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• W342668047 abstract "As DNA sequencing of multigene panels becomes routine for cancer samples in the clinical laboratory, an efficient process for classifying variants has become more critical. Determining which germline variants are significant for cancer disposition and which somatic mutations are integral to cancer development or therapy response remains difficult, even for well-studied genes such as BRCA1 and TP53. We compare and contrast the general principles and lines of evidence commonly used to distinguish the significance of cancer-associated germline and somatic genetic variants. The factors important in each step of the analysis pipeline are reviewed, as are some of the publicly available annotation tools. Given the range of indications and uses of cancer sequencing assays, including diagnosis, staging, prognostication, theranostics, and residual disease detection, the need for flexible methods for scoring of variants is discussed. The usefulness of protein prediction tools and multimodal risk-based or Bayesian approaches are highlighted. Using TET2 variants encountered in hematologic neoplasms, several examples of this multifactorial approach to classifying sequence variants of unknown significance are presented. Although there are still significant gaps in the publicly available data for many cancer genes that limit the broad application of explicit algorithms for variant scoring, the elements of a more rigorous model are outlined. As DNA sequencing of multigene panels becomes routine for cancer samples in the clinical laboratory, an efficient process for classifying variants has become more critical. Determining which germline variants are significant for cancer disposition and which somatic mutations are integral to cancer development or therapy response remains difficult, even for well-studied genes such as BRCA1 and TP53. We compare and contrast the general principles and lines of evidence commonly used to distinguish the significance of cancer-associated germline and somatic genetic variants. The factors important in each step of the analysis pipeline are reviewed, as are some of the publicly available annotation tools. Given the range of indications and uses of cancer sequencing assays, including diagnosis, staging, prognostication, theranostics, and residual disease detection, the need for flexible methods for scoring of variants is discussed. The usefulness of protein prediction tools and multimodal risk-based or Bayesian approaches are highlighted. Using TET2 variants encountered in hematologic neoplasms, several examples of this multifactorial approach to classifying sequence variants of unknown significance are presented. Although there are still significant gaps in the publicly available data for many cancer genes that limit the broad application of explicit algorithms for variant scoring, the elements of a more rigorous model are outlined. CME Accreditation Statement: This activity (“JMD 2015 CME Program in Molecular Diagnostics”) has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the American Society for Clinical Pathology (ASCP) and the American Society for Investigative Pathology (ASIP). ASCP is accredited by the ACCME to provide continuing medical education for physicians.The ASCP designates this journal-based CME activity (“JMD 2015 CME Program in Molecular Diagnostics”) for a maximum of 36 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.CME Disclosures: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose. CME Accreditation Statement: This activity (“JMD 2015 CME Program in Molecular Diagnostics”) has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the American Society for Clinical Pathology (ASCP) and the American Society for Investigative Pathology (ASIP). ASCP is accredited by the ACCME to provide continuing medical education for physicians. The ASCP designates this journal-based CME activity (“JMD 2015 CME Program in Molecular Diagnostics”) for a maximum of 36 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity. CME Disclosures: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose. Genetic differences in individuals include single nucleotide polymorphisms (SNPs), intragenic insertion and deletion polymorphisms (indels), and structural variants, such as copy number variations. These factors contribute to risk of cancer development and responses to therapy (pharmacogenomics). During tumor development, there is a complex interplay between somatic or acquired mutations in oncogenes, tumor suppressors, and epigenetic regulators and germline, or inherited, genetic variation. Initial work on localizing genetic variants associated with cancer susceptibility focused on well-defined clinical syndromes, such as alterations of TP53 in Li-Fraumeni syndrome, BRCA1 and BRCA2 in hereditary breast and ovarian cancer,1King M.C. Marks J.H. Mandell J.B. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2.Science. 2003; 302: 643-646Crossref PubMed Scopus (1819) Google Scholar and the mismatch repair genes in Lynch syndrome.2Lynch H.T. de la Chapelle A. Genetic susceptibility to non-polyposis colorectal cancer.J Med Genet. 1999; 36: 801-818PubMed Google Scholar The genes involved, typically tumor suppressors, were initially localized using linkage analysis and targeted DNA sequencing. The genetic changes observed in affected individuals include frameshift and nonsense mutations as well as inactivating mutations with loss of function linked to tumor initiation. More recently, genome-wide association studies (GWASs), which compare variant profiles of diseased versus healthy individuals, have accelerated the rate of discovery of cancer-associated variants.3Wellcome Trust Case Control ConsortiumGenome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature. 2007; 447: 661-678Crossref PubMed Scopus (7790) Google Scholar GWASs have identified many more cancer-associated variants in well-characterized cancer genes than family studies, including many missense mutations that have subtle or undetermined effects. They have also identified recurrent germline variants in genes whose association with carcinogenesis was not previously known. These include genes that serve core cellular functions, such as energy metabolism, chromatin maintenance, and protein translation. Connecting a given variant to its phenotypic effect(s) is more difficult for GWASs compared with classic genetic analyses. Even when published data are available to support interpretation of a particular mutation call, GWASs have often had low reproducibility attributable to weak or incomplete phenotypic penetrance, bias against effects due to more common variants and covariants, and inadequate statistical power.4Manolio T.A. Collins F.S. Cox N.J. Goldstein D.B. Hindorff L.A. Hunter D.J. McCarthy M.I. Ramos E.M. Cardon L.R. Chakravarti A. Cho J.H. Guttmacher A.E. Kong A. Kruglyak L. Mardis E. Rotimi C.N. Slatkin M. Valle D. Whittemore A.S. Boehnke M. Clark A.G. Eichler E.E. Gibson G. Haines J.L. Mackay T.F. McCarroll S.A. Visscher P.M. Finding the missing heritability of complex diseases.Nature. 2009; 461: 747-753Crossref PubMed Scopus (5902) Google Scholar As a result, only approximately one-third of germline variant associations reach statistical significance across multiple studies.5Dong L.M. Potter J.D. White E. Ulrich C.M. Cardon L.R. Peters U. Genetic susceptibility to cancer: the role of polymorphisms in candidate genes.JAMA. 2008; 299: 2423-2436Crossref PubMed Scopus (353) Google Scholar Unsurprisingly, GWASs, massively parallel exome sequencing projects, and routine targeted next-generation sequencing (NGS) in clinical laboratories have resulted in many more variants of undetermined significance (VUS) in cancer-associated genes.6McClellan J. King M.C. Genetic heterogeneity in human disease.Cell. 2010; 141: 210-217Abstract Full Text Full Text PDF PubMed Scopus (722) Google Scholar The first step in ensuring robust variant calling relies on accurate base-calling and alignment. To accomplish this goal, raw data from high-throughput sequencers are moved through an analysis pipeline to sequentially accomplish sequence alignment, read filtering, variant calling, and variant annotation (Figure 1).7Larson D.E. Harris C.C. Chen K. Koboldt D.C. Abbott T.E. Dooling D.J. Ley T.J. Mardis E.R. Wilson R.K. Ding L. SomaticSniper: identification of somatic point mutations in whole genome sequencing data.Bioinformatics. 2012; 28: 311-317Crossref PubMed Scopus (400) Google Scholar The initial steps may be accomplished by instrument vendor software, commercial or open source third-party software, or combinations. Guidelines from the College of American Pathologists and the New York State Department of Health (http://www.wadsworth.org/labcert/TestApproval/forms/NextGenSeq_ONCO_Guidelines.pdf, last accessed February 15, 2015) have benchmarked key data quality and analysis requirements for somatic mutation detection.8Aziz N. Zhao Q. Bry L. Driscoll D.K. Funke B. Gibson J.S. Grody W.W. Hegde M.R. Hoeltge G.A. Leonard D.G.B. Merker J.D. Nagarajan R. Palicki L.A. Robetorye R.S. Schrijver I. Weck K.E. Voelkerding K.V. College of American Pathologists' laboratory standards for next-generation sequencing clinical tests.Arch Pathol Lab Med. 2015; 139: 481-493Crossref PubMed Scopus (245) Google Scholar Measuring sequence quality, read depth, and coverage are critical at each step of the pipeline to ensure adequate sensitivity and control for biases introduced by the nucleic acid quality, sequencing chemistry, assay design, and alignment software. Data quality cutoffs will differ by application, but