Association of Pathway Mutations With Survival in Taiwanese Breast Cancers

Abstract

Breast cancer is the most common invasive cancer in women worldwide. Next-generation sequencing (NGS) provides a high-resolution profile of cancer genome. Our study ultimately gives the insight for genetic screening to identify the minority of patients with breast cancer with a poor prognosis, who might benefit from the most intensive possible treatment. The detection of mutations can polish the traditional method to detect high-risk patients who experience poor prognosis, recurrence and death early. In total, 147 breast cancer tumors were sequenced with targeted sequencing using a RainDance Cancer Hotspot Panel. The average age of all 147 breast cancer patients in the study was 51.7 years, with a range of 21-77 years. The average sequencing depth was 5,222x (range 2,900x-8,633x), and the coverage was approximately 100%. A total of 235 variants in 43 genes were detected in 147 patients by high-depth Illumina sequencing. A total of 219 single nucleotide variations were found in 42 genes from 147 patients, and 16 indel mutations were found in 13 genes from 84 patients. After filtering with the 1000 Genomes database and for synonymous SNPs, we focused on 54 somatic functional point mutations. The functional point mutations contained 54 missense mutations in 22 genes. Additionally, mutation of genes within the RET, PTEN, CDH1, MAP2K4, NF1, ERBB2, RUNX1, PIK3CA, FGFR3, KIT, KDR, APC, SMO, NOTCH1, and FBXW7 in breast cancer patients were with poor prognosis. Moreover, TP53 and APC mutations were enriched in triple-negative breast cancer. APC mutations were associated with a poor prognosis in human breast cancer (log-rank P<0.001). Our study identified tumor mutation hotspot profiles in Taiwanese breast cancer patients, revealing new targetable gene mutations in Asian breast cancer patients.

Keywords: breast cancer; cancer panel; next-generation sequencing; survival analysis; triple negative.

Figure 1

Mutations and in the 147 breast cancer patients. The top panel shows a summary of the mutations in 43 cancer hotspot genes (see text and Table S2 for details). Patients are arranged from left to right by the number of mutations, shown in the top track. Colored rectangles indicate the mutation category observed in a given gene.

Figure 2

Somatic missense mutation profiles of breast cancers identified by a hotspot panel of 22 cancer-associated genes.

Figure 3

Genetic mutations identified by the RainDance™ gene panel in 147 breast cancers. The Oncoprint illustrated the distribution of somatic mutations according to age at diagnosis.

Figure 4

Ten interaction networks constructed from canonical maps including the mutations found in 147 luminal breast tumors. In the concentric circle diagram, tumors are arranged as radial spokes and categorized by their mutation status in each network (concentric ring color). Orange, genome integrity, proteolysis, and apoptosis; green, MAPK signaling and PI3K signaling; blue, RTK signaling and Hippo signaling; purple, Notch signaling, Hedgehog signaling, and Wnt signaling.

Figure 5

Kaplan–Meier survival curves for overall survival based on kinase signaling pathway in breast cancer patients.

Figure 6

Kaplan–Meier survival curves for overall survival based on APC mutations in breast cancer patients.