{"id":1750,"date":"2018-03-01T12:51:54","date_gmt":"2018-03-01T12:51:54","guid":{"rendered":"https:\/\/www.mybiosource.com\/learn\/?page_id=1750"},"modified":"2023-03-02T10:10:19","modified_gmt":"2023-03-02T10:10:19","slug":"dna-provenance","status":"publish","type":"page","link":"https:\/\/www.mybiosource.com\/learn\/testing-procedures\/dna-provenance\/","title":{"rendered":"DNA provenance"},"content":{"rendered":"

Introduction<\/strong><\/h3>\n

DNA provenance test assay is a diagnostic<\/span> test which is used to assess specimen purity of a certain biopsy<\/span> specimen. It is used in cases of a histopathological condition like cancer. In any diagnostic<\/span> test algorithm for cancer, there is an assessment of a tissue specimen by a surgical pathologist. This specimen is collected from a patient, who is suspected of disease. The specimen is collected is collected at a clinically authorized place, labeled, and transported to the pathology<\/span> lab for evaluation. There the analysis takes place and reports are filed based on which an analysis treatment takes place. The whole process is based on the assumption that there is perfect continuity in the labeling and transport of the patient specimen which ensures that the specimen evaluated by the pathologist and the specimen obtained from the patient are same. If there is any uncertainty in the provenance of the specimen, the diagnostic<\/span> and therapeutic process carries significant risk to the patient.<\/p>\n

Number of cases have been reported on errors happening during all the phases of the analysis stage. They are termed as specimen identification errors or specimen provenance errors, or SPEs. These errors may arise during any of these stages preanalytic (collection and processing), analytic, or postanalytic (reporting) stages of the specimen test cycle. New technologies have significantly reduced these SPEs. Specimen mix-up is the major high-risk error in all surgical pathology<\/span> laboratories. Other errors like cross-contamination, floaters, or carryover artifacts still happen in most laboratories. Short Tandem repeat (STR) DNA based analysis method has evolved as a method for specimen identity testing. It is greatly applied in clinical laboratories. The panel of STRs or microsatellites, which is basics of this method, is based on the Combined DNA Index System loci. The feature of the loci system is extreme polyallelism and widespread distribution of the different alleles across different population groups.\u00a0 This aids in the high power discrimination in the STR-based testing for assigning specimen provenance in clinical settings. The clinical utility of the system is increased by the availability of commercial kit, the availability of technical resources to support test interpretation, and an extensive literature that has demonstrated clinical utility for the resolution of a wide variety of specimen labeling and identification issues.<\/p>\n

There are several diagnostics test which is currently applied for practical purposes all trough clinical society. These methods have found its application\u00a0 include bone marrow<\/span> engraftment analysis, diagnosis of hydatidiform moles, assessment of maternal cell contamination in prenatal<\/span> specimens, and identification of uniparental disomy patterns<\/span> of inheritance characteristic of some inherited disorders. Also, the STR based method can be applied to find the resolution of specimen labeling\/identification issues (which occur in about 6% of accessioned cases) and extraneous tissue contaminant issues (which can be identified in up to 2.9% of slides).<\/p>\n

The features of the STR which make it a go-to method for provenance testing is that it is a simple, relatively quick, inexpensive, and informative method of identity testing. The number of cases referred for this testing has increased with time. A generalized protocol for the collection STR typing is given below in this example<\/p>\n

Materials and Methods<\/strong><\/h3>\n

Tissue Sample Preparation<\/strong><\/h4>\n

The patient sample was prepared for testing in 1 of 2 ways, depending on the type of tissue sample and the clinical context.<\/p>\n

Recuts Only<\/strong><\/h4>\n

For the cases in which the provenance ambiguity concerned all tissue in the block, 4-\u03bcm sections of the formalinfixed, paraffin-embedded (FFPE) tissue block (up to 5 sections were used when the tissue fragment was \u226410 mm2 in area) were placed into a new sterile microfuge tube.<\/p>\n

To avoid possible contamination during production of the ribbon of sections, the microtome was cleaned thoroughly before cutting.<\/p>\n

For these cases, a prior laboratory specimen, a buccal swab, or peripheral blood lymphocytes<\/span> served as the reference sample.<\/p>\n

Microdissection From Glass Slides<\/strong><\/h4>\n

For cases containing a putative extraneous tissue contaminant (so-called floaters) and cases for which a putative mixture of tissue from more than 1 patient was present in the same block, the tissue in question was manually microdissected under direct microscopic examination.<\/p>\n

