Dataset Information

Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer.

ABSTRACT: Crohn's-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score were obtained according to the related density. And the CLR-I score was calculated by summing two scores. The development (Hospital 1, N = 370) and validation (Hospital 2 & 3, N = 144) cohorts were used to evaluate the prognostic value of the CLR-I score. The C-index and integrated area under the curve were used to assess the discrimination ability. A higher CLR-I score was associated with a better prognosis, which was identified by multivariable analysis in the development (hazard ratio for score 3 vs score 0 = 0.22, 95% confidence interval 0.12-0.40, P < 0.001) and validation cohort (0.21, 0.05-0.78, P = 0.020). The AI-based CLR-I score outperforms the single predictor in predicting OS which is objective and more prone to be deployed in clinical practice.

SUBMITTER: Xu Y

PROVIDER: S-EPMC9568693 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer.

Xu Yao Y Yang Shangqing S Zhu Yaxi Y Yao Su S Li Yajun Y Ye Huifen H Ye Yunrui Y Li Zhenhui Z Wu Lin L Zhao Ke K Huang Liyu L Liu Zaiyi Z

Computational and structural biotechnology journal 20221006

Crohn's-like lymphoid reaction (CLR) and tumor-infiltrating lymphocytes (TILs) are crucial for the host antitumor immune response. We proposed an artificial intelligence (AI)-based model to quantify the density of TILs and CLR in immunohistochemical (IHC)-stained whole-slide images (WSIs) and further constructed the CLR-I (immune) score, a tissue level- and cell level-based immune factor, to predict the overall survival (OS) of patients with colorectal cancer (CRC). The TILs score and CLR score ...[more]

PMID: 36284712

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Project description:BackgroundArtificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology.MethodsThe study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.ResultsThe evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037).ConclusionsWe quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

Project description:BackgroundCholangiocarcinoma is a challenging malignancy with limited responses to conventional therapies, particularly immune checkpoint inhibitor therapy. Tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLSs) are key components of the tumor microenvironment (TME) and have been implicated in the immune response to cancer. However, the role and difference of TLSs and TILs in patients with cholangiocarcinoma remains unclear. This study elucidates their contributions to the TME.MethodsWe examined 16 tumor samples from a single-arm, phase II trial of nivolumab plus modified gemcitabine and S-1 and various datasets. Immunohistochemistry and RNA sequencing were employed to assess TLSs and TILs presence and activity. Differential gene expression and signature of immune cell composition were examined by GeoMx Digital Spatial Profiler and Cancer Transcriptome Altas analysis.ResultsTLS-positive (N=7) patients demonstrated significantly better immunotherapy outcomes compared with TLS-negative (N=9) patients, including higher objective response rates (71% vs 0%) and disease control rates (100% vs 67%). The presence of TLSs correlated with improved progression-free and overall survival (p=0.03). TLSs were associated with "inflamed" tumors characterized by substantial immune infiltration, particularly involving T and B cells. Gene expression analyses identified significant upregulation of B cell-related genes in TLSs. Additionally, TLSs exhibited higher properties of memory B cells and myeloid dendritic cells but lower levels of innate immune cells compared with TILs. T cells within TLSs showed elevated expression of precursor-exhausted-related genes and lower cytotoxicity signature. Furthermore, TILs in TLS-positive tumors had higher levels of exhaustion signatures compared with TILs in TLS-negative tumors. Clinical data corroborated these findings, with higher PD-L1 and LAG-3 expression in TLS-positive tumors.ConclusionOur findings revealed that TILs in TLS-positive tumors have more exhausted T cell signature and PD-1 and LAG-3 protein expression in CCA which support our clinical finding. TLSs can predict favorable immunotherapy responses in patients with cholangiocarcinoma, highlighting their potential as a biomarker and therapeutic target to enhance treatment efficacy.

Dataset Information

Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer.

Publications

Artificial intelligence for quantifying Crohn's-like lymphoid reaction and tumor-infiltrating lymphocytes in colorectal cancer.

Similar Datasets

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