| 1. |
- Biermann, Jana, et al.
(författare)
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A 17-marker panel for global genomic instability in breast cancer.
- 2020
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Ingår i: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 112:2, s. 1151-1161
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Tidskriftsartikel (refereegranskat)abstract
- Genomic instability is a hallmark of cancer that plays a pivotal role in breast cancer development and evolution. A number of existing prognostic gene expression signatures for breast cancer are based on proliferation-related genes. Here, we identified a 17-marker panel associated with genome stability. A total of 136 primary breast carcinomas were stratified by genome stability. Matched gene expression profiles showed an innate segregation based on genome stability. We identified a 17-marker panel stratifying the training and validation cohorts into high- and low-risk patients. The 17 genes associated with genomic instability strongly impacted clinical outcome in breast cancer. Pathway analyses determined chromosome organisation, cell cycle regulation, and RNA processing as the underlying biological processes, thereby offering options for drug development and treatment tailoring. Our work supports the applicability of the 17-marker panel to improve clinical outcome prediction for breast cancer patients based on a signature accounting for genomic instability.
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| 2. |
- Biermann, Jana, et al.
(författare)
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A novel 18-marker panel predicting clinical outcome in breast cancer
- 2017
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Ingår i: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. - 1538-7755. ; 26:11, s. 1619-28
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiling has made considerable contributions to our understanding of cancer biology and clinical care. This study describes a novel gene expression signature for breast cancer-specific survival that was validated using external datasets. Gene expression signatures for invasive breast carcinomas (mainly Luminal B subtype) corresponding to 136 patients were analysed using Cox regression and the effect of each gene on disease-specific survival (DSS) was estimated. Iterative Bayesian Model Averaging was applied on multivariable Cox regression models resulting in an 18-marker panel, which was validated using three external validation datasets. The 18 genes were analysed for common pathways and functions using the Ingenuity Pathway Analysis software. This study complied with the REMARK criteria. The 18-gene multivariable model showed a high predictive power for DSS in the training and validation cohort and a clear stratification between high- and low-risk patients. The differentially expressed genes were predominantly involved in biological processes such as cell cycle, DNA replication, recombination, and repair. Furthermore, the majority of the 18 genes were found to play a pivotal role in cancer. Our findings demonstrated that the 18 molecular markers were strong predictors of breast cancer-specific mortality. The stable time-dependent area under the ROC curve function (AUC(t)) and high C-indices in the training and validation cohorts were further improved by fitting a combined model consisting of the 18-marker panel and established clinical markers. Our work supports the applicability of this 18-marker panel to improve clinical outcome prediction for breast cancer patients.
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| 3. |
- Biermann, Jana, et al.
(författare)
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Clonal relatedness in tumour pairs of breast cancer patients.
- 2018
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Ingår i: Breast cancer research : BCR. - : Springer Science and Business Media LLC. - 1465-542X. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Molecular classification of tumour clonality is currently not evaluated in multiple invasive breast carcinomas, despite evidence suggesting common clonal origins. There is no consensus about which type of data (e.g. copy number, mutation, histology) and especially which statistical method is most suitable to distinguish clonal recurrences from independent primary tumours.Thirty-seven invasive breast tumour pairs were stratified according to laterality and time interval between the diagnoses of the two tumours. In a multi-omics approach, tumour clonality was analysed by integrating clinical characteristics (n=37), DNA copy number (n=37), DNA methylation (n=8), gene expression microarray (n=7), RNA sequencing (n=3), and SNP genotyping data (n=3). Different statistical methods, e.g. the diagnostic similarity index (SI), were used to classify the tumours as clonally related recurrences or independent primary tumours.The SI and hierarchical clustering showed similar tendencies and the highest concordance with the other methods. Concordant evidence for tumour clonality was found in 46% (17/37) of patients. Notably, no association was found between the current clinical guidelines and molecular tumour features.A more accurate classification of clonal relatedness between multiple breast tumours may help to mitigate treatment failure and relapse by integrating tumour-associated molecular features, clinical parameters, and statistical methods. Guidelines need to be defined with exact thresholds to standardise clonality testing in a routine diagnostic setting.
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| 4. |
- Biermann, Jana, et al.
(författare)
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Radiation-induced genomic instability in breast carcinomas of the Swedish haemangioma cohort.
