Identification of tumor-specific mutations and fetal-specific SNPs in the pregnant women with lymphoma

Identification of tumor-specific mutations and fetal-specific SNPs in the pregnant woman with lymphoma

Wet lab protocol

1. DNA was extracted from 4mL of plasma using the QIAamp DSP DNA Blood Mini Kit (Qiagen) following the manufacturer’s protocol. The plasma DNA library was prepared using the KAPA HTP Library Preparation Kit (Kapa Biosystems) according to the manufacturer’s instructions. Half of the plasma DNA library was submitted to two runs of bisulfite conversion using the EpiTect Bisulfite Kit (Qiagen).

2. DNA was extracted from tumor tissue with the QIAamp DNA Mini kit (Qiagen). DNA was extracted from buccal swab and buffy coats using the QIAamp DNA Blood Mini Kit (Qiagen). The genomic DNA was then fragmented to around 200 bp using the S220 Focused-Ultrasonicator (Covaris) following the manufacturer’s protocol. DNA libraries were prepared following the manufacturer's instructions (KAPA Biosystems).

3. The size distribution of the DNA libraries was profiled using High Sensitivity D1000 ScreenTape and Reagents with the 4200 TapeStation instruments (Agilent Technologies). The size peak of the library should equal to the adaptor size (124bp) plus the size of the corresponding amplicon. The KAPA Library Quantification Kit (KAPA Biosystems) was used to quantify the DNA libraries according to the manufacturer’s protocol.

4. The non–bisulfite plasma DNA library, bisulfite-converted plasma DNA library, and DNA libraries prepared from genomic DNA were sequenced on a HiSeq or NextSeq system (Illumina) with a 76 × 2 paired-end mode.

Data analysis protocol

1. Align the sequenced reads to the human genome (GRCh37) using the Short Oligonucleotide Alignment Program 2 (SOAP2) (Li et al. Bioinformatics. 2009;25:1966–1967). The parameters for alignment were “-r 0 -v 2 -I 0 -X 600”.

2. Identify variants which are detected in the plasma DNA sequencing result of pregnant women affected by lymphoma (median sequencing depth: 106×) but absent in the sequencing result of white blood cells and normal cells harvested from buccal swab (median sequencing depth: 23×). 

3. Use dynamic cutoff algorithm (Chan et al., Proc Natl Acad Sci USA. 2016;113:E8159-E8168) to differentiate the putative variants from the sequencing errors. The dynamic cutoffs used in this study were listed in the table below.

4. Realign reads covering the putative variant alleles using Bowtie2 (Langmead et al. Nat Methods. 2012;9:357-9), with a “--very-sensitive” option. 

5. Remove variants that do not align to the same genomic location by the two alignment algorithms.

6. Discard variants with an unexpected high coverage (>1000×).

7. Select variants with a sequencing depth of at least 10×.

8. Classify the candidate variants into two categories: 

      (1) Variants present in the dbSNP database (Build 135) are regarded as candidate fetal-specific alleles. 

      (2) Variants not present in the dbSNP database are regarded as candidate tumor-specific mutations.

9. Compare the two categories of variants with the sequencing result of the tumor biopsy. 

• For the candidate fetal-specific alleles:
  • Variants absent in the tumor tissue are concluded as fetal-specific alleles.
  • Variants present in the tumor tissue are discarded.
• For the candidate tumor-specific mutations:
  • Same variant detected in the tumor tissue with three or more supporting reads are concluded as tumor-specific mutations.
  • Variants with two or less supporting reads in tumor tissue are discarded.

10. Identify the DNA molecules in the bisulfite sequencing result which contain the fetal-specific alleles and DNA molecules carrying the tumor-specific mutations for the downstream GETMap analysis (according to the software pipeline provided with the paper).

Category