| 1. |
- Friedrich, Stefanie, et al.
(författare)
-
Fusion transcript detection using spatial transcriptomics
- 2020
-
Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes.Method: We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5 ' gene and has an elevated number of poly(A) tails for the 3 ' gene. Its expression level is defined by the upstream promoter of the 5 ' gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score.Results: We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGeSLC45A3-ELK4in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands.Conclusions: STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level.
|
|
| 2. |
- Friedrich, Stefanie, et al.
(författare)
-
MetaCNV-a consensus approach to infer accurate copy numbers from low coverage data
- 2020
-
Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 13
-
Tidskriftsartikel (refereegranskat)abstract
- Background: The majority of copy number callers requires high read coverage data that is often achieved with elevated material input, which increases the heterogeneity of tissue samples. However, to gain insights into smaller areas within a tissue sample, e.g. a cancerous area in a heterogeneous tissue sample, less material is used for sequencing, which results in lower read coverage. Therefore, more focus needs to be put on copy number calling that is sensitive enough for low coverage data.Results: We present MetaCNV, a copy number caller that infers reliable copy numbers for human genomes with a consensus approach. MetaCNV specializes in low coverage data, but also performs well on normal and high coverage data. MetaCNV integrates the results of multiple copy number callers and infers absolute and unbiased copy numbers for the entire genome. MetaCNV is based on a meta-model that bypasses the weaknesses of current calling models while combining the strengths of existing approaches. Here we apply MetaCNV based on ReadDepth, SVDetect, and CNVnator to real and simulated datasets in order to demonstrate how the approach improves copy number calling.Conclusions: MetaCNV, available at https://bitbucket.org/sonnhammergroup/metacnv, provides accurate copy number prediction on low coverage data and performs well on high coverage data.
|
|
| 3. |
- Marklund, Maja, et al.
(författare)
-
Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones
- 2022
-
Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors. Spatial heterogeneity in prostate cancer can contribute to its resistance to androgen deprivation therapy (ADT). Here, the authors analyse prostate cancer samples before and after ADT using Spatial Transcriptomics, and find heterogeneous pre-treatment tumour cell populations and stromal cells that are associated with resistance.
|
|
