bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2024‒08‒18
seven papers selected by
Sergio Marchini, Humanitas Research
  1. Reducing ovarian cancer mortality through screening: an impossible dream?
  2. Integrated genomic/epigenomic analysis stratifies subtypes of clear cell ovarian carcinoma, highlighting their cellular origin.
  3. Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.
  4. Interactive visualisation of nanopore sequencing signal data with Squigualiser.
  5. DNA methylation in mammalian development and disease.
  6. DNA methylation markers for oral cancer detection in non- and minimally invasive samples: a systematic review.
  7. Single-cell RNA sequencing and cell-cell communication analysis reveal tumor microenvironment associated with chemotherapy responsiveness in ovarian cancer.
  1. J Natl Cancer Inst. 2024 Aug 12. pii: djae175. [Epub ahead of print]
    Reducing ovarian cancer mortality through screening: an impossible dream?
    Evan R Myers.
      
    DOI:  https://doi.org/10.1093/jnci/djae175
  2. Sci Rep. 2024 08 13. 14(1): 18797
    Integrated genomic/epigenomic analysis stratifies subtypes of clear cell ovarian carcinoma, highlighting their cellular origin.
    Akira Nishijima, Katsutoshi Oda, Kosei Hasegawa, Takahiro Koso, Kayo Asada, Yuji Ikeda, Ayumi Taguchi, Daichi Maeda, Genta Nagae, Shingo Tsuji, Kenji Tatsuno, Yuriko Uehara, Akira Kurosaki, Sho Sato, Michihiro Tanikawa, Kenbun Sone, Mayuyo Mori, Masako Ikemura, Keiichi Fujiwara, Tetsuo Ushiku, Yutaka Osuga, Hiroyuki Aburatani.
      The cellular origin of clear cell ovarian carcinoma (CCOC), a major histological subtype of ovarian carcinoma remains elusive. Here, we explored the candidate cellular origin and identify molecular subtypes using integrated genomic/epigenomic analysis. We performed whole exome-sequencing, microarray, and DNA methylation array in 78 CCOC samples according to the original diagnosis. The findings revealed that ARID1A and/or PIK3CA mutations were mutually exclusive with DNA repair related genes, including TP53, BRCA1, and ATM. Clustering of CCOC and other ovarian carcinomas (n = 270) with normal tissues from the fallopian tube, ovarian surface epithelium, endometrial epithelium, and pelvic peritoneum mesothelium (PPM) in a methylation array showed that major CCOC subtypes (with ARID1A and/or PIK3CA mutations) were associated with the PPM-lile cluster (n = 64). This cluster was sub-divided into three clusters: (1) mismatch repair (MMR) deficient with tumor mutational burden-high (n = 2), (2) alteration of ARID1A (n = 51), and (3) ARID1A wild-type (n = 11). The remaining samples (n = 14) were subdivided into (4) ovarian surface epithelium-like (n = 11) and (5) fallopian tube-like (considered as high-grade serous histotype; n = 3). Among these, subtypes (1-3) and others (4 and 5) were found to be associated with immunoreactive signatures and epithelial-mesenchymal transition, respectively. These results contribute to the stratification of CCOC into biological subtypes.
    Keywords:  Cellular origin; Clear cell ovarian carcinoma; Epigenomics; Genomics; Molecular subtypes
    DOI:  https://doi.org/10.1038/s41598-024-69796-4
  3. Bioinformatics. 2024 Aug 12. pii: btae506. [Epub ahead of print]
    Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.
    Samson Weiner, Bingjun Li, Sheida Nabavi.
      MOTIVATION: Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline.RESULTS: We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON employs a Gaussian Mixture Model (GMM) to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses.
    AVAILABILITY AND IMPLEMENTATION: SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.
    SUPPLEMENTARY MATERIAL: Supplementary material is available at XX.
    DOI:  https://doi.org/10.1093/bioinformatics/btae506
  4. Bioinformatics. 2024 Aug 13. pii: btae501. [Epub ahead of print]
    Interactive visualisation of nanopore sequencing signal data with Squigualiser.
    Hiruna Samarakoon, Kisaru Liyanage, James M Ferguson, Sri Parameswaran, Hasindu Gamaarachchi, Ira W Deveson.
      MOTIVATION: Nanopore sequencing current signal data can be 'basecalled' into sequence information or analysed directly, with the capacity to identify diverse molecular features, such as DNA/RNA base modifications and secondary structures. However, raw signal data is large and complex, and there is a need for improved visualisation strategies to facilitate signal analysis, exploration and tool development.RESULTS: Squigualiser (Squiggle visualiser) is a toolkit for intuitive, interactive visualisation of sequence-aligned signal data, which currently supports both DNA and RNA sequencing data from Oxford Nanopore Technologies (ONT) instruments. Squigualiser is compatible with a wide range of alternative signal-alignment software packages and enables visualisation of both signal-to-read and signal-to-reference aligned data at single-base resolution. Squigualiser generates an interactive signal browser view (HTML file), in which the user can navigate across a genome/transcriptome region and customise the display. Multiple independent reads are integrated into a 'signal pileup' format and different datasets can be displayed as parallel tracks. Although other methods exist, Squigualiser provides the community with a software package purpose-built for raw signal data visualisation, incorporating a range of new and existing features into a unified platform.
    AVAILABILITY: Squigualiser is an open source package under an MIT licence: Https://github.com/hiruna72/squigualiser. The software was developed using Python 3.8 and can be installed with pip or bioconda or executed directly using prebuilt binaries provided with each release.
