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ACMG technical standards and guidelines: microarray analysis for chromosome abnormalities in neoplastic disorders (2013) (view source)
Revision as of 19:11, 27 July 2016
, 19:11, 27 July 2016→Introduction
== Introduction ==
== Introduction ==
Neoplastic processes are a complex group of disorders that develop as a result of the accumulation of genetic alterations including gene mutations, chromosomal rearrangements, gain and loss of genetic material, epigenetic changes, loss of heterozygosity (LOH), and various other genetic changes. Defining and understanding the genetic alterations of specific neoplastic disorders influences the diagnoses, prognoses, and therapeutic choices for patients with both malignant and benign neoplasms.<ref>Swerdlow SH, Campo E, Harris NL, et al. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, 4th edn, International Agency for Research on Cancer: Lyon, France, 2008.</ref><ref>Astbury C. Clinical Cytogenetics in Clinics in Laboratory Medicine, vol. 31(4). Elsevier Saunders: Philadelphia, PA, 2011.</ref><ref>Byrd JC, Mrózek K, Dodge RK, et al.; Cancer and Leukemia Group B (CALGB 8461). Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325–4336.</ref><ref>Döhner H, Stilgenbauer S, Benner A, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000;343:1910–1916.</ref><ref>Heim S, Mitelman F. Cancer Cytogenetics, Chromosomal and Molecular Genetic Aberrations of Tumor Cells, 3rd edn. Wiley: Hoboken, New Jersey, 2009.</ref><ref>Moorman AV, Harrison CJ, Buck GA, et al.; Adult Leukaemia Working Party, Medical Research Council/National Cancer Research Institute. Karyotype is an independent prognostic factor in adult acute lymphoblastic leukemia (ALL): analysis of cytogenetic data from patients treated on the Medical Research Council (MRC) UKALLXII/Eastern Cooperative Oncology Group (ECOG) 2993 trial. Blood 2007;109:3189–3197.</ref><ref>Mrózek K, Heerema NA, Bloomfield CD. Cytogenetics in acute leukemia. Blood Rev 2004;18:115–136.</ref>
Neoplastic processes are a complex group of disorders that develop as a result of the accumulation of genetic alterations including gene mutations, chromosomal rearrangements, gain and loss of genetic material, epigenetic changes, loss of heterozygosity (LOH), and various other genetic changes. Defining and understanding the genetic alterations of specific neoplastic disorders influences the diagnoses, prognoses, and therapeutic choices for patients with both malignant and benign neoplasms.<ref>Swerdlow SH, Campo E, Harris NL, et al. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, 4th edn, International Agency for Research on Cancer: Lyon, France, 2008.</ref><ref>Astbury C. Clinical Cytogenetics in Clinics in Laboratory Medicine, vol. 31(4). Elsevier Saunders: Philadelphia, PA, 2011.</ref><ref>Byrd JC, Mrózek K, Dodge RK, et al.; Cancer and Leukemia Group B (CALGB 8461). Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325–4336 PMID 12393746</ref><ref>Döhner H, Stilgenbauer S, Benner A, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000;343:1910–1916.</ref><ref>Heim S, Mitelman F. Cancer Cytogenetics, Chromosomal and Molecular Genetic Aberrations of Tumor Cells, 3rd edn. Wiley: Hoboken, New Jersey, 2009.</ref><ref>Moorman AV, Harrison CJ, Buck GA, et al.; Adult Leukaemia Working Party, Medical Research Council/National Cancer Research Institute. Karyotype is an independent prognostic factor in adult acute lymphoblastic leukemia (ALL): analysis of cytogenetic data from patients treated on the Medical Research Council (MRC) UKALLXII/Eastern Cooperative Oncology Group (ECOG) 2993 trial. Blood 2007;109:3189–3197.</ref><ref>Mrózek K, Heerema NA, Bloomfield CD. Cytogenetics in acute leukemia. Blood Rev 2004;18:115–136.</ref>
Published clinically applicable data now show the utility of DNA microarray analysis in the assessment of multiple neoplastic disorders.<ref>Armengol G, Canellas A, Alvarez Y, et al. Genetic changes including gene copy number alterations and their relation to prognosis in childhood acute myeloid leukemia. Leuk Lymphoma 2010;51:114–124.</ref><ref name=Gunnarsson>Gunnarsson R, Staaf J, Jansson M, et al. Screening for copy-number alterations and loss of heterozygosity in chronic lymphocytic leukemia–a comparative study of four differently designed, high resolution microarray platforms. Genes Chromosomes Cancer 2008;47:697–711.</ref><ref>Okamoto R, Ogawa S, Nowak D, et al. Genomic profiling of adult acute lymphoblastic leukemia by single nucleotide polymorphism oligonucleotide microarray and comparison to pediatric acute lymphoblastic leukemia. Haematologica 2010;95:1481–1488.