bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒10‒23
ten papers selected by
Sergio Marchini
Humanitas Research
  1. Replication stress generates distinctive landscapes of DNA copy number alterations and chromosome scale losses.
  2. Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient.
  3. SPASCER: spatial transcriptomics annotation at single-cell resolution.
  4. Practical Considerations for the Use of Circulating Tumor DNA in the Treatment of Patients With Cancer: A Narrative Review.
  5. Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.
  6. DeepST: identifying spatial domains in spatial transcriptomics by deep learning.
  7. Reimagining Cancer: Moving from the Cellular to the Tissue Level.
  8. Targeting STING for cancer immunotherapy: From mechanisms to translation.
  9. Computational Approaches and Challenges in Spatial Transcriptomics.
  10. Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.
  1. Genome Biol. 2022 Oct 20. 23(1): 223
    Replication stress generates distinctive landscapes of DNA copy number alterations and chromosome scale losses.
    Nadeem Shaikh, Alice Mazzagatti, Simone De Angelis, Sarah C Johnson, Bjorn Bakker, Diana C J Spierings, René Wardenaar, Eleni Maniati, Jun Wang, Michael A Boemo, Floris Foijer, Sarah E McClelland.
      BACKGROUND: A major driver of cancer chromosomal instability is replication stress, the slowing or stalling of DNA replication. How replication stress and genomic instability are connected is not known. Aphidicolin-induced replication stress induces breakages at common fragile sites, but the exact causes of fragility are debated, and acute genomic consequences of replication stress are not fully explored.RESULTS: We characterize DNA copy number alterations (CNAs) in single, diploid non-transformed cells, caused by one cell cycle in the presence of either aphidicolin or hydroxyurea. Multiple types of CNAs are generated, associated with different genomic regions and features, and observed copy number landscapes are distinct between aphidicolin and hydroxyurea-induced replication stress. Coupling cell type-specific analysis of CNAs to gene expression and single-cell replication timing analyses pinpointed the causative large genes of the most recurrent chromosome-scale CNAs in aphidicolin. These are clustered on chromosome 7 in RPE1 epithelial cells but chromosome 1 in BJ fibroblasts. Chromosome arm level CNAs also generate acentric lagging chromatin and micronuclei containing these chromosomes.
    CONCLUSIONS: Chromosomal instability driven by replication stress occurs via focal CNAs and chromosome arm scale changes, with the latter confined to a very small subset of chromosome regions, potentially heavily skewing cancer genome evolution. Different inducers of replication stress lead to distinctive CNA landscapes providing the opportunity to derive copy number signatures of specific replication stress mechanisms. Single-cell CNA analysis thus reveals the impact of replication stress on the genome, providing insights into the molecular mechanisms which fuel chromosomal instability in cancer.
    DOI:  https://doi.org/10.1186/s13059-022-02781-0
  2. Front Immunol. 2022 ;13 985187
    Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient.
    Yanyu Ren, Runrong Li, Hanxiao Feng, Jieying Xie, Lin Gao, Shuai Chu, Yan Li, Fanliang Meng, Yunshan Ning.
      Cancer recurrence and chemoresistance are the leading causes of death in high-grade serous ovarian cancer (HGSOC) patients. However, the unique role of the immune environment in tumor progression for relapsed chemo-resistant patients remains elusive. In single-cell resolution, we characterized a comprehensive multi-dimensional cellular and immunological atlas from tumor, ascites, and peripheral blood of a chemo-resistant patient at different stages of treatment. Our results highlight a role in recurrence and chemoresistance of the immunosuppressive microenvironment in ascites, including MDSC-like myeloid and hypo-metabolic γδT cells, and of peripheral CD8+ effector T cells with chemotherapy-induced senescent/exhaustive. Importantly, paired TCR/BCR sequencing demonstrated relative conservation of TCR clonal expansion in hyper-expanded CD8+ T cells and extensive BCR clonal expansion without usage bias of V(D)J genes after chemotherapy. Thus, our study suggests strategies for ameliorating chemotherapy-induced immune impairment to improve the clinical outcome of HGSOC.