    SUPPLEMENTARY INFORMATION: Squigualiser usage, implementation and underlying methods development are outlined in detail in the Supplementary Notes 1-5.
    DOI:  https://doi.org/10.1093/bioinformatics/btae501
  5. Nat Rev Genet. 2024 Aug 12.
    DNA methylation in mammalian development and disease.
    Zachary D Smith, Sara Hetzel, Alexander Meissner.
      The DNA methylation field has matured from a phase of discovery and genomic characterization to one seeking deeper functional understanding of how this modification contributes to development, ageing and disease. In particular, the past decade has seen many exciting mechanistic discoveries that have substantially expanded our appreciation for how this generic, evolutionarily ancient modification can be incorporated into robust epigenetic codes. Here, we summarize the current understanding of the distinct DNA methylation landscapes that emerge over the mammalian lifespan and discuss how they interact with other regulatory layers to support diverse genomic functions. We then review the rising interest in alternative patterns found during senescence and the somatic transition to cancer. Alongside advancements in single-cell and long-read sequencing technologies, the collective insights made across these fields offer new opportunities to connect the biochemical and genetic features of DNA methylation to cell physiology, developmental potential and phenotype.
    DOI:  https://doi.org/10.1038/s41576-024-00760-8
  6. Clin Epigenetics. 2024 Aug 13. 16(1): 105
    DNA methylation markers for oral cancer detection in non- and minimally invasive samples: a systematic review.
    Óscar Rapado-González, Sofia Salta, Rafael López-López, Rui Henrique, María Mercedes Suárez-Cunqueiro, Carmen Jerónimo.
      More than 50% of oral cancer (OC) patients are diagnosed with advanced-stage disease associated with poor prognosis and quality of life, supporting an urgent need to improve early OC detection. The identification of effective molecular markers by minimally invasive approaches has emerged as a promising strategy for OC screening. This systematic review summarizes and evaluates the performance of the DNA methylation markers identified in non- or minimally invasive samples for OC detection. PubMed's MEDLINE, Scopus, Embase, and Cochrane Library databases were systematically searched for studies that evaluated DNA methylation markers in non-invasive and/or minimally invasive samples (oral rinse/saliva, oral brush, and blood) from OC patients. Two investigators independently extracted data on study population characteristics, candidate methylation markers, testing samples, DNA methylation assay, and performance diagnostic outcomes. Methodological study quality was assessed with the Quality Assessment for Studies of Diagnostic Accuracy-2 tool. Thirty-one studies met the inclusion criteria for this systematic review. DNA methylation markers were evaluated in oral rinse/saliva (n = 17), oral brush (n = 9), and blood (n = 7) samples. Methylation-specific PCR (MSP) and quantitative-MSP were the most common DNA methylation assays. Regarding diagnostic performance values for salivary, oral brush, and blood DNA methylation markers, sensitivity and specificity ranged between 3.4-100% and 21-100%, 9-100% and 26.8-100%, 22-70% and 45.45-100%, respectively. Different gene methylation panels showed good diagnostic performance for OC detection. This systematic review discloses the promising value of testing DNA methylation markers in non-invasive (saliva or oral rinse) or minimally invasive (oral brush or blood) samples as a novel strategy for OC detection. However, further validation in large, multicenter, and prospective study cohorts must be carried out to confirm the clinical value of specific DNA methylation markers in this setting.
    Keywords:  DNA methylation; Diagnosis; Epigenetics; Liquid biopsies; Oral cancer; Screening
    DOI:  https://doi.org/10.1186/s13148-024-01716-9
  7. Clin Transl Oncol. 2024 Aug 09.
    Single-cell RNA sequencing and cell-cell communication analysis reveal tumor microenvironment associated with chemotherapy responsiveness in ovarian cancer.
    Xiaoyan Jiang, Ningxuan Chen, Qinglv Wei, Xin Luo, Xiaoyi Liu, Lingcui Xie, Ping Yi, Jing Xu.
      BACKGROUND: To investigate the impact of the tumor microenvironment (TME) on the responsiveness to chemotherapy in ovarian cancer (OV).METHODS: We integrated single cell RNA-seq datasets of OV containing chemo-response information, and characterize their clusters based on different TME sections. We focus on analyzing cell-cell communication to elaborate on the mechanisms by which different components of the TME directly influence the chemo-response of tumor cells.
    RESULTS: scRNA-seq datasets were annotated according to specific markers for different cell types. Differential analysis of malignant epithelial cells revealed that chemoresistance was associated with the TME. Notably, distinct TME components exhibited varying effects on chemoresistance. Enriched SPP1+ tumor-associated macrophages in chemo-resistant patients could promote chemoresistance through SPP1 binding to CD44 on tumor cells. Additionally, the overexpression of THBS2 in stromal cells could promote chemoresistance through binding with CD47 on tumor cells. In contrast, GZMA in the lymphocytes could downregulate the expression of PARD3 through direct interaction with PARD3, thereby attenuating chemoresistance in tumor cells.
    CONCLUSION: Our study indicates that the non-tumor cell components of the TME (e.g. SPP1+ TAMs, stromal cells and lymphocytes) can directly impact the chemo-response of OV and targeting the TME was potentially crucial in chemotherapy of OV.
    Keywords:  Cell–cell communication; Chemoresistance; Ovarian cancer; Single-cell RNA sequencing; Tumor microenvironment
    DOI:  https://doi.org/10.1007/s12094-024-03655-6
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