WO2018170443A1 - Contrôle de qualité multidimensionnel dépendant des échantillons et dépendant des lots - Google Patents

Contrôle de qualité multidimensionnel dépendant des échantillons et dépendant des lots Download PDF

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WO2018170443A1
WO2018170443A1 PCT/US2018/022955 US2018022955W WO2018170443A1 WO 2018170443 A1 WO2018170443 A1 WO 2018170443A1 US 2018022955 W US2018022955 W US 2018022955W WO 2018170443 A1 WO2018170443 A1 WO 2018170443A1
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test sample
batch
parameter
sample
samples
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PCT/US2018/022955
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English (en)
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Kevin R. HAAS
Xin Wang
Hyunseok KANG
Helen Yamong WAN
Imran Saeedul Haque
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Counsyl, Inc.
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Publication of WO2018170443A1 publication Critical patent/WO2018170443A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/6851—Quantitative amplification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • the present invention relates to methods for controlling the quality of a test sample using multi-parameter data.
  • Assays performed on a test sample are subject to variability from sample to sample.
  • the variability may be caused by caused by fluctuations that affect a number of co-assayed samples (i.e., samples within a sample batch), such as changes in temperature or reagent quality. Other variability may be due to more sample-specific phenomena, such as poor sample preparation or a poor sample source.
  • the variability can obfuscate assay results, resulting in false positive or false negative results.
  • quality control has been monitored by evaluating one or more parameters measured for the sample and comparing the measured parameter to a corresponding quality control window. Samples falling within the quality control window for each parameter were considered accurate, whereas samples with a parameter falling outside of the quality control window for the parameter were considered inaccurate.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) evaluating a test sample batch quality comprising: (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (ii) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (c) evaluating a test sample quality, comprising: (i) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality
  • the method comprises reporting a no call for the test sample if the test sample batch or the test sample is failed. In some embodiments, the method comprises retesting the test sample if the test sample batch or the test sample is failed. In some embodiments, the method comprises passing the test sample if the test sample batch and the test sample is not failed. In some embodiments, the method comprises reporting test results for the test sample if the test sample batch and the test sample is not failed.
  • the batch quality control limit is based on historical batches. In some embodiments, the batch quality control limit is dynamically recalibrated based on historical batches limited to within a predetermined time of testing. In some embodiments, the sample quality control limit is based on historical samples. In some embodiments, the sample quality control limit is dynamically recalibrated based on historical samples limited to within a predetermined time of testing.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences between determined multiple parameters of reference samples and multi-parameter averages for a reference batch corresponding to each reference sample; (c) determining a test sample multi-parameter point based on a difference between the determined multiple parameters of the test sample and the test sample batch multi-parameter average; and (d) failing the test sample if the determined test sample multi-parameter point is outside of the sample quality control limit.
  • the method comprises reporting a no call for the test sample if the test sample is failed. In some embodiments, the method comprises retesting the test sample if the test sample is failed. In some embodiments, the method comprises passing the test sample if the test sample is not failed. In some embodiments, the method comprises reporting test results for the test sample if the test sample is not failed.
  • the sample quality control limit is based on historical samples. In some embodiments, the sample quality control limit is dynamically recalibrated based on historical batches limited to within a predetermined time of testing.
  • a method for controlling the quality of a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples to determine multiple parameters for the samples within the test sample batch; (b) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (c) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (d) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit.
  • the method comprises reporting a no call for the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method comprises retesting the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method comprises passing the test sample batch if the test sample batch is not failed. In some embodiments, the method comprises reporting test results for the samples in the test sample batch if the test sample batch is not failed. [0014] In some embodiments, the batch quality control limit is based on historical batches. In some embodiments, the batch quality control limit is dynamically recalibrated based on historical batches limited to within a predetermined time of testing.
  • the method further comprises adjusting one or more parameters to account for test results.
  • one of the one or more parameters is adjusted to account for test results by dividing a determined parameter of a sample with a first test result by a predetermined constant, wherein the predetermined constant is determined by dividing an average parameter for a plurality of samples with a second test result by an average parameter for a plurality of samples with the first test result.
  • one of the one or more parameters is adjusted to account for the test results by adding a predetermined constant to a determined parameter of a sample with a first test result, wherein the predetermined constant is determined by subtracting an average parameter for a plurality of samples with the first test result from an average parameter for a plurality of samples with the second test result.
  • the first test result is a copy number of 1 or a copy number of 3
  • the second test result is a copy number of 2.
  • the samples in the test sample batch are tested to determine a genetic variant. In some embodiments, the samples in the test sample batch are tested to determine a structural genetic variant. In some embodiments, the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene. In some embodiments, the multiple parameters comprise a size standard deviation of a calibration curve and a residual sum of squares of the calibration curve against a logistic curve model. In some embodiments, the samples in the test sample batch are tested to determine a number of copies of a region of interest. In some embodiments, the region of interest comprises an exon from SMN1. In some embodiments, the multiple parameters comprise an asymptote parameter and an efficiency parameter of a qPCR assay.
  • the sample quality control limit is defined as a pre-determined p-value of the reference sample multi-parameter distribution. In some embodiments, the pre-determined p-value of the reference sample multi-parameter distribution is about 0.05 or less. In some embodiments, the sample quality control limit is defined as a predetermined deviation from a fit of the reference sample multiparameter distribution.
  • the batch quality control limit is defined as a pre-determined p-value of the reference batch multi-parameter distribution. In some embodiments, the pre-determined p-value of the reference batch multi-parameter distribution is about 0.05 or less. In some embodiments, the batch quality control limit is defined as a predetermined deviation from a fit of the reference batch multi-parameter distribution.
  • the testing is performed using a high-throughput automated system.
  • the samples are biological samples.
  • FIG. 1 illustrates a flowchart of one embodiment of a method for controlling the quality of a test sample batch.
  • FIG. 2 illustrates a flowchart of one embodiment of a method for controlling the quality of a test sample.
  • FIG. 3 illustrates a flowchart of one embodiment of a method for controlling the quality of a test sample using a batch-level quality control and a sample-level quality control.
  • FIG. 4 illustrates an exemplary plot of log(ARn) against PCR cycle number with a sigma curve fit in the exponential phase for SMNl, exon 7 and for control genes ALB and RNase P.
  • FIGS. 5A-D plot batch multi-parameter points from the SMNl assay for SMNl, exon 7 tested in the presence of a TERT control (FIG. 5 A), the TERT control (FIG. 5B), SMNl, exon 7 tested in the presence of an RNase P control (FIG. 5C), and the RNase P control (FIG. 5D).
  • FIGS. 6A-D show sample multi-parameter points form a distribution form SMNl, exon 7 tested in the presence of indicated control genes
  • FIG. 6A shows the sample multiparameter point distribution for SMNl, exon 7 tested in the presence of a TERT control.
