Breast cancer (BC) is the second most common type of cancer and the second leading cause of death in women; it is also the most incident cancer worldwide [1]. The risk of BC increases with age: the majority of new cases are reported in women who are 60 to 65 years old. High BC mortality is explained by late diagnosis established when the disease has already progressed to the advanced stage. Metastatic BC is particularly dangerous, since it is resistant even to combination treatments based on chemotherapy, hormones and targeted drugs. The 5-year survival rate in patients with BC is 55 %. This brings the need for novel approaches towards more effective screening as well as targeted therapy of BC based on the molecular genetic profiling of tumors.

The rapid development of next generation sequencing (NGS) has yielded a bulk of information about genetic variants [2]. A lot of mutations are associated with BC, including somatic and germinal mutations in the genes PIK3CA, STK11/LKB1, CDH1, ATM, CHEK2, BRIP1, and PALB2 and mutant variants of the highly penetrant genes associated with hereditary BC, such as TP53, PTEN, MLH1, BRCA1, and BRCA2 [3].

The majority of tumor mutations are somatic; they have an important role in the pathogenesis of cancer and confer de novo resistance to treatment. Thus, a lot of ongoing studies utilize NGS in an attempt to profile mutant variants in tumors. As a result, it has been identified a significant amount of new mutations with unknown function. To describe these polymorphisms, mathematical algorithms are necessary that can automatically process huge data arrays, predict potentially pathogenic mutations and distinguish them from harmless variants. The resulting data can be used when developing screening or diagnostic tools (including liquid biopsy) and selecting adequate targeted therapies.

In this work we analyze a range of mutations identified in key BC oncogenes by NGS, using a previously developed bioinformatic pipeline for the functional annotation of mutations and assessment of their pathogenicity.

METHODS

We obtained tumor samples from 16 patients of Blokhin Russian Cancer Research Center, Moscow. The participants’ age range was 27 to 76 years, with a mean of 50.7 ± 11.3 years. All patients had breast malignancies and received combination therapy. The inclusion criteria were as follows: age of 18 to 70 years, sex (all patients were females), histologically and cytologically confirmed breast cancer. The exclusion criteria were a medical history of other tumor types and pregnancy.

Disease stages were determined according to the TNM classification [4]. The study was carried out in the patients with stages T1–3N0–3M0–1.

All patients gave voluntary informed consent. The study complied with the principles of confidentiality. Patients’ clinicopathologic features are summarized in tab. 1.

DNA isolation and quality control

DNA was isolated from the samples of tumor tissue using DNeasy Blood and Tissue Kit (Qiagen, USA). Tumor tissue was cut into small pieces, and buffer ATL was added to the samples. The samples were then treated with proteinase K, incubated at 56 °C until fully lysed, and treated with RNase A. Next, we added 200 μl buffer AL and 96 % ethanol. The resulting mixture was transferred to spin columns and centrifuged at 8,000 g for 1 min. The samples were washed with AW1 and AW2 buffers to remove salts (guanidine and SDS). The columns were eluted twice with 30 μl Low-TE buffer; the samples were incubated and centrifuged according to the manufacturer’s protocol. Quality control of the obtained DNA was performed on Qubit 3.0 (Thermo Fisher Scientific, USA). The samples were also run on 1 % agarose gel electrophoresis with ethidium bromide.

Sequencing of targeted oncogenes

DNA libraries were prepared using NEBNext Ultra DNA Library Kit for Illumina (New England Biolabs, USA). The libraries were dual-indexed by PCR using NEBNext Ultra DNA Library Prep Kit for Illumina and NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1, New England Biolabs). Quality control of the obtained DNA libraries was performed on Agilent Bioanalyzer 2100 (Agilent Technologies, USA) using High Sensitivity Kit by the same manufacturer according to the official protocol.

For targeted enrichment of the coding regions of tumor genomes we used MYbaits Onconome KL v1.5 Panel (MYcroarray, USA). The enriched fragments were sequenced with 100 b. p. paired-end reads on HiSeq 2500 (Illumina, USA). Sample preparation and sequencing were done according to Illumina’s protocols.

Bioinformatic analysis

Sequencing data were analyzed using an original algorithm developed previously [5]. First, the quality of reads was checked: sequences with read quality below 10 were removed from NGS data using Cutadapt software [6]. Then the reads were mapped to the reference genome hg19 (GRCh37. p13) using the Burrows–Wheeler Aligner algorithm [7]. PCR- duplicates were removed by running the rmdup command in SAMtools [8].

Mutations were called with MuTect [9]. DNA sequences covered by at least 12 reads were considered the most significant.

To assess the functional effect of the discovered mutations, they were annotated in SnpEff and their effect on the encoded protein was predicted based on the analysis of genomic coordinates [10].

RESULTS

Using Illumina-based NGS, we have screened 16 breast tumors for mutations harbored by cancer-associated genes BRCA1, BRCA2, ATM, CDH1, CHEK2, MRE11A, NBN, PALB2, PTEN, RAD50, RAD51C, TP53, and SEC23B. Out original bioinformatic algorithm has detected 58 point mutations in the genes BRCA1, BRCA2, ATM, CDH1, CHEK2 and TP53, including 19 homozygous and 39 heterozygous variants. The list of unique mutations is provided in tab. 2.

The figure below shows the frequency of mutations in the genes with the highest abundance of mutations, namely ATM, TP53 and BRCA1. The most frequent mutations were c.376- 283T>C (TP53), c.3994-193T>C, c.8010+186C>T (ATM), and c.5215+66G>A (BRCA1).

Based on the bioinformatic analysis and annotation of the identified polymorphisms, we selected those mutations that could significantly affect the regulatory or protein sequences. To assess pathogenicity and conservation of the mutations, we used data from COSMIC (Catalogue of Somatic Mutations In Cancer) [11] and dbNSFP [12]. Additionally, SIFT (Sorting Intolerant From Tolerant) and PolyPhen2 tools were used to predict pathogenicity of the mutations and assess their effect on the function of the encoded protein [13, 14]. Information about mutation frequencies was obtained from the 100 Genomes project and the Exome Aggregation Consortium [15, 16].