First, the relevant slides (whether H&E-stained sections of routinely processed FFPE tissue or frozen sections) were digitally imaged to guide subsequent microdissection and to create a permanent record of the slide for archiving with the patient\u2019s surgical pathology<\/span> report. Second, the slides were ecoverslipped by a xylene soak. Third, the relevant tissue fragments were collected by manual microdissection after a thorough\u00a0 cleaning of the work area with 70% ethanol. The microdissection was performed by a pathologist wearing clean disposable gloves that were changed between each target<\/span>, each target<\/span> was collected using new sterile needles and surgical scalpels, and each target<\/span> was placed into a new sterile microfuge tube. \u00a0For these cases, a prior laboratory specimen, other tissue within the block, a buccal swab, or peripheral blood lymphocytes<\/span> served as the reference sample.<\/p>\n

STR Typing<\/strong><\/h4>\n

Extraction of DNA from the target<\/span> samples should be done according to established protocols. STR typing was performed via multiplex fluorescent polymerase<\/span> chain reaction amplification using the AmpFISTR Profiler Plus ID amplification kit according to the manufacturer\u2019s instructions. Amplicons were separated by capillary electrophoresis on a Genetic Analyzer. STR marker profile was evaluated using the fragment analysis program GeneMapper.<\/p>\n

The advantages of using STR for the provenance testing:<\/p>\n

    \n
  • In most cases STR typing gives a definite result.<\/li>\n
  • Testing is rapid (maximum assay time of 4 days) and relatively inexpensive.<\/li>\n<\/ul>\n

    While surgical pathologists have long been aware of specimen identity issues and their impact on patient safety,\u00a0 increased awareness by other medical professionals, as well as the lay public, that STR based approaches are available to address putative or occult specimen identity issues has undoubtedly led to more proactive testing.<\/p>\n

    The fact a number of cases in which testing was performed owing to patient self-referral indicate that this group of stakeholders is becoming increasingly informed of specimen identity and contamination issues and empowered to exclude them.<\/p>\n

    The approach of digital whole slide imaging to create a record of the tissue sections destroyed by microdissection of problematic tissue fragments removes a significant barrier to testing; the digital image ensures that the original glass slide is not lost from the patient\u2019s medical record.<\/p>\n

    In the context of medical-legal risk, it is difficult to overemphasize the role of DNA-based STR typing in documenting specimen mix-ups that were previously difficult.\u00a0 The power of discrimination, the sensitivity, and the almost universal applicability of STR-based typing makes it more applicable then the other laboratory methods exist for detecting specimen switches.TR is capable to demonstrate and identify identity errors and the subsequent indirect diagnostic<\/span> errors that are not captured by current laboratory protocols given that there is no indication of specimen switch.<\/p>\n

    Other methods<\/strong><\/h4>\n

    But the magnitude of the switches is not addressed by this method.\u00a0 Analysis of mitochondrial DNA is one such method.\u00a0 There is demonstration that informative alleles of mitochondrial DNA loci can often be amplified from samples too small or too degraded for successful analysis of nuclear DNA<\/span>. Due to the inherent nature of the method, some complications in the interpretation of the test results. Single nucleotide polymorphism is another method. Forensically relevant single nucleotide polymorphisms have recently been identified that permit analysis of highly degraded samples, including in identity-testing .<\/p>\n

    Economic evaluation<\/strong><\/h4>\n

    An economic evaluation of the application of STR-based provenance testing, versus no testing, of transrectal prostate<\/span> biopsy<\/span> specimens is discussed and modeled below. The biopsy<\/span> samples are obtained as part of routine clinical care to rule out the presence of adenocarcinoma of the prostate<\/span>. The parameter values in our model were drawn from the published literature and an extensive sensitivity analysis is conducted to identify those factors, which are mostly associated with the cost-effectiveness of STR-based provenance testing.<\/p>\n

    Methods<\/strong><\/h4>\n

    A decision analytic model to compare the cost-effectiveness of identity testing to prevent SPEs for prostate cancer<\/span> biopsies positive for cancer, versus the current practice of no identity testing is done here. The parameters which are included in this modeling are estimates of the SPE rate in surgical pathology<\/span>, the percentage of men biopsied who are diagnosed with prostate cancer<\/span>, the usage of prostate cancer<\/span> treatments by age, the prevalence of side effects from treatment.<\/p>\n

