Background: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. Results: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Conclusions: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level.

The distinct molecular subtypes of lung cancer are defined by monogenic biomarkers, such as EGFR, KRAS, and ALK rearrangement. Tumor mutation burden (TMB) is a potential biomarker for response to immunotherapy, which is one of the measures for genomic instability. The molecular subtyping based on TMB has not been well characterized in lung adenocarcinomas in the Chinese population. Here we performed molecular subtyping based on TMB with the published whole exome sequencing data of 101 lung adenocarcinomas and compared the different features of the classified subtypes, including clinical features, somatic driver genes, and mutational signatures. We found that patients with lower TMB have a longer disease-free survival, and higher TMB is associated with smoking and aging. Analysis of somatic driver genes and mutational signatures demonstrates a significant association between somatic RYR2 mutations and the subtype with higher TMB. Molecular subtyping based on TMB is a potential prognostic marker for lung adenocarcinoma. Signature 4 and the mutation of RYR2 are highlighted in the TMB-High group. The mutation of RYR2 is a significant biomarker associated with high TMB in lung adenocarcinoma.

Tumor mutation burden (TMB) is a potential biomarker for response to immunotherapy. The subset of patients with TMB has not been well characterized in lung adenocarcinomas. Here we performed molecular subtyping based on TMB and compared the features of different subtypes including clinical features, somatic driver genes and mutational signatures. We found that patients with lower tumor mutation burden had a longer disease-free survival, while higher tumor mutation burden is associated with smoking and aging. Analysis of somatic driver genes and mutational signatures demonstrates a significant association between somaticRYR2mutations and the subtype with higher mutation burden. Overall, our study identified two molecular subtypes based on TMB and described the corresponding difference in their clinical and genomic levels.

Somatic mosaicism is widespread among tissues and could indicate distinct tissue origins of circulating cell-free DNA (cfDNA), DNA fragments released by lytic cells into the blood. By investigating the alignment patterns of whole genome sequencing reads with the genomic DNA of different tissues, we found that the read distributions formed type-specific patterns in some regions as a result of somatic mosaicism. We then utilized this information to construct a tissue-of-origin mapping model and evaluated its predictive performance on whole genome sequencing data from tissue and cfDNA samples. In total, 1,545 tissue samples associated with 13 cancer types were included, and identification of the tissue of origin achieved a specificity of 82% and a sensitivity of 80%. Furthermore, a total of 30 cfDNA samples from lung cancer and liver cancer patients and healthy controls were analyzed to predict their tissues of origin with a specificity of 87% and a sensitivity of 87%. Our results show that read distribution patterns from whole genome sequencing could be used to identify cfDNA tissues of origin with high accuracy, suggesting the potential application of our model to early cancer detection and diagnosis.