KR-102962090-B1 - Measurement of microsatellite instability

KR102962090B1KR 102962090 B1KR102962090 B1KR 102962090B1KR-102962090-B1

Abstract

A system and method for detecting microsatellite instability in biological samples are described. Signal data is received from a capillary electrophoresis gene analyzer, and this signal data is measured from the fluorescence of fragments containing nucleic acid sequences amplified from the biological sample via polymerase chain reaction (PCR). The nucleic acid sequences correspond to multiple different microsatellite loci and are obtained using multiple PCR primers configured to be positioned on the sides of the multiple microsatellite loci of the biological sample. When the PCR primers and the biological sample are combined and subjected to the PCR amplification process, fluorescently labeled DNA fragments containing multiple microsatellite loci are generated. Fluorescence data obtained from the multiple fluorescently labeled microsatellite loci is used to classify the microsatellite instability of the biological sample.

Inventors

렁 해리슨
바우도 찰스
히든 찰스 웬델

Assignees

라이프 테크놀로지스 코포레이션

Dates

Publication Date: 20260511
Application Date: 20201107
Priority Date: 20191108

Claims (20)

As a method for identifying microsatellite instability in biological samples, A step of obtaining a plurality of signals from a capillary electrophoresis gene analysis device—the signals are detected from the fluorescence of fragments containing a nucleic acid sequence amplified from the biological sample through a polymerase chain reaction, wherein the nucleic acid sequence corresponds to a plurality of different microsatellite loci, and each signal corresponds to one of the plurality of different microsatellite loci—; and A method for identifying microsatellite instability in a biological sample, comprising the step of applying one or more classifiers to a combined signal for each individual microsatellite locus of the plurality of microsatellite loci to identify whether the biological sample has high microsatellite instability, low microsatellite instability, or is a stable microsatellite.
In paragraph 1, the step of applying one or more classifiers is, A step of determining one or more signal features for the signal for each individual microsatellite locus of a plurality of different microsatellite loci; A step of combining one or more signal features determined for each individual microsatellite locus; and A method for identifying microsatellite instability in a biological sample, comprising the step of applying one or more classifiers to combined signal features to identify whether the biological sample has high microsatellite instability, low microsatellite instability, or stable microsatellites.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises comparing signal features derived from the biological sample with signal features derived from one or more samples of cancer-free tissue.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, at least one classifier includes a fragment size threshold.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, at least one classifier includes fragment size intervals.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises evaluating peak counts within specified size intervals.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises evaluating the relative peak count between tumor and normal tissue within a specified size interval.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises evaluating a peak envelope within a specified size interval.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises evaluating peak envelope separation within a specified size interval.
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A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers comprises evaluating a shift in one or more peak positions within a specified size interval.
A method for identifying microsatellite instability in a biological sample, wherein, in claim 1 or 2, the step of applying one or more classifiers includes analyzing a peak pattern input.
A method for identifying microsatellite instability in a biological sample, wherein the peak pattern input comprises two or more values of: peak amplitude along the fragment size axis and peak position along the fragment size axis; peak amplitude for the largest peak, peak position for the largest peak, peak envelope peak amplitude, peak envelope peak position, peak envelope peak width, or peak metric for the peak envelope metric.
In paragraph 2, A method for identifying microsatellite instability in a biological sample, further comprising the step of: designating the biological sample in a high microsatellite instability state if the percentage of microsatellite loci determined to be microsatellite instability exceeds a first predetermined threshold; designating the sample in a low microsatellite instability state if the percentage of microsatellite loci determined to be microsatellite instability exceeds a second predetermined threshold but is less than the first predetermined threshold; and designating the sample in a microsatellite stable state if the percentage of microsatellite loci determined to be microsatellite instability is less than the second predetermined threshold.
In paragraph 2, A step of analyzing the above signal characteristics to assign a stable value or an unstable value to each microsatellite locus; A step of calculating a weighted sum over the entire range of designated stable and unstable values of microsatellite loci; and A method for identifying microsatellite instability in a biological sample, further comprising the step of designating a high microsatellite instability state in the biological sample if the weighted sum across the entire microsatellite locus exceeds a first predetermined threshold, designating a low microsatellite instability state in the biological sample if the weighted sum across the entire microsatellite locus exceeds a second predetermined threshold but does not exceed the first predetermined threshold, or designating a microsatellite stable state if the weighted sum across the entire microsatellite locus is less than the second predetermined threshold.
A method for identifying microsatellite instability in a biological sample, wherein, in paragraph 15, the weighted sum is calculated using one or more classification functions that map a plurality of signal features to three distinct output values.
As a method for identifying microsatellite instability in biological samples, A step of obtaining a plurality of signals from a capillary electrophoresis gene analysis device—the signals are detected from the fluorescence of fragments containing nucleic acid sequences amplified from the biological sample via polymerase chain reaction, wherein the nucleic acid sequence corresponds to a plurality of different microsatellite loci, and each signal corresponds to one of the plurality of different microsatellite loci—; and A method for identifying microsatellite instability in a biological sample, comprising the step of applying one or more classifiers to a combined signal for each individual microsatellite locus of the plurality of microsatellite loci to identify whether the biological sample has high microsatellite instability, low microsatellite instability, or is in a microsatellite stable state.
A method for identifying microsatellite instability in a biological sample, wherein, in paragraph 17, the classifier comprises a non-linear classification function.
A method for identifying microsatellite instability in a biological sample, wherein the above-mentioned nonlinear classification function comprises a multilayer artificial neural network.
A method for identifying microsatellite instability in a biological sample, wherein the classifier comprises a deep learning neural network, in claim 17.

