MICROSATELLITE INSTABILITY DETERMINING METHOD AND SYSTEM THEREOF

REFERENCE TO A “SEQUENCE LISTING”, A TABLE, OR A COMPUTER PROGRAM LISTING APPENDIX SUBMITTTED ON A COMPACT DISC AND AN INCORPORATION-BY-REFERENCE OF THE MATERIAL ON THE COMPACT DISC

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created Jun. 11, 2021, is named “ACTG-7PCT_ST25.txt” and is 6,293 bytes in size

BACKGROUND OF THE INVENTION

This disclosure is related to the fields of molecular diagnostics, cancer genomics, and molecular biology.

Microsatellite instability (MSI) is a molecular phenotype indicative of underlying genomic hypermutability. The gain or loss of nucleotides from microsatellite tracts can arise from impairments in the mismatch repair (MMR) system, limiting the correction of spontaneous mutations in repetitive DNA sequences. MSI-affected tumors may, accordingly, be caused by mutational inactivation or epigenetic silencing of genes in the MMR pathway. MSI has been associated with improved prognosis. The ability of MSI to predict pembrolizumab response has led to the first tumor-agnostic drug approval by the FDA in May 2017. Additional evidence showed an improved response for microsatellite instability-high (MSI-H) patients to the anti-PD-1 agents nivolumab and MED10680, the anti-PD-L1 agent durvalumab, and the anti-CTLA-4 agent ipilimumab. With these results, MSI-H has been approved as the molecular marker for immune checkpoint inhibitors.

MSI is typically detected through PCR assay (MSI-PCR) by fragment analysis (FA) using the peak pattern of five microsatellite loci to determine the MSI status of individual samples. Samples with two or more unstable microsatellites are referred to as MSI-High, whereas samples with one or no unstable microsatellite detected are referred to as MSS. However, since each microsatellite locus should be evaluated by comparing the paired tumor and normal tissue, MSI-PCR assay is not always feasible for cases with limited tissue samples, especially the sample containing few normal cells. Immunohistochemistry (IHC) is another typical assay that may be used for MSI status detection. It detects samples with MSI through MMR protein expression testing. However, MMR-IHC cannot always detect loss of mutated proteins resulting from missense mutations and may have normal staining even for some protein-truncating mutations. Further, interpretation of both MSI-PCR and IHC data is manual and qualitative. There is a need in the art for developing a quantitative assay to determine the MSI status efficiently and accurately for patients. Currently several next-generation sequencing (NGS) assays are found to be feasible to determine MSI status. In general, NGS-based MSI testing offers the advantage of providing automated analysis based on quantitative statistics, which reduces analysis time and the variation derived from inter-observer and inter-laboratory compared to MSI-PCR assay. However, some NGS-based MSI-detection methods such as MANTIS and MSIsensor require a matched-normal sample for the evaluation. For other methods, e.g., MSIplus, though do not require a matched-normal sample in the assay, further improvement like adding more microsatellite loci may be needed. There is still space for improving NGS-based MSI testing

SUMMARY OF THE INVENTION

The present disclosure provides improved techniques for determining MSI status. The present disclosure uses a trained machine learning model to determine MSI status from large-panel clinical targeted NGS data accounting for at least six microsatellite loci, and preferably at least one hundred microsatellite loci. The trained machine learning model uses different weights on the different features, e.g., peak width, peak height, peak location, and simple sequence repeat (SSR) type, to achieve high robustness and efficiency for MSI status detection from NGS data without matched normal sample. Furthermore, through validating the trained machine learning model using an independent dataset of clinical samples across various cancer types, the trained machine learning model is proved to have high sensitivity and specificity for MSI status detection.

In one general aspect, the disclosure relates to a method of generating a model for predicting a MSI status, including:

(a) collecting a clinical sample and an estimated MSI status data thereof;
(b) sequencing, through NGS, at least six microsatellite loci of the clinical sample to generate sequencing data;
(c) extracting a MSI feature from the sequencing data;
(d) training a machine learning model by mapping a MSI feature data with the estimated MSI status data; and
(e) outputting a trained machine learning model.

In some embodiments, the MSI feature data is calculated by a baseline. In some embodiments, the baseline for calculating the MSI feature data is established by normal samples or samples with MSS status. In some embodiments, the baseline is established from the mean of each the MSI feature of each SSR region across the normal samples. Preferably, the baseline is established from the mean peak width of each SSR region.

In some embodiments, the estimated MSI status data is retrieved from a cancer patient through known assay method including but not limited to MSI-PCR assay, IHC, NGS-based MSI testing including MANTIS, MSIsensor, MSIplus, or Large Panel NGS. In some embodiments, the MSI status is microsatellite stability (MSS) or MSI-H. In some embodiments, the MSI features include peak width, peak height, peak location, SSR type, or any combination thereof.

In some embodiments, the machine learning model includes but is not limited to regression-based models, tree-based models, Bayesian models, support vector machines, boosting models, or neural network-based models. In some embodiments, the machine learning model includes but is not limited to a logistic regression model, a random forest model, an extremely randomized trees model, a polynomial regression model, a linear regression model, a gradient descent model, and an extreme gradient boost model.

In some embodiments, the trained machine learning model includes a defined weight of each microsatellite locus. In some embodiments, the trained machine learning model includes a defined weight of the MSI feature in each microsatellite locus. The trained machine learning model is predictive of MSI status.

In some embodiments, the machine learning model has a cutoff value of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, or 0.5.

In some embodiments, the estimated MSI status data or the computed MSI status data indicates microsatellite stability (MSS) or microsatellite instability-high (MSI-H).

In another general aspect, the disclosure relates to a computer-implemented method for determining MSI status, including:

(a) collecting a clinical sample from a subject;
(b) sequencing, through NGS, at least six microsatellite loci of the clinical sample so as to generate a sequencing data;
(c) extracting a MSI feature from the sequencing data;
(d) inputting a MSI feature data into the trained machine learning model; and
(e) generating a computed MSI status.

In some embodiments, the computer-implemented method further includes step (f): outputting the computed MSI status data to an electronic storage medium or a display.

In some embodiments, the method further includes a step of identifying a treatment for a subject based on the computed MSI status data and/or administering a therapeutically effective amount of treatment to the subject.

In some embodiments, the treatment includes but is not limited to surgery, individual therapy, chemotherapy, radiation therapy, immunotherapy, or any combination thereof. In some embodiments, the immunotherapy includes administering the drug including but not limited to anti-PD-1 agents pembrolizumab, nivolumab and MED10680, anti-PD-L1 agent durvalumab, and anti-CTLA-4 agent ipilimumab.

In some embodiments, the microsatellite loci is at least 7, 10, 15, 20, 30, 40, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, or 600 loci. In some embodiments, the microsatellite loci are identifying by sequencing SSR regions in the chromosomal regions. In some embodiments, the microsatellite loci are excluded due to low coverage, unstable peak call, high variability in peak width, or low weight. In some embodiments, the microsatellite loci with high variability in peak width has a peak width greater than 2 in 5 replicate runs, 3 in 6 replicate runs, 3 in 7 replicate runs, 3 in 8 replicate runs, 3 in 9 replicate runs, or 4 in 10 replicate runs.

In some embodiments, the sample originates from a cell line, biopsy, primary tissue, frozen tissue, formalin-fixed paraffin-embedded (FFPE), liquid biopsy, blood, serum, plasma, buffy coat, body fluid, visceral fluid, ascites, paracentesis, cerebrospinal fluid, saliva, urine, tears, seminal fluid, vaginal fluid, aspirate, lavage, buccal swab, circulating tumor cell (CTC), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), DNA, RNA, nucleic acid, purified nucleic acid, purified DNA, or purified RNA.

In some embodiments, the sample is a clinical sample. In some embodiments, the sample originates from a diseased patient. In some embodiments, the sample originates from a patient having cancer, solid tumor, hematologic malignancy, rare genetic disease, complex disease, diabetes, cardiovascular disease, liver disease, or neurological disease. In some embodiments, the sample originates from a patient having Adenocarcinoma, Adenoid cystic carcinoma, Adrenal cortical carcinoma, Ampulla Vater cancer, Anal cancer, Appendix cancer, Basal ganglia glioma, Bladder cancer, Brain cancer, Brain tumor glioma, Breast cancer, Buccal cancer, Cervical cancer, Cholangiocarcinoma, Chondrosarcoma, Clear cell carcinoma, Colon cancer, Colorectal cancer, Cystic duct carcinoma, Dedifferentiated liposarcoma, Desmoid, Diffuse midline glioma, Endometrial cancer, Endometrioid adenocarcinoma, Epithelioid rhabdomyosarcoma, Esophageal cancer, Extraskeletal chondroblastic osteosarcoma, Eyelid sebaceous carcinoma, Fallopian tube cancer, Gallbladder cancer, Gastric Cancer, Gastrointestinal stromal tumor, Glioblastoma multiforme, Head and Neck Cancers, Hepatocellular carcinoma, High grade glioma, Hypopharyngeal Cancer, Intima sarcoma, Infantile fibrosarcoma, Invasive ductal carcinoma, Kidney cancer, Leiomyosarcoma, Liposarcoma, Liver angiosarcoma, Liver cancer, Lung cancer, Melanoma, Metastasis of unknown origin, Nasopharyngeal cancer, NSCLC adenocarcinoma, Oesophageal cancer, Oral Cancer, Oropharyngeal cancer, Osteosarcoma, Ovarian cancer, Pancreatic cancer, Papillary Thyroid Carcinoma, Peritoneal cancer, Primary peritoneal serous carcinoma, Prostate cancer, Rectal cancer, Renal cancer, Salivary gland cancer, Sarcomatoid Carcinoma, Sigmoid cancer, Sinus cancer, Skin cancer, Soft tissue sarcoma, Squamous cell carcinoma, Stomach adenoacrinoma, Submandibular gland cancer, Thymic cancer, Thymoma involvement, Thyroid cancer, Tongue cancer, Tonsillar cancer, Transitional cell carcinoma, Uterine cancer, Uterine sarcoma, or Uterus leiomyosarcoma. In some embodiments, the sample originates from a pregnant woman, a child, an adolescent, an elder, or an adult. In some embodiments, the sample is a research sample. In some embodiments, the sample originates from a group of samples. In some embodiments, the group of samples is from related species. In some embodiments, the group of samples is from different species.

