bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023–06–11
eight papers selected by
Sergio Marchini, Humanitas Research
  1. Rare Epithelial Ovarian Cancers: Low Grade Serous and Mucinous Carcinomas.
  2. Spatial transcriptomics: new dimension of understanding biological complexity.
  3. Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.
  4. JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies.
  5. Defining and using immune archetypes to classify and treat cancer.
  6. Comprehensive Review of Serous Tumors of Tubo-Ovarian Origin: Clinical Behavior, Pathological Correlation, Current Molecular Updates, and Imaging Manifestations.
  7. The technological landscape and applications of single-cell multi-omics.
  8. DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.
  1. Cold Spring Harb Perspect Med. 2023 Jun 05. pii: a038190. [Epub ahead of print]
    Rare Epithelial Ovarian Cancers: Low Grade Serous and Mucinous Carcinomas.
    Olivia Craig, Abhimanyu Nigam, Genevieve V Dall, Kylie Gorringe.
      The ovarian epithelial cancer histotypes can be divided into common and rare types. Common types include high-grade serous ovarian carcinomas and the endometriosis-associated cancers, endometrioid and clear-cell carcinomas. The less common histotypes are mucinous and low-grade serous, each comprising less than 10% of all epithelial carcinomas. Although histologically and epidemiologically distinct from each other, these histotypes share some genetic and natural history features that distinguish them from the more common types. In this review, we will consider the similarities and differences of these rare histological types, and the clinical challenges they pose.
    DOI:  https://doi.org/10.1101/cshperspect.a038190
  2. Biophys Rep. 2022 Jun 30. 8(3): 119-135
    Spatial transcriptomics: new dimension of understanding biological complexity.
    Zhuxia Li, Guangdun Peng.
      Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.
    Keywords:  Histology; Single-cell sequencing; Spatial data analysis; Spatial multi-omics; Spatial transcriptomics
    DOI:  https://doi.org/10.52601/bpr.2021.210037
  3. bioRxiv. 2023 May 24. pii: 2023.05.24.542174. [Epub ahead of print]
    Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.
    Sasha Thalluri, Vishal H Oza, Tabea M Soelter, Jennifer L Fisher, Brittany N Lasseigne.
       Background: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment.
    Aims: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA).
    Methods and results: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex.
    Conclusions: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing to other studies.
    DOI:  https://doi.org/10.1101/2023.05.24.542174
  4. Bioinformatics. 2023 Jun 05. pii: btad366. [Epub ahead of print]
    JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies.
    Pengfei Lyu, Yan Li, Xiaoquan Wen, Hongyuan Cao.
       MOTIVATION: Replicability is the cornerstone of scientific research. The current statistical method for high-dimensional replicability analysis either cannot control the false discovery rate (FDR) or is too conservative.
    RESULTS: We propose a statistical method, JUMP, for the high-dimensional replicability analysis of two studies. The input is a high-dimensional paired sequence of p-values from two studies and the test statistic is the maximum of p-values of the pair. JUMP uses four states of the p-value pairs to indicate whether they are null or non-null. Conditional on the hidden states, JUMP computes the cumulative distribution function of the maximum of p-values for each state to conservatively approximate the probability of rejection under the composite null of replicability. JUMP estimates unknown parameters and uses a step-up procedure to control FDR. By incorporating different states of composite null, JUMP achieves a substantial power gain over existing methods while controlling the FDR. Analyzing two pairs of spatially resolved transcriptomic datasets, JUMP makes biological discoveries that otherwise cannot be obtained by using existing methods.
    AVAILABILITY: An R package JUMP implementing the JUMP method is available on CRAN (https://CRAN.R-project.org/package=JUMP).
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btad366
  5. Nat Rev Cancer. 2023 Jun 05.
    Defining and using immune archetypes to classify and treat cancer.
    Alexis J Combes, Bushra Samad, Matthew F Krummel.
      Tumours are surrounded by a host immune system that can suppress or promote tumour growth. The tumour microenvironment (TME) has often been framed as a singular entity, suggesting a single type of immune state that is defective and in need of therapeutic intervention. By contrast, the past few years have highlighted a plurality of immune states that can surround tumours. In this Perspective, we suggest that different TMEs have 'archetypal' qualities across all cancers - characteristic and repeating collections of cells and gene-expression profiles at the level of the bulk tumour. We discuss many studies that together support a view that tumours typically draw from a finite number (around 12) of 'dominant' immune archetypes. In considering the likely evolutionary origin and roles of these archetypes, their associated TMEs can be predicted to have specific vulnerabilities that can be leveraged as targets for cancer treatment with expected and addressable adverse effects for patients.
    DOI:  https://doi.org/10.1038/s41568-023-00578-2
  6. Curr Probl Diagn Radiol. 2023 May 14. pii: S0363-0188(23)00074-9. [Epub ahead of print]
    Comprehensive Review of Serous Tumors of Tubo-Ovarian Origin: Clinical Behavior, Pathological Correlation, Current Molecular Updates, and Imaging Manifestations.
    Susan M Folsom, Jessica Berger, T Rinda Soong, Balasubramanya Rangaswamy.
      Ovarian cancer is the eighth most common women's cancer worldwide, with the highest mortality rate of any gynecologic malignancy. On a global scale, the World Health Organization (WHO) reports that ovarian cancer has approximately 225,000 new cases every year with approximately 145,000 deaths. According to the National Institute of Health, Surveillance Epidemiology and End Results program (SEER) database, 5-year survival for women with ovarian cancer in the United States is 49.1%. High-grade serous ovarian carcinoma typically presents at an advanced stage and accounts for the majority of these cancer deaths. Given their prevalence and the lack of a reliable method for screening, early and reliable diagnosis of serous cancers is of paramount importance. Early differentiation of borderline, low and high-grade lesions can assist in surgical planning and support challenging intraoperative diagnoses. The objective of this article is to provide a review of the pathogenesis, diagnosis, and treatment of serous ovarian tumors, with a specific focus on the imaging characteristics that help to preoperatively differentiate borderline, low-grade, and high-grade serous ovarian lesions.
    DOI:  https://doi.org/10.1067/j.cpradiol.2023.05.010
  7. Nat Rev Mol Cell Biol. 2023 Jun 06.
    The technological landscape and applications of single-cell multi-omics.
    Alev Baysoy, Zhiliang Bai, Rahul Satija, Rong Fan.
      Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
    DOI:  https://doi.org/10.1038/s41580-023-00615-w
  8. Comput Struct Biotechnol J. 2023 ;21 3124-3135
    DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.
    Xiaolu Xu, Zitong Qi, Dawei Zhang, Meiwei Zhang, Yonggong Ren, Zhaohong Geng.
      Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.
    Keywords:  Cancer research; Driver gene; Driver pathway; MutSigCV; Statistical methods
    DOI:  https://doi.org/10.1016/j.csbj.2023.05.019
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