  • FIG. 6B shows the sample multi-parameter point distribution for the TERT control.
  • FIG. 6C shows the sample multi-parameter point distribution for SMNl, exon 7 tested in the presence of an RNase P control.
  • FIG. 6D shows the sample multi-parameter point distribution for and the RNase P control.
  • the sample multi-parameter point distributions for the SMNl, exon 7 samples (FIG. 6A and FIG.
  • FIGS. 7A-D show multi-parameter points from a distribution from SMNI, exon 7 tested in the presence of a control gene, wherein the multi-parameter points were adjusted to account for the test results (i.e., 1 copy, 2 copies, or 3 copies of the SMNI gene).
  • FIG. 7A shows the test result adjusted sample multi-parameter point distribution for SMNI, exon 7 tested in the presence of a TERT control.
  • FIG. 7B shows the test result adjusted sample multi-parameter point distribution for the TERT control.
  • FIG. 7C shows the test result adjusted sample multi-parameter point distribution for SMNI, exon 7 tested in the presence of an RNase P control.
  • FIG. 7D shows the test result adjusted sample multi-parameter point distribution for and the RNase P control.
  • FIG. 8 plots the log(log(SSD)) against the log(log(RSS)) for samples in a Fragile X assay.
  • FIG. 9 plots the frequency of samples for a given negative log-likelihood in the Fragile X assay. Most samples fit within a Gaussian fit. A Sample quality control limit was selected based on acceptable false-positive and false-negative rates.
  • Fig. 10 illustrates an exemplary computing system for implementing the examples of the disclosure.
  • the methods described herein provide quality control for high-throughput assays, particularly when assaying a biological sample.
  • an automated system may analyze hundreds or even thousands of samples per day. Samples are batched together, and the automated system can analyze the batch of samples simultaneously or in close temporal proximity before analyzing samples in a subsequent batch. Often, the results of the assays can lead to medical diagnoses and therapeutic decisions that can significantly impact the health of a patient. Accordingly, it is important for high-throughput assay systems to provide high-quality and accurate results. However, during sample collection, preparation, or analysis, errors can be introduced that yield inaccurate results.
  • sample-by-sample basis for example, by collecting an insufficient amount of an assay reagent added to a sample
  • a batch-by-batch basis for example, by analyzing a batch of samples at too high of a temperature.
  • Samples or batches that fail quality control should not be reported to the patient or treating physician as accurate samples. Instead, the sample or samples can be re-analyzed to obtain a more accurate result or discarded so that a new sample can be collected.
  • the quality control methods described herein ensure that only reliable sample assay results, particularly when collected using a high-throughput system, are provided to the patient or physician.
  • the sample is a biological sample, such as a saliva, blood, urine, or stool sample.
  • the batch-level quality control limit can be dynamically determined based on a recent set of sample batches. For example, due to systematic changes, a parameter measured in a batch assayed a year prior to a current test sample assay may no longer be relevant due to systematic changes in the high-throughput system. Accordingly, in some embodiments, the quality control limit (such as the batch quality control limit) is dynamically recalibrated using a recent set of historical sample batches.
  • Described herein is a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences between determined multiple parameters of reference samples and multi-parameter averages for a reference batch corresponding to each reference sample; (c) determining a test sample multi-parameter point based on a difference between the determined multiple parameters of the test sample and the test sample batch multi-parameter average; and (d) failing the test sample if the determined test sample multi-parameter point is outside of the sample quality control limit.
  • Also described herein is a method for controlling the quality of a test sample batch comprising: (a) testing a test sample batch to determine multiple parameters for the samples within the test sample batch; (b) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (c) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (d) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of test samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) evaluating a test sample batch quality comprising: (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (ii) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (c) evaluating a test sample quality, comprising: (i) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-
  • the methods described herein are useful for controlling the quality of an assay when reference data is available.
  • the reference data can be, for example, historical data.
  • the quality control methods can be performed at a test sample batch level, a test sample level, or a combination of test sample batch and test sample levels.
  • the methods include evaluating a test sample or test sample batch for a plurality of parameters, and determining whether determined parameters for the test sample or the test sample batch are within a multiparameter quality control limit. Previous methods of evaluating sample assay quality often relied on determining whether a determined parameter value falls within quality control limits for that parameter, and multiple parameters were evaluated independently.
  • the methods described herein include simultaneous multi-parameter evaluation (i.e., in multi-dimensional space) rather than independent evaluation of several parameters. The multi-parameter evaluation allows for a tighter control of the quality control metrics, which helps ensure fewer false positive and false negative assay results.
  • the methods described herein also use historical data (such as parameters from historical sample batches or parameters from historical samples) to control the quality of a test sample batch or a test sample.
  • historical data such as parameters from historical sample batches or parameters from historical samples
  • test sample batches can be compared against historical sample batches to determine if the quality of test sample batch is sufficient.
  • a test sample can be compared to historical samples rather than only the samples in the test sample batch.
  • references to "about” or “approximately” a value or parameter herein includes (and describes) variations that are directed to that value or parameter per se. For example, description referring to "about X” includes description of "X”.
  • average refers to either a mean or a median, or any value used to approximate the mean or the median, unless the context clearly indicates otherwise.
  • sample batch refers to a plurality of samples that are assayed contemporaneously and under the same conditions.
  • Reference batches or “reference samples” refer to a plurality of sample batches or a plurality of samples that include sample batches or samples other than the test sample or test sample batch.
  • the test sample or the test sample batch can be included or excluded from the reference batches or reference samples, although if included is preferably a minor component of the reference batches or reference samples so as to not cause a significant impact on the relevant quality control limit.
  • Test samples within a test sample batch can be testing using an assay that generates two or more measurable parameters.
  • the measureable parameters are independent of the test results or can be adjusted to account for the test results.
  • the assay can be, for example, a test to determine a number of copies of a region of interest (such as an exon from the SMN1 gene), a repeat number of a repetitive sequenced (such as a CGG repeats in an FMR1 gene in a test for diagnosing Fragile X), or detection of a small nucleotide polymorphism (a "S P") or an genetic insertion or deletion (an "indel").
  • the quality control method can be used with any other suitable test that generates two or more measureable parameters.
  • a test for detecting an SMN1 (or an exon of SMN1) copy number can be performed using a qPCR assay, for which an asymptote and a qPCR efficiency can be determined.
  • the asymptote and qPCR efficiency can be used as parameters in the methods described herein.
  • the asymptote in a qPCR assay is the determined maximal measurement (e.g., emission of a dye) after the qPCR has gone to completion.
