Detection method, computer program product, data processing unit and detection system for detecting polynucleotide mutations in biological sample

Abstract

One aspect of the present disclosure relates to a detection method for reliably detecting a mutation of at least one target polynucleotide in a biological sample. The method comprises the step of providing a control sample and a biological sample to a dPCR unit in the same batch and from a common sample plate. The detection method further comprises the steps of: generating a bioluminescence data set using the biological sample and generating a control fluorescence data set using the control sample; and obtaining, by a fluorescence data processing unit, a bioluminescence data set and a control fluorescence data set generated by the dPCR unit. The detection method comprises the step of detecting a mutant copy in a biological sample from a bioluminescence dataset using a control fluorescence dataset in a fluorescence data processing unit.

Description

[0001] Public domain

[0002] The present disclosure relates to detection methods, computer program products, fluorescence data processing units and detection systems for detecting mutations in at least one polynucleotide in a biological sample.

[0003] Public background

[0004] WO2016197028A1, published in December 2016 under the name of Life Technologies Corp., relates to a method for determining false positive calls in a biological data graph. The method includes identifying a first data cluster as non-amplified data points within the biological data graph, and identifying a second data cluster as wild-type positive within the biological data graph. The method further includes estimating the positions of the centers of the first and second data clusters in the biological data graph. The method further includes: determining a probability of belonging to the first data cluster for each data point within the first data cluster, and determining a probability of belonging to the second data cluster for each data point within the second data cluster. The method includes applying a probability threshold to each data point within the first and second data clusters to identify false positives.

[0005] CN111235240A, disclosed in June 2020 under the names of Guangzhou Forevergen Biotechnology Co Ltd and Guangdong Yongnuo MedicalTech Co Ltd, relates to a PCR reaction liquid and a kit for detecting a V600E site mutation of the human BRAF gene. The kit comprises a PCR reaction liquid. The PCR reaction liquid comprises a PCR reaction premix, an upstream primer Pri-F1, a downstream Pri-R1, a wild-type probe and a mutation probe. The final concentration and specificity of the primers and probes used are particularly suitable for digital PCR detection, and are detected using digital PCR technology.

[0006] Public Overview

[0007] Liquid biopsy provides the ability to monitor solid tumors in patients, allowing, for example, to assess the success of ongoing cancer treatment. However, the level of cell-free DNA (cfDNA) in liquid biopsy may be low, and the level of circulating tumor DNA (ctDNA) with mutations is usually even lower. Detecting tumor-specific mutations in ctDNA is difficult, and only very sensitive and specific detection methods are possible. Digital polymerase chain reaction (dPCR) is a quantitative PCR (qPCR) technology that allows detection, identification and quantification of specific DNA markers in biological samples, particularly in highly diluted biological samples such as plasma. The following disclosure is mainly with reference to droplet digital polymerase chain reaction (ddPCR), but other digital PCR methods and devices / units are equally applicable, such as PCR methods and devices based on solid compartments can be used. In the dPCR scheme, samples are usually divided into thousands of partitions, which are used as separate reaction chambers. These partitions can be liquid compartments (e.g., droplets in ddPCR) or solid compartments (e.g., holes and microchambers in nanoplates, chip-based or microfluidic chamber-based dPCR).

[0008] For analysis, the same reagents / assays as qPCR are used, but before amplification, the sample (and the polynucleotide molecules (DNA) together) are divided into multiple partitions (e.g., droplets), with the purpose that each partition obtains at most one target polynucleotide. After the amplification process, the fluorescent signals of all partitions (e.g., droplets) are read. dPCR devices (such as ddPCR devices) often use dual-channel fluorescent probes to quantify the target polynucleotide (DNA) molecules in the biological sample. The two channels are generally used to measure the wild type and at least one mutant variant of the target polynucleotide. Based on the readout fluorescent signal, it will be determined whether and in how many partitions (e.g., droplets) the target wild-type polynucleotide is amplified and in how many partitions (e.g., droplets) the mutant polynucleotide is amplified. Preferably, the concentration of the mutant copy of the target polynucleotide is determined. The concentration of the mutant copy of the target polynucleotide generally refers to the ratio of the mutant copy of the target polynucleotide in the sample to the total copy (mutant and wild-type copy). Based on this, the biological sample can be classified as a mutation including at least one target polynucleotide. However, the known methods do not take into account the batch-to-batch and plate-to-plate variations of dPCR, which can affect the fluorescent signals in the wild-type and mutant channels, respectively. Therefore, the detection method should provide the detection results in a reliable and robust manner.

[0009] An object of the present disclosure is to provide a detection method, a computer program product, a fluorescence data processing unit and a detection system for detecting a mutation of at least one polynucleotide in a biological sample. Specifically, an object of the present disclosure is to provide a detection method, a computer program product, a fluorescence data processing unit and a detection system for reliably detecting a mutation of at least one polynucleotide in a biological sample, wherein the detection method solves at least one shortcoming of the prior art. According to the present disclosure, these objects are achieved by the features of the independent claims. In addition, other advantageous embodiments can be derived from the dependent claims and the specification.

