| 1. |
- Berglund, Emelie, et al.
(författare)
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Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
- 2018
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.
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| 2. |
- Dantonello, Tobias M, et al.
(författare)
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Challenges in the Local Treatment of Large Abdominal Embryonal Rhabdomyosarcoma
- 2014
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Ingår i: Annals of Surgical Oncology. - : Springer Science and Business Media LLC. - 1068-9265 .- 1534-4681. ; 21:11, s. 3579-3586
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND:Embryonal rhabdomyosarcoma is the most common pediatric soft tissue sarcoma. The best local treatment in large, nonmetastatic primary unresected nongenitourinary embryonal rhabdomyosarcoma of the abdomen (LARME) is however unclear.METHODS:We analyzed patients with LARME treated in four consecutive CWS trials. All diagnoses were confirmed by reference reviews. Treatment included multiagent chemotherapy and local treatment of the primary tumor with surgery and/or radiotherapy. The impact of primary debulking surgery (PDS) also was studied.RESULTS:One hundred patients <21 years with a median age of 4 years had LARME. Sixty-one of them had a tumor >10 cm in diameter at diagnosis. PDS was performed in 19 of 100 children. The outcomes of patients with PDS were similar to those of the other patients. In 36 children, the tumor was resected after induction chemotherapy; 60 RME were irradiated. The toxic effects of radiochemotherapy were not significantly increased compared with the nonirradiated patients. With a median follow-up of 10 years, the 5-year EFS and OS were 52 ± 10 and 65 ± 9 %, respectively. Significant risk factors in multivariate analysis were age >10 years; no achievement of complete remission; and inadequate secondary local treatment, defined as incomplete secondary resection or no radiation.CONCLUSIONS:Children with LARME have a fair prognosis, despite an often huge tumor size and unfavorable primary site, if the tumors can either be resected or irradiated following induction chemotherapy. PDS was only performed in a small subgroup. Radiation performed concomitantly with chemotherapy did not increase the acute toxicity significantly.
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| 3. |
- Dantonello, Tobias M., et al.
(författare)
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Survival following disease recurrence of primary localized alveolar rhabdomyosarcoma
- 2013
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Ingår i: Pediatric Blood & Cancer. - : Wiley. - 1545-5009 .- 1545-5017. ; 60:8, s. 1267-1273
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Tidskriftsartikel (refereegranskat)abstract
- Background Recurrences in primary localized alveolar rhabdomyosarcoma (RMA) are common. Post-relapse survival is poor. We evaluated prognostic factors including relapse treatment in patients with recurrent RMA. Methods Relapses occurred in 115/235 patients with nonmetastatic RMA treated in four consecutive CWS-trials after achievement of a complete remission. Sufficient information about post-relapse treatment and outcome could be obtained in 99 patients and was retrospectively analyzed. Results Nine of 99 patients received no salvage therapy and died after a median of 2 months. The remaining 90 patients received multimodal relapse treatment including mandatory chemotherapy. Recurrences were grossly resected in 39 patients; 57 patients received radiation. At a median follow-up from relapse of 8 years, 20 patients were alive and disease-free (5-year post-relapse survival [PROS] 21.3 +/- 8). All surviving patients apart from a single individual had an isolated, circumscribed recurrence. Sixteen of 20 survivors were treated with adequate local relapse therapy (ALRT, i.e., either complete resection or gross resection+radiation). Survival in the subgroup of 27 individuals with circumscribed recurrences and ALRT was significantly better (PROS 53.7 +/- 19) compared with disseminated recurrences and/or tumors treated without ALRT. Absence of primary lymph node involvement, circumscribed relapses, ALRT, and achievement of a second CR were identified as independent favorable risk factors. Conclusion Post-relapse survival for primary localized RMA is generally poor. However, certain patient groups differed significantly in their likelihood of survival and 50% of patients with circumscribed relapses treated with ALRT survived. These findings may form the basis for an evidence-based risk-stratification for recurrent disease including relapse treatment.
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| 4. |
- Friedrich, Stefanie, et al.
(författare)
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Adverse drug event classification of health records using dictionary-based pre-processing and machine learning
- 2015
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Ingår i: Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis. - : Association for Computational Linguistics. - 9781941643327 ; , s. 121-130
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Konferensbidrag (refereegranskat)abstract
- A method to find adverse drug reactions in electronic health records written in Swedish is presented. A total of 14,751 health records were manually classified into four groups. The records are normalised by pre-processing using both dic- tionaries and manually created word lists. Three different supervised machine learning algorithm were used to find the best results; decision tree, random forest and LibSVM. The best performance on a test dataset was with LibSVM obtaining a pre- cision of 0.69 and a recall of 0.66, and a F-score of 0.67. Our method found 865 of 981 true positives (88.2%) in a 3-class dataset which is an improvement of 49.5% over previous approaches.
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| 5. |
- Friedrich, Stefanie, 1973-
(författare)
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Computational Analysis of Tumour Heterogeneity
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity.The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity.
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| 6. |
- Friedrich, Stefanie
(författare)
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Fusion transcript detection using spatial transcriptomics
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.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. We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-sore applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occured in the centre or periphery of inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands.
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| 7. |
- Friedrich, Stefanie, et al.
(författare)
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Fusion transcript detection using spatial transcriptomics
- 2020
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 13:1
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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.
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| 8. |
- Friedrich, Stefanie, et al.
(författare)
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MetaCNV-a consensus approach to infer accurate copy numbers from low coverage data
- 2020
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 13
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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.
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| 9. |
- Marklund, Maja, et al.
(författare)
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Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones
- 2022
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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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.
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| 10. |
- Marklund, Maja, et al.
(författare)
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Spatio-temporal analysis of prostate tumors in situ suggests the pre-existence of ADT-resistance
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The molecular mechanisms by which potentially lethal castration-resistant prostate cancer emerge in advanced metastatic prostate cancer are still poorly understood. Intratumor heterogeneity is believed to contribute to the fact that a majority of affected men succumb to the disease within a few years. In this study, we will challenge the conventional notion that castration-resistant prostate cancer cells evolve as a consequence of treatment. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in core needle biopsies collected pre-and post-treatment. 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 identified several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Strikingly, certain minor cell populations present before treatment exhibited gene expression profiles that matched those of resistant tumor cell clusters. Such resistant clusters were confirmed by the localization of the androgen receptor to the nuclei in cancer cells present after treatment. Our data also demonstrate that stromal cells adjacent to resistant tumor factors do not express AR before treatment (or after), which can be used to increase the power in predicting resistant tumors.
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