Background: Approximately 5-10% of epithelial ovarian and breast cancer cases are hereditary and germline mutations in BRCA1 and BRCA2 genes are the most common in these patients. Prevalence of clinically relevant mutations in BRCA1/2 differs among different populations, and specifically in Brazil, a country with great ethnic diversity and miscegenation, and only limited data are available to the present moment. In our country, the public health system (SUS) is responsible for the health care of almost 75% of the population; however, genetic tests are not yet covered. Our aim was to evaluate the complete coding sequence of BRCA1/2 genes and large rearrangements in patients diagnosed with epithelial ovarian and breast cancer in the largest cancer hospital from the Brazilian public health system. Methodology: The complete coding sequence of BRCA1/2 genes were evaluated through next-generation or capillary sequencing, and large deletions were investigated through multiplex ligation-dependent probe amplification (MLPA) in four cohorts of patients: 102 unrelated patients diagnosed with epithelial ovarian cancer unselected for family history of breast and/or ovarian cancer; 89 unrelated early-onset breast cancer patients (<35 years); 42 unrelated postmenopausal breast cancer patients (> 55 years) reporting positive breast and ovarian family history; and 49 unrelated breast cancer patients selected through Frank, BRCApro, and Evans algorithms (risk > 10%). Results: Pathogenic mutation in BRCA1/2 genes was detected in 44 patients (44/282: 15.6%; BRCA1, n=27; BRCA2, n=17) featuring 33 different mutations (16 in BRCA1 and 17 in BRCA2), including two large deletions in BRCA1 (exon 1-2 deleted and exon 5-7 deleted). Five mutations were detected more than once in BRCA1 gene (c.211A>G, c.3331_3334delCAAG, c.4484G>T, c.5074+2T>C, and c.5266dupC); however, all mutations in BRCA2 gene were detected only once. Only three ovarian cancer patients had a mutation localized in an ovarian cancer cluster region (OCCR) of BRCA1 and two breast cancer patients in BRCA1 breast cancer cluster regions (BCCR). Ovarian cancer patents most commonly presented BRCA1 mutations and young breast cancer mainly presented BRCA2 mutation. In this series of patients, 24 missense variants of uncertain significance were detected (BRCA1: n=6 and BRCA2: n=18). Considering distribution of pathogenic mutation among the patient groups, a high frequency was observed in two groups: patients diagnosed with epithelial ovarian cancer (18.36%) and breast cancer patients selected by Frank, BRCApro, and Evans algorithms (18.6%). Conclusion: The chance of carrying a BRCA1/2 germline mutation in Brazilian high-risk ovarian and breast cancer patients is 15.6%. The entire gene should be analyzed as mutations are detected in various regions. Financial support: FAPESP, CNPq. Citation Format: Simone Maistro, Giselly Encinas, Tauana Nagy, Natalia Teixeira, Maria Lucia H. Katayama, Ana Carolina R. C. Gouvêa, Maria del Pilar E. Diz, Roger Chammas, Geertruida H. de Boch, Maria Aparecida A. K. Folgueira. Germline BRCA1 and BRCA2 mutations in Brazilian ovarian and breast cancer patients [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A23.
Selection of cancer patients for new targeted therapies reached its dead end as next generation sequencing based precision oncology approaches failed to deliver breakthrough improvements to oncology practice. The problem with current approaches is that the effect of a mutation can be indirect by influencing the expression of various other genes, which in turn can act as new therapy targets. A large-scale analysis of such cascades was not yet executed in breast cancer. Here, we developed an analysis tool to identify targetable genes showing an altered expression in relation to a mutation in other genes. The background database includes two independent large patient's cohorts, the TCGA and the Metabric datasets. Mutation status for each gene was determined using the VCF files from the TCGA repository. RNA-seq gene expression data for the same patients was re-normalized using a scaling normalization. Gene expression for the Metabric samples was determined using Illumina gene arrays and mutation status for the same patients is available for 174 selected genes. The Metabric database includes 1,981 patients and the TCGA breast cancer database contains 1,091 patients. Expression is linked with mutation status for each gene across all patients using Mann-Whitney test. A p<0.05 and a false discovery rate of <10% was accepted as significant. We demonstrate the utility of the analysis platform by using it to uncover patient cohorts with higher expression of PD1 (PDCD1) and PD-L1 (CD274). Immune checkpoint inhibitors permbrolizumab and nivolumab target PD1. PD-L1 inhibitors include atezolizumab, avelumab, and durvalumab. None of these immunotherapy agents is approved to be used in breast cancer. In both settings, only one gene reached statistical significance. For PD1, the best performing gene was NOP14. Patients with mutation in NOP14 (1.3% of patients) had a 2.08x increased expression (4.58 in mutated vs. 2.20 in wild type) of PD1 (p=8.4e-05, FDR=0.0239). For PD-L1, the strongest gene was CCDC88A (mutated in 2.6% of patients), which had a 2.03x increased expression (10.42 in mutated vs. 5.13 in wild type) of PD-L1 (p=6.2e-05, FDR=0.0147). Both NOP14 and CCDC88A have been linked to cancer development and progression, but have not been investigated in relation to immune therapies. One can anticipate that patients with mutation in these genes will be prone to respond to immune checkpoint inhibitors. In summary, an online portal was set up capable to identify genes with altered expression in relation to a given mutation. The presented approach can help to increase speed and reduce cost of development for future anticancer treatments. The analysis tool also enables identification of patient cohorts for new agents and is accessible at www.mutarget.com. Citation Format: Győrffy B, Nagy T. muTarget.com: Linking gene expression and mutation status to identify patient cohorts eligible for targeted- and immunotherapy in breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-21-09.
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