Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer

Zhu, Ying; Ferri-Borgogno, Sammy; Sheng, Jianting; Yeung, Tsz-Lun; Burks, Jared K.; Cappello, Paola; Jazaeri, Amir A.; Kim, Jae-Hoon; Han, Gwan Hee; Birrer, Michael; Mok, Samuel C.; Wong, Stephen

doi:10.21203/rs.3.rs-67036/v1

Cited by 3 publications

(1 citation statement)

References 76 publications

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“…The TME, which is composed primarily of fibroblasts, endothelial cells, lymphocytic infiltrates, and extracellular matrix proteins, can directly affect cancer cell growth, migration, and differentiation and thus presents a unique opportunity for cancer diagnosis and treatment (7). The immune system is an important determinant of the TME; ongoing inflammation results in various immunologic gene products that create a favorable microenvironment for tumor growth and progression (8)(9)(10)(11)(12), and the presence of specific immune cell types, such as intratumor CD8 + T cells, is associated with improved survival in patients with various types of cancer, including ovarian cancer (8,9,13). These findings suggest that immune cell heterogeneity plays an important role in conferring the malignant phenotypes of cancer cells.…”

Section: Introductionmentioning

confidence: 99%

Long-term ovarian cancer survivors: spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface

Ferri-Borgogno

Zhu

Sheng

et al. 2022

Preprint

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Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The identification of predictive markers associated with tumors developed from these long-term survivors (LTS) is crucial to identifying therapeutic targets for the disease, and thus improving patient survival rates. Reports to date have not fully established the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer and its association with clinical outcomes. We used a spatial transcriptomics platform to generate spatially resolved transcript profiles in treatment naive advanced HGSC from LTS and short-term survivors (STS), and determined whether cancer-associated fibroblasts (CAFs) heterogeneity is associated with survival in patients with advanced HGSC. We integrated spatial transcriptomics with single-cell RNA sequencing data to distinguish tumor and stroma regions, and developed a method to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. In addition, we used multiplex immunohistochemistry techniques to validate our findings. We found that a specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlate with long-term survival in advanced HGSC patients. We also demonstrated that significant APOE- LRP5 crosstalk occurred at the stroma-tumor interface in tumor tissues from STS compared to LTS, suggesting that such crosstalk plays a crucial role in modulating the malignant phenotype of HGSC, and could serve as a predictive biomarker of patient survival.

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Section: Introductionmentioning

confidence: 99%

Long-term ovarian cancer survivors: spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface

Ferri-Borgogno

Zhu

Sheng

et al. 2022

Preprint

Self Cite

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Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer

Ruchitha¹,

Sai²,

Kodipalli³

et al. 2022

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)

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A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Pandya

Thakur

Saxena

et al. 2021

Sensors

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The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

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Deep learning on image-omics data in identifying prognostic immune biomarkers for ovarian cancer

Cited by 3 publications

References 76 publications

Long-term ovarian cancer survivors: spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface

Long-term ovarian cancer survivors: spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface

Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

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