PO.TB04.05 · 肿瘤生物学

结直肠癌患者来源肿瘤类器官的高内涵图像分析发现与药物活性和作用方式相关的高度患者内和患者间异质性

High-content image analysis of colorectal cancer patient-derived tumroids identifies high intra- and inter-patient heterogeneity associated with drug activity and mode-of-action

编号 734 展板 4 时间 4/19 02:00–05:00 区域 Section 30 主讲 Jarle Bruun
分会场 Noninvasive Imaging and Analysis of Animal and Tissue Models
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作者与单位 Authors & Affiliations

Peter W. Eide1, Nicolas Pasquir1, Christer A. Andreassen1, Anne Hansen Ree2, Knut M Augestad2, Sebastian Meltzer2, Jarle Bruun1

1Oncosyne AS, Oslo, Norway,2Akershus University Hospital, Lørenskog, Norway

摘要 Abstract

中文摘要
背景:结直肠癌(CRC)患者来源肿瘤类器官(tumoroid)的离体培养揭示了丰富且异质的生长形态谱,既存在于模型内部也存在于模型之间。对肿瘤类器官形态的定量分析可以揭示新的生物学机制和预测性关系。研究表明,紧凑型与囊性形态反映了药物敏感性,与紧凑型肿瘤类器官相比,囊性肿瘤类器官对Wnt抑制剂敏感而对MEK抑制剂耐药。 方法:基于对明场、核染色和核死细胞染色多通道图像的目视评估,对单个肿瘤类器官(n=4049)进行标注。使用明场或全部三个通道作为输入,在生成的数据集上训练ConvNeXt v2分类器。 结果:我们开发了一种表型分析算法(iCANdy),将CRC肿瘤类器官分层为10个类别:紧凑型、出芽型、腺泡型、囊性(厚壁)、囊性(薄壁)、浸润型、成纤维型、单层型、单细胞/凋亡型以及混合/其他型。明场和多通道输入的性能相似。根据最接近的形态对所有肿瘤类器官进行分类,得到F1分数0.80和平衡准确率0.75,最模糊的类别如预期为混合/其他型。iCANdy被应用于来自57个肿瘤类器官模型、共22k个药物处理/对照孔、使用37种药物或药物组合的1020万个肿瘤类器官的明场图像。对于未处理的对照组,就总面积而言,主导形态在不同模型间各不相同,以紧凑型(39%)、浸润型(32%)、单细胞/凋亡型(12%)、出芽型(9%)和囊性(厚壁)(7%)最为常见。我们观察到高度的患者内异质性,主导形态的比例范围为20%至54%,中位数为36%(不包括单细胞/凋亡结构)。患者间异质性也与若干药物的活性相关。虽然阿托伐他汀对紧凑型(82%)和浸润型(100%)结构均表现出类似的单细胞导向转变,但其他药物如5-FU则表现出强烈差异(分别为68%和18%)。其他对细胞活力影响有限的化合物诱导了指示作用方式(如细胞抑制或衰老)的形态学表型。有趣的是,甲氨蝶呤(methotrexate)使80%的囊性(厚壁)结构发生浸润导向转变,但仅使14%的紧凑型簇发生此类转变。 结论:对肿瘤类器官的定量表型分析可以识别新的预测性形态学关系、药物作用方式,并更准确地预测药物活性。
查看英文原文 English abstract
Background: Ex vivo culture of colorectal cancer (CRC) patient-derived tumoroids reveals a rich heterogenous pool of growth morphologies, within and among models. Quantitative analysis of tumoroid morphologies can uncover new biology and predictive relationships. Studies have shown that compact vs. cystic morphology reflects drug sensitivity, with cystic tumoroids being sensitive to Wnt inhibitors and resistant to MEK inhibitors as compared to compact tumoroids. Methods: Individual tumoroids (n=4049) were annotated based on visual assessment of brightfield, nuclear and nuclear dead stain multi-channel images. ConvNeXt v2 classifiers were trained on the resulting dataset, using brightfield or all three channels as inputs. Results: We developed a phenotyper algorithm (iCANdy) stratifying CRC tumoroids into 10 classes: compact, budding, acinar, cystic (thick-walled), cystic (thin-walled), invasive, fibroblastic, monolayer, single/apoptotic and mixed/other. Performance was similar for brightfield and multi-channel inputs. Classifying all tumoroids according to the nearest morphology yielded an F1-score of 0.80 and balanced accuracy of 0.75, the most ambiguous class being mixed/other as expected. iCANdy was applied on brightfield images of 10.2 million tumoroids from a total of 22k drug treatment/control wells from 57 tumoroid models, using 37 drugs or drug combinations. For untreated controls, the dominant morphology in terms of total area varied across models with compact (39%), invasive (32%), single/apoptotic (12%), budding (9%), and cystic (thick-walled) (7%) being most common. We observed high intra-patient heterogeneity, the proportion of the dominant morphology ranging from 20% to 54% with a median of 36% (excluding single/apoptotic structures). Inter-patient heterogeneity was also associated with the activity of several drugs. While atorvastatin showed a similar single-oriented shift for both compact (82%) and invasive (100%) structures, other drugs such as 5-FU exhibited strong differences (68% and 18% respectively). Other compounds with limited impact on cell viability induced morphological phenotypes indicating mode-of-actions such as cytostasis or senescence. Interestingly, methotrexate caused an invasive-oriented shift for 80% of the cystic (thick-walled) structures but only 14% of the compact clusters. Conclusions: Quantitative phenotyping of tumoroids can identify novel predictive morphological relationships, drug mode-of-action and more accurately predict drug activity.
利益披露 Disclosure
P. Eide, Oncosyne AS Employment, Stock. N. Pasquir, Oncosyne AS Employment. C. A. Andreassen, Oncosyne AS Employment, Stock Option. A. H. Ree, None.. K. Augestad, None.. S. Meltzer, None. J. Bruun, Oncosyne AS Employment, Stock.

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