In some embodiments, the machine learning model is trained by using a training set having MSI status data and MSI feature data.

In some embodiments, the NGS system includes but not limited to the MiSeq, HiSeq, MiniSeq, iSeq, NextSeq, and NovaSeq sequencers manufactured by Illumina, Inc., Ion Personal Genome Machine (PGM), Ion Proton, Ion S5 series, and Ion GeneStudio S5 series manufactured by Life Technologies, Inc., BGlseq series, DNBseq series and MGlseq series, manufactured by BGI, and MinION/PromethION sequencers manufactured by Oxford Nanopore Technologies.

In some embodiments, the sequencing reads are generated from nucleic acids that are amplified from the original sample or the nucleic acids captured by the bait. In some embodiments, the sequencing reads are generated from a sequencer that required the addition of an adapter sequence. In some embodiments, the sequencing reads are generated from a method that includes but is not limited to hybrid capture, primer extension target enrichment, a molecular inversion probe-based method, or multiplex target-specific PCR.

In another general aspect, the disclosure relates to a system for determining MSI status. The system includes a data storage device storing instructions for determining characteristics of MSI status and a processor configured to execute the instructions to perform a method. Further, the method includes the following steps:

(a) training a machine learning model, wherein the machine learning model maps the training data of one or more MSI features with the training estimated MSI status;
(b) collecting a clinical sample from a human subject;
(c) sequencing at least six microsatellite loci of the clinical sample to generate a sequence data by using NGS;
(d) computing the estimated MSI status by inputting a MSI features data extracting from the sequencing data into the trained machine learning model; and
(e) outputting the computed MSI status data.

BRIEF DESCRIPTION OF DRAWINGS

One or more embodiments are illustrated by ways of example, and not by limitation, in the figures of the accompanying drawings, wherein elements are having the same reference numeral designations represent like elements throughout. The drawings are not to scale unless otherwise disclosed.

FIGS. 1(a)-(c) are schematic diagrams illustrating the parameters used to characterize microsatellite instability.

FIG. 2 is a ROC curve of the MSI model.

FIG. 3 is Box plot of the MSI score in the validation data set.

The drawings are only schematic and are non-limiting. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes. The dimensions and the relative dimensions do not necessarily correspond to actual reductions to the practice of the disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The making and using of the embodiments of the disclosure are discussed in detail below. It should be appreciated, however, that the embodiments provide many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific ways to make and use the embodiments and do not limit the scope of the disclosure.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by a person skilled in the art to which this disclosure belongs. As used herein, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

As used herein, “microsatellite” means a tract of repetitive DNA in which certain DNA motifs are repeated. “Microsatellite loci” refers to the regions of the microsatellite. The terms “microsatellite” and “SSR,” as well as “microsatellite loci” and “SSR region” are used interchangeably, respectively, where the context allows. In some embodiments of the disclosure, type of microsatellite loci or SSR region refers to mono-, di-, tri-, tetra, or pentanucleotide repeats or certain complex nucleotide type in a nucleotide sequence. Preferably, type of the microsatellite loci or SSR region refers to mononucleotide with at least ten repeats, dinucleotide with at least six repeats, trinucleotide with at least five repeats, tetranucleotide with at least five repeats, pentanucleotide with at least five repeats, and the complex nucleotide type including but not limited to SEQ ID NOs: 1-37.

As used herein, “MSI status” or “MMR status” refers to the presence of “MSI” or “unstable microsatellite (loci),” a clonal or somatic change in the number of repeated DNA nucleotide units in microsatellites. The present disclosure estimates the MSI status as MSS or MSI-H. “MSI-H” refers to those in which the number of repeats present in microsatellite loci differs significantly from the number of repeats that are in the DNA of a normal cell. “MSS” refers to those who have no functional defects in DNA MMR and have no significant differences between tumor and normal cell in microsatellite loci.

As used herein, “cutoff value” or “threshold” refers to a numerical value or other representation whose value is used to arbitrate between two or more states of classification for a biological sample. In some embodiments of the disclosure, the cutoff value is set according to the training result of the machine learning model and is used to distinguish between MSI-H and MSS. If the MSI score is greater than the cutoff value, the MSI status is determined as MSI-H; or if the MSI score is less than the cutoff value, the MSI status is determined as MSS.

As used herein, “peak” refers to a microsatellite distribution pattern in the microsatellite loci. The peak may be analyzed using data generated by next-generation sequencing, where the number of allele repeat length within each microsatellite locus is considered as peak width, the read counts of the most frequently observed allele is referred to as peak height, and the location difference between the peak height in each microsatellite locus of tumor tissue and reference genome is referred to as peak location. In some embodiments of the disclosure, peak width, peak height, or peak location are used as MSI features to estimate the MSI status.

As shown in FIGS. 1(a) to 1(c), each locus is a short sequence repeat. When detected by PCR followed by Sanger sequencing or by Next-Generation Sequencing (NGS) methods, each microsatellite locus shows a pattern of a peak. A peak can be characterized by its peak width, peak height, and peak location. When a microsatellite locus becomes unstable, the peak width, peak height, and/or peak location may change. Here, the x-axis shows the alleles for each peak signal. For example, in FIG. 1(a), the first signal shows an allele with eight repeats of nucleotide A at that microsatellite locus. This peak has a peak width of 5, peak height of about 35%, and peak location at 11 A. Peak location can also be described by its chromosome position, such as chr4:55598211. The y-axis shows the percentage of reading count for a given peak signal as compared to the other peak signals. Therefore, the sum of peak height for a given peak is one. FIG. 1(a) shows the peak distribution when the peak width is widened from 5 to 8 when this locus becomes unstable. FIG. 1(b) shows that when a peak is unstable, the peak height may become lower. In this example, it went from 50% to 25%. FIG. 1(c) shows that when a peak is unstable, the peak location may change. In this example, it changed from 10 As to 12 As.

Generally, to understand the MSI status, a matched paired analysis would be performed to identify microsatellite loci in the tumor that are different compared to matched normal tissue. “Matched normal tissue” or “normal pair tissue” as used herein refers to normal tissue from the same patient. However, in some embodiments of the disclosure, the machine learning model detects MSI status from NGS data without matched normal tissue. A pooled normal sample is used to establish the mean of each the MSI feature of each SSR region across the normal population as a baseline for MSI detection. Data from individual clinical tumor tissue will be compared to the peak pattern of the baseline data to determine microsatellite status for each SSR region in that sample.

As used herein, “tumor purity” is the proportion of cancer cells in a tumor sample. Tumor purity impacts the accurate assessment of molecular and genomics features as assayed with NGS approaches. In some embodiments of the disclosure, the clinical sample has a tumor purity at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100%. Preferably, the present disclosure disclosure identifies the sample within the tumor purity at least 20%.

As used herein, “depth” or “total depth” refers to the number of sequencing reads per location. “Mean depth,” “mean total depth,” or “total mean depth” refers to the average number of reads across the entire sequencing region. Generally, the total mean depth has an impact on the performance of the NGS assay. The higher the mean total depth, the lower the variability in the variant frequency of the variant. In some embodiments of the disclosure, the mean depth of the sample across the entire sequencing region is at least 200x, 300x, 400, 500x, 600x, 700x, 800x, 900x, 1000x, 2000x, 3000x, 4000x, 5000x, 6000x, 8000x, 10000x, or 20000x. Preferably, the mean depth of the sample across the entire sequencing region is at least 500x.

As used herein, “coverage” refers to the total depth at a given locus and can be used interchangeably with “depth.” In some embodiments of the disclosure, “low coverage” means the read depth lower than 5x, 10x, 15x, 20x, 25x, 30x, 35x, 40x, 45x, or 50x from a sample on a locus.

