Functional diagnostics using fresh uncultured lung tumor cells to guide personalized treatments

Published: 21 July 2021| Version 1 | DOI: 10.17632/bb4vfjnfkf.1
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Description

A DNeasy Blood & Tissue kit (Qiagen) was used to extract genomic DNA from healthy lung and tumor tissue samples and from CR cultures (Table 1). Genomic dsDNA (382-500 ng) was fragmented with a Covaris E220 evolution instrument (Covaris) to a mean fragment size of 200 base pairs (bp). For sample library preparation and enrichment, a KAPA Hyper library preparation kit was used, following the SeqCap EZ HyperCap Workflow User’s Guide Version 1.0 (Roche Nimblegen). In brief, pre-capture amplification was performed using 9 cycles, and captures were performed in multiplexes of 3 to 4 samples using 0.667-1 μg of each library. To identify somatic mutations, targeted next-generation sequencing was performed using the Illumina HiSeq2500 system in HiSeq high output mode using v4 chemistry or HiSeq Rapid run mode using v2 chemistry (Illumina), with the NimbleGen Cancer Panel to capture the exons of 578 cancer-related genes. NimbleGen probes used were 120522_HG19_Onco_R_EZ. Instead of SeqCap HE-Oligos, we used xGen Universal Blocking oligo TS mix (IDT). For post-capture amplification, ten cycles were used in two replicate reactions. The library was quantified for sequencing using the 2100 Bioanalyzer High sensitivity kit. Read length for the paired-end run was 2x101 bp. Pre-processing of short read data was done using the Trimmomatic software and included correction of the sequence data for adapter sequences, bases with low quality, and reads less than 36 bp in length. The BWA-MEM algorithm was then used to map paired-end reads passing the pre-processing onto the human reference genome build 38 (EnsEMBL v82). Finally, variants were called according to the GATK best practice for somatic short variants (version 3.5.1), supplemented with cross-sample contamination and sequencing artifact filtering. In the process, tumor samples (tissue and CR culture) were paired with their patient-matched normal samples and variant calls were filtered against a panel of normals generated from the exome data of 24 healthy unrelated Finnish individuals sequenced in-house earlier. Annotation for single nucleotide variants and short indels was performed using the Annovar tool against the RefGene database. In brief, variants called from samples were filtered for false-positives by removing variants not passing all GATK filters, residing in intronic and intergenic regions, and causing a synonymous or non-frameshift change as well as variant with a minor allele frequency ≥ 1% in the EPS, 1KG, general ExAC (ExAC), East Asian ExAC (ExAC_EAS), non-Finnish European ExAC (ExAC_NFE), Finnish ExAC (ExAC_FIN) databases, strand odd ratio for single nucleotide variants ≥ 3.00, and strand odd ratio for indels ≥ 11.00, coverage ≤ 10, and variant quality value ≤ 40. Finally, variants were removed if the variant allele frequency between the tumor and normal was < 2%.

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Non-Small Cell Lung Cancer, Personalized Medicine, Next Generation Sequencing

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