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| 2. |
- 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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| 3. |
- 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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| 4. |
- Brandberg, John, 1966, et al.
(författare)
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Accurate tissue area measurements with considerably reduced radiation dose achieved by patient-specific CT scan parameters
- 2008
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Ingår i: British Journal of Radiology. - : British Institute of Radiology. - 1748-880X .- 0007-1285. ; 81:970, s. 801-8
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Tidskriftsartikel (refereegranskat)abstract
- A low-dose technique was compared with a standard diagnostic technique for measuring areas of adipose and muscle tissue and CT numbers for muscles in a body composition application. The low-dose technique was intended to keep the expected deviation in the measured area of adipose and muscle tissue to <1% of the total tissue area. The largest diameter of the patient determined the parameters for the low-dose technique. 17 patients - chosen to cover a wide range of diameters (31-47 cm) for both abdomen and thighs - were examined using both techniques. Tissue areas were compared, as were CT numbers for muscle tissue. Image noise was quantified by standard deviation measurements. The area deviation was <1%, except in the smallest subjects, in whom it was <2%. The integral radiation dose of the low-dose technique was reduced to 2-3% for diameters of 31-35 cm and to 7.5-50% for diameters of 36-47 cm as compared with the integral dose by the standard diagnostic technique. The CT numbers of muscle tissue remained unchanged with reduced radiation dose. Image noise was on average 20.9 HU (Hounsfield units) for subjects with diameters of 31-35 cm and 11.2 HU for subjects with diameters in the range of 36-47 cm. In conclusion, for body composition studies with CT, scan protocols can be adjusted so that the integral dose is lowered to 2-60% of the standard diagnostic technique at our centre without adversely altering area measurements of adipose and muscle tissue and without altering CT numbers of muscle tissue.
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| 6. |
- Larsson, Peter, et al.
(författare)
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Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties
- 2024
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Ingår i: Scientific Reports. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.
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| 7. |
- Larsson, Peter, et al.
(författare)
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Multi-omics analysis identifies repurposing bortezomib in the treatment of kidney-, nervous system-, and hematological cancers
- 2024
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Ingår i: Scientific Reports. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Repurposing of FDA-approved drugs is a quick and cost-effective alternative to de novo drug development. Here, we identify genes involved in bortezomib sensitivity, predict cancer types that may benefit from treatment with bortezomib, and evaluate the mechanism-of-action of bortezomib in breast cancer (BT-474 and ZR-75–30), melanoma (A-375), and glioblastoma (A-172) cells in vitro. Cancer cell lines derived from cancers of the blood, kidney, nervous system, and skin were found to be significantly more sensitive to bortezomib than other organ systems. The in vitro studies confirmed that although bortezomib effectively inhibited the β5 catalytic site in all four cell lines, cell cycle arrest was only induced in G2/M phase and apoptosis in A-375 and A-172 after 24h. The genomic and transcriptomic analyses identified 33 genes (e.g. ALDH18A1, ATAD2) associated with bortezomib resistance. Taken together, we identified biomarkers predictive of bortezomib sensitivity and cancer types that might benefit from treatment with bortezomib.
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| 8. |
- 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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| 9. |
- 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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