Nikfar Mehdi, Mi Haoyang, Gong Chang, Kimko Holly, Popel Aleksander S
Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Waltham, MA 02451, USA.
Cancers (Basel). 2023 May 13;15(10):2750. doi: 10.3390/cancers15102750.
Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.
空间异质性是癌症的一个标志。肿瘤异质性会随时间和位置而变化。肿瘤微环境（TME）包含各种细胞类型及其相互作用，这些相互作用赋予了对治疗的反应。因此，对肿瘤异质性进行定量评估对于开发有效的治疗方法至关重要。可以使用不同的方法，如多区域测序、空间转录组学、尸检样本分析和活检样本的纵向分析，来分析肿瘤内异质性（ITH）和时间演变，并揭示治疗反应的机制。然而，由于这些数据的局限性以及与样本采集时间点相关的不确定性，全面了解肿瘤内异质性的作用具有挑战性。在这里，我们使用了一种混合模型，该模型将全患者的房室定量系统药理学（QSP）模型与描述TME的基于空间智能体的模型（ABM）相结合；我们应用了四个空间指标来量化模型模拟的肿瘤内异质性，并对有效和无效抗PD-1治疗的代表性病例的TME免疫结构进行分类。这四个指标取自计算数字病理学，包括混合分数、平均邻域频率、香农熵和G交叉函数的曲线下面积（AUC）。使用第五个非空间指标来补充分析，即癌细胞与免疫细胞数量的比率。这些指标被用于将TME分类为“冷”、“分区化”和“混合”，这与治疗效果相关。这些有效和无效治疗指标的趋势与临床文献在定性上一致，表明分区化免疫结构可能会导致更有效的治疗结果。
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