Germline mutation profiling of breast cancer patients by using non-BRCA sequencing panel

Sonar Panigoro^1*Rafika Indah Paramita^2*Fadilah Fadilah²Septelia Inawati Wanandi³Aisyah Fitriannisa Prawiningrum⁴Linda Erlina²Wahyu Dian Utari²Ajeng Megawati Fajrin²

• ¹Surgical Oncology Division, Department of Surgery, Faculty of Medicine, Universitas Indonesia, Jakarta, Jakarta, Indonesia
• ²Department of Medical Chemistry, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
• ³Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Indonesia, Jakarta, Jakarta, Indonesia
• ⁴Bioinformatics Core Facilities - IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia

known to be associated with an increased risk of developing the disease. [2] While BRCA1 and BRCA2 mutations are well-known germline mutations associated with an increased risk of breast cancer, there are several other non-BRCA genes that can also harbor germline mutations linked to breast cancer susceptibility, for example, TP53, PTEN, STK11, PALB2, CHEK2, ATM, RAD51C, and RAD51D genes. [3] The aforementioned genes play a crucial role in the processes of DNA repair, regulation of the cell cycle, and inhibition of tumour formation. [4] Identifying germline mutations, not only in BRCA genes but also in other genes, can have important implications for both affected individuals and their families, allowing for prevention and treatment strategies. [5,6] In this cross-sectional study, we aimed to identify non-BRCA germline mutations found in breast cancer patients with less-invasive method that could serve as breast cancer subtyping biomarkers. A total of 28 female individuals diagnosed with breast cancer participated in this study, and blood samples were obtained from each participant. The DNA extraction procedure was conducted using the Genomic DNA Mini Kit® (Geneaid, New Taipei City, Taiwan), following the guidelines provided by the manufacturer. The analysis of DNA isolates' purity was conducted by assessing the absorbance ratio of 260/280 using a Nanodrop instrument (Thermo Fisher Scientific, Waltham, MA, USA). The quantification of DNA isolates was performed using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) on a Qubit® 4.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The library preparation was performed utilizing the Illumina AmpliSeq™ Cancer Hotspot Panel v2 (Illumina®, United States). The first step was the amplification of certain areas within the DNA sample. The amplicons were subsequently subjected to partial digestion using the FuPa reagent. The indexes were ligated using the Ligate program on a thermal cycler. In order to cleanse the libraries, a quantity of 30 μl of Agencourt AMPure XP beads (Beckman Coulter™, United States) was introduced into the mixes.The following amplification techniques were implemented in order to assure a sufficient quantity of the libraries. The second round of purification was subsequently performed twice to exclude high molecular-weight DNA and surplus primer. This was accomplished using Agencourt AMPure XP beads (Beckman Coulter™, United States). The libraries were prepared by diluting them to a final loading concentration and subsequently subjected to sequencing utilizing the Illumina MiSeq technology. The sequencing procedure yielded pairedend libraries in .fastq format, wherein both ends had a read length of 150 base pairs (bp). The data sequences were deposited into the Sequence Read Archive (SRA) database with the BioProject accession number PRJNA998562. Quality control check of the fastq data is to evaluate the quality of each sample's raw reads. FastQC software [7] can be used to do the fastq quality control. The total number of raw bases and Q30 percentage can be determined using q30 Python programs (https://github.com/dayedepps/q30/tree/master). If the sequence reads quality were poor, we could do some intervention to increase the sequence quality. Trimmomatic [8] was used to trim the poor-quality sequence reads. We used some parameters that included for adapter removal (ILLUMINACLIP:NexteraPE-PE.FA:2:30:10, LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, and MINLEN:35). Reads alignment were done using BWA-MEM [9] and GRCh38.p13 as the human reference genome. After the alignment process, the amplicon mean depth, the coverage uniformity, and the percentage of on target rate can be calculated by in-house script containing Mosdepth [10], Samtools [11], and Bedtools [11] software. The command-line script used for Q30 percentage, amplicon mean depth, the coverage uniformity, and the percentage of on target rate were provided in Supplementary File 1. Variant calling analysis were used to find likely-pathogenic and pathogenic variants in all samples. Variant calling methods used as describe in [12] were included reads alignment using BWA [13], SAM-BAM conversion using SAMTOOLS [14], variant calling using GATK [15], and variant annotation using SnpEff and SnpSift [16]. Germline variant classification was predicted by using Varsome [17] (https://varsome.com/) that used ACMG classification. A total