Here we describe the cohorts used for training and testing in the CMI-Flu prediction challenge. An overview of all the data, including links to more detailed descriptions of the other data, may be found at https://cmi-x.org/prediction-challenges/

Cohorts

LJI 2025 cohort. In year 1, we collected samples from 40 donors across 5 timepoints. The cohort consisted of 40 participants between the ages of 19 and 65 (Table 1). The study protocol was approved by the Institutional Review Board of the La Jolla Institute of Immunology (IRB #VD-319). Each participant received a standard Fluzone dose (15 μg of antigen) in the 2025-2026 influenza season (Table 2 shows flu strains present in the vaccine). Samples were collected according to Figure 1.

UGA 9 cohort. In year 0, we performed experiments on the UGA 9 cohort. This cohort consisted of 40 participants between the ages of 19 and 89 (Table 1). The study protocol was approved by the Institutional Review Board of the University of Georgia (IRB #20224877). Each participant received either a standard Fluzone dose (15 μg of antigen for ages < 65) or a high Fluzone dose (60 μg of antigen for ages >= 65) in the 2024-2025 influenza season (Table 2 shows flu strains present in the vaccine). Samples were collected according to Figure 2.

Publicly available data. In addition to the above cohorts, we have collected public data from across numerous resources. Details of these cohorts may be found in the related publications. Much of this data has been collected from ImmuneSpace.

As part of the CMI-Flu challenge, we have collected the following information for participants and the investigations they have been involved in:

Participants

Vaccination history

For the LJI 2025 cohort, and where information was provided for public studies, we collated influenza vaccination history for participants.

Vaccination History
Data dictionary
Column Description
subject subject ID. Combine with study_accession as studyID.subjectID to link to participants.tsv.
vaccine_year Influenza season (year) the vaccination record refers to.
received Whether the participant was vaccinated that season and the source of the record: Yes_Study (vaccinated, confirmed from study records), Yes_selfReported (self-reported as vaccinated), or No_selfReported (self-reported as not vaccinated).
 
vacc = read_tsv('../datasets/260512/train/participant_vaccineHistory.tsv')
head(vacc)
## # A tibble: 6 × 3
##   subject vaccine_year received        
##   <chr>          <dbl> <chr>           
## 1 ID_001          2013 Yes_selfReported
## 2 ID_001          2014 Yes_selfReported
## 3 ID_001          2015 No_selfReported 
## 4 ID_001          2016 Yes_Study       
## 5 ID_001          2017 Yes_selfReported
## 6 ID_001          2018 Yes_Study

HLA typing

In the participant_hla.tsv data we provide HLA typing for participants of the LJI 2025 cohort, as well as SDY314.

LJI 2025 cohort: Genomic DNA from PBMC was typed at the American Society for Histocompatibility and Immunogenetics (ASHI)–accredited Medical Genomics Laboratories, Murdoch University (Western Australia). Locus-specific, sample-indexed (multiplex identifier, MID) primers targeted polymorphic exons for Class I HLA-A, -B, and -C (exons 2–3) and Class II HLA-DQA1/-DQB1 (exons 2–3), -DRB1/3/4/5 (exon 2), and -DPB1 (exon 2). Libraries were prepared using NEBNext Ultra II Library Prep Kit and sequenced on an Illumina MiSeq (v3 600-cycle, 2 × 300 bp). Reads were demultiplexed by MID barcodes, adapter/primer sequences trimmed, quality-filtered, and merged. Alleles were called using the IIID HLA Allele Caller and the IIID HLA Analysis Suite, with the IMGT/HLA Database as the reference.

HLA Typing
Data dictionary
Column Description
subject subject ID. Combine with study_accession as studyID.subjectID to link to participants.tsv.
locus_name HLA locus typed: Class I (HLA-A, -B, -C) and Class II (HLA-DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4, -DRB5). One row per subject per locus.
allele_1 First called allele at the locus, in IMGT/HLA nomenclature (e.g. A*24:02). allele_1 and allele_2 are arbitrarily ordered.
allele_2 Second called allele. allele_1 and allele_2 are arbitrarily ordered.
study_accession Study identifier (2025LJI or SDY314). Links to the investigations.tsv file.
 
hla = read_tsv('../datasets/260512/train/participant_hla.tsv')
head(hla)
## # A tibble: 6 × 5
##   subject locus_name allele_1   allele_2   study_accession
##   <chr>   <chr>      <chr>      <chr>      <chr>          
## 1 SUB1829 HLA-A      A*24:02    A*24:02    2025LJI        
## 2 SUB1829 HLA-B      B*40:02    B*40:02    2025LJI        
## 3 SUB1829 HLA-C      C*03:03    C*03:04    2025LJI        
## 4 SUB1829 HLA-DPB1   DPB1*04:02 DPB1*05:01 2025LJI        
## 5 SUB1829 HLA-DQA1   DQA1*01:01 DQA1*01:02 2025LJI        
## 6 SUB1829 HLA-DQB1   DQB1*05:03 DQB1*06:02 2025LJI
print(paste('Number of donors with HLA typing information:', length(unique(hla$subject))))
## [1] "Number of donors with HLA typing information: 119"