[0010] In a preferred variant, a first aspect of the present disclosure relates to a detection method for reliably detecting mutations of at least one target polynucleotide in a biological sample, in particular for reliably quantifying low-concentration mutations of at least one target polynucleotide in a biological sample. The method includes providing a control sample and a biological sample in the same batch and / or from a common sample plate to a digital polymerase chain reaction (dPCR) unit. In a more preferred variant, a droplet digital polymerase chain reaction (ddPCR) unit is utilized. Alternatively, a solid compartment-based digital PCR unit can be used. The control sample generally comprises a predetermined amount of mutant and wild-type copies of a target polynucleotide. When a control sample and a biological sample in the same batch and / or from a common sample plate are provided, during the PCR process, the environmental factors of the two samples are substantially the same. The detection method includes the following steps: generating a bioluminescent data set using a biological sample by a dPCR unit, and generating a control fluorescent data set using a control sample; and obtaining a bioluminescent data set and a control fluorescent data set generated by a dPCR unit by a fluorescent data processing unit. Typically, each data set comprises a plurality of fluorescent spots, each of which has intensity values ​​in a wild-type channel and a mutant channel of a target polynucleotide, respectively. Each fluorescent spot typically corresponds to the fluorescent signal of a partition (e.g., a droplet) read out by a dPCR unit. A sample plate typically refers to a multi-well plate used to hold samples in wells (multiple samples analyzed together are analyzed on a common sample plate; inter-plate differences typically refer to differences associated with analysis of samples on different sample plates). In some variations, a semi-skirted PCR plate can be used as a sample plate.

[0011] The detection method generally includes using a control fluorescence data set in a fluorescence data processing unit to detect mutant copies in a biological sample from a bioluminescent data set. Using a control fluorescence data set obtained from a control sample (containing a predetermined amount of mutant and wild-type copies) in a dPCR unit under substantially the same conditions as the bioluminescent data set to detect mutant copies in the bioluminescent data set can reduce batch and / or plate differences. This will increase the reliability and robustness of the detection method.

[0012] Depending on the field of application, the detection method may alternatively or additionally comprise the following steps: in a fluorescence data processing unit, determining the amount of each mutant and wild-type copies of the target polynucleotide and / or the concentration of the mutant copies from the bioluminescence dataset using the control fluorescence dataset.

[0013] Preferably, the detection method comprises the following steps: using a control fluorescence data set, determining a mutant threshold by one or more processors of a fluorescence data processing unit. Alternatively or in addition, one or more processors of the fluorescence data processing unit determine a wild-type threshold using a control fluorescence data set. These thresholds are typically used to determine whether a fluorescent spot is positive in a corresponding channel, which means that at least one copy (mutant and / or wild-type) of the polynucleotide is present in a corresponding partition (e.g., droplet) of the dPCR unit. When the intensity value of a fluorescent spot is above a corresponding threshold in a corresponding channel, the fluorescent spot is typically considered positive.

[0014] In some variants, one or more processors of the fluorescence data processing unit determine the number of fluorescent spots in the mutant channel of the bioluminescent data set having an intensity value above the mutant threshold. In addition, they can determine the number of fluorescent spots in the wild-type channel of the bioluminescent data set having an intensity value above the wild-type threshold.

[0015] In order to account for the multiple polynucleotide molecules (mutant and / or wild type) of each partition (e.g., droplet), a Poisson correction should be calculated. In some variants, one or more processors of the fluorescence data processing unit calculate the Poisson correction of the number of fluorescent spots with intensity values ​​higher than the mutant threshold in the mutant channel of the bioluminescence data set. In addition, they can calculate the Poisson correction of the number of fluorescent spots with intensity values ​​higher than the wild-type threshold in the wild-type channel of the bioluminescence data set. In this way, the amount / number of mutant copies and / or wild-type copies can be determined from the number of fluorescent spots that are positive in the mutant channel and / or wild-type channel. The mutant concentration (ratio of mutant copies to wild-type copies) can be output by the fluorescence data processing unit.

[0016] Depending on the application area, one or more processors of the fluorescence data processing unit compare the number and / or concentration of the determined mutant copies with a predefined blank limit (lob) value to classify the biological sample. When the concentration exceeds the lob value, the biological sample is usually classified as a target polynucleotide mutation positive. The lob value can be understood as the lower limit of the number and / or concentration of mutant copies of the target polynucleotide that the biological sample is classified as including the target polynucleotide mutation.

[0017] Alternatively or additionally, one or more processors of the fluorescence data processing unit use the control fluorescence data set to determine an expected range of at least one of the following: clusters and cluster centers of mutant-positive and wild-type-negative partitions (e.g., droplets), wild-type-positive and mutant-negative partitions (e.g., droplets), and mutant-negative and wild-type-negative partitions (e.g., droplets), respectively. The expected range preferably includes at least one boundary in the mutant channel and / or the wild-type channel, in particular an upper boundary and a lower boundary.

[0018] In order to obtain reliable performance, the detection method comprises the following steps: one or more processors of the fluorescence data processing unit are used to determine the cross-reactivity threshold value using a control fluorescence data set. Cross-reactivity is usually indicated by a partition (e.g., droplet) (fluorescent spot) or a cluster center of a partition (e.g., droplet) located below the expected range, and is detected by counting the number of partitions (e.g., droplets) within different signal intensity ranges. If the number of (mutant / wild type) positive partitions (e.g., droplets) below the (mutant / wild type) cross-reactivity threshold value is greater than the number of (mutant / wild type) partitions (e.g., droplets) above the threshold, the sample may be cross-reactive. Alternatively, or in addition, if a partition (e.g., droplet) cluster is detected above the mutant negative and wild type negative clusters and below the (mutant / wild type) positive cluster and / or (mutant / wild type) cluster center expected range, the sample may be cross-reactive.