    Keywords:  TCR/BCR repertoire; chemotherapy; clonal expansion; immune microenvironment; ovarian cancer; single-cell sequencing
    DOI:  https://doi.org/10.3389/fimmu.2022.985187
  3. Nucleic Acids Res. 2022 Oct 16. pii: gkac889. [Epub ahead of print]
    SPASCER: spatial transcriptomics annotation at single-cell resolution.
    Zhiwei Fan, Yangyang Luo, Huifen Lu, Tiangang Wang, YuZhou Feng, Weiling Zhao, Pora Kim, Xiaobo Zhou.
      In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
    DOI:  https://doi.org/10.1093/nar/gkac889
  4. JAMA Oncol. 2022 Oct 20.
    Practical Considerations for the Use of Circulating Tumor DNA in the Treatment of Patients With Cancer: A Narrative Review.
    Matthew G Krebs, Umberto Malapelle, Fabrice André, Luis Paz-Ares, Martin Schuler, David M Thomas, Gilad Vainer, Takayuki Yoshino, Christian Rolfo.
      Importance: Personalized medicine based on tumor profiling and identification of actionable genomic alterations is pivotal in cancer management. Although tissue biopsy is still preferred for diagnosis, liquid biopsy of blood-based tumor analytes, such as circulating tumor DNA, is a rapidly emerging technology for tumor profiling.Observations: This review presents a practical overview for clinicians and allied health care professionals for selection of the most appropriate liquid biopsy assay, specifically focusing on circulating tumor DNA and how it may affect patient treatment and case management across multiple tumor types. Multiple factors influence the analytical validity, clinical validity, and clinical utility of testing. This review provides recommendations and practical guidance for best practice. Current methodologies include polymerase chain reaction-based approaches and those that use next-generation sequencing (eg, capture-based profiling, whole exome, or genome sequencing). Factors that may influence utility include sensitivity and specificity, quantity of circulating tumor DNA, detection of a small vs a large panel of genes, and clonal hematopoiesis of indeterminate potential. Currently, liquid biopsy appears useful in patients unable to undergo biopsy or where mutations detected may be more representative of the predominant tumor burden than for tissue-based assays. Other potential applications may include screening, primary diagnosis, residual disease, local recurrence, therapy selection, or early therapy response and resistance monitoring.
    Conclusions and Relevance: This review found that liquid biopsy is increasingly being used clinically in advanced lung cancer, and ongoing research is identifying applications of circulating tumor DNA-based testing that complement tissue analysis across a broad range of clinical settings. Circulating tumor DNA technologies are advancing quickly and are demonstrating potential benefits for patients, health care practitioners, health care systems, and researchers, at many stages of the patient oncologic journey.
    DOI:  https://doi.org/10.1001/jamaoncol.2022.4457
  5. Cell Syst. 2022 Oct 19. pii: S2405-4712(22)00354-4. [Epub ahead of print]13(10): 786-797.e13
    Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics.
    Cong Ma, Uthsav Chitra, Shirley Zhang, Benjamin J Raphael.
      Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.
    Keywords:  conformal maps; gene expression; layered tissues; segmented regression; spatial variation; spatially resolved transcriptomics
    DOI:  https://doi.org/10.1016/j.cels.2022.09.002
  6. Nucleic Acids Res. 2022 Oct 17. pii: gkac901. [Epub ahead of print]
    DeepST: identifying spatial domains in spatial transcriptomics by deep learning.
    Chang Xu, Xiyun Jin, Songren Wei, Pingping Wang, Meng Luo, Zhaochun Xu, Wenyi Yang, Yideng Cai, Lixing Xiao, Xiaoyu Lin, Hongxin Liu, Rui Cheng, Fenglan Pang, Rui Chen, Xi Su, Ying Hu, Guohua Wang, Qinghua Jiang.
      Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.
    DOI:  https://doi.org/10.1093/nar/gkac901
  7. Cancer Res. 2022 Oct 20. pii: CAN-22-1601. [Epub ahead of print]
    Reimagining Cancer: Moving from the Cellular to the Tissue Level.
    Romano Demicheli, William Jm Hrushesky.