score is computed as the sum of the points from the pathogenic rules, minus the sum of the points from benign rules. The total score is then compared to thresholds to assign the final verdict: Pathogenic if greater than or equal to 10, Likely Pathogenic if between 6 and 9 inclusive, Uncertain Significance if between 0 and 5, Likely Benign if between -6 and -1, Benign if less than or equal to -7. The command-line script used for variant calling analysis were also provided in GitHub: https://github.com/fikaparamita04/variant-calling. The highest number of pathogenic variant was visualize using MutationMapper [18] (https://www.cbioportal.org/mutation_mapper). We've successfully collected the blood sample from 28 patients diagnosed with breast cancer From Cipto Mangunkusumo National Hospital Jakarta, with age range from 40 to 71 (Table 1). The patients were categorized into 4 subtypes, namely Luminal A, Luminal B, HER2-positive and TNBC with total number of patients were 8, 9, 7, and 4, respectively. Among the subjects, there were four patients in the fourth stage. Raw fastq data were quality checked to ensure sequencing quality. Due to targeted sequencing, we evaluated the Q30 percentage, average amplicon depth, coverage uniformity, and target level percentage (Table 2). The Q30 result of 97.71% ± 0.44 indicates good sequencing quality. The average amplicon depth in this study was good, with a score of 1076 ± 256.36, but the deviation from one sample to another was quite wide. Coverage uniformity refers to how evenly sequencing reads are distributed across the genome or a specific region of interest. All samples showed a coverage uniformity score of 1, or close to 1 (0.9901±0.01), indicating that all target bases were covered to the same extent, with no regions significantly higher or lower coverage. The target level percentage also indicated good target sequencing, with a score of 95.57% ± 0.57. The raw data files in fastq format have been archived in the BioProject database under accession number PRJNA998562. These data are potentially valuable in the screening of gene mutation markers in breast cancer, and have the potential to predict treatment efficacy related to mutations. We got highest germline frameshift mutation in FBXW7 gene (4:g.152324246del) around 35.7% that predicted as likely-pathogenic variants from Varsome (prediction score = 9), due to possibility of loss of protein functions (Figure 1). Interestingly, this mutation was found in all Luminal B and one HER2-positive patients. FBXW7 is a tumor suppressor that modulates the degradation of oncogenic substrates, including c-Jun, c-Myc, Notch1 intracellular domain (ICD), and cyclin E, by acting as the substrate recognition protein within the Skp1-Cullin-Fbox (SCF) ubiquitin ligase complex. Chromosome 4q31 deletion, encompassing FBXW7, occurs in approximately 30% of primary breast tumors. [19] In line with previous studies, deletion mutation in the FBXW7 gene closely resembles the human breast cancer luminal B subtype, characterized by ERα+, PR-, and elevated Ki67 staining. [20,21] Furthermore, luminal B tumors exhibit the lowest FBXW7 mRNA expression among breast cancer subtypes. Lower FBXW7 expression is associated with a high Ki-67 labeling index and positive cyclin E protein expression, both of which indicate proliferation. Breast cancer patients with the greatest FBXW7 gene expression have a longer disease-free survival rate. [22] The process by which FBXW7 regulates breast cancer growth, cell cycle, and metastasis involves many signaling pathways and gene crossover. For example, FBXW7deficient breast tumors inhibit the NF-κB signaling pathway, which normally involves E3 ubiquitin ligase binding and degradation. This results in enhanced NF-κB DNA binding activity, which promotes tumor development and metastasis. [20] As seen in Figure 1, mutation found in FBXW7 gene is found as well in OncoKB database. [21] Based on the database, it's stated that The FBXW7 N598Mfs*30 is a truncating mutation in a tumor suppressor gene, and therefore is likely oncogenic. There is promising scientific and anecdotal clinical data to support the use of Lunresertib and Camonsertib in patients with FBXW7-mutated solid tumors. Lunresertib is an orally available, small molecule PKMYT1 inhibitor, whereas Camonsertib is an orally available, small molecule ATR inhibitor. In the Phase I MYTHIC trial of Lunresertib plus Camonsertib in patients with advanced tumors harboring CCNE1 amplifications, FBXW7 deleterious mutations, or PPP2R1A deleterious mutations, the lunresertib + camonsertib cohort (n=59 [n=17 endometrial; n=13 colorectal; n=11 ovarian; n=3, breast; n=3, lung; n=12, other]) revealed an overall response rate of 23.6% in all evaluable patients across tumor types (n=55). [23] However, this study needs to be expanded to include larger patient populations and integrated with other -omics approaches for precise subtyping and therapy. We hope that our small contribution can lead to significant advances in precision therapy for breast cancer, particularly in Indonesia.