  • Parameters that can be determined for controlling the quality of a test sample or attest sample batch when testing the test sample or test sample batch to determine a number of CGG repeats in the FMR1 gene include a size standard deviation of a calibration curve or a residual sum of squares of the calibration curve against a logistic curve model.
  • one or more of the parameters may tangential to the test results, such as test conditions (e.g., temperature or one or more incubation times).
  • the parameters can include sequencing depth, allele balance, or indel size (for example, when assaying for an indel).
  • Samples in a test sample batch are contemporaneously tested so that the testing conditions for each sample in the test sample batch are the substantially the same. There may be some slight variation, for example, in incubation times or temperatures due to variability in a working laboratory space.
  • each test sample within the sample batch is treated using the same equipment and using the same assay reagents.
  • a test sample batch includes a plurality of samples, such as about 3 or more, about 5 or more, about 8 or more, about 12 or more, about 16 or more, about 24 or more, about 48 or more, about 96 or more, or about 384 or more samples.
  • one or more samples in the test sample batch is a control sample.
  • the test sample batch also in includes the test sample for methods in which the quality of the test sample is evaluated.
  • the samples in the test sample batch are each tested to determine multiple parameters (e.g., an asymptote and a qPCR efficiency).
  • a sample multi-parameter point can be determined using the determined multiple parameters, with each parameter providing a coordinate for the multi-parameter point.
  • a sample batch multi-parameter average (such as a sample batch multi-parameter median) can be determined by averaging (or determining the median of) the multi-parameter points from the sample within the sample batch.
  • one or more of the parameters is affected by the data results.
  • one or more of the multiple parameters can be adjusted to account for result dependency (e.g., a number of copies).
  • One or more parameters of a sample can be adjusted by multiplying the determined parameter by a predetermined constant or by adding a predetermined constant from the determined parameter. The predetermined constant is based on the test results (e.g., the determined copy number).
  • the predetermined constant can be the difference between an average parameter for a first result (e.g., a copy number of 1) determined from a plurality of samples or batches (which may be historical samples or historical batches) from an average parameter for a second result (e.g., a copy number of 2) determined from a plurality of samples or batches (which may be historical samples or historical batches), and the predetermined constant can be added to the determined parameter for samples with the first result.
  • the predetermined constant can be the ratio of the average parameter for the second result to the average parameter for the first result, and the determined parameter for samples with the first result can be multiplied by the predetermined constant.
  • the methods described herein include the use of historical samples or historical sample batches, for example to determine a batch quality control limit or a sample quality control limit.
  • the number of historical samples or historical sample batches used can be determined by the user. As processes, reagents, and consumables can change over time without any significant impact on the test results, outdated historical samples or historical sample batches can optionally be excluded. This ensures that the historical data used to determine quality control limits is relevant to a contemporary test sample or test sample batch. Limiting historical data to within a predetermined time of testing the test sample or test sample batch allows for a dynamic recalibration of the quality control limits.
  • the historic samples or the historic sample batches were tested about 6 months or less, about 5 months or less, about 4 months or less, about 3 months or less, about 2 months or less, about 30 days or less, about 14 days or less, about 7 days or less, about 5 days or less, about 3 days or less, about 2 days or less, or about 1 day or less prior to testing the test sample or the test sample batch.
  • test sample batches are tested to generate a test result.
  • the test results can be used to diagnose a patient (i.e., the patient from which the sample was obtained) with a disease, such as Fragile X or spinal muscular atrophy (SMA). However, it is important to ensure that the quality of the test is controlled to minimize false negative or false positive test results.
  • Test results from failed test samples or test sample batches are preferably not used to diagnose the patent. In some embodiments, failed test samples or failed test sample batches are retested. In some embodiments, test results that are not failed (i.e., that pass) after controlling the quality of the test sample or the test sample batch are reported (e.g., to the patient or a healthcare provide) or are used to diagnose or assist in the diagnosis of a disease.
  • the quality of a test sample batch can be evaluated to determine if a test sample batch multi-parameter point based on the test sample batch multi-parameter average is within or outside of a batch quality control limit.
  • the batch quality control limit is based on a reference batch multi-parameter distribution.
  • the reference batch multi-parameter distribution includes multi-parameter points, wherein each multi-parameter point is based on a multi-parameter average for a reference batch. If the test sample batch multi-parameter average is within the reference batch quality control limit, then the test sample batch passes the batch-level quality control (although one or more samples within the test sample batch could still fail the sample-level quality control even though the test sample batch passed the quality control). If the test sample batch multi-parameter average is outside of the batch quality control limit, then the test sample batch fails the batch-level quality control (and all samples within the sample batch are also considered to have failed the quality control).
  • the batch quality control limit is based on a reference batch multi-parameter distribution determined using a plurality of reference batches. In some embodiments, about 3 or more, about 5 or more, about 10 or more, about 20 or more, about 50 or more, about 100 or more, about 250 or more, about 500 or more, about 1000 or more, or about 5000 or more reference batches are used. A multi-parameter point is determined for each of the reference batches using the multi-parameter average for the reference batch. In some embodiments, the reference batches are historical batches. The multi-parameter average can be based on parameters that were adjusted to account for test results (such as copy number). For example, for each sample within the reference batch, one or more of the parameters can be adjusted by multiplying the determined parameter by a predetermined constant or by adding a
  • the predetermined constant can be, for example, the difference between an average parameter for a first result (e.g., a copy number of 1) determined from a plurality of samples or batches (which may be historical samples or historical batches) from an average parameter for a second result (e.g., a copy number of 2) determined from a plurality of samples or batches (which may be historical samples or historical batches), and the predetermined constant can be added to the determined parameter for samples with the first result.
  • the predetermined constant can be the ratio of the average parameter for the second result to the average parameter for the first result, and the determined parameter for samples with the first result can be multiplied by the predetermined constant.
  • the parameter average for the reference batch can be an average of the parameters adjusted based on the result.
  • the multi-parameter distribution can be a multidimensional if (e.g., a Gaussian fit) trained on the reference batch multi-parameter points.
  • the batch quality control limit can be a predetermined p-value of the multi-parameter distribution.
  • the predetermined p-value can be, for example about 0.10 or less, about 0.05 or less, about 0.01 or less, about 0.005 or less, about 0.002 or less, or about 0.0001 or less.
  • the predetermined p-value established an expected or desired failure rate of the samples.
  • the batch quality control limit is defined as a predetermined deviation from a fit of the multi-parameter distribution.
  • the batch quality control limit can be defined as an outlier limit of the fit, which can be selected based on previously validated samples. For example, a desired (or acceptable) false-negative and/or false-positive rate can be selected, and based on results from historical validated samples, the outlier limit is selected.
  • test sample batch multi-parameter point is outside of the t batch quality control limit, then the test sample batch is failed.