</ref><ref name=Slovak>Slovak ML, Bedell V, Hsu YH, et al. Genomic alterations in Hodgkin and Reed/Sternberg (HRS) cells at disease onset reveals distinct signatures for chemo-sensitive and primary refractory Hodgkin lymphoma. Clin Cancer Res 2011;17:3443–3454.</ref><ref>Walter MJ, Payton JE, Ries RE, et al. Acquired copy number alterations in adult acute myeloid leukemia genomes. Proc Natl Acad Sci USA 2009;106:12950–12955.</ref><ref>Yu L, Slovak ML, Mannoor K, et al. Microarray detection of multiple recurring submicroscopic chromosomal aberrations in pediatric T-cell acute lymphoblastic leukemia. Leukemia 2011;25:1042–1046.</ref> Data indicate that microarray technologies provide information about gain and loss of genetic material in neoplastic disorders, including hematologic malignancies and solid tumors.<ref name=Bungaro>Bungaro S, Dell’Orto MC, Zangrando A, et al. Integration of genomic and gene expression data of childhood ALL without known aberrations identifies subgroups with specific genetic hallmarks. Genes Chromosomes Cancer 2009;48:22–38.</ref><ref name=Carrasco>Carrasco DR, Tonon G, Huang Y, et al. High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell 2006;9:313–325.</ref><ref name=Gunn>Gunn SR, Mohammed MS, Gorre ME, et al. Whole-genome scanning by array comparative genomic hybridization as a clinical tool for risk assessment in chronic lymphocytic leukemia. J Mol Diagn 2008;10:442–451.</ref><ref name=Hagenkord>Hagenkord JM, Gatalica Z, Jonasch E, Monzon FA. Clinical genomics of renal epithelial tumors. Cancer Genet 2011;204:285–297.</ref> These gains and losses, represented as an increase or decrease in the proportion of genetic material as compared with a reference genome, are collectively referred to as copy-number variants (CNVs). Microarray methodologies are appropriate complementary methods to standard methods of chromosome and fluorescence in situ hybridization (FISH) analyses for detection of genetic anomalies in neoplastic disorders.
Published clinically applicable data now show the utility of DNA microarray analysis in the assessment of multiple neoplastic disorders.<ref>Armengol G, Canellas A, Alvarez Y, et al. Genetic changes including gene copy number alterations and their relation to prognosis in childhood acute myeloid leukemia. Leuk Lymphoma 2010;51:114–124.</ref><ref name=Gunnarsson>Gunnarsson R, Staaf J, Jansson M, et al. Screening for copy-number alterations and loss of heterozygosity in chronic lymphocytic leukemia–a comparative study of four differently designed, high resolution microarray platforms. Genes Chromosomes Cancer 2008;47:697–711.</ref><ref>Okamoto R, Ogawa S, Nowak D, et al. Genomic profiling of adult acute lymphoblastic leukemia by single nucleotide polymorphism oligonucleotide microarray and comparison to pediatric acute lymphoblastic leukemia. Haematologica 2010;95:1481–1488.</ref><ref name=Slovak>Slovak ML, Bedell V, Hsu YH, et al. Genomic alterations in Hodgkin and Reed/Sternberg (HRS) cells at disease onset reveals distinct signatures for chemo-sensitive and primary refractory Hodgkin lymphoma. Clin Cancer Res 2011;17:3443–3454.</ref><ref>Walter MJ, Payton JE, Ries RE, et al. Acquired copy number alterations in adult acute myeloid leukemia genomes. Proc Natl Acad Sci USA 2009;106:12950–12955.</ref><ref>Yu L, Slovak ML, Mannoor K, et al. Microarray detection of multiple recurring submicroscopic chromosomal aberrations in pediatric T-cell acute lymphoblastic leukemia. Leukemia 2011;25:1042–1046.</ref> Data indicate that microarray technologies provide information about gain and loss of genetic material in neoplastic disorders, including hematologic malignancies and solid tumors.<ref name=Bungaro>Bungaro S, Dell’Orto MC, Zangrando A, et al. Integration of genomic and gene expression data of childhood ALL without known aberrations identifies subgroups with specific genetic hallmarks. Genes Chromosomes Cancer 2009;48:22–38.</ref><ref name=Carrasco>Carrasco DR, Tonon G, Huang Y, et al. High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell 2006;9:313–325.</ref><ref name=Gunn>Gunn SR, Mohammed MS, Gorre ME, et al. Whole-genome scanning by array comparative genomic hybridization as a clinical tool for risk assessment in chronic lymphocytic leukemia. J Mol Diagn 2008;10:442–451.</ref><ref name=Hagenkord>Hagenkord JM, Gatalica Z, Jonasch E, Monzon FA. Clinical genomics of renal epithelial tumors. Cancer Genet 2011;204:285–297.</ref> These gains and losses, represented as an increase or decrease in the proportion of genetic material as compared with a reference genome, are collectively referred to as copy-number variants (CNVs). Microarray methodologies are appropriate complementary methods to standard methods of chromosome and fluorescence in situ hybridization (FISH) analyses for detection of genetic anomalies in neoplastic disorders.
