Dataset Information

Deconvolution of Adult T-Cell Leukemia/Lymphoma With Single-Cell RNA-Seq Using Frozen Archived Skin Tissue Reveals New Subset of Cancer-Associated Fibroblast.

ABSTRACT: Adult T-cell Leukemia/Lymphoma (ATLL) is a rare aggressive T-cell malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) infection. However, little is known about the underlying activated molecular pathways at the single cell level. Moreover, the intercellular communications between the tumor microenvironment (TME) and tumor cells in this malignancy are currently unknown. Difficulties in harvesting fresh tissue in a clinical setting have hampered our deeper understanding of this malignancy. Herein, we examined ATLL using archived fresh frozen tissue after biopsy using single-cell RNA sequencing (scRNA-seq) with T-cell receptor (TCR) clonal analysis. Highly clonal tumor cells showed multiple activating pathways, suggesting dynamic evolution of the malignancy. By dissecting diverse cell types comprising the TME, we identified a novel subset of cancer-associated fibroblast, which showed enriched epidermal growth factor receptor (EGFR)-related transcripts including early growth response 1 and 2 (EGR1 and EGR2). Cancer associated fibroblasts (CAFs) of ATLL play an important role for CD4 T-cell proliferation via FGF7-FGF1 and PDGFA-PDGFRA/B signaling, and CAFs, particularly EGR-enriched, are also associated with CD8 and NKT expansion by EGFR. These findings suggest a potential targeted therapeutic pathway to better treat this neoplasm.

SUBMITTER: Joo EH

PROVIDER: S-EPMC9021607 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Deconvolution of Adult T-Cell Leukemia/Lymphoma With Single-Cell RNA-Seq Using Frozen Archived Skin Tissue Reveals New Subset of Cancer-Associated Fibroblast.

Joo Eun-Hye EH Bae Jai Hee JH Park Jihye J Bang Yoon Ji YJ Han Joseph J Gulati Nicholas N Kim Jong-Il JI Park Chung-Gyu CG Park Woong-Yang WY Kim Hyun Je HJ

Frontiers in immunology 20220407

Adult T-cell Leukemia/Lymphoma (ATLL) is a rare aggressive T-cell malignancy caused by human T-cell leukemia virus type 1 (HTLV-1) infection. However, little is known about the underlying activated molecular pathways at the single cell level. Moreover, the intercellular communications between the tumor microenvironment (TME) and tumor cells in this malignancy are currently unknown. Difficulties in harvesting fresh tissue in a clinical setting have hampered our deeper understanding of this malign ...[more]

PMID: 35464471

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Project description:BACKGROUND:The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. RESULTS:In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3'-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. CONCLUSIONS:Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2-3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies.

Project description:BackgroundCell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.MethodsWe developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.ResultsHASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients' overall survival.ConclusionHASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD .

Project description:Background: The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging. Results: In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3'-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions. Conclusions: Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2-3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies.

Dataset Information

Deconvolution of Adult T-Cell Leukemia/Lymphoma With Single-Cell RNA-Seq Using Frozen Archived Skin Tissue Reveals New Subset of Cancer-Associated Fibroblast.

Publications

Deconvolution of Adult T-Cell Leukemia/Lymphoma With Single-Cell RNA-Seq Using Frozen Archived Skin Tissue Reveals New Subset of Cancer-Associated Fibroblast.

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