bims-tumhet 2022-04-24 papers

Issue of 2022–04–24
four papers selected by
Sergio Marchini, Humanitas Research

Brief Bioinform. 2022 Apr 23. pii: bbac150. [Epub ahead of print]

scDeconv: an R package to deconvolve bulk DNA methylation data with scRNA-seq data and paired bulk RNA-DNA methylation data.

Yu Liu.

Many DNA methylation (DNAm) data are from tissues composed of various cell types, and hence cell deconvolution methods are needed to infer their cell compositions accurately. However, a bottleneck for DNAm data is the lack of cell-type-specific DNAm references. On the other hand, scRNA-seq data are being accumulated rapidly with various cell-type transcriptomic signatures characterized, and also, many paired bulk RNA-DNAm data are publicly available currently. Hence, we developed the R package scDeconv to use these resources to solve the reference deficiency problem of DNAm data and deconvolve them from scRNA-seq data in a trans-omics manner. It assumes that paired samples have similar cell compositions. So the cell content information deconvolved from the scRNA-seq and paired RNA data can be transferred to the paired DNAm samples. Then an ensemble model is trained to fit these cell contents with DNAm features and adjust the paired RNA deconvolution in a co-training manner. Finally, the model can be used on other bulk DNAm data to predict their relative cell-type abundances. The effectiveness of this method is proved by its accurate deconvolution on the three testing datasets here, and if given an appropriate paired dataset, scDeconv can also deconvolve other omics, such as ATAC-seq data. Furthermore, the package also contains other functions, such as identifying cell-type-specific inter-group differential features from bulk DNAm data. scDeconv is available at: https://github.com/yuabrahamliu/scDeconv.

Keywords: DNA methylation; cell deconvolution; cell-type-specific inter-group differential features; co-training; ensemble; scRNA-seq

DOI: https://doi.org/10.1093/bib/bbac150

Clin Cancer Res. 2022 Apr 20. pii: clincanres.2834.2021. [Epub ahead of print]

Improved T cell immunity following neoadjuvant chemotherapy in ovarian cancer.

PURPOSE: Although local tissue-based immune responses are critical for elucidating direct tumor-immune cell interactions, peripheral immune responses are increasingly recognized as occupying an important role in anti-cancer immunity. We evaluated serial blood samples from patients with epithelial ovarian cancer(EOC) undergoing standard-of-care neoadjuvant carboplatin and paclitaxel chemotherapy (including dexamethasone for prophylaxis of paclitaxel-associated hypersensitivity reactions) to characterize the evolution of the peripheral immune cell function and composition during therapy.
EXPERIMENTAL DESIGN: Serial blood samples from ten patients with advanced high-grade serous ovarian cancer treated with neoadjuvant chemotherapy(NACT) were collected before initiation of chemotherapy, after third and sixth cycles, and ~2 months after completion of chemotherapy. T-cell function was evaluated using ex vivo IFN-γ ELISpot assays, and the dynamics of T-cell repertoire and immune cell composition were assessed using bulk and single-cell RNA sequencing.
RESULTS: T-cells exhibited an improved response to viral antigens after NACT, which paralleled the decrease in CA125 levels. Single-cell analysis revealed increased numbers of memory T-cell receptor(TCR) clonotypes and increased central memory CD8+ and regulatory T-cells throughout chemotherapy. Finally, administration of NACT was associated with increased monocyte frequency and expression of HLA class II and antigen presentation genes; single cell RNAseq analyses showed that while driven largely by classical monocytes, increased class II gene expression was observed across monocyte subpopulations after chemotherapy.
CONCLUSIONS: NACT may alleviate tumor-associated immunosuppression by reducing tumor burden and may enhance antigen processing and presentation. These findings have implications for the successful combinatorial applications of immune checkpoint blockade and therapeutic vaccine approaches in EOC.

DOI: https://doi.org/10.1158/1078-0432.CCR-21-2834

Diagnostics (Basel). 2022 Apr 13. pii: 978. [Epub ahead of print]12(4):

Cell-Free DNA Fragmentomics in Liquid Biopsy.

Spencer C Ding, Y M Dennis Lo.

Cell-free DNA (cfDNA) in bodily fluids has rapidly transformed the development of noninvasive prenatal testing, cancer liquid biopsy, and transplantation monitoring. Plasma cfDNA consists of a mixture of molecules originating from various bodily tissues. The study of the fragmentation patterns of cfDNA, also referred to as 'fragmentomics', is now an actively pursued area of biomarker research. Clues that cfDNA fragmentation patterns might carry information concerning the tissue of origin of cfDNA molecules have come from works demonstrating that circulating fetal, tumor-derived, and transplanted liver-derived cfDNA molecules have a shorter size distribution than the background mainly of hematopoietic origin. More recently, an improved understanding of cfDNA fragmentation has provided many emerging fragmentomic markers, including fragment sizes, preferred ends, end motifs, single-stranded jagged ends, and nucleosomal footprints. The intrinsic biological link between activities of various DNA nucleases and characteristic fragmentations has been demonstrated. In this review, we focus on the biological properties of cell-free DNA unveiled recently and their potential clinical applications.

Keywords: NIPT; cancer detection; cell-free DNA; fragmentomics; liquid biopsy

DOI: https://doi.org/10.3390/diagnostics12040978

Cancer Cell Int. 2022 Apr 19. 22(1): 155

Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment.

Hailan Yan, Jinghua Shi, Yi Dai, Xiaoyan Li, Yushi Wu, Jing Zhang, Zhiyue Gu, Chenyu Zhang, Jinhua Leng.

BACKGROUND: The tumor microenvironment contributes to tumor initiation, growth, invasion, and metastasis. The tumor microenvironment is heterogeneous in cellular and acellular components, particularly structural features and their gene expression at the inter-and intra-tumor levels.
MAIN TEXT: Single-cell RNA sequencing profiles single-cell transcriptomes to reveal cell proportions and trajectories while spatial information is lacking. Spatially resolved transcriptomics redeems this lack with limited coverage or depth of transcripts. Hence, the integration of single-cell RNA sequencing and spatial data makes the best use of their strengths, having insights into exploring diverse tissue architectures and interactions in a complicated network. We review applications of integrating the two methods, especially in cellular components in the tumor microenvironment, showing each role in cancer initiation and progression, which provides clinical relevance in prognosis, optimal treatment, and potential therapeutic targets.
CONCLUSION: The integration of two approaches may break the bottlenecks in the spatial resolution of neighboring cell subpopulations in cancer, and help to describe the signaling circuitry about the intercommunication and its exact mechanisms in producing different types and malignant stages of tumors.

Keywords: Integration; Single-cell RNA sequencing; Spatially resolved transcriptomics; Tumor microenvironment

DOI: https://doi.org/10.1186/s12935-022-02580-4