Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer

Wu, Rui; Guo, Wu; Qiu, Xinyao; Wang, Shicheng; Sui, Chengjun; Lian, Qiuyu; Wu, Jianmin; Shan, Yiran; Zhao, Yang; Yang, Shuai; Wu, Tong; Wang, Kaiting; Zhu, Yanjing; Wang, Shan; Liu, Changyi; Zhang, Yangqianwen; Zheng, Bo; Li, Zhixuan; Zhang, Yani; Shen, Siyun; Zhao, Yan; Wang, Wenwen; Bao, Jinxia; Hu, Ji; Wu, Xuan; Jiang, Xiaoqing; Wang, Hongyang; Gu, Jin; Chen, Lei

doi:10.1101/2021.05.24.445446

Cited by 16 publications

(27 citation statements)

References 65 publications

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“…Considering that numerous studies have achieved multi-class tissue segmentation across various cancer types 38,39 , further exploration of the impact of these ready-made segmentation maps on prognosis may lead to new discoveries. Moreover, benefiting from its simple and easy-to-use features, PathFinder can be easily migrated to similar tasks such as spatial multi-omics and three-dimensional pathological prognosis to discover new biomarkers in different modalities [40][41][42] . We expect Pathfinder as a fundamental mechanism to better integrate the two fields of clinical prognosis and AI, and inspire more meaningful discoveries.…”

Section: Discussionmentioning

confidence: 99%

AI inspired discovery of new biomarkers for clinical prognosis of liver cancer

Liang

Zhang

Yang

et al. 2022

Preprint

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Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment, and treatment planning. However, few of current biomarkers used in clinics are robust enough to show a true analytical and clinical value. Thus the search for additional tissue biomarkers, including the strategies to identify them, is imperative. Recently, the capabilities of deep learning (DL)-based computational pathology in cancer diagnosis and prognosis have been explored, but the limited interpretability and generalizability make the results difficult to be accepted in clinical practice. Here we present an interpretable human-centric DL-guided framework—PathFinder (Pathological-biomarker-finder)— that can inspire pathologists to discover new tissue biomarkers from well-performing DL models, which bridges the gap between DL and clinical prognosis. By combining sparse multi-class tissue spatial distribution information of whole slide images (WSIs) with attribution methods, PathFinder can achieve localization, characterization, and verification of potential biomarkers, while guaranteeing state-of-the-art prognostic performance. With the inspiration of PathFinder, we discovered that tumor necrosis in liver cancer, a long-neglected factor, has a strong relationship with patient prognosis. Thus we proposed two clinically independent indicators, including necrosis area fraction and tumor necrosis distribution, for practical prognosis, and verified their potentials in clinical prognosis according to Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)-derived criteria. Our work demonstrates a successful example of introducing artificial intelligence (AI) into clinical practice in a knowledge discovery way, which can be adopted in identifying biomarkers in various cancer types and modalities.

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Section: Discussionmentioning

confidence: 99%

AI inspired discovery of new biomarkers for clinical prognosis of liver cancer

Liang

Zhang

Yang

et al. 2022

Preprint

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“…A study applied the ST technology in the tumor tissues with 4 LUAD and 8 LUSC from 12 NSCLC patients, tracked the subclone of the specific sate of tumors through spatial pseudo time, and further described the process of epithelial-mesenchymal transition in detail [265]. Besides, the ST technology of 21 primary tumor tissues from 7 HCC patients found that the fibroblasts and endothelial cells can form a barrier-like capsules to prevent immune cell infiltration which may hinder immunotherapy [266]. In NPC, the efficacy of PD-1 inhibitor treatment can be improved by combination therapy, plus systemic therapy in first-line therapy [267] or radiotherapy [268].…”

Section: Single-cell and Spatial Transcriptomics In Time And Stie Res...mentioning

confidence: 99%

Reshaping the systemic tumor immune environment (STIE) and tumor immune microenvironment (TIME) to enhance immunotherapy efficacy in solid tumors

