Methods for diagnosing infectious disease and determining HLA status using immune repertoire sequencing

Patent metadata

Field	Value
Publication number	US-12428682-B2
Application number	US-202117343552-A
Country	US
Kind code	B2
Filing date	Jun 9, 2021
Priority date	Feb 24, 2015
Publication date	Sep 30, 2025
Grant date	Sep 30, 2025

Abstract

Methods are provided for predicting a subject's infection status using high-throughput T cell receptor sequencing to match the subject's TCR repertoire to a known set of disease-associated T cell receptor sequences. The methods of the present invention may be used to predict the status of several infectious agents in a single sample from a subject. Methods are also provided for predicting a subject's HLA status using high-throughput immune receptor sequencing.

First claim

What is claimed is:

1. A method comprising:
(a) determining an immune receptor profile of unique T-cell receptor (TCR) rearranged DNA sequences for each of a plurality of subjects, each subject having a known HLA allele status;
(b) categorizing the plurality of subjects based on (i) said known HLA allele status of the subject and (ii) a presence or absence in the subject's immune receptor profile of a feature comprising a unique TCR rearranged DNA sequence;
(c) determining a statistical score for an association between a set of features and a positive HLA allele status based on (b);
(d) training a machine learning model using said set of features to define a set of classifiers for each HLA allele status;
(e) inputting one or more unique TCR rearranged DNA sequences of a subject with an unknown HLA allele status into said machine learning model to identify one or more features that match the set of classifiers;
(f) predicting an HLA allele status of said subject based on said one or more matched features; and
(g) transplanting bone marrow, stem cells, or an organ from said subject to a recipient having the same HLA allele status as said subject.

2. The method of claim 1 , wherein determining an immune receptor profile comprises determining total number of the unique TCR rearranged DNA sequences and frequency of each unique TCR rearranged DNA sequence.

3. The method of claim 1 , wherein determining a statistical score comprises determining a p-value using a Fisher exact two-tailed test.

4. The method of claim 3 , further comprising determining a cutoff p-value for identifying a set of features that are significantly associated with an HLA allele status.

5. The method of claim 1 , further comprising determining a false discovery rate (FDR) of the association of a feature with an HLA allele status.

6. The method of claim 5 , further comprising determining a number of false-positive associations between said feature and said HLA allele status.

7. The method of claim 1 , wherein training a machine learning model comprises training a logistic regression model using said set of identified features and said known HLA allele statuses of each subject.

8. The method of claim 1 , wherein training a machine learning model comprises performing a leave-one out cross validation method.

9. The method of claim 8 , further comprising performing said leave-one out cross validation method for multiple rounds.

10. The method of claim 1 , wherein said prediction is at least 80% accurate.

11. The method of claim 1 , wherein said prediction is at least 90% accurate.

12. The method of claim 1 , wherein said TCR rearranged DNA sequence is a TCRA, TCRB, TCRG or TCRD rearranged DNA sequence.

13. The method of claim 1 , wherein said HLA allele is an HLA-A2 allele or an HLA-24 allele.

14. A method comprising:
(a) performing amplification and high throughput sequencing of genomic DNA obtained from a sample comprising T cells from a subject of unknown HLA allele status to determine a T-cell receptor (TCR) profile comprising unique TCR rearranged DNA sequences;
(b) comparing the TCR profile of the subject with a set of previously identified TCR profiles in a database, wherein each of the previously identified TCR profiles comprises TCR rearranged DNA sequences statistically significantly associated with a known HLA allele status for a plurality of subjects;
(c) generating a score for the subject, wherein the score is the proportion of unique TCR rearranged DNA sequences in the profile of the subject that match the TCR rearranged DNA sequences of the previously identified TCR profiles in the database;
(d) inputting the score from (c) into an algorithm, wherein the algorithm compares the score of the subject and the HLA allele status from the plurality of subjects of known HLA allele status;
(e) determining an estimated probability of the HLA allele status of the subject as an output of the algorithm;
(f) predicting the HLA allele status of the subject based on the estimated probability determined at step (e); and
(g)) transplanting bone marrow, stem cells, or an organ from the subject to a recipient having the same HLA allele status as the subject.

15. The method of claim 14 , wherein the database classifies the plurality of subjects based on (i) the known HLA allele status of the subject and (ii) a presence or absence in the subject's immune receptor profile of a feature comprising a unique TCR rearranged DNA sequence.

16. The method of claim 14 , wherein generating the score comprises determining a p-value using a Fisher exact two-tailed test.

17. The method of claim 16 , further comprising determining a cutoff p-value for identifying a set of features that are significantly associated with an HLA allele status, wherein the cutoff p-value is less than or equal to 1*10-4.

18. The method of claim 14 , wherein the algorithm comprises a logistic regression model.

19. The method of claim 18 , wherein the logistic regression model performs a leave-one out cross validation method for at least one round.

20. The method of claim 14 , wherein said HLA allele status is of an HLA-A2 allele or an HLA-24 allele.

Assignees

• Adaptive Biotechnologies Corp
• Fred Hutchinson Cancer Center

Inventors

• Emerson Ryan O
• Robins Harlan S
• Rieder Mark J
• Dewitt Iii William S
• Carlson Christopher S

Classifications

• C12Q2600/172

  Haplotypes · CPC title

• C12Q2600/158

  Expression markers · CPC title

• C12Q1/6883

  for diseases caused by alterations of genetic material · CPC title

• C12Q1/6869

  Methods for sequencing · CPC title

• C12Q1/70

  involving virus or bacteriophage {(immunoassay for viruses G01N33/56983)} · CPC title