- 2019
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Ingår i: Genes, chromosomes & cancer. - : Wiley. - 1098-2264 .- 1045-2257. ; 58:9, s. 627-35
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Tidskriftsartikel (refereegranskat)abstract
- Radiation-induced genomic instability (GI) is hypothesized to persist after exposure and ultimately promote carcinogenesis. Based on the absorbed dose to the breast, an increased risk of developing breast cancer was shown in the Swedish haemangioma cohort that was treated with radium-226 for skin haemangioma as infants. Here, we screened 31 primary breast carcinomas for genetic alterations using the OncoScan CNV Plus Assay to assess GI and chromothripsis-like patterns associated with the absorbed dose to the breast. Higher absorbed doses were associated with increased numbers of copy number alterations (CNAs) in the tumour genome and thus a more unstable genome. Hence, the observed dose-dependent GI in the tumour genome is a measurable manifestation of the long-term effects of irradiation. We developed a highly predictive Cox regression model for overall survival based on the interaction between absorbed dose and GI. The Swedish haemangioma cohort is a valuable cohort to investigate the biological relationship between absorbed dose and GI in irradiated humans. This work gives a biological basis for improved risk assessment to minimize carcinogenesis as a secondary disease after radiation therapy. This article is protected by copyright. All rights reserved.
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| 5. |
- Biermann, Jana, et al.
(författare)
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Tumour clonality in paired invasive breast carcinomas
- 2019
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Ingår i: Cancer Research. - 0008-5472.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Multiple invasive breast tumours may represent either independent primary tumours or clonal recurrences of the first tumour, where the same progenitor cell gives rise to all of the detected tumours. Consequently, the driver events for the progenitor cell need to have been identical in early tumour development. Molecular classification of tumour clonality is not currently evaluated in multiple invasive breast carcinomas, despite evidence suggesting common clonal origins. Furthermore, there is no consensus about which type of biological data (e.g. copy number, mutation, histology) and especially which statistical method is most suitable to distinguish clonal recurrences from independent primary tumours. Methods: Thirty-seven invasive breast tumour pairs were stratified by laterality (bilateral vs. ipsilateral) and the time interval between the diagnoses of the first and second tumours (synchronous vs. metachronous). Both tumours from the same patient were analysed by integrating clinical characteristics (n = 37), DNA copy number (n = 37), DNA methylation (n = 8), gene expression microarray (n = 7), RNA sequencing (n = 3), and SNP genotyping data (n = 3). Different statistical methods, e.g. the diagnostic similarity index (SI), distance measure, shared segment analysis etc., were used to classify the tumours from the same patient as clonally related recurrences or independent primary tumours. Results: The SI applied on DNA copy numbers derived from aCGH (array comparative genomic hybridization) data was determined as the strongest indicator of clonal relatedness as it showed the highest concordance with all other methods. The distance measure was the most conservative method and the shared segment analysis most liberal. Concordant evidence for tumour clonality was found in 46% (17/37) of the patients. Notably, no significant association was found between the clinical characteristics and molecular tumour features. Conclusions: A more accurate classification of clonal relatedness between multiple breast tumours may help to mitigate treatment failure and relapse by integrating tumour-associated molecular features, clinical parameters, and statistical methods. In cases of extremely similar or different tumour pairs, the results showed consistency regardless of the method used. The SI can be easily integrated into clinical routine using FFPE samples to obtain copy number data. However, clinical guidelines with exact thresholds need to be defined to standardize clonality testing in a routine diagnostic setting.
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| 6. |
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| 7. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Immunohistochemical validation of COL3A1, GPR158 and PITHD1 as prognostic biomarkers in early-stage ovarian carcinomasn
- 2019
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 19:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Ovarian cancer is the main cause of gynecological cancer-associated death. However, 5- Methods: Here, we evaluated the prognostic role of 29 genes for early-stage (I and II) ovarian Results: We provide evidence of aberrant protein expression patterns for Collagen type III alpha 1 Conclusions: The novel biomarkers identified here may improve prognostication at the time of
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| 8. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.
- 2020
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Ovarian cancer comprises multiple subtypes (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with differing molecular and clinical behavior. However, robust histotype-specific biomarkers for clinical use have yet to be identified. Here, we utilized a multi-omics approach to identify novel histotype-specific genetic markers associated with ovarian carcinoma histotypes (CCC, EC, HGSC, and MC) using DNA methylation, DNA copy number alteration and RNA sequencing data for 96 primary invasive early-stage (stage I and II) ovarian carcinomas. More specifically, the DNA methylation analysis revealed hypermethylation for CCC in comparison with the other histotypes. Moreover, copy number imbalances and novel chromothripsis-like rearrangements (n=64) were identified in ovarian carcinoma, with the highest number of chromothripsis-like patterns in HGSC. For the 1000 most variable transcripts, underexpression was most prominent for all histotypes in comparison with normal ovarian samples. Overall, the integrative approach identified 46 putative oncogenes (overexpressed, hypomethylated and DNA gain) and three putative tumor suppressor genes (underexpressed, hypermethylated and DNA loss) when comparing the different histotypes. In conclusion, the current study provides novel insights into molecular features associated with early-stage ovarian carcinoma that may improve patient stratification and subclassification of the histotypes.