[0019] In a preferred variant, one or more processors of the fluorescence data processing unit determine the number of fluorescent spots of the bioluminescence data set in the mutant channel with an intensity value higher than the mutant threshold and lower than the cross-reactivity threshold, i.e., the so-called weak positive partitions (e.g., droplets). Specifically, the number of weak positive partitions (e.g., droplets) (fluorescent spots between the mutant threshold and the cross-reactivity threshold) is compared with the number of strong positive partitions (e.g., droplets) (fluorescent spots higher than the cross-reactivity threshold). Preferably, in the case where the number of weak positive partitions (e.g., droplets) is greater than the number of strong positive partitions (e.g., droplets), one or more processors of the fluorescence data processing unit issue a warning. The same cross-reactivity check can be performed similarly in the wild-type channel. A combination of cross-reactivity checks is also possible.

[0020] In a preferred variant, one or more processors of the fluorescence data processing unit determine the subarea (for example, droplet) density of the negative (and mutant negative) subarea (for example droplet) of the wild-type. Specifically, by determining the density peak between the clusters, check whether the density has an unexpected cluster above the negative / wild-type negative cluster and below the positive cluster of the mutant (wild-type). Alternatively, or in addition, by determining the density peak between the clusters, unexpected clusters can be found within the expected range of (mutant / wild-type) cluster center. Usually, it is not expected that there are more than two clusters in a channel, so more than two clusters indicate cross-reactivity samples or other problems. Preferably, one or more processors of the fluorescence data processing unit issue a warning when finding such a cluster.

[0021] In some variants, the detection method includes the following steps: identifying mutant-positive clusters of fluorescent dots and mutant-negative clusters of fluorescent dots from a control fluorescence data set in a mutant channel by one or more processors of a fluorescence data processing unit, and deriving a mutant threshold value based on the clusters. Alternatively, or in addition, one or more processors of a fluorescence data processing unit identify wild-type positive clusters of fluorescent dots and wild-type negative clusters of fluorescent dots from a control fluorescence data set in a wild-type channel, and derive a wild-type threshold value based on the clusters.

[0022] When one or more processors of fluorescence data processing unit determine mutant type threshold value and / or cross reactivity threshold value based on at least one of the following, good result is possible. The center of the center of mutant type positive cluster and the center of mutant type negative cluster separately and the distribution of the fluorescence point in the mutant type positive cluster and / or the fluorescence point distribution in the mutant type negative cluster, wherein said distribution is particularly fitted distribution. If suitable, use continuous probability distribution to fit partition (for example, droplet) (fluorescence data point), such as lognormal distribution.

[0023] Similarly, the detection method may include determining, by one or more processors of the fluorescence data processing unit, a wild-type threshold and / or a cross-reactivity wild-type threshold based on at least one of the following: the center of a wild-type positive cluster and / or the center of a wild-type negative cluster, respectively, and the distribution of fluorescent dots in the wild-type positive cluster and / or the distribution of fluorescent dots in the wild-type negative cluster, wherein the distribution is particularly a fitted distribution.

[0024] Depends on configuration, the initial mutant threshold value is calculated as a certain quantile of the discrete distribution of the continuous probability distribution or partition (e.g., droplet) of the fitting. The control sample is designed to have a large number of partitions (e.g., droplets) (fluorescent spots) comprising wild-type polynucleotides. The distribution is fitted to the cluster, and the quantile is predefined as a correspondingly large quantile, particularly 1-1e-4 to 1-1e-6, preferably about 1-1e-5. The quantile can be used as the initial mutant threshold value. Preferably, the mutant threshold value is calculated based on the center of the initial mutant threshold value and the mutant positive cluster. Specifically, the mutant threshold value is calculated as the weighted average between the initial mutant threshold value and the mutant positive cluster center, preferably with a weight of 2:1. In a similar manner, the (mutant) cross-reactivity threshold value can be calculated as the weighted average between the initial mutant threshold value and the mutant positive cluster center, preferably with a weight of 1:3. Depending on the application area, weights can be selected differently without departing from the present disclosure.

[0025] If appropriate, the detection method includes the step of determining the result of the biological sample by one or more processors of the fluorescence data processing unit. Specifically, the result is determined by comparing the amount and / or concentration of the mutant copy with a predefined lob value. In the case where the amount and / or concentration of the mutant copy is higher than the predefined lob value, a "positive" result value can be assigned to the pathological sample. The result generally includes at least one of the following: the amount of the mutant copy, the concentration of the mutant copy, the result value, the confidence value or interval, and the recommendation of at least one specific test. The recommendation of at least one specific test preferably depends on the result value. In the case of determining a "positive" result value, a specific test for verifying the result value can form a part of the result. The specific test recommendation may depend on the application field, and in some cases, it can be a computed tomography (CT) scan of the biological sample source, particularly a positron-emission-tomography (PET) CT scan thereof.

[0026] Preferably, the method further comprises displaying the result via a display interconnected to the fluorescence data processing unit. Alternatively, or in addition, the method comprises at least one of the following steps: storing the result on a storage medium, printing the result on a printer interconnected to the fluorescence data processing unit, and transmitting the result to a communication device interconnected to the fluorescence data processing unit via a communication network.

