Clinical application of amplicon-based next-generation sequencing in cancer

      Next-generation sequencing (NGS) technology has revolutionized genomic research by decreasing the cost of sequencing while increasing the throughput. The focus now is on potential clinical applications of NGS technology for diagnostics and therapeutics. Clinical applications of NGS in cancer can detect clinically actionable genetic/genomic alterations that are critical for cancer care. These alterations can be of diagnostic, prognostic, or therapeutic significance. In certain cancers, patient risk and prognosis can be predicted based on the mutation profile identified by NGS. Many targeted therapies have been developed for cancer patients who bear specific mutations; however, choosing the right NGS technique for the appropriate clinical application can be challenging, especially in clinical oncology, where the material for NGS tests is often limited and the turnaround time (TAT) for cancer tests is constrained to a few days. Currently, amplicon-based NGS approaches have emerged as the best fit for clinical oncology. In this review, we focus on amplicon-based library preparation, sequencing, sequence data alignment and annotation, and post-analytic interpretation and reporting.

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      References

        • Rusk N.
        • Kiermer V.
        Primer: sequencing: the next generation.
        Nat Methods. 2008; 5: 15
        • Shyr D.
        • Liu Q.
        Next generation sequencing in cancer research and clinical application.
        Biol Proced Online. 2012; 15: 4
        • D'Antonio M.
        • Pendino V.
        • Sinha S.
        • et al.
        Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes.
        Nucleic Acids Res. 2012; 40: D978-D983
        • Abdel-Wahab O.
        Molecular genetics of acute myeloid leukemia: clinical implications and opportunities for integrating genomics into clinical practice.
        Hematology. 2012; 17: S39-S42
        • Druker B.J.
        • Talpaz M.
        • Resta D.J.
        • et al.
        Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia.
        N Engl J Med. 2001; 344: 1031-1037
        • Mitsudomi T.
        • Morita S.
        • Yatabe Y.
        • et al.
        Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised Phase 3 trial.
        Lancet Oncol. 2009; 11: 121-128
        • Mok T.S.
        • Wu Y.L.
        • Thongprasert S.
        • et al.
        Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinama.
        N Engl J Med. 2009; 361: 947-957
        • Rosell R.
        • Moran T.
        • Queralt C.
        • et al.
        Screening for epidermal growth factor receptor mutations in lung cancer.
        N Engl J Med. 2009; 361: 958-967
        • Peters S.
        • Taron M.
        • Bubendorf L.
        • et al.
        Treatment and detection of ALK-rearranged NSCLC.
        Lung Cancer. 2013; 81: 145-154
        • Schweiger M.R.
        • Kerick M.
        • Timmermann B.
        • et al.
        The power of NGS technologies to delineate the genome organization in cancer: from mutations to structural variations and epigenetic alterations.
        Cancer Metastasis Rev. 2011; 30: 199-210
        • Klee E.W.
        • Hoppman-Chaney N.L.
        • Ferber M.J.
        Expanding DNA diagnostic panel testing: is more better?.
        Expert Rev Mol Diagn. 2011; 11: 703-709
        • Mardis E.R.
        The impact of next-generation sequencing technology on genetics.
        Trends Genet. 2008; 24: 133-141
        • Shendure J.
        • Ji H.
        Next-generation DNA sequencing.
        Nat Biotechnol. 2008; 26: 1135-1145
        • Metzker M.L.
        Sequencing technologies - the next generation.
        Nat Rev Genet. 2010; 11: 31-46
        • Liu L.
        • Li Y.
        • Li S.
        • et al.
        Comparison of next-generation sequencing systems.
        J Biomed Biotechnol. 2012; 2012: 251364
        • Zhou X.
        • Ren L.
        • Meng Q.
        • et al.
        The next-generation sequencing technology and application.
        Protein Cell. 2010; 1: 520-536
        • Voelkerding K.V.
        • Dames S.A.
        • Durtschi J.D.
        Next-generation sequencing: from basic research to diagnostics.
        Clin Chem. 2009; 55: 641-658
        • Rothberg J.M.
        • Leamon J.H.
        The development and impact of 454 sequencing.
        Nat Biotechnol. 2008; 26: 1117-1124
        • Margulies M.
        • Egholm M.
        • Altman W.E.
        • et al.
        Genome sequencing in microfabricated high density picolitre reactors.
        Nature. 2005; 437: 376-380
        • Rothberg J.M.
        • Hinz W.
        • Rearick T.M.
