Study of the Genetic Variants in BRCA1/2 and Non-BRCA Genes in a Population-Based Cohort of 2155 Breast/Ovary Cancer Patients, Including 443 Triple-Negative Breast Cancer Patients, in Argentina.

Gene/s sequencing in hereditary breast/ovary cancer (HBOC) in routine diagnosis is challenged by the analysis of panels. We aim to report a retrospective analysis of BRCA1/2 and non-BRCA gene sequencing in patients with breast/ovary cancer (BOC), including triple-negative breast cancer (TNBC), in our population. In total 2155 BOC patients (1900 analyzed in BRCA1/2 and 255 by multigenic panels) gave 372 (17.2.6%) and 107 (24.1%) likely pathogenic/pathogenic variants (LPVs/PVs), including BRCA and non-BRCA genes, for the total and TNBC patients, respectively. When BOC was present in the same proband, a 51.3% rate was found for LPVs/PVs in BRCA1/2. Most of the LPVs/PVs in the panels were in BRCA1/2; non-BRCA gene LPVs/PVs were in CDH1, CHEK2, CDKN2A, MUTYH, NBN, RAD51D, and TP53. TNBC is associated with BRCA1/2 at a higher rate than the rest of the breast cancer types. The more prevalent PVs in BRCA1/2 genes (mostly in BRCA1) do not rule out the importance to panels of genes, although they are certainly far from shedding light on the gap of the 85% predicted linkage association of BOC with BRCA1/2 and are still not elucidated.

Onco-omics Approaches and Applications in Clinical Trials for Cancer Patients.

Omics technologies have revolutionised fundamental and medical research. Oncology is perhaps the field where these technologies have been most rapidly adopted and where they have had their biggest impact, dramatically transforming clinical practice guidelines over a very short period of time. Along with this transformation has come an even larger array of technologies, tools and jargon, that make following the most recent developments in the field a truly daunting task for those not involved in it. This chapter is intended to provide a general overview of evolving topics in oncology research in the era of big data analysis and precision medicine, with a specific focus on the use of tumour biomarkers, tumour biomarker tests, targeted drugs and the changing landscape of clinical trial designs.

Potential Role of Genomic Sequencing in the Early Diagnosis of Treatable Genetic Conditions.

We present cases of 3 children diagnosed with the same genetic condition, Gitelman syndrome, at different stages using various genetic methods: panel testing, targeted single gene sequencing, and exome sequencing. We discuss the advantages and disadvantages of each method and review the potential of genomic sequencing for early disease detection.

Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice.

Cancer gene panels (CGPs) are already used in clinical practice to match tumor's genetic profile with available targeted therapies. We aimed to determine if CGPs could also be applied to estimate tumor mutational load and predict clinical benefit to PD-1 and CTLA-4 checkpoint blockade therapy. Whole-exome sequencing (WES) mutation data obtained from melanoma and non-small cell lung cancer (NSCLC) patients published by Snyder et al. 2014 and Rizvi et al. 2015, respectively, were used to select nonsynonymous somatic mutations occurring in genes included in the Foundation Medicine Panel (FM-CGP) and in our own Institutional Panel (HSL-CGP). CGP-mutational load was calculated for each patient using both panels and was associated with clinical outcomes as defined and reported in the original articles. Higher CGP-mutational load was observed in NSCLC patients presenting durable clinical benefit (DCB) to PD-1 blockade (FM-CGP P=0.03, HSL-CGP P=0.01). We also observed that 69% of patients with high CGP-mutational load experienced DCB to PD-1 blockade, as compared to 20% of patients with low CGP-mutational load (FM-CGP and HSL-CGP P=0.01). Noteworthy, predictive accuracy of CGP-mutational load for DCB was not statistically different from that estimated by WES sequencing (P=0.73). Moreover, a high CGP-mutational load was significantly associated with progression-free survival (PFS) in patients treated with PD-1 blockade (FM-CGP P=0.005, HR 0.27, 95% IC 0.105 to 0.669; HSL-CGP P=0.008, HR 0.29, 95% IC 0.116 to 0.719). Similar associations between CGP-mutational load and clinical benefit to CTLA-4 blockade were not observed. In summary, our data reveals that CGPs can be used to estimate mutational load and to predict clinical benefit to PD-1 blockade, with similar accuracy to that reported using WES.