== Verification and Validation of Hardware, Software, Reagents, and Processes ==
== Verification and Validation of Hardware, Software, Reagents, and Processes ==
'''Verification.''' Verification is a confirmation, through provision of objective evidence, that specified requirements have been fulfilled. This is a one-time process completed to determine or confirm test performance characteristics before the test system is used for patient testing. Verification is a quality assurance process to determine that instruments, software, and associated data are accurate per the manufacturer’s description and specifications, i.e., does the system (hardware, software, probes) function as described by the vendor/manufacturer?
''' Definitions '''
'''Validation.''' Validation is a confirmation through the provision of objective evidence that requirements for a specific intended use or application have been fulfilled. Validation is a QC process to determine that the data from test samples are accurate for the intended use when compared with a validated method, i.e., does the system (processes) provide the correct (accurate, reproducible) result(s) when test samples or test data are analyzed?
''Verification.'' Verification is a confirmation, through provision of objective evidence, that specified requirements have been fulfilled. This is a one-time process completed to determine or confirm test performance characteristics before the test system is used for patient testing. Verification is a quality assurance process to determine that instruments, software, and associated data are accurate per the manufacturer’s description and specifications, i.e., does the system (hardware, software, probes) function as described by the vendor/manufacturer?
''Validation.'' Validation is a confirmation through the provision of objective evidence that requirements for a specific intended use or application have been fulfilled. Validation is a QC process to determine that the data from test samples are accurate for the intended use when compared with a validated method, i.e., does the system (processes) provide the correct (accurate, reproducible) result(s) when test samples or test data are analyzed?
'''Platform'''
Initiation of microarray technologies requires the laboratory verify that the instrumentation, software, and probes perform as specified by the vendor. All platforms intended for clinical testing must be verified and validated. The method and scope of the verification and validation must be documented. A new platform is defined as any new methodology or microarray type introduced into the laboratory. A single microarray vendor may produce multiple similar platforms, but each must be assessed independently. A new version is defined as a minor modification to probe coverage, either through manufacturing of the microarray or by in silico probe filtering.
Initiation of microarray technologies requires the laboratory verify that the instrumentation, software, and probes perform as specified by the vendor. All platforms intended for clinical testing must be verified and validated. The method and scope of the verification and validation must be documented. A new platform is defined as any new methodology or microarray type introduced into the laboratory. A single microarray vendor may produce multiple similar platforms, but each must be assessed independently. A new version is defined as a minor modification to probe coverage, either through manufacturing of the microarray or by in silico probe filtering.
'''Laboratory with little or no experience with microarray technologies'''
The laboratory with little or no experience with microarray technology should become familiar with all aspects of the new technology through the verification process, consultation with vendor support, and if possible, other laboratories with demonstrated proficiency using the same platform before beginning the validation process. Familiarization includes understanding the processes, features, and capabilities of the technology selected. The laboratory should gain experience with the instrumentation, platform design, software, reagents, methodologies, technological limitations, workflows, and DNA quality parameters by experimental sample runs. Similarly, the laboratory should become familiar with the features of each sample type the laboratory will process.
The laboratory with little or no experience with microarray technology should become familiar with all aspects of the new technology through the verification process, consultation with vendor support, and if possible, other laboratories with demonstrated proficiency using the same platform before beginning the validation process. Familiarization includes understanding the processes, features, and capabilities of the technology selected. The laboratory should gain experience with the instrumentation, platform design, software, reagents, methodologies, technological limitations, workflows, and DNA quality parameters by experimental sample runs. Similarly, the laboratory should become familiar with the features of each sample type the laboratory will process.
The laboratory should demonstrate expertise in technical aspects of the processing of sample types to be used for clinical testing, technical performance of the microarray, reproducibility of results, and data analysis and interpretation. Expertise should be documented for each microarray platform used for clinical testing, regardless of whether the laboratory has prior experience with a different platform.
The laboratory should demonstrate expertise in technical aspects of the processing of sample types to be used for clinical testing, technical performance of the microarray, reproducibility of results, and data analysis and interpretation. Expertise should be documented for each microarray platform used for clinical testing, regardless of whether the laboratory has prior experience with a different platform.
'''New platform'''
A minimum of 30 samples should be processed and interpreted by the laboratory to verify and validate any new platform. This includes changing to a platform of the same type from a different manufacturer or a different platform type, e.g., array comparative genomic hybridization to SNP. Samples with known abnormalities should be used to gain expertise with the new methodology and assess performance.
A minimum of 30 samples should be processed and interpreted by the laboratory to verify and validate any new platform. This includes changing to a platform of the same type from a different manufacturer or a different platform type, e.g., array comparative genomic hybridization to SNP. Samples with known abnormalities should be used to gain expertise with the new methodology and assess performance.