[0027] A second aspect of the present disclosure relates to a computer program product comprising a non-transitory computer-readable medium having stored thereon a computer program code, the computer program code being configured to direct one or more processors, in particular one or more processors of a fluorescence data processing unit, to obtain, through the fluorescence data processing unit, a biological fluorescence dataset and a control fluorescence dataset generated by a dPCR unit, each dataset comprising a plurality of fluorescence spots having intensity values ​​in a wild-type channel and a mutant channel of a target polynucleotide, respectively; and to detect mutant copies in a biological sample from the biological fluorescence dataset in the fluorescence data processing unit using the control fluorescence dataset.

[0028] In a preferred variant, the computer readable medium has stored thereon further computer program code configured to instruct one or more processors of a fluorescence data processing unit for determining in the fluorescence data processing unit the amount of each of mutant and wild-type copies of the target polynucleotide and / or the concentration of the mutant copies from the bioluminescence dataset using the control fluorescence dataset.

[0029] Depending on the design, the computer readable medium has stored thereon further computer program code, the further computer program code being configured to direct one or more processors of the fluorescence data processing unit for determining a mutant threshold value and / or a wild-type threshold value from the fluorescence data obtained from the control sample. In addition, the one or more processors may be directed by the computer program code for determining the number of fluorescent spots obtained from the biological sample having an intensity value in the mutant channel above the mutant threshold value, and determining the number of fluorescent spots obtained from the biological sample having an intensity value in the wild-type channel above the wild-type threshold value.

[0030] For improved reliability, the computer readable medium has stored thereon further computer program code, the further computer program code being configured to instruct one or more processors of the fluorescence data processing unit to determine the cross-reactivity threshold using the control fluorescence data set. And further for determining the number of weak positive fluorescent spots as the number of fluorescent spots of the biological fluorescence data set whose intensity values ​​in the mutant channel are higher than the mutant threshold and lower than the cross-reactivity threshold, and determining the number of strong positive fluorescent spots as the number of fluorescent spots of the biological fluorescence data set whose intensity values ​​in the mutant channel are higher than the cross-reactivity threshold. Preferably, in addition, the number of weak positive fluorescent spots is compared with the number of strong positive fluorescent spots to evaluate whether the biological sample has cross-reactivity.

[0031] Preferably, the computer readable medium has stored thereon further computer program code, the further computer program code being configured to direct one or more processors of the fluorescence data processing unit to identify mutant-positive clusters of fluorescent dots and mutant-negative clusters of fluorescent dots in the mutant channel from the fluorescence data obtained from the control sample, and derive mutant thresholds based on the clusters. Alternatively, or in addition, the one or more processors may be directed by the computer program code to identify wild-type positive clusters of fluorescent dots and wild-type negative clusters of fluorescent dots in the wild-type channel from the fluorescence data obtained from the control sample, and derive wild-type thresholds based on the clusters.

[0032] Good results are possible when the computer readable medium has stored thereon further computer program code configured to instruct one or more processors of the fluorescence data processing unit for determining, by the one or more processors of the fluorescence data processing unit, the mutant threshold based on at least one of: the center of the mutant-positive cluster and the center of the mutant-negative cluster, respectively, and the distribution of fluorescent dots in the mutant-positive clusters and / or the distribution of fluorescent dots in the mutant-negative clusters, wherein the distribution is in particular a fitted distribution.

[0033] Similarly, the computer readable medium has stored thereon further computer program code configured to instruct one or more processors of the fluorescence data processing unit for determining, by the one or more processors of the fluorescence data processing unit, a wild-type threshold based on at least one of the following: the center of the wild-type positive cluster and the center of the wild-type negative cluster, respectively, and the distribution of fluorescent dots in the wild-type positive cluster and / or the distribution of fluorescent dots in the wild-type negative cluster, wherein the distribution is in particular a fitted distribution.

[0034] The computer readable medium preferably stores thereon further computer program code, the further computer program code being configured to direct one or more processors of the fluorescence data processing unit for determining an outcome of the biological sample, in particular for determining an outcome value by comparing the amount and / or concentration of mutant copies with a predefined lob value, the outcome comprising at least one of the following: the amount of mutant copies, the concentration of mutant copies, an outcome value, a confidence value or interval, and a recommendation for at least one specific test; and displaying the outcome on a display interconnected to the fluorescence data processing unit. The lob value typically defines a lower limit for the amount and / or concentration of mutant copies of the target polynucleotide for classifying the biological sample as comprising a mutation of the target polynucleotide.

[0035] Another aspect of the present disclosure relates to a fluorescence data processing unit for use in a detection method as described above. The fluorescence data processing unit generally comprises one or more processors configured to obtain a bioluminescence data set and a control fluorescence data set generated by a dPCR unit, each data set comprising a plurality of fluorescence spots having intensity values ​​in a wild-type channel and a mutant channel of a target polynucleotide, respectively. In addition, the one or more processors are preferably configured to detect mutant copies in a biological sample from the bioluminescence data set using the control fluorescence data set and / or determine the amount of mutant and wild-type copies of a target polynucleotide and / or the concentration of mutant copies from the bioluminescence data set using the control fluorescence data set.

