Abstract
Molecular biomarker assays are the key to unlocking the promise of personalized medicine. Molecular assays need to be developed on clinically relevant samples and be rigorously validated. First and foremost, we need to establish the assay’s clinical relevance using appropriate patient populations, in order to answer the fundamental question “why this molecular assay is useful and what unmet medical need does it fill?” Once the medical utility of the assay has been demonstrated, we need to ensure the assay can be accurately measured. One major branch of molecular assays is genomic alterations, including DNA point mutations, fusions, copy number alterations, loss of heterozygosity (LOH), and translocations. Efficient measurements on these DNA alterations across the genome become increasingly feasible with advancements in sequencing technology. However, reliable detection and accurate quantification of these important genomic-based assays may not be as straightforward as we think. While clinical validation on an independent test set against a reference standard is critical and necessary, it is only the first step. The final performance observed in the test set must be maintained in real-life settings amidst commonly encountered interfering substances and across different reagent lots, sequencing machines, and human operators. Adding to the complexity, when the genomic alteration is rare, it poses additional challenges on cost effectiveness. Innovation on methodology becomes key in keeping the highest scientific rigor while conducting the evaluation in the most efficient manner.
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References
Biomarkers Definitions Working Group. (2001). Clinical Pharmacology and Therapeutics, 69: 89–95.
Knezevic D, Goddard AD, Natraj N, Cherbavaz DB, Clark-Langone KM, Snable J, Watson D, FAlzarano SM, Magi-Galluzzi C, Klein EA, Quale C. (2013). Analytical validation of the Oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics, 14:690
Diggans J, Kim SY, Hu Z, Pankratz D, Wong M, Reynolds J, Tom E, Pagan M, Monroe R, Rosai J, Livolsi VA, Lanman RB, Kloos RT, Walsh PS, Kennedy GC. (2015). Machine learning from concept to clinic: reliable detection of BRAF V600E DNA mutations in thyroid nodules using high-dimensional RNA expression data. Pac Symp Biocomputing, 371–82.
FDA. Statistical guidance on reporting results from studies evaluating diagnostic tests. 2007. Available from: http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071148.htm.
Kloos RT, Reynolds JD, Walsh PS, Wilde JI, Tom EY, Pagan M, Barbacioru C, Chudova DI, Wong M, Friedman L, LiVolsi VA, Rosai J, Lanman RB, Kennedy GC. (2013). Does addition of BRAF V600E mutation testing modify sensitivity or specificity of the Afirma Gene Expression Classifier in cytologically indeterminate thyroid nodules? J Clin Endocrinol Metab, 98:E761–8.
Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, Abecasis GR et al. (2012). Detecting and estimating contamination of human DNA samples in sequencing and array -based genotype data. American Journal of Human Genetics, 91, 839–848.
Cibulskis K, McKenna A, Fennell T, Banks E, DePristo M, Getz G. (2012). ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics. 27(18):2601–2602. doi:https://doi.org/10.1093/bioinformatics/btr446.
Huang J, Chen J, Lathrop M and Liang L. (2013). A tool for RNA sequencing sample identity check. Bioinformatics, 29, 1463–1464.
Pengelly RJ, Gibson J, Andreoletti G, Collins A, Mattocks CJ, Ennis S (2013). A SNP profiling panel for sample tracking in whole-exome sequencing studies. Genome Medicine, 5, 89.
Choi Y, Babiarz J, Tom E, Kennedy GC, Huang J (2017a). Repurpo12sing kinship coefficients as a sample integrity method for next generation sequencing data in a clinical setting. Model Assisted Statistics and Applications 12:265–273.
Buness A, Huber W, Steiner K et al (2005). arrayMagic: two-colour cDNA microarray quality control and preprocessing. Bioinformatics 21: 554–556.
Cohen Freue GV, Hollander Z, Shen E, Zamar RH, Balshaw R, Scherer A, McManus B, Keown P, McMaster WR, Ng RT (2007), MDQC: a new quality assessment method for microarrays based on quality control reports. Bioinformatics 23: 3162–3169.
Kauffmann A, Huber W (2010). Microarray data quality control improves the detection of differentially expressed genes. Genomics 95: 138–142.