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| 9. |
- Engqvist, Hanna, 1985
(författare)
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Novel biomarkers associated with histotype and clinical outcome in early-stage ovarian carcinoma
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ovarian cancer is a collective name for multiple malignancies deriving from or involving the ovary, mainly comprising five histotypes of epithelial origin (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with varying clinical (e.g. risk factors, survival outcome, response to therapy) and molecular behavior (e.g. origin, genetic characteristics). Despite known differences in disease states, the majority of ovarian carcinomas are still treated as one entity with surgery, followed by chemotherapy. This treatment regimen is not adequate, which is reflected in relatively poor 5-year overall survival rates (55%) for ovarian cancer patients. Hence, there is a strong need for novel biomarkers for improved stratification of ovarian carcinoma patients based on a combination of individual molecular tumor characteristics and conventional clinicopathological features, which can further form the basis for the future development of novel targeted treatment options for ovarian cancer histotypes. This doctoral thesis focuses on early-stage (stage I and II) ovarian carcinomas for which limited information is available regarding molecular profiles associated with the diagnosis and prognosis of the different histotypes. In the first work, novel mutation and gene signatures were associated with histotype, overall survival (e.g. the tumor suppressor MTUS1), ovarian cancer (e.g. gene expression patterns for the long non-coding RNA MALAT1), and tumor aggressiveness (e.g. COL3A1). In the second and third works, histotype-specific prognostic gene signatures were validated on the protein level using immunohistochemistry identifying 20 prognostic biomarkers (11 CCC-associated biomarkers (ARPC2, CCT5, GNB1, KCTD10, NUP155, PITHD1, RPL13A, RPL37, SETD3, SMYD2, and TRIO), three EC-associated biomarkers (CECR1, KIF26B, and PIK3CA), five MC-associated biomarkers (CHEK1, FOXM1, GPR158, KIF23, and PARPBP), and COL3A1 for the main histotypes). In the fourth work, a multi-omics approach (genome- and transcriptome-wide analyses) integrating DNA methylation, DNA copy number alteration, and RNA sequencing data was applied to identify novel putative oncogenes and tumor suppressor genes associated with the CCC, EC, HGSC and MC histotypes. Taken together, the current doctoral thesis presents novel insights into molecular features associated with early-stage ovarian carcinoma that may improve patient stratification and subclassification based on histotype and clinical outcome.
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| 10. |
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| 11. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Validation of Novel Prognostic Biomarkers for Early-Stage Clear-Cell, Endometrioid and Mucinous Ovarian Carcinomas Using Immunohistochemistry
- 2020
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Early-stage (I and II) ovarian carcinoma patients generally have good prognosis. Yet, some patients die earlier than expected. Thus, it is important to stratify early-stage patients into risk groups to identify those in need of more aggressive treatment regimens. The prognostic value of 29 histotype-specific biomarkers identified using RNA sequencing was evaluated for early-stage clear-cell (CCC), endometrioid (EC) and mucinous (MC) ovarian carcinomas (n = 112) using immunohistochemistry on tissue microarrays. Biomarkers with prognostic significance were further evaluated in an external ovarian carcinoma data set using the web-based Kaplan-Meier plotter tool. Here, we provide evidence of aberrant protein expression patterns and prognostic significance of 17 novel histotype-specific prognostic biomarkers [10 for CCC (ARPC2, CCT5, GNB1, KCTD10, NUP155, RPL13A, RPL37, SETD3, SMYD2, TRIO), three for EC (CECR1, KIF26B, PIK3CA), and four for MC (CHEK1, FOXM1, KIF23, PARPBP)], suggesting biological heterogeneity within the histotypes. Combined predictive models comprising the protein expression status of the validated CCC, EC and MC biomarkers together with established clinical markers (age, stage, CA125, ploidy) improved the predictive power in comparison with models containing established clinical markers alone, further strengthening the importance of the biomarkers in ovarian carcinoma. Further, even improved predictive powers were demonstrated when combining these models with our previously identified prognostic biomarkers PITHD1 (CCC) and GPR158 (MC). Moreover, the proteins demonstrated improved risk prediction of CCC-, EC-, and MC-associated ovarian carcinoma survival. The novel histotype-specific prognostic biomarkers may not only improve prognostication and patient stratification of early-stage ovarian carcinomas, but may also guide future clinical therapy decisions.