As used herein, “target base coverage” refers to the percentage of the sequenced region that is sequenced at a depth above a predefined value. Target base coverage needs to specify the depth at which it is evaluated. In some embodiments, the target base coverage at 100x is 85%. That means 85% of the target sequenced bases is covered by at least 100x depth of sequencing reads. In some embodiments, the target base coverage at 30x, 40x, 50x, 60x, 70x, 80x, 90x, 100x, 125x, 150x, 175x, 200x, 300x, 400x, 500x, 750x, 1000x is above 70%, 75%, 80%, 85%, 90%, or 95%.

As used herein, “human subject” refers to those with formally diagnosed disorders, those without formally recognized disorders, those receiving medical attention, those at risk of developing the disorders, etc.

As used herein, “treat,” “treatment,” and “treating” includes therapeutic treatments, prophylactic treatments, and applications in which one reduces the risk that a subject will develop a disorder or other risk factor. Treatment does not require the complete curing of a disorder and encompasses embodiments in which one reduces symptoms or underlying risk factors.

As used herein, “therapeutically effective amount” means an amount of a therapeutically active molecule needed to elicit the desired biological or clinical effect. In preferred embodiments of the disclosure, “a therapeutically effective amount” is the amount of drug needed to treat cancer patients with MSI-H.

The present disclosure is further illustrated by the following Examples, which are provided for the purpose of demonstration rather than limitation.

EXAMPLE 1
Training a Machine Learning Model for Detection of MSI Status

Formalin-fixed paraffin-embedded (FFPE) samples were prepared from cancer patients through surgical or needle biopsy samples. Genomic DNA was extracted using QIAamp DNA FFPE Tissue Kit (QIAGEN, Hilden, Germany). Eighty nanograms of DNA were amplified using multiplexed PCR targeting a panel of 440 genes and 1.8 Mbps. The samples were sequenced by using Ion Proton or Ion S5 Prime (Thermo Fisher Scientific, Waltham, Mass.) system with the Ion PI or 540 Chip (Thermo Fisher Scientific, Waltham, Mass.) following manufacturer recommended protocol. Raw sequence reads were processed by the manufacturer-provided software Torrent Variant Caller (TVC) v5.2, and .bam and .vcf files were generated.

(1) Candidate Loci Selection

Using the MIcroSAtellite identification tool (MISA, Beier, Thiel, Munch, Scholz, & Mascher,

2017), SSR regions in the chromosomal regions covered by the ACTOnco Panel assay were identified. A total of 600 SSR regions, including mononucleotide with at least ten repeats, dinucleotide with at least six repeats, trinucleotide with at least five repeats, tetranucleotide with at least five repeats, pentanucleotide with at least five repeats, and complex nucleotide type, were identified by MISA. The sequences of the complex SSR regions are provided in Table 1.

TABLE 1

Complex microsate11ite loci

SEQ

ID

Size

NO
Microsatellite sequence
(bp)

1
(A)11(T)10
21

2
(CA)10ctctctctct(CA)6ctcagt(CA)13
74

3
(AC)7atacttc(T)12
33

4
(TA)12(T)21
45

5
(A)19caaac(A)11
35

6
(T)16(TG)8
32

7
(A)10(AT)9
28

8
(AT)6tcttttctctatacatttatgcaaactt
77

g(T)10catttgatgacatcatattttgcagg

9
(T)10ctttttc(T)12
29

10
(TG)9(AG)9acagagac(AG)6
56

11
(T)10acaagaccatttttcattatgaatttg
68

taccatgtgtcagcacc(T)14

12
(GATG)10(GACG)5
60

13
(CAC)5catgc(CCA)6
38

14
(CAG)7caa(CAG)7
45

15
(A)12c(A)12
25

16
(AC)14(CA)7
42

17
(A)11g(A)10
22

18
(CT)8ata(TG)6(TA)6
43

19
(TG)9(AG)11
40

20
(TG)7tatgtatgtg(TA)7tc(TA)6gat
79

(ATAG)6

21
(A)13gaaaaag(A)11
31

22
(TA)11(T)10
32

23
(T)10caatccattcagacaactt(TTG)6ttt
75

tgtgtttttcggtg(T)11

24
(GCT)7gaagttgctgttgctgttgca(GCT)5
57

25
(ATG)8ataatgatgatagct(ATG)6
57

26
(A)12t(TA)11tttcgtggcaa(T)19
65

27
(T)11caaactttctc(T)14
36

28
(A)14gggaatagatact(A)14
41

29
(T)12cc(T)13
27

30
(T)27(GA)6
39

31
(TG)9(T)25
43

32
(T)11(A)11
22

33
(A)12g(A)10gaa(AAG)7
47

34
(AC)6(GC)6(AC)16
56

35
(TCTG)5(TC)10(TA)8
56

36
(GA)10ggg(AAAT)11
67

37
(TG)11tttttt(C)11(T)11
50

Note: The uppercase sequences in parenthesis are the sequences being repeated by the number of times indicated by the number following it. Lowercase sequences not in parenthesis are sequences between two repetition regions within one identified loci.

We first examined the chromosomal location of each SSR region. A total of 34 SSR loci were found located on the X chromosome and were excluded.

In order to develop a robust MSI prediction algorithm for ACTOnco assay, we plan to include only SSR regions from the remaining 566 candidate loci, which shows reproducible peak patterns in clinical FFPE samples in the prediction model. To identify SSRs with good reproducibility across different sequencing runs, we examined the coverage and peak pattern of the 566 SSR regions in a set of 10 FFPE clinical samples across six replicate runs.

In order to include only highly confident reads on each SSR region for the prediction model, a minimum read depth of 30x from a sample on a locus was required. Additionally, to determine the total number of repeats of different lengths (peak width) on a SSR region, a minimum of 5% of allele frequency for a repeated length was required to be included. For example, for a sample on a locus with segments of mononucleotide repeats, if the allele frequencies are detected as 2% for 15 bases, 10% for 16 bases, 20% for 17 bases, 30% for 18 bases, 20% for 19 bases, 10% for 20 bases, and 8% for 21 bases, the total number of repeats of different lengths (peak width) will be 6 with the length of 15 bases uncounted.

We excluded 138 SSR regions due to their low coverage (<30 reads for the SSR region), unstable peak call (missing peak width data in any sequencing run), high variability in peak width (variation in peak width greater than 3 in 6 replicate runs) or low weight (the MSI feature data around the last 5% contributions to the prediction model). The remaining 428 microsatellite loci were used for the subsequent baseline establishment and model training.

(2) Baseline Establishment

Population baseline for all 428 loci was established. The mean peak width of 77 normal samples sequenced in the Ion Proton sequencer was used to establish a baseline. The mean peak width of 81 normal samples sequenced in the Ion S5 Prime sequencer was used to establish another baseline. The MSI baseline was established from the mean peak width of each SSR region across the normal population. The standard deviation of peak width was also calculated for each candidate locus. For a given locus, it is considered unstable if the difference in peak width between a given clinical sample and the baseline falls outside of two times the standard deviation. The total unstable loci percentage is calculated by dividing the number of unstable loci by the total number of loci used.

(3) MSI Prediction Model and Model Validation

A total of 122 colorectal cancer (FFPE samples) sequenced on Ion Proton and Ion S5 Prime were used in training the machine learning model. Of those samples, 76 are MSS, and 46 are MSI-H samples based on a 5-marker MSI-PCR detection system (Promega MSI Analysis System, version 1.2). For each sample, the loci with read depth less than 30x were not considered in model training and were reported as missing information. Additionally, to determine the peak width on a SSR region, a minimum of 5% of allele frequency for a repeated length (allele) was required to be included in training the model. The difference in the peak width between the MSS baseline and clinical samples were used for calculation in the following logistic regression model:

MSI status (MSS/MSI-H)=β0+β1loci1+β2loci2+β3loci3+ . . . +β428loci428 where β is a weight.

We divided 122 training data by 7:3 ratio for training and testing and randomly assigned samples to train and test the data for 1000 iterations. Due to the small sample size, all 122 training data were used to set the cutoff value. The MSI score used for setting the cutoff value is calculated by selecting the median MSI score for each sample when it is selected as testing data during the 1000 iterations. The ROC curve for the model performance is shown in FIG. 2. According to analysis results, we decided to select 0.15 as the cutoff value of the MSI prediction model to achieve high sensitivity (100%) and specificity (100%).

EXAMPLE 2
Using the MSI Model to Determine the MSI Status of Cancer Samples

We next used an independent set of 439 clinical FFPE samples, including 30 MSI-H and 409 MSS samples, to validate the MSI model. Samples include but are not limited to lung cancer, colorectal cancer, breast cancer, ovarian cancer, pancreatic cancer, cholangiocarcinoma, gastric cancer, glioblastoma, sarcoma, cervical cancer, leiomyosarcoma, and liposarcoma. These samples were processed using the same method as described in Example 1 to sequence the 428 loci region to a mean sequencing depth of at least 500x and 85% of the target region reaching a target base coverage of 100x.