        • et al.
        An integrated semiconductor device enabling non-optical genome sequencing.
        Nature. 2011; 475: 348-352
        • Tewhey R.
        • Warner J.B.
        • Nakano M.
        • et al.
        Microdroplet-based PCR enrichment for large-scale targeted sequencing.
        Nat Biotechnol. 2009; 11: 1025-1031
        • Moonsamy P.V.
        • Williams T.
        • Bonella P.
        • et al.
        High throughput HLA genotyping using 454 sequencing and the Fluidigm Access Array system for simplified amplicon library preparation.
        Tissue Antigens. 2013; 81: 141-149
        • Langmead B.
        • Salzberg S.L.
        Fast gapped-read alignment with Bowtie 2.
        Nat Methods. 2012; 9: 357-359
        • Li H.
        • Durbin R.
        Fast and accurate short read alignment with Burrows-Wheeler transform.
        Bioinformatics. 2009; 25: 1754-1760
        • Li H.
        • Durbin R.
        Fast and accurate long-read alignment with Burrows-Wheeler transform.
        Bioinformatics. 2010; 26: 589-595
        • Li R.
        • Yu C.
        • Li Y.
        • et al.
        SOAP2: an improved ultrafast tool for short read alignment.
        Bioinformatics. 2009; 25: 1966-1967
        • Li H.
        • Ruan J.
        • Durbin R.
        Mapping short DNA sequencing reads and calling variants using mapping quality scores.
        Genome Res. 2008; 18: 1851-1858
        • Trapnell C.
        • Pachter L.
        • Salzberg S.L.
        TopHat: discovering splice junctions with RNA-Seq.
        Bioinformatics. 2009; 25: 1105-1111
        • Li H.
        • Handsaker B.
        • Wysoker A.
        • et al.
        The Sequence Alignment/Map format and SAMtools.
        Bioinformatics. 2009; 25: 2078-2079
        • Dalca A.V.
        • Rumble S.M.
        • Levy S.
        • et al.
        VARiD: a variation detection framework for color-space and letter-space platforms.
        Bioinformatics. 2010; 26: i343-i349
        • Koboldt D.C.
        • Zhang Q.
        • Larson D.E.
        • et al.
        VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.
        Genome Res. 2012; 22: 568-576
        • Au K.F.
        • Jiang H.
        • Lin L.
        • et al.
        Detection of splice junctions from paired-end RNA-seq data by SpliceMap.
        Nucleic Acids Res. 2010; 38: 4570-4578
        • Jia W.
        • Qiu K.
        • He M.
        • et al.
        SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data.
        Genome Biol. 2013; 14: R12
        • Piazza R.
        • Pirola A.
        • Spinelli R.
        • et al.
        FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery.
        Nucleic Acids Res. 2012; 40: e123
        • Fiume M.
        • Williams V.
        • Brook A.
        • et al.
        Savant: genome browser for high-throughput sequencing data.
        Bioinformatics. 2010; 26: 1938-1944
        • Adzhubei I.A.
        • Schmidt S.
        • Peshkin L.
        • et al.
        A method and server for predicting damaging missense mutations.
        Nat Methods. 2010; 7: 248-249
        • Kumar P.
        • Henikoff S.
        • Ng P.C.
        Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.
        Nat Protoc. 2009; 4: 1073-1081
        • Wong W.C.
        • Kim D.
        • Carter H.
        • et al.
        CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer.
        Bioinformatics. 2011; 27: 2147-2148
        • Wang K.
        • Li M.
        • Hakonarson H.
        ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.
        Nucleic Acids Res. 2010; 38: e164
        • Wheeler D.L.
        • Barrett T.
        • Benson D.A.
        • et al.
        Database resources of the National Center for Biotechnology Information.
        Nucleic Acids Res. 2007; 35: D5-D12
        • Cooper D.N.
        • Stenson P.D.
        • Chuzhanova N.A.
        The Human Gene Mutation Database (HGMD) and its exploitation in the study of mutational mechanisms.
        Curr Protoc Bioinformatics. 2006; (Chapter 1:Unit 1.13)
        • Cook-Deegan R.
        • Conley J.M.
        • Evans J.P.
        • et al.
        The next controversy in genetic testing: clinical data as trade secrets?.
        Eur J Hum Genet. 2013; 21: 585-588
        • Soden S.E.
        • Farrow E.G.
        • Saunders C.J.
        • et al.
        Genomic medicine: evolving science, evolving ethics.
        Per Med. 2012; 9: 523-528
        • Korf B.R.
        • Rehm H.L.
        New approaches to molecular diagnosis.
        JAMA. 2013; 309: 1511-1521
        • Fialho A.M.
        • Chakrabarty A.M.
        Patent controversies and court cases: cancer diagnosis, therapy and prevention.
        Cancer Biol Ther. 2012; 13: 1229-1234