[0036] Good performance is possible when the fluorescence data processing unit includes a memory having stored thereon a lob value for assigning a result value to a biological sample, wherein the lob value defines a lower limit on the amount and / or concentration of mutant copies of a target polynucleotide for classifying the biological sample as comprising a mutation of the target polynucleotide.

[0037] Another aspect of the present disclosure relates to a detection system comprising a dPCR unit and a fluorescence data processing unit for performing the detection method as described above. The previously described embodiments of the method for reliably detecting mutations in at least one target polynucleotide in a biological sample simultaneously disclose embodiments of a correspondingly designed device, and vice versa.

[0038] Although the terms "droplet digital PCR" and "ddPCR" are trademarks of Bio-Rad Laboratories Inc., and the technologies they refer to fall within the meaning of ddPCR in the present disclosure, the terms are not limited thereto. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present disclosure will be explained in more detail by way of examples and with reference to the accompanying drawings, in which:

[0041] Figure 1 The block diagram shown schematically illustrates variations of the detection method and the fluorescence data processing unit according to the present disclosure.

[0042] Figure 2 The flowchart shown illustrates an exemplary sequence of steps for a detection method according to the present disclosure.

[0043] Figure 3 The flowchart shown illustrates an exemplary sequence of steps for a detection method according to the present disclosure.

[0044] Figure 4 A scatter plot of fluorescent spots for an exemplary bioluminescence dataset is shown.

[0045] Implementation Detail

[0046] In the following paragraphs, reference is made to Figures 1 to 3 A possible sequence of steps for detecting method changes is described. Figure 1 An overview of a detection method for reliably detecting a mutation of at least one target polynucleotide in a biological sample 1 according to the present disclosure is illustrated. In a preparation step S0 , a control sample 3 and a biological sample 1 are provided to a droplet digital polymerase chain reaction (ddPCR) unit 4 .

[0047] In step S1, the ddPCR unit 4 generates a bioluminescent data set using the biological sample 1. The ddPCR unit 4 further generates a control fluorescent data set using the control sample 3. The samples and the accompanying target polynucleotide molecules are divided into thousands of droplets, with the goal of obtaining only one target polynucleotide per droplet. After undergoing an amplification process to form a bioluminescent data set and a control fluorescent data set, the fluorescent signals in the mutant channel and in the wild-type channel are read from all droplets.

[0048] In step S2, the fluorescence data processing unit 5 obtains a biological fluorescence data set and a control fluorescence data set generated by the ddPCR unit 4, each data set comprising a plurality of fluorescence spots, each of which has an intensity value in a wild-type channel and a mutant channel of the target polynucleotide. The fluorescence data processing unit 5 can read the data set from a storage device interconnected with the fluorescence data processing unit 5. In other variants, the fluorescence data processing unit 5 is remotely interconnected to the ddPCR unit 4 locally or via a communication network to receive the data set therefrom. The fluorescence data processing unit 5 can be implemented as a mobile processing unit, such as a laptop computer, a tablet computer, etc. In other variants, the fluorescence data processing unit 5 is implemented as a remote processing unit, such as a cloud computing unit.

[0049] In step S3, the fluorescence data processing unit 5 detects mutant copies in the biological sample 1 from the biological fluorescence data set using the control fluorescence data set. Figure 2 As shown in , the fluorescence data processing unit 5 can alternatively or additionally use the control fluorescence data set in step S3.1 to determine the amount of each mutant and wild-type copy of the target polynucleotide and / or the concentration of the mutant copy from the biological fluorescence data set. To achieve this, the processor 6 of the fluorescence data processing unit 5 determines the number of fluorescent spots 8 in the mutant channel of the biological fluorescence data set whose intensity values ​​are higher than the mutant threshold 9, and determines the number of fluorescent spots 8 in the wild-type channel of the biological fluorescence data set whose intensity values ​​are higher than the wild-type threshold 10.

[0050] In step S6, the processor 6 of the fluorescence data processing unit 5 assigns a result value to the biological sample 1 by comparing the amount and / or concentration of the mutant copies with a predefined lob value stored in the memory 7 of the fluorescence data processing unit 5. In step S7, the result is output, which can be achieved, for example, by displaying the result on a display (not shown) interconnected to the fluorescence data processing unit 5 or integrated into the fluorescence data processing unit 5.

[0051] like Figure 3 As shown, the variation shown in the present disclosure includes determining the cross-reactivity threshold 11 using the control fluorescence data set by the processor 6 of the fluorescence data processing unit 5 in step S5.1. Based on this, the processor determines the number of weak positive fluorescent spots 8 as the number of fluorescent spots 8 whose intensity values ​​in the mutant channel of the biological fluorescence data set are higher than the mutant threshold 9 and lower than the cross-reactivity threshold 11, and determines the number of strong positive fluorescent spots 8 as the number of fluorescent spots 8 whose intensity values ​​in the mutant channel of the biological fluorescence data set are higher than the cross-reactivity threshold 11 in step S5.2. The cross-reactivity threshold can be calculated for the mutant channel 11 and / or the wild-type channel 16. By comparing the number of weak positive fluorescent spots 8 with the number of strong positive fluorescent spots 8 in step S5.3, the fluorescence data processing unit 5 is able to evaluate whether the biological sample has cross-reactivity. Specifically, if the weak positive fluorescent spots 8 are more than the strong positive fluorescent spots 8, the biological sample is considered to have cross-reactivity. Alternatively, or in addition, if a cluster of droplets accumulated above the mutant-negative and wild-type-negative clusters 13 and below the mutant- or wild-type-positive clusters 14 and / or below the expected range of the (mutant / wild-type) cluster center 15 is detected, the sample 3 may have cross-reactivity. In order to determine the presence of unexpected clusters, the fluorescence data processing unit 5 determines that the density of the droplets 8 of the wild-type-negative (mutant-negative) droplets can be determined. Specifically, by looking for a peak in the density, it can be checked whether the density has an unexpected cluster above the mutant / wild-type negative cluster 13 and below the mutant (wild-type) positive cluster 12 (14) and / or below the expected range of the (mutant / wild-type) cluster center 15.

