Development and validation of next generation sequencing based 35-gene hereditary cancer panel.

BACKGROUND: Understanding the genetic basis of cancer risk is a major international endeavor. The emergence of next-generation sequencing (NGS) in late 2000's has further accelerated the discovery of many cancer susceptibility genes. The use of targeted NGS-based multigene testing panels to provide comprehensive analysis of cancer susceptible genes has proven to be a viable option, with the accurate and robust detection of a wide range of clinically relevant variants in the targeted genes being crucial. METHODS: We have developed and validated a targeted NGS-based test for hereditary cancer risk assessment using Illumina's NGS platform by analyzing the protein-coding regions of 35 hereditary cancer genes with a bioinformatics pipeline that utilizes standard practices in the field. This 35-gene hereditary cancer panel is designed to identify germline cancer-causing mutations for 8 different cancers: breast, ovarian, prostate, uterine, colorectal, pancreatic, stomach cancers and melanoma. The panel was validated using well-characterized DNA specimens [NIGMS Human Genetic Cell Repository], where DNA had been extracted using blood of individuals whose genetic variants had been previously characterized by the 1000 Genome Project and the Coriell Catalog. RESULTS: The 35-gene hereditary cancer panel shows high sensitivity (99.9%) and specificity (100%) across 4820 variants including single nucleotide variants (SNVs) and small insertions and deletions (indel; up to 25 bp). The reproducibility and repeatability are 99.8 and 100%, respectively. CONCLUSIONS: The use of targeted NGS-based multigene testing panels to provide comprehensive analysis of cancer susceptible genes has been considered a viable option. In the present study, we developed and validated a 35-gene panel for testing 8 common cancers using next-generation sequencing (NGS). The performance of our hereditary cancer panel is assessed across a board range of variants in the 35 genes to support clinical use.

Single layer linear array of microbeads for multiplexed analysis of DNA and proteins.

In this study, a microfluidic platform was developed to generate single layer, linear array of microbeads for multiplexed high-throughput analysis of biomolecules. The microfluidic device is comprised of eight microbead-trapping units, where microbeads were immobilized in a linear array format by the exertion of a negative pressure in the control channel connected to each sieving microstructure. Multiplexed assays were achieved by using a mixture of different spectrally-encoded microbeads functionalized with specific probes, followed by on-chip reaction and detection. The microfluidic-based microbeads array platform was employed for multiplexed analysis of DNA and proteins, as demonstrated by the simultaneous discrimination of four HPV genotypes and the parallel detection of six different proteins. Compared with the off-chip protocols, the on-chip analysis exhibited better reaction efficiency, higher sensitivity and wider linear detection range. Visual inspection and identification of functionalized microbeads were facilitated by the single layer arrangement of microbeads so that accurate data acquisition can be performed during the detection process.

Prevalence and genotype distribution of cervical human papillomavirus infection in Macao.

Population-specific epidemiological data on human papillomavirus (HPV) infection are essential for formulating strategies to prevent cervical cancer. The age-specific prevalence of HPV infection was determined among 1,600 women enrolled for cervical screening in Macao. A U-shaped age-specific prevalence curve with a first peak (prevalence rate, 10%) at 20-25 years and a second peak (13%) at 51-55 years was observed. Co-infections with multiple types were detected in 32.5% of HPV-positive subjects and without significant variation among different age groups (P = 0.318). The majority (84.6%) of the positive samples harbored high- or probable high-risk HPV types, and these types also exhibited a similar U-shaped age-specific prevalence curve. In contrast, low and unknown-risk HPV types remained at a low prevalence (1.5-2.5%) throughout the age groups between 20 and 50 years, and with a small peak (4.5%) at 51-55 years. HPV 52 was the most common type found in 26.8% of positive samples, followed by HPV 16 (15.5%), HPV 68 (11.4%), HPV 18 and HPV 58 (8.9% each), HPV 54 (8.1%), HPV 53 (7.3%), HPV 39 (6.5%), HPV 33 and HPV 66 (5.7% each). In conclusion, because of the early peak of infection, vaccination and educational campaigns in Macao should start early and target at teenagers. The presence of a second peak containing mainly high-risk HPV types in older women indicates the need to evaluate the cover of the cervical screening programme for older women. Further study to determine the contribution of HPV 52 in high-grade cervical neoplasia and invasive cancers in Macao is warranted.

Deep proteome profiling of sera from never-smoked lung cancer patients.

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.