  • a failed test sample batch means all samples within that batch are considered unreliable due to poor quality.
  • one or more samples in the test sample batch are retested. If the test sample batch multi-parameter point is within the batch quality control limit, then the test sample batch passes the quality control at the test sample-batch level. In some embodiments, one or more test samples within the test sample batch are further evaluated for a sample-level quality control.
  • FIG. 1 presents a flowchart of one embodiment of a method for controlling the quality of a test sample batch.
  • a test sample batch is tested to determine multiple parameters for each sample within the test sample batch.
  • the multiple parameters that are determined need not be, but can be, related to the test results.
  • the parameter(s) are adjusted to account for the test results at step 120. Adjustment of the one or more parameters to account for the test results can include multiplying the parameter by a predetermined constant or by adding a predetermined constant to the parameter.
  • a test sample batch multi -parameter average is determined based on the determined multiple parameters (which may be adjusted to account for the test results at step 120), and the multi-parameter average is a basis for a multi-parameter point.
  • a batch quality control limit is determined based on a multi-parameter distribution of reference batches.
  • the reference batch multi-parameter distribution includes a plurality of reference batch multi-parameter points, and each of the reference batch multi-parameter points are based on a multi-parameter average for a reference batch.
  • the reference batch multi-parameter points are determined in a similar manner as the multi-parameter point for the test sample batch.
  • test sample batch multi-parameter point is evaluated against the batch quality control limit. If the test sample batch multi-parameter point is within the batch quality control limit, the test sample batch passes the quality control (step 160). If the test sample batch multi -parameter point is outside of the batch quality control limit, the test sample batch fails the quality control (step 170).
  • a method for controlling the quality of a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples to determine multiple parameters for the samples within the test sample batch; (b) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (c) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (d) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit.
  • the method further comprises reporting a no call for the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method further comprises retesting the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method comprises passing the test sample batch if the test sample batch is not failed. In some embodiments, the method comprises reporting test results for the samples in the test sample batch if the test sample batch is not failed. In some embodiments, the batch quality control limit is based on historical batches. In some embodiments, the samples in the test sample batch are tested to determine a genetic variant. In some embodiments, the samples in the test sample batch are tested to determine a structural genetic variant.
  • the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene. In some embodiments, the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1). In some
  • the batch quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference batch multi-parameter distribution.
  • the batch quality control limit is defined as a predetermined deviation from a fit of the reference batch multi-parameter distribution.
  • there is a method for controlling the quality of a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples to determine multiple parameters for the samples within the test sample batch; (b) adjusting one or more of the multiple parameters to account for test results of the samples in the test sample batch; (c) determining a batch quality control limit based on a reference batch
  • the method further comprises reporting a no call for the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method further comprises retesting the samples in the test sample batch if the test sample batch is failed. In some embodiments, the method comprises passing the test sample batch if the test sample batch is not failed.
  • the method comprises reporting test results for the samples in the test sample batch if the test sample batch is not failed.
  • the batch quality control limit is based on historical batches.
  • the samples in the test sample batch are tested to determine a genetic variant.
  • the samples in the test sample batch are tested to determine a structural genetic variant.
  • the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene.
  • the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1).
  • the batch quality control limit is defined as a predetermined p-value (such as about 0.05 or less) of the reference batch multi-parameter distribution.
  • the batch quality control limit is defined as a
  • the quality of a test sample selected from within a test sample batch can be evaluated to determine if a test sample multi-parameter point is within a sample quality control limit based on a reference sample multi-parameter distribution.
  • the test sample multi-parameter point is based on a difference (i.e., a deviation vector) between determined multiple parameters of the test sample and a test sample batch multi-parameter average.
  • the sample quality control limit is based on a reference sample multi-parameter distribution, which includes a plurality of multi-parameter points.
  • the multi-parameter points are based on a difference between determined multiple parameters for reference samples and a multiparameter average for the corresponding reference batch.
  • the "corresponding" reference batch is the reference batch that included any given reference sample. If the test sample multi-parameter point is within the sample quality control limit, then the test sample passes the sample-level quality control. If the test sample multi-parameter average is outside of the sample quality control limit, then the test sample fails the sample-level quality control.
  • the multiple parameters for the samples (including the test sample) within the test sample batch are determined, a test sample batch multi-parameter average is determined, and the difference between the multiple parameters for the test sample and the test sample batch multi-parameter average is determined.
  • one or more of the multiple parameters (for the samples in the test sample batch and the test sample batch) are adjusted to account for result dependency (e.g., a number of copies).
  • One or more parameters of a sample can be adjusted by multiplying the determined parameter by a predetermined constant or by adding a predetermined constant from the determined parameter. The predetermined constant is based on the test results (e.g., the determined copy number).
  • the predetermined constant can be the difference between an average parameter for a first result (e.g., a copy number of 1) determined from a plurality of samples or batches (which may be historical samples or historical batches) from an average parameter for a second result (e.g., a copy number of 2) determined from a plurality of samples or batches (which may be historical samples or historical batches), and the predetermined constant can be added to the determined parameter for samples with the first result.
  • the predetermined constant can be the ratio of the average parameter for the second result to the average parameter for the first result, and the determined parameter for samples with the first result can be multiplied by the predetermined constant.
  • the sample quality control limit is based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution includes a plurality of multi-parameter points. Each of the multi-parameter points are based on a difference between the determined multiple parameters of a reference sample and a multi-parameter average for a reference batch corresponding to the reference sample. In some embodiments, about 3 or more, about 5 or more, about 10 or more, about 20 or more, about 50 or more, about 100 or more, about 250 or more, about 500 or more, about 1000 or more, or about 5000 or more reference batches are used.
  • the reference batches each include about 3 or more, about 5 or more, about 8 or more, about 12 or more, about 16 or more, about 24 or more, about 48 or more, about 96 or more, or about 384 or more samples.
  • a multi-parameter average for each reference batch is determined, along with a difference between the reference sample and the multi-parameter average for the corresponding reference batch.
  • the multi-parameter average can be based on parameters that were adjusted to account for test results (such as copy number). For example, for each sample within the reference batch, one or more of the parameters can be adjusted by multiplying the determined parameter by a predetermined constant or by adding a predetermined constant from the determined parameter.
  • the predetermined constant can be, for example, the difference between an average parameter for a first result (e.g., a copy number of 1) determined from a plurality of samples or batches (which may be historical samples or historical batches) from an average parameter for a second result (e.g., a copy number of 2) determined from a plurality of samples or batches (which may be historical samples or historical batches), and the predetermined constant can be added to the determined parameter for samples with the first result.
  • the predetermined constant can be the ratio of the average parameter for the second result to the average parameter for the first result, and the determined parameter for samples with the first result can be multiplied by the predetermined constant.