    A modeling parameter which estimates how quality-of-life, effects of different combinations of side effects from treatment, estimates for costs of DNA testing, prostate cancer<\/span> treatment, and treatment of side effects were applied to the model on the basis of expert opinion and Medicare allowable. Uncertainty in the model stemming from variability in the values of the parameters was tested by using one-way and two-way sensitivity analyses.\u00a0 An analysis was conducted from a payer perspective while assuming that quality-adjusted-life-year (QALY) has some meaning to a third-party payer. Costs were estimated by using the Medicare allowable. The treatment for prostate cancer<\/span> differs by age, and so the model was stratified by age, with individuals aging throughout the modeling process (and mortality risk-adjusted accordingly). Individuals whose specimen is correctly labeled face the same risk and benefit in both arms of our model; therefore, their outcomes do not affect the incremental result. Similarly, individuals with a mislabeled specimen who have prostate cancer<\/span> in spite of the mislabeled sample (i.e., a surreptitious \u201ctrue positive\u201d) also face the same risk and benefit in both arms of the model; An\u00a0 assumption that genetic testing is 100% accurate; therefore, no one in the \u201cIdentity Test\u201d branch faces iatrogenic harm due to misdiagnosis; however, these patients do face the cost of a second biopsy<\/span> to correct the initial erroneous diagnosis.\u00a0 Another assumption of the second biopsy<\/span> is 100% accurate with no identification error. In the \u201cNo Test\u201d arm, misclassified patients undergo treatment for their incorrectly diagnosed cancer, facing the potential for adverse outcomes of treatment.<\/p>\n

    A Markov model was constructed to estimate the cost and benefit of long-term outcomes of treatment. The Markov model is a mathematical method of representing an iterative process, in this context, the medical\/surgical process faced by a patient following a positive finding by the biopsy<\/span>. The Markov process consists of a series of cycles (in this context, the cycle is equal to 1 year) during which the patient faces consequences associated with the treatment of their disease, including the risk of adverse outcomes and additional costs. In modeling side effects, assuming that it is possible for side effects to either develop or resolve during the current or subsequent Markov cycle. Therefore, for the first two cycles (years), a patient could move between treatment options. After this period, the patient remains in the final treatment state for the rest of his life and thus experiences the consequence of only that treatment for his remaining lifetime.<\/p>\n

    Data and assumptions<\/strong><\/h4>\n

    Estimates of the short- and long-term side effects, the probabilities of complications following surgery, the costs of primary treatments, and the costs of treating side effects were determined. The treatment decision following a diagnosis of prostate cancer<\/span> is typically based on the presumed risk of progression and age of the patient.\u00a0 As relevant subgroup lacked a malignancy<\/span> to progress, an assumption that treatment decisions would largely be based on the patient\u2019s age.<\/p>\n

    Estimating the utility of health states<\/strong><\/h4>\n

    To determine the effects of a treatment with an individual\u2019s health, a quantitative measure of the quality of life must be used. Quality of life in terms of \u201cutility.\u201d The utility is a preference-based measure that quantifies an individual\u2019s perception of the importance of functional limitation. The scale used to measure utility ranges from 0.0 to 1.0, with zero representing a health state comparable to death and 1.0 representing perfect health. The utilities for each health state are then used to calculate the QALYs expected by living in that health state.<\/p>\n

    Utilities were used for three single health states and those complications resulting from active treatment, \u201cerectile dysfunction,\u201d \u201curinary incontinence,\u201d and \u201cbowel complications,\u201d along with all possible combinations of these health states. Utilities measuring the quality of life during primary androgen deprivation therapy<\/span> and watchful waiting with active surveillance were also used in the construction of the model.<\/p>\n

    Short-term disutility of treatment resulting from the invasiveness of surgery and radiation therapies was not included because the magnitude of these effects is unknown and omitting these effects provided a conservative estimate of the cost-effectiveness of identity testing.<\/p>\n