'''New/different version of an established platform'''
Analysis of a minimum of five known abnormal samples should be run on a new platform version. Data from a new version should be compared with data from the established version to determine if the platform and software perform as expected to detect known CNVs. New probe additions for enhanced coverage or improved performance should be investigated with samples known to have variation in the region of new content (when possible).
Analysis of a minimum of five known abnormal samples should be run on a new platform version. Data from a new version should be compared with data from the established version to determine if the platform and software perform as expected to detect known CNVs. New probe additions for enhanced coverage or improved performance should be investigated with samples known to have variation in the region of new content (when possible).
New versions of established platforms will vary with the manufacturer and platform type. A manufacturer may define minor upgrades as new versions. There are no definitive criteria for a new version; however, a different version should be limited to minimal probe changes, e.g., removal and/or replacement of probes to improve performance and/or coverage over a limited number of genomic regions. These types of changes to an established platform are likely to be rare, with most changes of platforms requiring a full validation.
New versions of established platforms will vary with the manufacturer and platform type. A manufacturer may define minor upgrades as new versions. There are no definitive criteria for a new version; however, a different version should be limited to minimal probe changes, e.g., removal and/or replacement of probes to improve performance and/or coverage over a limited number of genomic regions. These types of changes to an established platform are likely to be rare, with most changes of platforms requiring a full validation.
'''Validation of a new clinical test or assay'''
Any assay intended for clinical diagnosis must be verified and validated before offering as a clinical test. Proficiency in test performance, analysis, and interpretation must be demonstrated.
Any assay intended for clinical diagnosis must be verified and validated before offering as a clinical test. Proficiency in test performance, analysis, and interpretation must be demonstrated.
Exchanging samples with another laboratory conducting similar assays in a blind, split-sample comparison using both normal and abnormal samples and comparing results at the appropriate detection levels declared by the laboratories can provide valuable feedback during the validation process. After the validation period, sample sharing can be used for external proficiency testing (PT). All validation data for each disease and sample type, including discordant results and limitations, should be documented.
Exchanging samples with another laboratory conducting similar assays in a blind, split-sample comparison using both normal and abnormal samples and comparing results at the appropriate detection levels declared by the laboratories can provide valuable feedback during the validation process. After the validation period, sample sharing can be used for external proficiency testing (PT). All validation data for each disease and sample type, including discordant results and limitations, should be documented.
'''Clonality detection and limits'''
Samples from neoplastic disorders can be expected to have varying amounts of nonneoplastic cells admixed with neoplastic cells. The proportion of clonal and nonclonal cells may or may not be clinically relevant but will affect assay sensitivity. Detectable clonality can be influenced by several factors including microarray platform used, sample source, DNA quality, size and copy-number state of the abnormality, and probe coverage. Noise from poor-quality DNA may mask clonality. Each laboratory will need to challenge their microarray with mosaic, aneuploid, and clonally diverse samples to gain experience in their detection. The various factors should be considered with data analysis.
Samples from neoplastic disorders can be expected to have varying amounts of nonneoplastic cells admixed with neoplastic cells. The proportion of clonal and nonclonal cells may or may not be clinically relevant but will affect assay sensitivity. Detectable clonality can be influenced by several factors including microarray platform used, sample source, DNA quality, size and copy-number state of the abnormality, and probe coverage. Noise from poor-quality DNA may mask clonality. Each laboratory will need to challenge their microarray with mosaic, aneuploid, and clonally diverse samples to gain experience in their detection. The various factors should be considered with data analysis.
Visual inspection and manual review of the data should be employed to detect clonality and gain experience with data interpretation. The software may not flag low-level clonality. A call made by visual/manual inspection, when the call was not made by the software, should be verified by another method, e.g., interphase FISH, qPCR.
Visual inspection and manual review of the data should be employed to detect clonality and gain experience with data interpretation. The software may not flag low-level clonality. A call made by visual/manual inspection, when the call was not made by the software, should be verified by another method, e.g., interphase FISH, qPCR.
'''Determination of levels of detectable clonality'''
Methods to evaluate levels of detectable clonality will differ with sample type, e.g., fresh, fixed, or FFPE. Dilution studies are one method that may be used to create different levels of clonality for test purposes.<ref name=Nowak>Nowak NJ, Miecznikowski J, Moore SR, et al. Challenges in array CGH for the analysis of cancer samples. Genet Med 2007;9(9):585–595.</ref> Flow cytometric analysis and interphase FISH analysis of fresh (uncultured) samples provide reliable methods for confirmation of clonality level(s). Conventional cytogenetic analysis of metaphase cells provides information about clonal populations but does not reliably reflect levels of clonality.
Methods to evaluate levels of detectable clonality will differ with sample type, e.g., fresh, fixed, or FFPE. Dilution studies are one method that may be used to create different levels of clonality for test purposes.<ref name=Nowak>Nowak NJ, Miecznikowski J, Moore SR, et al. Challenges in array CGH for the analysis of cancer samples. Genet Med 2007;9(9):585–595.</ref> Flow cytometric analysis and interphase FISH analysis of fresh (uncultured) samples provide reliable methods for confirmation of clonality level(s). Conventional cytogenetic analysis of metaphase cells provides information about clonal populations but does not reliably reflect levels of clonality.
