

Background. Cell-free DNA (cfDNA) sequencing enables serial temporal sampling, which offers the possibility of following the dynamics of biomarkers and clonal evolution in Multiple Myeloma (MM) over time. The use of cfDNA in clinical practice as a molecular biomarker and for monitoring response/resistance is dependent on a comprehensive profile of matched cfDNA and tumor DNA (tDNA) samples. Here we performed Ultra-Low Pass Whole Genome Sequencing (ULP-WGS) followed by whole-exome sequencing (WES) and targeted deep sequencing of matched cfDNA/tDNA samples from MM patients.

Methods. We performed next generation sequencing of matched cfDNA/tDNA samples for 63 patients with newly diagnosed or relapsed MM, SMM, or MGUS. Libraries were constructed using the Kappa Hyper kit and sequenced by ultra-low-pass whole-genome sequencing (ULP-WGS, 0.1x coverage) to quantify tumor fraction within cfDNA. WES was performed on 30 matched samples cfDNA/tDNA/germline DNA from 10 patients with more than 5% of tumor fraction. Libraries were hybridized to the Nextera Rapid Capture Exome kit (Illumina) and then sequenced on HiSeq 4000 (Illumina). Targeted deep sequencing was performed on 32 matched cfDNA/tDNA samples from 16 patients using the HaloPlex HS technology (Agilent), allowing for molecular barcoding. Libraries were constructed according to the manufacturer's instructions and sequenced on HiSeq 2500 (Illumina). Sequencing data were analyzed using the Firehose pipelines, including MuTect, ABSOLUTE, ReCapSeg, GISTIC and MutSig.

Results. We first used a cost-effective approach to establish the tumor content of cfDNA in a large-scale manner by ULP-WGS. Among 63 tested samples (53 MM, 6 SMM and 4 MGUS patient samples), the tumor fraction within cfDNA ranged from 0 to 81% with a mean of 10%. About 43% of these samples had tumor fraction greater than 5% within cfDNA. To assess whether cfDNA can capture the genetic diversity of MM and inform clinical management, we performed WES of matched cfDNA/tDNA/germline DNA samples for 10 patients (mean target coverage 194x). Copy number alterations (CNAs) assessed by WES (ReCapSeg) were consistent between cfDNA and tumor DNA. Similarly, focal CNAs assessed by GISTIC were consistent between tDNA and cfDNA. We then examined the overlap of somatic single nucleotide variants (SSNVs) between WES of cfDNA and matched tDNA. We found, on average, 100% of the clonal and 96% of the subclonal (range 54-100%) SSNVs that were detected in the tumor were confirmed to be present in cfDNA. Similarly, for mutations detected in the cfDNA, we found, on average, 100% of the clonal and 99% of the subclonal (range 98-100%) SSNVs were confirmed in the tumor. To assess whether targeted deep sequencing of cfDNA could be a good proxy for tumor biopsy we used a targeted deep sequencing approach of known MM driver genes. Libraries were prepared using unique molecular barcodes to avoid duplication rates, for 32 matched cfDNA/tDNA samples from 16 patients with MM. The mean target coverage was 596x. We found similar frequencies of altered MM driver genes in both cfDNA and tDNA, including KRAS, NRAS, and TP53, indicating that cfDNA can be used for precision medicine.

Conclusions. Our study demonstrates that both WES and targeted deep sequencing of cfDNA are consistently representative of tumor DNA alterations in terms of CNAs, focal CNAs and SSNVs. This approach could therefore be used to longitudinally follow clonal evolution across the course of the disease and precision medicine in patients with MM.