    Estimating the incremental cost-effectiveness<\/strong><\/h3>\n

    The incremental cost-effectiveness of one treatment compared with another is determined by dividing the incremental cost of a treatment by the incremental effectiveness (in the context of this study, effectiveness is estimated by QALYs). This is known as the incremental cost-effectiveness ratio (ICER). A treatment is considered cost-effective if the value that society places on a QALY is greater than the cost required to acquire the QALY via the proposed treatment. This value is based on society\u2019s \u201cwillingness to pay\u201d (WTP), or the value that society places on a QALY.\u00a0 The influence of model assumptions on the ICER was tested by using one- and two-way sensitivity analyses. The overall model stability was evaluated with probabilistic sensitivity analysis presented as a net benefits acceptability curve. This was developed from the results of a second-order Monte Carlo simulation in which the model resampled 10,000 times to evaluate the influence of parameter uncertainty on the cost-effectiveness decision. As the costs and effects of\u00a0 treatment occur over a patient\u2019s remaining lifetime, future costs and benefits were discounted at a 3% rate.<\/p>\n

    Results<\/strong><\/h3>\n

    Sensitivity analysis<\/strong><\/h3>\n

    In one-way sensitivity analysis concludes that the cost-effectiveness decision is sensitive to the rate of SPE in prostate cancer<\/span> biopsies and the cost of the test. No other parameters with clinically relevant changes in assumed value resulted in a change of treatment decision, given a decision threshold between $50,000 and $100,000 per QALY gained.<\/p>\n

    Conclusion<\/strong><\/h3>\n

    The results showed that STR-based testing is likely to produce a net benefit for society by reducing iatrogenic harm for patients. As a consequence, health policymakers should consider providing coverage for testing in patients undergoing a prostate<\/span> biopsy<\/span>. While our findings are robust, the demonstration that this result is real and not an artifact of our modeling process\u00a0 requires measurement of the frequency of occult SPEs in routine clinical practice.<\/p>\n

    Economic evaluation<\/strong><\/h3>\n

    We conducted an economic evaluation of the application ofSTR-based provenance testing, versus no testing, of transrectal prostate<\/span> biopsy<\/span> specimens obtained as part of routine clinical care to rule out the presence of adenocarcinoma of the prostate<\/span>. The parameter values in our model were drawn from the published literature, and we conducted extensive sensitivity analyses to identify those factors most associated with the cost-effectiveness of STR-based provenance testing.<\/p>\n

    Methods<\/strong><\/h3>\n

    We constructed a decision analytic model to compare the cost effectiveness of identity testing to prevent SPEs for prostate cancer<\/span> biopsies positive for cancer, versus the current practice of no identity testing. Parameters in this model included estimates of the SPE rate in surgical pathology<\/span>, the percentage of men biopsied who are diagnosed with prostate cancer<\/span>, the usage of prostate cancer<\/span> treatments by age, and the prevalence of side effects from treatment. We also included estimates for the quality-of-life effects of different combinations of side effects from treatment. Estimates for costs of DNA testing, prostate cancer<\/span> treatment, and treatment of side effects were applied to the model on the basis of expert opinion and Medicare allowable. Uncertainty in the model stemming from variability in the values of the parameters was tested by using one-way and two-way sensitivity analyses. Analysis was conducted from a payer perspective, while assuming that quality adjusted life-year (QALY) has meaning to a third-party payer.<\/p>\n

    Costs were estimated by using the Medicare allowable.<\/p>\n

    Modeling the costs and effectiveness of identity testing for\u00a0 prostate cancer<\/span> biopsies. The treatment for prostate cancer<\/span> differs by age, and so the model was stratified by age, with individuals aging throughout the modeling process (and mortality risk adjusted accordingly). Individuals whose specimen is correctly labeled face the same risk and benefit in both arms of our model; therefore, their outcomes do not affect the incremental result.<\/p>\n

    Similarly, individuals with a mislabeled specimen who have prostate cancer<\/span> in spite of the mislabeled sample (i.e., a surreptitious \u201ctrue positive\u201d) also face the same risk and benefit in both arms of the model;<\/p>\n

    We assume that genetic testing is 100% accurate; therefore, no one in the \u201cIdentity Test\u201d branch faces iatrogenic harm due to misdiagnosis; however, these patients do face the cost of a second biopsy<\/span> to correct the initial erroneous diagnosis. We make the assumption that this second biopsy<\/span> is 100% accurate with no identification error. In the \u201cNo Test\u201d arm, misclassified patients undergo treatment for their incorrectly diagnosed cancer, facing the potential for adverse outcomes of treatment.<\/p>\n

