BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck malignancy. Although the survival rate of patients with advanced-stage disease remains approximately 20% to 60%, when detected at an early stage, the survival rate approaches 80%, posing a pressing need for a well validated profiling method to assess patients who have a high risk of developing OCSCC. Tumor DNA detection in saliva may provide a robust biomarker platform that overcomes the limitations of current diagnostic tests. However, there is no routine saliva-based screening method for patients with OCSCC. METHODS: The authors designed a custom nextgeneration sequencing panel with unique molecular identifiers that covers coding regions of 7 frequently mutated genes in OCSCC and applied it on DNA extracted from 121 treatment-naive OCSCC tumors and matched preoperative saliva specimens. RESULTS: By using stringent variant-calling criteria, mutations were detected in 106 tumors, consistent with a predicted detection rate ≥88%. Moreover, mutations identified in primary malignancies were also detected in 93% of saliva samples. To ensure that variants are not errors resulting in false-positive calls, a multistep analytical validation of this approach was performed: 1) re-sequencing of 46 saliva samples confirmed 88% of somatic variants; 2) no functionally relevant mutations were detected in saliva samples from 11 healthy individuals without a history of tobacco or alcohol; and 3) using a panel of 7 synthetic loci across 8 sequencing runs, it was confirmed that the platform developed is reproducible and provides sensitivity on par with droplet digital polymerase chain reaction. CONCLUSIONS: The current data highlight the feasibility of somatic mutation identification in driver genes in saliva collected at the time of OCSCC diagnosis.

Human Leukocyte Antigen (HLA) encoding genes are part of the major histocompatibility complex (MHC) on human chromosome 6. This region is one of the most polymorphic regions in the human genome. Prior knowledge of HLA allelic polymorphisms is clinically important for matching donor and recipient during organ/tissue transplantation. HLA allelic information is also useful in predicting immune responses to various infectious diseases, genetic disorders and autoimmune conditions. India harbors over a billion people and its population is untapped for HLA allelic diversity. In this study, we explored and compared three HLA typing methods for South Indian population, using Sequence-Specific Primers (SSP), NGS (Roche/454) and single-molecule sequencing (PacBio RS II) platforms. Over 1020 DNA samples were typed at low resolution using SSP method to determine the major HLA alleles within the South Indian population. These studies were followed up with medium resolution HLA typing of 80 samples based on exonic sequences on the Roche/454 sequencing system and high-resolution (6-8 digit) typing of 8 samples for HLA alleles of class I genes (HLA-A, B and C) and class II genes (HLA-DRB1 and DQB1) using PacBio RS II platform. The long reads delivered by SMRT technology, covered the full-length class I and class II genes/alleles in contiguous reads including untranslated regions, exons and introns, which provided phased SNP information. We have identified three novel alleles from PacBio data that were verified by Roche 454 sequencing. This is the first case study of HLA typing using second and third generation NGS technologies for an Indian population. The PacBio platform is a promising platform for large-scale HLA typing for establishing an HLA database for the untapped ethnic populations of India.

Breast cancer (BC) is a heterogeneous disease. Numerous chemotherapeutic agents are available for early stage or advanced/metastatic breast cancer to provide maximum benefit with minimum side effects. However, the clinical outcome of patients with the same clinical and pathological characteristics and treated with similar treatments may show major differences and a vast majority of patients still develop treatment resistance and eventually succumb to disease. It remains an unmet need to identify specific molecular defects, new biomarkers to enable clinicians to adopt individualized treatment for every patient in terms of endocrine, chemotherapy or targeted therapy which will improve clinical outcomes in BC. Our study aimed to identify frequent hotspot mutation profile in BC by targeted deep sequencing in cancer-related genes using Illumina Truseq amplicon/Swift Accel-Amplicon panel and MiSeq technology in an IRB-approved prospective study in a CLIA compliant laboratory. All the cases had pathology review for stage, histological type, hormonal status and Ki-67. Data was processed using Strand NGS™. Mutations identified in the tumor were assessed for ‘actionability’ i.e. response to therapy and impact on prognosis.

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Background Multi–gene panel sequencing using next-generation sequencing (NGS) methods is a key tool for genomic medicine. However, with an estimated 140 000 genomic tests available, current system inefficiencies result in high genetic-testing costs. Reduced testing costs are needed to expand the availability of genomic medicine. One solution to improve efficiency and lower costs is to calculate the most cost-effective set of panels for a typical pattern of test requests. Methods We compiled rare diseases, associated genes, point prevalence, and test-order frequencies from a representative laboratory. We then modeled the costs of the relevant steps in the NGS process in detail. Using a simulated annealing-based optimization procedure, we determined panel sets that were more cost-optimal than whole exome sequencing (WES) or clinical exome sequencing (CES). Finally, we repeated this methodology to cost-optimize pharmacogenomics (PGx) testing. Results For rare disease testing, we show that an optimal choice of 4–6 panels, uniquely covering genes that comprise 95% of the total prevalence of monogenic diseases, saves $257–304 per sample compared with WES, and $66–135 per sample compared with CES. For PGx, we show that the optimal multipanel solution saves $6–7 (27%–40%) over a single panel covering all relevant gene–drug associations. Conclusions Laboratories can reduce costs using the proposed method to obtain and run a cost-optimal set of panels for specific test requests. In addition, payers can use this method to inform reimbursement policy.