Purpose: The frequency of BRCA1 and BRCA2 germ-line mutations in women with ovarian cancer is unclear; reports vary from 3% to 27%. The impact of germ-line mutation on response requires further investigation to understand its impact on treatment planning and clinical trial design. Patients and Methods: Women with nonmucinous ovarian carcinoma (n = 1,001) enrolled onto a population-based, case-control study were screened for point mutations and large deletions in both genes. Survival outcomes and responses to multiple lines of chemotherapy were assessed. Results: Germ-line mutations were found in 14.1% of patients overall, including 16.6% of serous cancer patients (high-grade serous, 22.6%); 44% had no reported family history of breast or ovarian cancer. Patients carrying germ-line mutations had improved rates of progression-free and overall survival. In the relapse setting, patients carrying mutations more frequently responded to both platin- and nonplatin-based regimens than mutation-negative patients, even in patients with early relapse after primary treatment. Mutation-negative patients who responded to multiple cycles of platin-based treatment were more likely to carry somatic BRCA1/2 mutations. Conclusion: BRCA mutation status has a major influence on survival in ovarian cancer patients and should be an additional stratification factor in clinical trials. Treatment outcomes in BRCA1/2 carriers challenge conventional definitions of platin resistance, and mutation status may be able to contribute to decision making and systemic therapy selection in the relapse setting. Our data, together with the advent of poly(ADP-ribose) polymerase inhibitor trials, supports the recommendation that germ-line BRCA1/2 testing should be offered to all women diagnosed with nonmucinous, ovarian carcinoma, regardless of family history.

BACKGROUND: Precision medicine is dependent on identifying actionable mutations in tumors. Accurate detection of mutations is often problematic in formalin-fixed paraffin-embedded (FFPE) tissues. DNA extracted from formalin-fixed tissues is fragmented and also contains DNA lesions that are the sources of sequence artifacts. Sequence artifacts can be difficult to distinguish from true mutations, especially in the context of tumor heterogeneity, and are an increasing interpretive problem in this era of massively parallel sequencing. Understanding of the sources of sequence artifacts in FFPE tissues and implementation of preventative strategies are critical to improve the accurate detection of actionable mutations. CONTENT: This mini-review focuses on DNA template lesions in FFPE tissues as the source of sequence artifacts in molecular analysis. In particular, fragmentation, base modification (including uracil and thymine deriving from cytosine deamination), and abasic sites are discussed as indirect or direct sources of sequence artifacts. We discuss strategies that can be implemented to minimize sequence artifacts and to distinguish true mutations from sequence artifacts. These strategies are applicable for the detection of actionable mutations in both single amplicon and massively parallel amplicon sequencing approaches. SUMMARY: Because FFPE tissues are usually the only available material for DNA analysis, it is important to maximize the accurate informational content from FFPE DNA. Careful consideration of each step in the work flow is needed to minimize sequence artifacts. In addition, validation of actionable mutations either by appropriate experimental design or by orthogonal methods should be considered.

In this article, we show that high resolution melting analysis (HRM) is a sensitive and specific method for the detection of methylation. Methylated DNA and unmethylated DNA acquire different sequences after bisulphite treatment resulting in PCR products with markedly different melting profiles. We used PCR to amplify both methylated and unmethylated sequences and assessed HRM for the determination of the methylation status of the MGMT promoter region. Reconstruction experiments showed that MGMT methylation could be detected at levels as low as 0.1%. Moreover, MS-HRM allows for estimation of the methylation level by comparing the melting profiles of unknown PCR products to the melting profiles of PCR products derived from standards with a known unmethylated to methylated template ratio. We used MS-HRM for the analysis of eight cell lines of known methylation status and a panel of colorectal cancer specimens. The simplicity and high reproducibility of the MS-HRM protocol makes MS-HRM the method of choice for methylation assessment in many diagnostic and research applications.

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis.

Antibody-dependent cellular cytotoxicity (ADCC) is a major mechanism of action of therapeutic monoclonal antibodies (mAbs) such as cetuximab, rituximab and trastuzumab. Fc gamma receptors (FcgR) on human white blood cells are an integral part of the ADCC pathway. Differential response to therapeutic mAbs has been reported to correlate with specific polymorphisms in two of these genes: FCGR2A (H131R) and FCGR3A (V158F). These polymorphisms are associated with differential affinity of the receptors for mAbs. This review critically examines the current evidence for genotyping the corresponding single nucleotide polymorphisms (SNPs) to predict response to mAbs in patients with cancer.

Background: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results.Results: In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment.Conclusion: This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.