Assessment of levels of neoplastic to nonneoplastic cells or sizes of different clonal populations in fresh or fixed (FFPE) tissue samples is more difficult. Dissection of fresh tumor with an inverted microscope can reduce the amount of nonneoplastic tissues. Microdissection of FFPE tumors can enrich the DNA sample for tumor. Estimation of clonality in tumor tissue samples can be useful when analyzing data from these tumor types.<ref name=Slovak></ref><ref name=Nowak></ref>
Assessment of levels of neoplastic to nonneoplastic cells or sizes of different clonal populations in fresh or fixed (FFPE) tissue samples is more difficult. Dissection of fresh tumor with an inverted microscope can reduce the amount of nonneoplastic tissues. Microdissection of FFPE tumors can enrich the DNA sample for tumor. Estimation of clonality in tumor tissue samples can be useful when analyzing data from these tumor types.<ref name=Slovak></ref><ref name=Nowak></ref>
'''Determination of ploidy'''
Polyploidy may be detected by microarray analysis but may be difficult to appreciate. The allelic states of SNP probes can assist in determining ploidy levels. The validation process should include samples with varying levels of ploidy to gain experience in analysis and recognition of different ploidies. The manufacturer should provide the method used for normalization. The laboratory must understand the effect that normalization may have on polyploidy detection and subsequent interpretation of gains and losses in the context of polyploidy.
Polyploidy may be detected by microarray analysis but may be difficult to appreciate. The allelic states of SNP probes can assist in determining ploidy levels. The validation process should include samples with varying levels of ploidy to gain experience in analysis and recognition of different ploidies. The manufacturer should provide the method used for normalization. The laboratory must understand the effect that normalization may have on polyploidy detection and subsequent interpretation of gains and losses in the context of polyploidy.
'''Clonal diversity'''
Clonal diversity, common to neoplastic disorders, should be visible by microarray when the cell populations of different clones reach the threshold for detection. However, determination of the composition of clones or the sequence of progression of clonal evolution will not be possible. Correlation with conventional cytogenetic analysis may facilitate interpretation of the microarray results.
Clonal diversity, common to neoplastic disorders, should be visible by microarray when the cell populations of different clones reach the threshold for detection. However, determination of the composition of clones or the sequence of progression of clonal evolution will not be possible. Correlation with conventional cytogenetic analysis may facilitate interpretation of the microarray results.
'''Software experience and evaluation'''
Software may not be specifically designed for analysis of cancer specimens. Laboratories may choose to design their own software programs or modify parameters of the platform’s standard software program. The laboratory should recognize software limitations and the need for manual and visual inspection of the data for aberration and clonality detection.
Software may not be specifically designed for analysis of cancer specimens. Laboratories may choose to design their own software programs or modify parameters of the platform’s standard software program. The laboratory should recognize software limitations and the need for manual and visual inspection of the data for aberration and clonality detection.
== Quality Control ==
== Quality Control ==
'''Identification'''
For each microarray, the slide ID, sample sex, control sex (when appropriate), and sample-tracking control (for multiplex microarrays) should be verified. Discrepancies in the documentation from the physical sample should be investigated and resolved before processing.
For each microarray, the slide ID, sample sex, control sex (when appropriate), and sample-tracking control (for multiplex microarrays) should be verified. Discrepancies in the documentation from the physical sample should be investigated and resolved before processing.
'''Sample requirements'''
The laboratory should establish parameters for the minimum DNA quality and quantity requirements for each sample type used for clinical testing. The laboratory should demonstrate proficiency in sample preparation, DNA extraction, and DNA purification for each sample type. Fresh or frozen tumor tissue is preferable to fixed tumor tissue for quality. FFPE tumor samples should be evaluated by a surgical pathologist to assess the quality and quantity of tumor in the sample used for microarray analysis. A minimum of 25% tumor is recommended to prevent masking of clonal changes by normal tissue DNA.
The laboratory should establish parameters for the minimum DNA quality and quantity requirements for each sample type used for clinical testing. The laboratory should demonstrate proficiency in sample preparation, DNA extraction, and DNA purification for each sample type. Fresh or frozen tumor tissue is preferable to fixed tumor tissue for quality. FFPE tumor samples should be evaluated by a surgical pathologist to assess the quality and quantity of tumor in the sample used for microarray analysis. A minimum of 25% tumor is recommended to prevent masking of clonal changes by normal tissue DNA.