    A Markov model was constructed to estimate the cost and benefit of long-term outcomes of treatment. The Markov model is a mathematical method of representing an iterative process, in this context, the medical\/surgical process faced by a patient following a positive finding by the biopsy<\/span>. The Markov process consists of a series of cycles (in this context, the cycle is equal to 1 year) during which the patient faces consequences associated with the treatment of their disease, including the risk of adverse outcomes and additional costs. In modeling side effects, we assumed that it is possible for side effects to either develop or resolve during the current or subsequent Markov cycle. Therefore, for the first two cycles (years), a patient could move between treatment options. After this period, the patient remains in the final treatment state for the rest of his life and thus experiences the consequence of only that treatment for his remaining lifetime.<\/p>\n

    Data and assumptions<\/strong><\/h3>\n

    From this report, estimates of the short- and long-term side effects, the probabilities of complications following surgery, the costs of primary treatments, and the costs of treating side effects were determined.<\/p>\n

    The treatment decision following a diagnosis of prostate cancer<\/span> is typically based on the presumed risk of progression and age of the patient . As our relevant subgroup lacked a malignancy<\/span> to progress, we assumed that treatment decisions would largely be based on the patient\u2019s age.<\/p>\n

    Estimating the utility of health states<\/strong><\/h3>\n

    To determine the effects of a treatment with an individual\u2019s health, a quantitative measure of quality of life must be used. Quality of life in terms of \u201cutility\u201d is measured. Utility is a preference-based measure that quantifies an individual\u2019s perception of the importance of functional limitation. The scale used to measure utility ranges from 0.0 to 1.0, with zero representing a health state comparable to death and 1.0 representing perfect health. The utilities for each health state are then used to calculate the QALYs expected by living in that health state.<\/p>\n

    Utilities were used for three single health states and those complications resulting from active treatment, \u201cerectile dysfunction,\u201d \u201curinary incontinence,\u201d and \u201cbowel complications,\u201d alongwith all possible combinations of these health states. Utilities measuring the quality of life during primary androgen deprivation therapy<\/span> and watchful waiting with active surveillance were also used in the construction of the model.<\/p>\n

    Short-term disutility of treatment resulting from the invasiveness of surgery and radiation therapies was not included because the magnitude of these effects is unknown and omitting these effects provided a conservative estimate of the cost-effectiveness of identity testing.<\/p>\n

    Estimating the incremental cost-effectiveness<\/strong><\/h3>\n

    The incremental cost-effectiveness of one treatment compared with another is determined by dividing the incremental cost of a treatment by the incremental effectiveness (in the context of this study, effectiveness is estimated by QALYs). This is known as the incremental cost-effectiveness ratio (ICER). A treatment is considered cost-effective if the value that society places on a QALY is greater than the cost required to acquire the QALY via the proposed treatment. This value is based on society\u2019s \u201cwillingness to pay\u201d (WTP), or the value that society places on a QALY.\u00a0 The influence of model assumptions on the ICER was tested by using one- and two-way sensitivity analyses. The overall model stability was evaluated with probabilistic sensitivity analysis presented as a net benefits acceptability curve. This was developed from the results of a second-order Monte Carlo simulation in which the model resampled 10,000 times to evaluate the influence of parameter uncertainty on the cost-effectiveness decision. As the costs and effects of treatment occur over a patient\u2019s remaining lifetime, future costs and benefits were discounted at a 3% rate.<\/p>\n

    Results<\/strong><\/h3>\n

    Sensitivity analysis<\/strong><\/h4>\n

    In one-way sensitivity analysis we found that the cost-effectiveness decision is sensitive to the rate of SPE in prostate cancer<\/span> biopsies and the cost of the test. No other parameters with clinically relevant changes in assumed value resulted in a change of treatment decision, given a decision threshold between $50,000 and $100,000 per QALY gained.<\/p>\n

    Conclusion<\/strong><\/h4>\n

    Results demonstrate that STR-based testing is likely to produce a net benefit for society by reducing iatrogenic harm for patients. As a consequence, health policymakers should consider providing coverage for testing in patients undergoing prostate<\/span> biopsy<\/span>.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Introduction DNA provenance test assay is a diagnostic test which is used to assess specimen purity of a certain biopsy specimen. It is used in cases of a histopathological condition like cancer. In any diagnostic test algorithm for cancer, there is an assessment of a tissue specimen by a surgical pathologist. This specimen is collected […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":401,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-1750","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/pages\/1750","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/comments?post=1750"}],"version-history":[{"count":0,"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/pages\/1750\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/pages\/401"}],"wp:attachment":[{"href":"https:\/\/www.mybiosource.com\/learn\/wp-json\/wp\/v2\/media?parent=1750"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}