FIG. 3 shows the resulting MSI scores of the MSI-H and MSS samples are clearly distinguished. The results of model validation demonstrate that the positive percent agreement (PPA) and negative percent agreement (NPA) of this model are 93.3% and 98.5%, respectively. The validation results are provided in Tables 2-5.

TABLE 2

MSI detection of clinical samples

Target base

Sample

Tumor
Mean
coverage
MSI
MSI Status
Unstable
MSI status

ID
Cancer type
purity
depth
at 100x
score
by MSI model
Loci %
by 5-loci PCR

F00173
Lung cancer
NA
1877
0.97
0.01
MSS
3.49
MSS

F00212
Oesophagus cancer
50%
900.7
0.94
0.01
MSS
3.94
MSS

F01597
Pancreatic cancer
60%
1488
0.95
0.01
MSS
3.59
MSS

F02095
Adenocarcinoma
NA
1155
0.96
0.02
MSS
5.01
MSS

F01143
Lung cancer
40%
1127
0.96
0.06
MSS
3.4
MSS

F01407
Unknown primary
5%
1355
0.96
0
MSS
4.81
MSS

E00708
Adenoid cystic carcinoma
50%
1454
0.94
0.01
MSS
4.99
MSS

F01911
Adenoid cystic carcinoma
45%
983.3
0.96
0.01
MSS
3.33
MSS

F02161
Adenoid cystic carcinoma
40%
1238
0.97
0
MSS
3.86
MSS

F01464
Adrenal cortical carcinoma
40%
1174
0.96
0.01
MSS
5.57
MSS

F00249
Ampulla Vater cancer
25%
1097
0.96
0.01
MSS
2.21
MSS

F01517
Appendix cancer
90%
1441
0.96
0
MSS
4.07
MSI-L

F00507
Brain cancer
25%
1142
0.96
0.03
MSS
3.5
MSS

F02040
Brain cancer
30%
2237
0.99
0.05
MSS
5.8
MSS

F01581
Basal ganglia glioma
70%
794.5
0.92
0.01
MSS
3.57
MSS

F01530
Brain tumor glioma
40%
2411
0.97
0.01
MSS
4.58
MSS

F02387
Breast cancer
NA
1640
0.98
0
MSS
10.52
MSI-L

F02197
Breast cancer
20%
1226
0.95
0.02
MSS
5.14
MSS

E00086
Breast cancer
55%
1064
0.94
0.01
MSS
7.1
MSS

E00494
Breast cancer
30%
1479
0.96
0.02
MSS
7.09
MSS

E00557
Breast cancer
40%
1525
0.94
0.02
MSS
5.14
MSS

F02573
Breast cancer
45%
674.4
0.92
0.01
MSS
6.73
MSS

F02092
Breast cancer
40%
753
0.94
0
MSS
6.2
MSS

F00107
Breast cancer
20%
1054
0.95
0.02
MSS
5.44
MSS

F01141
Breast cancer
70%
844.1
0.92
0.01
MSS
5.53
MSS

F01409
Breast cancer
70%
641.4
0.93
0
MSS
8.08
MSS

F01898
Breast cancer
35%
1264
0.96
0.01
MSS
4.07
MSS

E00086
Breast cancer
55%
828.7
0.93
0
MSS
7.81
MSS

F02386
Breast cancer
55%
1391
0.96
0.01
MSS
8.38
MSS

D01394
Breast cancer
45%
1003
0.94
0.01
MSS
5.18
MSS

F02385
Breast cancer
50%
1666
0.97
0.3
MSS
10.28
MSS

D01491
Breast cancer
65%
1206
0.95
0
MSS
5.63
MSS

F00564
Breast cancer
80%
1309
0.97
0
MSS
4.63
MSS

F00201
Breast cancer
80%
1518
0.96
0.02
MSS
3.56
MSS

F01424
Breast cancer
10%
1247
0.96
0
MSS
3.69
MSS

F00486
Breast cancer
85%
1605
0.98
0.04
MSS
3.62
MSS

F01178
Breast cancer
25%
1334
0.96
0.01
MSS
3.33
MSS

F01459
Breast cancer
40%
1265
0.95
0.02
MSS
4.31
MSS

F01333
Breast cancer
60%
1414
0.97
0.02
MSS
4.03
MSS

F00110
Breast cancer
70%
1812
0.97
0.02
MSS
6.42
MSS

F00678
Breast cancer
50%
1936
0.98
0
MSS
3.27
MSS

F01362
Breast cancer
85%
1634
0.94
0.03
MSS
5.79
MSS

F01468
Breast cancer
60%
1009
0.93
0.01
MSS
7.29
MSS

F00817
Breast cancer
NA
2227
0.97
0.01
MSS
4.36
MSS

F01130
Breast cancer
40%
2128
0.98
0
MSS
3.09
MSS

F01933
Breast cancer
15%
1042
0.94
0.06
MSS
6.12
MSS

F02365
Breast cancer
60%
1498
0.98
0.01
MSS
5.63
MSS

F02208
Buccal cancer
40%
861.3
0.94
0.01
MSS
4.26
MSS

D01571
Bladder cancer
65%
886.3
0.95
0.02
MSS
5.46
MSS

E00495
Colon cancer
55%
1574
0.88
0.01
MSS
10.3
MSS

F00369
Oesophageal cancer
50%
2115
0.96
0.01
MSS
2.8
MSS

F00716
Prostate cancer
75%
2231
0.97
0.04
MSS
5.81
MSI-L

F01155
Rectum cancer
60%
708.6
0.92
0.01
MSS
4.17
MSS

E00705
Gastric Cancer
40%
1045
0.94
0.04
MSS
6.94
MSS

F00426
Uterine sarcoma
90%
1122
0.94
0.01
MSS
4.91
MSS

D01878
Cervical cancer
60%
1302
0.95
0.01
MSS
6.62
MSS

D01878
Cervical cancer
60%
1671
0.95
0.03
MSS
6.17
MSS

D01870
Cervical cancer
40%
876.5
0.94
0.01
MSS
10.31
MSS

D01870
Cervical cancer
40%
969.7
0.95
0
MSS
5.76
MSS

E00208
Cervical cancer
55%
840.8
0.94
0.01
MSS
11.47
MSS

F01426
Cervical cancer
70%
991.8
0.94
0
MSS
4.73
MSS

F01287
Cervical cancer
25%
1663
0.96
0.02
MSS
3.33
MSS

E01827
Cholangiocarcinoma
25%
1217
0.96
0.11
MSS
6.57
MSS

F00381
Cholangiocarcinoma
60%
1498
0.96
0.03
MSS
6.25
MSS

E00224
Cholangiocarcinoma
60%
883.4
0.94
0
MSS
5.12
MSS

F00137
Cholangiocarcinoma
50%
1021
0.96
0.01
MSS
3.89
MSS

F01536
Cholangiocarcinoma
60%
1068
0.95
0
MSS
4.1
MSS

F02049
Cholangiocarcinoma
15%
1348
0.96
0.01
MSS
4.49
MSS

F02132
Cholangiocarcinoma
10%
1949
0.98
0.01
MSS
6.38
MSS

F02086
Chondrosarcoma
60%
764.2
0.94
0.01
MSS
6.45
MSS

E00167
Brain cancer
85%
541.1
0.88
0
MSS
7.25
MSI-L

F00844
Ovarian cancer
90%
1100
0.97
0
MSS
3.34
MSS

F02495
Colon cancer
30%
1360
0.97
0.01
MSS
4.38
MSS

F02346
Colon cancer
15%
2403
0.98
0
MSS
9.65
MSS

D01774
Colon cancer
60%
706.8
0.94
0.03
MSS
5.48
MSS

D01124
Colon cancer
NA
1488
0.95
0.02
MSS
4.11
MSS

F00409
Colon cancer
15%
1215
0.96
0.01
MSS
3.73
MSS

F00556
Colon cancer
50%
1227
0.95
0.01
MSS
3.36
MSS

F00003
Colon cancer
35%
1349
0.95
0.02
MSS
7.12
MSS

F01115
Colon cancer
30%
1727
0.96
0.04
MSS
4.39
MSS

F02580
Colon cancer
15%
1487
0.95
0.01
MSS
3.59
MSS

F01402
Colon cancer
10%
2262
0.98
0.03
MSS
4.14
MSS

F02414
Colon cancer
35%
1600
0.98
0.01
MSS
4.37
MSS

F02071
Colon cancer
5%
1430
0.95
0.02
MSS
6.45
MSS

D00846
NA
NA
511.8
0.93
1
MSI-H
24.47
MSI-H

D00923
NA
NA
608.8
0.94
1
MSI-H
17.92
MSI-H

D00854
NA
NA
674.8
0.94
0.99
MSI-H
18.3
MSI-H

D00927
NA
NA
712.1
0.94
1
MSI-H
19.81
MSI-H

D00932
NA
NA
716.2
0.95
0.99
MSI-H
20.57
MSI-H

D00938
NA
NA
755.2
0.95
1
MSI-H
25.18
MSI-H

D00868
NA
NA
768.1
0.95
0.96
MSI-H
18.66
MSI-H

D00881
NA
NA
788.4
0.95
1
MSI-H
17.57
MSI-H

D00848
NA
NA
803.9
0.95
1
MSI-H
17.2
MSI-H

D00900
NA
NA
815.9
0.95
0.02
MSS
6.21
MSI-H

D00849
NA
NA
821.8
0.96
1
MSI-H
26.77
MSI-H

D00895
NA
NA
828.2
0.95
0.97
MSI-H
17.29
MSI-H

D00864
NA
NA
864.1
0.95
1
MSI-H
20.08
MSI-H

D00918
NA
NA
906.7
0.96
1
MSI-H
13.6
MSI-H

D00847
NA
NA
979.4
0.96
1
MSI-H
18.6
MSI-H

D00893
NA
NA
986.2
0.96
0.99
MSI-H
18.48
MSI-H

D00879
NA
NA
1054
0.96
0.99
MSI-H
12.45
MSI-H

D00926
NA
NA
1116
0.97
0.99
MSI-H
20.11
MSI-H

D00915
NA
NA
1330
0.95
0.79
MSI-H
20.98
MSI-H

D00878
NA
NA
1377
0.96
0.87
MSI-H
14.44
MSI-H

D00873
NA
NA
1498
0.96
0.16
MSS
10.17
MSI-H

D00909
NA
NA
1575
0.96
0.05
MSS
13.73
MSI-H

D00853
NA
NA
1995
0.97
0.76
MSI-H
9.26
MSI-L

F00124
Colorectal cancer
90%
1058
0.94
0.01
MSS
4.58
MSI-L

F01012
Colorectal cancer
10%
592.7
0.94
0.01
MSS
6.49
MSS

F01495
Colorectal cancer
40%
857.8
0.96
0
MSS
7.28
MSS

F01460
Colorectal cancer
35%
1731
0.97
0.01
MSS
5.44
MSS

F01944
Colorectal cancer
15%
3667
0.98
0.01
MSS
3.99
MSI-L

F01080
Rectal cancer
60%
1735
0.98
0
MSS
3.27
MSS

F02388
Cystic duct carcinoma
40%
1328
0.98
0.01
MSS
7.35
MSS

F01194
Dedifferentiated liposarcoma
85%
1144
0.94
0
MSS
4.17
MSS

F00950
Desmoid
50%
1675
0.97
0.01
MSS
2.92
MSS

F00211
Diffuse midline glioma
70%
945.6
0.95
0.07
MSS
4.31
MSS

F00713
Endometrial carcinoma
50%
1006
0.95
0.01
MSS
4.49
MSS

F00318
Endometrial cancer
60%
2074
0.97
0.06
MSS
1.83
MSS

F01480
Endometrial cancer
30%
948.9
0.94
0.23
MSS
11.22
MSI-L

F01425
Esophageal cancer
20%
965.4
0.93
0.02
MSS
4.1
MSS

F01313
Esophageal cancer
25%
629
0.94
0.03
MSS
11.74
MSS

F00145
Esophagus cancer
10%
1452
0.94
0.02
MSS
4.19
MSS

F01089
Esophageal cancer
75%
1146
0.93
0.01
MSS
5.74
MSS

F01383
Extraskeletal chondroblastic
65%
1708
0.95
0
MSS
3.74
MSS

osteosarcoma

F01410
Eyelid sebaceous carcinoma
40%
1019
0.96
0.09
MSS
3.53
MSS

E02217
Fallopian tube cancer
85%
1394
0.95
0.43
MSS
6.18
MSI-H

F01537
Gallbladder cancer
40%
1317
0.95
0.09
MSS
3.74
MSS

D00304
Gastric cancer
13%
836.6
0.95
0.03
MSS
9.21
MSS

F02397
Gastric cancer
15%
1326
0.98
0.01
MSS
7.4
MSS

F00108
Gastric cancer
15%
1571
0.97
0.02
MSS
7.26
MSS

F00292
Gastric cancer
20%
1809
0.98
0.04
MSS
5.47
MSS

F01291
Gastric cancer
55%
1156
0.97
0.05
MSS
4.77
MSS

E00545
Glioblastoma multiforme
70%
2408
0.96
0
MSS
4.22
MSS

F01907
Glioblastoma multiforme
40%
1389
0.97
0
MSS
5.08
MSS

F01781
Glioblastoma multiforme
45%
1370
0.95
0.01
MSS
5.66
MSI-L

F00041
Glioblastoma Multiforme
65%
1169
0.95
0.08
MSS
3.62
MSS

F00766
Glioblastoma Multiforme
80%
648.3
0.93
0.02
MSS
5.38
MSS

F01073
Glioblastoma multiforme
50%
1138
0.95
0.02
MSS
2.62
MSS

F00345
Glioblastoma multiforme
60%
1715
0.96
0
MSS
4.1
MSS

F00120
Glioblastoma multiforme
45%
1318
0.96
0.01
MSS
4.81
MSI-L

F02320
Gastrointestinal stromal tumor
70%
1114
0.95
0
MSS
5.61
MSS

F00620
Gastrointestinal stromal
65%
602.6
0.88
0.01
MSS
7.75
MSS

tumors (GIST)

