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Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8⁺ tumor-infiltrating lymphocytes in breast cancer.

ABSTRACT: Infiltration of CD8⁺ T cells and their spatial contexture, represented by immunophenotype, predict the prognosis and therapeutic response in breast cancer. However, a non-surgical method using radiomics to evaluate breast cancer immunophenotype has not been explored. Here, we assessed the CD8⁺ T cell-based immunophenotype in patients with breast cancer undergoing upfront surgery (n = 182). We extracted radiomic features from the four phases of dynamic contrast-enhanced magnetic resonance imaging, and randomly divided the patients into training (n = 137) and validation (n = 45) cohorts. For predicting the immunophenotypes, radiomic models (RMs) that combined the four phases demonstrated superior performance to those derived from a single phase. For discriminating the inflamed tumor from the non-inflamed tumor, the feature-based combination model from the whole tumor (RM-whole_FC) showed high performance in both training (area under the receiver operating characteristic curve [AUC] = 0.973) and validation cohorts (AUC = 0.985). Similarly, the feature-based combination model from the peripheral tumor (RM-peri_FC) discriminated between immune-desert and excluded tumors with high performance in both training (AUC = 0.993) and validation cohorts (AUC = 0.984). Both RM-whole_FC and RM-peri_FC demonstrated good to excellent performance for every molecular subtype. Furthermore, in patients who underwent neoadjuvant chemotherapy (n = 64), pre-treatment images showed that tumors exhibiting complete response to neoadjuvant chemotherapy had significantly higher scores from RM-whole_FC and lower scores from RM-peri_FC. Our RMs predicted the immunophenotype of breast cancer based on the spatial distribution of CD8⁺ T cells with high accuracy. This approach can be used to stratify patients non-invasively based on the status of the tumor-immune microenvironment.

SUBMITTER: Jeon SH

PROVIDER: S-EPMC9806253 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer.

Jeon Seung Hyuck SH Kim So-Woon SW Na Kiyong K Seo Mirinae M Sohn Yu-Mee YM Lim Yu Jin YJ

Frontiers in immunology 20221219

Infiltration of CD8+ T cells and their spatial contexture, represented by immunophenotype, predict the prognosis and therapeutic response in breast cancer. However, a non-surgical method using radiomics to evaluate breast cancer immunophenotype has not been explored. Here, we assessed the CD8+ T cell-based immunophenotype in patients with breast cancer undergoing upfront surgery (n = 182). We extracted radiomic features from the four phases of dynamic contrast-enhanced magn ...[more]

PMID: 36601118

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Project description:BackgroundWe sought to investigate associations between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features and tumor-infiltrating lymphocytes (TILs) in breast cancer, as well as to study if MRI features are complementary to molecular markers of TILs.MethodsIn this retrospective study, we extracted 17 computational DCE-MRI features to characterize tumor and parenchyma in The Cancer Genome Atlas cohort (n = 126). The percentage of stromal TILs was evaluated on H&E-stained histological whole-tumor sections. We first evaluated associations between individual imaging features and TILs. Multiple-hypothesis testing was corrected by the Benjamini-Hochberg method using false discovery rate (FDR). Second, we implemented LASSO (least absolute shrinkage and selection operator) and linear regression nested with tenfold cross-validation to develop an imaging signature for TILs. Next, we built a composite prediction model for TILs by combining imaging signature with molecular features. Finally, we tested the prognostic significance of the TIL model in an independent cohort (I-SPY 1; n = 106).ResultsFour imaging features were significantly associated with TILs (P < 0.05 and FDR < 0.2), including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding background parenchymal enhancement (BPE), and proportion of BPE. Among molecular and clinicopathological factors, only cytolytic score was correlated with TILs (ρ = 0.51; 95% CI, 0.36-0.63; P = 1.6E-9). An imaging signature that linearly combines five features showed correlation with TILs (ρ = 0.40; 95% CI, 0.24-0.54; P = 4.2E-6). A composite model combining the imaging signature and cytolytic score improved correlation with TILs (ρ = 0.62; 95% CI, 0.50-0.72; P = 9.7E-15). The composite model successfully distinguished low vs high, intermediate vs high, and low vs intermediate TIL groups, with AUCs of 0.94, 0.76, and 0.79, respectively. During validation (I-SPY 1), the predicted TILs from the imaging signature separated patients into two groups with distinct recurrence-free survival (RFS), with log-rank P = 0.042 among triple-negative breast cancer (TNBC). The composite model further improved stratification of patients with distinct RFS (log-rank P = 0.0008), where TNBC with no/minimal TILs had a worse prognosis.ConclusionsSpecific MRI features of tumor and parenchyma are associated with TILs in breast cancer, and imaging may play an important role in the evaluation of TILs by providing key complementary information in equivocal cases or situations that are prone to sampling bias.

Project description:BackgroundThe tumor immune microenvironment, particularly tumor-infiltrating lymphocytes (TILs), plays a critical role in disease progression and treatment response in triple-negative breast cancers (TNBCs). This study was aimed to characterize the composition of TILs and investigate their clinicopathological and prognostic significance with a special focus on the spatial distribution of TILs in TNBCs.MethodsWe analyzed TNBC samples through PanCancer Immune Profiling using NanoString nCounter assays to identify immune-related genes that are expressed differentially in relation to TIL levels and evaluated protein expression of selected markers through immunohistochemical staining on tissue microarrays. For a comprehensive assessment of the expression of cytotoxic T lymphocyte (CTL) and natural killer (NK) cell markers, a CTL-NK score was devised based on CD8+, CD56+, CD57+, GNLY+, and GZMB+ TIL levels.ResultsGene expression analysis revealed significant upregulation of CTL and NK cell-associated genes including GNLY, KLRC2, and GZMB in TIL-high TNBCs. Immunohistochemical validation confirmed that TNBCs with higher TILs had a greater amount of CD56+, CD57+, GNLY+, and GZMB+ TILs not only in absolute number but also in proportion relative to CD4+ or CD8+ TILs. High TIL and its subset (CD4+, CD8+, CD56+, CD57+, GNLY+, and GZMB+ TIL) infiltration correlated with favorable clinicopathological features of tumor. In survival analysis, high CTL-NK score was found to be an independent prognostic factor for better disease-free survival (DFS) of the patients. Furthermore, uniformly high TIL infiltration was linked to better DFS, whereas cases with heterogeneous TIL infiltration showed no difference in survival compared to those with uniformly low TIL infiltration.ConclusionOur study showed that CTL and NK cell-associated gene expression and protein levels differ significantly according to TIL levels and that CTL-NK score and distribution of TILs within tumors have a prognostic value. These findings emphasize the importance of CTLs and NK cells as well as the spatial uniformity of TIL infiltration in clinical outcome of TNBC patients, providing valuable insights for refining prognostic assessments and guiding immunotherapeutic strategies.

Dataset Information

Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8⁺ tumor-infiltrating lymphocytes in breast cancer.

Publications

Radiomic models based on magnetic resonance imaging predict the spatial distribution of CD8<sup>+</sup> tumor-infiltrating lymphocytes in breast cancer.

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