US-20260125757-A1 - Methods for Diagnosing Infectious Disease and Determining HLA Status Using Immune Repertoire Sequencing

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.

Inventors

• RYAN O. EMERSON
• Harlan S. Robins
• Mark J. RIEDER
• William S. Dewitt, III
• Christopher S. Carlson

Assignees

• ADAPTIVE BIOTECHNOLOGIES CORPORATION
• FRED HUTCHINSON CANCER CENTER

Dates

Publication Date

20260507

Application Date

20250905

Claims (8)

1. 1 .- 24 . (canceled)
2. 25 . A method for predicting a human leukocyte antigen (HLA) allele status of a subject, comprising: (a) performing 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 a weighted function based on the unique TCR rearranged DNA sequences and/or abundance thereof 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; and (f) predicting the HLA allele status of the subject based on the estimated probability determined at step (e).
3. 26 . The method of claim 25 , 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.
4. 27 . The method of claim 25 , wherein generating the score comprises determining a p-value using a Fisher exact two-tailed test.
5. 28 . The method of claim 27 , further comprising determining a cutoff p-value for identifying the 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 .
6. 29 . The method of claim 25 , wherein the algorithm comprises a logistic regression model.
7. 30 . The method of claim 29 , wherein the logistic regression model performs a leave-one out cross validation method for at least one round.
8. 31 . The method of claim 25 , wherein said HLA allele status is of an HLA-A2 allele or an HLA-24 allele.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 17/343,552, filed on Jun. 9, 2021, which is a divisional of U.S. patent application Ser. No. 15/553,040, filed on Aug. 23, 2017, now U.S. Pat. No. 11,047,008, which is a national stage filing under 35 U.S.C. 371 of International Patent Application No. PCT/US2016/019343, filed on Feb. 24, 2016, which claims the benefit of U.S. Provisional Patent Application No. 62/120,249, filed on Feb. 24, 2015, U.S. Provisional Patent Application No. 62/157,249, filed on May 5, 2015, and U.S. Provisional Patent Application No. 62/215,630, filed on Sep. 8, 2015. The contents of each of these applications are herein incorporated by reference in their entirety. REFERENCE TO AN ELECTRONIC SEQUENCE LISTING The contents of the electronic sequence listing (ADBS-024CON_SEQLIST; Size: 141,920 bytes; and Date of Creation: Sep. 5, 2025) is herein incorporated by reference in its entirety. BACKGROUND OF THE INVENTION The cellular adaptive immune system conveys broad protection against infection by pathogens through the development of a vast and highly diverse repertoire of T cell receptor (TCR) genes, which encode cell-surface T cell receptors with randomized antigen specificity. The ability of a subject's adaptive immune system to adequately address an incipient infection relies on activation of an appropriate antigen-specific T-cell receptor (TCR). TCR-antigen interaction is mediated by the cell-surface presentation of foreign peptides by pathogen-infected cells in the context of major histocompatibility complex (MHC) proteins. Specifically, CD8+ T cells recognize antigen in the context of MHC class I proteins. Since MHC class I proteins are encoded by the human leukocyte antigen (HLA) loci A, B, and C, which are highly polymorphic, the antigen specificity of a TCR is modulated across individuals by HLA context. When an antigen has been encountered, activated T cells proliferate by clonal expansion and reside in the memory T cell compartment for many years as a clonal population of cells (clones) with identical-by-descent rearranged TCR genes (Arstila T P, et al. A direct estimate of the human alphabeta T cell receptor diversity. Science 286:958-961, 1999). Protection against future exposure to disease-causing pathogens is conferred by the ability of activated T cells to form long-lasting memory responses. The majority of TCR diversity resides in the ß chain of the TCR alpha/beta heterodimer. Each T cell clone is encoded by a single TCRß allele that has been randomly rearranged from the germ-line TCRß locus to form a mature TCRß gene. Immense diversity is generated by combining noncontiguous TCRß variable (V), diversity (D), and joining (J) region gene segments, which collectively encode the CDR3 region, the primary region of the TCRß locus for determining antigen specificity. Deletion and template-independent insertion of nucleotides during rearrangement at the Vβ-Dβ and Dβ-Jβ junctions further add to the potential diversity of receptors that can be encoded (Cabaniols J P, et al. Most alpha/beta T cell receptor diversity is due to terminal deoxynucleotidyl transferase. J Exp Med 194:1385-1390, 2001). Typically, at a given point in time, a healthy adult expresses approximately 10 million unique TCRß chains on their 1012 circulating T cells (Robins H S, et al. (2009) Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114:4099-4107). However, observing the same TCRß chain independently in two individuals is thousands of times more common than would be expected if all rearrangements were equally likely (Robins H S, et al. (2010) Overlap and effective size of the human CD8+ T cell receptor repertoire. Science Translational Medicine 2: 47ra64). It is expected that there are many TCRβ sequences (especially those with few or no insertions) that are present in the naïve repertoires of most individuals and that these TCRβ sequences will reliably proliferate upon exposure to their associated antigen (V. Venturi, et al., The molecular basis for public T-cell responses? Nature reviews. Immunology 8, 231-238 (2008); published online EpubMar (10.1038/nri2260)). This over-representation of specific TCRß sequence rearrangements in the naïve T cell repertoire forms the basis for public T cell responses. Public T-cell responses occur when T cells bearing identical T-cell receptors (TCRs) are observed to dominate the response to the same antigenic epitope in multiple individuals. Many pathogenic antigens are known to induce such a public T cell response, in which a pathogenic antigen is targeted by the same T cell receptor sequence (and found to be immunodominant) in multiple individuals with specific HLA isotypes. H. Li et al., Determinants of public T cell responses. Cell research 22, 33-42 (2012); published online EpubJan (10.1038/cr.2012.1); H. Li, et al., Recombinatorial biases and convergent rec