F02142
Gastrointestinal stromal
80%
1187
0.96
0.01
MSS
5.24
MSS

tumor

E00413
Hepatocellular carcinoma
70%
1461
0.96
0.01
MSS
2.59
MSS

F00052
Hepatocellular carcinoma
90%
1240
0.96
0.03
MSS
3.68
MSS

F01560
Hepatocellular carcinoma
60%
1723
0.97
0.02
MSS
2.93
MSS

F00881
Hepatocellular carcinoma
35%
789.9
0.93
0.02
MSS
5.02
MSS

F00882
Cholangiocarcinoma
40%
835.6
0.94
0.03
MSS
5.7
MSS

E00787
High grade glioma
40%
729.1
0.93
0.01
MSS
3.85
MSS

E00421
Intima sarcoma
90%
1097
0.95
0.01
MSS
3.2
MSS

E00421
Intima sarcoma
90%
840.8
0.94
0.01
MSS
5.33
MSS

F02066
Invasive ductal carcinoma
50%
1065
0.96
0.02
MSS
5.6
MSS

F01380
Kidney cancer
85%
1627
0.97
0.03
MSS
4.92
MSS

E01811
Leiomyosarcoma
45%
1627
0.97
0.01
MSS
12.84
MSS

F02519
Leiomyosarcoma
90%
1298
0.96
0
MSS
9.94
MSS

E00237
Leiomyosarcoma
85%
1108
0.94
0.01
MSS
10.19
MSS

F02519
Leiomyosarcoma
90%
1298
0.96
0
MSS
9.94
MSS

F02065
Leiomyosarcoma
75%
1016
0.97
0.03
MSS
5.51
MSS

F00988
Leiomyosarcoma
90%
544.3
0.93
0.07
MSS
9.47
MSS

D00546
Liposarcoma
98%
1090
0.96
0.01
MSS
11.5
MSS

F02026
Liposarcoma
90%
1234
0.97
0
MSS
6.04
MSS

F00942
Liposarcoma
75%
1152
0.96
0.05
MSS
4.82
MSS

F00805
Liposarcoma
40%
1260
0.96
0.03
MSS
6.36
MSS

F00962
Liposarcoma
90%
1511
0.96
0
MSS
3.56
MSS

F01154
Liver cancer
NA
1929
0.96
0.01
MSS
3.53
MSS

F02019
Liver angiosarcoma
5%
964.5
0.95
0.02
MSS
4.17
MSS

F01489
Liver cancer
55%
1219
0.97
0.01
MSS
3.49
MSS

E00811
Lung cancer
10%
660.2
0.95
0
MSS
5.93
MSS

E00695
Lung cancer
5%
861.3
0.94
0.01
MSS
5.47
MSS

F00593
Lung cancer
40%
948.3
0.95
0
MSS
9.51
MSS

F00679
Lung cancer
0%
1137
0.95
0.05
MSS
7.87
MSS

E00704
Lung Cancer
60%
1415
0.96
0.01
MSS
7.02
MSS

F01960
Lung cancer
3%
1474
0.96
0.22
MSS
8.67
MSI-H

E00561
Lung cancer
85%
1522
0.96
0.01
MSS
4.25
MSS

E01825
Lung cancer
35%
1598
0.97
0
MSS
6.49
MSS

F01282
Lung cancer
50%
1840
0.96
0.01
MSS
3.11
MSS

F02483
Lung cancer
10%
1297
0.96
0.01
MSS
9.29
MSS

F00269
Lung cancer
2%
811.8
0.95
0.03
MSS
7.33
MSI-L

F00815
Lung cancer
60%
1410
0.96
0.01
MSS
4.28
MSS

F02497
Lung cancer
10%
1491
0.96
0.01
MSS
3.56
MSS

F00758
Lung cancer
60%
1154
0.95
0.2
MSS
17.29
MSS

F01494
Lung cancer
15%
1329
0.96
0.01
MSS
6.2
MSI-L

F02514
Lung cancer
40%
2222
0.97
0.02
MSS
3.49
MSS

F01321
Lung cancer
80%
1498
0.97
0.04
MSS
5.45
MSS

F01196
Lung cancer
35%
1639
0.96
0.04
MSS
8.52
MSS

F01151
Lung cancer
15%
1813
0.96
0.03
MSS
2.79
MSI-L

F02043
Lung cancer
30%
1162
0.97
0.07
MSS
7.08
MSS

F02483
Lung cancer
10%
1297
0.96
0.01
MSS
9.29
MSS

F02096
Lung cancer
55%
1710
0.95
0.02
MSS
6.24
MSS

D01492
Lung cancer
65%
714.5
0.93
0.02
MSS
5.56
MSS

F01782
Lung cancer
20%
2187
0.96
0
MSS
6.15
MSS

E00639
Lung cancer
45%
1619
0.96
0.01
MSS
4.34
MSS

F00946
Lung cancer
35%
757.1
0.93
0.06
MSS
8.66
MSS

F00251
Lung cancer
60%
871.1
0.97
0.11
MSS
5.19
MSS

F00762
Lung cancer
30%
543.8
0.93
0.02
MSS
5.96
MSS

F00159
Lung cancer
70%
1085
0.95
0.02
MSS
3.93
MSS

F00317
Lung cancer
50%
1142
0.96
0.01
MSS
4.07
MSS

F00790
Lung cancer
10%
742.8
0.95
0.04
MSS
6.65
MSS

F00141
Lung cancer
45%
1302
0.96
0
MSS
4.26
MSI-L

F00892
Lung cancer
40%
1213
0.95
0.06
MSS
4.51
MSS

F00895
Lung cancer
30%
1256
0.96
0.08
MSS
4.98
MSS

F00286
Lung cancer
15%
1416
0.95
0.13
MSS
4.84
MSS

F00654
Lung cancer
35%
1471
0.95
0.01
MSS
3.37
MSS

F00114
Lung cancer
25%
1499
0.97
0.01
MSS
5.74
MSS

F00479
Lung cancer
55%
1511
0.95
0
MSS
5.45
MSS

F01596
Lung cancer
60%
921.1
0.94
0.01
MSS
4.34
MSI-L

F00408
Lung cancer
60%
1636
0.96
0.01
MSS
4.41
MSS

F00994
Lung cancer
30%
911.5
0.94
0.01
MSS
4.18
MSS

F00038
Lung cancer
20%
1930
0.98
0.01
MSS
3.24
MSS

F00675
Lung cancer
15%
1836
0.97
0.01
MSS
3.48
MSS

F00610
Lung cancer
50%
1613
0.98
0.01
MSS
3.26
MSS

F00509
Lung cancer
40%
1872
0.96
0
MSS
4.24
MSS

F00559
Lung cancer
20%
1947
0.98
0.12
MSS
3.43
MSS

F02212
Lung cancer
25%
697.5
0.94
0.03
MSS
9.35
MSS

F00856
Lung cancer
85%
1557
0.96
0.03
MSS
5.36
MSS

F00413
Lung cancer
35%
1998
0.98
0.03
MSS
4.55
MSS

F01404
Lung cancer
25%
927.3
0.96
0
MSS
6.65
MSS

F02060
Lung cancer
20%
857
0.96
0
MSS
6.48
MSS

F01116
Lung cancer
10%
1303
0.95
0
MSS
3.36
MSS

F01290
Lung cancer
8%
1284
0.96
0.01
MSS
5.52
MSS

F00412
Lung cancer
25%
2380
0.98
0.05
MSS
4.71
MSS

F00894
Lung cancer
5%
1863
0.96
0.08
MSS
2.99
MSS

F00725
Lung cancer
40%
2578
0.99
0.03
MSS
4.68
MSS

F02579
Lung cancer
30%
1345
0.96
0.01
MSS
3.02
MSS

F02296
Lung cancer
10%
1670
0.96
0
MSS
5.91
MSS

F01125
Lung cancer
65%
2208
0.97
0.02
MSS
4.03
MSS

F01109
Lung cancer
80%
1961
0.96
0.01
MSS
2.77
MSS

F01163
Pancreatic cancer
10%
1497
0.96
0.01
MSS
6.33
MSS

E00784
Sarcomatoid Carcinoma
10%
1339
0.95
0.02
MSS
4.1
MSS

F00712
Melanoma
80%
1611
0.97
0.01
MSS
14.18
MSS

F00712
Melanoma
80%
720.3
0.94
0.01
MSS
3.01
MSS

F00040
Meningioma
85%
2058
0.98
0.01
MSS
2.89
MSS

F02202
Ovarian cancer
NA
1683
0.97
0.08
MSS
4.04
MSS

E00674
Breast Cancer
40%
3108
0.95
0.06
MSS
4.11
MSS

E00674
Breast Cancer
40%
1168
0.95
0
MSS
3.72
MSS

F02451
Epithelioid rhabdomyosarcoma
75%
1211
0.97
0.02
MSS
4.66
MSS

F02478
Melanoma
25%
1808
0.96
0.02
MSS
3.9
MSS

F01075
Pancreatic cancer
20%
2340
0.98
0.03
MSS
2.52
MSS

F00793
Tonsil cancer
35%
670.8
0.92
0.02
MSS
5.71
MSS

F01305
Metastasis of unknown
35%
1654
0.98
0.01
MSS
2.53
MSS

origin (MUO)

F01576
Metastasis of unknown
10%
1042
0.95
0.02
MSS
3.38
MSS

origin (MUO)

