Identifying Genomic Alterations in Patients With Stage IV Breast Cancer Using MammaSeq: An International Collaborative Study.

BACKGROUND: Identification of genomic alterations present in cancer patients may aid in cancer diagnosis, prognosis and therapeutic target discovery. In this study, we aimed to identify clinically actionable variants present in stage IV breast cancer (BC) samples. MATERIALS AND METHODS: DNA was extracted from formalin-fixed paraffin-embedded samples of BC (n = 41). DNA was sequenced using MammaSeq, a BC-specific next-generation sequencing panel targeting 79 genes and 1369 mutations. Ion Torrent Suite 4.0 was used to make variant calls on the raw data, and the resulting single nucleotide variants were annotated using the CRAVAT toolkit. Single nucleotide variations (SNVs) were filtered to remove common polymorphisms and germline variants. CNVkit was employed to identify copy number variations (CNVs). The Precision Medicine Knowledgebase (PMKB) and OncoKB Precision Oncology Database were used to associate clinical significance with the identified variants. RESULTS: A total of 41 samples from Turkish patients with BC were sequenced (read depth of 94-13,340; median of 1529). These patients were diagnosed with various BC subtypes including invasive ductal carcinoma, invasive lobular carcinoma, apocrine BC, and micropapillary BC. In total, 59 different alterations (49 SNVs and 10 CNVs) were identified. From these, 8 alterations (3 CNVs - ERBB2, FGFR1, and AR copy number gains and 5 SNVs - IDH1.R132H, TP53.E204∗, PI3KCA.E545K, PI3KCA.H1047R, and PI3KCA.R88Q) were identified to have some clinical significance by PMKB and OncoKB. Moreover, the top 5 genes with the most SNVs included PIK3CA, TP53, MAP3K1, ATM, and NCOR1. Additionally, copy number gains and losses were found in ERBB2, GRB7, IGFR1, AR, FGFR1, MYC, and IKBKB, and BRCA2, RUNX1, and RB1, respectively. CONCLUSION: We identified 59 unique alterations in 38 genes in 41 stage IV BC tissue samples using MammaSeqTM. Eight of these alterations were found to have some clinical significance by OncoKB and PKMB. This study highlights the potential use of cancer specific next-generation sequencing panels in clinic to get better insight into the patient-specific genomic alterations.

Reinterpretation of Chromosomal Microarrays with Detailed Medical History.

OBJECTIVE: To investigate the utility of a detailed medical history in the interpretation of chromosomal microarray results for pediatric patients with a constitutional disease. STUDY DESIGN: A retrospective review and reinterpretation of test results from chromosomal microarrays performed from 2011 to 2013. Previously reported genetic variants were reanalyzed after review of the patient's complete electronic medical record (cEMR). A 3-tier system was used for reclassification of variants: pathogenic or likely pathogenic (P/LP); variant of uncertain significance (VUS); or benign or likely benign (B/LB). RESULTS: Over an 18-month period, 998 patients with chromosomal microarray results were identified. The most common reasons for chromosomal microarray testing were developmental delay (n = 336), autism spectrum disorder (n = 241), and seizures (n = 143). Chromosomal microarray testing identified 1 or more variants in 48% (482 of 998) of patients; 516 patients had a negative report. For the 482 patients with variants, the original interpretations were composed of 19.3% P/LP (93 of 482), 44.8% VUS (216 of 482), and 35.9% B/LB (173 of 482) variants. After review of the cEMR, 34% of patient results (164 of 482) were changed in interpretation. One case changed from B/LB to VUS, 7 VUS were upgraded to P/LP, and 156 VUS were downgraded to B/LB. No P/LP variants had a change in interpretation. CONCLUSIONS: Overall, 16.4% (164 of 998) of patients with chromosomal microarray testing had a change in interpretation. Access to the patient's cEMR improves the interpretation of chromosomal microarrays by decreasing the number of uncertain (VUS) interpretations.

[Clinical Applications of Next-Generation Sequencing].

The significant drop in sequencing costs boosted by chemistries optimization and sample multiplexation has resulted in an immense growth within the field of next-generation sequencing (NGS) in the last decade. This has allowed a diversification of techniques, promoting a rapid advance in knowledge on the molecular basis of human disease. Due to the applicability and importance of this technology in basic research, it has quickly migrated to the clinical setting. NGS enables clinicians to make improved diagnostic and treatment decisions, which ultimately may influence precision medicine.

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes.

Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

3697G>A in MT-ND1 is a causative mutation in mitochondrial disease.

Mitochondrial diseases are a group of clinically heterogeneous disorders that can be difficult to diagnose. We report a two and a half year old girl with clinical symptoms compatible with Leigh disease but with no definitive diagnosis. Using next generation sequencing we found that mutation 3697G>A was responsible for the patient's clinical symptoms. Corroboration was performed via segregation analysis in mother and sister and by evolutionary analysis that showed that the mutation is located in a highly conserved region across a wide range of species. Functional analyses corroborated the mutation effect and indicated that the pathophysiological alterations were partially restored by Coenzyme Q10. In addition, we proposed that the presence of the mutation at high frequencies causes the phenotype in the patient, while other family members with intermediate levels of heteroplasmy are symptoms-free.