  • the parameter average for the reference batch can be an average of the parameters adjusted based on the result.
  • the reference sample multi-parameter distribution can be a multidimensional Gaussian trained on the reference sample multi-parameter points.
  • the sample quality control limit can be a predetermined p-value of the reference sample multi-parameter distribution.
  • the predetermined p-value can be, for example, about 0.10 or less, about 0.05 or less, about 0.01 or less, about 0.005 or less, about 0.002 or less, or about 0.0001 or less.
  • the sample quality control limit is defined as a predetermined deviation from a fit of the multi-parameter distribution.
  • the sample quality control limit can be defined as an outlier limit of the fit, which can be selected based on previously validated samples. For example, a desired (or acceptable) false-negative and/or false-positive rate can be selected, and based on results from historical validated samples, the outlier limit is selected.
  • test sample multi-parameter point is outside of the sample quality control limit, then the test sample is failed.
  • a failed test sample means that the sample test results are considered unreliable.
  • the test sample is retested. If the test sample multiparameter point is within the sample quality control limit, then the test sample passes the quality control at the test sample level.
  • FIG. 2 presents a flowchart of one embodiment of a method for controlling the quality of a test sample.
  • a test sample batch which includes a test sample, is tested to determine multiple parameters for the samples within the test sample batch.
  • the multiple parameters that are determined can be, but need not be, related to the test results.
  • the parameter(s) are adjusted to account for the test results at step 220. Adjustment of the one or more parameters to account for the test results can include multiplying the parameter by a predetermined constant.
  • a multi-parameter point for the test sample is determined, which is based on the difference between the determined multiple parameters for the test sample and a multiparameter average for the test sample batch.
  • a sample quality control limit is determined based on a multi-parameter distribution of reference samples.
  • the reference sample multi-parameter distribution includes a plurality of reference sample multi-parameter points, and each of the reference sample multi-parameter points is based on the difference between multiple parameters for a reference sample and a multi-parameter average for a reference batch corresponding to the reference sample.
  • multi-parameter points are determined in a similar manner as the multi-parameter point for the test sample. That is, if one or more of the multiple parameters are adjusted to account for test results in the test samples, the one or more multiple parameters are also adjusted to account for test results in the reference samples.
  • a predetermined p-value of the reference sample multi-parameter distribution defines the sample quality control limit.
  • the test sample multi-parameter point is evaluated against the sample quality control limit. If the test sample multi-parameter point is within the sample quality control limit, the test sample passes the quality control (step 260). If the test sample multi-parameter point is outside of the sample quality control limit, the test sample fails the quality control (step 270).
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences between determined multiple parameters of reference samples and multi-parameter averages for a reference batch corresponding to each reference sample; (c) determining a test sample multi-parameter point based on a difference between the determined multiple parameters of the test sample and the test sample batch multi-parameter average; and (d) failing the test sample if the determined test sample multi-parameter point is outside of the sample quality control limit.
  • the method further comprises retesting the test sample if the test sample is failed. In some embodiments, the method comprises passing the test sample if the test sample is not failed. In some embodiments, the method comprises reporting test results for the test sample if the test sample is not failed. In some embodiments, the sample quality control limit is based on historical samples. In some embodiments, the samples in the test sample batch are tested to determine a genetic variant. In some embodiments, the samples in the test sample batch are tested to determine a structural genetic variant. In some
  • the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene. In some embodiments, the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1).
  • the sample quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference batch multi-parameter distribution. In some embodiments, the sample quality control limit is defined as a predetermined deviation from a fit of the reference sample multi-parameter distribution.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) adjusting one or more of the multiple parameters to account for test results of the samples in the test sample batch; (c) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences between determined multiple parameters of reference samples and multiparameter averages for a reference batch corresponding to each reference sample; (d) determining a test sample multi-parameter point based on a difference between the determined multiple parameters of the test sample and the test sample batch multi-parameter average; and (e) failing the test sample if the determined test sample multi-parameter point is outside of the sample quality control limit.
  • the method further comprises retesting the test sample if the test sample
  • the method comprises reporting test results for the test sample if the test sample is not failed.
  • the sample quality control limit is based on historical samples.
  • the samples in the test sample batch are tested to determine a genetic variant.
  • the samples in the test sample batch are tested to determine a structural genetic variant.
  • the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene.
  • the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1).
  • the sample quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference batch multi-parameter distribution. In some embodiments, the sample quality control limit is defined as a predetermined deviation from a fit of the reference sample multiparameter distribution.
  • quality control of a sample is evaluated at the batch level and the sample level. That is, a test sample batch (which includes a test sample) is controlled for quality of the test sample batch, and the test sample itself is controlled for quality.
  • the test sample will fail the quality control if the test sample batch fails the batch-level quality control or if the test sample fails the sample-level quality control.
  • the test sample will pass the quality control if the test sample batch passes the batch-level quality control and the test sample passes the sample-level quality control.
  • the quality of the test sample batch and the test sample can be controlled in any order.
  • the test sample batch is controlled for quality prior to the test sample.
  • the test sample is controlled for quality prior to the test sample batch.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a test sample to determine multiple parameters for the samples within the test sample batch; (b) evaluating a sample batch quality comprising: (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multiparameter distribution comprises a plurality of multi-parameter points based on multiparameter averages for reference batches; (ii) determining a test sample batch multiparameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (c) evaluating a test sample quality, comprising: (i) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences
  • one or more parameters are adjusted to account for test results (such as copy number). For example, for each sample within the reference batch, one or more of the parameters can be adjusted by multiplying the determined parameter by a predetermined constant or by adding a predetermined constant from the determined parameter.
  • the predetermined constant can be, for example, the difference between an average parameter for a first result (e.g., a copy number of 1) determined from a plurality of samples or batches (which may be historical samples or historical batches) from an average parameter for a second result (e.g., a copy number of 2) determined from a plurality of samples or batches (which may be historical samples or historical batches), and the predetermined constant can be added to the determined parameter for samples with the first result.
  • the predetermined constant can be the ratio of the average parameter for the second result to the average parameter for the first result, and the determined parameter for samples with the first result can be multiplied by the predetermined constant.
  • the parameter average for the reference batch can be an average of the parameters adjusted based on the result.
  • FIG. 3 presents a flowchart of one embodiment of a method for controlling the quality of a test sample using a batch-level quality control and a sample-level quality control.
  • a test sample batch which includes the test sample, is tested to determine multiple parameters for the samples within the test sample batch.
  • the multiple parameters that are determined can be, but need not be, related to the test results.
  • the parameter or parameters are adjusted to account for the test results at step 310. Adjustment of the one or more parameters to account for the test results can include multiplying the parameter by a predetermined constant.