F00585
Nasopharyngeal cancer
50%
1482
0.96
0.02
MSS
7.42
MSS

F01438
Nasopharyngeal carcinoma
30%
1519
0.97
0.01
MSS
5.63
MSS

F02024
Lung cancer
3%
1718
0.97
0
MSS
9.44
MSS

F02429
Adenocarcinoma
40%
672.9
0.95
0.05
MSS
6.03
MSS

F02329
Lung cancer
35%
1508
0.94
0
MSS
7.9
MSS

F00414
NSCLC adenocarcinoma
85%
1062
0.97
0
MSS
4.39
MSS

F00673
NSCLC, adenocarcinoma
65%
995
0.93
0.04
MSS
6.8
MSS

E00744
Oesophageal Cancer
25%
1974
0.96
0
MSS
9.26
MSS

F00288
Oropharyngeal cancer
50%
838.3
0.95
0.03
MSS
4.29
MSS

F01785
Osteosarcoma
35%
1004
0.91
0
MSS
3.68
MSS

F02155
Ovarian cancer
40%
2518
0.99
0.03
MSS
3.93
MSS

D01410
Ovarian cancer
70%
757.5
0.94
0.38
MSS
15.75
MSI-H

F01265
Ovarian cancer
60%
1101
0.96
0.02
MSS
5.02
MSS

E00608
Endometrial cancer
40%
1611
0.96
0.04
MSS
2.41
MSS

F02083
Ovarian cancer
50%
837.3
0.94
0.01
MSS
5.64
MSS

F00893
Ovarian cancer
35%
759.7
0.94
0.01
MSS
5.63
MSS

F02494
Ovarian cancer
85%
1540
0.97
0.02
MSS
5.12
MSS

F01200
Ovarian cancer
50%
1174
0.94
0.01
MSS
4.73
MSS

F01145
Ovarian cancer
95%
2072
0.96
0.01
MSS
2.43
MSS

F02390
Ovarian cancer
35%
1081
0.94
0.11
MSS
9.04
MSS

D00944
Clear cell carcinoma
85%
1506
0.96
0.01
MSS
5.59
MSI-L

F00298
Ovarian cancer
60%
1001
0.96
0.05
MSS
3.7
MSS

F00698
Ovarian cancer
60%
834.9
0.95
0.03
MSS
7.52
MSS

F00724
Ovarian cancer
20%
1259
0.97
0.01
MSS
3.88
MSS

F00920
Ovarian cancer
75%
1483
0.97
0.04
MSS
6.42
MSS

F00983
Ovarian cancer
60%
764.5
0.96
0.01
MSS
8.6
MSS

F01090
Ovarian cancer
90%
1260
0.96
0.01
MSS
5.45
MSS

F02070
Ovarian cancer
15%
1281
0.96
0.01
MSS
4.08
MSS

F01467
Ovarian cancer
35%
1523
0.97
0.01
MSS
5.28
MSI-L

F01763
Ovarian cancer
NA
1624
0.95
0.03
MSS
4.1
MSS

F01400
Ovarian cancer
70%
2197
0.98
0.01
MSS
5.1
MSS

F02059
Ovarian cancer
75%
1710
0.98
0.01
MSS
4.52
MSS

F02010
Ovarian cancer
70%
854.9
0.94
0
MSS
4.75
MSS

F02194
Ovarin cancer
70%
1051
0.95
0
MSS
5.28
MSS

F00898
Ovarian cancer
80%
841.6
0.92
0
MSS
5.8
MSS

F00955
Ovarian cancer
45%
1547
0.97
0.02
MSS
5.84
MSS

F00900
Ovarian cancer
40%
1771
0.96
0.05
MSS
5.22
MSS

F02517
Ovary cancer
70%
1774
0.98
0.04
MSS
4.39
MSI-L

F02025
Pancreatic cancer
70%
1646
0.97
0
MSS
7.13
MSS

F00880
Pancreatic cancer
25%
1165
0.95
0.04
MSS
5.59
MSS

F00627
Pancreatic cancer
20%
1624
0.96
0.01
MSS
3.58
MSS

F01909
Pancreatic cancer
40%
1231
0.96
0
MSS
5.33
MSS

F00936
Pancreatic cancer
5%
2249
0.98
0.02
MSS
5.23
MSS

F01771
Pancreatic cancer
15%
1912
0.97
0.01
MSS
4.6
MSS

F02526
Pancreatic cancer
35%
1359
0.97
0.01
MSS
8.82
MSS

F02525
Pancreatic cancer
10%
869.2
0.95
0
MSS
3.75
MSS

E00666
Pancreatic cancer
5%
1357
0.94
0.01
MSS
5.75
MSS

F00081
Pancreatic cancer
80%
909.1
0.95
0.01
MSS
9.63
MSS

F01436
Pancreatic cancer
40%
1782
0.97
0.09
MSS
5.28
MSS

F01769
Pancreatic cancer
40%
1557
0.96
0
MSS
4.53
MSS

F00296
Pancreatic cancer
15%
1299
0.97
0.03
MSS
6.04
MSS

F00728
Pancreatic cancer
15%
1570
0.97
0.01
MSS
14.15
MSS

F00788
Pancreatic cancer
15%
1490
0.97
0.02
MSS
3.62
MSS

E01854
Papillary Thyroid Carcinoma
40%
1538
0.97
0
MSS
5.96
MSS

F00992
Gastric cancer
50%
1156
0.96
0.01
MSS
3.31
MSI-L

F00834
Primary peritoneal serous
40%
695.5
0.95
0.01
MSS
4.15
MSS

carcinoma (PPSC)