'''DNA extraction, purification, measurement, and amplification with different sample types'''
DNA extraction methods should ensure the highest-quality DNA possible from the sample type(s) tested by the laboratory. Samples from neoplastic disorders present unique challenges for generating high-quality, tumor-specific DNA. Written protocols should be available in the laboratory procedure manual and/or quality management program for optimizing DNA extraction and labeling, DNA quantification (e.g., fluorometer, spectrophotometer), DNA quality and concentration (e.g., examination by gel electrophoresis), DNA fragmentation (e.g., via sonication or digestion), fluorescent labeling (e.g., examination by gel electrophoresis, visual inspection, ultraviolet/visible spectroscopy), and amplification (e.g., significant increase in product). For any labeling method, acceptable ranges should be determined for proper dye incorporation. Protocols for optimization, e.g., reextraction, repurification, tumor cell enrichment for hematological samples (cell sorting or concentration), and/or microdissection for paraffin-embedded tumor, should be available as appropriate. Laboratories should be aware that fixatives other than formalin may influence DNA quality and that decalcification of bony tumors may adversely affect DNA quality.
DNA extraction methods should ensure the highest-quality DNA possible from the sample type(s) tested by the laboratory. Samples from neoplastic disorders present unique challenges for generating high-quality, tumor-specific DNA. Written protocols should be available in the laboratory procedure manual and/or quality management program for optimizing DNA extraction and labeling, DNA quantification (e.g., fluorometer, spectrophotometer), DNA quality and concentration (e.g., examination by gel electrophoresis), DNA fragmentation (e.g., via sonication or digestion), fluorescent labeling (e.g., examination by gel electrophoresis, visual inspection, ultraviolet/visible spectroscopy), and amplification (e.g., significant increase in product). For any labeling method, acceptable ranges should be determined for proper dye incorporation. Protocols for optimization, e.g., reextraction, repurification, tumor cell enrichment for hematological samples (cell sorting or concentration), and/or microdissection for paraffin-embedded tumor, should be available as appropriate. Laboratories should be aware that fixatives other than formalin may influence DNA quality and that decalcification of bony tumors may adversely affect DNA quality.
'''Suboptimal samples'''
The laboratory should establish sample adequacy requirements. Samples that do not meet the laboratory requirements should be rejected with a repeat sample requested from the referring physician.
The laboratory should establish sample adequacy requirements. Samples that do not meet the laboratory requirements should be rejected with a repeat sample requested from the referring physician.
When a repeat sample is not available, whole-genome amplification may be a reasonable alternative if the laboratory has expertise with the method and if potential biases inherent in the technique are detailed in the report. Laboratory policies and protocols should describe when and how whole-genome amplification is performed.
When a repeat sample is not available, whole-genome amplification may be a reasonable alternative if the laboratory has expertise with the method and if potential biases inherent in the technique are detailed in the report. Laboratory policies and protocols should describe when and how whole-genome amplification is performed.
'''Equipment calibration, maintenance, and QC'''
Equipment, instrumentation, and methodologies employed during the validation and use of microarray platforms should be calibrated, receive regular maintenance, and be monitored for QC. Quality metrics should be established for each step of the assay.
Equipment, instrumentation, and methodologies employed during the validation and use of microarray platforms should be calibrated, receive regular maintenance, and be monitored for QC. Quality metrics should be established for each step of the assay.
'''QC metrics'''
Every microarray platform has defined quality metric values, e.g., adequate dye incorporation and/or amplification, fluorescence intensities variance, signal-to-background-noise ratio, and SD or error. Standard cutoff values and acceptable limits should be established for these metrics to ensure that the generated results are reliable and sufficiently precise to be used for a clinical assessment. Quality metrics should be monitored for DNA labeling, hybridization efficiency, data generation and analysis, and other platform-specific parameters. QC metrics should be incorporated into the laboratory quality assurance and quality improvement programs to monitor analytical variables.
Every microarray platform has defined quality metric values, e.g., adequate dye incorporation and/or amplification, fluorescence intensities variance, signal-to-background-noise ratio, and SD or error. Standard cutoff values and acceptable limits should be established for these metrics to ensure that the generated results are reliable and sufficiently precise to be used for a clinical assessment. Quality metrics should be monitored for DNA labeling, hybridization efficiency, data generation and analysis, and other platform-specific parameters. QC metrics should be incorporated into the laboratory quality assurance and quality improvement programs to monitor analytical variables.
'''Microarray content'''
It is not feasible for a laboratory to validate the identity and copy-number responsiveness of every probe on a microarray. The laboratory should obtain documentation from the microarray manufacturer that the probes on each microarray are the intended sequence, located appropriately by the software, empirically selected for appropriate copy-number responsiveness and/or SNP allele specificity, and stable for these assessments from lot to lot.
It is not feasible for a laboratory to validate the identity and copy-number responsiveness of every probe on a microarray. The laboratory should obtain documentation from the microarray manufacturer that the probes on each microarray are the intended sequence, located appropriately by the software, empirically selected for appropriate copy-number responsiveness and/or SNP allele specificity, and stable for these assessments from lot to lot.