greater read depths are needed for somatic mutation studies. This is especially true when suboptimal sample quality is expected because off-target reads and sequencing artifacts are more common. Other important quality measures include base and read quality filters, cutoffs for uniformity of target coverage, and maximum allowable strand bias for paired-end or bidirectional sequencing methods. Translation of germline variant calls into clinical decisions relies on proper annotation (Figure 1). There are now several public sources that catalog the frequency and population characteristics of germline variants. For SNPs, the International HapMap Project, the Exome Sequencing Project, and the 1000 Genomes Project report population-based data.9The International HapMap Project.Nature. 2003; 426: 789-796Crossref PubMed Scopus (4975) Google Scholar, 10Tennessen J.A. Bigham A.W. O'Connor T.D. Fu W. Kenny E.E. Gravel S. McGee S. Do R. Liu X. Jun G. Kang H.M. Jordan D. Leal S.M. Gabriel S. Rieder M.J. Abecasis G. Altshuler D. Nickerson D.A. Boerwinkle E. Sunyaev S. Bustamante C.D. Bamshad M.J. Akey J.M. Evolution and functional impact of rare coding variation from deep sequencing of human exomes.Science. 2012; 337: 64-69Crossref PubMed Scopus (1215) Google Scholar, 11Abecasis G.R. Auton A. Brooks L.D. DePristo M.A. Durbin R.M. Handsaker R.E. Kang H.M. Marth G.T. McVean G.A. An integrated map of genetic variation from 1,092 human genomes.Nature. 2012; 491: 56-65Crossref PubMed Scopus (5685) Google Scholar The online SNP catalogs Genevar (Sanger Institute)12Yang T.P. Beazley C. Montgomery S.B. Dimas A.S. Gutierrez-Arcelus M. Stranger B.E. Deloukas P. Dermitzakis E.T. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies.Bioinformatics. 2010; 26: 2474-2476Crossref PubMed Scopus (259) Google Scholar and the Single-Nucleotide Polymorphism database (dbSNP; NIH database of germline variation13Database resources of the National Center for Biotechnology Information.Nucleic Acids Res. 2014; 42: D7-D17Crossref PubMed Scopus (322) Google Scholar) house records for >100 million variants. The Database of Genomic Structural Variation (dbVAR) and the Database of Genomic Variants (DGV) catalog large-scale genomic variation (copy number variations), including large insertions, deletions, and inversions.13Database resources of the National Center for Biotechnology Information.Nucleic Acids Res. 2014; 42: D7-D17Crossref PubMed Scopus (322) Google Scholar, 14MacDonald J.R. Ziman R. Yuen R.K. Feuk L. Scherer S.W. The Database of Genomic Variants: a curated collection of structural variation in the human genome.Nucleic Acids Res. 2014; 42: D986-D992Crossref PubMed Scopus (768) Google Scholar Most DNA sequencing pipelines routinely query these sources, and the provided variant frequencies can be used to filter out commonly occurring changes. Presumed benign variants are typically regarded as those with minor allele frequencies (MAFs) >1% to 5%.15Gorlov I.P. Gorlova O.Y. Sunyaev S.R. Spitz M.R. Amos C.I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.Am J Hum Genet. 2008; 82: 100-112Abstract Full Text Full Text PDF PubMed Scopus (253) Google Scholar MAF segregation by race, provided by the Exome Sequencing Project, can be used if population demographics are relevant for tested patients. The assumption that major variants are of limited diagnostic utility may occasionally be erroneous but is essential for reporting large gene panels to narrow the number of variants requiring further analysis. However, most SNPs occur at MAFs under 0.5% (<1% of the population),15Gorlov I.P. Gorlova O.Y. Sunyaev S.R. Spitz M.R. Amos C.I. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.Am J Hum Genet. 2008; 82: 100-112Abstract Full Text Full Text PDF PubMed Scopus (253) Google Scholar highlighting the difficulty of variant annotation.16Abecasis G.R. Altshuler D. Auton A. Brooks L.D. Durbin R.M. Gibbs R.A. Hurles M.E. McVean G.A. A map of human genome variation from population-scale sequencing.Nature. 2010; 467: 1061-1073Crossref PubMed Scopus (5947) Google Scholar Most germline analysis pipelines also query the Human Genome Mutation Database, the Online Mendelian Inheritance in Man, the Clinical Genome Resource, and ClinVar to report clinical associations of the best-characterized pathogenic germline variants.13Database resources of the National Center for Biotechnology Information.Nucleic Acids Res. 2014; 42: D7-D17Crossref PubMed Scopus (322) Google Scholar, 17Landrum M.J. Lee J.M. Riley G.R. Jang W. Rubinstein W.S. Church D.M. Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype.Nucleic Acids Res. 2014; 42: D980-D985Crossref PubMed Scopus (1635) Google Scholar At this time, curated gene-specific databases cover only a few cancer-associated genes (eg, TP53 and BRCA1/2). Therefore, locally curated variant databases are essential for reporting and identifying significant co-occurrences with other variants. Having excluded common (presumed nonpathogenic) variants and highlighted pathogenic ones, indeterminate calls must be scored (Figure 1). The American College of Medical Genetics and Genomics (ACMG)18Richards C.S. Bale S. Bellissimo D.B. Das S. Grody W.W. Hegde M.R. Lyon E. Ward B.E. ACMG recommendations for standards for interpretation and reporting of sequence variations: revisions 2007.Genet Med. 2008; 10: 294-300Abstract Full Text Full Text PDF PubMed Scopus (637) Google Scholar and the International Agency for Research on Cancer (IARC)19Tavtigian S.V. Greenblatt M.S. Goldgar D.E. Boffetta P. Assessing pathogenicity: overview of results from the IARC Unclassified Genetic Variants Working Group.Hum Mutat. 2008; 29: 1261-1264Crossref PubMed Scopus (66) Google Scholar have released guidelines on germline sequence variant interpretation to promote standardized nomenclature. The ACMG and IARC systems rely on lines of evidence (LOE) to stratify germline variants into tiers from nonpathogenic (benign) to definitively pathogenic. LOEs include linkage and segregation data from pedigree analysis and family studies, population-based data on relative risk, clinical correlations, in vitro functional studies, predictions of protein structural effects, evolutionary conservation, and frequency distributions (Table 1). Most laboratories do not categorically score variants for each factor but rely most heavily, for cancer genes, on relative risk and clinical correlations.Table 1Germline and Somatic Variants Compared and ContrastedGermline sequence variantsSomatic mutationsGeneral features Disease association: Single gene and single disease (eg, CFTR in cystic fibrosis), single gene and multiple diseases, multiple genes and single disease (Lynch syndrome)Variant effects: Directly pathogenic (dominant or recessive), interacting effects, linkage to altered gene or locusAllele frequency: Linked to patient population and/or racial groupLevel: Usually present in all cells and detected at ∼50% or ∼100% levelsRetained throughout disease course except if locus is deletedClinical uses: Disease predisposition, family risk and reproductive guidanceMutation specificity: Single gene and single cancer type (eg, BCR-ABL1 in CML), single gene and multiple cancer types (eg, KRAS), multiple genes and single cancer (CALR, JAK2 V617F in MPN)Mutation effects: Tumor initiation, promotion, outgrowth, metastasis, progression, or therapy resistanceMutation frequency: Highly variable based on oncogenicity, tumor type, prior treatment(s)Level: Variable due to percentage of tumor present, gene copy number (ploidy), and subclonal occurrenceMutation persists, lost, and/or reacquired due to tumor evolution or treatment selectionClinical uses: Diagnosis, prognosis, recurrence monitoring therapy (Table 2)Challenges for annotation and reporting Limited clinical correlations for poorly penetrant or uncommon variantsLinking variant effect to phenotypeInferring magnitude of variant effect(s)Distinguishing somatic from germline variants if no normal or nonneoplastic reference sequenceLinking mutation to tumor category or outcomeLinking mutation to treatment responseStrong evidence for pathogenicity or oncogenicity Disease and variant segregation studiesCase-controlled studies comparing prevalence of variant in affected versus healthy populationsFunctional studies reveal damaging effect of variantsClinical studies linking mutation to therapy outcome or prognosis in multivariate models are fewHighly powered correlative studies on mutation frequency by tumor type and/or clinical stageIn vitro animal or cell studies revealing transforming or tumor suppressive effects of a mutationWeaker or inferential evidence for pathogenicity or oncogenicity No occurrence or rarity in SNP databasesFamily studies to trace inheritance patterns and identify de novo changesBayesian risk assessment based on pre-test probability in an individual patientCo-occurrence with known pathogenic variants reduce riskcis- or trans-inheritance patterns (eg, VUS on the same allele as a known pathogenic would favor benign classification of VUS)Computational tools predict pathogenic variantsFrequency in somatic mutation databases and literatureMutation burden tracking with disease response and recurrenceBayesian risk assessment based on other high-risk clinicopathologic features (age, stage, histology)Co-occurrence with more definitive disease-defining, prognostic or response-predicting mutationsPathway analysis to detect complementing mutations or those that are known to be mutually exclusive with variant detectedComputational tools predict contribution of variant to diseaseCML, chronic myelogenous leukemia; MPN, myeloproliferative neoplasm; SNP, single nucleotide polymorphism; VUS, variant of unknown significance. Open table in a new tab CML, chronic myelogenous leukemia; MPN, myeloproliferative neoplasm; SNP, single nucleotide polymorphism; VUS, variant of unknown significance. The best-studied cancer susceptibility genes, particularly BRCA1, BRCA2, and the Lynch syndrome–associated mismatch repair genes, have publicly available and curated databases.20Goldgar D.E. Easton D.F. Deffenbaugh A.M. Monteiro A.N. Tavtigian S.V. Couch F.J. Integrated evaluation of DNA sequence variants of unknown clinical significance: application to BRCA1 and BRCA2.Am J Hum Genet. 