Lung adenocarcinoma (LUAD), the most common type of lung cancer, has an average 5-year survival rate of 15%. In LUAD, interaction between tumor and immune cells has been shown to be highly associated with the likelihood of disease progression and metastases. We have previously demonstrated the association between spatial architecture and arrangement of tumor-infiltrating lymphocytes (TILs) with likelihood of recurrence in early stage NSCLC. Recently, gene set enrichment analysis-derived immune scores have been found to be prognostic of outcome. However, this requires transcriptomics techniques as a precursor, which involves mechanical disruption of cells and tissues. In this work (N = 170), we extracted graph-based histomorphometric features on segmented nuclei from digitized H and E biopsy images and then performed principal component analysis (PCA) to select the most representative tiles from each patient. We then identified TILs and quantitative histomorphometric attributes of different nuclei groups (all-nuclei, TILs, non-TILs) prognostic of overall patient survival (OS) and further investigated their associations with immune scores and biological pathways implicated immune response using gene-set enrichment analysis (GSEA). We found TIL-compactness (a set of TIL density features) derived risk scores were prognostic of OS (Hazard Ratio (HR) = 3.26, p = 0.012, C-index = 0.634). The median immune score (IS) in the cohort was used as a threshold to divide the cases into low and high IS expression groups. The TIL compactness measures prognostic of OS were also statistically significantly correlated with the IS and biological pathways related to immune response (Immune System Process, Immune Response, Adaptive Immune Response, and Humoral Immune Response Mediated by Circulating Immunoglobulin).
A number of papers have established that a high density of tumor-infiltrating lymphocytes (TILs) is highly correlated with a better prognosis for many different cancer types. More recently, some studies have shown that the spatial interplay between different subtypes of TILs (e.g. CD3, CD4, CD8) is more prognostic of disease outcome compared to just metrics related to TIL density. A challenge with TIL subtyping is that it relies on quantitative immunofluoresence or immunohistochemistry, complex and tissue-destructive technologies. In this paper we present a new approach called PhenoTIL to identify TIL sub-populations and quantify the interplay between these sub-populations and show the association of these interplay features with recurrence in early stage lung cancer. The approach comprises a Dirichlet Process Gaussian Mixture Model that clusters lymphocytes on H&E images. The approach was evaluated on a cohort of N=178 early stage non-small cell lung cancer patients, N=100 being used for model training and N=78 being used for independent validation. A Linear Discriminant Analysis classifier was trained in conjunction with 186 PhenoTIL features to predict the likelihood of recurrence in the test set. The PhenoTIL features yielded an AUC=0.84 compared to an approach involving just TIL density alone (AUC=0.58). In addition, a Kaplan-Meier analysis showed that the PhenoTIL features were able to statistically significantly distinguish early from late recurrence (p = 4 ∗ 10 −5 ).
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