bims-tumhet 2023-08-13 papers

Issue of 2023–08–13
six papers selected by
Sergio Marchini, Humanitas Research

Brief Bioinform. 2023 Aug 07. pii: bbad278. [Epub ahead of print]

Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks.

Xuejing Shi, Juntong Zhu, Yahui Long, Cheng Liang.

MOTIVATION: Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging.
RESULTS: To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.

Keywords: different similarity measures; multi-view graph convolution networks; spatial domain identification; spatially resolved transcriptomics

DOI: https://doi.org/10.1093/bib/bbad278

Cold Spring Harb Perspect Med. 2023 Aug 08. pii: a041314. [Epub ahead of print]

The Emerging Role of the Single-Cell and Spatial Tumor Microenvironment in High-Grade Serous Ovarian Cancer.

Inga-Maria Launonen, Anna Vähärautio, Anniina Färkkilä.

The development of single-cell and spatial technologies has enabled a more detailed understanding of the tumor microenvironment and its role in therapy response and clinical outcome of high-grade serous ovarian cancer (HGSC). Interestingly, emerging evidence suggests that HGSCs with different genetic drivers harbor distinct tumor-immune microenvironments. Further, spatial cell-cell interactions have been shown to shape the CD8+ T-cell phenotypes and responses to immune checkpoint blockade therapies. The heterogeneous stroma consisting of cancer-associated fibroblast (CAF) subtypes, endothelia, and site-specific stromal types such as mesothelium modulates treatment responses via increasing stiffness and by producing ligands that promote drug resistance, angiogenesis, or immune escape. Chemotherapy itself shifts CAFs toward an inflammatory phenotype that associates with poor survival and immune-suppressive signaling. New emerging immunotherapies include combinational approaches and agents targeting, for example, the tumor-intrinsic endoplasmic reticulum pathway. A more detailed understanding of the spatial interplay of tumor, immune, and stromal cells in the tumor microenvironment is needed to develop more efficient immunotherapeutic strategies for HGSC.

DOI: https://doi.org/10.1101/cshperspect.a041314

bioRxiv. 2023 Jul 25. pii: 2023.07.24.549256. [Epub ahead of print]

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data.

Danni Luo, Sophie Robertson, Yuanchun Zhan, Ruichen Rong, Shidan Wang, Xi Jiang, Sen Yang, Suzette Palmer, Liwei Jia, Qiwei Li, Guanghua Xiao, Xiaowei Zhan.

Motivation: Spatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing and visualizing large image datasets together with molecular information.
Results: We introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers.
Availability: ScopeViewer is available at: https://datacommons.swmed.edu/scopeviewer.
Contact: Xiaowei.Zhan@UTSouthwestern.edu , Guanghua.Xiao@UTSouthwestern.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1101/2023.07.24.549256

Med Oncol. 2023 Aug 10. 40(9): 265

Liquid biopsy in ovarian cancer: advantages and limitations for prognosis and diagnosis.

Farzaneh Karimi, Omid Azadbakht, Ali Veisi, Mohammad Sabaghan, Maryam Owjfard, Ebrahim Kharazinejad, Negar Dinarvand.

Ovarian cancer (OC) is a highly fatal gynecologic malignancy, often diagnosed at an advanced stage which presents significant challenges for disease management. The clinical application of conventional tissue biopsy methods and serological biomarkers has limitations for the diagnosis and prognosis of OC patients. Liquid biopsy is a novel sampling method that involves analyzing distinctive tumor elements secreted into the peripheral blood. Growing evidence suggests that liquid biopsy methods such as circulating tumor cells, cell-free RNA, circulating tumor DNA, exosomes, and tumor-educated platelets may improve early prognosis and diagnosis of OC, leading to enhanced therapeutic management of the disease. This study reviewed the evidence demonstrating the utility of liquid biopsy components in OC prognosis and diagnosis, and evaluated the current advantages and limitations of these methods. Additionally, the existing obstacles and crucial topics for future studies utilizing liquid biopsy in OC patients were discussed.

Keywords: Diagnosis; Extracellular vesicles; Liquid biopsy; Ovarian cancer; Prognosis

DOI: https://doi.org/10.1007/s12032-023-02128-0

Genome Res. 2023 Aug 08.

Partial alignment of multislice spatially resolved transcriptomics data.

Xinhao Liu, Ron Zeira, Benjamin J Raphael.

Spatially resolved transcriptomics (SRT) technologies measure messenger RNA (mRNA) expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two-dimensional (2D) slices extracted from a 3D tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multislice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated because of biological or technical reasons. We introduce PASTE2, a method for partial alignment and 3D reconstruction of multislice SRT data sets, allowing only partial overlap between aligned slices and/or slice-specific cell types. PASTE2 formulates a novel partial fused Gromov-Wasserstein optimal transport problem, which we solve using a conditional gradient algorithm. PASTE2 includes a model selection procedure to estimate the fraction of overlap between slices, and optionally uses information from histological images that accompany some SRT experiments. We show on both simulated and real data that PASTE2 obtains more accurate alignments than existing methods. We further use PASTE2 to reconstruct a 3D map of gene expression in a Drosophila embryo from a 16 slice Stereo-seq data set. PASTE2 produces accurate alignments of multislice data sets from multiple SRT technologies, enabling detailed studies of spatial gene expression across a wide range of biological applications.

DOI: https://doi.org/10.1101/gr.277670.123

Cancers (Basel). 2023 Jul 28. pii: 3849. [Epub ahead of print]15(15):

Tumor Immune Microenvironment in Gynecologic Cancers.

Daniel Margul, Camilla Yu, Mariam M AlHilli.

Gynecologic cancers have varying response rates to immunotherapy due to the heterogeneity of each cancer's molecular biology and features of the tumor immune microenvironment (TIME). This article reviews key features of the TIME and its role in the pathophysiology and treatment of ovarian, endometrial, cervical, vulvar, and vaginal cancer. Knowledge of the role of the TIME in gynecologic cancers has been rapidly developing with a large body of preclinical studies demonstrating an intricate yet dichotomous role that the immune system plays in either supporting the growth of cancer or opposing it and facilitating effective treatment. Many targets and therapeutics have been identified including cytokines, antibodies, small molecules, vaccines, adoptive cell therapy, and bacterial-based therapies but most efforts in gynecologic cancers to utilize them have not been effective. However, with the development of immune checkpoint inhibitors, we have started to see the rapid and successful employment of therapeutics in cervical and endometrial cancer. There remain many challenges in utilizing the TIME, particularly in ovarian cancer, and further studies are needed to identify and validate efficacious therapeutics.

Keywords: cervical cancer; checkpoint inhibitors; endometrial cancer; gynecologic oncology; ovarian cancer; pembrolizumab; tumor immune microenvironment; tumor infiltrating lymphocytes; vaginal cancer; vulvar cancer

DOI: https://doi.org/10.3390/cancers15153849