E01902
prostate cancer
5%
1551
0.97
0.02
MSS
8.74
MSS

F02364
Prostate cancer
25%
1139
0.97
0.02
MSS
4.78
MSS

F00044
Prostate cancer
35%
2999
0.98
0.02
MSS
3.26
MSS

E00755
Renal cell carcinoma
60%
830.9
0.92
0
MSS
12.65
MSS

E00755
Renal cell carcinoma
60%
1279
0.94
0
MSS
3.48
MSS

F00394
Renal cell carcinoma
85%
1182
0.96
0.01
MSS
3.94
MSS

F01081
Rectal cancer
10%
1240
0.95
0
MSS
5.31
MSS

F00326
Rectal cancer
50%
1468
0.96
0.01
MSS
2.79
MSS

F02135
Rectal cancer
10%
2202
0.97
0.01
MSS
4.8
MSS

F00586
Rectum cancer
25%
1393
0.95
0
MSS
3.74
MSS

F00119
Renal cancer
60%
1837
0.96
0.01
MSS
4.45
MSS

F00035
Uterine cancer
45%
1554
0.98
0.06
MSS
3.45
MSS

D02004
Skin cancer
65%
805.9
0.93
0
MSS
13.93
MSS

D02004
Skin cancer
65%
526.5
0.91
0.01
MSS
5.27
MSS

F02332
Sarcoma
5%
2019
0.96
0.01
MSS
6.79
MSS

F00987
Sarcoma
70%
1701
0.97
0.01
MSS
3.28
MSS

F00887
Sarcoma
40%
555.2
0.93
0.03
MSS
6.65
MSS

F00144
Sarcoma
60%
1140
0.97
0.02
MSS
3.31
MSS

F00603
Sarcoma
10%
1608
0.97
0.1
MSS
4.25
MSS

F01472
Sarcoma
50%
1062
0.97
0.03
MSS
3.66
MSS

F01520
Sarcoma
80%
1080
0.95
0.01
MSS
3.95
MSS

E01878
Sigmoid cancer
5%
1435
0.92
0.01
MSS
6.12
MSS

F02430
Squamous cell carcinoma
40%
903.3
0.95
0
MSS
8.21
MSS

E00318
Stomach adenoacrinoma
40%
1456
0.96
0.02
MSS
4.81
MSS

F01162
Gastric cancer
10%
920.3
0.94
0.02
MSS
4.91
MSS

F00171
Gastric cancer
10%
1565
0.96
0.02
MSS
3.31
MSS

F01377
Gastric cancer
75%
1421
0.97
0.05
MSS
5.28
MSS

F00274
Submandibular gland cancer
75%
1012
0.97
0.01
MSS
5.17
MSS

F00172
Thymic cancer
80%
1273
0.95
0
MSS
3.56
MSS

F01274
Thymoma involvement
35%
1109
0.94
0.02
MSS
3.4
MSS

F00245
Thyriod cancer
40%
871.4
0.94
0.05
MSS
3.58
MSS

F02375
Breast cancer
40%
1242
0.94
0
MSS
4.96
MSS

F00656
Breast cancer
85%
2417
0.98
0.01
MSS
2.53
MSS

F02369
Tongue cancer
40%
1473
0.96
0.01
MSS
5.54
MSS

E00764
Tonsillar cancer
50%
1304
0.94
0.01
MSS
6.54
MSS

E00764
Tonsillar cancer
50%
1655
0.94
0
MSS
2.51
MSS

F01546
Transitional cell carcinoma
45%
680.3
0.95
0.02
MSS
6.38
MSI-L

F01014
Endometrioid adenocarcinoma
40%
1646
0.97
0.03
MSS
3.65
MSS

F00624
Uterus leiomyosarcoma
40%
1422
0.95
0.02
MSS
3.61
MSS

F01281
Hypopharyngeal Cancer
60%
2083
0.96
0
MSS
3.53
MSS

F01414
Oral Cancer
35%
521.5
0.92
0.03
MSS
11.35
MSS

D01425
Colon cancer
60%
858.9
0.95
0.01
MSS
5.83
MSS

F01837
Endometrial cancer
25%
1477
0.96
0.93
MSI-H
9.98
MSI-H

F00956
Endometrial cancer
10%
1485
0.95
0
MSS
2.64
MSS

F02435
Endometrial cancer
60%
1934
0.97
0.02
MSS
4.4
MSS

F00891
Endometrial cancer
35%
922.7
0.94
0.01
MSS
6.21
MSS

F01833
Leiomyosarcoma
60%
1693
0.97
0.03
MSS
4.04
MSS

F00763
Unknown primary
10%
1383
0.98
0.01
MSS
3.43
MSS

F01174
Unknown primary
25%
809
0.94
0.06
MSS
6.79
MSS

F00811
Unknown primary
80%
1318
0.97
0.03
MSS
6.07
MSS

F00113
Unknown primary
60%
1737
0.96
0.01
MSS
3.31
MSS

F00765
Breast cancer
70%
1272
0.97
0.01
MSS
4.62
MSS

F01780
Thyroid cancer
10%
703.7
0.92
0
MSS
5.98
MSI-L

F02213
Skin cancer
60%
907.3
0.97
0.01
MSS
4.66
MSS

F02485
Ovarian cancer
40%
1026
0.95
0.03
MSS
3.82
MSS

F02415
Ovarian cancer
65%
1581
0.96
0.09
MSS
15.76
MSS

F01318
Ovarian cancer
20%
1420
0.96
0
MSS
3.66
MSS

F01267
Ovarian cancer
20%
1729
0.96
0.03
MSS
3.53
MSS

F00696
Ovarian cancer
70%
828.9
0.94
0.01
MSS
5.36
MSS

F02644
Ovarian cancer
50%
2333
0.98
0.01
MSS
4.32
MSS

F01519
Ovarian cancer
40%
1407
0.97
0
MSS
4.61
MSS

D00465
Ovarian cancer
80%
1545
0.96
0.02
MSS
7.28
MSS

F02189
Ovarian cancer
35%
1528
0.98
0.06
MSS
3.82
MSS

F02443
Ovarian cancer/Endometrial
70%
1940
0.97
0
MSS
4.41
MSS

cancer

F02100
Cholangiocarcinoma
45%
1639
0.97
0.03
MSS
4.44
MSS

E00771
Breast Cancer
50%
963
0.94
0.02
MSS
14.75
MSS

F00730
Breast cancer
35%
1905
0.98
0.01
MSS
17.6
MSS

F01173
Breast cancer
45%
1282
0.95
0.05
MSS
4.36
MSS

F00984
Breast cancer
35%
1744
0.97
0.07
MSS
3.07
MSS

E00771
Breast Cancer
50%
1238
0.95
0.01
MSS
4.75
MSS

F00985
Breast cancer
30%
1463
0.96
0.09
MSS
3.94
MSS

F01399
Rectal cancer
5%
797.4
0.93
0
MSS
4.78
MSS

F01401
Rectal cancer
30%
1021
0.95
0
MSS
6.77
MSI-L

F01118
Lung cancer
NA
1564
0.96
0.07
MSS
2.22
MSS

F01539
Lung cancer/Thyroid cancer
20%
1353
0.98
0.08
MSS
8.01
MSS

F00421
Gastric cancer
50%
1420
0.96
0.01
MSS
4.11
MSS

F01598
Gastric cancer
15%
965.3
0.96
0
MSS
6.02
MSS

F01478
Gastric cancer
20%
683.9
0.95
0.01
MSS
5.42
MSS

F01482
Gastric cancer
15%
760.4
0.94
0.01
MSS
5.83
MSS

F02434
Gastric cancer
25%
879.4
0.95
0.16
MSS
5.28
MSS

F01929
Esophageal cancer
65%
547.5
0.92
0
MSS
8.38
MSS

F00396
Unknown primary
10%
1741
0.97
0.01
MSS
3.81
MSS

F02028
Pancreatic cancer
40%
680.9
0.96
0.01
MSS
6.9
MSS

F01198
Pancreatic cancer
40%
1600
0.97
0.02
MSS
7.51
MSS

F01903
Pancreatic cancer
15%
1194
0.97
0
MSS
3.67
MSS

F01912
Pancreatic cancer
10%
1501
0.97
0
MSS
3.61
MSS

F00360
Pancreatic cancer
20%
1167
0.97
0.01
MSS
3.85
MSS

F00789
Pancreatic cancer
35%
861.8
0.94
0.03
MSS
4.95