'''Data quality'''
Detection of genomic aberrations is dependent on the size of the DNA targets, the probe density, the probe performance, and the distance between the sequences naturally located on the chromosome. The quality of the data will affect the ability to detect genomic aberrations; thus, the laboratory needs to understand the within-array metrics provided by the analysis software and how each metric reflects the quality of the data. One metric that provides a measurement of noise or random variance unrelated to genomic location in the data is the derivative log ratio. The derivative log ratio is the difference between the log ratio values of consecutive probes (derivative log ratio spread), i.e., the spread of the derivative log ratio values after outlier rejection. For SNP arrays, quality may be assessed using data from such parameters as call rates and variability (spread) of allele frequency.
Detection of genomic aberrations is dependent on the size of the DNA targets, the probe density, the probe performance, and the distance between the sequences naturally located on the chromosome. The quality of the data will affect the ability to detect genomic aberrations; thus, the laboratory needs to understand the within-array metrics provided by the analysis software and how each metric reflects the quality of the data. One metric that provides a measurement of noise or random variance unrelated to genomic location in the data is the derivative log ratio. The derivative log ratio is the difference between the log ratio values of consecutive probes (derivative log ratio spread), i.e., the spread of the derivative log ratio values after outlier rejection. For SNP arrays, quality may be assessed using data from such parameters as call rates and variability (spread) of allele frequency.
Laboratories should ensure that the software manufacturer provides documentation and safeguards such that data are processed and summarized in a consistent manner for every clinical analysis. Most analysis software provides a hierarchy of users with customizable permissions, which enables the laboratory to prevent modification of analysis settings so that all specimens are analyzed consistently. Any changes to data processing should be validated and documented.
Laboratories should ensure that the software manufacturer provides documentation and safeguards such that data are processed and summarized in a consistent manner for every clinical analysis. Most analysis software provides a hierarchy of users with customizable permissions, which enables the laboratory to prevent modification of analysis settings so that all specimens are analyzed consistently. Any changes to data processing should be validated and documented.
'''Verification of new lots of microarrays and/or reagents'''
Verification should ensure that new lots of microarray slides and/or reagents perform in the same manner as the previous lot. The manufacturer should supply documentation of the QC comparison between lots of microarray slides, e.g., oligo synthesis verification, accuracy of SNP calls, or other defined control parameters. A new lot of microarray slides should be tested to ensure equivalency by testing, either before or concurrently with new patient specimens, preferably using a patient specimen with an abnormal result that has been tested on a previous lot. Manufacturers may include a normal control and request that it be run. New lots of reagents, e.g., new labeling kits and consumables, should have documented equivalency between runs. This may be accomplished by documenting that the QC metrics meet certain set parameters for the new lot of reagents.
Verification should ensure that new lots of microarray slides and/or reagents perform in the same manner as the previous lot. The manufacturer should supply documentation of the QC comparison between lots of microarray slides, e.g., oligo synthesis verification, accuracy of SNP calls, or other defined control parameters. A new lot of microarray slides should be tested to ensure equivalency by testing, either before or concurrently with new patient specimens, preferably using a patient specimen with an abnormal result that has been tested on a previous lot. Manufacturers may include a normal control and request that it be run. New lots of reagents, e.g., new labeling kits and consumables, should have documented equivalency between runs. This may be accomplished by documenting that the QC metrics meet certain set parameters for the new lot of reagents.
== Quality Assurance ==
== Quality Assurance ==
'''Laboratory accreditation and personnel qualifications'''
Laboratory personnel must have documentation of education, degrees, and certifications as appropriate for the level of testing, as well as training, competency assessments, and continuing education as required by appropriate regulatory bodies, e.g., College of American Pathologists (CAP), Clinical Laboratory Improvement Amendments (CLIA), Center for Medicare and Medicaid Services (CMS). The testing laboratory must have CLIA certification and state certifications as required to provide clinical testing. CAP accreditation is strongly encouraged.
Laboratory personnel must have documentation of education, degrees, and certifications as appropriate for the level of testing, as well as training, competency assessments, and continuing education as required by appropriate regulatory bodies, e.g., College of American Pathologists (CAP), Clinical Laboratory Improvement Amendments (CLIA), Center for Medicare and Medicaid Services (CMS). The testing laboratory must have CLIA certification and state certifications as required to provide clinical testing. CAP accreditation is strongly encouraged.
'''Indications and ordering for microarray analysis of neoplastic disorders'''
Microarray analysis of tumors should be limited to specimens that contain ample tumor, e.g., diagnostic or relapse. The sample should be accompanied by an appropriate indication for the test. Clinical testing should be limited to neoplastic disorders for which unbalanced genomic anomalies are well documented to have diagnostic, prognostic, and/or therapeutic implication(s).
Microarray analysis of tumors should be limited to specimens that contain ample tumor, e.g., diagnostic or relapse. The sample should be accompanied by an appropriate indication for the test. Clinical testing should be limited to neoplastic disorders for which unbalanced genomic anomalies are well documented to have diagnostic, prognostic, and/or therapeutic implication(s).
