Table 1 Recent NGS-based studies in cancer

Colon cancer

72 WES, 68 RNA-seq, 2 WGS

Identify multiple gene fusions such as RSPO2 and RSPO3 from RNA-seq that may function in tumorigenesis

[15]

Breast cancer

65 WGS/WES, 80 RNA-seq

36% of the mutations found in the study were expressed. Identify the abundance of clonal frequencies in an epithelial tumor subtype

[11]

Hepatocellular carcinoma

1 WGS, 1 WES

Identify TSC1 nonsense substitution in subpopulation of tumor cells, intra-tumor heterogeneity, several chromosomal rearrangements, and patterns in somatic substitutions

[17]

Breast cancer

510 WES

Identify two novel protein-expression-defined subgroups and novel subtype-associated mutations

[5]

Colon and rectal cancer

224 WES, 97 WGS

24 genes were found to be significantly mutated in both cancers. Similar patterns in genomic alterations were found in colon and rectum cancers

[14]

squamous cell lung cancer

178 WES, 19 WGS, 178 RNA-seq, 158 miRNA-seq

Identify significantly altered pathways including NFE2L2 and KEAP1 and potential therapeutic targets

[16]

Ovarian carcinoma

316 WES

Discover that most high-grade serous ovarian cancer contain TP53 mutations and recurrent somatic mutations in 9 genes

[13]

Melanoma

25 WGS

Identify a significantly mutated gene, PREX2 and obtain a comprehensive genomic view of melanoma

[20]

Acute myeloid leukemia

8 WGS

Identify mutations in relapsed genome and compare it to primary tumor. Discover two major clonal evolution patterns

[21]

Breast cancer

24 WGS

Highlights the diversity of somatic rearrangements and analyzes rearrangement patterns related to DNA maintenance

[8]

Breast cancer

31 WES, 46 WGS

Identify eighteen significant mutated genes and correlate clinical features of oestrogen-receptor-positive breast cancer with somatic alterations

[7]

Breast cancer

103 WES, 17 WGS

Identify recurrent mutation in CBFB transcription factor gene and deletion of RUNX1. Also found recurrent MAGI3-AKT3 fusion in triple-negative breast cancer

[6]

Breast cancer

100 WES

Identify somatic copy number changes and mutations in the coding exons. Found new driver mutations in a few cancer genes

[9]

Acute myeloid leukemia

24 WGS

Discover that most mutations in AML genomes are caused by random events in hematopoietic stem/progenitor cells and not by an initiating mutation

[22]

Breast cancer

21 WGS

Depict the life history of breast cancer using algorithms and sequencing technologies to analyze subclonal diversification

[12]

Head and neck squamous cell carcinoma

32 WES

Identify mutation in NOTCH1 that may function as an oncogene

[19]

Renal carcinoma

30 WES

Examine intra-tumor heterogeneity reveal branch evolutionary tumor growth

[18]