Description

Measurement of microsatellite instability Cross-reference of related applications This application claims the benefit of priority to U.S. Provisional Application No. 62/932,987, filed November 8, 2019; U.S. Provisional Application No. 62/932,910, filed November 8, 2019; and U.S. Provisional Application No. 62/932,752, filed November 8, 2019. These U.S. applications and all other external materials discussed herein are incorporated by reference in their entirety. The present disclosure generally relates to DNA fragment analysis. The causative agent of cancer is thought to be a failure of the biomolecular machinery responsible for DNA repair. During cell replication, DNA repair mechanisms are critical to the integrity of the replicating cell. If these mechanisms break down, incorrect DNA can accumulate in the DNA carried by the generated cell. There are anticancer drugs that utilize this breakdown to identify and destroy tumors. Such drugs are most effective when tumors exhibit a high mutation rate—which is ultimately associated with a high degree of dysfunction in the DNA repair biomolecular machinery. One method to detect the conditions under which such drugs are most effective is to examine the degree of DNA deviation at loci composed of many repeating DNA sequences. These sequences are called microsatellites. Microsatellite markers (locuses), also known as short tandem repeats (STRs), are polymorphic DNA loci composed of repeating nucleotide sequences. In a typical microsatellite analysis, microsatellite loci are amplified by a polymerase chain reaction (PCR) using fluorescently labeled forward primers and unlabeled reverse primers. PCR amplicons are separated by size using electrophoresis. Applications include linkage mapping; animal husbandry; typification of humans, animals, and plants; subtyping of pathogens; genetic diversity; microsatellite instability; loss of heterozygosity (LOH); inter-simple sequence repeats (ISSR); multilocus variant analysis (MLVA); and companion diagnostics for cancer treatment. When the number of microsatellites at a given DNA locus differs substantially from normal, the microsatellites are considered unstable microsatellites (MSUs). If a majority of microsatellite loci exhibit instability, the DNA sample is considered to have high microsatellite instability and a high MSI. If only a few loci exhibit instability, the DNA sample is considered to have a low MSI. If none exhibit instability, the DNA sample is considered to have stable microsatellites (MSS). For a given microsatellite locus, capillary electrophoresis (CE) can be used to measure the number of microsatellites using fragment analysis. Automated CE uses fluorescent dyes and separates with higher resolution and higher accuracy compared to other methods such as agarose or polyacrylamide gel electrophoresis. To perform fragment analysis in the CE system, probes and primers can be designed to be positioned on the sides of the region of interest. This can be accomplished by attaching a fluorescent dye to the primers or probes used in conjunction with the polymerase chain reaction (PCR) to amplify the DNA locus of interest prior to electrophoresis, and by providing the amplicon to the CE. There is also a size determination standard, which is a collection of fragments of known size labeled in a color different from that of the test fragment. The labeled PCR product and the size determination standard are then electrokinetically injected into a capillary. During electrophoresis, negatively charged DNA fragments move from the negative electrode to the positively charged anode through the polymer-filled capillary when a high voltage is applied between the electrodes. DNA fragment analysis using CE can be multiplexed, meaning there are multiple fragments within the reaction well that pass well through capillaries like the one described above. Small fragments generally run quickly, while large fragments run slowly. Fluorescently labeled DNA fragments, separated by size, travel across the path of a laser beam just before reaching the anode. The laser beam causes the dyes on the fragments to fluorescence at various emission wavelengths. A CCD camera detects this fluorescence, and the fluorescence intensity is digitized, color-coded, and displayed as peaks on the electrophoretic graph. Longer fragments appear later in the data compared to shorter fragments. If the proportion of DNA with microsatellites different from normal molecules is low, it can be very difficult to detect the presence of abnormal DNA molecules. More accurate methods are required to analyze CE data to resolve uncertainty sufficiently to reliably distinguish between high MSI and stable MSI at a given DNA locus, and to determine whether the entire gene profile can be considered high or low MSI. There are possible alternatives to the use of CE fragment analysis. As a simple example, MSI status can be determined by performing sequencing of DNA loci of interest usin