bims-tumhet 2023-02-05 papers

Issue of 2023–02–05
four papers selected by
Sergio Marchini, Humanitas Research

PeerJ. 2023 ;11 e14653

Next-generation sequencing shows the genomic features of ovarian clear cell cancer and compares the genetic architectures of high-grade serous ovarian cancer and clear cell carcinoma in ovarian and endometrial tissues.

Meifu Gan, Zaixian Tai, Yijian Yu, Chao Zhang, Juan Xu.

Ovarian clear cell carcinoma (OCCC) is a special histological type of epithelial ovarian cancer (EOC) that is not derived from epithelial cells of the ovarian or fallopian tube as the most common type of ovarian cancer, high-grade serous ovarian carcinoma (HGSOC), but is closely related to endometriosis and similar to endometrial clear cell carcinoma (ECCC) at morphologic and phenotypic features. However, limited data was shown in OCCC genomic features and compared with that in OCCC, HGSOC and ECCC. Herein, we utilized next-generation sequencing analysis of a panel of 1,021 genes to profile the mutational alterations in 34 OCCC and compared them to those from HGSOC (402 cases) and ECCC (30 cases). In result, the ARID1A and PIK3CA are high-frequency mutations of OCCC. Clonal architectures showed that all the mutations of genes occur in the later stage in the OCCC progress, whereas KRAS mutation is the earlier event compared with mutation of ARID1A or PIK3CA, which usually occurs in a group of ARID1A or PIK3CA mutations. The mutation frequency of main driver genes is similar between OCCC and ECCC, while TP53 is the main mutation in HGSOC and ECCC. Shared mutational signatures between OCCC and ECCC tissues with commonly observed a C>T change indicated a common carcinogens-exposed between these two carcinomas, but HGSOC and ECCC have common and distinct mutational signatures across cohorts respectively. In addition, we identified some novel CNV gains in NF1, ASXL1, TCF7L2, CREBBP and LRP1B and loss in ATM, FANCM, RB1 and FLT in OCCC. Our study offered a new perspective for OCCC tumorigenesis from two organs, the ovary and uterus, at genomic architectures and revealed novel CNV events for helping to provide theoretical support for OCCC treatment.

Keywords: ECCC; Genetic architectures; Genomic features; HGSOC; OCCC

DOI: https://doi.org/10.7717/peerj.14653

JCO Precis Oncol. 2023 Jan;7 e2200258

Homologous Recombination Repair Gene Mutations to Predict Olaparib Plus Bevacizumab Efficacy in the First-Line Ovarian Cancer PAOLA-1/ENGOT-ov25 Trial.

PURPOSE: The PAOLA-1/ENGOT-ov25 trial of maintenance olaparib plus bevacizumab for newly diagnosed advanced high-grade ovarian cancer demonstrated a significant progression-free survival (PFS) benefit over placebo plus bevacizumab, particularly in patients with homologous recombination deficiency (HRD)-positive tumors. We explored whether mutations in non-BRCA1 or BRCA2 homologous recombination repair (non-BRCA HRRm) genes predicted benefit from olaparib plus bevacizumab in PAOLA-1.
METHODS: Eight hundred and six patients were randomly assigned (2:1). Tumors were analyzed using the Myriad MyChoice HRD Plus assay to assess non-BRCA HRRm and HRD status; HRD was based on a genomic instability score (GIS) of ≥ 42. In this exploratory analysis, PFS was assessed in patients harboring deleterious mutations using six non-BRCA HRR gene panels, three devised for this analysis and three previously published.
RESULTS: The non-BRCA HRRm prevalence ranged from 30 of 806 (3.7%) to 79 of 806 (9.8%) depending on the gene panel used, whereas 152 of 806 (18.9%) had non-BRCA1 or BRCA2 mutation HRD-positive tumors. The majority of tumors harboring non-BRCA HRRm had a low median GIS; however, a GIS of > 42 was observed for tumors with mutations in five HRR genes (BLM, BRIP1, RAD51C, PALB2, and RAD51D). Rates of gene-specific biallelic loss were variable (0% to 100%) in non-BRCA HRRm tumors relative to BRCA1-mutated (99%) or BRCA2-mutated (86%) tumors. Across all gene panels tested, hazard ratios for PFS (95% CI) ranged from 0.92 (0.51 to 1.73) to 1.83 (0.76 to 5.43).
CONCLUSION: Acknowledging limitations of small subgroup sizes, non-BRCA HRRm gene panels were not predictive of PFS benefit with maintenance olaparib plus bevacizumab versus placebo plus bevacizumab in PAOLA-1, irrespective of the gene panel tested. Current gene panels exploring HRRm should not be considered a substitute for HRD determined by BRCA mutation status and genomic instability testing in first-line high-grade ovarian cancer.

DOI: https://doi.org/10.1200/PO.22.00258

Nat Commun. 2023 Feb 02. 14(1): 568

Estimation of cell lineages in tumors from spatial transcriptomics data.

Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape, Peng Jiang.

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

DOI: https://doi.org/10.1038/s41467-023-36062-6

Methods Mol Biol. 2023 ;2624 1-6

DNA Methylation Data Analysis Using Msuite.

Xiaojian Liu, Pengxiang Yuan, Kun Sun.

DNA methylation is a widespread epigenetic modification responsible for many biological regulation pathways. The development of various powerful biochemical assays, including conventional bisulfite treatment-based and emerging bisulfite-free techniques, has promised high-resolution DNA methylome profiling and significantly propelled the DNA methylation research field. However, the analysis of large-scale data generated from such assays is still complex and challenging. In this paper, we present a step-by-step protocol for using Msuite for whole-spectrum DNA methylation data analysis, from quality control, read alignment, to methylation call and data visualization. The Msuite package and a testing dataset are freely available at https://github.com/hellosunking/Msuite.

Keywords: Bisulfite sequencing; CpG dinucleotides; Data visualization

DOI: https://doi.org/10.1007/978-1-0716-2962-8_1