[0052] In step S5.4, a warning is issued by the fluorescence data processing unit 5. The warning is usually output in the same way as the result or together with it.

[0053] In a preferred variation of the present disclosure, the step of determining a threshold value is as follows.

[0054] For each Assay A and Batch B on the sample plate:

[0055] For each positive control sample (control fluorescence dataset):

[0056] Identifying clusters and cluster centers

[0057] ● K-means algorithm to separate in 1-dimension by mutation channel (S4a)

[0058] ● K-means algorithm to separate in 1-dimension by wild-type channel (S4b)

[0059] ● Calculate the cluster center 15 as the median of the respective assigned droplets

[0060] Threshold for identification of mutant channels (S4, S4a)

[0061] ● Fitting the wild-type positive cluster 14 along the mutant channel to a 1-dimensional log-normal distribution

[0062] ● Calculate the initial mutation threshold as the (1-1e-5) quantile of the lognormal distribution

[0063] ● Calculate the mutation threshold as a weighted average (2:1) between the initial mutation threshold and the center of the mutation-positive cluster 12

[0064] ● Calculate the cross-reactivity threshold 11 as a weighted average (1:3) between the initial mutant threshold and the center of the mutant-positive cluster 12

[0065] Threshold for identification of wild-type channels (S4, S4b)

[0066] ● Fit wild-type negative cluster 13 to a 1-dimensional log-normal distribution along the wild-type channel

[0067] ● Calculate the initial wild-type threshold as the (1-1e-5) quantile of the lognormal distribution

[0068] ● Calculate the wild-type threshold as a weighted average (2:1) between the initial wild-type threshold and the center of the wild-type positive cluster 14

[0069] ● Calculate the cross-reactivity threshold 16 as a weighted average (1:3) between the initial wild-type threshold and the center of the wild-type positive cluster 14

[0070] The final channel threshold was derived by taking the median of the thresholds of all positive control samples.

[0071] In a preferred variation of the present disclosure, the step of classifying the biological sample based on the determined threshold value is as follows.

[0072] For each biological sample (bioluminescence dataset):

[0073] For each droplet in the bioluminescence dataset:

[0074] ● Classify the droplets into clusters by applying the corresponding thresholds (NN - mutant and wild type negative 13, PN - mutant positive 12, NP - wild type positive 14, PP - mutant and wild type positive) (S6)

[0075] For each channel (S3.1):

[0076] ● Calculate the number of mutant and wild-type positive polynucleotides as the Poisson-corrected number of positive droplets

[0077] - Calculate the mutant concentration as 100*(number of mutant-positive polynucleotides) / (total number of polynucleotides), where the total number of polynucleotides is defined as the sum of the number of mutant-positive polynucleotides and the number of wild-type positive polynucleotides.

[0078] Classify biological samples (S6):

[0079] ●If (the number and / or concentration of mutant copies > lob): Positive

[0080] Otherwise: Negative

[0081] It should be noted that in the specification, the order of steps has been presented in a specific order, but those skilled in the art will understand that the order of at least some steps can be changed without departing from the scope of the present disclosure. The same is true for computer program codes associated with specific functional modules in the specification, and those skilled in the art will understand that the computer program codes can be constructed differently without departing from the scope of the present disclosure.

[0082] exist Figure 4 A typical scatter plot of a bioluminescence data set is shown in , which has the following clusters of fluorescent spots: mutant / wild-type negative 13, mutant positive 12, wild-type positive 14, and mutant / wild-type positive. The centers of the individual clusters 15 are indicated by cross symbols. Each threshold is indicated by a dotted line (mutant threshold 9, cross-reactivity threshold 11 (for mutant channels), cross-reactivity threshold 16 (for wild-type channels), and wild-type threshold 10).

Claims

1. A detection method for reliably detecting a mutation of at least one target polynucleotide in a biological sample (1), in particular for reliably quantifying a low-concentration mutation of at least one target polynucleotide in a biological sample (1), the detection method comprising the following steps: a. providing (S0) a control sample (3) and a biological sample (1) in the same batch and from a common sample plate (2) to a digital polymerase chain reaction (dPCR) unit (4), wherein the control sample comprises a predetermined amount of mutant and wild-type copies of a target polynucleotide; b. generating (S1) a biological fluorescence data set using the biological sample (1) and generating (S1) a control fluorescence data set using the control sample (3) by a dPCR unit (4); c. obtaining (S2) a biological fluorescence data set and a control fluorescence data set generated by the dPCR unit (4) through a fluorescence data processing unit (5), each data set comprising a plurality of fluorescence spots (8), each of which has intensity values ​​in a wild-type channel and a mutant channel of the target polynucleotide; and d. In the fluorescence data processing unit (5), the mutant copies in the biological sample (1) are detected (S3) from the biological fluorescence data set using the control fluorescence data set.