Walsh PS, Wilde JI, Tom EY, Reynolds JD, Chen DC, Chudova DI, Pagan M, Pankratz DG, Wong M, Veitch J, Friedman L, Monroe R, Steward DL, Lupo MA, Lanman RB, Kennedy GC (2012). Analytical performance verification of a molecular diagnostic for cytology-indeterminate thyroid nodules. J Clin Endocrinol Metab 97(12): E2297–306.
Hu Z, Whitney D, Anderson JR, Cao M, Ho C, Choi Y, Huang J, Frink R, Smith KP, Monroe R, Kennedy GC, Walsh PS (2016) Analytical performance of a bronchial genomic classifier. BMC Cancer 16:161.
Choi Y, Lu J, Hu Z, Pankratz DG, Jiang H, Cao M, Marchisano C, Huiras J, Fedorowicz G, Wong MG, Anderson JR, Tom EY, Babiarz J, Imtiaz U, Barth NM, Walsh PS, Kennedy GC, Huang J (2017b). Analytical performance of Envisia: a genomic classifier for Usual Interstitial Pneumonia. BMC Pulmonary Medicine BMC Pulm Med. 17:141.
Linnet, K., Boyd, J. C. (2012) Selection and and analytical evaluation of methods—with statistical techniques. In Tietz textbook of clinical chemistry and molecular diagnostics 5. ed. (Chapter 2, pp. 7–48). St Louis: Elsevier Sci. Intl. Congress Series 1100.
Teutsch S, Bradley L, Palomaki G, Haddow J, Piper M, Calonge N, et al. (2009). The evaluation of genomic applications in practice and prevention (egapp) initiative: methods of the egapp working group. Genetics in Medicine. 11(1), 3–14.
Sun F, Bruening W, Uhl S, Ballard R, Tipton K, Scoelles K (2010). Quality, Regulation and Clinical Utility of Laboratory-Developed Molecular Tests. Agency for Healthcare Research and Quality, Technology Assessment Program. Available online: https://www.cms.gov/Medicare/Coverage/DeterminationProcess/downloads/id72TA.pdf, accessed on 2017.
Saah AJ, Hoover DR. (1997). Sensitivity and specificity reconsidered: the meaning of these terms in analytical and diagnostic settings. Ann Intern Med. 126(1):91–4.
Chudova D, Wilde JI, Wang ET, Wang H, Rabbee N, Egidio CM, Reynolds J, Tom E, Pagan M, Rigl CT, Friedman L, Wang CC, Lanman RB, Zeiger M, Kebebew E, Rosai J, Fellegara G, LiVolsi VA, Kennedy GC (2010). Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data, The Journal of Clinical Endocrinology & Metabolism, 95(12):5296–5304.
Johnson WE, Rabinovic A, and Li C (2007). Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics 8(1):118–127.
Leek JT and Storey JD. (2007). Capturing heterogeneity in gene expression studies by ‘surrogate variable analysis’. PLoS Genetics 3:e161.
Gagnon-Bartsch JA, Speed TP. (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics, 13(3):539–552.
Jacob L, Gagnon-Bartsch JA, Speed TP. (2016). Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Biostatistics (Oxford, England), 17(1), 16–28. https://doi.org/10.1093/biostatistics/kxv026
Leek JT (2014) svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Research, Vol 42, No. 21 e161
Risso D, Ngai J, Speed TP, Dudoit S. (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nature biotechnology, 32: 896–902.
Amado RG, Wolf M, Peeters M, Van Cutsem E, Siena S, Freeman DJ, Juan T, Sikorski R, Suggs S, Radinsky R, Patterson SD, Chang DD. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(10):1626–34.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64.
Whitney DH, Elashoff MR, Porta-Smith K, Gower AC, Vachani A, Ferguson JS, et al. (2015) Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics, 8:18.
Silvestri GA, Vachani A, Whitney DH, Elashoff MR, Porta-Smith K, Ferguson JS, et al. (2015) A Bronchial Genomic Classifier for the diagnostic evaluation of lung cancer. N Engl J Med 373(3):243–51.
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We would like to thank Giulia C. Kennedy, Marla Johnson, and Andrea Danforth for carefully reviewing the manuscript and providing valuable feedback.
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Choi, Y., Huang, J. (2019). Validation of Genomic-Based Assay. In: Fang, L., Su, C. (eds) Statistical Methods in Biomarker and Early Clinical Development. Springer, Cham. https://doi.org/10.1007/978-3-030-31503-0_7
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