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| 12. |
- Larsson, Peter, et al.
(författare)
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Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens.
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Cancer drug development has been riddled with high attrition rates, in part, due to poor reproducibility of preclinical models for drug discovery. Poor experimental design and lack of scientific transparency may cause experimental biases that in turn affect data quality, robustness and reproducibility. Here, we pinpoint sources of experimental variability in conventional 2D cell-based cancer drug screens to determine the effect of confounders on cell viability for MCF7 and HCC38 breast cancer cell lines treated with platinum agents (cisplatin and carboplatin) and a proteasome inhibitor (bortezomib). Variance component analysis demonstrated that variations in cell viability were primarily associated with the choice of pharmaceutical drug and cell line, and less likely to be due to the type of growth medium or assay incubation time. Furthermore, careful consideration should be given to different methods of storing diluted pharmaceutical drugs and use of DMSO controls due to the potential risk of evaporation and the subsequent effect on dose-response curves. Optimization of experimental parameters not only improved data quality substantially but also resulted in reproducible results for bortezomib- and cisplatin-treated HCC38, MCF7, MCF-10A, and MDA-MB-436 cells. Taken together, these findings indicate that replicability (the same analyst re-performs the same experiment multiple times) and reproducibility (different analysts perform the same experiment using different experimental conditions) for cell-based drug screens can be improved by identifying potential confounders and subsequent optimization of experimental parameters for each cell line.
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| 13. |
- Larsson, Peter, et al.
(författare)
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Pan-cancer analysis of genomic and transcriptomic data reveals the prognostic relevance of human proteasome genes in different cancer types.
- 2022
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Ingår i: BMC cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- The human proteasome gene family (PSM) consists of 49 genes that play a crucial role in cancer proteostasis. However, little is known about the effect of PSM gene expression and genetic alterations on clinical outcome in different cancer forms.Here, we performed a comprehensive pan-cancer analysis of genetic alterations in PSM genes and the subsequent prognostic value of PSM expression using data from The Cancer Genome Atlas (TCGA) containing over 10,000 samples representing up to 33 different cancer types. External validation was performed using a breast cancer cohort and KM plotter with four cancer types.The PSM genetic alteration frequency was high in certain cancer types (e.g. 67%; esophageal adenocarcinoma), with DNA amplification being most common. Compared with normal tissue, most PSM genes were predominantly overexpressed in cancer. Survival analysis also established a relationship with PSM gene expression and adverse clinical outcome, where PSMA1 and PSMD11 expression were linked to more unfavorable prognosis in≥30% of cancer types for both overall survival (OS) and relapse-free interval (PFI). Interestingly, PSMB5 gene expression was associated with OS (36%) and PFI (27%), and OS for PSMD2 (42%), especially when overexpressed.These findings indicate that several PSM genes may potentially be prognostic biomarkers and novel therapeutic targets for different cancer forms.
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| 14. |
- Parris, Toshima Z, 1978, et al.
(författare)
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Genome-wide multi-omics profiling of the 8p11-p12 amplicon in breast carcinoma.
- 2018
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Ingår i: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 9:35, s. 24140-24154
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Tidskriftsartikel (refereegranskat)abstract
- Genomic instability contributes to the neoplastic phenotype by deregulating key cancer-related genes, which in turn can have a detrimental effect on patient outcome. DNA amplification of the 8p11-p12 genomic region has clinical and biological implications in multiple malignancies, including breast carcinoma where the amplicon has been associated with tumor progression and poor prognosis. However, oncogenes driving increased cancer-related death and recurrent genetic features associated with the 8p11-p12 amplicon remain to be identified. In this study, DNA copy number and transcriptome profiling data for 229 primary invasive breast carcinomas (corresponding to 185 patients) were evaluated in conjunction with clinicopathological features to identify putative oncogenes in 8p11-p12 amplified samples. Illumina paired-end whole transcriptome sequencing and whole-genome SNP genotyping were subsequently performed on 23 samples showing high-level regional 8p11-p12 amplification to characterize recurrent genetic variants (SNPs and indels), expressed gene fusions, gene expression profiles and allelic imbalances. We now show previously undescribed chromothripsis-like patterns spanning the 8p11-p12 genomic region and allele-specific DNA amplification events. In addition, recurrent amplification-specific genetic features were identified, including genetic variants in the HIST1H1E and UQCRHL genes and fusion transcripts containing MALAT1 non-coding RNA, which is known to be a prognostic indicator for breast cancer and stimulated by estrogen. In summary, these findings highlight novel candidate targets for improved treatment of 8p11-p12 amplified breast carcinomas.
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| 16. |
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