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Adenocarcinoma, the most common histologic variant of lung cancer, is morphologically diverse. The International Association for the Study of Lung Cancer (IASLC) grading system, based on the percentages of growth patterns within the tumour, is highly prognostic (Moreira et al. 2020). However, the clinicopathological significance of transitions between growth patterns, and the combinatorial effects of growth pattern and inflammatory cell infiltration are not yet known. We used a deep learning model to delineate six growth patterns (lepidic, acinar, papillary, micropapillary, solid, and cribriform) at pixel level on hematoxylin and eosin diagnostic whole slide images. The model was trained on 49 slides from the TRACERx cohort (AbdulJabbar et al. 2020), and subsequently applied to 4324 slides from 970 adenocarcinoma cases from the Leicester Archival Thoracic Tumor Investigatory Cohort (Moore et al. 2019). To examine how tumor growth patterns are spatially intermixed, we created a graph network of growth patterns. A linking criterion based on effective cell-cell communication distance was established, whereby adjacent compact tumor islands were linked together. Frequencies of 15 types of pairwise links were further evaluated. A higher intermixing score, measured as the Shannon diversity of link percentages, was associated with adverse relapse free survival (RFS) (p Citation Format: Anca-Ioana Grapa, Hanyun Zhang, Xiaoxi Pan, Khalid AbdulJabbar, Jose Coelho-Lima, Ho Kwan Alvin Cheung, Sarah J. Aitken, David A. Moore, Charles Swanton, John Le Quesne, Yinyin Yuan. Clinical relevance of spatial intermixing of growth patterns and immune infiltration in lung adenocarcinoma-from TRACERx to LATTICe-A [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB153. |
