US-12624402-B2 - Variant classification through high-confidence mutation detection from fluorescence signals measured with a multiple mutation assay

Abstract

A system and method for SARS-CoV-2 variant classification through mutation detection from qPCR fluorescence signals. The system receives fluorescence signals, wherein a first fluorescence signal indicates quantitative presence of a first genomic region as a control for SARS-CoV-2, and a second fluorescence signal indicates quantitative presence of a second genomic region of a first mutation present in a subset of variants of SARS-CoV-2. The system measures a first Cq for the first fluorescence signal and a second Cq for the second fluorescence signal as the signals cross a threshold RFU. The system calculates a delta Cq as a difference between the two. Further, the system identifies a first peak RFU for the first fluorescence signal and a second peak RFU for the second fluorescence signal, and calculates a RFU ratio of the two. The system detects presence of the first mutation based on the delta Cq and/or the RFU ratio.

Inventors

• Steven Okino

Assignees

• BIO-RAD LABORATORIES, INC.

Dates

Publication Date

20260512

Application Date

20230816

Claims (20)

1. 1 . A method comprising: receiving fluorescence signals over a plurality of thermal cycles of a quantitative Polymerase Chain Reaction (qPCR) for a test sample, wherein a first fluorescence signal indicates quantitative presence of a first genomic region as a control for SARS-COV-2, and a second fluorescence signal indicates quantitative presence of a second genomic region of a first mutation present in a subset of variants of SARS-COV-2; measuring a first quantitative cycle (Cq) for the first fluorescence signal as the first fluorescence signal crosses a threshold relative fluorescence unit (RFU); measuring a second Cq for the second fluorescence signal as the second fluorescence signal crosses the threshold RFU; identifying a first peak RFU for the first fluorescence signal; identifying a second peak RFU for the second fluorescence signal; calculating a delta Cq as a difference between the second Cq and the first Cq; calculating a RFU ratio of the second peak RFU to the first peak RFU; and detecting presence of the first mutation based on the delta Cq being below a Cq tolerance or the RFU ratio being above a RFU tolerance.
2. 2 . The method of claim 1 , wherein a number of thermal cycles is selected between 30 and 100.
3. 3 . The method of claim 2 , wherein the number of thermal cycles is further selected between 40 and 60.
4. 4 . The method of claim 1 , wherein the threshold RFU is selected between 5 RFU and 500 RFU.
5. 5 . The method of claim 4 , wherein the threshold RFU is determined based on the first fluorescence signal.
6. 6 . The method of claim 1 , wherein the first peak RFU is a maximum RFU of the first fluorescence signal over the plurality of thermal cycles, and wherein the second peak RFU is a maximum RFU of the second fluorescence signal over the plurality of thermal cycles.
7. 7 . The method of claim 1 , wherein the first peak RFU is a RFU of the first fluorescence signal at a sample thermal cycle of the plurality of thermal cycles, and wherein the second peak RFU is a RFU of the second fluorescence signal at the sample thermal cycle.
8. 8 . The method of claim 7 , wherein the sample thermal cycle is a final thermal cycle of the plurality of thermal cycles.
9. 9 . The method of claim 1 , wherein the Cq tolerance is a Cq value selected from the range of 1 Cq to 10 Cq.
10. 10 . The method of claim 9 , wherein the Cq tolerance is further selected from the range of 2 Cq to 5 Cq.
11. 11 . The method of claim 9 , wherein the Cq tolerance is selected to minimize false positive detection of the first mutation.
12. 12 . The method of claim 1 , wherein the RFU tolerance is a ratio selected from the range of 0.3 to 1.1.
13. 13 . The method of claim 12 , wherein the RFU tolerance is further selected from the range of 0.9 to 1.0.
14. 14 . The method of claim 1 , wherein detecting the presence of the first mutation is based on the delta Cq being below the Cq tolerance and the RFU ratio being above the RFU tolerance.
15. 15 . The method of claim 1 , further comprising: in response to detecting the presence of the first mutation, reporting the test sample as having one of the subset of variants of SARS-COV-2.
16. 16 . The method of claim 15 , wherein the reporting further comprises reporting a treatment recommendation for treating COVID.
17. 17 . The method of claim 1 , further comprising: in response to detecting absence of the first mutation, reporting the test sample as not having any of the subset of variants of SARS-COV-2.
18. 18 . The method of claim 17 , further comprising: in response to detecting the absence of the first mutation, identifying the test sample as having a candidate new variant of SARS-COV-2; and providing the test sample for genetic sequencing to sequence the candidate new variant of SARS-COV-2.
19. 19 . A non-transitory computer-readable storage medium storing instructions that, when executed by a computer processor, cause the computer processor to perform operations comprising: receiving fluorescence signals over a plurality of thermal cycles of a quantitative Polymerase Chain Reaction (qPCR) for a test sample, wherein a first fluorescence signal indicates quantitative presence of a first genomic region as a control for SARS-COV-2, and a second fluorescence signal indicates quantitative presence of a second genomic region of a first mutation present in a subset of variants of SARS-COV-2; measuring a first quantitative cycle (Cq) for the first fluorescence signal as the first fluorescence signal crosses a threshold relative fluorescence unit (RFU); measuring a second Cq for the second fluorescence signal as the second fluorescence signal crosses the threshold RFU; identifying a first peak RFU for the first fluorescence signal; identifying a second peak RFU for the second fluorescence signal; calculating a delta Cq as a difference between the second Cq and the first Cq; calculating a RFU ratio of the second peak RFU to the first peak RFU; and detecting presence of the first mutation based on the delta Cq being below a Cq tolerance or the RFU ratio being above a RFU tolerance.