Laboratories may facilitate appropriate ordering by providing a directive or disease-specific testing menu. The test requisition should provide sufficient clinical and/or pathological information for the laboratory to assess the appropriateness of the test order.
Laboratories may facilitate appropriate ordering by providing a directive or disease-specific testing menu. The test requisition should provide sufficient clinical and/or pathological information for the laboratory to assess the appropriateness of the test order.
'''Proficiency testing (PT)'''
The laboratory should participate in PT for sample types and tumor types that are included in the laboratory test menu by participating in an external PT program when available through an appropriate-deemed organization, e.g., CAP. In addition, the laboratory may establish external PT of normal and abnormal specimens by the exchange of DNAs, in a blinded manner, with another laboratory performing microarray testing for neoplastic disorders.
The laboratory should participate in PT for sample types and tumor types that are included in the laboratory test menu by participating in an external PT program when available through an appropriate-deemed organization, e.g., CAP. In addition, the laboratory may establish external PT of normal and abnormal specimens by the exchange of DNAs, in a blinded manner, with another laboratory performing microarray testing for neoplastic disorders.
Failure to achieve agreement on external or internal proficiency tests should be documented and followed by investigation of the discrepancy with resolution. If indicated, appropriate remediation should be undertaken.
Failure to achieve agreement on external or internal proficiency tests should be documented and followed by investigation of the discrepancy with resolution. If indicated, appropriate remediation should be undertaken.
'''Turnaround time'''
Laboratory policies should define acceptable standards for microarray analysis test prioritization and turnaround times. Turnaround time should be clinically appropriate so the results are available for patient care management decisions.
Laboratory policies should define acceptable standards for microarray analysis test prioritization and turnaround times. Turnaround time should be clinically appropriate so the results are available for patient care management decisions.
It is suggested that 90% of cases should have a final written report by 21 calendar days. A longer turnaround time is acceptable when custom probes, oligos, or primer sequences must be designed, ordered, validated, and used. Normal or preliminary abnormal results should be available within 14 calendar days.
It is suggested that 90% of cases should have a final written report by 21 calendar days. A longer turnaround time is acceptable when custom probes, oligos, or primer sequences must be designed, ordered, validated, and used. Normal or preliminary abnormal results should be available within 14 calendar days.
'''Documentation of problems'''
A logbook, database, or sample processing form should be created and used to track problems that may occur throughout the processing of samples for neoplasia, from sample intake to final report, e.g., sample adequacy and/or errors. Data from the QC metrics program can provide information for oversight of all processes. Ongoing collection of sample or process variances allows patterns or trends to be recognized and promptly addressed.
A logbook, database, or sample processing form should be created and used to track problems that may occur throughout the processing of samples for neoplasia, from sample intake to final report, e.g., sample adequacy and/or errors. Data from the QC metrics program can provide information for oversight of all processes. Ongoing collection of sample or process variances allows patterns or trends to be recognized and promptly addressed.
<li>Database of Genomic Variants (http://projects.tcag.ca/variation/),</li>
<li>Database of Genomic Variants (http://projects.tcag.ca/variation/),</li>
<li>Online Mendelian Inheritance in Man (http:www.ncbi.nlm.nih.gov/omim/),</li>
<li>Online Mendelian Inheritance in Man (http://www.ncbi.nlm.nih.gov/omim/),</li>
<li>DECIPHER (http:www.sanger.ac.uk/research/areas/),</li>
<li>DECIPHER (http://www.sanger.ac.uk/research/areas/),</li>
<li>dbVar—database of Structural Variation (http:www.ncbi.nlm.nih.gov/dbvar),</li>
<li>dbVar—database of Structural Variation (http://www.ncbi.nlm.nih.gov/dbvar),</li>
<li>dbGaP—database of Genotypes and Phenotypes (http:www.ncbi.nlm.nih.gov/gap),</li>
<li>dbGaP—database of Genotypes and Phenotypes (http://www.ncbi.nlm.nih.gov/gap),</li>
<li>Memorial Sloan-Kettering Cancer Center (http://cbio.mskcc.org/CancerGenes),</li>
<li>Memorial Sloan-Kettering Cancer Center (http://cbio.mskcc.org/CancerGenes),</li>
<li>The Cancer Genome Anatomy Project (http:www.ncbi.nlm.nih.gov/ncicgap/),</li>
<li>The Cancer Genome Anatomy Project (http://www.ncbi.nlm.nih.gov/ncicgap/),</li>
<li>UCSC Genome Bioinformatics (http://genome.ucsc.edu/cgi-bin/hgGateway),</li>
<li>UCSC Genome Bioinformatics (http://genome.ucsc.edu/cgi-bin/hgGateway),</li>
<li>The Cancer Genome Atlas (http://cancergenome.nih.gov/),</li>
<li>The Cancer Genome Atlas (http://cancergenome.nih.gov/),</li>