  • a test sample batch multi -parameter average is determined based on the determined multiple parameters (which may be adjusted to account for the test results at step 310), and the multi-parameter average is used as a basis for a multi-parameter point.
  • a batch quality control limit is determined based on a reference batch multi-parameter distribution.
  • the reference batch multi-parameter distribution includes a plurality of reference batch multi-parameter points, and each of the reference batch multi-parameter points are based on a multi-parameter average for a reference batch.
  • the reference batch multi-parameter points are determined in a similar manner as the test sample multi-parameter point.
  • the test sample batch multi-parameter point is evaluated against the batch quality control limit. If the test sample batch multi-parameter point is within the batch quality control limit, the test sample batch passes the quality control (step 330). If the test sample batch multi-parameter point is outside of the batch quality control limit, the test sample batch fails the quality control (step 335).
  • a test sample multi-parameter point is determined, which is based on the difference between the determined multiple parameters for the test sample and the multi-parameter average for the test sample batch.
  • a sample quality control limit is determined based on a reference sample multiparameter distribution.
  • the reference sample multi-parameter distribution includes a plurality of reference sample multi-parameter points, and each of the reference sample multiparameter points is based on the difference between multiple parameters for a reference sample and a multi-parameter average for a reference batch corresponding to the reference sample.
  • the reference sample multi-parameter points are determined in a similar manner as the multi-parameter point for the test sample.
  • test sample multi -parameter point is evaluated against the sample quality control limit. If the test sample multi-parameter point is within the sample quality control limit, the test sample passes the quality control (step 355). If the test sample multi-parameter point is outside of the sample quality control limit, the test sample fails the quality control (step 360).
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) evaluating a test sample batch quality comprising: (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (ii) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (c) evaluating a test sample quality, comprising: (i) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality
  • the samples in the test sample batch are tested to determine a genetic variant (such as a structural genetic variant). In some embodiments, the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene. In some embodiments, the multiple parameters comprise a size standard deviation of a calibration curve and a residual sum of squares of the calibration curve against a logistic curve model. In some embodiments, the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1). In some embodiments, at least one of the multiple parameters is adjusted to account for the number of copies. In some embodiments, the multiple parameters comprise an asymptote parameter and an efficiency parameter of a qPCR assay.
  • the sample quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference sample multi-parameter distribution. In some embodiments, the sample quality control limit is defined as a predetermined deviation from a fit of the reference sample multi-parameter distribution. In some embodiments, the batch quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference batch multi-parameter distribution. In some embodiments, the batch quality control limit is defined as a predetermined deviation from a fit of the reference batch multiparameter distribution.
  • a method for controlling the quality of a test sample in a test sample batch comprising: (a) testing a test sample batch comprising a plurality of samples including a test sample to determine multiple parameters for the samples within the test sample batch; (b) adjusting one or more of the multiple parameters to account for test results of the samples in the test sample batch; (c) evaluating a test sample batch quality comprising: (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (ii) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (d) evaluating a test sample quality, comprising: (i) determining a sample quality control limit based
  • the samples in the test sample batch are tested to determine a genetic variant (such as a structural genetic variant). In some embodiments, the samples in the test sample batch are tested to determine a number of CGG repeats in a FMR1 gene. In some embodiments, the multiple parameters comprise a size standard deviation of a calibration curve and a residual sum of squares of the calibration curve against a logistic curve model. In some embodiments, the samples in the test sample batch are tested to determine a number of copies of a region of interest (such as an exon from SMN1). In some embodiments, at least one of the multiple parameters is adjusted to account for the number of copies. In some embodiments, the multiple parameters comprise an asymptote parameter and an efficiency parameter of a qPCR assay.
  • the sample quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference sample multi-parameter distribution. In some embodiments, the sample quality control limit is defined as a predetermined deviation from a fit of the reference sample multi-parameter distribution. In some embodiments, the batch quality control limit is defined as a pre-determined p-value (such as about 0.05 or less) of the reference batch multiparameter distribution. In some embodiments, the batch quality control limit is defined as a predetermined deviation from a fit of the reference batch multi-parameter distribution.
  • the following example describes a method for controlling the quality of a test sample and a test sample batch using a qPCR assay for evaluating the number of copies of exon 7 of the SMNI gene.
  • the SMNI copy number assay was performed substantially as described in Pyatt & Prior, A feasibility study for the newborn screening of spinal muscular atrophy, Genetics in Medicine, vol. 8, no. 7, pp. 428-437 (2006), with some variations.
  • multiplex quantitative PCR (qPCR) assay was used to determine a copy number for the SMNI exon 7 in about 1000 samples in 10 sample batches.
  • the samples were nucleic acid samples that were extracted from patient blood, saliva, or pre-extracted DNA.
  • Exon 7 of SMNI was PCR amplified, and fluorescence was monitored using a TaqMan protocol.
  • a fluorescently labeled probe hybridizes to the PCR product and the hybridized duplex is degraded using exonuclease activity of a Taq polymerase, allowing fluorescence of the fluorophore on the probe.
  • a non-fluorescent blocking probe that more specifically binds to the SMN2 gene was also included during the qPCR.
  • a reference gene RNase P, TERT, or ALB was also subjected to qPCR along with SMNI, exon 7. Fluorescence was recorded during several qPCR cycles.
  • FIG. 4 An exemplary plot of log(ARn) against PCR cycle number with a sigma curve fit in the exponential phase is shown in FIG. 4 (showing qPCR results for SMNI, exon 7; ALB, and RNase P (ALB and RNaseP are labeled as "Reference” and SMNI, exon 7 is labeled as "SMA”).
  • the AC T value can then be determined, which is the C T for SMN1 minus the C T for the reference gene.
  • the qPCR assay provides two at least two deducible parameters: PCR efficiency and the upper asymptote of the of the sigma curve. These parameters can be used to control the quality of the assay. Additionally, these two parameters depend, in part, on the assay results (i.e., the number of copies of SMN1, exon 7), and can be adjusted to account for the results dependency.
  • FIGS. 5A-D plot batch multi-parameter points from the SMN1 assay for SMN1, exon 7 tested in the presence of a TERT control (FIG. 5 A), the TERT control (FIG. 5B), SMN1, exon 7 tested in the presence of an RNase P control (FIG. 5C), and the RNase P control (FIG. 5D).
  • Each test sample batch multi-parameter point is a test sample multi-parameter average that has been adjusted to account for the number of copies of the SMN1, exon 7 in each sample of the sample batch.
  • the batch multi-parameter points form a multi-parameter distribution, and a multi-parameter Gaussian was trained on the multi-parameter distribution.
  • a batch quality control limit was determined by setting a p-value of 0.005 such that approximately 1 out of every 200 batches was outside the quality control limit. Sample batches that fell outside of the batch quality control limit were failed.
