Abstract 3014: Association between HLA-KIR allele interaction combinations and density of T-cell subsets in colorectal cancer
Bibliographic record
Abstract
Abstract Background: Germline genetic factors central to immunity, such as human leukocyte antigen (HLA) variants, have been associated with several immune-related phenotypes such as response to immune checkpoint inhibitors. However, HLA and killer-cell immunoglobulin-like (KIR) gene combinations, which may modulate immune function, have not been studied in relation to T-cell density in cancer. Methods: This study was conducted within 3 well characterized epidemiologic studies that collected colorectal tumor tissue blocks (N=484). We profiled the in-situ T cell landscape of CRC using digital imaging, machine learning, and a customized 9-plex multiplexed immunofluorescence panel with antibodies directed against CD3, CD4, CD8, CD45RA, CD45RO, FOXP3, and MKI67. HLA and KIR variants were imputed from genome-wide array datasets through SNP2HLA and KIR*IMP methods. We used multivariable ordinal logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between HLA-KIR activation/inhibition ligand combinations with quartile of T cell densities in CRC adjusting for age, sex, GWAS panel, and the first two principal components of ancestry. Results: Presence of KIR2DL2+HLAC1+ and KIR2DS2+HLAC1+ combinations were associated with lower odds of greater CD3+CD8+ T-cell density quartile, however these findings were not statistically significant [OR = 0.73, 95% CI (0.52, 1.02), p-value = 0.067; OR = 0.74 95% CI (0.53, 1.04), p-value= 0.082, respectively]. There was no association between HLA-KIR combinations and CD3+CD4+ T-cell density quartile. Further results will examine HLA and KIR genes individually, as well as additional combination variables and more specific T-cell density subsets. Conclusions: Further investigation is needed to determine if germline genetics related to immune profile plays a role in T-cell densities in CRC. Citation Format: Claire Elizabeth Thomas, Jeroen Huyghe, Tomotaka Ugai, Hang Yin, Yasutoshi Takashima, Daniel D. Buchanan, Conghui Qu, Li Hsu, Andressa Dias Costa, Stephen Gallinger, Robert Grant, Sushma Thomas, Shuji Ogino, Amanda I. Phipps, Jonathan Nowak, Ulrike Peters. Association between HLA-KIR allele interaction combinations and density of T-cell subsets in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".