MSS

F00160
Pancreatic cancer
10%
1472
0.95
0.04
MSS
2.82
MSS

F01264
Pancreatic cancer
80%
1383
0.98
0.03
MSS
5.8
MSS

F01473
Pancreatic cancer
10%
557.8
0.93
0.02
MSS
5.3
MSS

F00674
Pancreatic cancer
65%
2158
0.97
0.01
MSS
2.54
MSS

F01582
Pancreatic cancer
30%
771.1
0.93
0.01
MSS
5.27
MSS

F01969
Pancreatic cancer
2%
1669
0.98
0.01
MSS
4.01
MSI-L

F01997
Pancreatic cancer
35%
1013
0.94
0.01
MSS
7.13
MSS

F01986
Pancreatic cancer
10%
1923
0.99
0.03
MSS
4.89
MSS

F01773
Pancreatic cancer
10%
1450
0.97
0.04
MSS
4.55
MSS

F01550
Pancreatic cancer
40%
1781
0.96
0.01
MSS
5.57
MSS

F02116
Pancreatic cancer
60%
1966
0.98
0
MSS
3.09
MSS

F02433
Pancreatic cancer
20%
953.9
0.95
0.04
MSS
6.02
MSS

F02527
Pancreatic cancer
10%
2167
0.98
0.01
MSS
5.82
MSS

F02041
Pancreatic cancer
40%
1960
0.99
0.17
MSS
7.01
MSS

F00868
Thymic carcinoma
25%
911.8
0.95
0.01
MSS
4.92
MSS

F02432
Osteosarcoma
90%
1298
0.95
0
MSS
5.86
MSS

F02646
Osteosarcoma
10%
1453
0.93
0.01
MSS
4.84
MSS

F00190
Salivary gland cancer
2%
1620
0.96
0
MSS
3.9
MSS

F01171
Sarcoma
35%
1193
0.91
0
MSS
4.31
MSS

F01427
Kidney cancer
80%
1084
0.94
0
MSS
4.97
MSS

E01792
Melanoma
40%
1383
0.95
0.03
MSS
13.13
MSS

E00467
Peritoneal carcinoma
40%
996.4
0.94
0.01
MSS
5.44
MSS

F01169
Peritoneal cancer
25%
861.6
0.95
0.01
MSS
5.28
MSS

F00129
Peritoneal cancer
60%
1257
0.96
0.02
MSS
5.44
MSS

F00803
Bladder cancer
80%
704.9
0.94
0.03
MSS
3.2
MSS

F02403
Nasopharyngeal carcinoma
85%
1633
0.98
0.01
MSS
7.01
MSS

F01176
Sinus cancer
40%
1373
0.95
0.03
MSS
2.6
MSS

F02171
Head and Neck Cancers
40%
1302
0.93
0.01
MSS
4.54
MSS

F00731
Cholangiocarcinoma
40%
1525
0.97
0.99
MSI-H
15.72
MSI-H

E00407
Cholangiocarcinoma
NA
1555
0.97
0
MSS
4.02
MSS

F01172
Cholangiocarcinoma
25%
944.7
0.93
0
MSS
3.03
MSS

F00836
Cholangiocarcinoma
20%
2087
0.97
0.01
MSS
3.68
MSS

F01120
Cholangiocarcinoma
65%
1250
0.97
0.02
MSS
2.93
MSS

D00831
Cholangiocarcinoma
70%
1498
0.97
0
MSS
3.85
MSS

F00068
Cholangiocarcinoma
60%
991.8
0.95
0.02
MSS
10.69
MSS

F00493
Cholangiocarcinoma
2%
1447
0.96
0.02
MSS
3.89
MSS

F00727
Cholangiocarcinoma
20%
1244
0.97
0.02
MSS
4.03
MSS

F02115
Cholangiocarcinoma
10%
3378
0.98
0.01
MSS
3.26
MSS

F00246
Cholangiocarcinoma
40%
1803
0.96
0.02
MSS
3.29
MSS

F01288
Cholangiocarcinoma
65%
1336
0.97
0.01
MSS
4.74
MSS

F00976
Cholangiocarcinoma
20%
1825
0.97
0.01
MSS
4.17
MSS

F01060
Cholangiocarcinoma
10%
1797
0.97
0
MSS
3.86
MSS

F00186
Gallbladder cancer
40%
1244
0.97
0.01
MSS
5.47
MSS

F01266
Lung cancer
40%
507.6
0.93
0.02
MSS
6.47
MSS

F02384
Prostate cancer
35%
1302
0.98
0.01
MSS
7.07
MSS

ACT0744
NA
NA
554.2
0.92
1
MSI-H
27.02
MSI-H

ACT0953
NA
NA
983.7
0.94
0.95
MSI-H
36.59
MSI-H

ACT0893
NA
NA
1105
0.96
0
MSS
4.37
MSS

ACT0897
NA
NA
1209
0.96
0.02
MSS
4.66
MSS

ACT0894
NA
NA
1403
0.97
0.05
MSS
6.92
MSS

ACT0887
NA
NA
1682
0.97
0.99
MSI-H
19.78
MSI-H

ACT1217
NA
NA
1731
0.96
0.05
MSS
10.2
MSS

F03491
Anal cancer
75%
1394
0.96
0
MSS
4.98
MSS

TABLE 3

MSI Model Validation Results

5-marker MSI-PCR detection system

MSI-H
MSS
Total

MSI Model
MSI-H
28
6
34

MSS
2
403
405

Total
30
409
439

TABLE 4

MSI Model Performance

Performance Summary

Agreement Statistic
Point Estimate
Wilson Score 95% CI

PPA
93%
79%, 98%

NPA
99%
97%, 99%

PPV
82%
66%, 92%

NPV
100%
98%, 100%

EXAMPLE 3
MSI detection for Samples of Different Tumor Purity

Total of three cancer cell lines with MSI-H were utilized (where they come from) for the determination of the lowest amount of tumor purity required to determine MSI status. These three cancer cell lines were diluted with their own matched normal cell to form a series of diluted samples with 100%, 80%, 50%, 40%, 30%, and 20% of tumor content. The MSI score for each of these samples is shown in Table 5.

TABLE 5

MSI status determined by MSI model for

cell lines of different tumor purity

Mean
Target base
Tumor/

Cell
sequencing
coverage
Normal
MSI
MSI

line
depth
at 100x
percentage
score
status

RKO
746.6
0.91
100%/0%
0.85
MSI-H

RKO
623.3
0.92
80%/20%
0.98
MSI-H

RKO
800.4
0.93
50%/50%
1
MSI-H

RKO
824.1
0.92
40%/60%
1
MSI-H

RKO
702.3
0.92
30%/70%
1
MSI-H

RKO
712
0.92
20%/80%
0.92
MSI-H

C33A
894.4
0.92
100%/0%
0.99
MSI-H

C33A
687.3
0.92
80%/20%
1
MSI-H

C33A
789.3
0.92
50%/50%
1
MSI-H

C33A
763.8
0.92
40%/60%
1
MSI-H

C33A
680.1
0.92
30%/70%
0.99
MSI-H

C33A
694
0.92
20%/80%
0.97
MSI-H

SW48
1670
0.92
100%/0%
1
MSI-H

SW48
832.4
0.92
80%/20%
1
MSI-H

SW48
721.8
0.92
50%/50%
1
MSI-H

SW48
870.8
0.93
40%/60%
1
MSI-H

SW48
784.5
0.93
30%/70%
0.99
MSI-H

SW48
848
0.93
20%/80%
0.66
MSI-H

MICROSATELLITE INSTABILITY DETERMINING METHOD AND SYSTEM THEREOF

Information

Publication Number

Date Filed

Date Published

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CPC

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Abstract

Description

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Provisional Applications (1)