Ontology highlight
ABSTRACT:
PROVIDER: PRJNA485695 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7687780.fastq.gz | Fastqsanger.gz | |||
| SRR7687790.fastq.gz | Fastqsanger.gz | |||
| SRR7687800.fastq.gz | Fastqsanger.gz | |||
| SRR7687810.fastq.gz | Fastqsanger.gz | |||
| SRR7687771.fastq.gz | Fastqsanger.gz |
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Project description:Triple-negative breast cancer represents approximately 15–20% of all reported breast cancer cases, and is characterized by a shorter survival time and higher mortality rates compared to other breast cancer sub-types. Tumor microenvironment (TME) refers to the internal and external environment of tumor tissue. Increasing evidence indicates that a tumor’s microenvironment is tightly associated with the immunological surveillance and defense during the development of breast cancer. Although oncology studies employing digital dissection methodologies have provided some insight on the biological features of TME, the development of methods to investigate the cellular composition of the tumor microenvironment remain an important research priority. In this study, we extracted whole transcriptome from 30 Triple-negative breast cancer (TNBC) patients and then used bioinformatics approaches to characterize cell type content in tumor tissue compared with para-cancerous tissue. We identified 4 types of enriched immune cells and 6 types of downregulated immune cells in the tumor tissue samples. After comprehensive bioinformatics analyses, we developed an ‘immune infiltration score’ (IIS) to quantitatively model immune cell infiltration in TNBC. To demonstrate the utility of the IIS, we used 2 independent datasets for validation. We found that patients with a higher IIS showing a longer progression-free survival time and significantly better prognosis than those with a lower IIS value. In sum, we explored the immune infiltration landscape in 30 TNBC patients and provided a novel and reliable biomarker IIS to evaluate the progression-free survival and prognosis in the TNBC patients.
2021-10-18 | E-MTAB-10886 | biostudies-arrayexpress
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Project description:With the advent of cancer immunotherapy, intense investigation has been focused on tumor-infiltrating immune cells. With only a fraction of patients responding to these new therapies, a better understanding of all elements of the tumor microenvironment (TME) that may influence therapeutic outcome is needed. Stromal elements of the TME, chiefly fibroblasts, have emerged as potential contributors to tumor progression and most recently resistance to immunotherapy, but their precise composition and clinical relevance remain incompletely understood. Here we use single-cell transcriptomics to chart the fibroblastic landscape during pancreatic ductal adenocarcinoma (PDAC) progression in animal models, identifying two healthy tissue fibroblast subsets that co-evolve along individual trajectories into four subsets of carcinoma-associated fibroblasts (CAFs).
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Project description:Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. 6 samples of stroma surrounding invasive breast primary tumors; 6 matched samples of normal stroma. 6 samples of stroma surrounding invasive prostate primary tumors; 6 matched samples of normal stroma.
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