Abstract
BackgroundThe immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor.MethodsImmunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The ISBC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.ResultsAn ISBC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the ISBC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49–3.93; P<0.01). A radiomic signature of 21 features of the ISBC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).ConclusionsThe RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.
Highlights
The tumor immune microenvironment (TIME) displays key actions in tumor development, metastasis, and the response to therapy [1, 2]
Radiography entails a wealth of knowledge comprising tumor phenotypes [8] that are controlled by the inherent biology of tumor cells and regulated by the tumor microenvironment (TME)
Following application of the datascreening criterion, overall survival data from 335 clinically annotated breast cancer specimens were accessible for additional analyses
Summary
The tumor immune microenvironment (TIME) displays key actions in tumor development, metastasis, and the response to therapy [1, 2]. Many researchers have confirmed the prognosis and potentially predictive importance of the infiltration of immune cells into tumors [3–6]. The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor. Methods: Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis
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