Fig 1.
(A) Experimental design. The workflow from sample recruitment to sequencing data analysis is shown. Panel 1. Sample selection. Thirty-eight BC patients were selected from the Terry Fox National Tumor Bank (National Cancer Institute in Colombia) diagnosed in a period of 10 years (2007–2017). Panel 2. Sample preparation and barcoding. Genomic DNA was extracted by affinity column followed by library preparation using exome enrichment (176 genes for tumor samples) and shallow whole genome sequencing for cfDNA. The samples were sequenced on an Illumina HiSeq in a 2X150 cycle format. Panel 3. Sequencing and data analysis. Identification of SCNAs in both tumor and cfDNA, and SNV in tumor tissues. BC: Breast Cancer; cfDNA: circulating free DNA; sWGS: Shallow Whole Genome Sequencing; SCNA: Somatic Copy Number alteration; SNV: Single Nucleotide Variant. (B) Samples used for this study. cfDNA samples obtained from plasma are depicted in blue, and tumor samples are shown in red. Created with Biorender.
Table 1.
Clinical and epidemiological characteristics in our cohort of study.
Fig 2.
Tumor mutation rate in 38 breast cancer tumor tissues.
Number of mutations segmented by sample type in ascending order. The clinical stages of these tumors are shown in the lower track. The horizontal, dotted line represents the mean mutational load.
Fig 3.
Pathogenic variants and VUS identified in 38 breast cancer patients.
The total number of driver genes with variants in each sample are shown in the top panel. Signaling pathways and classification of variants following the recommendations of American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) are shown in the left panel. The clinical characteristics are shown in the bottom panel. UTR: untranslated region; IDC: intraductal carcinoma; CTx: chemotherapy; RTx: radiotherapy; Sx: surgery.
Fig 4.
cfDNA concentration between the groups with different stages and treatments.
(A) cfDNA concentration per patient in ascending order, (B) cfDNA concentration by different stages of the disease (ANOVA test, p = 0.60), and (C) distribution of cfDNA concentration and age.
Fig 5.
Total somatic copy number alterations events identified in tumor tissues and cfDNA.
(A) Venn diagram depicting the differences and similarity of the SCNAs detected in cfDNA (blue) and tumor tissues (orange). For this analysis only the paired samples were considered (cfDNA-tumor tissues). (B) Number of SCNAs identified in cfDNA and tumor tissues by patient.
Fig 6.
Tumor and cfDNA SCNA events in driver genes and clinical characteristics in the cohort.
Samples are presented in two groups, the cfDNA:tumor pairs on the left, and tumor only on the right. The alterations in the cfDNA and tumor samples are illustrated as left and right flags, respectively. The number of affected driver genes in each sample is shown in the top panel. The ASCO/CAP classification, mutation signaling pathways, and relative frequency are shown on the left side. The absolute frequency of SCNA events per gene and the legends are shown on the right. Clinical characteristics are shown in the bottom panel.
Fig 7.
Alterations found according to ASCO/CAP classification in BC.
The figure shows the different types of alterations (top), the existing therapeutic targets (left) and the principal pathways affected (right). Created with Biorender.