2. The detection method according to claim 1, wherein the detection method comprises the following steps: In the fluorescence data processing unit (5), the amount of each of mutant and wild-type copies of the target polynucleotide and / or the concentration of mutant copies is determined (S3.1) from the bioluminescence data set using the control fluorescence data set.

3. The detection method according to at least one of the preceding claims, wherein the detection method comprises the following steps: a. determining (S4) a mutant threshold (9) and / or a wild-type threshold (10) using a control fluorescence data set by one or more processors (6) of a fluorescence data processing unit (5); b. Determine (S3.1) the number of fluorescent spots (8) in the mutant channel of the bioluminescence data set whose intensity values ​​are higher than the mutant threshold value (9) through one or more processors (6) of the fluorescence data processing unit (5), and determine the number of fluorescent spots (8) in the wild-type channel of the bioluminescence data set whose intensity values ​​are higher than the wild-type threshold value (10).

4. The detection method according to claim 3, wherein the detection method comprises the following steps: a. determining (S5.1) a cross-reactivity threshold (11) using a control fluorescence data set by one or more processors (6) of a fluorescence data processing unit (5); b. determining (S5.2) the number of weak positive fluorescent spots (8) as the number of fluorescent spots (8) in the bioluminescence data set whose intensity values ​​in the mutant channel are higher than the mutant threshold value (9) and lower than the cross-reactivity threshold value (11), and determining the number of strong positive fluorescent spots (8) as the number of fluorescent spots (8) in the bioluminescence data set whose intensity values ​​in the mutant channel are higher than the cross-reactivity threshold value (11) by one or more processors (6) of the fluorescence data processing unit (5); and c. One or more processors (6) of the fluorescence data processing unit (5) compare the number of weak positive fluorescence spots (8) with the number of strong positive fluorescence spots (8) (S5.3) to evaluate whether the biological sample has cross-reactivity.

5. The detection method according to claim 3 or 4, wherein the detection method comprises the following steps: a. identifying (S4a) mutant-positive clusters (12) of fluorescent dots (8) and mutant-negative clusters (13) of fluorescent dots (8) from a control fluorescence data set in a mutant channel by one or more processors (6) of a fluorescence data processing unit (5), and deriving a mutant threshold value (9) based on the clusters; and / or b. One or more processors (6) of the fluorescence data processing unit (5) identify (S4b) wild-type positive clusters (14) of fluorescent spots (8) and wild-type negative clusters (13) of fluorescent spots (8) in the wild-type channel from the control fluorescence data set, and derive a wild-type threshold value (10) based on the clusters.

6. The detection method according to claim 5, wherein the detection method comprises the following steps: a. determining (S4a) a mutant threshold (9) and / or a cross-reactive mutant threshold (11) by one or more processors (6) of a fluorescence data processing unit (5) based on at least one of the following: i. the center (15) of the mutant-positive cluster (12) and the center (15) of the mutant-negative cluster (13), respectively; and ii. the distribution of fluorescent spots in the mutant-positive cluster (12) and / or the distribution of fluorescent spots in the mutant-negative cluster (13), wherein the distribution is particularly a fitted distribution; and / or b. determining (S4b) a wild-type threshold (10) and / or a cross-reactive wild-type threshold by one or more processors (6) of the fluorescence data processing unit (5) based on at least one of the following: i. the center (15) of the wild-type positive cluster (14) and / or the center (15) of the wild-type negative cluster (13), respectively; and ii. The distribution of fluorescent spots in the wild-type positive cluster (14) and / or the distribution of fluorescent spots in the wild-type negative cluster (13), wherein the distribution is particularly a fitted distribution.

7. The detection method according to at least one of the preceding claims, wherein the detection method comprises the following steps: a. determining (S6) a result of the biological sample (1) by one or more processors (6) of the fluorescence data processing unit (5), in particular for determining a result value by comparing the amount and / or concentration of mutant copies with a predefined blank limit (lob) value, the result comprising at least one of the following: the amount of mutant copies, the concentration of mutant copies, a result value, a confidence value or interval and a recommendation for at least one specific test; and b. Displaying (S7) the results via a display interconnected with the fluorescence data processing unit (5).

8. A computer program product comprising a non-transitory computer readable medium having stored thereon computer program code configured to direct one or more processors (6) of a fluorescence data processing unit (5) to: a. obtaining (S2) a biological fluorescence data set and a control fluorescence data set generated by a dPCR unit (4) through a fluorescence data processing unit (5), each data set comprising a plurality of fluorescence spots (8), wherein the plurality of fluorescence spots (8) have intensity values ​​in a wild-type channel and a mutant channel of the target polynucleotide, respectively; and b. Using the control fluorescence dataset in the fluorescence data processing unit (5) to detect (S3) mutant copies in the biological sample (1) from the biological fluorescence dataset.