2004; 75: 535-544Abstract Full Text Full Text PDF PubMed Scopus (314) Google Scholar, 21Thompson B.A. Spurdle A.B. Plazzer J.P. Greenblatt M.S. Akagi K. Al-Mulla F. et al.Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database.Nat Genet. 2014; 46: 107-115Crossref PubMed Scopus (347) Google Scholar These efforts have helped to limit reporting in routine clinical assays to those variants for which there is strong suspicion for an inherited basis for a patient's tumor. Reporting a poorly characterized VUS, even in a well-studied cancer gene,22Easton D.F. Deffenbaugh A.M. Pruss D. Frye C. Wenstrup R.J. Allen-Brady K. Tavtigian S.V. Monteiro A.N. Iversen E.S. Couch F.J. Goldgar D.E. A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast cancer-predisposition genes.Am J Hum Genet. 2007; 81: 873-883Abstract Full Text Full Text PDF PubMed Scopus (375) Google Scholar can have detrimental consequences. The lack of clear guidance may lead some patients to avoid beneficial standard therapies, whereas others may opt for unnecessary procedures. These unintended effects can be exacerbated if the testing has reproductive or screening implications for family members. In multigene cancer susceptibility panels, tens to hundreds of genes that are not as well annotated as BRCA1 are now being reported. In these panels, VUS calls have increased exponentially, encompassing variants for which there is not yet sufficiently strong evidence of clinical and/or functional significance, those with limited population frequency data, or in which the existing data are contradictory. Given the limited association of some of these genes with genetically defined cancer syndromes, annotation of large NGS panels will require different approaches. Interpretation of a VUS call can be based, in part, on the pretest probability of a positive test result, as determined by demographic and/or clinical risk factors. Effective implementation of this type of Bayesian or tiered-risk approach depends on the availability of reliable, correlative data. Given the need for well-annotated reference patient populations, it is most easily used in highly structured clinical programs and can be difficult to implement in a reference laboratory setting. Below, we describe how such multivariate approaches can be applied to the even more complex task of annotating somatic sequence variants in cancer samples. Genetic changes that arise during the development of a tumor are termed somatic mutations and possess commonalities and differences with germline changes (Table 1). Acquired somatic mutations in cancer cells are propagated through clonal expansion from founder cancer stem cells or tumor subpopulations. If a given genetic change promotes tumor development, it is regarded as a driver mutation and is typically retained during the disease course.23Greaves M. Maley C.C. Clonal evolution in cancer.Nature. 2012; 481: 306-313Crossref PubMed Scopus (1981) Google Scholar Such pathogenic mutations are typically classified as gain-of-function changes in tumor-promoting oncogenes or loss-of-function changes in tumor suppressor genes. The latter effects can also be produced by deletion of the entire gene. Other cancer-associated mutations have dominant-negative or hypofunctional effects that do not fit this oncogene and tumor suppressor duality. The latter mutations are common in genes that serve core metabolic functions or those that regulate epigenetic properties, such as histone and DNA methylation or acetylation and posttranscriptional RNA or protein modifications. Once a tumor becomes established, additional mutations that were present in the selected abnormal cell population but that are not integral to tumorigenesis can arise as passengers. The differentiation of driver from passenger is not always clear-cut because many somatic mutations have cooperating and subtle effects on tumor growth. This is particularly true for epigenetic regulators, such as TET2. Tumor evolution, often represented as a linear progression in historical models, has now been found to have complex branched patterns in cancers.24Gerlinger M. Rowan A.J. Horswell S. Larkin J. Endesfelder D. Gronroos E. Martinez P. Matthews N. Stewart A. Tarpey P. Varela I. Phillimore B. Begum S. McDonald N.Q. Butler A. Jones D. Raine K. Latimer C. Santos C.R. Nohadani M. Eklund A.C. Spencer-Dene B. Clark G. Pickering L. Stamp G. Gore M. Szallasi Z. Downward J. Futreal P.A. Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N Engl J Med. 2012; 366: 883-892Crossref PubMed Scopus (5749) Google Scholar Complex tumor progression patterns result in different secondary mutations present in various tumor subclones and highlight the importance of adequate tumor sampling and appropriately sensitive mutation detection techniques. The level of a particular mutation has important implications for interpretation (Table 1). Tumor-associated aneuploidy and genomic instability often produce unpredictable increases or decreases in the copy number of a mutated gene. Finally, some driver mutations in early-stage tumors can be lost as the neoplasm evolves and spreads, whereas others may be required for tumor persistence (also known as oncogene addiction). Similar to germline variant annotation, approaches to somatic variant annotation in cancers differs based on type of assay (full exome versus hotspot or targeted panels) and assay goal(s). Common indications for mutation detection include establishing clonality (ie, differentiating a reactive from neoplastic process), tumor subclassification, prognostic risk stratification, therapy response prediction (theranostics), and minimal residual disease assessment (Table 2).Table 2Uses of Multigene Sequencing Assays in Cancer TestingExamplesAnalytic considerationsDiagnostic classification and subclassification Molecular subclassification of hematologic malignant tumors (eg, NPM1- and CEBPA-mutated normal karyotype AML)Cancers presenting with unknown primaryIdentifying high-risk molecular changes in tumor with typical histologic featuresStrength of association between a specific mutation and tumor subtype is highly context dependentMost tumor types lack mutations with high diagnostic specificitySeparate sampling of histologically divergent areas may be necessary for molecular analysisClonality assessment T-cell and B-cell clonality by TCR and BCR repertoire profilingDifferentiating reactive hyperplasia from early-stage neoplastic lesionsDetecting recurrent tumor in small or limited biopsy specimens in which histologic analysis is compromisedMay change definition of clonality in many lymphoid malignant tumors due to increased sensitivityThe full range of normal findings in hyperplastic lesions must be fully understoodMay not reflect clinically significant findings if molecular results are interpreted in the absence of definitive microscopic findingsPrognostication Multimutation models of outcome within a standard clinicopathologic risk groupReplacement for multimodal testing for upfront risk assessment for decision to treat (eg, CLL prognostic models)Association between a mutation and outcome linked to specific data sets, may not be generally applicableComparability of mutation and gold standard nonmutation testing models (eg, FISH panels in CLL) needs to be establishedTheranostics Multigene hotspot panels assessing actionable mutations for treatment options in refractory or relapsed diseaseMultimutation models to select patients for neoadjuvant or maintenance therapiesDeep sequencing to detect emerging resistance mutation (eg, ABL1 kinase domain mutations in CML)Many mutations identified will be lightly annotated and not linked to well-established therapies, limited clinical trials optionsInterpretation dependent on strength and relevance of model dataLimited guidance on patient management in such preemergent low-level mutation settingsMinimal residual disease assessment Molecular fingerprinting tumors at diagnosis for followup monitoringStability of mutation profile during disease course will depend on oncogenic strength and treatment effectsAML, acute myeloid leukemia; BCR, B-cell receptor (IGH, IGK); CLL, chronic lymphocytic leukemia; CML, chronic myelogenous leukemia; FISH, fluorescence in situ hybridization; TCR, T-cell receptor genes (ie, TCRG, TCRB). Open table in a new tab AML, acute myeloid leukemia; BCR, B-cell receptor (IGH, IGK); CLL, chronic lymphocytic leukemia; CML, chronic myelogenous leukemia; FISH, fluorescence in situ hybridization; TCR, T-cell receptor genes (ie, TCRG, TCRB). NGS clonality assays include T-cell receptor (TCR) and B-cell antigen receptor (BCR) profiling in lymphoproliferative disorders,25Qi Q. Liu Y. Cheng Y. Glanville J. Zhang D. Lee J.Y. Olshen R.A. Weyand C.M. Boyd S.D. Goronzy J.J. Diversity and clonal selection in the human T-cell repertoire.Proc N" @default.
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• W342668047 title "Annotation of Sequence Variants in Cancer Samples" @default.
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