PO.PS01.03 · 人群科学
结直肠癌T细胞图谱中与遗传祖先相关的特征
Genetic ancestry-associated features of the colorectal cancer T-cell landscape
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:肿瘤浸润淋巴细胞(TIL)是结直肠癌(CRC)的阳性预后指标,然而除微卫星不稳定性(MSI)之外,驱动其变异的因素尚不明确。既往研究表明遗传祖先可能影响全身及肿瘤相关的免疫反应。我们检验了以下假设:全局遗传祖先与CRC肿瘤免疫微环境中不同的T细胞特征独立相关。
方法:在来自拉丁裔结直肠癌联盟的230名CRC患者中,我们使用immunoSEQ TCRbeta检测量化了肿瘤相关T细胞库,以评估T细胞受体(TCR)丰度和克隆性(经对数转换);由病理学家对每个高倍视野的TIL评分(二分为≥2 vs <2;N=180);以及基于全外显子组测序(WES)推断的TCRalpha(TCRA)比例(二分为≥0.03 vs <0.03;N=192)。遗传祖先比例(欧洲[EUR]、非洲[AFR]、东亚[EAS]、美洲原住民[NAT]、南亚[SAS])通过监督式ADMIXTURE方法(使用1kGP/HGDP参考数据集)从WES的种系/正常基因型中估算。TCR特征之间的相关性使用Spearman系数进行评估。逻辑/线性回归检验了免疫特征(TCR丰度、TCR克隆性、TIL和TCRA比例)与个体祖先比例之间的关联,并对年龄、性别、肿瘤位置和MSI状态进行校正。为处理祖先成分之和受限的问题,我们应用了加性对数比转换(ALR),将AFR、EAS、SAS和NAT祖先表示为相对于EUR的对数比。将这些ALR拟合到多变量逻辑/线性回归模型中,并使用4自由度似然比检验(4-df LRT)检验遗传祖先对每个免疫特征的总体贡献。
结果:各种T细胞定量指标呈正相关,其中病理学家评估的TIL与TCR丰度、克隆性和TCRA比例显示出显著相关性(ρ分别为0.29、0.29和0.22)。TCRA比例较高的CRC患者更可能具有较高的EUR祖先(比值比[OR]:12.85;95%置信区间[CI]:1.09-151.70,p=0.043)和较低的NAT祖先(OR:0.06,95% CI:0.005-0.70,p=0.025)。较高的TCR克隆性与较低的EUR比例(p=0.076)和较高的NAT比例(p=0.084)显示出提示性关联。在成分分析中,我们未发现总体遗传祖先与任何T细胞特征之间存在关联(4-df LRT>0.05)。
结论:我们的研究结果表明,特定的遗传祖先成分可能与CRC中的T细胞图谱相关。这些结果提示遗传背景可能影响CRC中宿主的抗肿瘤免疫反应,凸显了将遗传祖先纳入个体化风险分层和精准医学方法的重要性。
查看英文原文 English abstract
Introduction: Tumor-infiltrating lymphocytes (TILs) are positive prognostic indicators in colorectal cancer (CRC), yet factors driving their variability beyond microsatellite instability (MSI) are poorly defined. Prior studies suggest that genetic ancestry may influence systemic and tumor-associated immune responses. We tested the hypothesis that global genetic ancestry is independently associated with distinct T-cell features in the CRC tumor immune microenvironment.
Methods: In 230 patients with CRC from the Latino Colorectal Cancer Consortium, we quantified tumor-associated T-cell repertoires using immunoSEQ TCRbeta assays for T-cell receptor (TCR) abundance and clonality (log-transformed), pathologist-scored TILs per high-powered field (dichotomized ≥2 vs <2; N=180), and whole-exome sequencing (WES)-inferred TCRalpha (TCRA) fractions (dichotomized ≥0.03 vs <0.03; N=192). Genetic ancestry proportions (European [EUR], African [AFR], East Asian [EAS], Indigenous American [NAT], South Asian [SAS]) were estimated from germline/normal genotypes from WES via supervised ADMIXTURE (using 1kGP/HGDP references). Correlations between TCR features were evaluated using Spearman coefficients. Logistic/linear regression examined the associations between immune features (TCR abundance, TCR clonality, TILs, and TCRA fractions) and individual ancestry proportions, adjusting for age, sex, tumor location, and MSI status. To account for the constrained sum of ancestral components, we applied an additive log-ratio transformation (ALR), expressing AFR, EAS, SAS, and NAT ancestries as log ratios relative to EUR. These ALRs were fitted into a multivariable logistic/linear regression model, and the overall contribution of genetic ancestry to each immune feature was tested using a 4-df likelihood-ratio test (4-df LRT).
Results: Various T-cell quantification metrics were positively correlated, with pathologist-reviewed TILs showing significant correlations with TCR abundance, clonality, and TCRA fractions (ρ=0.29, 0.29, and 0.22, respectively). CRC patients with higher TCRA fractions were more likely to have higher EUR ancestry (Odds Ratio [OR]: 12.85; 95% Confidence Interval [CI]: 1.09-151.70, p=0.043) and lower NAT ancestry (OR: 0.06, 95% CI: 0.005-0.70, p=0.025). Higher TCR clonality showed a suggestive association with lower EUR proportions (p=0.076) and higher NAT proportions (p=0.084). We found no association with overall genetic ancestry and any T-cell feature in the compositional analysis (4-df LRT>0.05).
Conclusion: Our findings suggest that specific genetic ancestry components may be associated with T-cell landscapes in CRC. These results indicate that genetic background may influence the host anti-tumor immune response in CRC, highlighting the importance of integrating genetic ancestry into personalized risk stratification and precision medicine approaches.
利益披露 Disclosure
Y. Tsai, None..
M. Matejcic, None..
D. Sobieski, None..
E. M. Cockman, None..
E. Jean-Baptiste, None..
N. T. Nguyen, None..
E. Gordian, None..
J. Oliveras Torres, None..
H. J. Hoehn, None..
K. Shankar, None..
D. B. Diaz, None..
R. Wilson, None..
K. Brito, None..
A. Koepfler, None..
N. C. Lorona, None..
D. Coppola, None..
O. Saglam, None..
C. Fulmer, None..
K. Jiang, None..
S. Felder, None..
J. Sanchez, None..
M. C. Stern, None..
D. Cress, None..
E. M. Siegel, None..
J. K. Teer, None..
J. C. Figueiredo, None..
S. L. Schmit, None.