9. The computer program product of claim 8, wherein the computer readable medium has stored thereon further computer program code, the further computer program code being configured to instruct one or more processors (6) of a fluorescence data processing unit (5) to determine (S3.1) the respective amounts of mutant and wild-type copies of a target polynucleotide and / or the concentration of mutant copies from a biological fluorescence data set using a control fluorescence data set in the fluorescence data processing unit (5).

10. The computer program product of claim 8 or 9, wherein the computer readable medium has stored thereon further computer program code configured to instruct one or more processors (6) of the fluorescence data processing unit (5) to: a. determining (S4) a mutant threshold (9) and / or a wild-type threshold (10) from the fluorescence data obtained from the control sample (3); b. Determine (S3.1) the number of fluorescent spots (8) obtained from the biological sample whose intensity values ​​in the mutant channel are higher than the mutant threshold value (9), and determine the number of fluorescent spots (8) obtained from the biological sample (1) whose intensity values ​​in the wild-type channel are higher than the wild-type threshold value (10).

11. The computer program product of claim 10, wherein the computer readable medium has stored thereon further computer program code configured to direct one or more processors (6) of the fluorescence data processing unit (5) to: a. Determine (S5.1) the cross-reactivity threshold (11) using a control fluorescence dataset; b. Determine (S5.2) the number of weakly positive fluorescent spots (8) as the number of fluorescent spots (8) in the mutant channel of the bioluminescence data set whose intensity values ​​are higher than the mutant threshold (9) and lower than the cross-reactivity threshold (11), and determine the number of strongly positive fluorescent spots (8) as the number of fluorescent spots (8) in the mutant channel of the bioluminescence data set whose intensity values ​​are higher than the cross-reactivity threshold (11); and c. Compare the number of weak positive fluorescent spots (8) with the number of strong positive fluorescent spots (8) (S5.3) to evaluate whether the biological sample has cross-reactivity.

12. The computer program product of claim 10 or 11, wherein the computer readable medium has stored thereon further computer program code configured to instruct one or more processors (6) of the fluorescence data processing unit (5) to: a. identifying (S4a) mutant-positive clusters (12) of fluorescent spots (8) and mutant-negative clusters (13) of fluorescent spots (8) in the mutant channel from the fluorescence data obtained from the control sample (3), and deriving a mutant threshold value (9) based on the clusters; and / or b. Identifying (S4b) a wild-type positive cluster (14) of fluorescent spots (8) and a wild-type negative cluster (13) of fluorescent spots (8) in the wild-type channel from the fluorescence data obtained from the control sample (3), and deriving a wild-type threshold value (10) based on the clusters.

13. The computer program product of claim 12, wherein the computer readable medium has stored thereon further computer program code configured to direct one or more processors (6) of the fluorescence data processing unit (5) to: a. determining (S4a) a mutant threshold (9) and / or a cross-reactive mutant threshold (11) by one or more processors (6) of a fluorescence data processing unit (5) based on at least one of the following: i. the center (15) of the mutant-positive cluster (12) and / or the center (15) of the mutant-negative cluster (13), respectively; and ii. the distribution of fluorescent spots in the mutant-positive cluster (12) and / or the distribution of fluorescent spots in the mutant-negative cluster (13), wherein the distribution is particularly a fitted distribution; and / or b. determining (S4b) a wild-type threshold (10) and / or a cross-reactive wild-type threshold by one or more processors (6) of the fluorescence data processing unit (5) based on at least one of the following: i. the center (15) of the wild-type positive cluster (14) and / or the center (15) of the wild-type negative cluster (13), respectively; and ii. The distribution of fluorescent spots in the wild-type positive cluster (14) and / or the distribution of fluorescent spots in the wild-type negative cluster (13), wherein the distribution is particularly a fitted distribution.

14. The computer program product of at least one of claims 8 to 13, wherein the computer readable medium has stored thereon further computer program code configured to instruct one or more processors (6) of the fluorescence data processing unit (5) to: a. determining (S6) a result of a biological sample (1), in particular for determining a result value by comparing the amount and / or concentration of mutant copies with a predefined blank limit (lob) value, the result comprising at least one of the following: the amount of mutant copies, the concentration of mutant copies, a result value, a confidence value or interval and a recommendation for at least one specific test; and b. Displaying (S7) the results on a display interconnected with the fluorescence data processing unit (5).

15. A fluorescence data processing unit (5) for the detection method according to at least one of claims 1 to 6, comprising one or more processors (6), wherein the one or more processors (6) are configured to: a. Obtaining (S2) a bioluminescence data set and a control fluorescence data set generated by a dPCR unit (4), each data set comprising a plurality of fluorescence spots (8), wherein the plurality of fluorescence spots (8) respectively have intensity values ​​in a wild-type channel and a mutant channel of a target polynucleotide; and b. using a control fluorescence dataset, detecting (S3) mutant copies in the biological sample (1) from the biological fluorescence dataset; and / or c. Using the control fluorescence dataset, determine (S3.1) the amount of each of the mutant and wild-type copies of the target polynucleotide and / or the concentration of the mutant copies from the bioluminescence dataset.

16. A fluorescence data processing unit (5) according to claim 15, wherein the memory (7) of the fluorescence data processing unit (5) has stored thereon a lob value for assigning (S6) a result value to a biological sample (1), wherein the lob value defines a lower limit of the amount and / or concentration of mutant copies of a target polynucleotide, and is used to classify the biological sample (1) as comprising a mutation of the target polynucleotide.

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