  • sample multi-parameter point For each sample within each batch, a sample multi-parameter point was determined.
  • the sample multi-parameter points were the determined as the vector difference between the determined multiple parameters for the sample and the batch multi-parameter median for the sample' s corresponding batch.
  • These sample multi -parameter points form a distribution, which is shown in FIGS. 6A-D (samples with 1 copy of SMN1, exon are indicated with: ⁇ ; samples with 2 copies of SMN1, exon are indicated with: ⁇ ; and samples with 3 copies of SMN1, exon are indicated with: A ).
  • FIG. 6A shows the sample multi-parameter point distribution for SMN1, exon 7 tested in the presence of a TERT control, FIG.
  • FIG. 6B shows the sample multi-parameter point distribution for the TERT control
  • FIG. 6C shows the sample multi-parameter point distribution for SMN1, exon 7 tested in the presence of an RNase P control
  • FIG. 6D shows the sample multi-parameter point distribution for and the RNase P control.
  • the sample multi-parameter point distributions for the SMN1, exon 7 samples (FIG. 6A and FIG. 6C) exhibited copy number dependent clustering, whereas the control genes TERT (FIG. 6B) and RNase P (FIG. 6D) did not. TERT and RNase P are expected to each have two copies of their respective genes, so no results dependent clustering was expected.
  • FIG. 7A shows the test result adjusted sample multi-parameter point distribution for SMN1, exon 7 tested in the presence of a TERT control
  • FIG. 7B shows the test result adjusted sample multi-parameter point distribution for the TERT control
  • FIG. 7C shows the test result adjusted sample multi-parameter point distribution for SMN1, exon 7 tested in the presence of an RNase P control
  • FIG. 7D shows the test result adjusted sample multi-parameter point distribution for and the RNase P control.
  • a multi-parameter Gaussian was trained on the multi-parameter distribution of the test result adjusted samples.
  • a sample quality control limit was determined by setting a p- value of 0.002 such that approximately 1 out of every 500 batches was outside the quality control limit, and is shown by the dashed line in FIGS. 7A-7D. Samples that fell outside of the sample quality control limit were failed.
  • the following example describes a method for controlling the quality of a test sample batch using a capillary electrophoresis assay for determining a number of CGG repeats in the FMRl gene, which is an assay used to diagnose Fragile X.
  • the test was performed similarly to the methods described in Chen et al., An Information-Rich CGG Repeat Primed PCR That Detects the Full Range of Fragile X Expanded Alleles and Minimizes the Need for Southern Blot Analysis, J. Molecular Diagnostics, vol. 12, no. 5, pp. 589-600 (2010), with some variations.
  • DNA samples from isolated from patient blood were combined with FMRl gene-specific primers and a (CGG) n repeat primer and PCR amplified. About 10 batches, with about 92 samples per batch, were analyzed. The PCR products were then analyzed by capillary electrophoresis. CGG-specific peaks were enumerated using a ROX- labeled DNA reference ladder.
  • FIG. 8 plots the log(log(SSD)) against the log(log(RSS)) for the samples. This forms a reference sample multi-parameter distribution with a plurality of multi-parameter points, wherein the first parameter is the log(log(SSD)) and the second parameter is the log(log(RSS)). A Gaussian was fit to the reference sample multi -parameter distribution.
  • the sample quality control limit was selected as the threshold of the Gaussian with a negative log- likelihood of approximately zero, which provided acceptable false-positive and false-negative rates. That is, samples with a log-likelihood greater than zero for the Gaussian fit were failed. This is illustrated in FIG. 9.
  • Fig. 10 illustrates an exemplary computing system for implementing the examples of the disclosure.
  • System 1000 may include, but is not limited to known components such as central processing unit (CPU) 1001, storage 1002, memory 1003, network adapter 1004, power supply 1005, input/output (I/O) controllers 1006, electrical bus 1007, one or more displays 1008, one or more user input devices 1009, and other external devices 1010.
  • CPU central processing unit
  • I/O input/output
  • system 1000 may contain other well-known components which may be added, for example, via expansion slots 1012, or by any other method known to those skilled in the art.
  • Such components may include, but are not limited, to hardware redundancy components (e.g., dual power supplies or data backup units), cooling components (e.g., fans or water-based cooling systems), additional memory and processing hardware, and the like.
  • System 1000 may be, for example, in the form of a client-server computer capable of connecting to and/or facilitating the operation of a plurality of workstations or similar computer systems over a network.
  • system 1000 may connect to one or more workstations over an intranet or internet network, and thus facilitate communication with a larger number of workstations or similar computer systems.
  • system 1000 may include, for example, a main workstation or main general purpose computer to permit a user to interact directly with a central server.
  • the user may interact with system 1000 via one or more remote or local workstations 1013.
  • CPU 1001 may include one or more processors, for example Intel® CoreTM i7 processors, AMD FXTM Series processors, or other processors as will be understood by those skilled in the art (e.g., including graphical processing unit (GPU)-style specialized computing hardware used for, among other things, machine learning applications, such as training and/or running the machine learning algorithms of the disclosure).
  • processors for example Intel® CoreTM i7 processors, AMD FXTM Series processors, or other processors as will be understood by those skilled in the art (e.g., including graphical processing unit (GPU)-style specialized computing hardware used for, among other things, machine learning applications, such as training and/or running the machine learning algorithms of the disclosure).
  • CPU 1001 may further communicate with an operating system, such as Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system by Windows NT® operating system
  • Storage 1002 may include one or more types of storage, as is known to one of ordinary skill in the art, such as a hard disk drive (HDD), solid state drive (SSD), hybrid drives, and the like. In one example, storage 1002 is utilized to persistently retain data for long-term storage.
  • Memory 1003 may include one or more types of memory as is known to one of ordinary skill in the art, such as random access memory (RAM), read-only memory (ROM), hard disk or tape, optical memory, or removable hard disk drive. Memory 1003 may be utilized for short-term memory access, such as, for example, loading software applications or handling temporary system processes.
  • storage 1002 and/or memory 1003 may store one or more computer software programs.
  • Such computer software programs may include logic, code, and/or other instructions to enable processor 1001 to perform the tasks, operations, and other functions as described herein (e.g., the quality control functions described herein), and additional tasks and functions as would be appreciated by one of ordinary skill in the art.
  • Operating system 1002 may further function in cooperation with firmware, as is well known in the art, to enable processor 1001 to coordinate and execute various functions and computer software programs as described herein.
  • firmware may reside within storage 1002 and/or memory 1003.
  • I/O controllers 1006 may include one or more devices for receiving, transmitting, processing, and/or interpreting information from an external source, as is known by one of ordinary skill in the art.