20. 20 . A system comprising: a computer processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the computer processor, cause the computer processor to perform operations: receiving fluorescence signals over a plurality of thermal cycles of a quantitative Polymerase Chain Reaction (qPCR) for a test sample, wherein a first fluorescence signal indicates quantitative presence of a first genomic region as a control for SARS-COV-2, and a second fluorescence signal indicates quantitative presence of a second genomic region of a first mutation present in a subset of variants of SARS-COV-2; measuring a first quantitative cycle (Cq) for the first fluorescence signal as the first fluorescence signal crosses a threshold relative fluorescence unit (RFU); measuring a second Cq for the second fluorescence signal as the second fluorescence signal crosses the threshold RFU; identifying a first peak RFU for the first fluorescence signal; identifying a second peak RFU for the second fluorescence signal; calculating a delta Cq as a difference between the second Cq and the first Cq; calculating a RFU ratio of the second peak RFU to the first peak RFU; and detecting presence of the first mutation based on the delta Cq being below a Cq tolerance or the RFU ratio being above a RFU tolerance.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This applications claims the benefit of and priority to U.S. Provisional Application No. 63/403,581 filed on Sep. 2, 2022, which is incorporated by reference in its entirety. BACKGROUND Field of the Art This present disclosure generally relates to classification of virus variants from nucleic acid samples. Traditional systems detect presence of a polynucleotide sequence in a quantitative Polymerase Chain Reaction (qPCR) operation by measuring whether there is sufficiently high fluorescence signal, e.g., above an agnostic baseline level of noise. These traditional systems, however, are prone to high levels of detecting false positives as true positives. Utilizing the simple noise baseline is inadequately positioned to provide accurate high-confidence detection. SUMMARY A system is disclosed for identifying infections (e.g., viral or bacterial) based on analysis of quantitative fluorescence signal targeting polynucleotide sequences. In some embodiments, the system is implemented for SARS-CoV-2 variant classification. The system may include a thermal cycler and an analytics system. The thermal cycler is configured to cycle through various temperature points for varying durations. For example, in a polymerase-based amplification reaction (e.g., quantitative Polymerase Chain Reaction (qPCR)), the thermal cycle can cycle through a denaturation phase, an annealing phase, and an extension phase to amplify target genomic regions. The target probes used in the qPCR operation may include primers with fluorophores bound to the primers. The thermal cycler includes one or more light sources for exciting the fluorophores on the extended amplificons and one or more light detectors for measuring fluorescence signal from a sample. The system may use a multiple mutation assay that includes a control probe targeting a genomic region common to the virus or the bacterium and probes targeting particular mutations of variants of the virus or the bacterium. In embodiments screening for SARS-CoV-2 variants, the multiple mutation assay includes the control probe common to all SARS-CoV-2 variants and one or more mutation probes targeting genomic regions encompassing various mutations present in SARS-CoV-2 variants. The multiple mutation assay may distinctly target mutations, such that each variant has a distinct set of target mutations from other variants. The analytics system analyses the fluorescence signals to determine whether the sample has one of the variants. The analytics system measures a quantitative cycle (Cq) for each fluorescence signal that crosses a threshold relative fluorescence unit (RFU). The analytic system may calculate a delta Cq for each mutation fluorescence signal by taking a difference between the Cq of the mutation fluorescence signal and the Cq of the control fluorescence signal. The analytics system may measure a peak RFU for each fluorescence signal. The peak RFU may be a maximum RFU over the duration of the fluorescence signal, or at a sample thermal cycle (e.g., the final thermal cycle). The analytics system may calculate a RFU ratio for each mutation fluorescence signal as a ratio of the peak RFU for the mutation fluorescence signal to the peak RFU for the control fluorescence signal. The analytics system may, with high-confidence, detect presence of a target polynucleotide sequence (e.g., a mutation) by comparing a strength of the fluorescence signal for the target polynucleotide sequence to a strength of the fluorescence signal for the control sequence. The comparative strengths of the fluorescence signals may be based on the delta Cq being below a Cq tolerance and/or the RFU ratio being above a RFU tolerance. The analytics system may further determine a mutation pattern for the sample based on the detected presence or absence of the mutations screened for in the multiple mutation assay. The analytics system searches for known variants matching the mutation pattern of the sample. In response to the analytics, the analytics system may perform actions based on the results. The analytics system may generate and report a notification based on the results. If a known variant is matched, having the same set of mutations (also referred to as “mutation pattern”) as the test sample, then the analytics system may report the known variant. The analytics system may further provide a treatment recommendation based on the identified known variant. The analytics system may also report variant metrics calculated based on classified samples. For example, the analytics system may provide aggregated statistics on how many samples are being classified as each variant. These statistics can inform researchers on variant behavior. In embodiments with no identified variant, the analytics system may report the closest match. The analytics system may also notify that there may be a candidate new variant for sequencing. The analytics system may transmit such notifications to a client com