PO.BCS01.08 · 生物信息与计算
多重免疫荧光图像的图论空间异质性分析实现对输卵管中HGSC前驱病变的定量鉴别
Graph theoretic spatial heterogeneity analysis of multiplexed immunofluorescence images enables quantitative differentiation of HGSC precursor lesions in the fallopian tube
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：p53特征、浆液性输卵管上皮内病变（STILs）和浆液性输卵管上皮内癌（STICs）代表了高级别浆液性癌（HGSC）的前驱病变谱，在50-60%的HGSC病例中可发现STICs。这些病变携带TP53突变，并表现出与浸润性HGSC相似的基因组改变，但只有其中一部分会进展为恶性肿瘤。研究表明，从STIC形成到浸润性疾病之间存在近十年的潜伏期，这为干预创造了关键窗口。然而，决定恶性转化的因素仍知之甚少且缺乏量化。至关重要的是，这些前驱病变的免疫微环境在定量上仍未被表征。具体而言，我们缺乏将p53特征、STIL和STIC病变中的淋巴细胞、免疫抑制细胞和免疫检查点表达关联起来、并与HGSC风险相符的定量指标。定量理解免疫逃逸机制是在早期建立还是在进展过程中形成，可能识别出预测个体病变发生浸润风险的微环境生物标志物。
方法与结果：为此，我们开发了一个用于多重免疫荧光（mIF）的空间异质性分析（SHEAN）框架，该框架利用输卵管前驱组织样本的图论表征来识别定量空间免疫特征，这些特征能够（1）表征并相互区分正常上皮（Norm）、p53特征、STICs和HGSCs；（2）对TP53突变状态敏感；（3）能够根据STICs的扁平型（FLAT）和出芽型、松散黏附或脱离型（BLAD）状态对其进行区分。这些经图像水平确认的统计学显著特征，包括与T淋巴细胞、M2极化巨噬细胞和上皮细胞之间浸润程度及相互作用相关的特征。SHEAN在基于自助法（bootstrap）模型（该模型纳入53个正常、68个p53特征、73个STIC和32个HGSC区域并使用XGBoost分类器）区分Norm、p53特征、STICs和HGSCs方面，还取得了交叉验证的ROC曲线下面积（AUROC）87.4%。
结论：SHEAN能够量化HGSC前驱微环境的免疫空间指标，提供了一个可解释的、空间信息化的模型，能够区分病变类别并实现对HGSC风险的可靠分层。
查看英文原文 English abstract
Background: p53 signatures, serous tubal intraepithelial lesions (STILs), and serous tubal intraepithelial carcinomas (STICs) represent the precursor spectrum of high-grade serous carcinoma (HGSC), with STICs identified in 50-60% of HGSC cases. These lesions harbor TP53 mutations and exhibit similar genomic alterations to invasive HGSC, yet only a subset of these progress to malignancy. Studies suggest a close to a decade long latency period between STIC formation and invasive disease, creating a critical window for intervention. However, the factors determining malignant transformation remain poorly understood and quantified. Critically, the immune microenvironment of these precursors remains quantitatively uncharacterized. Specifically, we lack quantitative metrics associating lymphocytes, immunosuppressive cells, and immune checkpoint expressions in p53 signatures, STIL, and STIC lesions that are concordant with HGSC risk. Quantitative understanding of whether immune escape mechanisms are established early or develop during progression could identify microenvironment biomarkers that predict risk of individual lesions becoming invasive.
Method and Results: Toward this goal, we have developed a spatial heterogeneity analysis (SHEAN) framework for multiplexed immunofluorescence (mIF) that utilizes graph-theoretic representations of fallopian precursor tissue samples to identify quantitative spatial immune signatures that (1) characterize and distinguish normal epithelium (Norm), p53 signatures, STICs, and HGSCs from each other; (2) are sensitive to TP53 mutation status; and (3) can differentiate STICs based on their flat (FLAT) and budding, loosely adherent or detached (BLAD) status. These statistically significant features, confirmed at the image level, include signatures associated with degree of infiltration into, and interaction between T-lymphocytes, M2 polarized macrophages, and epithelial cells. SHEAN also achieved a cross-validated area under the ROC curve (AUROC) of 87.4% in discriminating Norm, p53 signatures, STICs, and HGSCs based on a bootstrapped model that included 53 normal, 68 p53 signatures, 73 STIC, and 32 HGSC regions and used an XGBoost classifier.
Conclusions: SHEAN allows the quantification of immune spatial metrics of the HGSC precursor microenvironment, providing an interpretable, spatially informed model capable of discriminating between lesion categories and enabling the reliable stratification of HGSC risk.
利益披露 Disclosure
T. Jacob, None..
T. Soong, None..
S. Uttam, None.