  • I/O controllers 1006 may include functionality to facilitate connection to one or more user devices 1009, such as one or more keyboards, mice, microphones, trackpads, touchpads, or the like.
  • I/O controllers 1006 may include a serial bus controller, universal serial bus (USB) controller, FireWire controller, and the like, for connection to any appropriate user device.
  • I/O controllers 1006 may also permit communication with one or more wireless devices via technology such as, for example, near-field communication (NFC) or BluetoothTM.
  • NFC near-field communication
  • BluetoothTM BluetoothTM
  • I/O controllers 1006 may include circuitry or other functionality for connection to other external devices 1010 such as modem cards, network interface cards, sound cards, printing devices, external display devices, or the like.
  • I/O controllers 1006 may include controllers for a variety of display devices 1008 known to those of ordinary skill in the art. Such display devices may convey information visually to a user or users in the form of pixels, and such pixels may be logically arranged on a display device in order to permit a user to perceive information rendered on the display device.
  • Such display devices may be in the form of a touch-screen device, traditional non-touch screen display device, or any other form of display device as will be appreciated be one of ordinary skill in the art.
  • CPU 1001 may further communicate with I/O controllers 1006 for rendering a graphical user interface (GUI) on, for example, one or more display devices 1008.
  • GUI graphical user interface
  • CPU 1001 may access storage 1002 and/or memory 1003 to execute one or more software programs and/or components to allow a user to interact with the system as described herein.
  • a GUI as described herein includes one or more icons or other graphical elements with which a user may interact and perform various functions.
  • GUI 1007 may be displayed on a touch screen display device 1008, whereby the user interacts with the GUI via the touch screen by physically contacting the screen with, for example, the user's fingers.
  • GUI may be displayed on a traditional non-touch display, whereby the user interacts with the GUI via keyboard, mouse, and other conventional I/O components 1009.
  • GUI may reside in storage 1002 and/or memory 1003, at least in part as a set of software instructions, as will be appreciated by one of ordinary skill in the art.
  • the GUI is not limited to the methods of interaction as described above, as one of ordinary skill in the art may appreciate any variety of means for interacting with a GUI, such as voice-based or other disability-based methods of interaction with a computing system.
  • network adapter 1004 may permit system 1000 to communicate with network 1011.
  • Network adapter 1004 may be a network interface controller, such as a network adapter, network interface card, LAN adapter, or the like.
  • network adapter 1004 may permit communication with one or more networks 1011, such as, for example, a local area network (LAN), metropolitan area network (MAN), wide area network (WAN), cloud network (IAN), or the Internet.
  • LAN local area network
  • MAN metropolitan area network
  • WAN wide area network
  • IAN cloud network
  • One or more workstations 1013 may include, for example, known components such as a CPU, storage, memory, network adapter, power supply, I/O controllers, electrical bus, one or more displays, one or more user input devices, and other external devices. Such components may be the same, similar, or comparable to those described with respect to system 1000 above. It will be understood by those skilled in the art that one or more workstations 1013 may contain other well-known components, including but not limited to hardware redundancy components, cooling components, additional memory/processing hardware, and the like.
  • a computer system comprising one or more processors, and a non-transitory computer readable storage medium storing one or more programs configured to be executed by the one or more processors, the one or more programs comprising instructions for (a) receiving multiple parameters for samples within a test sample batch comprising a plurality of samples including a test sample; (b) evaluating a test sample batch quality, comprising (i) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (ii) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (iii) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit; and (c) evaluating a test sample quality, comprising (i) determining a sample quality control limit based on a
  • a computer system comprising one or more processors, and a non-transitory computer readable storage medium storing one or more programs configured to be executed by the one or more processors, the one or more programs comprising instructions for (a) receiving multiple parameters for samples within a test sample batch comprising a plurality of samples including a test sample; (b) determining a sample quality control limit based on a reference sample multi-parameter distribution, wherein the reference sample multi-parameter distribution comprises a plurality of multi-parameter points based on differences between determined multiple parameters of reference samples and multi-parameter averages for a reference batch corresponding to each reference sample; (c) determining a test sample multi-parameter point based on a difference between the determined multiple parameters of the test sample and the test sample batch multi-parameter average; and (d) failing a test sample if the determined test sample multi-parameter point is outside of the sample quality control limit.
  • a computer system comprising one or more processors, and a non-transitory computer readable storage medium storing one or more programs configured to be executed by the one or more processors, the one or more programs comprising instructions for (a) receiving multiple parameters for samples within a test sample batch comprising a plurality of samples including a test sample; (b) determining a batch quality control limit based on a reference batch multi-parameter distribution, wherein the reference batch multi-parameter distribution comprises a plurality of multi-parameter points based on multi-parameter averages for reference batches; (c) determining a test sample batch multi-parameter point based on a test sample batch multi-parameter average; and (d) failing the test sample batch if the determined test sample batch multi-parameter point is outside of the batch quality control limit.

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Abstract

La présente invention concerne des procédés de contrôle de qualité d'un échantillon d'essai d'un lot d'échantillons d'essai. L'échantillon peut être un échantillon biologique, qui peut être testé au moyen d'un système de dosage à rendement élevé. En contrôlant la qualité des échantillons analysés, des résultats de dosage plus précis peuvent être déterminés et des échantillons imprécis peuvent être en échec. Dans certains modes de réalisation, le procédé comprend l'évaluation d'un lot d'échantillons d'essai comprenant un échantillon d'essai pour déterminer des paramètres multiples pour les échantillons dans le lot ; l'évaluation d'une qualité de lot d'échantillons d'essai et l'échec du lot d'échantillons d'essai si un point multiparamètre de lot d'échantillons d'essai déterminé est en dehors d'une limite de contrôle de qualité de lot ; et l'évaluation d'une qualité d'échantillon d'essai et l'échec de l'échantillon de test si un point multiparamètre d'échantillon d'essai déterminé est en dehors d'une limite de contrôle de qualité d'échantillon.
PCT/US2018/022955 2017-03-16 2018-03-16 Contrôle de qualité multidimensionnel dépendant des échantillons et dépendant des lots WO2018170443A1 (fr)

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US20070011201A1 (en) * 2005-06-30 2007-01-11 Yerramalli Subramaniam Interface method and system for genetic analysis data
US20130103321A1 (en) * 2011-10-24 2013-04-25 Somalogic, Inc. Selection of Preferred Sample Handling and Processing Protocol for Identification of Disease Biomarkers and Sample Quality Assessment
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CN112712853A (zh) * 2020-12-31 2021-04-27 北京优迅医学检验实验室有限公司 一种无创产前检测装置
CN112712853B (zh) * 2020-12-31 2023-11-21 北京优迅医学检验实验室有限公司 一种无创产前检测装置

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