Method for differentially quantifying naturally processed HLA-restricted peptides for cancer, autoimmune and infectious diseases immunotherapy development

Patent metadata

Field	Value
Publication number	US-9791443-B2
Application number	US-201113087948-A
Country	US
Kind code	B2
Filing date	Apr 15, 2011
Priority date	Apr 16, 2010
Publication date	Oct 17, 2017
Grant date	Oct 17, 2017

Abstract

The invention relates to a method for quantitatively identifying relevant HLA-bound peptide antigens from primary tissue specimens on a large scale without labeling approaches. This method can not only be used for the development of peptide vaccines, but is also highly valuable for a molecularly defined immunomonitoring and the identification of new antigens for any immunotherapeutic strategy in which HLA-restricted antigenic determinants function as targets, such as a variety of subunit vaccines or adoptive T-cell transfer approaches in cancer, or infectious and autoimmune diseases.

First claim

The invention claimed is:

1. A method for the identification and label-free quantification of at least one MHC ligand peptide suitable for use in an immunotherapeutic composition, said method comprising
a) isolating at least one MHC ligand peptide from at least one primary healthy tissue sample and at least one primary diseased tissue sample by immune-precipitating the at least one MHC ligand peptide using an antibody that specifically binds to a human leukocyte antigen (HLA)-A, HLA-B, and/or HLA-C,
b) performing an HPLC-MS analysis on said MHC ligand peptides in order to generate peptide signals therefor, wherein a plurality of replicate runs of HPLC-MS analysis is performed for each sample,
c) extracting the precursor ion signal area for each of said MHC ligand peptide signals,
d) identifying the sequences for each of said MHC ligand peptides,
e) normalizing the areas within replicate runs by calculating a mean presentation value for each peptide in each replicate run; and computing a normalization factor for each peptide and each replicate run using the mean presentation value; and averaging over all peptides results in run-wise normalization factors; and applying the run-wise normalization factors to all peptides of a particular replicate run,
f) determining relative quantities of the MHC ligand peptides that are comparable between samples,
g) calculating a presentation profile and/or a presentation score for the MHC ligand peptide of said diseased sample and said healthy sample based on said average and relative quantities of the MHC ligand peptide, and
i) label-free quantification of the MHC ligand peptides,
j) formulating the at least one MHC ligand peptide in an immunotherapeutic composition if the at least one MHC ligand peptide is over-presented in the diseased sample.

2. The method of claim 1 , wherein f) comprises:
f1) assigning the MHC ligand peptide to an allele-specific sequence subgroup for a comparison between different samples by identifying at least one MHC allele to which the MHC ligand peptide is expected to bind, and
f2) normalizing between different samples using the allele-specific subgroups to account for different sample sizes and/or MHC expression levels; and
f3) performing a data quality control based on peptide reproducibility for each sample.

3. The method of claim 1 , wherein said method allows handling of tissue samples of different amounts and MHC expression levels.

4. The method of claim 1 , wherein said tissue samples comprise primary tissue samples.

5. The method of claim 1 , wherein the method is capable of being performed on a high-throughput basis.

6. The method of claim 1 , wherein said method is capable of being performed incrementally by increasing an existing dataset from earlier tested samples with data of new samples.

7. The method of claim 1 , wherein at least five label-free replicate LCMS runs are performed for each sample.

8. The method of claim 2 , wherein normalizing between different samples is performed by a method comprising:
(f2α) calculating a mean presentation value of each peptide for all samples of a defined preparation antibody;
(f2β) computing a normalization factor for each peptide and sample using a mean presentation value; and
(f2γ) averaging over all peptides results in sample-wise normalization factors, and
(f2δ) applying sample-wise normalization factors to all peptides of the particular sample.

9. The method of claim 1 , wherein the presentation score is a p-value of a linear mixed model or a p-value of a t-test.

10. The method of claim 9 , wherein the presentation score is adjusted for multiple testing using false discovery rate.

11. The method of claim 1 , wherein the sequence is identified by generating a consensus spectrum from the HPLC-MS analysis and at least one previous HPLC-MS analysis.

12. The method of claim 11 , further comprising performing fragment spectra clustering, wherein clustering is performed by a growing k-means clustering algorithm.

Assignees

• Weinschenk Toni
• Fritsche Jens
• Immatics Biotechnologies Gmbh

Inventors

• Weinschenk Toni
• Fritsche Jens

Classifications

• G01N33/6842 Primary

  Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins · CPC title

• G01N33/6848

  Methods of protein analysis involving mass spectrometry · CPC title

• G01N33/6878

  in epitope analysis · CPC title

• G01N33/56977 Primary

  HLA or MHC typing · CPC title

• G01N2333/70539

  MHC-molecules, e.g. HLA-molecules · CPC title