      The current universally accepted explanation of cancer origin and behavior, the somatic mutation theory, is cell-centered and rooted in perturbation of gene function independent of the external environmental context. However, tumors consist of various epithelial and stromal cell populations temporally and spatially organized into an integrated neoplastic community, and they can have properties similar to normal tissues. Accordingly, we review specific normal cellular and tissue traits and behaviors with adaptive temporal and spatial self-organization that result in ordered patterns and structures. A few recent theories have described these tissue-level cancer behaviors, invoking a conceptual shift from the cellular level and highlighting the need for methodological approaches based on the analysis of complex systems. We propose extending the analytical approach of regulatory networks to the tissue level and introduce the concept of "cancer attractors." These concepts require re-evaluation of cancer imaging and investigational approaches and challenge the traditional reductionist approach of cancer molecular biology.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-22-1601
  8. Int Immunopharmacol. 2022 Oct 14. pii: S1567-5769(22)00788-3. [Epub ahead of print]113(Pt A): 109304
    Targeting STING for cancer immunotherapy: From mechanisms to translation.
    Ruilei Huang, Qian Ning, Jihui Zhao, Xuhong Zhao, Luting Zeng, Yi Yi, Shengsong Tang.
      Cancer immunotherapy with immune checkpoint inhibitors has achieved unprecedented success in cancer treatment; However, only a subset of patients achieved clinical benefit from this treatment, underscoring the urgent need to identify new strategies to enhance the clinical efficacy of immune checkpoint inhibitors. Given the essential role of innate immunity in cancer immune surveillance, tremendous effort has been focused on the innate immune pathways that can be pharmacologically modulated to improve the clinical outcome of checkpoint inhibitors. The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway plays essential roles in host defense against cancers. Activation of the cGAS-STING signaling pathway induces the expression of type I interferons and proinflammatory cytokines, culminating in promotion of a robust adaptive antitumor immunity. As part of this innate immune signaling pathway, STING is ubiquitously expressed in immune and nonimmune cells. STING activation has been demonstrated to propagate the cancer immunity cycle, remodel the tumor microenvironment, and ultimately eliminate tumor cells. The immunomodulatory roles of STING enable it to be an appealing target for cancer immunotherapy. As such, STING agonists that are capable of triggering antitumor immune responses have been developed in recent years, and several of them have advanced into clinical trials. In this review, we first give an overview on the STING signaling pathway, then dissect the roles of STING activation in different steps of the cancer immunity cycle and finally discuss the development of STING agonists as well as challenges with STING activation, with the potential to make cancer immunotherapy with STING agonists more effective.
    Keywords:  Cancer immunity cycle; Cancer immunotherapy; STING agonists
    DOI:  https://doi.org/10.1016/j.intimp.2022.109304
  9. Genomics Proteomics Bioinformatics. 2022 Oct 14. pii: S1672-0229(22)00129-2. [Epub ahead of print]
    Computational Approaches and Challenges in Spatial Transcriptomics.
    Shuangsang Fang, Bichao Chen, Yong Zhang, Haixi Sun, Longqi Liu, Shiping Liu, Yuxiang Li, Xun Xu.
      The development of spatial transcriptomics technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these spatial transcriptomics technologies, which contains spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework towards the in-depth understanding of biological processes. However, algorithms designed specifically for spatial transcriptomics technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
    Keywords:  Computational approaches; Data interpretation; Data quality; Multi-omics integration; Spatial transcriptomics
    DOI:  https://doi.org/10.1016/j.gpb.2022.10.001
  10. J Pathol Inform. 2022 ;13 100105
    Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.
    Hongming Xu, Jean René Clemenceau, Sunho Park, Jinhwan Choi, Sung Hak Lee, Tae Hyun Hwang.
      Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival.
    Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.
    Keywords:  BLCA, Urothelial Bladder Carcinoma; Bladder cancer; Computational pathology; Spatial Heterogeneity; Survival prognosis; TCGA, The Cancer Genome Atlas Project; TIL, Tumor-Infiltrating Lymphocyte; TMB, Tumor Mutation Burden; Tumor Immune Microenvironment; Tumor mutation burden prediction; WSI, Whole Slide Image
    DOI:  https://doi.org/10.1016/j.jpi.2022.100105
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