Zou

Zhang

et al. 2022

J Hematol Oncol

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The development of combination immunotherapy based on the mediation of regulatory mechanisms of the tumor immune microenvironment (TIME) is promising. However, a deep understanding of tumor immunology must involve the systemic tumor immune environment (STIE) which was merely illustrated previously. Here, we aim to review recent advances in single-cell transcriptomics and spatial transcriptomics for the studies of STIE, TIME, and their interactions, which may reveal heterogeneity in immunotherapy responses as well as the dynamic changes essential for the treatment effect. We review the evidence from preclinical and clinical studies related to TIME, STIE, and their significance on overall survival, through different immunomodulatory pathways, such as metabolic and neuro-immunological pathways. We also evaluate the significance of the STIE, TIME, and their interactions as well as changes after local radiotherapy and systemic immunotherapy or combined immunotherapy. We focus our review on the evidence of lung cancer, hepatocellular carcinoma, and nasopharyngeal carcinoma, aiming to reshape STIE and TIME to enhance immunotherapy efficacy.

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“…1A). All of these public datasets were collected from the 10x Genomics website (Methods) except the liver samples, which were collected from [13]. Main quality statistics, such as the final number of genes and spots for each processed VISIUM data, were considered sufficient for a reliable downstream analysis (Table S1).…”

Section: Spatial Transcriptomics Illustrates the Spatial Variability ...mentioning

confidence: 99%

“…Fifteen publicly available VISIUM tumor datasets from seven tissues (Fig. 1A) were downloaded from a recent publication [13] (three liver leading-edge tumor samples; HCC-1L, cHC-1L, ICC-1L) and the 'spatial gene expression' panel of 10x Genomics (https://www.10xgenomics.com/resources/datasets). The samples were obtained from the following sections: 'Visium Spatial Targeted Demonstration Data (v1)', 'Visium Spatial Fluorescent Demonstration (v1)', 'Visium Spatial for FFPE Demonstration (v1)'.…”

Section: Sequencing Data Processing and Filteringmentioning

confidence: 99%

The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology

Sibai

Cervilla

Grases

et al. 2022

Preprint

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Cancer Hallmarks have been indispensable to rationalize the complex phenotypic properties acquired by cancer cells and their surrounding tumor microenvironment (TME) as they progress from normalcy to malignancy. While great insights have been gained from their use to understand bulk tissue transcriptomics, our knowledge remained incomplete, as it was not possible to determine the origin and relative localization of the activity of each Cancer Hallmark within tumor tissues. Thanks to recent advances in spatial transcriptomics, we can now explore the spatial contribution of each compartment within a tumor to each Cancer Hallmark at near single-cell resolution. Here, we used spatial transcriptomics from fifteen different primary tumor samples from seven tissues to quantify the contribution of the main tumor compartments -cancer cells and the TME- to each of thirteen Cancer Hallmarks. Our results revealed a Pan-Cancer architecture: with the activity of some hallmarks consistently coming from the cancer compartment across all cancer types (Cancer-associated hallmarks) and the activity of others consistently coming from the TME (TME-associated hallmarks). Furthermore, we observed that even within each compartment (cancer or TME) there was a consistent internal architecture, as many of these hallmarks exhibited higher activity at the border between compartments. Our study provides the first spatial map of Cancer Hallmark activities in tumors at almost cellular resolution, paving the way for a better understanding of the underpinnings of hallmark acquisition by tumor cell types.

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Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer

Cited by 16 publications

References 65 publications

AI inspired discovery of new biomarkers for clinical prognosis of liver cancer

AI inspired discovery of new biomarkers for clinical prognosis of liver cancer

Reshaping the systemic tumor immune environment (STIE) and tumor immune microenvironment (TIME) to enhance immunotherapy efficacy in solid tumors

The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology

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